{"meta":{"query_hash":"d45f391d3a64","filters":{"topic":"Anomaly Detection Techniques and Applications"},"cohort_total":1158,"direct_labels_cover":1,"predictions_cover":1158,"exported":1158,"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/d45f391d3a64","api":"https://metacan.xera.ac/api/v1/cohort?topic=Anomaly+Detection+Techniques+and+Applications"},"results":[{"id":"W143725752","doi":"10.1007/978-3-642-30353-1_34","title":"Anomaly Detection via Coupled Gaussian Kernels","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Anomaly detection; Computer science; Artificial intelligence; Margin (machine learning); Support vector machine; Kernel (algebra); Pattern recognition (psychology); Classifier (UML); Multiple kernel learning; Machine learning; Gaussian; Feature vector; Kernel method; Ensemble learning; Anomaly (physics); Mathematics","score_opus":0.012976191230729045,"score_gpt":0.2388189143152819,"score_spread":0.22584272308455286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W143725752","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":[0.00010232912,0.00038301878,0.99452966,0.00037214937,0.00088659796,0.0005130193,0.0000019732956,0.0005691326,0.002642113],"genre_scores_gemma":[0.76647794,0.000055560395,0.23166394,0.0006347423,0.0005897257,0.00004770214,0.000002862572,0.00003978965,0.0004877434],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99673486,0.000024106988,0.00053100876,0.0013167511,0.00070228364,0.0006909656],"domain_scores_gemma":[0.997457,0.00015017523,0.00036602776,0.0015782495,0.000198361,0.00025015592],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006572545,0.00050347915,0.00042775949,0.00077910296,0.00042857416,0.00040196686,0.0024131176,0.00042627534,0.0000596828],"category_scores_gemma":[0.000019745527,0.00048149054,0.00017059463,0.0008017607,0.00046170023,0.00076266145,0.00084472273,0.0007731472,0.0001652207],"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":[0.000002688194,0.000029963803,0.000035453944,0.000014693594,0.000007736494,0.000009772796,0.00014325631,0.0006943777,0.002470275,0.012513097,0.0000045998495,0.98407406],"study_design_scores_gemma":[0.00026240564,0.00029283177,0.00092102744,0.00015002298,0.000021385682,0.0002933115,1.278584e-7,0.7573982,0.032443937,0.19774646,0.009137644,0.0013326424],"about_ca_topic_score_codex":0.00007161551,"about_ca_topic_score_gemma":0.000094290255,"teacher_disagreement_score":0.9827414,"about_ca_system_score_codex":0.0003837402,"about_ca_system_score_gemma":0.00020427967,"threshold_uncertainty_score":0.99976367},"labels":[],"label_agreement":null},{"id":"W1481707143","doi":"10.5555/1760894.1760948","title":"HOT: hypergraph-based outlier test for categorical data","year":2003,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Simon Fraser University","funders":"","keywords":"Categorical variable; Outlier; Data mining; Computer science; Robustness (evolution); Anomaly detection; Hypergraph; Curse of dimensionality; Linear subspace; Missing data; Computation; Cluster analysis; Artificial intelligence; Machine learning; Algorithm; Mathematics","score_opus":0.04766541032808504,"score_gpt":0.2906593674277747,"score_spread":0.24299395709968968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1481707143","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":[0.00007196903,0.000014529173,0.9916417,0.0010153198,0.000047103174,0.00022340473,0.000009788501,0.00037436496,0.0066018156],"genre_scores_gemma":[0.57385814,0.0000026878233,0.4240417,0.0007272346,0.000020361324,0.000117710646,0.000010803881,0.0000065835848,0.0012147916],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992956,0.000009956755,0.000120692675,0.00034454215,0.00008165388,0.00014756167],"domain_scores_gemma":[0.99864775,0.00012386769,0.000030445717,0.001084751,0.000051153158,0.00006201015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015361737,0.00007083351,0.00007001337,0.000049698076,0.00012952393,0.00007162721,0.00081491924,0.000041178388,0.000028477714],"category_scores_gemma":[0.00006481308,0.000059147205,0.000037121245,0.0003146132,0.000022447835,0.00016358442,0.00007189833,0.00004663307,0.000032691885],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000012623693,0.00019960226,0.00049013406,0.00000489365,0.000004546982,6.7445166e-7,0.0000072859043,0.000009312684,0.0010296815,0.93667215,0.045298703,0.016281728],"study_design_scores_gemma":[0.00025207398,0.00011803123,0.0003509871,0.0000011228442,0.000006748863,0.0000090753665,0.0000066548882,0.11653771,0.021615608,0.019062323,0.8418285,0.00021115706],"about_ca_topic_score_codex":0.000013561346,"about_ca_topic_score_gemma":0.0000058262985,"teacher_disagreement_score":0.9176099,"about_ca_system_score_codex":0.000011537667,"about_ca_system_score_gemma":0.000052504685,"threshold_uncertainty_score":0.24119529},"labels":[],"label_agreement":null},{"id":"W1484098979","doi":"10.1016/j.neucom.2015.05.112","title":"Modeling and recognition of smart grid faults by a combined approach of dissimilarity learning and one-class classification","year":2015,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Smart grid; Computer science; Grid; Fault (geology); Electric power system; Data mining; Transformer; Class (philosophy); Artificial intelligence; Electricity; Fuzzy logic; Machine learning; Power (physics); Engineering","score_opus":0.06051662701551754,"score_gpt":0.26616191326946526,"score_spread":0.2056452862539477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1484098979","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":[0.38326496,0.000020345244,0.6160255,0.000099222605,0.000014516338,0.00012681022,0.0000016104395,0.00008358644,0.00036344174],"genre_scores_gemma":[0.92987263,0.000015344593,0.07004574,0.000020726711,0.000014307256,0.000010401245,0.000007202299,0.0000060590314,0.000007555403],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991466,0.000069804286,0.00026825033,0.0002794303,0.00013646323,0.000099441866],"domain_scores_gemma":[0.9994442,0.00004931317,0.00017734064,0.00013226525,0.00013039682,0.00006651217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030054763,0.00007933102,0.00014348356,0.000064141364,0.0000977735,0.000028870336,0.0001263792,0.000051831717,1.827979e-7],"category_scores_gemma":[0.00004607117,0.00008566035,0.000020288675,0.00020873958,0.00004008537,0.00015331233,0.00014552049,0.00015120693,2.2396335e-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":[0.0001536083,0.0013144293,0.022384444,0.00072035927,0.00006995281,0.0000013151163,0.0051657306,0.007401706,0.14350134,0.026724542,0.00043810337,0.79212445],"study_design_scores_gemma":[0.00023713862,0.00014274813,0.00074511097,0.000022681694,0.0000063792354,0.0000060046805,0.000084811756,0.9928536,0.004101998,0.0016413901,0.0000780781,0.000080058046],"about_ca_topic_score_codex":0.000034482502,"about_ca_topic_score_gemma":6.148007e-7,"teacher_disagreement_score":0.9854519,"about_ca_system_score_codex":0.000009835437,"about_ca_system_score_gemma":0.00001875634,"threshold_uncertainty_score":0.34931278},"labels":[],"label_agreement":null},{"id":"W1507654104","doi":"10.1007/11596448_113","title":"Grid-ODF: Detecting Outliers Effectively and Efficiently in Large Multi-dimensional Databases","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University; Dalhousie University","funders":"","keywords":"Outlier; Computer science; Grid; Anomaly detection; Data mining; Local outlier factor; Rank (graph theory); Database; Algorithm; Artificial intelligence; Mathematics","score_opus":0.01891582833929225,"score_gpt":0.2710187547318289,"score_spread":0.2521029263925367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1507654104","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020634222,0.00035442706,0.9957484,0.0002260361,0.00043421355,0.0006106763,0.00002004306,0.0002447597,0.0002980123],"genre_scores_gemma":[0.37862793,0.000031150117,0.61997294,0.0008821121,0.00025609275,0.00004285303,0.0000067157944,0.00003313966,0.00014707196],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966913,0.000043464377,0.00047732444,0.001613169,0.00054713315,0.00062761724],"domain_scores_gemma":[0.99808437,0.00051093707,0.00024423312,0.00087263144,0.00012737277,0.0001604548],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010636656,0.0004501287,0.00042711853,0.00097645784,0.00039636952,0.0002512625,0.0012714707,0.00020841298,0.000009338881],"category_scores_gemma":[0.000102252765,0.00042999152,0.0000860339,0.0007210755,0.00041166972,0.0005026597,0.0014712123,0.0008540085,0.000018600453],"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":[0.000010756107,0.00016015973,0.00074835826,0.000050500523,0.000011431349,0.000065870336,0.00062372076,0.020774705,0.00083073194,0.012693203,0.000016966946,0.9640136],"study_design_scores_gemma":[0.0006392384,0.00015520479,0.0026817187,0.00039437733,0.0000075530015,0.00011931429,5.658466e-7,0.9836573,0.004893082,0.004039329,0.0026020173,0.0008103247],"about_ca_topic_score_codex":0.000056423192,"about_ca_topic_score_gemma":0.0003040672,"teacher_disagreement_score":0.96320325,"about_ca_system_score_codex":0.00028790862,"about_ca_system_score_gemma":0.00020174682,"threshold_uncertainty_score":0.99981517},"labels":[],"label_agreement":null},{"id":"W1520514216","doi":"10.1002/atr.1231","title":"More robust and better: a multiple kernel support vector machine ensemble approach for traffic incident detection","year":2013,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Support vector machine; Computer science; Artificial intelligence; Multiple kernel learning; Kernel (algebra); Ensemble learning; Machine learning; Resampling; Classifier (UML); Ranking SVM; Pattern recognition (psychology); Construct (python library); Set (abstract data type); Data mining; Algorithm; Kernel method; Mathematics","score_opus":0.010407021546226792,"score_gpt":0.23206672141979384,"score_spread":0.22165969987356704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1520514216","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":[0.3950983,0.000031012525,0.6042464,0.00023073959,0.000049199683,0.00029480955,0.0000037219797,0.000041151474,0.0000047118965],"genre_scores_gemma":[0.71788096,0.00003289009,0.28183624,0.00007255721,0.00004301386,0.00009529779,0.000010151528,0.000008962998,0.000019899326],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907243,0.000011499573,0.0004256439,0.00018756256,0.00016842766,0.00013444015],"domain_scores_gemma":[0.99913275,0.00004166747,0.00037210606,0.00013298682,0.00023223877,0.00008824318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013137296,0.00011279104,0.000167337,0.00012535302,0.000114384384,0.000054710665,0.00016701683,0.000056586334,0.0000047344633],"category_scores_gemma":[0.000010757461,0.00010123803,0.000098855395,0.0001770136,0.000022357654,0.0008627992,0.0000038595026,0.00012970855,8.165491e-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":[0.00014256864,0.00037079313,0.0019532174,0.0001509408,0.00006976176,0.000006072174,0.0029252933,0.24069233,0.10719987,0.0005975077,0.00021094903,0.64568067],"study_design_scores_gemma":[0.0037782413,0.0017399,0.3861153,0.000048911425,0.00011110615,0.00022058646,0.00067135703,0.53560215,0.06661161,0.0017470282,0.0027427566,0.0006110055],"about_ca_topic_score_codex":0.000014183686,"about_ca_topic_score_gemma":0.000024890982,"teacher_disagreement_score":0.64506966,"about_ca_system_score_codex":0.000039324226,"about_ca_system_score_gemma":0.000022778013,"threshold_uncertainty_score":0.4128367},"labels":[],"label_agreement":null},{"id":"W1528124672","doi":"10.1007/978-3-642-15549-9_41","title":"Anomalous Behaviour Detection Using Spatiotemporal Oriented Energies, Subset Inclusion Histogram Comparison and Event-Driven Processing","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Histogram; Anomaly detection; Representation (politics); Event (particle physics); Set (abstract data type); Focus (optics); Artificial intelligence; Range (aeronautics); Pattern recognition (psychology); Image processing; Image (mathematics); Data mining","score_opus":0.015388278378212524,"score_gpt":0.27161810450019913,"score_spread":0.2562298261219866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1528124672","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":[0.027865037,0.0002912114,0.97021085,0.00012000703,0.000579128,0.0004883171,0.000005227162,0.00036521058,0.00007503602],"genre_scores_gemma":[0.6701359,0.000012736233,0.32953256,0.00009868646,0.00014161505,0.00001373227,0.000007404644,0.000024256044,0.000033080763],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99686766,0.000032337735,0.0006148948,0.001339006,0.0006944987,0.00045163403],"domain_scores_gemma":[0.9980302,0.00006558301,0.000586317,0.0008375153,0.0003052313,0.00017518514],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00045915405,0.00046619488,0.00045230976,0.00078222534,0.001392756,0.0004224536,0.001261162,0.0005038587,0.000005641864],"category_scores_gemma":[0.00002204878,0.00046775446,0.000091533475,0.0007145664,0.0006023329,0.0006309339,0.0023355116,0.0010130961,0.000002244514],"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":[0.000008229185,0.00007378222,0.0014495677,0.000038205657,0.000004722214,0.000017155895,0.0009273463,0.0043403176,0.011478982,0.0012128403,0.0000015961439,0.98044723],"study_design_scores_gemma":[0.00019666279,0.00022632067,0.000708251,0.00019560575,0.000018671404,0.00013555527,5.402266e-7,0.96201634,0.023969157,0.010275457,0.0015958892,0.0006615307],"about_ca_topic_score_codex":0.00025893017,"about_ca_topic_score_gemma":0.0005504368,"teacher_disagreement_score":0.97978574,"about_ca_system_score_codex":0.00040677635,"about_ca_system_score_gemma":0.00035878184,"threshold_uncertainty_score":0.9999073},"labels":[],"label_agreement":null},{"id":"W1530890182","doi":"10.1109/ijcnn.2005.1556429","title":"Detection of disease outbreaks in pharmaceutical sales: neural networks and threshold algorithms","year":2006,"lang":"en","type":"article","venue":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","topic":"Anomaly Detection Techniques and Applications","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 Guelph","funders":"","keywords":"Outbreak; Computer science; Artificial neural network; Population; Disease; Data mining; Backpropagation; Machine learning; Artificial intelligence; Environmental health; Medicine; Pathology","score_opus":0.03509483939807372,"score_gpt":0.2873200302629358,"score_spread":0.25222519086486206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1530890182","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":[0.17592946,0.0005623479,0.81206834,0.0055497796,0.0014366845,0.0010924055,0.00002394059,0.00063311914,0.0027039077],"genre_scores_gemma":[0.99716604,0.000351905,0.0011286152,0.00038784667,0.0006094299,0.00012724151,0.000011711265,0.000021969672,0.00019526177],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99768203,0.000020674806,0.00067114737,0.0006840283,0.00043366844,0.000508445],"domain_scores_gemma":[0.9990072,0.000042379725,0.00031146055,0.00018900211,0.00022552926,0.00022442543],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027324786,0.0003141592,0.00028920537,0.00029510082,0.00011824791,0.0002665623,0.0006030796,0.00014245119,0.00002659955],"category_scores_gemma":[0.000014830321,0.00030517258,0.000115550836,0.00031502385,0.00016219464,0.00064108806,0.00016229904,0.00053912,0.0000047704016],"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":[0.0011217332,0.0022333437,0.025255246,0.00014748951,0.000158331,0.00009246755,0.00021928211,0.3391196,0.018246954,0.2686096,0.0089144735,0.33588144],"study_design_scores_gemma":[0.0004480988,0.000084791194,0.008539318,0.0000654363,0.000011746615,0.000027726017,0.000011548927,0.9864371,0.0016582165,0.0017125437,0.0007134919,0.00028997177],"about_ca_topic_score_codex":0.000054415294,"about_ca_topic_score_gemma":0.000050791852,"teacher_disagreement_score":0.82123655,"about_ca_system_score_codex":0.00012389159,"about_ca_system_score_gemma":0.000025421085,"threshold_uncertainty_score":0.99994004},"labels":[],"label_agreement":null},{"id":"W1539371187","doi":"10.1109/sai.2015.7237166","title":"A fast noise resilient anomaly detection using GMM-based collective labelling","year":2015,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Dalhousie University; University of Ottawa","funders":"","keywords":"Constant false alarm rate; Computer science; Anomaly detection; Mixture model; Noise (video); Pattern recognition (psychology); Probabilistic logic; Labelling; Artificial intelligence; Anomaly (physics); Similarity (geometry)","score_opus":0.041464782502188054,"score_gpt":0.2736132519836677,"score_spread":0.23214846948147966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1539371187","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":[0.0907147,0.000017501901,0.90388113,0.0001708532,0.00008854269,0.0002555825,9.889287e-7,0.00053997245,0.0043307114],"genre_scores_gemma":[0.8263445,7.294364e-7,0.17265587,0.0001792066,0.000037554306,0.000050301533,4.77855e-7,0.00000857478,0.0007227997],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899817,0.000046419078,0.00019243243,0.00035798145,0.00020614448,0.00019883565],"domain_scores_gemma":[0.9990908,0.00003175857,0.00009106944,0.00041108808,0.00023522339,0.0001400489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022753244,0.000116202755,0.00010827589,0.00017475309,0.00025022958,0.00012783268,0.0003116777,0.0000670144,0.000008528879],"category_scores_gemma":[0.000021443826,0.00011151276,0.000055428874,0.0009856386,0.00003280163,0.00025205998,0.00008663471,0.00010093517,0.00003488738],"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":[0.0003764042,0.0021557508,0.0085996315,0.00008657581,0.00016936171,0.000059114747,0.0048835394,0.18511152,0.4671517,0.07743986,0.0053753913,0.24859114],"study_design_scores_gemma":[0.00024944136,0.0001483055,0.00027616348,0.000006892928,0.000005721944,0.000012124007,0.000060628176,0.77561766,0.22012378,0.0017187989,0.0016197832,0.00016073258],"about_ca_topic_score_codex":0.00026678102,"about_ca_topic_score_gemma":0.00006181226,"teacher_disagreement_score":0.7356298,"about_ca_system_score_codex":0.00030233356,"about_ca_system_score_gemma":0.00023062016,"threshold_uncertainty_score":0.45473585},"labels":[],"label_agreement":null},{"id":"W1546720985","doi":"10.4271/2000-01-0363","title":"Detecting Malfunctions in Dynamic Systems","year":2000,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":23,"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":"Computer science; Systems engineering; Engineering","score_opus":0.009701051809827896,"score_gpt":0.24281116535292557,"score_spread":0.23311011354309769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1546720985","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":[0.68459296,0.0013201265,0.0016357192,0.014223739,0.0009827674,0.005496522,0.00010726908,0.024346806,0.26729408],"genre_scores_gemma":[0.98389035,0.00037568228,0.011188647,0.0009770758,0.00008921911,0.0012370102,0.000014618319,0.00008211606,0.0021452743],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99521977,0.00018961026,0.001254541,0.0015727086,0.0007381422,0.001025246],"domain_scores_gemma":[0.99673945,0.00036234176,0.00022434565,0.0022322088,0.00009539679,0.000346232],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007327409,0.0006658884,0.00073926285,0.0004182066,0.0006704954,0.00032257498,0.002029103,0.00073917967,0.0005762032],"category_scores_gemma":[0.00017971962,0.0006241659,0.00038963024,0.002263234,0.0004734778,0.00090680993,0.00041467216,0.0014692367,0.0004303245],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000068442765,0.00040967052,0.000100066434,0.000031475214,0.000020649608,0.00004399972,0.000028939146,0.00023793952,0.88838696,0.052590977,0.0009928662,0.057088014],"study_design_scores_gemma":[0.0004937158,0.0008907028,0.8802988,0.00020022872,0.00002636875,0.00034715518,0.00007191581,0.000017341488,0.00010738868,0.004754558,0.11191908,0.0008727258],"about_ca_topic_score_codex":0.0002011859,"about_ca_topic_score_gemma":0.04833952,"teacher_disagreement_score":0.88827956,"about_ca_system_score_codex":0.0005229153,"about_ca_system_score_gemma":0.00010296934,"threshold_uncertainty_score":0.999621},"labels":[],"label_agreement":null},{"id":"W155411511","doi":"","title":"Boosting expert ensembles for rapid concept recall","year":2006,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Boosting (machine learning); Computer science; Artificial intelligence; Machine learning; AdaBoost; Adversary; Adversarial system; Recall; Classifier (UML); Task (project management); Multi-task learning; Ensemble learning","score_opus":0.01682377459474949,"score_gpt":0.25801248072321825,"score_spread":0.24118870612846877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W155411511","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003838795,0.00010695997,0.9778686,0.0012861966,0.000042076957,0.0001908092,0.0000010364231,0.000574741,0.019545702],"genre_scores_gemma":[0.39282095,0.000008239672,0.60175794,0.00077367475,0.00015261851,0.0001962514,0.0000029468008,0.0000064457563,0.0042809285],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99948245,0.0000072210014,0.00013045681,0.00019481828,0.000055866672,0.00012919771],"domain_scores_gemma":[0.99959,0.00006844355,0.000039125953,0.00022833381,0.000049885948,0.00002420367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006721026,0.000058010282,0.00006029827,0.000027403883,0.00014409368,0.00006446443,0.00025041975,0.000033649667,0.000025435527],"category_scores_gemma":[0.0000069762577,0.0000508603,0.000048506223,0.00011143488,0.000018239656,0.00012342805,0.0000471577,0.000026284111,0.000009556079],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012724793,0.000035228535,0.000014428604,0.0000021241858,0.0000024161857,3.5236062e-7,0.00006887248,0.000015690879,0.014963578,0.65739816,0.08527466,0.2422232],"study_design_scores_gemma":[0.00017085722,0.000084046274,0.00012179793,0.000005490959,0.0000013563516,0.000008760815,0.000026011176,0.0341059,0.33447978,0.023231195,0.60757995,0.00018486315],"about_ca_topic_score_codex":0.00009566505,"about_ca_topic_score_gemma":0.000005774358,"teacher_disagreement_score":0.63416696,"about_ca_system_score_codex":0.000013304152,"about_ca_system_score_gemma":0.000011873041,"threshold_uncertainty_score":0.20740229},"labels":[],"label_agreement":null},{"id":"W1569851992","doi":"10.1007/978-3-540-73540-3_7","title":"Efficiently Mining Regional Outliers in Spatial Data","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Outlier; Computer science; Data mining; Spatial analysis; Statistic; Cluster analysis; Object (grammar); Anomaly detection; Pattern recognition (psychology); Artificial intelligence; Mathematics; Statistics","score_opus":0.05377628463019086,"score_gpt":0.2965726735091381,"score_spread":0.24279638887894722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1569851992","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008795348,0.00014729585,0.99440575,0.0006980031,0.00057124306,0.00035883478,0.000007886236,0.00020776626,0.0035152475],"genre_scores_gemma":[0.13471358,0.000041702246,0.86218894,0.0020323794,0.0005412519,0.000014674823,0.000028683215,0.00003801197,0.0004008129],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962005,0.00001930062,0.00056260556,0.0017966257,0.00084168435,0.00057926995],"domain_scores_gemma":[0.9967898,0.00030428052,0.00026530272,0.002377164,0.00012218962,0.00014124416],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0012661592,0.00036998044,0.00035049274,0.0011595559,0.00021593088,0.00030692221,0.0054696356,0.00030763453,0.000014966596],"category_scores_gemma":[0.00005081068,0.0003691693,0.00006821093,0.0009073542,0.00056002353,0.000459132,0.0023128404,0.0007146439,0.000026987333],"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":[0.0000044379917,0.000035763725,0.000095640404,0.000011565189,0.0000028818142,0.00004949398,0.00032295735,0.0053580673,0.000037994505,0.01238646,0.000092356124,0.9816024],"study_design_scores_gemma":[0.00027734324,0.00013350394,0.00052531116,0.0002767206,0.000005219236,0.000090629874,5.3283327e-7,0.95173454,0.00052779156,0.030369759,0.015277706,0.00078092905],"about_ca_topic_score_codex":0.00011527315,"about_ca_topic_score_gemma":0.000525824,"teacher_disagreement_score":0.98082143,"about_ca_system_score_codex":0.00028732105,"about_ca_system_score_gemma":0.00043089926,"threshold_uncertainty_score":0.99991125},"labels":[],"label_agreement":null},{"id":"W1574832543","doi":"10.1016/b978-012088469-8.50123-6","title":"HOS-Miner","year":2004,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Linear subspace; Subspace topology; Outlier; Anomaly detection; Computer science; Data mining; Dimension (graph theory); Range (aeronautics); Pattern recognition (psychology); Mathematics; Algorithm; Artificial intelligence; Combinatorics; Engineering","score_opus":0.012547547365516457,"score_gpt":0.23036390050184416,"score_spread":0.2178163531363277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1574832543","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[8.0979686e-7,0.00020973031,0.03571669,0.00025631263,0.00014523379,0.00035496627,0.0000063542484,0.0005867228,0.9627232],"genre_scores_gemma":[0.00058068696,0.00005124571,0.023448573,0.0005837837,0.00020285053,0.00008921638,0.00000443594,0.000042355066,0.97499686],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99871415,0.000005828915,0.000302162,0.0005281908,0.00024085687,0.00020883803],"domain_scores_gemma":[0.9985295,0.000016033831,0.00018713076,0.0010737794,0.000083857325,0.0001097252],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009115254,0.00029119302,0.0002652217,0.00014068911,0.00013740566,0.000101817954,0.0008910402,0.00027079193,0.00015937604],"category_scores_gemma":[0.000002327197,0.00027639192,0.000217221,0.000022321394,0.00007927774,0.000066502005,0.0002866845,0.0003543384,0.00060823373],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.5540962e-7,0.000002443997,1.09894955e-7,0.000006134378,0.000008805258,0.0000050858903,0.00001964567,2.9093158e-7,0.000016000584,0.38506055,0.00019222438,0.61468846],"study_design_scores_gemma":[0.00005770692,0.000033770168,0.0000027712454,0.00006721805,0.000011848224,0.000018738749,3.641774e-7,0.000020409178,0.00022866989,0.20002115,0.79927367,0.000263674],"about_ca_topic_score_codex":6.276223e-7,"about_ca_topic_score_gemma":0.000002424005,"teacher_disagreement_score":0.79908144,"about_ca_system_score_codex":0.000107834996,"about_ca_system_score_gemma":0.00013125267,"threshold_uncertainty_score":0.9999688},"labels":[],"label_agreement":null},{"id":"W1584325974","doi":"10.1007/978-3-642-13769-3_1","title":"Y-Means: An Autonomous Clustering Algorithm","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Cluster analysis; Computer science; Robustness (evolution); Dependency (UML); Canopy clustering algorithm; CURE data clustering algorithm; Outlier; Centroid; Data mining; Algorithm; Pattern recognition (psychology); Correlation clustering; Artificial intelligence","score_opus":0.013655174058695428,"score_gpt":0.25067947409963837,"score_spread":0.23702430004094294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1584325974","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000131338575,0.00006895326,0.9943388,0.000400254,0.0010090509,0.00038764358,0.000006039119,0.0006861754,0.0030899672],"genre_scores_gemma":[0.009232198,0.000023535775,0.9886798,0.00094663625,0.0005494842,0.000031348394,0.00000482352,0.00003689196,0.0004952266],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969089,0.000017038574,0.00045315368,0.0015260448,0.0005592853,0.00053557946],"domain_scores_gemma":[0.9972848,0.00011059956,0.00024492954,0.0019417761,0.00019069121,0.00022714895],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005780197,0.0004428379,0.00036928183,0.0006443482,0.00043836862,0.0006690311,0.003659493,0.000450783,0.000036174828],"category_scores_gemma":[0.000015785214,0.00043351293,0.00011608744,0.00046838066,0.0005331928,0.0007623841,0.0012045085,0.0011650602,0.000054311735],"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":[4.934918e-7,0.000018646546,0.0000017646191,0.0000058264236,0.0000025103054,0.00001623069,0.00017990782,0.0018680271,0.00040643237,0.008555176,0.000002918618,0.9889421],"study_design_scores_gemma":[0.000085409214,0.00016100057,0.000028093624,0.000062834006,0.000003909665,0.00013536958,8.493473e-8,0.857691,0.0045328056,0.12579559,0.010937578,0.0005663044],"about_ca_topic_score_codex":0.00004373853,"about_ca_topic_score_gemma":0.00014513222,"teacher_disagreement_score":0.9883758,"about_ca_system_score_codex":0.00020216634,"about_ca_system_score_gemma":0.0003569999,"threshold_uncertainty_score":0.99981165},"labels":[],"label_agreement":null},{"id":"W1606663572","doi":"","title":"A web-based interactive data visualization system for outlier subspace analysis","year":2010,"lang":"en","type":"article","venue":"University of Southern Queensland ePrints (University of Southern Queensland)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Dalhousie University","funders":"","keywords":"Outlier; Linear subspace; Computer science; Anomaly detection; Visualization; Subspace topology; Data mining; Data visualization; Data point; Artificial intelligence; Pattern recognition (psychology); Mathematics","score_opus":0.01372013817120976,"score_gpt":0.22428748818532493,"score_spread":0.21056735001411517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1606663572","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":[0.31414652,0.0000054373786,0.6832174,0.0002724185,0.00004330023,0.00032322822,0.0013689736,0.00021924589,0.0004034617],"genre_scores_gemma":[0.9672773,0.000006400109,0.03163334,0.000011946513,0.000022924296,2.9475527e-7,0.00016506681,0.000016504318,0.00086622447],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841636,0.00010163509,0.00021269929,0.00071624515,0.00029212807,0.00026090752],"domain_scores_gemma":[0.99723756,0.00014219664,0.0005896645,0.0014208294,0.0004613092,0.00014841676],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048700906,0.0002218424,0.00047495932,0.00053701503,0.00039239431,0.000034319313,0.0018582602,0.00022787639,0.00013642736],"category_scores_gemma":[0.000022914788,0.000263968,0.00031672517,0.0008479258,0.0002509742,0.00029236305,0.00048710266,0.00018914738,0.00013110868],"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":[0.0043120575,0.0031517984,0.71529114,0.0016443004,0.009282488,0.00016120271,0.1435613,0.0040094485,0.022177946,0.046374306,0.0033131014,0.04672089],"study_design_scores_gemma":[0.0070730704,0.00028594417,0.015264042,0.0002061153,0.0021589994,0.000012567293,0.12145115,0.82233167,0.0016044523,0.0010667632,0.027089642,0.0014555723],"about_ca_topic_score_codex":0.0037346557,"about_ca_topic_score_gemma":0.0037957602,"teacher_disagreement_score":0.81832224,"about_ca_system_score_codex":0.00005975771,"about_ca_system_score_gemma":0.00014498524,"threshold_uncertainty_score":0.9999812},"labels":[],"label_agreement":null},{"id":"W1614841168","doi":"","title":"Crowd analysis with target tracking, K-means clustering and hidden Markov models","year":2012,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Anomaly Detection Techniques and Applications","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":"Institut National d'Optique","funders":"","keywords":"Crowds; Hidden Markov model; Cluster analysis; Centroid; Computer science; Tracking (education); Artificial intelligence; Pattern recognition (psychology); Crowd psychology; Markov chain; k-means clustering; Data mining; Machine learning","score_opus":0.02469062103090949,"score_gpt":0.2624692680882453,"score_spread":0.23777864705733578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1614841168","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":[0.008754692,0.000005613144,0.95009464,0.0009103385,0.00008894487,0.00013326152,0.000011688144,0.00016849818,0.039832316],"genre_scores_gemma":[0.9362257,0.000035792847,0.06307151,0.00036030056,0.000034001616,0.0000459556,0.00004159251,0.0000033478582,0.00018176],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991097,0.00001700416,0.00025457898,0.00013358297,0.0003334733,0.00015167103],"domain_scores_gemma":[0.99924266,0.00002046373,0.00017905454,0.00022359107,0.00024447852,0.00008973647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018509149,0.00011566198,0.000103402046,0.00037955947,0.00015043352,0.00031960668,0.00035042065,0.000055422894,0.00015803626],"category_scores_gemma":[0.000008105832,0.00009746476,0.000043288113,0.00033438392,0.00002755066,0.0033797838,0.0001374179,0.000109076616,0.000034546538],"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":[0.00005308169,0.000090672256,0.0037903266,0.000014088807,0.00015090709,5.929735e-7,0.0029639388,0.004299294,0.00024694446,0.79257625,0.00044407885,0.19536985],"study_design_scores_gemma":[0.00017597504,0.000055073553,0.0061863353,0.000019334804,0.000016588949,0.000012210632,0.00013856057,0.9859002,0.0006582641,0.002162169,0.00450824,0.00016702269],"about_ca_topic_score_codex":0.00004164084,"about_ca_topic_score_gemma":0.000009412431,"teacher_disagreement_score":0.98160094,"about_ca_system_score_codex":0.00005116554,"about_ca_system_score_gemma":0.000024069763,"threshold_uncertainty_score":0.3974498},"labels":[],"label_agreement":null},{"id":"W1617852548","doi":"10.1109/ijcnn.2015.7280433","title":"Adaptive skew-sensitive fusion of ensembles and their application to face re-identification","year":2015,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Skew; Robustness (evolution); Classifier (UML); Pattern recognition (psychology); A priori and a posteriori; Histogram; Sensor fusion; Machine learning","score_opus":0.025479888688096402,"score_gpt":0.2575039041311094,"score_spread":0.23202401544301302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1617852548","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":[0.032567594,0.000019269339,0.9637986,0.0009438228,0.000018755547,0.00037788553,0.00000388964,0.0001642713,0.0021059492],"genre_scores_gemma":[0.9462853,0.000010031163,0.05322259,0.00011590774,0.00001358299,0.000072850504,0.0000024522787,0.000004129911,0.00027316858],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993329,0.000027963977,0.00017731432,0.00027873146,0.00010266641,0.0000804396],"domain_scores_gemma":[0.9991659,0.00003748127,0.00010074899,0.00037130696,0.000236967,0.0000875923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021591657,0.000076337965,0.000092650895,0.00008437137,0.00006602125,0.00003234163,0.00019622009,0.000043166063,0.000001081478],"category_scores_gemma":[0.000016150254,0.00006350814,0.000019152058,0.00033967814,0.000029494233,0.0001727772,0.00014657639,0.000038572496,0.000023416658],"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":[0.000026506119,0.00011456234,0.00009203498,0.000008264627,0.000014599694,2.620781e-7,0.006574839,0.0002045184,0.29474083,0.31102523,0.0031050867,0.38409328],"study_design_scores_gemma":[0.00012700223,0.00019133957,0.0030164868,0.000010649223,0.000004074096,0.0000071059617,0.0019119554,0.082844704,0.8871588,0.020569908,0.003978045,0.00017990771],"about_ca_topic_score_codex":0.00012365323,"about_ca_topic_score_gemma":0.0000265516,"teacher_disagreement_score":0.9137177,"about_ca_system_score_codex":0.000027352988,"about_ca_system_score_gemma":0.000022308885,"threshold_uncertainty_score":0.25897866},"labels":[],"label_agreement":null},{"id":"W1689060643","doi":"10.5210/ojphi.v7i1.5779","title":"Detecting Outbreaks in Time-Series Data with RecentMax","year":2015,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Outbreak; Computer science; Series (stratigraphy); Time series; Data mining; Disease surveillance; Virology; Disease; Medicine; Machine learning; Biology; Pathology","score_opus":0.12112709208470575,"score_gpt":0.34951332439599286,"score_spread":0.22838623231128713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1689060643","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0097261,0.00006594142,0.9735273,0.015933895,0.00008101541,0.00013971135,0.000013536461,0.00006842366,0.00044406872],"genre_scores_gemma":[0.0248997,0.0001625865,0.97308505,0.0015990882,0.0001239487,0.000002980825,0.000018081952,0.000009264462,0.00009929598],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99800724,0.00006809195,0.0011548243,0.00007099071,0.0004286313,0.00027023317],"domain_scores_gemma":[0.99752855,0.000039339026,0.0011166045,0.0005765271,0.00038487228,0.0003541287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039333142,0.00009147662,0.00022559539,0.0002930033,0.00007519967,0.00019502564,0.0014165663,0.000045532783,0.0000033953593],"category_scores_gemma":[0.00030321607,0.00006871006,0.000018675451,0.00073180767,0.000028966018,0.0028466529,0.00032577023,0.0003454357,0.000008137801],"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":[0.000016694226,0.00033656144,0.002874111,0.00008581987,0.00002393454,0.000006536094,0.0043605953,0.00022398106,0.0000032733478,0.0023876259,0.011129489,0.9785514],"study_design_scores_gemma":[0.0011766393,0.0014723096,0.0012113658,0.00016448695,0.000004248887,0.001257221,0.0026459594,0.15733212,0.000055531233,0.0008558827,0.83356655,0.00025770254],"about_ca_topic_score_codex":0.000021773978,"about_ca_topic_score_gemma":0.000044927816,"teacher_disagreement_score":0.97829366,"about_ca_system_score_codex":0.000167085,"about_ca_system_score_gemma":0.0014192746,"threshold_uncertainty_score":0.2801915},"labels":[],"label_agreement":null},{"id":"W1753758320","doi":"10.1007/978-3-642-20039-7_44","title":"A New Frontier in Novelty Detection: Pattern Recognition of Stochastically Episodic Events","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Novelty; Novelty detection; Frontier; Pattern recognition (psychology); Artificial intelligence","score_opus":0.022533240776718984,"score_gpt":0.23810055349162254,"score_spread":0.21556731271490356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1753758320","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011183584,0.00007812668,0.9973275,0.00016490753,0.0005504584,0.0004678953,0.000006509037,0.00012277273,0.0011699874],"genre_scores_gemma":[0.46669012,0.000036065885,0.5323636,0.00037825565,0.00022593005,0.000037409816,0.0000038378107,0.000029706962,0.00023507241],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99744,0.00002373471,0.0006245463,0.0010253913,0.0005123765,0.00037396612],"domain_scores_gemma":[0.99831456,0.00011834116,0.00035508323,0.0008788914,0.00019373477,0.00013940559],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004362578,0.0003351061,0.00041150948,0.00085185945,0.0000956023,0.00007156971,0.001594722,0.00031926628,0.00007439584],"category_scores_gemma":[0.000036069596,0.0003350685,0.00012169263,0.0006653545,0.00021296271,0.00037831423,0.0005676246,0.0006017855,0.00004731011],"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":[0.0000047910808,0.000029619538,0.00005153323,0.00001402112,0.0000040701684,0.0000049609257,0.00017108108,0.00020286959,0.00014778327,0.00075778353,0.000006687926,0.9986048],"study_design_scores_gemma":[0.0006662336,0.0006721603,0.0025216462,0.0008207079,0.000018733534,0.00010083525,2.704084e-7,0.15450318,0.019311056,0.8195326,0.0007602387,0.0010923874],"about_ca_topic_score_codex":0.00023991262,"about_ca_topic_score_gemma":0.00032483588,"teacher_disagreement_score":0.9975124,"about_ca_system_score_codex":0.00020380494,"about_ca_system_score_gemma":0.00031259767,"threshold_uncertainty_score":0.9999101},"labels":[],"label_agreement":null},{"id":"W176952057","doi":"10.1007/978-3-642-23199-5_9","title":"Parameter-Free Anomaly Detection for Categorical Data","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Categorical variable; Anomaly detection; Computer science; Outlier; Data mining; Preprocessor; Set (abstract data type); Similarity (geometry); Artificial intelligence; Data set; Measure (data warehouse); Similarity measure; Data pre-processing; Pattern recognition (psychology); Machine learning; Image (mathematics)","score_opus":0.050451300976941706,"score_gpt":0.2740340784227595,"score_spread":0.2235827774458178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W176952057","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000011633995,0.00014242715,0.99562716,0.00039524143,0.0010224516,0.0007752578,0.000029263385,0.00041209848,0.0015844854],"genre_scores_gemma":[0.14000611,0.000046363155,0.8580442,0.0006272363,0.00063489215,0.00010865664,0.000020628324,0.000046504243,0.00046539225],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9964756,0.000018786115,0.00051227777,0.0019816877,0.00047474395,0.00053692784],"domain_scores_gemma":[0.9942669,0.0004088198,0.00031380527,0.0046156975,0.00023397224,0.00016075141],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0007347935,0.0004271327,0.00040710618,0.00058886985,0.00036780818,0.0003804206,0.008154604,0.0003802656,0.000015008912],"category_scores_gemma":[0.0001502781,0.0004031951,0.00012802873,0.00054693635,0.00042939145,0.0007626934,0.0030789354,0.0005514214,0.000025898526],"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":[0.000006465938,0.00003158824,0.000005768053,0.00001942124,0.000008941019,0.0000072339303,0.00007235081,0.00015529137,0.00015072433,0.04051868,0.00013559821,0.95888793],"study_design_scores_gemma":[0.00017620849,0.0002941822,0.000041518306,0.000034669436,0.000013795988,0.00008013435,4.499948e-8,0.3478071,0.005265739,0.62966776,0.016087135,0.00053168595],"about_ca_topic_score_codex":0.00007308959,"about_ca_topic_score_gemma":0.00016440438,"teacher_disagreement_score":0.95835626,"about_ca_system_score_codex":0.00020436151,"about_ca_system_score_gemma":0.00028366724,"threshold_uncertainty_score":0.999842},"labels":[],"label_agreement":null},{"id":"W182693664","doi":"10.1007/978-3-642-30353-1_32","title":"Bayesian Multiple Imputation Approaches for One-Class Classification","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Imputation (statistics); Bayesian probability; Class (philosophy); Computer science; Artificial intelligence; Machine learning; Missing data","score_opus":0.05957947064101748,"score_gpt":0.26559070127584306,"score_spread":0.20601123063482557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W182693664","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000132503,0.00014685965,0.99522805,0.0010931762,0.00040080058,0.0010920701,0.000010125255,0.00035928248,0.001656356],"genre_scores_gemma":[0.4133708,0.000020258705,0.5855733,0.00028226935,0.000367278,0.00016283758,0.000019877489,0.000027844497,0.00017551934],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99730766,0.000020746109,0.00048228394,0.0011892699,0.00049140654,0.00050864153],"domain_scores_gemma":[0.99773836,0.000382974,0.0003854198,0.0011208156,0.00021635811,0.00015605072],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007019013,0.00036532813,0.00033010056,0.00056917476,0.00038354888,0.00036420563,0.001735302,0.00036276237,0.000007662462],"category_scores_gemma":[0.00005430298,0.00036921934,0.00014851996,0.0004691994,0.00032569683,0.00066932314,0.0003925766,0.0004042971,0.000023887513],"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":[0.0000029917112,0.00003947109,0.000031417305,0.000027963586,0.000006114392,3.3232064e-7,0.0001983835,0.0027238415,0.0002527311,0.110920936,0.000017682249,0.8857781],"study_design_scores_gemma":[0.00014088688,0.00008323602,0.0002463946,0.00005377285,0.000009416467,0.000010607122,2.4732662e-7,0.8216946,0.002591425,0.17086643,0.0039015138,0.00040147663],"about_ca_topic_score_codex":0.00000916311,"about_ca_topic_score_gemma":0.000028722363,"teacher_disagreement_score":0.88537663,"about_ca_system_score_codex":0.000321193,"about_ca_system_score_gemma":0.00023275717,"threshold_uncertainty_score":0.99987596},"labels":[],"label_agreement":null},{"id":"W1944494027","doi":"10.1007/978-3-642-21596-4_33","title":"A New Image-Based Method for Event Detection and Extraction of Noisy Hydrophone Data","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Hydrophone; Computer science; Spectrogram; Artificial intelligence; Noise (video); Orientation (vector space); Computer vision; Pattern recognition (psychology); Image (mathematics); Acoustics; Mathematics","score_opus":0.03198752506988693,"score_gpt":0.3102790461874813,"score_spread":0.27829152111759436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1944494027","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000007857663,0.00013909601,0.99827105,0.00025768243,0.00035205594,0.00061846926,0.000014400381,0.00012786475,0.00021154169],"genre_scores_gemma":[0.038185216,0.000027344482,0.9612965,0.00015694753,0.00015583925,0.000024410976,0.000005795352,0.000019372657,0.00012855309],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978079,0.00002126144,0.00042328754,0.0011734172,0.00032606596,0.0002480546],"domain_scores_gemma":[0.9974357,0.00030795424,0.00040879502,0.0015519338,0.00018406915,0.000111561705],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007980289,0.00026963837,0.00032035715,0.0005036229,0.00016807835,0.0001318206,0.0017264882,0.00021358693,0.000010559141],"category_scores_gemma":[0.000046996593,0.00025969913,0.00007817933,0.00037778442,0.0001928007,0.00052037253,0.0006630724,0.0003041151,0.0000029725252],"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":[0.000010314326,0.000018053428,0.0000011726548,0.000032958564,0.000005305787,0.0000012582583,0.000060678474,0.0006755858,0.011762233,0.0019711037,0.000017604605,0.9854437],"study_design_scores_gemma":[0.00019678811,0.00024535356,0.00003879245,0.00007722608,0.000015955386,0.00003806774,6.2278524e-8,0.7821313,0.09963347,0.11468734,0.002656867,0.0002787323],"about_ca_topic_score_codex":0.00017899284,"about_ca_topic_score_gemma":0.00010268866,"teacher_disagreement_score":0.985165,"about_ca_system_score_codex":0.00009366638,"about_ca_system_score_gemma":0.0003272569,"threshold_uncertainty_score":0.9999855},"labels":[],"label_agreement":null},{"id":"W1966836840","doi":"10.1109/icdm.2013.132","title":"Explaining Outliers by Subspace Separability","year":2013,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":103,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Outlier; Anomaly detection; Computer science; Linear subspace; Subspace topology; Focus (optics); Data mining; Heuristic; Domain (mathematical analysis); Artificial intelligence; Pattern recognition (psychology); Ranking (information retrieval); Mathematics","score_opus":0.006427386465298589,"score_gpt":0.22479125703206673,"score_spread":0.21836387056676815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966836840","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":[0.02555161,0.000010422103,0.94522387,0.0024655964,0.000025971693,0.00016753118,3.855751e-7,0.0006492176,0.02590537],"genre_scores_gemma":[0.9104714,0.0000032630335,0.08534028,0.00044715335,0.00000865162,0.0001476262,6.350191e-7,0.0000032053858,0.003577801],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994507,0.000014279036,0.00009938678,0.00022065428,0.00008023554,0.00013476203],"domain_scores_gemma":[0.99943,0.000027724629,0.000029507632,0.0004009544,0.000040735493,0.00007108276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008784972,0.000058928857,0.00005646516,0.00002164098,0.00010698602,0.00010631806,0.00035078765,0.000032204083,0.00020355366],"category_scores_gemma":[0.0000072049083,0.000050800776,0.00003080702,0.00018698283,0.00002536195,0.00035856807,0.00008530766,0.00005772057,0.00034237164],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.8807514e-7,0.00013813502,0.0042997333,0.0000065407044,0.000012861645,4.116157e-7,0.00064695295,0.000014609814,0.018260004,0.2856919,0.5265213,0.16440663],"study_design_scores_gemma":[0.00047800117,0.000292571,0.014634592,0.00000991547,0.000006914608,0.000024675872,0.00088654784,0.2219938,0.25424793,0.06905144,0.43717405,0.0011995329],"about_ca_topic_score_codex":0.00016269454,"about_ca_topic_score_gemma":0.0000026039331,"teacher_disagreement_score":0.88491976,"about_ca_system_score_codex":0.000021553526,"about_ca_system_score_gemma":0.0000095567775,"threshold_uncertainty_score":0.4400609},"labels":[],"label_agreement":null},{"id":"W1967656454","doi":"10.1109/isi.2008.4565051","title":"Hiding clusters in adversarial settings","year":2008,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Adversarial system; Cluster analysis; Computer science; Outlier; Domain (mathematical analysis); Convexity; Data mining; Artificial intelligence; Theoretical computer science; Mathematics","score_opus":0.013549000302208049,"score_gpt":0.2253913494248426,"score_spread":0.21184234912263455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967656454","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":[0.031023262,0.00000368212,0.9505409,0.001405216,0.000049415983,0.00008084836,1.1419369e-7,0.00030883992,0.016587699],"genre_scores_gemma":[0.9063986,0.000006273862,0.09237513,0.0005094755,0.000022392165,0.000009408594,2.2318751e-7,0.0000021471456,0.0006763414],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995834,0.000008491514,0.0000966707,0.00014817803,0.000064503096,0.000098720295],"domain_scores_gemma":[0.9997522,0.000017049628,0.000022528133,0.00017094219,0.000011476064,0.000025764557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000066881206,0.00003945434,0.000045152126,0.00006553209,0.00008440994,0.000014840148,0.00024184411,0.000026229,0.000015398373],"category_scores_gemma":[0.000005291281,0.000037656293,0.000022690778,0.00027172375,0.000015682332,0.00019277463,0.00008628831,0.0000551585,0.00003401461],"study_design_candidate":"theoretical_or_conceptual","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":[0.000017403718,0.0002946588,0.021523122,0.000019146968,0.00001712308,0.00009762795,0.004456287,0.00058199244,0.012764447,0.7573292,0.086974606,0.11592434],"study_design_scores_gemma":[0.0019191587,0.00025270172,0.034200646,0.000041031108,0.0000048659886,0.00046479623,0.00032280842,0.5689557,0.085919194,0.014892532,0.29181936,0.0012071526],"about_ca_topic_score_codex":0.00005085514,"about_ca_topic_score_gemma":0.000005198271,"teacher_disagreement_score":0.87537533,"about_ca_system_score_codex":0.000027143487,"about_ca_system_score_gemma":0.000017223332,"threshold_uncertainty_score":0.15355791},"labels":[],"label_agreement":null},{"id":"W1968089381","doi":"10.2174/1874120700903010001","title":"A Fall and Near-Fall Assessment and Evaluation System","year":2009,"lang":"en","type":"article","venue":"The Open Biomedical Engineering Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Wearable computer; Bayes' theorem; Fall prevention; Simulation; Artificial intelligence; Data mining; Poison control; Machine learning; Real-time computing; Embedded system; Injury prevention; Bayesian probability; Medicine; Medical emergency","score_opus":0.01650568103901463,"score_gpt":0.30460935518882115,"score_spread":0.28810367414980653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968089381","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":[0.02575468,0.00014892232,0.96836597,0.0048793037,0.00008006289,0.00023296387,4.3439064e-7,0.00007358657,0.00046406826],"genre_scores_gemma":[0.91252667,0.00003966778,0.08720045,0.0001261254,0.00006773512,0.000016413303,4.0246718e-7,0.0000031554382,0.000019412008],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931616,0.000034287485,0.00015997903,0.00012099419,0.00025216094,0.00011640601],"domain_scores_gemma":[0.99957997,0.00003061091,0.00005506473,0.00016286249,0.000041530748,0.00012996995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012568035,0.00006663557,0.000086578584,0.000039363433,0.00024543543,0.0006598381,0.00049714145,0.00003538497,0.000002931131],"category_scores_gemma":[0.000011883546,0.000043517422,0.00001604657,0.00016456771,0.000029859631,0.00020711783,0.00015956472,0.00019606696,0.0000013690332],"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":[0.000005474679,0.00008012395,0.000082671264,0.000021531536,0.000042302134,0.00001457519,0.0004390141,0.00061580737,0.0028368996,0.13990599,0.0017134936,0.8542421],"study_design_scores_gemma":[0.0002679866,0.00013685535,0.01172136,0.00005267696,0.000011377188,0.0007213666,0.000024074687,0.9768752,0.000028440862,0.0010043778,0.009075277,0.000081019716],"about_ca_topic_score_codex":0.000016096606,"about_ca_topic_score_gemma":4.144582e-7,"teacher_disagreement_score":0.9762594,"about_ca_system_score_codex":0.000062672196,"about_ca_system_score_gemma":0.00006166637,"threshold_uncertainty_score":0.6362834},"labels":[],"label_agreement":null},{"id":"W1968487146","doi":"10.1109/cisda.2014.7035633","title":"Smoothing gamma ray spectra to improve outlier detection","year":2014,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"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 Ottawa","funders":"Health Canada","keywords":"Smoothing; Preprocessor; Anomaly detection; Computer science; Outlier; Poisson distribution; Noise (video); Variance (accounting); Energy (signal processing); Gamma distribution; Algorithm; Data mining; Artificial intelligence; Pattern recognition (psychology); Statistics; Mathematics; Image (mathematics); Computer vision","score_opus":0.006236290456180425,"score_gpt":0.22529737805728298,"score_spread":0.21906108760110254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968487146","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":[0.0040792204,0.0000017585595,0.97431,0.0012031397,0.00015571654,0.00019188982,2.2764121e-7,0.0008167425,0.0192413],"genre_scores_gemma":[0.7947047,8.909094e-7,0.20200233,0.00090412237,0.000113632326,0.00006889664,1.3944593e-7,0.000006963291,0.0021983332],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99921554,0.000019189085,0.00014449422,0.0003266719,0.000117385214,0.0001766894],"domain_scores_gemma":[0.9992837,0.00002655932,0.000039903993,0.0005059393,0.000045435474,0.00009848415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021859218,0.000086131535,0.000078038785,0.00009206869,0.00015297745,0.00014053205,0.00038332757,0.000047633024,0.00003167299],"category_scores_gemma":[0.000021198211,0.00007738025,0.000048828184,0.00031556733,0.000010197806,0.00021023411,0.00011039299,0.00009292044,0.00029208665],"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":[0.000002109405,0.000026948204,0.000026466992,0.0000031859774,0.0000041306494,2.8646787e-7,0.00014243259,0.000050375696,0.1459671,0.11119788,0.0006832194,0.74189585],"study_design_scores_gemma":[0.00014269256,0.0002688641,0.0018461484,0.0000045031516,0.000003789372,0.000007842486,0.000018335,0.090008855,0.7078236,0.013055466,0.18652004,0.0002998728],"about_ca_topic_score_codex":0.000065414955,"about_ca_topic_score_gemma":0.000023741139,"teacher_disagreement_score":0.79062545,"about_ca_system_score_codex":0.000038870545,"about_ca_system_score_gemma":0.00000871097,"threshold_uncertainty_score":0.37542802},"labels":[],"label_agreement":null},{"id":"W1977437156","doi":"10.1007/s10115-010-0347-3","title":"Class separation through variance: a new application of outlier detection","year":2010,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Mahalanobis distance; Linear subspace; Outlier; Subspace topology; Anomaly detection; Pattern recognition (psychology); Computer science; Artificial intelligence; Ranking (information retrieval); Variance (accounting); Class (philosophy); Data mining; Mathematics; Algorithm","score_opus":0.008671805795515073,"score_gpt":0.26656406260817367,"score_spread":0.2578922568126586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977437156","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":[0.003024511,0.000063575964,0.9738854,0.000088003275,0.00034934972,0.00042665246,0.0000025890695,0.00019776245,0.021962157],"genre_scores_gemma":[0.9929659,0.000025134854,0.006387071,0.0000406039,0.00012540864,0.00013787637,0.000008481117,0.0000036264707,0.00030591176],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99921954,0.000019075671,0.00041813636,0.0001297049,0.000117803276,0.00009574486],"domain_scores_gemma":[0.9990529,0.000024222789,0.00028025673,0.0003515299,0.00023599157,0.000055129825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021879606,0.00009098149,0.00012115571,0.00010905864,0.0001358579,0.00013142182,0.00019700587,0.00012493411,0.0000025730676],"category_scores_gemma":[0.000014524379,0.000085241394,0.000032472806,0.00041888515,0.000025938974,0.0030278238,0.000050440634,0.000121127094,0.00010053465],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000050453573,0.000029441166,0.00011039145,0.000075991935,0.000009886669,1.890105e-8,0.0025288472,0.00004184838,0.017638681,0.73964965,0.0016090974,0.23830108],"study_design_scores_gemma":[0.00025038226,0.00006139628,0.00097239343,0.000010960773,0.0000062664553,0.000023286166,0.00008582873,0.19543631,0.020319602,0.0018629775,0.7808281,0.00014248202],"about_ca_topic_score_codex":0.000115344585,"about_ca_topic_score_gemma":0.00002315689,"teacher_disagreement_score":0.98994136,"about_ca_system_score_codex":0.000019129197,"about_ca_system_score_gemma":0.00005555339,"threshold_uncertainty_score":0.34760433},"labels":[],"label_agreement":null},{"id":"W1980290952","doi":"10.1080/00085030.2002.10757543","title":"Are Mouth Alcohol Defenses “Valid” or “Invalid”? The BAC Datamaster C™ “Invalid Sample” Status Message","year":2002,"lang":"en","type":"article","venue":"Canadian Society of Forensic Science Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Provincial Health Services Authority","funders":"","keywords":"Sample (material); Exhalation; Test (biology); Protocol (science); Alcohol; Medicine; Biology; Chromatography; Pathology; Chemistry; Anesthesia","score_opus":0.07981706598229137,"score_gpt":0.2863685890023563,"score_spread":0.2065515230200649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980290952","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":[0.40863937,0.0012830663,0.5341688,0.044331368,0.0021473365,0.0017569708,0.00075717707,0.000516825,0.006399122],"genre_scores_gemma":[0.8532636,0.00031146765,0.14004105,0.0053762654,0.00020551583,0.000019421883,0.0000020098153,0.000019191697,0.0007614877],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99730206,0.00005136061,0.00045097715,0.00045983223,0.0008134517,0.00092233106],"domain_scores_gemma":[0.997105,0.00014795018,0.00053579913,0.0009498699,0.00040622047,0.0008551795],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0011622071,0.00020799998,0.00024048422,0.00018289128,0.0018671314,0.0006963315,0.0022477638,0.000087850494,0.00031862772],"category_scores_gemma":[0.00018909397,0.00014089947,0.00026703984,0.0017583707,0.0013693172,0.000998759,0.00020514542,0.00042929605,0.000020422765],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007875538,0.000108420965,0.009631544,0.000041098185,0.00012337128,0.000049514,0.01828208,0.00016667534,0.00211879,0.009499528,0.5945196,0.36545146],"study_design_scores_gemma":[0.0025345148,0.0006669522,0.08695069,0.000502344,0.00020704638,0.0038741108,0.028555665,0.1066446,0.058796562,0.023165379,0.685417,0.0026851385],"about_ca_topic_score_codex":0.0034122502,"about_ca_topic_score_gemma":0.0049650893,"teacher_disagreement_score":0.44462422,"about_ca_system_score_codex":0.00040403832,"about_ca_system_score_gemma":0.0008009238,"threshold_uncertainty_score":0.9994323},"labels":[],"label_agreement":null},{"id":"W1983103633","doi":"10.1109/cvprw.2009.5206686","title":"Abnormal events detection based on spatio-temporal co-occurences","year":2009,"lang":"en","type":"article","venue":"2009 IEEE Conference on Computer Vision and Pattern Recognition","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":150,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Office of Naval Research; U.S. Department of Homeland Security; National Science Foundation","keywords":"Computer science; Background subtraction; Pixel; Artificial intelligence; Event (particle physics); Pattern recognition (psychology); Markov random field; Markov chain; Path (computing); Abnormality; Computer vision; Object detection; Contrast (vision); Image segmentation; Image (mathematics); Machine learning","score_opus":0.034956409123311175,"score_gpt":0.2899651683389382,"score_spread":0.25500875921562705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983103633","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":[0.07732032,0.0000044086887,0.9199808,0.00091250305,0.00033324107,0.00032296323,0.000023749893,0.00034217886,0.000759797],"genre_scores_gemma":[0.98949605,0.000028702105,0.007591177,0.0025859536,0.00015704337,0.000032794826,0.00006768187,0.0000076041288,0.000033014523],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983922,0.00012208277,0.0003152422,0.0005931911,0.00033467074,0.00024261777],"domain_scores_gemma":[0.99911565,0.00006211229,0.00019049612,0.00034650994,0.00013850241,0.00014672063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021051905,0.00025573923,0.00019369503,0.0002763676,0.0002875307,0.000256699,0.00030585346,0.00013920157,0.00007767673],"category_scores_gemma":[0.0000036916867,0.00022899483,0.000080195656,0.00023872056,0.000030248733,0.00037766455,0.000020959893,0.0002452958,0.00017777285],"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":[0.000027333037,0.00017463922,0.00021702243,0.0000059573636,0.0000028283375,0.00000440256,0.00003117726,0.000050860315,0.00022997777,0.00012952629,0.00059221685,0.9985341],"study_design_scores_gemma":[0.0008178629,0.00420821,0.029621836,0.00026002008,0.000008532831,0.000024266268,0.000005558427,0.9434538,0.014571103,0.0048058587,0.0016888782,0.0005340847],"about_ca_topic_score_codex":0.000022240767,"about_ca_topic_score_gemma":0.0000140542,"teacher_disagreement_score":0.99799997,"about_ca_system_score_codex":0.00003287737,"about_ca_system_score_gemma":0.000032533102,"threshold_uncertainty_score":0.9338138},"labels":[],"label_agreement":null},{"id":"W1984457016","doi":"10.1002/acp.1152","title":"Improving police decision making: general principles and practical applications of receiver operating characteristic analysis","year":2005,"lang":"en","type":"article","venue":"Applied Cognitive Psychology","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Receiver operating characteristic; Context (archaeology); Identification (biology); Statement (logic); Psychology; Computer science; Machine learning; Law","score_opus":0.026726464975567483,"score_gpt":0.3611728707117704,"score_spread":0.33444640573620293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984457016","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":[0.082254,0.000019238063,0.91164684,0.000318469,0.000013057435,0.00040101886,0.0000102273625,0.00010661852,0.005230513],"genre_scores_gemma":[0.7183148,0.000023598292,0.2804871,0.00072914636,0.00007100652,0.00033394838,0.0000061843707,0.000007407815,0.000026815236],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9986576,0.000040642608,0.00039086092,0.00057739037,0.00012578254,0.0002077681],"domain_scores_gemma":[0.99880904,0.0003006323,0.00027956686,0.0003791379,0.00016176995,0.000069853726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024322806,0.00013937362,0.00023364786,0.00028100074,0.00018312877,0.00005793697,0.0002202616,0.00010974642,0.00003489135],"category_scores_gemma":[0.00003416281,0.00013957142,0.00006073419,0.00082774245,0.00012785105,0.00015776769,0.00019506174,0.00017507975,0.000025211753],"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":[0.000029604442,0.00019601943,0.0010222049,0.000007988579,0.00013517703,8.563279e-7,0.00037676297,0.000018472801,0.013830508,0.09881065,0.000035189765,0.88553655],"study_design_scores_gemma":[0.0037957884,0.0007770586,0.61592674,0.00009527278,0.0018303063,0.0005533072,0.0008061422,0.2852657,0.041882265,0.020278115,0.026219001,0.0025702652],"about_ca_topic_score_codex":0.000020749769,"about_ca_topic_score_gemma":0.000018758064,"teacher_disagreement_score":0.8829663,"about_ca_system_score_codex":0.000020926434,"about_ca_system_score_gemma":0.00002718149,"threshold_uncertainty_score":0.5691557},"labels":[],"label_agreement":null},{"id":"W1989730063","doi":"10.1109/iccrd.2011.5763846","title":"Hard hat detection in video sequences based on face features, motion and color information","year":2011,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"IntelliView Technologies (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Construct (python library); Face (sociological concept); Face detection; Computer vision; Artificial intelligence; Motion (physics); Video processing; Motion detection; Computer graphics (images); Facial recognition system; Feature extraction","score_opus":0.016981594105945113,"score_gpt":0.21819932832660935,"score_spread":0.20121773422066425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989730063","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":[0.033236567,0.0000043345995,0.9608261,0.00035199942,0.00004461892,0.00025018636,9.501931e-7,0.0002617358,0.0050234976],"genre_scores_gemma":[0.9664133,0.00000669376,0.032944605,0.00044883162,0.000005690171,0.000086129025,0.0000016025352,0.0000019210943,0.00009117835],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994952,0.000023303515,0.00013571954,0.00015083377,0.00010100449,0.000093935],"domain_scores_gemma":[0.9996641,0.00001881965,0.00006120745,0.00018637483,0.000034642362,0.000034834033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015222188,0.00007140463,0.00005783787,0.00016866556,0.000090986905,0.000080495585,0.00014694338,0.00006658416,0.000012252966],"category_scores_gemma":[0.000012929961,0.0000618427,0.000017639475,0.00029139814,0.000022587476,0.00090512127,0.000029028977,0.00008602421,0.000022260661],"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":[0.00004311221,0.00010365514,0.0013693734,0.000023500419,0.0000041127764,0.0000012788812,0.0011407577,0.00037348265,0.0048045833,0.07246728,0.00032858388,0.91934025],"study_design_scores_gemma":[0.00038078811,0.00043890317,0.1980512,0.000019937796,0.000003377208,0.000014579223,0.00014908703,0.5274997,0.2652705,0.0048489505,0.0030593108,0.0002636786],"about_ca_topic_score_codex":0.00038119077,"about_ca_topic_score_gemma":0.00010715099,"teacher_disagreement_score":0.93317676,"about_ca_system_score_codex":0.000044807206,"about_ca_system_score_gemma":0.000011709059,"threshold_uncertainty_score":0.25218722},"labels":[],"label_agreement":null},{"id":"W1989769965","doi":"10.1145/1460412.1460451","title":"Safety assurance for archeologists using sensor network","year":2008,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science","score_opus":0.05167146054778457,"score_gpt":0.2879282298506234,"score_spread":0.23625676930283881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989769965","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031370097,0.000022005162,0.99322397,0.00057425094,0.000058006834,0.00020698117,0.0000012712572,0.00038249567,0.0023939938],"genre_scores_gemma":[0.3277579,0.00002015933,0.6706933,0.000273994,0.00006755732,0.000028575227,5.485939e-7,0.000003301941,0.0011547243],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994287,0.000013417695,0.00012715768,0.00020019274,0.000058102574,0.0001724156],"domain_scores_gemma":[0.99948245,0.000092926995,0.00004576984,0.00028810388,0.000054144846,0.000036623285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010799748,0.0000592119,0.00007773606,0.000017823437,0.00030692416,0.000014898455,0.00026110653,0.000037589492,0.000008440932],"category_scores_gemma":[0.000013012023,0.000052524996,0.00005154874,0.00020736484,0.00004633681,0.00010148561,0.00006145283,0.000042674936,0.000008236684],"study_design_candidate":"theoretical_or_conceptual","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":[0.000024204734,0.00008798233,0.004429341,0.000016933569,0.000025615702,0.000007632911,0.00013378603,0.012583339,0.0037251227,0.9242293,0.02158498,0.03315172],"study_design_scores_gemma":[0.00031661763,0.00010371676,0.011852052,0.000010419506,0.0000047884046,0.00022321827,0.000006149186,0.80086476,0.01194931,0.023853928,0.15043712,0.0003779173],"about_ca_topic_score_codex":0.000019632795,"about_ca_topic_score_gemma":0.0000036736917,"teacher_disagreement_score":0.9003754,"about_ca_system_score_codex":0.000022750271,"about_ca_system_score_gemma":0.000032701613,"threshold_uncertainty_score":0.23606434},"labels":[],"label_agreement":null},{"id":"W1989986853","doi":"10.1016/j.media.2012.11.007","title":"Regional heart motion abnormality detection: An information theoretic approach","year":2013,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CARE Canada; McGill University; London Health Sciences Centre; St Joseph's Health Care; University of Alberta","funders":"","keywords":"Abnormality; Artificial intelligence; Computer science; Motion (physics); Computer vision; Pattern recognition (psychology); Medicine","score_opus":0.008462233525864483,"score_gpt":0.24652221636517618,"score_spread":0.2380599828393117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989986853","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":[0.010219525,0.0000067190076,0.9850606,0.0020388903,0.000020267635,0.00016218417,9.2124986e-7,0.00036197924,0.0021289305],"genre_scores_gemma":[0.94022906,0.000010904673,0.058060367,0.0013520736,0.000065046755,0.00018780527,0.000040271585,0.000003422112,0.000051065243],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848825,0.00012315376,0.0003479786,0.00026293573,0.0005903354,0.00018736774],"domain_scores_gemma":[0.99876106,0.00003181094,0.00010600105,0.0006185492,0.00023132247,0.0002512656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000586244,0.000108332984,0.00017161282,0.0002812861,0.0002286326,0.00028481154,0.00052732055,0.000115370574,0.00063064945],"category_scores_gemma":[0.000056587618,0.0000912422,0.0001807244,0.0016409167,0.00013322251,0.0024240962,0.000097558805,0.00019327203,0.00023334073],"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":[0.000010461087,0.00094417384,0.0027100097,0.00006978837,0.000553886,0.000004554587,0.0012502299,0.00038352588,0.0012506958,0.10004853,0.0067337356,0.8860404],"study_design_scores_gemma":[0.00011084877,0.000047137764,0.022436345,0.0000019298627,0.00008759176,0.00003952864,0.00008268538,0.96687376,0.0013496301,0.006254827,0.002539018,0.00017669881],"about_ca_topic_score_codex":0.00041718653,"about_ca_topic_score_gemma":0.000010814946,"teacher_disagreement_score":0.9664902,"about_ca_system_score_codex":0.000039790433,"about_ca_system_score_gemma":0.000029117267,"threshold_uncertainty_score":0.6905169},"labels":[],"label_agreement":null},{"id":"W1990106316","doi":"10.1145/2487575.2487676","title":"Subsampling for efficient and effective unsupervised outlier detection ensembles","year":2013,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":190,"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":"","keywords":"Outlier; Anomaly detection; Detector; Computer science; Intuition; Artificial intelligence; Ensemble learning; Pattern recognition (psychology); Data mining; Local outlier factor; Machine learning","score_opus":0.008596002049371925,"score_gpt":0.22885866899211763,"score_spread":0.2202626669427457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990106316","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":[0.19640173,0.000015850272,0.8018174,0.00020614604,0.000037423386,0.0008460184,4.798767e-7,0.0003048895,0.0003700364],"genre_scores_gemma":[0.92765063,0.000003459568,0.0710739,0.00011387966,0.000023486862,0.0009872372,3.9581954e-7,0.0000058801525,0.0001411318],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943084,0.000012946639,0.00010510235,0.00025717978,0.00005848325,0.00013546168],"domain_scores_gemma":[0.99950355,0.00011880537,0.000030377572,0.00019839293,0.0000952801,0.000053593478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010197989,0.00007752311,0.0000750441,0.00006417216,0.00020139175,0.00012971659,0.00013008146,0.000043205324,0.000009140155],"category_scores_gemma":[0.000014080666,0.00006471188,0.000037771322,0.00015345185,0.000020143732,0.00012970946,0.00005976423,0.000041148338,0.0000216457],"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":[0.0000046470395,0.000063323314,0.00015768655,0.000022867453,0.000014514345,9.936662e-8,0.0002984254,0.00016353794,0.11041977,0.046575114,0.00018109499,0.8420989],"study_design_scores_gemma":[0.00034616175,0.00020771312,0.008909455,0.0000074267023,0.0000072773796,0.000010052602,0.0000789496,0.61461425,0.36133805,0.011229499,0.0030181585,0.00023301571],"about_ca_topic_score_codex":0.00006756769,"about_ca_topic_score_gemma":0.0000053694366,"teacher_disagreement_score":0.8418659,"about_ca_system_score_codex":0.00002050935,"about_ca_system_score_gemma":0.0000053066365,"threshold_uncertainty_score":0.26388738},"labels":[],"label_agreement":null},{"id":"W1991959600","doi":"10.1007/s10115-008-0145-3","title":"Resolution-based outlier factor: detecting the top-n most outlying data points in engineering data","year":2008,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Outlier; Anomaly detection; Data mining; Computer science; Identification (biology); Local outlier factor; Rank (graph theory); Nonparametric statistics; Domain (mathematical analysis); Artificial intelligence; Mathematics; Statistics","score_opus":0.05467301865057345,"score_gpt":0.27022438014433875,"score_spread":0.2155513614937653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991959600","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":[0.0077521675,0.0003638553,0.989426,0.00016483403,0.00023585919,0.00039657977,0.000055065655,0.00024059598,0.0013650467],"genre_scores_gemma":[0.99668777,0.000026441814,0.003007786,0.00006665395,0.000061302075,0.00003903719,0.0000719672,0.000004710284,0.000034352775],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990644,0.00003502484,0.00040406207,0.00019903296,0.00013736526,0.00016012859],"domain_scores_gemma":[0.99827373,0.000113415445,0.00012736073,0.0013607689,0.00007928891,0.00004541376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054812594,0.00010015873,0.0001114876,0.00016321069,0.00031504995,0.00019540197,0.0011298617,0.00005829458,0.0000016970275],"category_scores_gemma":[0.00010974147,0.0000785164,0.000011715091,0.00048212914,0.000021166526,0.0034002485,0.00057248725,0.00014731912,0.00004179456],"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":[0.00005546916,0.00047651495,0.09279066,0.0020645945,0.00021220559,0.000012443346,0.058679115,0.019774754,0.0010366612,0.09381883,0.05334174,0.677737],"study_design_scores_gemma":[0.0001465762,0.0000075224125,0.0036550632,0.000037278554,0.0000014537954,0.000013333463,0.000103935,0.8664418,0.00007154227,0.0000037300472,0.12941802,0.000099780955],"about_ca_topic_score_codex":0.00009437228,"about_ca_topic_score_gemma":0.000013651289,"teacher_disagreement_score":0.9889356,"about_ca_system_score_codex":0.0000426557,"about_ca_system_score_gemma":0.00006961913,"threshold_uncertainty_score":0.3201806},"labels":[],"label_agreement":null},{"id":"W1992276041","doi":"10.1145/2791347.2791352","title":"On the internal evaluation of unsupervised outlier detection","year":2015,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":39,"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":"Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Outlier; Anomaly detection; Computer science; Cluster analysis; Artificial intelligence; Pattern recognition (psychology); Data mining; Domain (mathematical analysis); Contrast (vision); Mathematics","score_opus":0.08291572072490964,"score_gpt":0.30411249232140336,"score_spread":0.2211967715964937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992276041","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":[0.10044751,0.0000050979047,0.8769707,0.00067277893,0.00006796767,0.00017484972,2.071384e-7,0.00009375243,0.021567112],"genre_scores_gemma":[0.99682856,6.3217857e-7,0.0026769426,0.00018126071,0.00001603205,0.000060436145,1.5838113e-7,0.0000021715782,0.00023381655],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993543,0.000066095134,0.00010660055,0.000108328495,0.00031414456,0.000050555453],"domain_scores_gemma":[0.99932355,0.00003728827,0.00004750885,0.00031407317,0.00025009247,0.000027503056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079874665,0.00003988709,0.000037797778,0.00004298883,0.00004080569,0.000026722133,0.00028595742,0.000022042228,0.000043983262],"category_scores_gemma":[0.00005916819,0.00002472359,0.000028444143,0.00019558119,0.000017110744,0.00009426895,0.000047099245,0.000049702365,0.000043494852],"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":[0.000011763951,0.000109694374,0.000094051116,0.000001646327,0.00001534216,9.919886e-8,0.00052841555,0.0005033374,0.0073718256,0.5129696,0.0033998336,0.47499436],"study_design_scores_gemma":[0.0002674619,0.00020526198,0.0010633724,0.0000054422503,0.000008898327,0.0000040973487,0.00010702012,0.6380404,0.25794688,0.10026844,0.0020054001,0.00007731362],"about_ca_topic_score_codex":0.000045747118,"about_ca_topic_score_gemma":0.0000107179485,"teacher_disagreement_score":0.896381,"about_ca_system_score_codex":0.00004166605,"about_ca_system_score_gemma":0.00003289583,"threshold_uncertainty_score":0.10081987},"labels":[],"label_agreement":null},{"id":"W1995539694","doi":"10.1002/atr.130","title":"The analysis of motor vehicle crash clusters using the vector quantization technique","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Vector quantization; Crash; A priori and a posteriori; Probability density function; Computer science; Quantization (signal processing); Self-organizing map; Motor vehicle crash; Data mining; Artificial intelligence; Mathematics; Algorithm; Statistics; Poison control; Cluster analysis","score_opus":0.008781043849719599,"score_gpt":0.27114097302222656,"score_spread":0.26235992917250694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995539694","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":[0.37727243,0.000014803028,0.62217706,0.000299037,0.000095204814,0.00012002165,0.0000022403106,0.00001507471,0.000004117686],"genre_scores_gemma":[0.9246458,0.00004202811,0.07523453,0.000025529733,0.000027927405,0.000011156759,0.0000014383465,0.000004618544,0.0000069798716],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9991347,0.000027520271,0.00044853,0.00008881042,0.00021938127,0.00008105626],"domain_scores_gemma":[0.99872327,0.00009649839,0.0005666274,0.00025347265,0.0003307529,0.000029392151],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037155647,0.00006361552,0.00012638155,0.00015175322,0.00020076297,0.00003607432,0.00040192815,0.000042742624,0.0000026962687],"category_scores_gemma":[0.000019609857,0.000039252027,0.00016458072,0.0010005604,0.000056713525,0.0003711677,0.0000042967476,0.00018745527,1.3224505e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027381357,0.000043909848,0.0010525457,0.0000062141485,0.00012274062,9.538676e-7,0.00054063444,0.06268634,0.905191,0.015620901,0.0000060368425,0.01470135],"study_design_scores_gemma":[0.0004639651,0.0002738886,0.4819716,0.000037655063,0.00067736435,0.000017885312,0.0004863304,0.18611863,0.32225323,0.0047014724,0.0027519462,0.0002460443],"about_ca_topic_score_codex":0.000014608931,"about_ca_topic_score_gemma":0.00014069668,"teacher_disagreement_score":0.5829378,"about_ca_system_score_codex":0.000017728025,"about_ca_system_score_gemma":0.00004301988,"threshold_uncertainty_score":0.16006511},"labels":[],"label_agreement":null},{"id":"W1995957263","doi":"10.1007/s00530-011-0253-x","title":"Concept-based near-duplicate video clip detection for novelty re-ranking of web video search results","year":2011,"lang":"en","type":"article","venue":"Multimedia Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Computer science; Novelty; Information retrieval; Novelty detection; Ranking (information retrieval); Learning to rank; Perspective (graphical); Rank (graph theory); Artificial intelligence; Semantics (computer science); Cluster analysis; Pattern recognition (psychology)","score_opus":0.06603045666467774,"score_gpt":0.2847049081483572,"score_spread":0.21867445148367945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995957263","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":[0.009466421,0.00006647221,0.9870625,0.00013638435,0.0004584727,0.0014854608,0.00008165029,0.00051973114,0.000722886],"genre_scores_gemma":[0.8676594,0.0000037488878,0.13141051,0.000038081173,0.00012129626,0.00060665276,0.0000110606425,0.00002179827,0.00012747358],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979117,0.0001035644,0.000716971,0.00059501233,0.00032135885,0.00035141708],"domain_scores_gemma":[0.99781704,0.00034740215,0.00038132293,0.0009402419,0.00038327204,0.00013073206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009636988,0.00018836129,0.00030689803,0.00015643987,0.000264474,0.000095679716,0.0007347099,0.00017930275,0.0000067986757],"category_scores_gemma":[0.00010793438,0.000179245,0.00015641314,0.00055878196,0.00014339546,0.0002313428,0.00009774496,0.0001688004,0.00003323314],"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":[0.0009301529,0.0010322431,0.0014321588,0.00084423454,0.00021533179,0.00000921205,0.011856696,0.0025810134,0.36580446,0.0056090257,0.005083753,0.60460174],"study_design_scores_gemma":[0.0009589008,0.00022273151,0.0005762709,0.000060466988,0.000009994846,0.0000041456597,0.00006288334,0.79292953,0.19800426,0.00005413282,0.006936186,0.00018051839],"about_ca_topic_score_codex":0.0011644742,"about_ca_topic_score_gemma":0.00005156238,"teacher_disagreement_score":0.858193,"about_ca_system_score_codex":0.000085469226,"about_ca_system_score_gemma":0.00012304925,"threshold_uncertainty_score":0.7309399},"labels":[],"label_agreement":null},{"id":"W1998451966","doi":"10.1145/1341012.1341075","title":"A parallel multi-scale region outlier mining algorithm for meteorological data","year":2007,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Anomaly detection; Outlier; Scalability; Data mining; Wavelet; Scale (ratio); Image resolution; Spatial analysis; Artificial intelligence; Remote sensing; Database; Geography","score_opus":0.10236016729276912,"score_gpt":0.33800060823131356,"score_spread":0.23564044093854444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998451966","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022390645,0.000020272228,0.9973292,0.00064974703,0.000052165466,0.0003062639,0.000004235015,0.00048995175,0.00092425814],"genre_scores_gemma":[0.015114317,0.0000066136295,0.98241335,0.0005749544,0.00006247722,0.00006347063,0.000011136366,0.0000058232376,0.001747868],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990286,0.000011015503,0.00019644595,0.0004465758,0.0000893356,0.00022802985],"domain_scores_gemma":[0.9988623,0.000098277975,0.000060050985,0.0008508951,0.000051193965,0.00007725495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045889942,0.00008494557,0.000099971585,0.000052161184,0.00016888508,0.00005677262,0.0009976143,0.000081309154,0.0000074563914],"category_scores_gemma":[0.000015814683,0.00006825672,0.0000450923,0.00018852258,0.000036814614,0.00025323842,0.00037383108,0.00006091323,0.000012952194],"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":[0.0000036827337,0.000098810764,0.00012484868,0.0000015863321,0.00000714696,0.0000028381942,0.00006777466,0.0000027567803,0.00020634367,0.009392953,0.0065686614,0.9835226],"study_design_scores_gemma":[0.00033831593,0.00011821952,0.0011983409,0.0000022905597,0.000006441307,0.000033568394,0.000067723064,0.89073217,0.001642749,0.0013130098,0.104371525,0.00017564437],"about_ca_topic_score_codex":0.00001865905,"about_ca_topic_score_gemma":0.000025624193,"teacher_disagreement_score":0.98334694,"about_ca_system_score_codex":0.000014551913,"about_ca_system_score_gemma":0.0000119964,"threshold_uncertainty_score":0.2783428},"labels":[],"label_agreement":null},{"id":"W1999301031","doi":"10.1142/s0219878911002379","title":"ABNORMAL MOTION DETECTION IN REAL TIME USING VIDEO SURVEILLANCE AND BODY SENSORS","year":2011,"lang":"en","type":"article","venue":"International Journal of Information Acquisition","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"University of Illinois at Urbana-Champaign","keywords":"Computer science; Computer vision; Artificial intelligence; Optical flow; Motion (physics); Accelerometer; Orientation (vector space); Motion detection; Image (mathematics)","score_opus":0.010891257160208497,"score_gpt":0.24665212318614077,"score_spread":0.23576086602593227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999301031","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":[0.4107575,0.000005085166,0.5877298,0.000088877605,0.000197292,0.000072167386,0.000002897,0.0000429334,0.0011033865],"genre_scores_gemma":[0.97969574,0.00006382667,0.020037187,0.0001072019,0.00007739914,0.0000036701451,0.0000047701233,0.000003339024,0.00000686006],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888027,0.000048932194,0.000587927,0.00007916292,0.00030731654,0.00009639756],"domain_scores_gemma":[0.998726,0.000024579378,0.0005790607,0.0000998431,0.00052104297,0.000049488906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005131071,0.000084342195,0.00010381763,0.00056559336,0.000057176356,0.00011855801,0.000273288,0.00006801419,0.000042103904],"category_scores_gemma":[0.000027862543,0.00008456511,0.000049424918,0.00022886936,0.000025124107,0.0037792327,0.000060330305,0.00011615848,0.000019364406],"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":[0.0011252159,0.00066702196,0.028283868,0.00007423696,0.0002873396,0.00009393584,0.0117047075,0.005781967,0.14464799,0.07044939,0.0004919589,0.7363924],"study_design_scores_gemma":[0.0017765447,0.00037657825,0.3544038,0.000112820344,0.000012675103,0.002055279,0.00023918081,0.54599553,0.08575096,0.00787672,0.0009700327,0.00042987068],"about_ca_topic_score_codex":0.000076133685,"about_ca_topic_score_gemma":0.0000030593576,"teacher_disagreement_score":0.7359625,"about_ca_system_score_codex":0.0001650583,"about_ca_system_score_gemma":0.000029111807,"threshold_uncertainty_score":0.34484652},"labels":[],"label_agreement":null},{"id":"W1999518899","doi":"10.1007/s10618-014-0398-2","title":"Mining outlying aspects on numeric data","year":2015,"lang":"en","type":"article","venue":"Data Mining and Knowledge Discovery","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Subspace topology; Object (grammar); Outlier; Computer science; Data mining; Set (abstract data type); Rank (graph theory); Heuristic; Curse of dimensionality; Measure (data warehouse); Data set; Mathematics; Artificial intelligence; Combinatorics","score_opus":0.16263658103088974,"score_gpt":0.34844677869115825,"score_spread":0.18581019766026852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999518899","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":[0.07254567,0.0016884267,0.88175964,0.00066967914,0.0008041531,0.00027593801,0.0004258714,0.000728557,0.04110204],"genre_scores_gemma":[0.937066,0.000054843054,0.060456537,0.00017805956,0.0002590801,0.000018811255,0.00057472585,0.000018663843,0.0013732614],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986051,0.00004334907,0.00019207786,0.0008007951,0.00014208707,0.00021659571],"domain_scores_gemma":[0.99695504,0.000121453006,0.00008735331,0.0026680815,0.000036856356,0.00013119263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005112116,0.00014537357,0.00015623852,0.00009960863,0.00019491969,0.00045489884,0.0021428978,0.00004924795,0.0000017522864],"category_scores_gemma":[0.0001560878,0.00013275303,0.000014611388,0.00034874293,0.00005128846,0.0019661728,0.0028872425,0.00009488208,0.00004499666],"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":[0.000020024052,0.00031613483,0.0017566299,0.000040877363,0.00005550854,0.000023881714,0.0035272837,0.000006282246,0.00025223912,0.028677413,0.18960401,0.7757197],"study_design_scores_gemma":[0.0011330822,0.0005093457,0.00179157,0.0003607581,0.00007027188,0.00013664975,0.004546369,0.47009313,0.0015384731,0.0017368217,0.5167896,0.0012939202],"about_ca_topic_score_codex":0.000032066182,"about_ca_topic_score_gemma":0.00003073827,"teacher_disagreement_score":0.8645204,"about_ca_system_score_codex":0.000022532917,"about_ca_system_score_gemma":0.00014128882,"threshold_uncertainty_score":0.54135114},"labels":[],"label_agreement":null},{"id":"W2000219982","doi":"10.1109/icde.2014.6816641","title":"Scalable distance-based outlier detection over high-volume data streams","year":2014,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":119,"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":"Ministère de l'Économie, de la Science et de l'Innovation - Québec; National Science Foundation","keywords":"Anomaly detection; Outlier; Computer science; Scalability; Data mining; Data stream mining; Ranging; Data point; Cluster analysis; Scale (ratio); Big data; Process (computing); Credit card fraud; Object (grammar); Artificial intelligence; Credit card; Database","score_opus":0.013463939228918179,"score_gpt":0.23764094288604612,"score_spread":0.22417700365712795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000219982","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":[0.0034442497,0.0000056741014,0.990861,0.0006473613,0.00011465425,0.00013820006,0.000009599684,0.00076738215,0.0040118955],"genre_scores_gemma":[0.9153566,0.0000021705257,0.081356965,0.0003984295,0.00007296508,0.00004069547,0.000016648488,0.000009234127,0.0027462924],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894,0.000031087897,0.0001697374,0.00049267523,0.00017942564,0.0001870961],"domain_scores_gemma":[0.9980195,0.000033154487,0.00007045591,0.0017498557,0.000049943825,0.00007710055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021407608,0.000106375155,0.00010156292,0.00005836173,0.00019060512,0.00016936331,0.0010218214,0.0000614682,0.00013422007],"category_scores_gemma":[0.000017362372,0.00009430935,0.000031711726,0.00035324454,0.000036126152,0.0005033141,0.00025083544,0.00009001168,0.00016821662],"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":[0.000008787462,0.00020696995,0.0020051955,0.000015391626,0.00001395084,7.358485e-7,0.000014968415,0.00026932533,0.0038254354,0.15279633,0.024266837,0.81657606],"study_design_scores_gemma":[0.00016706985,0.00006255346,0.003059517,0.000004038318,0.0000046897403,0.0000011618027,0.0000020690195,0.7184916,0.01840877,0.003463312,0.25617597,0.0001592512],"about_ca_topic_score_codex":0.00032958895,"about_ca_topic_score_gemma":0.00016918746,"teacher_disagreement_score":0.9119123,"about_ca_system_score_codex":0.00003938044,"about_ca_system_score_gemma":0.000022581944,"threshold_uncertainty_score":0.38458237},"labels":[],"label_agreement":null},{"id":"W2001022356","doi":"10.1109/dexa.2014.45","title":"eXsight: An Analytical Framework for Quantifying Financial Loss in the Aftermath of Catastrophic Events","year":2014,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Geospatial analysis; Computer science; Big data; Context (archaeology); Data science; Portfolio; Event (particle physics); Analytics; Data analysis; Categorical variable; Risk analysis (engineering); Data mining; Finance; Machine learning; Geography; Business; Cartography","score_opus":0.04519814737575957,"score_gpt":0.33507642159713014,"score_spread":0.2898782742213706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001022356","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":[0.11765942,0.00000266002,0.8814543,0.0005896553,0.000022763961,0.00015475685,0.0000019807146,0.000037709837,0.00007679288],"genre_scores_gemma":[0.83931315,0.0000011251888,0.16032681,0.00024656535,0.0000358204,0.00006329396,0.0000012179896,0.0000024206574,0.000009571518],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993923,0.000037929043,0.00017447595,0.00016984661,0.000103238664,0.00012222354],"domain_scores_gemma":[0.99936724,0.00010244787,0.000052853004,0.00042035506,0.00003052449,0.000026556974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031031595,0.00005688173,0.00009003,0.000052196603,0.000066958506,0.000024485942,0.0005917352,0.000050571463,0.000003702585],"category_scores_gemma":[0.00004399531,0.000039092418,0.00004631162,0.00026234472,0.000025793632,0.00013052764,0.00004971707,0.00008518999,0.0000030747522],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030357673,0.00006295748,0.0013496055,0.0000059983795,8.1888777e-7,2.0310132e-7,0.00014183053,0.000013033993,0.00005265972,0.98968357,0.000055303113,0.0086309565],"study_design_scores_gemma":[0.00034067163,0.0005588259,0.13593896,0.000052245596,0.000012116862,0.000015642287,0.000057041183,0.4083005,0.0042648357,0.44242007,0.007744895,0.00029422427],"about_ca_topic_score_codex":0.000020180069,"about_ca_topic_score_gemma":0.000014432947,"teacher_disagreement_score":0.72165376,"about_ca_system_score_codex":0.0000076138385,"about_ca_system_score_gemma":0.000017528564,"threshold_uncertainty_score":0.15941425},"labels":[],"label_agreement":null},{"id":"W2001171101","doi":"10.4271/2014-01-2388","title":"Analysis of Video Event Recorder Data Used for Accident Reconstruction","year":2014,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Nuclear Waste Management Organization","funders":"","keywords":"Computer science; Event (particle physics); Computer graphics (images); Accident (philosophy); Computer vision; Artificial intelligence","score_opus":0.02205592320618488,"score_gpt":0.2904983003012483,"score_spread":0.2684423770950634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001171101","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":[0.67208415,0.000745007,0.20082344,0.04620992,0.0024870897,0.013551459,0.0012216106,0.021987773,0.040889572],"genre_scores_gemma":[0.92341274,0.0001505833,0.074635774,0.0007286626,0.00009621323,0.00059922703,0.00012737239,0.00004675577,0.00020264162],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9955169,0.00016219125,0.0013781784,0.0016662831,0.0006778451,0.0005986278],"domain_scores_gemma":[0.9939052,0.0007402628,0.0005978181,0.004258539,0.00023756352,0.00026065137],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013955104,0.0004979411,0.0010026348,0.0005511604,0.00039020236,0.00014293058,0.0032441146,0.00053740194,0.00021064846],"category_scores_gemma":[0.0006972941,0.00044881323,0.00065096124,0.0023794037,0.0004644726,0.0009022632,0.0010611899,0.00053873466,0.000023621353],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008213663,0.00032743544,0.0006028325,0.000027125521,0.00024000829,0.0000010977043,0.000016481325,0.000063228465,0.8391706,0.06078107,0.0026794437,0.09600859],"study_design_scores_gemma":[0.00048219608,0.0010466828,0.89759207,0.00008114487,0.00046256627,0.0000338815,0.000029700726,0.00006232479,0.00081447704,0.009091433,0.08968935,0.00061416236],"about_ca_topic_score_codex":0.00008227257,"about_ca_topic_score_gemma":0.045873456,"teacher_disagreement_score":0.8969892,"about_ca_system_score_codex":0.00018101606,"about_ca_system_score_gemma":0.000090389345,"threshold_uncertainty_score":0.9997964},"labels":[],"label_agreement":null},{"id":"W2003570164","doi":"10.1016/j.cogsys.2008.08.001","title":"Brain activation detection by neighborhood one-class SVM","year":2008,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Support vector machine; Hyperplane; Pattern recognition (psychology); Artificial intelligence; Computer science; Cluster analysis; Fuzzy logic; Voxel; Kernel (algebra); Class (philosophy); Consistency (knowledge bases); Data mining; Mathematics","score_opus":0.10219537301556328,"score_gpt":0.3559179362765997,"score_spread":0.25372256326103637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003570164","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":[0.02217558,0.0001245657,0.9603502,0.0008185953,0.00008113183,0.0009467764,0.000011294331,0.00039759974,0.01509423],"genre_scores_gemma":[0.9956118,0.000045675064,0.00021861905,0.00009154821,0.0001495775,0.0007811139,0.000011269541,0.000017890665,0.0030724967],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997535,0.00047024505,0.00026584658,0.0005443062,0.00076387543,0.00042073964],"domain_scores_gemma":[0.99780947,0.0005665176,0.000094783856,0.00039774246,0.0009865632,0.00014490083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009124551,0.00012824348,0.00015723436,0.0003395391,0.0009075138,0.00018167136,0.0004814382,0.00014296737,0.000017031389],"category_scores_gemma":[0.00032310307,0.0001324697,0.000056194578,0.0014556535,0.00016159304,0.0005571812,0.00019002713,0.00046491125,0.00031339822],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008704054,0.000879952,0.0009413529,0.00013335873,0.00014602326,0.000023046898,0.0016639037,0.0000100679945,0.5156612,0.050209563,0.042516217,0.38772827],"study_design_scores_gemma":[0.0012605716,0.0010956856,0.0060459673,0.00024002758,0.0000072794583,0.00017832941,0.00095193577,0.062880866,0.84987444,0.0034137834,0.07328445,0.00076668424],"about_ca_topic_score_codex":0.00027653895,"about_ca_topic_score_gemma":0.000005224638,"teacher_disagreement_score":0.97343624,"about_ca_system_score_codex":0.00018829766,"about_ca_system_score_gemma":0.000101472375,"threshold_uncertainty_score":0.6979954},"labels":[],"label_agreement":null},{"id":"W2004882387","doi":"10.1109/isspa.2012.6310529","title":"An introduction to deep learning","year":2012,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":137,"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","funders":"","keywords":"MNIST database; Artificial intelligence; Computer science; Deep learning; Machine learning; Representation (politics); Stack (abstract data type); Layer (electronics); Pattern recognition (psychology); Feature learning; Support vector machine","score_opus":0.008580058003336756,"score_gpt":0.26111845433381736,"score_spread":0.2525383963304806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004882387","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":[0.009152542,0.000004947749,0.9853438,0.0023132702,0.00007909573,0.000055691104,1.793874e-8,0.00056666305,0.002483942],"genre_scores_gemma":[0.7765576,8.1569954e-7,0.2221767,0.00020394467,0.00036606402,0.000022980328,3.8751733e-7,0.0000021833546,0.00066927396],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996536,0.000014055896,0.000052755393,0.00011784874,0.000050446186,0.000111340494],"domain_scores_gemma":[0.99963325,0.000003957543,0.000013210795,0.00023625292,0.000022887705,0.000090463036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012412411,0.00003207166,0.000027140215,0.000043529286,0.00009793153,0.000041849922,0.00017194834,0.000016527672,0.0000662662],"category_scores_gemma":[0.000005815932,0.000029217328,0.000011276778,0.00022391918,0.000003935045,0.0004453901,0.00004009358,0.000046894533,0.00018562573],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.434468e-7,0.00004913337,0.0009880735,6.207234e-7,0.0000012815053,3.1248383e-8,0.0003216244,0.00017173994,0.013195501,0.60100216,0.0020474126,0.3822219],"study_design_scores_gemma":[0.00003135181,0.00014841929,0.009723189,4.2759416e-7,0.0000016070551,0.000015906588,0.00008635672,0.027239773,0.07238413,0.0011275689,0.8890766,0.0001646902],"about_ca_topic_score_codex":0.000008041334,"about_ca_topic_score_gemma":0.0000013428252,"teacher_disagreement_score":0.8870292,"about_ca_system_score_codex":0.000013976926,"about_ca_system_score_gemma":0.0000020257241,"threshold_uncertainty_score":0.23859051},"labels":[],"label_agreement":null},{"id":"W2008513768","doi":"10.4028/www.scientific.net/aef.6-7.621","title":"Outlier Detection Algorithm Basing on Similarity Measurement Relation","year":2012,"lang":"en","type":"article","venue":"Advanced engineering forum","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"La Cité Collégiale","funders":"Government of Jiangsu Province","keywords":"Outlier; Anomaly detection; Similarity (geometry); Data mining; Relation (database); Computer science; Intrusion detection system; Pattern recognition (psychology); Cluster analysis; Metric (unit); Field (mathematics); Data set; Algorithm; Artificial intelligence; Dimension (graph theory); Credit card fraud; Credit card; Mathematics; Image (mathematics); Engineering","score_opus":0.011662999962652569,"score_gpt":0.21727306626434292,"score_spread":0.20561006630169035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008513768","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":[0.0018062952,0.000071761366,0.99625987,0.00021439153,0.0003950118,0.00016404862,8.701995e-7,0.0007367283,0.00035104813],"genre_scores_gemma":[0.7873828,0.0000065306986,0.2123802,0.00007602557,0.00005650626,0.000062520616,8.047744e-7,0.000011686249,0.000022945895],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991704,0.000008592503,0.00014079433,0.00017878736,0.00021577188,0.00028565514],"domain_scores_gemma":[0.9994982,0.000018171033,0.00004677277,0.00030665885,0.00005086059,0.00007932301],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020382693,0.00011182359,0.000075894546,0.00009038592,0.0001386875,0.000029972456,0.00014432885,0.000057116344,0.0000021939702],"category_scores_gemma":[0.000030160447,0.00011842464,0.0000468107,0.00025497028,0.0000055326877,0.00058800733,0.00004407332,0.00015230544,0.000024199704],"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":[0.000002547297,0.00006837134,0.00011979056,0.00000825597,0.000011667832,3.3227934e-7,0.000078772224,0.020845853,0.027842104,0.018606856,0.000050988237,0.93236446],"study_design_scores_gemma":[0.00027463402,0.00014290588,0.011331798,0.000044966775,0.000008910913,0.000014763172,0.000020499145,0.5790918,0.31778908,0.001320212,0.08951789,0.0004425764],"about_ca_topic_score_codex":0.0000030008189,"about_ca_topic_score_gemma":8.5784126e-7,"teacher_disagreement_score":0.9319219,"about_ca_system_score_codex":0.00019640481,"about_ca_system_score_gemma":0.000005895715,"threshold_uncertainty_score":0.4829217},"labels":[],"label_agreement":null},{"id":"W2009144907","doi":"10.1145/502512.502532","title":"Robust space transformations for distance-based operations","year":2001,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Estimator; Outlier; Computation; Cluster analysis; Transformation (genetics); Space (punctuation); Data mining; Stability (learning theory); Key (lock); Algorithm; Property (philosophy); Focus (optics); Mathematics; Artificial intelligence; Machine learning","score_opus":0.028802898010399848,"score_gpt":0.2518570733434828,"score_spread":0.22305417533308294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009144907","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00020349406,0.000009782093,0.9787984,0.010259449,0.00003218319,0.00040267376,0.000007699286,0.0005066979,0.009779649],"genre_scores_gemma":[0.43312994,0.000011827201,0.56354815,0.00048388855,0.000024951318,0.00051503564,0.00000939995,0.0000047634085,0.002272029],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950767,0.0000067275832,0.00013409561,0.00015692566,0.0000682937,0.00012629254],"domain_scores_gemma":[0.99952525,0.000029857458,0.000015683763,0.00029389095,0.00008496706,0.00005033268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006913058,0.00006366462,0.0000563607,0.000056465036,0.00033630972,0.0001196458,0.0002740602,0.000030140794,0.00004610244],"category_scores_gemma":[0.0000053167996,0.000057353704,0.00006028685,0.00035220166,0.000017800221,0.00034750704,0.0000099292165,0.000036763922,0.000021534288],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000016215191,0.000053048483,0.000023696191,0.0000030898166,0.000002441434,1.3225926e-7,0.00004659829,0.010152101,0.00032224797,0.9777047,0.0032616511,0.008428666],"study_design_scores_gemma":[0.00017800998,0.00004243615,0.00008834265,0.0000028314828,0.000003103794,0.000003526279,0.000017556109,0.7629683,0.0062144343,0.0030580896,0.22731589,0.00010747818],"about_ca_topic_score_codex":0.000017992867,"about_ca_topic_score_gemma":0.00011168427,"teacher_disagreement_score":0.9746466,"about_ca_system_score_codex":0.000025966117,"about_ca_system_score_gemma":0.000042575713,"threshold_uncertainty_score":0.25866565},"labels":[],"label_agreement":null},{"id":"W2011388517","doi":"10.1109/icde.2008.4497638","title":"SPOT: A System for Detecting Projected Outliers From High-dimensional Data Streams","year":2008,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"STREAMS; Computer science; Data stream mining; Outlier; Anomaly detection; Data mining; Artificial intelligence; Computer network","score_opus":0.04782631951930627,"score_gpt":0.2634963900847217,"score_spread":0.21567007056541543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011388517","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":[0.11348225,0.000011781454,0.88420814,0.0001740809,0.000114664144,0.0004607814,0.00007345756,0.0011413711,0.00033346438],"genre_scores_gemma":[0.66582775,9.5226136e-7,0.3336922,0.000053817963,0.00006631512,0.00009569138,0.000044201028,0.000007500525,0.00021154876],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988623,0.000019128418,0.00022394003,0.00054537295,0.00016274369,0.00018650723],"domain_scores_gemma":[0.998547,0.00010764874,0.00009745544,0.0010899812,0.000090548616,0.00006733772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012571056,0.00011061636,0.00013287037,0.00006553512,0.00038718537,0.000052089443,0.000929089,0.00006221186,0.000009230135],"category_scores_gemma":[0.000024561323,0.00009531613,0.000040121184,0.0002889946,0.000028225808,0.000310749,0.00036603012,0.0000759535,0.000024975034],"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":[0.00014463,0.0007842878,0.002551577,0.00014918856,0.0004168934,0.00008006595,0.0013882447,0.00042679723,0.077623904,0.17267086,0.04516934,0.6985942],"study_design_scores_gemma":[0.00078521826,0.00016656112,0.0018904833,0.00004014905,0.000023493225,0.00012003741,0.00017201848,0.8680175,0.12080045,0.001152754,0.006341145,0.0004902108],"about_ca_topic_score_codex":0.0009185527,"about_ca_topic_score_gemma":0.0000360843,"teacher_disagreement_score":0.86759067,"about_ca_system_score_codex":0.000051700954,"about_ca_system_score_gemma":0.00007863977,"threshold_uncertainty_score":0.38868788},"labels":[],"label_agreement":null},{"id":"W2015887370","doi":"10.1002/sam.11161","title":"A survey on unsupervised outlier detection in high‐dimensional numerical data","year":2012,"lang":"en","type":"article","venue":"Statistical Analysis and Data Mining The ASA Data Science Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":856,"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":"","keywords":"Outlier; Curse of dimensionality; Anomaly detection; Computer science; Linear subspace; Data mining; Euclidean distance; Clustering high-dimensional data; Task (project management); Machine learning; Artificial intelligence; Mathematics; Cluster analysis","score_opus":0.09488812902028622,"score_gpt":0.35905860971230336,"score_spread":0.26417048069201715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015887370","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":[0.02928215,0.00005216212,0.9673868,0.0006725916,0.0001221815,0.00007216251,0.0023668362,0.000025827194,0.000019278965],"genre_scores_gemma":[0.8600723,0.00004010812,0.13854095,0.00023864592,0.00009072051,0.0000022712773,0.0010037324,0.0000045623424,0.000006724349],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99707144,0.0002722957,0.00046297206,0.00092886254,0.0007991829,0.00046522103],"domain_scores_gemma":[0.994402,0.0007537588,0.00018626168,0.0042437688,0.000096305506,0.00031789448],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.009776866,0.0001470646,0.0002570498,0.00036984106,0.00088842574,0.0007597383,0.0070698815,0.00003997956,0.000054655557],"category_scores_gemma":[0.0014013097,0.00009415863,0.000018025463,0.0030376134,0.00037915207,0.0035075126,0.0056568696,0.00038392792,0.000017365968],"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":[0.00007280549,0.000668614,0.11395758,0.0000049152122,0.00027094525,0.000019825624,0.00030068436,0.00014988027,0.00039867795,0.011782773,0.013643708,0.8587296],"study_design_scores_gemma":[0.00008789653,0.00003687949,0.44425097,0.000004506081,0.00007415842,0.000029491232,0.00003298025,0.55447155,0.00001861326,0.0002257187,0.0006587137,0.00010853015],"about_ca_topic_score_codex":0.00086478546,"about_ca_topic_score_gemma":0.00035016725,"teacher_disagreement_score":0.85862106,"about_ca_system_score_codex":0.00004101557,"about_ca_system_score_gemma":0.00014620055,"threshold_uncertainty_score":0.99830234},"labels":[],"label_agreement":null},{"id":"W2016710592","doi":"10.1016/j.knosys.2014.02.008","title":"Graph-based approach for outlier detection in sequential data and its application on stock market and weather data","year":2014,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Outlier; Data mining; Anomaly detection; Stock market; Data type; Flexibility (engineering); Artificial intelligence","score_opus":0.05077237689347459,"score_gpt":0.29383664306459134,"score_spread":0.24306426617111676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016710592","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":[0.001849838,0.00023752445,0.99482507,0.000106311934,0.00011201928,0.0016805397,0.00011074211,0.00021787548,0.00086007017],"genre_scores_gemma":[0.9886916,0.000004273423,0.0098338295,0.000051802872,0.0001359436,0.00093735603,0.00016828952,0.000023255396,0.00015362815],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99814415,0.00019093553,0.00031828214,0.0009960532,0.00014206988,0.00020850354],"domain_scores_gemma":[0.9976162,0.00018071545,0.00015437219,0.0018709473,0.00008837787,0.00008944142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013730689,0.00018521782,0.00021593136,0.00024809968,0.00019630506,0.00016017484,0.0010127559,0.00013368482,0.0000011168592],"category_scores_gemma":[0.000049209826,0.00017569307,0.00002442109,0.00038747626,0.00004221099,0.00027875885,0.00024486487,0.00012454264,0.000005011561],"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":[0.000569294,0.0027144789,0.0032694086,0.003735468,0.00014940342,0.0000014891908,0.00048769012,0.0029785347,0.035382308,0.053956877,0.016038023,0.88071704],"study_design_scores_gemma":[0.0006759235,0.00011916625,0.00024262049,0.000033063185,0.000013989988,0.000002853548,0.000010494622,0.9656054,0.0020019964,0.00013129471,0.030969463,0.00019377892],"about_ca_topic_score_codex":0.000052619012,"about_ca_topic_score_gemma":0.00008453104,"teacher_disagreement_score":0.9868418,"about_ca_system_score_codex":0.000046218105,"about_ca_system_score_gemma":0.000052053594,"threshold_uncertainty_score":0.7164556},"labels":[],"label_agreement":null},{"id":"W2018960802","doi":"10.1016/s0263-2241(01)00022-7","title":"Refining visually estimated arrival times of short duration signals","year":2001,"lang":"en","type":"article","venue":"Measurement","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"PotashCorp (Canada); University of Saskatchewan","funders":"","keywords":"Refining (metallurgy); Duration (music); Arrival time; Computer science; Statistics; Engineering; Acoustics; Mathematics; Materials science; Transport engineering; Physics; Metallurgy","score_opus":0.08736030753413024,"score_gpt":0.3095955709263465,"score_spread":0.22223526339221625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018960802","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":[0.015989013,0.000049643764,0.9762739,0.0003660861,0.000037841528,0.00011633644,5.111233e-7,0.00025871617,0.006907929],"genre_scores_gemma":[0.9624291,0.000006464614,0.037326373,0.0000604052,0.000019044875,0.00004406528,0.0000012223395,0.000004809518,0.00010848012],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989959,0.0000274179,0.0002688917,0.00018538287,0.00040715246,0.00011525413],"domain_scores_gemma":[0.99935865,0.000009473287,0.00008100875,0.00027944683,0.00023149831,0.0000399097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005123628,0.000074465,0.00009690422,0.00006703658,0.00008595164,0.000037632708,0.00025350746,0.000031520944,0.000045007655],"category_scores_gemma":[0.000025812482,0.00007023268,0.000040048068,0.00027853448,0.00001611662,0.00014021211,0.000035423753,0.00004644699,0.000016889417],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014630634,0.0002458608,0.0041475827,0.000016479938,0.000044146032,0.000004423902,0.00019977271,0.000699174,0.7805042,0.029072922,0.0017025517,0.18334825],"study_design_scores_gemma":[0.00022876715,0.00035570434,0.021190153,0.00010010203,0.000021963657,0.000025293479,0.00002402129,0.08153689,0.8812832,0.0026367763,0.012270345,0.0003267649],"about_ca_topic_score_codex":0.00002140648,"about_ca_topic_score_gemma":0.0000071803584,"teacher_disagreement_score":0.9464401,"about_ca_system_score_codex":0.00005593352,"about_ca_system_score_gemma":0.000041398744,"threshold_uncertainty_score":0.28640056},"labels":[],"label_agreement":null},{"id":"W2030836983","doi":"10.1145/1066677.1066788","title":"Mining web content outliers using structure oriented weighting techniques and N-grams","year":2005,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":31,"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":"Computer science; Information retrieval; Web page; Identification (biology); Categorization; Web mining; Weighting; Outlier; World Wide Web; Artificial intelligence","score_opus":0.02546715169442273,"score_gpt":0.25703315584886716,"score_spread":0.23156600415444442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030836983","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":[0.22678712,0.000052208194,0.76926893,0.0007418342,0.000042924985,0.0001908271,0.0000015990313,0.00088648667,0.0020280783],"genre_scores_gemma":[0.5229769,0.000009684256,0.4765694,0.00017350687,0.000066905144,0.00000835219,5.565491e-7,0.000005287378,0.00018941925],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992171,0.000014411759,0.0001931719,0.00028893392,0.0001099125,0.00017645577],"domain_scores_gemma":[0.9995049,0.000017621462,0.00008292711,0.00025896382,0.000063099804,0.00007247654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000089037545,0.00011038938,0.000103804836,0.00009001865,0.00022384955,0.0001084904,0.00021617321,0.00006591721,0.000016183232],"category_scores_gemma":[0.0000059082536,0.00009337717,0.00003056987,0.00025702713,0.00004350034,0.0003134856,0.0001508115,0.00009326457,0.0000014232054],"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":[0.000003924722,0.00004053752,0.0018876189,0.000010444391,0.00002140269,0.0000045369857,0.00054324645,0.000016206694,0.09085222,0.13296567,0.00060950895,0.7730447],"study_design_scores_gemma":[0.00031378906,0.00011614481,0.00016202989,0.000045099594,0.0000145749655,0.0002466682,0.00040822022,0.3720405,0.4193204,0.0013397654,0.20549674,0.0004960482],"about_ca_topic_score_codex":0.00002970286,"about_ca_topic_score_gemma":0.000015823034,"teacher_disagreement_score":0.7725486,"about_ca_system_score_codex":0.00004594411,"about_ca_system_score_gemma":0.00002099736,"threshold_uncertainty_score":0.38078105},"labels":[],"label_agreement":null},{"id":"W2032490573","doi":"10.1080/13658810110060442","title":"Detecting outliers in irregularly distributed spatial data sets by locally adaptive and robust statistical analysis and GIS","year":2001,"lang":"en","type":"article","venue":"International Journal of Geographical Information Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":41,"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":"University of Waterloo","keywords":"Outlier; Computer science; Data mining; Anomaly detection; Sorting; Exploratory data analysis; Set (abstract data type); Spatial analysis; Data set; Block (permutation group theory); Artificial intelligence; Geography; Mathematics; Algorithm; Remote sensing","score_opus":0.01631648945668758,"score_gpt":0.26292331543499803,"score_spread":0.24660682597831046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032490573","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":[0.026762707,0.00007746286,0.9719304,0.00070996553,0.00012437049,0.00011598465,0.00019859693,0.000028726474,0.000051823623],"genre_scores_gemma":[0.9881693,0.000097319324,0.011514651,0.00007820407,0.00003318543,0.000007206904,0.000095248026,0.0000027337376,0.0000021380417],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998269,0.000075803684,0.0008259859,0.00016165538,0.0005323581,0.00013516136],"domain_scores_gemma":[0.9985369,0.00013248218,0.0005232796,0.0002105634,0.00046196405,0.00013481358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007405762,0.00010940825,0.00022632034,0.0007250442,0.00007541808,0.00051481905,0.00066700554,0.0000867254,0.000004152531],"category_scores_gemma":[0.000104135106,0.00009801566,0.00004774355,0.00075675466,0.000086374435,0.0019907018,0.00023225823,0.0002384348,0.0000011855014],"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":[0.0004280808,0.00032876333,0.2912869,0.000043706194,0.0023033202,0.00010031738,0.0012157925,0.009907588,0.00016634498,0.03348477,0.0022276258,0.6585068],"study_design_scores_gemma":[0.0007534046,0.00017307133,0.12440586,0.000053475884,0.00006953763,0.0004516393,0.00039382852,0.86787426,0.000021414236,0.00058715965,0.0050139353,0.00020239422],"about_ca_topic_score_codex":0.00077591074,"about_ca_topic_score_gemma":0.00010566943,"teacher_disagreement_score":0.9614066,"about_ca_system_score_codex":0.00005098801,"about_ca_system_score_gemma":0.000033806165,"threshold_uncertainty_score":0.49644122},"labels":[],"label_agreement":null},{"id":"W2036301107","doi":"10.1140/epjst/e2009-01099-1","title":"Analysis of complex networks associated to seismic clusters near the Itoiz reservoir dam","year":2009,"lang":"en","type":"article","venue":"The European Physical Journal Special Topics","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Geology; Seismology","score_opus":0.02320989688108497,"score_gpt":0.27337493313653666,"score_spread":0.25016503625545167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036301107","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":[0.0906542,0.00002443246,0.778712,0.0310192,0.000363578,0.00040934022,0.0000068402665,0.00018031213,0.09863012],"genre_scores_gemma":[0.98489714,0.000017944923,0.0010012155,0.001725478,0.011907994,0.0000011941049,0.0000019481315,0.000008636899,0.00043847994],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870557,0.00039583803,0.0002719512,0.00015308715,0.00027550716,0.00019806433],"domain_scores_gemma":[0.99901783,0.000078982215,0.00020758624,0.0004966667,0.00010910585,0.000089795954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046325815,0.0001025062,0.00020245202,0.000053522344,0.0006498842,0.0003138793,0.001373977,0.000018429444,0.0000071568297],"category_scores_gemma":[0.000025480407,0.000060472656,0.0002602031,0.0013632941,0.000088291876,0.000088924666,0.00017163939,0.00029737098,0.000014569668],"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":[0.000015577813,0.0002589488,0.00005154069,5.3496063e-7,0.00023611782,0.0000100635,0.0021350738,0.14154696,0.00015853484,0.007002049,0.022894619,0.82569],"study_design_scores_gemma":[0.00018797928,0.00037874025,0.11073373,0.000010370553,0.00020842857,0.000010163043,0.000054367352,0.63391584,0.00007945701,0.008368995,0.24584351,0.00020843385],"about_ca_topic_score_codex":0.000007521781,"about_ca_topic_score_gemma":0.0000051785023,"teacher_disagreement_score":0.8942429,"about_ca_system_score_codex":0.00004727586,"about_ca_system_score_gemma":0.000016294061,"threshold_uncertainty_score":0.49984497},"labels":[],"label_agreement":null},{"id":"W2036540625","doi":"10.1007/s00500-010-0575-1","title":"Detecting anomalies from high-dimensional wireless network data streams: a case study","year":2010,"lang":"en","type":"article","venue":"Soft Computing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University; Dalhousie University","funders":"","keywords":"Anomaly detection; Computer science; Data stream mining; Anomaly (physics); STREAMS; Data mining; Wireless network; Ground truth; Wireless sensor network; Outlier; Wireless; Data stream; Real-time computing; Computer network; Artificial intelligence; Telecommunications","score_opus":0.023566608437096924,"score_gpt":0.2793680976559714,"score_spread":0.2558014892188745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036540625","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":[0.5707784,0.000015040798,0.4280753,0.000056032502,0.00031612677,0.00018292542,0.000006863369,0.0005397355,0.000029539995],"genre_scores_gemma":[0.8382515,2.566609e-7,0.16101637,0.000097914985,0.0005855069,0.0000106149,0.000011633455,0.000015784266,0.000010421242],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981503,0.00007661727,0.00036960727,0.0008216656,0.00023094665,0.0003508583],"domain_scores_gemma":[0.9974798,0.00042795824,0.00021570771,0.0016750973,0.00009418723,0.000107232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005707406,0.00019466088,0.00021157946,0.00006072634,0.0009372951,0.0002859097,0.0013399335,0.000082953986,0.000014530028],"category_scores_gemma":[0.00004254034,0.00019367014,0.00004114083,0.00048206278,0.000051147526,0.00034467506,0.0020683608,0.00047327354,0.000018647152],"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":[0.000005605079,0.0003771341,0.028415514,0.0000068494874,0.00009056926,0.0008196852,0.0013195728,0.0015226015,0.002635739,0.0050022183,0.00051833125,0.95928615],"study_design_scores_gemma":[0.00035254352,0.00010880207,0.0039765094,0.000023692759,0.00002516485,0.0008324603,0.00064814946,0.9900008,0.000938569,0.0022920063,0.0003893185,0.00041197502],"about_ca_topic_score_codex":0.0032735222,"about_ca_topic_score_gemma":0.0011658509,"teacher_disagreement_score":0.9884782,"about_ca_system_score_codex":0.0000180004,"about_ca_system_score_gemma":0.00006161707,"threshold_uncertainty_score":0.7897639},"labels":[],"label_agreement":null},{"id":"W2036679198","doi":"10.1109/acssc.2014.7094542","title":"Detecting convoys in networks of short-ranged sensors","year":2014,"lang":"en","type":"article","venue":"2014 48th Asilomar Conference on Signals, Systems and Computers","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; License; Markov chain; Markov process; Construct (python library); Hidden Markov model; Property (philosophy); Process (computing); False positive paradox; Series (stratigraphy); Data mining; Artificial intelligence; Real-time computing; Machine learning; Computer network; Mathematics","score_opus":0.022842448125661222,"score_gpt":0.24266104400788227,"score_spread":0.21981859588222105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036679198","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":[0.08260153,0.000123456,0.91468227,0.00014619326,0.00023795129,0.00039177766,0.0000019396869,0.00017053427,0.0016443479],"genre_scores_gemma":[0.99591386,0.000051565425,0.0037567287,0.00009520291,0.00007490497,0.00005695594,0.0000016095702,0.0000127168305,0.000036467718],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817765,0.00023719581,0.0005319678,0.0005173078,0.00023043367,0.00030544837],"domain_scores_gemma":[0.99875665,0.00022765141,0.00023312711,0.0005226765,0.00012816176,0.00013175195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006828114,0.00022412557,0.0004327811,0.00022132637,0.00012121145,0.00017443865,0.0004962564,0.00013064838,0.0000042623165],"category_scores_gemma":[0.000013584626,0.00020892633,0.00007088208,0.0002970802,0.00007635636,0.00015857934,0.00012866977,0.00021193856,0.000008635369],"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":[0.000084052816,0.00033121917,0.011979212,0.0003338139,0.00013002637,0.000022551207,0.0011725705,0.038231015,0.012862763,0.50333935,0.0020971987,0.42941624],"study_design_scores_gemma":[0.00028767248,0.00032753765,0.0048984974,0.00023039192,0.00000575026,0.00002232605,0.00009353085,0.99137926,0.0010026073,0.0005892811,0.0008745226,0.00028863264],"about_ca_topic_score_codex":0.00021498631,"about_ca_topic_score_gemma":0.000015321546,"teacher_disagreement_score":0.95314825,"about_ca_system_score_codex":0.00002585933,"about_ca_system_score_gemma":0.000030199,"threshold_uncertainty_score":0.8519768},"labels":[],"label_agreement":null},{"id":"W2036901142","doi":"10.1108/13552511311315977","title":"Vibration‐ and acoustic‐emissions based novelty detection of fretted bearings","year":2013,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Novelty detection; Fault detection and isolation; Novelty; Engineering; Feature vector; Vibration; Feature (linguistics); Fault (geology); Acoustic emission; Pattern recognition (psychology); Principal component analysis; Dimensionality reduction; Condition monitoring; Reduction (mathematics); Identification (biology); Computer science; Artificial intelligence; Acoustics; Mathematics","score_opus":0.0120034111469476,"score_gpt":0.24681536769942766,"score_spread":0.23481195655248005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036901142","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":[0.19529238,0.000018677358,0.8037998,0.00072736107,0.00004744049,0.000070994734,5.8653256e-7,0.000026738466,0.00001601862],"genre_scores_gemma":[0.91313714,0.000010147426,0.086733736,0.00007464801,0.000020970334,0.00000868805,9.0614265e-8,0.0000041636476,0.000010433739],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917465,0.000020599067,0.00046505604,0.0000917067,0.00013831539,0.00010966568],"domain_scores_gemma":[0.99923265,0.00011442417,0.0002609769,0.0001511254,0.00018160585,0.000059196667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046250556,0.00006932105,0.00015522195,0.00016603353,0.000029713268,0.000035197092,0.00019046057,0.000049008286,0.0000046912814],"category_scores_gemma":[0.00023046641,0.00006134105,0.000045892448,0.00031369994,0.00001863355,0.0003769621,0.000035991638,0.00019558404,5.649658e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004775765,0.00005219534,0.00052732334,0.00005867429,0.000008595662,0.0000013421021,0.00019747578,0.021482568,0.96540576,0.0035620998,0.000066367575,0.008632805],"study_design_scores_gemma":[0.0004125382,0.00010740715,0.11485816,0.00018227013,0.0000052847654,0.00003396526,0.00008191679,0.7685704,0.113795914,0.0014759606,0.00031447416,0.00016166201],"about_ca_topic_score_codex":0.000056113873,"about_ca_topic_score_gemma":0.0000037274801,"teacher_disagreement_score":0.8516099,"about_ca_system_score_codex":0.000049763497,"about_ca_system_score_gemma":0.000026667289,"threshold_uncertainty_score":0.25014156},"labels":[],"label_agreement":null},{"id":"W2042980166","doi":"10.3141/1908-15","title":"Collision Frequency Analysis Using Tree-Based Stratification","year":2005,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Collision; Stratification (seeds); Tree (set theory); Environmental science; Computer science; Engineering; Mathematics; Biology; Computer security","score_opus":0.0990629223251541,"score_gpt":0.39859232935072486,"score_spread":0.29952940702557074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042980166","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":[0.5023234,0.00007481828,0.49289513,0.0038551302,0.00009464122,0.000567073,0.000018862851,0.00007697892,0.00009399233],"genre_scores_gemma":[0.89207184,0.00013496197,0.10734006,0.000058928425,0.00012798115,0.00006979148,0.000010850687,0.000028624318,0.00015694798],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99335694,0.0009270906,0.0014377417,0.0005143772,0.0031236792,0.00064019597],"domain_scores_gemma":[0.99368334,0.0005619399,0.00063845055,0.00093875505,0.0038501413,0.00032736466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038921651,0.00021685271,0.00038482228,0.0020356006,0.0009545878,0.00030321677,0.002023037,0.00019146901,0.00009341936],"category_scores_gemma":[0.00007109449,0.0001708873,0.00058887916,0.008111108,0.0003530178,0.0012211165,0.000008941446,0.0013986651,0.000019027604],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009805922,0.0026040527,0.3641632,0.0003180723,0.0014315192,0.000107569416,0.0060526873,0.17589627,0.1047216,0.19506389,0.005740015,0.14292055],"study_design_scores_gemma":[0.0012797529,0.0006376175,0.8535122,0.00015727693,0.00025978757,6.8328666e-7,0.0005375385,0.088197604,0.035681386,0.010190514,0.009129101,0.00041651764],"about_ca_topic_score_codex":0.003667909,"about_ca_topic_score_gemma":0.022800887,"teacher_disagreement_score":0.48934904,"about_ca_system_score_codex":0.0004166918,"about_ca_system_score_gemma":0.00082055334,"threshold_uncertainty_score":0.99503046},"labels":[],"label_agreement":null},{"id":"W2043075161","doi":"10.1080/07359683.2015.1000704","title":"How Health Managers Can Use Data Mining for Predicting Individuals’ Risks of Contracting Nosocomial Pneumonia","year":2015,"lang":"en","type":"article","venue":"Health Marketing Quarterly","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Université Laval","funders":"","keywords":"Overfitting; Decision tree; Trimming; Boosting (machine learning); Computer science; Health care; Genetic algorithm; Data mining; Actuarial science; Machine learning; Business; Economics","score_opus":0.1918826882951795,"score_gpt":0.37681639786333476,"score_spread":0.18493370956815527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043075161","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":[0.14216627,0.00019492132,0.81665874,0.037495133,0.00047187493,0.0019370154,0.00028771575,0.00068712205,0.00010122076],"genre_scores_gemma":[0.7616035,0.000013900044,0.23733303,0.0006703689,0.00015083414,0.00010092295,0.00004799035,0.000023564513,0.00005590942],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99708694,0.0004928962,0.0007496445,0.0006817111,0.00035916912,0.0006296137],"domain_scores_gemma":[0.99585515,0.001134661,0.0013338687,0.0011191338,0.00017803819,0.00037913627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006366669,0.00019503111,0.0004092388,0.00017385454,0.0005198219,0.00041162162,0.0010552137,0.000083200044,4.843856e-7],"category_scores_gemma":[0.0005953541,0.00021009638,0.00006304899,0.0003700183,0.000047108493,0.00084380177,0.00017133141,0.00024228382,5.0435824e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005387204,0.00011505702,0.018266208,0.0004810763,0.00004888542,0.0000016982478,0.009331307,0.000015688764,0.00010207848,0.0010492117,0.012478306,0.9580566],"study_design_scores_gemma":[0.0077616707,0.009943034,0.14058109,0.002645006,0.00010436072,0.00023059525,0.05703023,0.61717606,0.00047438813,0.0021742152,0.15921383,0.0026655232],"about_ca_topic_score_codex":0.0015578459,"about_ca_topic_score_gemma":0.00033329675,"teacher_disagreement_score":0.9553911,"about_ca_system_score_codex":0.000174722,"about_ca_system_score_gemma":0.0006199551,"threshold_uncertainty_score":0.85674816},"labels":[],"label_agreement":null},{"id":"W2046201795","doi":"10.1016/j.jocs.2013.10.008","title":"EigenBlock algorithm for change detection – An application of adaptive dictionary learning techniques","year":2013,"lang":"en","type":"article","venue":"Journal of Computational Science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan; York University; Statistics Canada","funders":"","keywords":"Computer science; Dictionary learning; Change detection; Algorithm; Artificial intelligence; Machine learning; Pattern recognition (psychology); Sparse approximation","score_opus":0.02018068831839023,"score_gpt":0.2893790152961578,"score_spread":0.2691983269777676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046201795","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":[0.010733741,0.000027330405,0.98836035,0.00021768294,0.00007193541,0.00044139172,0.0000018097826,0.00007963131,0.0000661258],"genre_scores_gemma":[0.5605764,0.0000049025616,0.43917164,0.000041101746,0.0000965969,0.0000995198,7.572217e-7,0.000003354327,0.000005768003],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878824,0.000033003525,0.00040449103,0.00020817925,0.00042439433,0.00014171342],"domain_scores_gemma":[0.99735075,0.0000996059,0.00063588016,0.00013481856,0.0016753214,0.00010361859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006698594,0.00008496282,0.00013135665,0.00036257206,0.00033260084,0.0000789715,0.00060682243,0.000043296164,0.0000034310663],"category_scores_gemma":[0.000026158634,0.00007732613,0.00007648513,0.0009365127,0.00015524746,0.0020106798,0.00006623456,0.00012849219,0.0000026767734],"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":[0.0000032427845,0.000075318734,0.00006507976,0.0000028714203,0.0000048801685,1.0121623e-7,0.00013881814,0.0012691463,0.015233452,0.008038288,0.0000107168435,0.9751581],"study_design_scores_gemma":[0.000108079046,0.00089988,0.011951452,0.000012561771,0.000005653036,0.00007502553,0.00006791405,0.88574344,0.04769282,0.052387055,0.0009500292,0.000106080064],"about_ca_topic_score_codex":0.00004322152,"about_ca_topic_score_gemma":9.103299e-7,"teacher_disagreement_score":0.975052,"about_ca_system_score_codex":0.00009581959,"about_ca_system_score_gemma":0.00012774703,"threshold_uncertainty_score":0.3153268},"labels":[],"label_agreement":null},{"id":"W2047016883","doi":"10.1109/cvpr.2013.337","title":"Online Dominant and Anomalous Behavior Detection in Videos","year":2013,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":169,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Codebook; Artificial intelligence; Parsing; Pixel; Construct (python library); Contrast (vision); Spatial contextual awareness; Hidden Markov model; Pattern recognition (psychology); Ranging; Computer vision; Machine learning","score_opus":0.009394701437294407,"score_gpt":0.24243583290980888,"score_spread":0.23304113147251448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047016883","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":[0.5535236,0.000012325544,0.4455361,0.00027227454,0.00001871669,0.00024268239,3.3841624e-7,0.00011936021,0.00027458818],"genre_scores_gemma":[0.96238303,0.000014644705,0.03685334,0.0001234086,0.00001263326,0.0002594492,3.8596468e-7,0.0000033148997,0.00034976206],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9995038,0.000010664982,0.00013116021,0.00019571622,0.00005168651,0.00010695243],"domain_scores_gemma":[0.9996787,0.000013984945,0.00002867789,0.00020774268,0.000028193188,0.0000427085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000047743157,0.00005900826,0.000062949526,0.00008848526,0.000053379514,0.000064697146,0.00014590961,0.00004026314,0.00003011068],"category_scores_gemma":[0.000003825992,0.000050333078,0.00001582981,0.00021710497,0.000018959241,0.00028215916,0.00008333232,0.00006422801,0.000030996795],"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":[7.818803e-7,0.00019290144,0.0028244893,0.0000030024453,0.0000011634413,0.0000026934397,0.000059858525,7.448867e-7,0.06286026,0.0051833326,0.00009273741,0.92877805],"study_design_scores_gemma":[0.0004503741,0.00026948896,0.67521864,0.000010343596,0.0000056980043,0.00013632061,0.00009244898,0.07758567,0.23604152,0.006036163,0.0037856074,0.00036770824],"about_ca_topic_score_codex":0.00076360133,"about_ca_topic_score_gemma":0.00018956003,"teacher_disagreement_score":0.92841035,"about_ca_system_score_codex":0.000019871144,"about_ca_system_score_gemma":0.0000066654,"threshold_uncertainty_score":0.20525233},"labels":[],"label_agreement":null},{"id":"W2048432737","doi":"10.1109/qr2mse.2013.6625907","title":"Linear correlation-based sparseness method for time series prediction with LS-SVR","year":2013,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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 Alberta","funders":"","keywords":"Series (stratigraphy); Computer science; Time series; Correlation; Support vector machine; Pattern recognition (psychology); Artificial intelligence; Machine learning; Mathematics; Geology","score_opus":0.009292180441281371,"score_gpt":0.2417998053916644,"score_spread":0.23250762495038302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048432737","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00034890012,0.0000023101463,0.994781,0.0016745542,0.000037546306,0.00064624,0.000005421989,0.00070802245,0.0017959891],"genre_scores_gemma":[0.034857325,6.862786e-7,0.95855945,0.0002645115,0.00004689972,0.0007560289,0.000013481639,0.000009293247,0.0054923026],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993882,0.000018819781,0.00013894695,0.0002371431,0.00009858198,0.00011828204],"domain_scores_gemma":[0.99929756,0.00008596322,0.00006542942,0.00031088805,0.00019082922,0.00004932739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000114817,0.00008478979,0.000088033,0.000057087334,0.0001690214,0.00009075522,0.00019586549,0.000053397172,0.00011278598],"category_scores_gemma":[0.000007895187,0.00006526653,0.00003560757,0.00025510797,0.000022612914,0.0004937046,0.000023654928,0.000046405956,0.0001116834],"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":[0.0001248974,0.00062897167,0.004938676,0.00011942039,0.00013478169,0.0000015388018,0.00044079305,0.05348304,0.025625695,0.56068695,0.06989225,0.28392297],"study_design_scores_gemma":[0.00015270775,0.00019718848,0.00084156654,0.0000051420193,0.000005685026,0.000005402725,0.0000073193896,0.9566341,0.025884485,0.0024769758,0.013692542,0.000096908836],"about_ca_topic_score_codex":0.00006585027,"about_ca_topic_score_gemma":0.000004512542,"teacher_disagreement_score":0.90315104,"about_ca_system_score_codex":0.000019415893,"about_ca_system_score_gemma":0.000045531826,"threshold_uncertainty_score":0.2661492},"labels":[],"label_agreement":null},{"id":"W2050439513","doi":"10.1145/502512.502554","title":"Mining top-n local outliers in large databases","year":2001,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":330,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Local outlier factor; Outlier; Computer science; Anomaly detection; Object (grammar); Pruning; Data mining; Computation; Artificial intelligence; Pattern recognition (psychology); Algorithm","score_opus":0.02431659145844005,"score_gpt":0.28705390993502766,"score_spread":0.2627373184765876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050439513","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":[0.019703584,0.000013348865,0.96160215,0.0005588006,0.000024823366,0.0000598507,7.8186076e-7,0.00024738422,0.017789284],"genre_scores_gemma":[0.91252166,0.000013758247,0.085732184,0.00053998537,0.00001418522,0.000024021872,0.0000016165194,0.0000026357873,0.001149952],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994882,0.000009425469,0.0001045346,0.00018040095,0.0000668145,0.00015065563],"domain_scores_gemma":[0.9996193,0.000019245743,0.000018347298,0.00029233008,0.000012114301,0.00003864591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010713987,0.00004715777,0.000052174317,0.00007165519,0.000054179727,0.000027829752,0.00025464292,0.00001772534,0.0000797969],"category_scores_gemma":[0.0000055036226,0.00004316939,0.000018358043,0.0003654278,0.000014469789,0.00022096155,0.00012273443,0.000047714304,0.000042487067],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000034445773,0.000193506,0.035443503,0.0000035207217,0.000004008322,0.000031193926,0.00030321995,0.00006742946,0.0002765226,0.6923964,0.006379934,0.26489735],"study_design_scores_gemma":[0.0004867009,0.000078205194,0.018329421,0.000018783683,0.0000025156178,0.00006744767,0.0007906964,0.32849082,0.010855499,0.002182422,0.63829625,0.00040123158],"about_ca_topic_score_codex":0.00009137489,"about_ca_topic_score_gemma":0.00014330407,"teacher_disagreement_score":0.8928181,"about_ca_system_score_codex":0.000020478503,"about_ca_system_score_gemma":0.000013971688,"threshold_uncertainty_score":0.17603967},"labels":[],"label_agreement":null},{"id":"W2052100537","doi":"10.1504/ijista.2014.059302","title":"Video event detection for fault monitoring in assembly automation","year":2014,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems Technologies and Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Ontario Centres of Excellence","keywords":"Downtime; Testbed; Computer science; Event (particle physics); Real-time computing; Fault detection and isolation; Automation; Fault (geology); Artificial intelligence; Similarity (geometry); Measure (data warehouse); Computer vision; Data mining; Embedded system; Engineering; Image (mathematics); Operating system","score_opus":0.015610814967175507,"score_gpt":0.29805997172964055,"score_spread":0.28244915676246507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052100537","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":[0.012696773,0.00030927634,0.98470145,0.0011745227,0.0003446295,0.00046155488,0.0000027028707,0.0002370289,0.00007204957],"genre_scores_gemma":[0.9801454,0.00039289307,0.018685173,0.000012733683,0.00016941177,0.0005524742,0.0000010829277,0.0000073362553,0.00003347774],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99879044,0.000021966402,0.0006328278,0.0002014009,0.0002294544,0.00012391497],"domain_scores_gemma":[0.9986285,0.00015535225,0.0004807438,0.00023355776,0.0004706838,0.000031183437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005024293,0.000106892934,0.0001636158,0.0004604826,0.00009258026,0.00016973342,0.00078368204,0.00011063265,3.4343552e-7],"category_scores_gemma":[0.00010860359,0.0000964698,0.00007903727,0.0002738564,0.000033725613,0.00030801303,0.00010581344,0.00015125344,0.0000028693414],"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":[0.000007341965,0.000079788864,0.0003974091,0.000020150266,0.000032501983,5.2214426e-7,0.000055860382,0.0016940089,0.010346241,0.13961153,0.00006901364,0.84768564],"study_design_scores_gemma":[0.0007637106,0.0005163715,0.0020800605,0.00039478394,0.00002794438,0.00038383887,0.0022919816,0.31345925,0.37022594,0.055571035,0.25377673,0.0005083653],"about_ca_topic_score_codex":0.000022278164,"about_ca_topic_score_gemma":0.000005518233,"teacher_disagreement_score":0.96744865,"about_ca_system_score_codex":0.00015143224,"about_ca_system_score_gemma":0.000020821428,"threshold_uncertainty_score":0.3933924},"labels":[],"label_agreement":null},{"id":"W2053613207","doi":"10.1109/ghtc.2014.6970316","title":"MicroFilters: Harnessing twitter for disaster management","year":2014,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":16,"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":"University of Waterloo","keywords":"Social media; Computer science; Key (lock); Emergency management; Data science; Natural disaster; Computer security; World Wide Web","score_opus":0.016482572802291946,"score_gpt":0.2589696728225679,"score_spread":0.24248710002027593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053613207","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008534584,0.0000021283665,0.9777489,0.0014449917,0.000056508405,0.00017374312,2.0752897e-7,0.00023765882,0.019482424],"genre_scores_gemma":[0.32927126,9.963424e-7,0.66164273,0.0018856797,0.000044976525,0.00015551054,8.950715e-7,0.0000049769046,0.006992984],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99956226,0.00000673854,0.00008662867,0.00018922318,0.000044819713,0.00011034704],"domain_scores_gemma":[0.99960625,0.000015683838,0.000025494548,0.0003086237,0.000017884853,0.000026071984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000084603285,0.000052422107,0.00004620721,0.0000377361,0.0000903034,0.00012047801,0.00028441424,0.000017094824,0.00001383349],"category_scores_gemma":[9.724699e-7,0.00004327933,0.000039745737,0.00008132862,0.000011925081,0.00013476083,0.000096025724,0.000019254794,0.00004564313],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017391524,0.000035240868,0.00006304945,0.000023096456,0.000009745348,1.7423554e-7,0.00012710443,0.0000043174023,0.0030067102,0.5814374,0.026829032,0.38846236],"study_design_scores_gemma":[0.00029688468,0.0000624413,0.0005904457,0.00001451319,0.000007802799,0.0000049741507,0.000055941196,0.06355077,0.04101819,0.03816473,0.85599005,0.00024327652],"about_ca_topic_score_codex":0.0000018198274,"about_ca_topic_score_gemma":3.0326638e-7,"teacher_disagreement_score":0.829161,"about_ca_system_score_codex":0.000009051737,"about_ca_system_score_gemma":0.0000014823577,"threshold_uncertainty_score":0.17648798},"labels":[],"label_agreement":null},{"id":"W2054874205","doi":"10.1145/2656045.2656071","title":"SiPTA","year":2014,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"TRACE (psycholinguistics); Computer science; Focus (optics); Domain (mathematical analysis); Set (abstract data type); Programming language; Mathematics","score_opus":0.004802739330868844,"score_gpt":0.20546395203095646,"score_spread":0.20066121270008763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054874205","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":[0.00030619506,0.0000010968108,0.86331517,0.0009246141,0.000017732153,0.000018813953,2.0468583e-8,0.0003811781,0.13503516],"genre_scores_gemma":[0.811441,7.9335183e-7,0.184883,0.0006168409,0.000017864715,0.000009921825,6.512974e-8,0.0000010068022,0.0030294922],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998063,0.0000045728657,0.000035181547,0.000078557685,0.000031918004,0.00004347851],"domain_scores_gemma":[0.99972427,0.000008217365,0.000008690583,0.0002280724,0.0000109274615,0.000019800842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004796701,0.000018977747,0.000019287603,0.0000144241185,0.000038488488,0.000027290338,0.00021911802,0.000010398695,0.000029112081],"category_scores_gemma":[0.0000030556698,0.000015524804,0.000012664284,0.00009049828,0.000005317552,0.000066826986,0.00004551875,0.000016913946,0.00017520659],"study_design_candidate":"theoretical_or_conceptual","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":[1.943655e-8,0.0000030828176,0.000019448638,1.5296091e-7,2.0202452e-7,1.354431e-8,0.0000032497437,5.337024e-7,0.0003430042,0.9131752,0.00266912,0.08378592],"study_design_scores_gemma":[0.00003461549,0.000031657066,0.0012261345,6.038885e-7,4.0360993e-7,0.000003902025,0.0000015298647,0.084845506,0.024970045,0.07343373,0.81538063,0.000071211485],"about_ca_topic_score_codex":0.000005998305,"about_ca_topic_score_gemma":7.943773e-7,"teacher_disagreement_score":0.8397415,"about_ca_system_score_codex":0.0000025983952,"about_ca_system_score_gemma":0.0000023706943,"threshold_uncertainty_score":0.22519848},"labels":[],"label_agreement":null},{"id":"W2056227284","doi":"10.1007/s11069-014-1486-8","title":"A multi-tier hazard: Part II—meteorological analysis","year":2014,"lang":"en","type":"article","venue":"Natural Hazards","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Anomaly (physics); Natural hazard; Climatology; Environmental science; Precipitation; Flooding (psychology); Wind speed; Meteorology; Hazard; Landslide; Geography; Geology","score_opus":0.012648662592385389,"score_gpt":0.26656929764103676,"score_spread":0.25392063504865137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056227284","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":[0.06844021,0.00012211206,0.9272848,0.0013582915,0.0002287602,0.00014826856,0.000003970699,0.0007575472,0.0016560868],"genre_scores_gemma":[0.8457312,0.000009424202,0.15121,0.0008301214,0.00009679001,0.000058873382,0.000006210441,0.0000053362505,0.0020520482],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867195,0.00007455309,0.00023413906,0.0004810961,0.0002578011,0.0002804816],"domain_scores_gemma":[0.9989606,0.00004232546,0.00009211996,0.00067786593,0.00011813268,0.00010891505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033774378,0.00015306652,0.00024183416,0.00018887443,0.0003322124,0.000096581316,0.00075442565,0.00013845057,0.000073743395],"category_scores_gemma":[0.00006271301,0.000118361015,0.0002747234,0.0012538794,0.000063319036,0.00020924541,0.00031247237,0.00028073284,0.000072092356],"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":[0.000021253289,0.00043029815,0.0037183682,0.000010724078,0.00052477536,0.000008403482,0.00026218605,0.00032461138,0.0041917837,0.19684233,0.018763086,0.77490216],"study_design_scores_gemma":[0.00027206974,0.00017784532,0.025864514,0.0000032308624,0.00010162387,0.000011617586,0.000004958736,0.7474198,0.003332517,0.0019020197,0.22055927,0.0003505746],"about_ca_topic_score_codex":0.000023988288,"about_ca_topic_score_gemma":0.000031745058,"teacher_disagreement_score":0.777291,"about_ca_system_score_codex":0.000038436578,"about_ca_system_score_gemma":0.000020758169,"threshold_uncertainty_score":0.4826622},"labels":[],"label_agreement":null},{"id":"W2056424540","doi":"10.1115/1.4006624","title":"Robust Pose Estimation With an Outlier Diagnosis Based on a Relaxation of Rigid Body Constraints","year":2012,"lang":"en","type":"article","venue":"Journal of Dynamic Systems Measurement and Control","topic":"Anomaly Detection Techniques and Applications","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":"National Research Council Canada; Toronto Metropolitan University","funders":"","keywords":"Outlier; Artificial intelligence; Anomaly detection; Pose; Rigid body; Computer science; Point (geometry); Relaxation (psychology); Pattern recognition (psychology); Computer vision; Mathematics; Physics; Geometry","score_opus":0.01993078550301111,"score_gpt":0.2280658901816503,"score_spread":0.2081351046786392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056424540","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":[0.040725682,0.0001688066,0.95827127,0.0002208569,0.00013699559,0.00033029643,0.0000027707454,0.000024132365,0.00011920653],"genre_scores_gemma":[0.9902773,0.000006943039,0.009573552,0.000039676575,0.000047292793,0.000043421394,5.583703e-7,0.0000059067625,0.0000053314575],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987211,0.00011480392,0.0004240091,0.00010211341,0.00051883643,0.00011909952],"domain_scores_gemma":[0.9984942,0.00004899835,0.0007521722,0.00018910709,0.00041234883,0.00010314912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013166678,0.000098594486,0.00021904791,0.00015824311,0.0000721685,0.000058152295,0.00013637319,0.000046499223,0.0000027040398],"category_scores_gemma":[0.000038956223,0.00007116053,0.000048266378,0.00012070808,0.000035343062,0.00042874343,0.0000052328173,0.00008878906,7.725781e-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":[0.001742108,0.006957629,0.23943983,0.00076492847,0.0012774928,0.000024017108,0.0024151385,0.07652416,0.046028946,0.05430959,0.0007294563,0.5697867],"study_design_scores_gemma":[0.0018853538,0.001395201,0.034754995,0.00034438638,0.00009469056,0.00006674038,0.000090183974,0.96049887,0.000545718,0.00008951958,0.000090309426,0.00014404695],"about_ca_topic_score_codex":0.0000122532165,"about_ca_topic_score_gemma":0.000002766504,"teacher_disagreement_score":0.94955164,"about_ca_system_score_codex":0.000104045626,"about_ca_system_score_gemma":0.000063974956,"threshold_uncertainty_score":0.29018423},"labels":[],"label_agreement":null},{"id":"W2057433016","doi":"10.1080/07408170600899565","title":"Data mining of resilience indicators","year":2007,"lang":"en","type":"article","venue":"IIE Transactions","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":15,"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":"McGill University","keywords":"Shock (circulatory); Resilience (materials science); Warning system; Psychological resilience; Fuzzy logic; Early warning system; Financial market; Financial crisis; Economics; State (computer science); Macroeconomics; Computer science; Finance; Artificial intelligence","score_opus":0.029601304543182844,"score_gpt":0.30421487429150457,"score_spread":0.27461356974832174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057433016","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":[0.008526361,0.000026781794,0.9877716,0.00023034125,0.00006180574,0.00008082277,0.00001858672,0.00020173848,0.0030820104],"genre_scores_gemma":[0.8390886,0.000014032058,0.16065222,0.000037433405,0.000011429427,0.0000062083072,0.000001988968,0.0000035168432,0.0001845494],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99928004,0.000008993587,0.00021043632,0.00023941736,0.00012655494,0.0001345637],"domain_scores_gemma":[0.9988881,0.00007223387,0.000073683215,0.00087803876,0.000027304543,0.00006064223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029888534,0.00005560916,0.00006960519,0.00018643368,0.0001447579,0.000016932858,0.0009554327,0.00004229226,0.00003250383],"category_scores_gemma":[0.0000063962652,0.000056646062,0.000030090616,0.0008709037,0.00006715522,0.00034536023,0.000029932206,0.0000847024,0.000007962043],"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":[0.000008979916,0.00023034192,0.0004918422,0.000015459056,0.000026467327,0.0000039515025,0.0006588511,0.00013141954,0.010857407,0.021440156,0.0008274998,0.96530765],"study_design_scores_gemma":[0.0007791126,0.0003944735,0.061373103,0.00009017032,0.000094561015,0.0001509063,0.0011160714,0.05713876,0.66601217,0.004714326,0.20706719,0.0010691704],"about_ca_topic_score_codex":0.000027803204,"about_ca_topic_score_gemma":0.000051058258,"teacher_disagreement_score":0.96423846,"about_ca_system_score_codex":0.000013308562,"about_ca_system_score_gemma":0.000039529405,"threshold_uncertainty_score":0.23099594},"labels":[],"label_agreement":null},{"id":"W2058118558","doi":"10.1109/bigdata.congress.2014.19","title":"Contextual Anomaly Detection in Big Sensor Data","year":2014,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Anomaly detection; Computer science; Context (archaeology); Big data; Anomaly (physics); Cluster analysis; Data mining; Artificial intelligence; Geography","score_opus":0.03762660552174608,"score_gpt":0.26806899619420677,"score_spread":0.2304423906724607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058118558","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":[0.017554615,0.000004846452,0.97321165,0.00046240655,0.000070168535,0.00010605824,0.0000013797594,0.00036161588,0.008227257],"genre_scores_gemma":[0.96374816,0.0000036164406,0.035267018,0.0003100725,0.000059841226,0.000019484734,0.0000022918596,0.000004023575,0.00058551924],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992654,0.000035811627,0.00015239147,0.00033745272,0.000082885155,0.00012609891],"domain_scores_gemma":[0.99888194,0.00004621332,0.000039135324,0.0009672797,0.00002626585,0.000039171202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026288207,0.000064349304,0.00007527657,0.00009453913,0.00006432429,0.00006665744,0.0007207771,0.000044335422,0.000011470496],"category_scores_gemma":[0.000029004874,0.000058744663,0.000016265178,0.0003419075,0.000020086272,0.00025635326,0.00027519203,0.000078549194,0.00008239861],"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":[0.0000020737111,0.000041644853,0.0004638225,0.000002298158,0.0000022818515,7.020012e-7,0.00003060222,0.0000065459894,0.007670554,0.04796379,0.00041677975,0.9433989],"study_design_scores_gemma":[0.00041880063,0.0001635373,0.015960645,0.00000708189,0.0000032352611,0.000037240196,0.00004647702,0.712135,0.0673376,0.004748046,0.19881421,0.00032811417],"about_ca_topic_score_codex":0.00027863373,"about_ca_topic_score_gemma":0.0005338757,"teacher_disagreement_score":0.9461935,"about_ca_system_score_codex":0.0000173695,"about_ca_system_score_gemma":0.00001204733,"threshold_uncertainty_score":0.23955378},"labels":[],"label_agreement":null},{"id":"W2061240327","doi":"10.1007/s007780050006","title":"Distance-based outliers: algorithms and applications","year":2000,"lang":"en","type":"article","venue":"The VLDB Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1196,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Outlier; Computer science; Anomaly detection; Simple (philosophy); Algorithm; Data mining; Curse of dimensionality; Identification (biology); Time complexity; Credit card fraud; Task (project management); Artificial intelligence; Machine learning; Pattern recognition (psychology); Credit card","score_opus":0.009606939193727806,"score_gpt":0.23790982533144633,"score_spread":0.22830288613771854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061240327","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":[0.001174469,0.00022313686,0.9922234,0.00367721,0.000019654583,0.0001290657,0.0000014409571,0.000111159,0.0024404968],"genre_scores_gemma":[0.86437434,0.0005941439,0.12985371,0.0018090813,0.0005290942,0.00021776551,0.0000010701245,0.000016389968,0.0026043751],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99948424,0.00002705092,0.00013174632,0.00011951901,0.000115438525,0.000121991805],"domain_scores_gemma":[0.99949783,0.000031863918,0.000055977704,0.00030579208,0.00003405972,0.00007448502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020413689,0.00006188159,0.000056440116,0.000032952645,0.0005590137,0.00021158886,0.00047479337,0.000023021017,0.000081258455],"category_scores_gemma":[0.0000017348323,0.000040986884,0.000038097256,0.00023271285,0.00007168726,0.0001381454,0.000023811483,0.00016746328,0.0000419698],"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":[0.0000024162757,0.00003081308,0.000056799574,0.0000012789249,0.0000058951064,0.0000010932301,0.00009286433,0.00009874982,0.000082797924,0.021260304,0.0012086303,0.97715837],"study_design_scores_gemma":[0.00026448895,0.00006770319,0.001558429,0.00001074585,0.000011685461,0.00029202734,0.000037266156,0.042887863,0.001168249,0.04698056,0.90655196,0.00016902688],"about_ca_topic_score_codex":0.0000046236514,"about_ca_topic_score_gemma":9.050422e-7,"teacher_disagreement_score":0.9769893,"about_ca_system_score_codex":0.00001992156,"about_ca_system_score_gemma":0.000029957953,"threshold_uncertainty_score":0.42995375},"labels":[],"label_agreement":null},{"id":"W2064366273","doi":"10.1520/jfs2004277","title":"A Large-Scale Statistical Analysis of Barefoot Impressions","year":2005,"lang":"en","type":"article","venue":"Journal of Forensic Sciences","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Statistics Canada; Royal Canadian Mounted Police","funders":"","keywords":"TRACE (psycholinguistics); Footprint; Sample (material); Odds; Population; Statistics; Scale (ratio); Tracing; Null hypothesis; Computer science; Econometrics; Mathematics; Demography; Geography; Cartography","score_opus":0.01616196828473719,"score_gpt":0.30953987227543356,"score_spread":0.29337790399069635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064366273","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":[0.11420603,0.00007095297,0.8838897,0.0009360245,0.000056480214,0.000029368826,0.000008086837,0.00001570166,0.00078766927],"genre_scores_gemma":[0.6917764,0.000013965664,0.3080695,0.00007891618,0.000034136152,0.0000010877825,1.7086347e-7,9.575155e-7,0.000024870114],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988842,0.00002798098,0.00040106202,0.00013239715,0.00040658057,0.00014780337],"domain_scores_gemma":[0.9990837,0.000109912304,0.00034190755,0.00018253631,0.00019030717,0.000091654925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006620209,0.000056733465,0.00020546703,0.000421636,0.00015695217,0.00005137941,0.00064240635,0.000027774968,0.0000620194],"category_scores_gemma":[0.00004529955,0.000038275608,0.00014579722,0.0016504023,0.00019457386,0.00035774853,0.00008373113,0.00008847248,0.0000022047413],"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":[0.000023582113,0.0008171446,0.018134007,0.000012541705,0.00053637376,0.000012479209,0.0037696543,0.009296639,0.016059166,0.5071639,0.016270313,0.42790422],"study_design_scores_gemma":[0.0005519035,0.0012480887,0.116058625,0.00006434319,0.00058887707,0.00015166745,0.001189503,0.7681668,0.06452221,0.026418054,0.020647984,0.0003919557],"about_ca_topic_score_codex":0.0000071960376,"about_ca_topic_score_gemma":0.000034295223,"teacher_disagreement_score":0.7588702,"about_ca_system_score_codex":0.000019805508,"about_ca_system_score_gemma":0.00009454631,"threshold_uncertainty_score":0.15608339},"labels":[],"label_agreement":null},{"id":"W2069479841","doi":"10.1167/3.9.401","title":"Detecting changes of velocity of smoothly moving objects","year":2010,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia Hospital","funders":"","keywords":"Geology; Geodesy","score_opus":0.011109798613485683,"score_gpt":0.278226871523913,"score_spread":0.2671170729104273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069479841","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":[0.7010312,0.00003383042,0.29817337,0.00023324131,0.00014790028,0.000041587147,4.094419e-7,0.000017480734,0.0003209467],"genre_scores_gemma":[0.8736648,0.000016051024,0.12622935,0.000018162122,0.000054958586,6.298164e-7,2.4909582e-8,0.000002980311,0.000013055745],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993533,0.000016546575,0.00028762568,0.00007307305,0.0001979108,0.0000715367],"domain_scores_gemma":[0.9988205,0.00006297335,0.00063507824,0.0001912698,0.00025000583,0.000040210318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048554924,0.000048637827,0.00013724102,0.00014807656,0.000054122684,0.000016955972,0.00036030353,0.000051699895,0.000009515579],"category_scores_gemma":[0.0000816826,0.000039648483,0.000070216614,0.00025277818,0.000026291724,0.00018643014,0.0000904219,0.00020366943,7.4000957e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000046026457,0.000041045132,0.00014701561,0.000013509909,0.0000045759407,0.0000010026928,0.0002214893,0.0000075198122,0.784492,0.00070999865,0.000054150227,0.21430306],"study_design_scores_gemma":[0.00016142249,0.0005431169,0.021982435,0.00009151806,0.0000072201683,0.00006226036,0.00005525991,0.007143881,0.96627015,0.0025720277,0.001039311,0.00007139345],"about_ca_topic_score_codex":0.000015006459,"about_ca_topic_score_gemma":0.0000135369,"teacher_disagreement_score":0.21423167,"about_ca_system_score_codex":0.000010104167,"about_ca_system_score_gemma":0.000036220998,"threshold_uncertainty_score":0.16168182},"labels":[],"label_agreement":null},{"id":"W2071339967","doi":"10.1504/ijbidm.2012.051713","title":"Multi-level relationship outlier detection","year":2012,"lang":"en","type":"article","venue":"International Journal of Business Intelligence and Data Mining","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Simon Fraser University","funders":"","keywords":"Computer science; Anomaly detection; Data mining; Outlier; Data science; Artificial intelligence","score_opus":0.2305228817311848,"score_gpt":0.37782925602002526,"score_spread":0.14730637428884047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071339967","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":[0.013304302,0.00028426983,0.98488206,0.00056171324,0.0008058651,0.000038261293,0.000011267048,0.000029223103,0.00008302429],"genre_scores_gemma":[0.76334715,0.0001337411,0.23608942,0.00009592784,0.00026756298,0.0000022625927,0.000005528784,0.0000045081847,0.000053926426],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9990523,0.000024352306,0.00038561376,0.00015214326,0.00026389232,0.000121689496],"domain_scores_gemma":[0.9986354,0.00012893477,0.00032148734,0.00031938223,0.0005181548,0.00007662239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061152934,0.00008311229,0.00009611535,0.00023612294,0.00009559727,0.00015893516,0.0011964083,0.00005036892,0.00001145229],"category_scores_gemma":[0.0003568902,0.000073768446,0.000026275495,0.00028543733,0.000042125594,0.0026119405,0.00041163823,0.00013654638,0.000012031286],"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":[0.000015294538,0.00013780671,0.017343147,0.0000058271207,0.000047235644,0.0000067987316,0.00069227745,0.000086268505,0.0013380116,0.004843972,0.00024592673,0.9752374],"study_design_scores_gemma":[0.0006446542,0.00014910256,0.7253172,0.00036056642,0.00008795001,0.0039309277,0.0015860726,0.17615294,0.028083863,0.0037934934,0.059016712,0.00087651814],"about_ca_topic_score_codex":0.000015892105,"about_ca_topic_score_gemma":0.000005822008,"teacher_disagreement_score":0.97436094,"about_ca_system_score_codex":0.000032706066,"about_ca_system_score_gemma":0.000040165578,"threshold_uncertainty_score":0.300819},"labels":[],"label_agreement":null},{"id":"W2072607026","doi":"10.1109/icphm.2012.6299543","title":"Software architecture for condition monitoring of mobile underground mining machinery: A framework extensible to intelligent signal processing and analysis","year":2012,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Laurentian University","funders":"","keywords":"Computer science; Software; Embedded system; Extensibility; Object-oriented programming; MATLAB; SIGNAL (programming language); Real-time computing; Operating system","score_opus":0.021479493132464226,"score_gpt":0.31219170280381736,"score_spread":0.29071220967135314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072607026","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":[0.06122238,0.00016892047,0.9380462,0.00009040848,0.000022468636,0.0002708537,0.0000037757907,0.00014365769,0.000031311778],"genre_scores_gemma":[0.531178,0.0000026452956,0.4685824,0.000037377293,0.00003553479,0.00012413428,0.0000016313868,0.0000038822827,0.000034414195],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99930614,0.000012918847,0.00018607065,0.0002133032,0.00010625539,0.0001752824],"domain_scores_gemma":[0.99940485,0.00013860225,0.000089682275,0.00018297079,0.000087019645,0.00009688153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016347511,0.00009128122,0.00015449262,0.00021832083,0.00014831389,0.00007366167,0.00015342454,0.000055259767,0.000008957663],"category_scores_gemma":[0.000016056594,0.000080428355,0.00006651023,0.0007409457,0.000020725707,0.00019271323,0.00008659396,0.000067287045,6.847058e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015045908,0.00014890227,0.031326197,0.0001096992,0.00015274555,2.3076855e-7,0.0033512323,0.0066579483,0.004480139,0.015820961,0.00009281085,0.9378441],"study_design_scores_gemma":[0.000620892,0.0019537595,0.06878228,0.00066638255,0.0011639964,0.00007668149,0.0052829497,0.25061804,0.48356518,0.17586271,0.009035471,0.0023716683],"about_ca_topic_score_codex":0.000014139271,"about_ca_topic_score_gemma":0.0000021575343,"teacher_disagreement_score":0.9354724,"about_ca_system_score_codex":0.000023589599,"about_ca_system_score_gemma":0.000015052613,"threshold_uncertainty_score":0.32797733},"labels":[],"label_agreement":null},{"id":"W2075949491","doi":"10.1007/s10618-012-0300-z","title":"Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection","year":2012,"lang":"en","type":"article","venue":"Data Mining and Knowledge Discovery","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":294,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Locality; Anomaly detection; Computer science; Outlier; Generalization; Data mining; Artificial intelligence; Graph; Machine learning; Pattern recognition (psychology); Theoretical computer science; Mathematics","score_opus":0.040746817394101985,"score_gpt":0.2935640976154808,"score_spread":0.25281728022137884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075949491","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":[0.027717065,0.0007182951,0.970082,0.0001362797,0.00014174628,0.0005194106,0.000050158636,0.00020674376,0.00042832282],"genre_scores_gemma":[0.97569793,0.00011169349,0.022839852,0.00032459162,0.0003221848,0.00042117774,0.00004611711,0.000021778325,0.00021465779],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984279,0.00010555279,0.0002846618,0.00070845586,0.0001272138,0.00034616518],"domain_scores_gemma":[0.9983899,0.00012199876,0.00011610314,0.0010886695,0.00006225076,0.00022106334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005413844,0.00022289561,0.00024282865,0.00010838146,0.0004881249,0.00028351386,0.0003408306,0.00010118148,0.0000038000542],"category_scores_gemma":[0.000025783189,0.00019088594,0.000027961023,0.00046771948,0.00010329315,0.0009820189,0.00047158377,0.0001488488,0.000027547785],"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":[0.00004324258,0.000121400924,0.0007853568,0.00003055074,0.000031161773,4.2094402e-7,0.00033456396,0.000029193558,0.00026399802,0.0015561595,0.00097341073,0.99583054],"study_design_scores_gemma":[0.0022257746,0.0016681388,0.027088301,0.0005228881,0.0003534322,0.00038049204,0.0012071362,0.14439769,0.022983732,0.0020875407,0.7944059,0.0026790153],"about_ca_topic_score_codex":0.00018465739,"about_ca_topic_score_gemma":0.0011695654,"teacher_disagreement_score":0.99315155,"about_ca_system_score_codex":0.00005321857,"about_ca_system_score_gemma":0.00004449581,"threshold_uncertainty_score":0.7784103},"labels":[],"label_agreement":null},{"id":"W2081836198","doi":"10.1109/isi.2012.6284089","title":"Outlier detection using semantic sensors","year":2012,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Anomaly detection; Outlier; Intuition; Computer science; Data mining; Singular value decomposition; Anomaly (physics); Data set; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.02648263575102783,"score_gpt":0.27154211775699716,"score_spread":0.24505948200596933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081836198","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":[0.17203623,0.000010419303,0.82446295,0.00006941395,0.000101461985,0.00007169992,8.985084e-8,0.0003886815,0.0028590688],"genre_scores_gemma":[0.882892,0.0000017377708,0.116445936,0.0000975293,0.000069802816,0.000008595755,7.6806806e-8,0.000004172644,0.00048014],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995398,0.000013179287,0.00009126121,0.00011413345,0.00007677749,0.00016484111],"domain_scores_gemma":[0.9996141,0.000009235441,0.000032655968,0.00025753785,0.000024981944,0.000061494065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000107761574,0.000054887205,0.000047472517,0.00005841854,0.0001289249,0.000040306637,0.00013981674,0.000035374407,0.000027130509],"category_scores_gemma":[0.000003894102,0.000048726397,0.000034341883,0.00025285705,0.000011372859,0.00037344336,0.00006246945,0.00005011941,0.00011586099],"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":[0.000004216711,0.000362445,0.008767949,0.0000249214,0.00004269213,0.0000019033702,0.0011803034,0.00025213655,0.2390088,0.2758189,0.0007335679,0.47380218],"study_design_scores_gemma":[0.00010967607,0.00003858158,0.0071631297,0.000004933096,0.000012070827,0.00013967889,0.00007302202,0.37031278,0.57856405,0.0022996492,0.04094082,0.00034158485],"about_ca_topic_score_codex":0.000041612224,"about_ca_topic_score_gemma":0.0000019952104,"teacher_disagreement_score":0.7108558,"about_ca_system_score_codex":0.000027565085,"about_ca_system_score_gemma":0.0000052946584,"threshold_uncertainty_score":0.19870047},"labels":[],"label_agreement":null},{"id":"W2084845753","doi":"10.3166/ria.22.401-420","title":"Algorithme interactif pour la sélection de dimensions en détection d'outlier","year":2008,"lang":"fr","type":"article","venue":"Revue d intelligence artificielle","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Humanities; Philosophy","score_opus":0.051708046584534026,"score_gpt":0.3065514050775559,"score_spread":0.25484335849302187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084845753","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":[0.011567127,0.00081080745,0.96826035,0.007074449,0.0011943216,0.00037019423,0.0000065695153,0.00046153972,0.010254614],"genre_scores_gemma":[0.80399656,0.0025220567,0.07875972,0.00030068535,0.00062207854,0.00016410745,0.0000049717687,0.00004746143,0.113582365],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975251,0.00035108387,0.000612695,0.0007335467,0.00020979991,0.0005677927],"domain_scores_gemma":[0.9979947,0.00048339594,0.00023225382,0.00077791454,0.0002815325,0.00023019421],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006585059,0.00029853193,0.00026191765,0.00024597603,0.0008147251,0.00017540858,0.0005518987,0.0003358719,0.000609539],"category_scores_gemma":[0.00017239961,0.00035022738,0.00024925504,0.0011702789,0.00024984585,0.00065264525,0.00022760176,0.0007202759,0.0020906387],"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":[0.000030948107,0.0014641583,0.00040079595,0.00009364782,0.00008897709,0.00015544277,0.01204168,0.023535168,0.04295647,0.10417299,0.022644874,0.79241484],"study_design_scores_gemma":[0.000033632943,0.00013828304,0.00025231013,0.0000942825,0.0000241861,0.0023519967,0.00033275,0.62883276,0.1559029,0.0068922634,0.20482251,0.00032209858],"about_ca_topic_score_codex":0.0006376521,"about_ca_topic_score_gemma":0.000030144107,"teacher_disagreement_score":0.8895007,"about_ca_system_score_codex":0.00036048828,"about_ca_system_score_gemma":0.0002282957,"threshold_uncertainty_score":0.999895},"labels":[],"label_agreement":null},{"id":"W2087414729","doi":"10.1007/s10115-006-0020-z","title":"Detecting outlying subspaces for high-dimensional data: the new task, algorithms, and performance","year":2006,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":143,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Linear subspace; Outlier; Subspace topology; Pruning; Computer science; Dimension (graph theory); Heuristic; Anomaly detection; Task (project management); Algorithm; Pattern recognition (psychology); Clustering high-dimensional data; Measure (data warehouse); Point (geometry); Process (computing); Artificial intelligence; Data point; Mathematics; Data mining; Cluster analysis; Combinatorics","score_opus":0.0196734787435495,"score_gpt":0.2493453736811592,"score_spread":0.2296718949376097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087414729","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":[0.016596075,0.001124521,0.9797473,0.00038443142,0.00025471576,0.00050948304,0.000014730287,0.00017611796,0.0011926171],"genre_scores_gemma":[0.9860743,0.000046996312,0.013052841,0.00005373231,0.00019957894,0.00008487619,0.00003112161,0.0000040054388,0.000452508],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993365,0.000015420484,0.0002844697,0.00014449406,0.00009272856,0.00012637794],"domain_scores_gemma":[0.99929947,0.00008638251,0.00015042422,0.000317427,0.00010744212,0.000038853654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042097186,0.00008859661,0.000097392025,0.000071867806,0.0005302755,0.00036137938,0.0002788234,0.000045648078,4.2136793e-7],"category_scores_gemma":[0.000012413883,0.00006308322,0.00001240741,0.0001782208,0.00002597897,0.0025729886,0.0001485814,0.000063423104,0.000011776164],"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":[0.000013360251,0.000025736592,0.000723471,0.00030532503,0.000027644857,1.198611e-7,0.0016621514,0.00030280053,0.00046351977,0.23348196,0.0423804,0.72061354],"study_design_scores_gemma":[0.00025967383,0.000063211504,0.001898043,0.00003551926,0.0000072410317,0.000037668517,0.00011203804,0.7220818,0.00090809976,0.00027532564,0.2741825,0.00013892072],"about_ca_topic_score_codex":0.000163025,"about_ca_topic_score_gemma":0.00001474463,"teacher_disagreement_score":0.96947825,"about_ca_system_score_codex":0.0000148015915,"about_ca_system_score_gemma":0.000039046015,"threshold_uncertainty_score":0.4078504},"labels":[],"label_agreement":null},{"id":"W2091448942","doi":"10.1109/isi.2012.6284274","title":"Anomaly detection in spatiotemporal data in the maritime domain","year":2012,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Variety (cybernetics); Vulnerability (computing); Computer science; Anomaly detection; Domain (mathematical analysis); Volume (thermodynamics); Data mining; Anomaly (physics); Data modeling; Data science; Computer security; Artificial intelligence; Database","score_opus":0.03096412403105024,"score_gpt":0.28049064623994097,"score_spread":0.2495265222088907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091448942","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":[0.06981944,0.00003050455,0.921908,0.0013541583,0.00004424947,0.0002256971,0.0000016484018,0.00011094647,0.006505354],"genre_scores_gemma":[0.95886344,0.0000044194107,0.04062027,0.00033109466,0.000045768473,0.000055626893,0.000004503347,0.0000029594826,0.00007189637],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99927425,0.000074541764,0.00016525497,0.00019203422,0.00011365218,0.0001802928],"domain_scores_gemma":[0.99900997,0.00004284994,0.000036042587,0.0008765845,0.000008714093,0.000025860021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083311205,0.00006048187,0.00005786778,0.000101203164,0.000057166177,0.00006211244,0.00095709285,0.000044358527,0.000019393718],"category_scores_gemma":[0.00000964585,0.000045020184,0.000014126869,0.00059600314,0.000018129294,0.0008037374,0.00023704678,0.00011964766,0.000038340346],"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":[0.000010192999,0.00074540527,0.14079238,0.000013622724,0.0000064432165,0.000007654035,0.0017796418,0.000007380436,0.0026622317,0.42103294,0.0024884332,0.43045366],"study_design_scores_gemma":[0.00040429135,0.00008351625,0.8384329,0.000010194561,0.0000028196732,0.00007800589,0.00033297885,0.049048476,0.0065850164,0.02219278,0.082459174,0.00036981844],"about_ca_topic_score_codex":0.00086072856,"about_ca_topic_score_gemma":0.001328031,"teacher_disagreement_score":0.88904405,"about_ca_system_score_codex":0.00003005704,"about_ca_system_score_gemma":0.0000122778265,"threshold_uncertainty_score":0.18358698},"labels":[],"label_agreement":null},{"id":"W2091566335","doi":"10.1258/1357633054068946","title":"An intelligent emergency response system: preliminary development and testing of automated fall detection","year":2005,"lang":"en","type":"article","venue":"Journal of Telemedicine and Telecare","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":235,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Emergency response; Computer science; Medical emergency; Medicine","score_opus":0.01674263717618528,"score_gpt":0.27632314073509473,"score_spread":0.25958050355890944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091566335","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":[0.7881439,0.00048364463,0.2108866,0.00020305764,0.000046898254,0.000093443494,4.1202557e-7,0.00010950528,0.000032541728],"genre_scores_gemma":[0.9303016,0.000056228455,0.06951438,0.000019343208,0.00008440981,0.0000053794856,2.6320257e-7,0.0000051894604,0.000013187271],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989459,0.00005567486,0.00057975133,0.0001296721,0.00018450819,0.000104487444],"domain_scores_gemma":[0.99897367,0.00005894418,0.00038884496,0.00014139242,0.00032804225,0.00010913741],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006081724,0.00009057006,0.00017528127,0.00021778455,0.00011047373,0.000014695781,0.00017750869,0.000046347697,0.0000019059298],"category_scores_gemma":[0.00006696985,0.00007108765,0.000023319182,0.00029401339,0.000024486857,0.00022352645,0.00004927742,0.00011827517,4.1000902e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008472227,0.000075090655,0.0035677159,0.000100842386,0.00002754804,0.000013606635,0.0016143308,0.000055003085,0.035751816,0.000115651215,0.00017885689,0.9584148],"study_design_scores_gemma":[0.0010771512,0.008835997,0.1891448,0.000706889,0.000104193125,0.0035983445,0.003437282,0.23038997,0.5443873,0.000114831535,0.01773518,0.00046807583],"about_ca_topic_score_codex":0.000013856785,"about_ca_topic_score_gemma":0.000009203036,"teacher_disagreement_score":0.9579467,"about_ca_system_score_codex":0.00004816508,"about_ca_system_score_gemma":0.00006698543,"threshold_uncertainty_score":0.289887},"labels":[],"label_agreement":null},{"id":"W2093729845","doi":"10.5539/cis.v7n1p94","title":"3N-Q: Natural Nearest Neighbor with Quality","year":2014,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; k-nearest neighbors algorithm; Outlier; Cluster analysis; Nearest-neighbor chain algorithm; Data mining; Pattern recognition (psychology); Nearest neighbor search; Best bin first; Artificial intelligence; Quality (philosophy); Value (mathematics); Nearest neighbor graph; Function (biology); Machine learning; Fuzzy clustering; Canopy clustering algorithm","score_opus":0.007062638474805923,"score_gpt":0.24652349331798726,"score_spread":0.23946085484318133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093729845","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":[0.019351088,0.0000042264883,0.974636,0.0005496483,0.00010598236,0.00009627517,5.2750624e-7,0.00021070476,0.0050455234],"genre_scores_gemma":[0.8785577,0.0000041945477,0.1199551,0.0014241568,0.000031219028,0.000009699953,0.0000010775537,0.0000010527102,0.000015838621],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991971,0.000014740556,0.00018665292,0.00017096238,0.00027540373,0.00015512513],"domain_scores_gemma":[0.999234,0.000035394878,0.000097960234,0.00034180869,0.00020028371,0.00009051045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004829628,0.0000738136,0.00007401976,0.00013700922,0.00038666264,0.00062687683,0.00056277827,0.000018137693,0.0000021088497],"category_scores_gemma":[0.0000151328795,0.000054168966,0.000014371787,0.0007146681,0.00021541683,0.0066151386,0.00021876056,0.000076572585,0.000028668546],"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":[0.000001919137,0.000006887531,0.00043659093,0.0000070875926,9.663744e-7,4.6367393e-8,0.00036391884,0.0000603298,0.00012050176,0.622433,0.0002132354,0.37635553],"study_design_scores_gemma":[0.0002368441,0.00015349581,0.12560771,0.0000110216515,0.000001132009,0.000033350672,0.000017663206,0.8014057,0.0019547786,0.0013558152,0.069003895,0.00021856565],"about_ca_topic_score_codex":0.000012173463,"about_ca_topic_score_gemma":8.928016e-7,"teacher_disagreement_score":0.85920656,"about_ca_system_score_codex":0.000017325829,"about_ca_system_score_gemma":0.000049394497,"threshold_uncertainty_score":0.6044988},"labels":[],"label_agreement":null},{"id":"W2096896574","doi":"10.1109/aiccsa.2006.205203","title":"Crime Hot-Spots Prediction Using Support Vector Machine","year":2006,"lang":"en","type":"article","venue":"IEEE International Conference on Computer Systems and Applications, 2006.","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":30,"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":"Support vector machine; Computer science; Artificial intelligence; Machine learning; Data mining; Domain (mathematical analysis); Pattern recognition (psychology); Mathematics","score_opus":0.0375228688986308,"score_gpt":0.288582350875924,"score_spread":0.25105948197729316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096896574","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":[0.001789441,0.00006723365,0.98833394,0.00036798927,0.0008929698,0.0006048767,0.00013110995,0.00041535893,0.0073970747],"genre_scores_gemma":[0.97626996,0.00004968905,0.020068852,0.00021904486,0.0016045666,0.00046339654,0.000074656295,0.00001971919,0.0012301188],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982858,0.000041067895,0.0004993654,0.00060530857,0.00035704576,0.0002114284],"domain_scores_gemma":[0.998826,0.00003685813,0.00025373508,0.00048978,0.00030118044,0.000092429626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018038276,0.00022279411,0.00019961476,0.00021817611,0.00025968277,0.00049771916,0.0006573877,0.000105741,0.000024583163],"category_scores_gemma":[0.0000013021681,0.00021911899,0.00006654338,0.00024130614,0.00005735672,0.00034483822,0.000102937105,0.0001626513,0.000062629275],"study_design_candidate":"theoretical_or_conceptual","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":[0.00000718653,0.00016909908,0.0003494573,0.000017368233,0.000026061569,0.0000029864768,0.000017560153,0.0009804119,0.0026007758,0.97508216,0.006231726,0.014515185],"study_design_scores_gemma":[0.0003653485,0.00012138738,0.0065324293,0.00006039317,0.000013021813,0.00016885414,0.00000934906,0.86232215,0.0015989817,0.0032674056,0.12516424,0.00037644984],"about_ca_topic_score_codex":0.0004569044,"about_ca_topic_score_gemma":0.000005231852,"teacher_disagreement_score":0.9744805,"about_ca_system_score_codex":0.00010128945,"about_ca_system_score_gemma":0.00006488645,"threshold_uncertainty_score":0.8935413},"labels":[],"label_agreement":null},{"id":"W2097627964","doi":"10.3233/ida-2006-10604","title":"A comprehensive survey of numeric and symbolic outlier mining techniques","year":2006,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":164,"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":"Outlier; Computer science; Data science; Data mining; Anomaly detection; Information retrieval; Artificial intelligence","score_opus":0.05757818261835768,"score_gpt":0.3155999382805493,"score_spread":0.2580217556621916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097627964","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":[0.031028349,0.00038685574,0.96785873,0.00006424512,0.000010648753,0.00010598158,0.000112833746,0.00014803193,0.00028431191],"genre_scores_gemma":[0.91798306,0.00025096032,0.0813512,0.000051779443,0.000013458275,0.000016424034,0.00024688512,0.0000059161976,0.000080321464],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9988181,0.0000714346,0.00036615782,0.0004535676,0.00015880291,0.00013194843],"domain_scores_gemma":[0.99813163,0.00012813328,0.00017969642,0.0013436404,0.00017711417,0.000039807044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027722915,0.000112108035,0.00028187147,0.00036328827,0.000073884665,0.000077244,0.0009650643,0.000046309913,0.000015405396],"category_scores_gemma":[0.000019393654,0.00010485268,0.00006438285,0.0020249984,0.0000734787,0.00021513799,0.0005647252,0.000058596353,0.0000051562133],"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":[0.000015584103,0.00054651476,0.3511015,0.000060603234,0.0016879073,0.000008864066,0.00035430127,0.00033469833,0.00451217,0.015673652,0.009200302,0.6165039],"study_design_scores_gemma":[0.00010446831,0.00015579347,0.50688624,0.00003049814,0.0008458324,0.000013919379,0.00016282227,0.34919515,0.07636771,0.0023776062,0.06303361,0.00082631304],"about_ca_topic_score_codex":0.006019918,"about_ca_topic_score_gemma":0.00034724432,"teacher_disagreement_score":0.8869547,"about_ca_system_score_codex":0.000013049059,"about_ca_system_score_gemma":0.000017210552,"threshold_uncertainty_score":0.9100355},"labels":[],"label_agreement":null},{"id":"W2100150752","doi":"10.1109/icpr.1990.119414","title":"Implementation of an MHT-based object detection algorithm on a 2-D processor mesh","year":2002,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Queen's University","funders":"","keywords":"Speedup; Computer science; Overhead (engineering); Algorithm; Parallel computing; Function (biology); Object (grammar); Artificial intelligence","score_opus":0.01635266539352884,"score_gpt":0.2838669753830472,"score_spread":0.26751430998951836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100150752","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":[0.017720727,0.0000035422995,0.9801597,0.00023898824,0.000026495814,0.00025703982,0.0000026768334,0.00037295543,0.0012178515],"genre_scores_gemma":[0.87445664,0.000002534703,0.12499041,0.0002681659,0.000020092672,0.00012956711,0.0000015625002,0.000005350713,0.00012564124],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992829,0.000023885872,0.00017997659,0.00023736649,0.00016580783,0.00011007619],"domain_scores_gemma":[0.9994222,0.000016711216,0.00009944954,0.00030920628,0.00010892079,0.000043492448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000097832904,0.00007718236,0.00007398003,0.00012437633,0.00009941029,0.000040015777,0.00023488497,0.00003662747,0.00013833711],"category_scores_gemma":[0.0000034259533,0.000070190035,0.000040255534,0.00043258662,0.000014922549,0.0002605819,0.000018567078,0.000051316325,0.00002103705],"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":[0.0000012550165,0.000101754595,0.000014268947,0.000004668973,0.0000029264684,2.7118008e-7,0.000070372655,0.00001489208,0.00465063,0.0025396051,0.00009898778,0.99250036],"study_design_scores_gemma":[0.00020615419,0.00051865925,0.0005720859,0.0000026815337,0.0000029726118,0.0000029118655,0.000055672543,0.32575727,0.6714088,0.0005747282,0.00081103,0.00008708533],"about_ca_topic_score_codex":0.0001230437,"about_ca_topic_score_gemma":0.000059762246,"teacher_disagreement_score":0.9924133,"about_ca_system_score_codex":0.00003112481,"about_ca_system_score_gemma":0.000013937739,"threshold_uncertainty_score":0.28622666},"labels":[],"label_agreement":null},{"id":"W2101549186","doi":"10.1145/2594473.2594476","title":"Ensembles for unsupervised outlier detection","year":2014,"lang":"en","type":"article","venue":"ACM SIGKDD Explorations Newsletter","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":273,"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":"","keywords":"Computer science; Anomaly detection; Outlier; Cluster analysis; Artificial intelligence; Focus (optics); Machine learning; Unsupervised learning; Core (optical fiber); Ensemble learning; Data mining; Pattern recognition (psychology); Data science","score_opus":0.025405331016139964,"score_gpt":0.2520752218889637,"score_spread":0.22666989087282374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101549186","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":[0.006314207,0.000008645937,0.9750851,0.01672688,0.00018764546,0.00047386627,0.0000032357966,0.0006554846,0.0005449311],"genre_scores_gemma":[0.78647304,0.000006516992,0.20564054,0.005538486,0.00036253495,0.001402254,0.000015369984,0.000023616381,0.00053763774],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988793,0.000053573083,0.0002717203,0.0004144664,0.00014091174,0.000240003],"domain_scores_gemma":[0.99829346,0.00020546914,0.000085926426,0.001194462,0.00014646834,0.00007421961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002037994,0.00015210595,0.0001334638,0.00015597243,0.00044387637,0.00021285337,0.00075663114,0.000091194175,0.000018243856],"category_scores_gemma":[0.00014098475,0.00014779455,0.00011172874,0.00039116104,0.00003353989,0.0007242203,0.00014096628,0.0001021698,0.00018755521],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019915286,0.00021629503,0.00018051539,0.00003215574,0.000053039705,8.2876215e-7,0.0012777802,0.0003665082,0.2771104,0.1591019,0.07778127,0.4838594],"study_design_scores_gemma":[0.0005373735,0.00023295722,0.00037718457,0.000009045884,0.000020840213,0.000008951378,0.000052979398,0.0540096,0.23203392,0.060020402,0.65224487,0.00045188473],"about_ca_topic_score_codex":0.00001195514,"about_ca_topic_score_gemma":0.000023765197,"teacher_disagreement_score":0.7801588,"about_ca_system_score_codex":0.000034742287,"about_ca_system_score_gemma":0.000018073599,"threshold_uncertainty_score":0.60268867},"labels":[],"label_agreement":null},{"id":"W2101821559","doi":"","title":"Proceedings of the UAI 2014 Workshop Causal Inference: Learning and Prediction Quebec City, Quebec, Canada July 27, 2014","year":2014,"lang":"en","type":"article","venue":"Radboud Repository (Radboud University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Inference; Causal inference; Geography; Econometrics; Artificial intelligence; Computer science; Mathematics","score_opus":0.0041735364698600045,"score_gpt":0.17816225940898942,"score_spread":0.1739887229391294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101821559","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":[0.62819576,0.0003748404,0.28533572,0.0022418923,0.002704205,0.001461154,0.000018119159,0.001268048,0.07840025],"genre_scores_gemma":[0.94784874,0.00006280431,0.0005283297,0.00003555116,0.00013313092,0.0000036367603,0.000001441458,0.000011596202,0.051374774],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985437,0.00007809525,0.000251627,0.00046824955,0.0004048888,0.00025348386],"domain_scores_gemma":[0.99879783,0.00011261043,0.00033620314,0.00028133093,0.00033008252,0.00014193282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019252674,0.00019112129,0.00020829476,0.00016334603,0.0007031952,0.00020676374,0.0007095345,0.00015238934,0.0000034951036],"category_scores_gemma":[0.00003898736,0.00017622848,0.00006753851,0.0005486141,0.00020226074,0.0004652089,0.00040055343,0.00040013407,0.0000019489576],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008970578,0.00033026905,0.14743944,0.00025912473,0.00028035545,0.00002710011,0.0018057375,0.004573205,0.026215725,0.46658298,0.34375447,0.008641893],"study_design_scores_gemma":[0.0004907575,0.000117615586,0.036401488,0.00011473395,0.00007709264,0.00006072823,0.00049818086,0.02821725,0.008895966,0.00029259108,0.92431444,0.00051915366],"about_ca_topic_score_codex":0.2656419,"about_ca_topic_score_gemma":0.098339945,"teacher_disagreement_score":0.58055997,"about_ca_system_score_codex":0.00037778943,"about_ca_system_score_gemma":0.0005388286,"threshold_uncertainty_score":0.918113},"labels":[],"label_agreement":null},{"id":"W2102558090","doi":"10.1109/34.868687","title":"Multiobject behavior recognition by event driven selective attention method","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":75,"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":"University of British Columbia","keywords":"Computer science; Artificial intelligence; Robustness (evolution); Soundness; Pattern recognition (psychology); Security token; Outlier; Event (particle physics); Detector; Computer vision","score_opus":0.017047577888189377,"score_gpt":0.2985126661347729,"score_spread":0.2814650882465835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102558090","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":[0.007231934,0.000022148082,0.99176216,0.00023328145,0.000041911168,0.00028569932,0.0000893432,0.00021843368,0.000115083996],"genre_scores_gemma":[0.98294675,0.00039794346,0.015433609,0.00025698985,0.000011686543,0.00030074085,0.00002342003,0.000011496527,0.0006173548],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984924,0.00012692243,0.00036455443,0.00060656236,0.00021001026,0.00019953634],"domain_scores_gemma":[0.99926263,0.00006721907,0.000103174316,0.00036968823,0.000088503875,0.0001088107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018893469,0.00019880488,0.0002339299,0.000337953,0.00032673244,0.00011229887,0.0002726928,0.00008688958,0.00051896874],"category_scores_gemma":[0.0000011587807,0.00019418824,0.00028102906,0.0011832421,0.000036851354,0.0002628877,0.0000030359831,0.00025470386,0.00009059607],"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":[0.0000055766186,0.00025658665,0.00012837867,0.0000027791461,0.00014560253,0.000001208973,0.00008401912,0.0018340986,0.0017140795,0.000008047655,0.000026292617,0.99579334],"study_design_scores_gemma":[0.0001685469,0.00036269834,0.0032173765,0.00002105707,0.0009592906,0.000036799865,0.000037702986,0.44281238,0.5508421,0.00047781918,0.00050856237,0.00055567233],"about_ca_topic_score_codex":0.0012823165,"about_ca_topic_score_gemma":0.00038932372,"teacher_disagreement_score":0.99523765,"about_ca_system_score_codex":0.000059137885,"about_ca_system_score_gemma":0.000010803107,"threshold_uncertainty_score":0.7918766},"labels":[],"label_agreement":null},{"id":"W2103521794","doi":"10.1177/0962280215591236","title":"Global tests for novelty","year":2015,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Anomaly Detection Techniques and Applications","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":"HEC Montréal","funders":"Jenny ja Antti Wihurin Rahasto; Natural Sciences and Engineering Research Council of Canada; Turun Yliopistosäätiö; Magnus Ehrnroothin Säätiö","keywords":"Novelty; Novelty detection; Computer science; Outlier; Permutation (music); Set (abstract data type); Relation (database); Range (aeronautics); Data mining; Null hypothesis; Resampling; Machine learning; Artificial intelligence; Statistical hypothesis testing; Data set; Statistics; Mathematics","score_opus":0.35127345044304936,"score_gpt":0.6502540705804974,"score_spread":0.2989806201374481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103521794","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004496147,0.000049135015,0.98640335,0.004389458,0.00010859654,0.0003675337,0.000022622591,0.000099876466,0.008514485],"genre_scores_gemma":[0.014367106,0.000010061606,0.98475033,0.00025579,0.000082363134,0.0003970107,0.0000027542676,0.0000056753393,0.00012892587],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9966846,0.00086863374,0.00031005865,0.00043502008,0.0011794842,0.00052220834],"domain_scores_gemma":[0.9926899,0.0057567363,0.000023237119,0.00042998916,0.0003855427,0.0007146089],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.012790143,0.000079772995,0.00017399686,0.000109703346,0.000093511204,0.00007557866,0.0010447949,0.00015130625,0.000092398106],"category_scores_gemma":[0.03190062,0.00006721531,0.000028180482,0.0012346744,0.00034134777,0.00007954263,0.0004504028,0.00041505363,0.00003081181],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006534765,0.000051052866,0.00007929924,0.000005713438,0.0000011273336,0.000007229956,0.000013427612,4.7240317e-7,0.00000986533,0.5099526,0.0073270653,0.48254558],"study_design_scores_gemma":[0.00034083298,0.00024543743,0.0019471254,0.0000144159,8.4328985e-7,0.0000135493865,0.000025867017,0.1061651,0.00016942465,0.81485003,0.07614131,0.00008604013],"about_ca_topic_score_codex":0.0001206346,"about_ca_topic_score_gemma":0.000038756334,"teacher_disagreement_score":0.48245955,"about_ca_system_score_codex":0.00020576364,"about_ca_system_score_gemma":0.0006435743,"threshold_uncertainty_score":0.9762541},"labels":[],"label_agreement":null},{"id":"W2112791183","doi":"10.1109/icdm.2009.17","title":"Unsupervised Class Separation of Multivariate Data through Cumulative Variance-Based Ranking","year":2009,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"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; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mahalanobis distance; Outlier; Computer science; Artificial intelligence; Pattern recognition (psychology); Ranking (information retrieval); Variance (accounting); Anomaly detection; Multivariate statistics; Data mining; Class (philosophy); Machine learning; Feature (linguistics)","score_opus":0.06972007756453508,"score_gpt":0.3563563778973447,"score_spread":0.2866363003328096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112791183","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":[0.0012037467,0.000012652645,0.9891558,0.0014685573,0.000031438696,0.0002497088,0.0000065273452,0.00029919035,0.0075723687],"genre_scores_gemma":[0.651388,0.000002962039,0.34797817,0.00048087467,0.000017103044,0.000007859792,0.000017314966,0.0000026072219,0.00010512132],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990376,0.000045567493,0.00026315605,0.00036268393,0.0001658019,0.00012517368],"domain_scores_gemma":[0.9986169,0.00006626406,0.00012884969,0.001057632,0.00010365215,0.000026708989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001974095,0.00009551944,0.00013102948,0.000045508932,0.00010959815,0.000051798725,0.0008600386,0.000056042223,0.000023155637],"category_scores_gemma":[0.000017495868,0.00008510139,0.000036643927,0.00046624566,0.00002426078,0.0007827096,0.00010107221,0.00007158486,0.000009146887],"study_design_candidate":"theoretical_or_conceptual","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":[0.000020063713,0.00024974055,0.000059714,0.000008341468,0.000017666025,0.0000010990882,0.00044011776,0.0030417838,0.02853286,0.9132017,0.0009665878,0.05346033],"study_design_scores_gemma":[0.00039053,0.00008935195,0.001728135,0.000013111675,0.000006494908,9.868183e-7,0.000007262357,0.9278763,0.04415014,0.019033873,0.006576444,0.00012740587],"about_ca_topic_score_codex":0.000090856454,"about_ca_topic_score_gemma":0.000005373943,"teacher_disagreement_score":0.9248345,"about_ca_system_score_codex":0.000020689076,"about_ca_system_score_gemma":0.000056009143,"threshold_uncertainty_score":0.34703338},"labels":[],"label_agreement":null},{"id":"W2118145999","doi":"10.5539/cis.v3n4p240","title":"Using Visual Analytics to Develop Situation Awareness in Network Intrusion Detection System","year":2010,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Intrusion detection system; Visual analytics; Analytics; Visualization; Usability; Computer security; Network security; Context (archaeology); Human–computer interaction; Data science; Data mining","score_opus":0.019150212660753386,"score_gpt":0.2920165015688231,"score_spread":0.2728662889080697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118145999","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":[0.26738194,8.3640305e-7,0.7317726,0.000050056453,0.0003524839,0.00016113634,2.6761276e-7,0.00012011431,0.00016055489],"genre_scores_gemma":[0.7951849,0.0000015989432,0.20453644,0.00019786032,0.00006378492,0.000011961696,9.1657114e-7,0.0000015525507,9.529555e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989803,0.000014880619,0.00033143917,0.00020784696,0.00026379054,0.00020174227],"domain_scores_gemma":[0.99911666,0.00002355934,0.00011647979,0.00022482275,0.00041116774,0.000107293294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008394868,0.00008766807,0.00009216858,0.00047528814,0.00047513243,0.0004839738,0.00039119078,0.000052806576,8.351986e-7],"category_scores_gemma":[0.00003147784,0.00008430111,0.0000115383955,0.003549866,0.000060403818,0.004511252,0.00035127773,0.000121701654,0.000014347229],"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":[0.000007090431,0.000023793284,0.003034468,0.000036860078,0.000002213935,6.2281924e-7,0.0018605979,0.030684898,0.017524898,0.09350932,0.000044636054,0.8532706],"study_design_scores_gemma":[0.00007260432,0.000032279353,0.026075142,0.000018453824,9.614665e-7,0.000026324293,0.000030223424,0.9657475,0.0065917484,0.00014924911,0.0011415128,0.00011402655],"about_ca_topic_score_codex":0.000040637602,"about_ca_topic_score_gemma":0.000024047864,"teacher_disagreement_score":0.9350626,"about_ca_system_score_codex":0.00009151382,"about_ca_system_score_gemma":0.00015925546,"threshold_uncertainty_score":0.46669707},"labels":[],"label_agreement":null},{"id":"W2120607659","doi":"10.1109/crv.2006.14","title":"Automated Detection of Unusual Events on Stairs","year":2006,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Stairs; Hidden Markov model; Background subtraction; Computer science; Conditional random field; Segmentation; Affine transformation; Artificial intelligence; Set (abstract data type); Markov random field; Computation; Data set; Pattern recognition (psychology); Field (mathematics); Optical flow; Activity recognition; Subtraction; Computer vision; Image segmentation; Pixel; Image (mathematics); Algorithm; Mathematics","score_opus":0.006603018516616361,"score_gpt":0.24327362369710415,"score_spread":0.2366706051804878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120607659","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":[0.101172574,0.000002268107,0.88877124,0.000101559956,0.000035423927,0.00009475895,8.3347317e-7,0.0012978658,0.00852349],"genre_scores_gemma":[0.98904186,7.907567e-7,0.010232041,0.000033185264,0.0000133720605,0.000018658868,7.2530923e-7,0.0000029969049,0.0006563434],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99953693,0.0000120687755,0.00013299621,0.00013436888,0.0001066606,0.000076989265],"domain_scores_gemma":[0.9996528,0.000013457664,0.000057337114,0.00021971007,0.000040437233,0.000016271837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000055386645,0.000048371377,0.000051724175,0.0000791116,0.000054181917,0.000010503249,0.000174778,0.00003262113,0.000007290613],"category_scores_gemma":[0.0000022442596,0.00004272023,0.00003175801,0.00031596317,0.000010826448,0.00008831583,0.000032322398,0.000033783628,0.000026877507],"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":[0.000012806147,0.0005416103,0.00071946485,0.0000147493265,0.000014703957,0.0000014132162,0.000037064918,0.0009124349,0.199232,0.66558003,0.0039207945,0.12901293],"study_design_scores_gemma":[0.000120050674,0.00018155223,0.0191149,0.000004415592,0.000001750765,0.0000035340784,0.000005450546,0.11328024,0.855292,0.0094166035,0.0024872848,0.00009220607],"about_ca_topic_score_codex":0.00016456952,"about_ca_topic_score_gemma":0.000013871857,"teacher_disagreement_score":0.8878693,"about_ca_system_score_codex":0.000022846652,"about_ca_system_score_gemma":0.00000956385,"threshold_uncertainty_score":0.17420805},"labels":[],"label_agreement":null},{"id":"W2121741220","doi":"10.1109/pst.2011.5971981","title":"Data preprocessing for distance-based unsupervised Intrusion Detection","year":2011,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":12,"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":"Mahalanobis distance; Normalization (sociology); Euclidean distance; Computer science; Pattern recognition (psychology); Intrusion detection system; Artificial intelligence; Preprocessor; Data mining; Outlier; Anomaly detection; Curse of dimensionality; Principal component analysis; Feature extraction; Data pre-processing; Distance measures","score_opus":0.10303045526846452,"score_gpt":0.29035393987486563,"score_spread":0.1873234846064011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121741220","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":[0.0009938685,0.000012763648,0.9957371,0.0001431799,0.0000544744,0.0002800027,0.000008133663,0.00061526726,0.002155242],"genre_scores_gemma":[0.65029037,0.0000016341303,0.3493417,0.00015225446,0.000017532344,0.00009474441,0.000007498316,0.000004896794,0.00008937074],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992618,0.00000983529,0.00014170572,0.00039464873,0.000074856325,0.00011713613],"domain_scores_gemma":[0.9988299,0.00002405973,0.000056855977,0.000978899,0.00006975722,0.00004054637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016912122,0.00006924841,0.000060815953,0.000049222126,0.00021071047,0.000063735875,0.0008233471,0.000040966315,0.000023244635],"category_scores_gemma":[0.000017541839,0.00006053764,0.000025691656,0.000249767,0.000020658816,0.00048201586,0.0001654473,0.00004125707,0.000007941065],"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":[0.000036393372,0.00017188709,0.0002616115,0.00004465135,0.0000071720983,4.060801e-7,0.00017620072,0.000010897044,0.014684424,0.03890339,0.00074564706,0.9449573],"study_design_scores_gemma":[0.00019219189,0.0000800761,0.00048376835,0.000009912699,0.000005030277,0.0000017652554,0.000015912701,0.6659109,0.29899344,0.010116652,0.024049027,0.00014129792],"about_ca_topic_score_codex":0.00002646446,"about_ca_topic_score_gemma":0.00003968805,"teacher_disagreement_score":0.944816,"about_ca_system_score_codex":0.000019038005,"about_ca_system_score_gemma":0.000031643947,"threshold_uncertainty_score":0.24686533},"labels":[],"label_agreement":null},{"id":"W2124086367","doi":"10.1109/pacrim.2009.5291297","title":"Classification of categorical sequences","year":2009,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Categorical variable; Computer science; Classifier (UML); Artificial intelligence; Pattern recognition (psychology); Class (philosophy); k-nearest neighbors algorithm; Matching (statistics); Domain (mathematical analysis); Noise (video); Data mining; Machine learning; Mathematics; Statistics","score_opus":0.02642165014761104,"score_gpt":0.2826094087252807,"score_spread":0.2561877585776696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124086367","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":[0.004029856,0.000008419931,0.96723527,0.0024181376,0.000011061915,0.00004512432,9.371129e-8,0.00017757897,0.026074432],"genre_scores_gemma":[0.939658,0.0000062571376,0.059927482,0.00012379467,0.000008395125,0.000005128247,2.8667003e-7,5.364436e-7,0.00027010532],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996786,0.0000064810733,0.000098984136,0.000100441684,0.00006708951,0.000048409547],"domain_scores_gemma":[0.99968916,0.000008892528,0.000039244205,0.00020387661,0.000038325892,0.000020526628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000050920706,0.000027429332,0.00003954191,0.000033118868,0.000030063395,0.00001425615,0.00025059367,0.00002157087,0.000011131933],"category_scores_gemma":[0.000003193909,0.00002208302,0.000020627125,0.00024875684,0.000014192732,0.000117972,0.00001158239,0.000025127478,0.000012743852],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.6116415e-7,0.000017054503,0.00004720631,3.0424923e-7,3.5873768e-7,7.338382e-8,0.0000129268155,0.000001189136,0.014493411,0.9298839,0.00040250295,0.0551409],"study_design_scores_gemma":[0.00010692023,0.0003600929,0.122657955,0.0000031493444,0.0000037777536,0.000023850089,0.000044036708,0.072505005,0.2299136,0.55768055,0.01648172,0.00021935652],"about_ca_topic_score_codex":0.000010809755,"about_ca_topic_score_gemma":5.8952475e-7,"teacher_disagreement_score":0.9356282,"about_ca_system_score_codex":0.000008864166,"about_ca_system_score_gemma":0.000014012204,"threshold_uncertainty_score":0.09005195},"labels":[],"label_agreement":null},{"id":"W2126966660","doi":"10.1007/s10115-005-0233-6","title":"Capabilities of outlier detection schemes in large datasets, framework and methodologies","year":2006,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Outlier; Computer science; Anomaly detection; Data mining; Scheme (mathematics); Credit card fraud; Matching (statistics); Artificial intelligence; Machine learning; Pattern recognition (psychology); Credit card; Mathematics; Statistics","score_opus":0.016619104645449252,"score_gpt":0.28303760100419423,"score_spread":0.266418496358745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126966660","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":[0.024747571,0.0008766822,0.9720842,0.000029543913,0.00008447139,0.00021114486,0.000029789719,0.00009607807,0.0018404662],"genre_scores_gemma":[0.9841559,0.000074553944,0.0156149445,0.000012304313,0.000022421284,0.00006869684,0.000012999972,0.0000017174739,0.000036449605],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993566,0.000052733772,0.00034359508,0.00009200653,0.000061632396,0.00009338761],"domain_scores_gemma":[0.9994852,0.000113044174,0.0001229879,0.00019410042,0.00006690444,0.000017792956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048669978,0.00006728902,0.00012849607,0.00017171465,0.00007489952,0.0000853056,0.00010515928,0.000079867146,0.0000010784433],"category_scores_gemma":[0.000048793077,0.00005913479,0.000014298465,0.00024751827,0.000038012302,0.0014279209,0.000090606,0.00006999655,0.0000051798997],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000055581636,0.00003665255,0.0035506394,0.00037396746,0.0000072458856,9.920094e-8,0.0024974623,0.000028451284,0.00038789306,0.9295632,0.0007257841,0.06282304],"study_design_scores_gemma":[0.00078826805,0.0002087126,0.043026894,0.00025322844,0.000013241259,0.000047082634,0.004335439,0.09365165,0.029970787,0.024954472,0.8021916,0.00055857725],"about_ca_topic_score_codex":0.0001203423,"about_ca_topic_score_gemma":0.000023462846,"teacher_disagreement_score":0.95940834,"about_ca_system_score_codex":0.000016512222,"about_ca_system_score_gemma":0.000011946108,"threshold_uncertainty_score":0.24114467},"labels":[],"label_agreement":null},{"id":"W2128404879","doi":"10.1109/86.867873","title":"Gait event detection for FES using accelerometers and supervised machine learning","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Rehabilitation Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":163,"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":"","keywords":"Detector; Accelerometer; Gait; Swing; Phase detector; Computer science; Filter (signal processing); Algorithm; Sample (material); Pattern recognition (psychology); Artificial intelligence; Mathematics; Computer vision; Engineering; Physics; Acoustics; Physical medicine and rehabilitation; Medicine; Telecommunications","score_opus":0.010198351997878432,"score_gpt":0.23039724998410874,"score_spread":0.2201988979862303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128404879","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":[0.23397104,0.000014857896,0.7650841,0.00013374568,0.00007680702,0.00032041039,0.0000042104825,0.00038761194,0.000007199305],"genre_scores_gemma":[0.86062163,0.00001952953,0.13900243,0.000023017838,0.000014169587,0.00022744865,8.5882687e-7,0.000017611013,0.0000733007],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999176,0.000025201689,0.00022347127,0.0003024197,0.000107387496,0.0001655211],"domain_scores_gemma":[0.99942183,0.00023970644,0.00003272813,0.00019173816,0.000047743753,0.000066248176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001660427,0.00013472792,0.00011621774,0.00023335712,0.00028004573,0.00007213101,0.00011600686,0.00006242837,0.000028458537],"category_scores_gemma":[0.000009937473,0.00014962055,0.00009615006,0.0003681352,0.000020422774,0.00036174635,0.0000011779281,0.00015116719,0.000004013349],"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":[0.000015276115,0.000053837233,0.000010984148,0.00004539248,0.000016591428,1.2508664e-7,0.00034484852,0.5126079,0.049330447,0.00016701147,0.0000012601676,0.43740636],"study_design_scores_gemma":[0.00025330356,0.0002964693,0.00036176495,0.000021660027,0.000011845418,0.0000064927212,0.000027944008,0.9674014,0.030439723,0.0001286085,0.0008874364,0.00016339523],"about_ca_topic_score_codex":0.000028802784,"about_ca_topic_score_gemma":0.0000039496654,"teacher_disagreement_score":0.6266506,"about_ca_system_score_codex":0.00008646555,"about_ca_system_score_gemma":0.00000945382,"threshold_uncertainty_score":0.6101349},"labels":[],"label_agreement":null},{"id":"W2128406375","doi":"10.1109/icpr.2000.906157","title":"Learning sparse multiple cause models","year":2002,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Inference; Artificial intelligence; Classifier (UML); Pattern recognition (psychology); Dimensionality reduction; Cluster analysis; Feature selection; Machine learning; Data mining","score_opus":0.05577234649733437,"score_gpt":0.23887409591908165,"score_spread":0.18310174942174728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128406375","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":[0.0037872328,0.000016844224,0.9622536,0.00044658518,0.000018161823,0.00006840984,1.4563143e-7,0.0008160642,0.032593004],"genre_scores_gemma":[0.9011673,0.00003054201,0.08887659,0.00016625259,0.000015602436,0.000028956652,1.9945149e-7,0.000003830247,0.009710738],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954444,0.00001127836,0.000084660016,0.00017293336,0.00007393653,0.00011274585],"domain_scores_gemma":[0.9996147,0.00002204826,0.000025381314,0.00026554885,0.000025643216,0.00004666065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000042960753,0.000051142364,0.000045762936,0.000039178183,0.00013052653,0.000058210244,0.00025789437,0.000027768774,0.00011614758],"category_scores_gemma":[0.0000061911596,0.000046477526,0.000029385716,0.00018965772,0.000011549472,0.00029010177,0.00008502288,0.000082123544,0.00025220803],"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":[9.5002497e-7,0.00019446478,0.0009946568,0.000004928962,0.000016953536,0.000008679186,0.0009807793,0.014251986,0.002512545,0.68554723,0.018201446,0.27728537],"study_design_scores_gemma":[0.000049515274,0.000024102364,0.00005160371,0.0000010193966,8.597511e-7,0.000007132838,0.000009928051,0.97501874,0.002727213,0.0032406603,0.018798215,0.00007102627],"about_ca_topic_score_codex":0.000030837098,"about_ca_topic_score_gemma":0.0000050637573,"teacher_disagreement_score":0.96076673,"about_ca_system_score_codex":0.000011518362,"about_ca_system_score_gemma":0.0000022992558,"threshold_uncertainty_score":0.3241708},"labels":[],"label_agreement":null},{"id":"W2128510565","doi":"10.1109/ifsa-nafips.2013.6608627","title":"Anomaly detection in time series data using a fuzzy c-means clustering","year":2013,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures","keywords":"Anomaly detection; Series (stratigraphy); Cluster analysis; Anomaly (physics); Time series; Autocorrelation; Data mining; Pattern recognition (psychology); Representation (politics); Computer science; Subsequence; Fuzzy clustering; Artificial intelligence; Mathematics; Machine learning; Statistics; Geology","score_opus":0.02898698928399773,"score_gpt":0.25947487740825215,"score_spread":0.23048788812425441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128510565","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":[0.051546864,0.000006588913,0.94428074,0.00027702097,0.00003348956,0.0002300227,0.0000012980536,0.00038352428,0.0032404668],"genre_scores_gemma":[0.7789842,0.0000044777103,0.22000077,0.00010987339,0.000031994747,0.000042993015,0.0000022625234,0.0000075141256,0.0008159327],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992007,0.000021532993,0.00018152304,0.00033181813,0.00009950069,0.00016492409],"domain_scores_gemma":[0.9990437,0.000013468138,0.00004651269,0.00082191767,0.000032230197,0.000042159434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013854692,0.00008333329,0.00008480449,0.00011401204,0.000106223284,0.0002235509,0.0008623597,0.000048103146,0.000067541754],"category_scores_gemma":[0.0000072421235,0.00008020007,0.000017889708,0.00043194284,0.000020874159,0.0016638865,0.00067303894,0.00007795816,0.0001724489],"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":[0.000012265662,0.00026710934,0.0038856682,0.000046599453,0.000031672596,0.0000097438415,0.0006621437,0.0014351273,0.46336365,0.008944911,0.0013931834,0.51994795],"study_design_scores_gemma":[0.000076377815,0.000034395063,0.0031751993,0.000008675366,0.0000018124239,0.00004570968,0.000021334834,0.97763014,0.014940862,0.0017004918,0.002208401,0.00015657688],"about_ca_topic_score_codex":0.0010318208,"about_ca_topic_score_gemma":0.00028060828,"teacher_disagreement_score":0.97619504,"about_ca_system_score_codex":0.00004295944,"about_ca_system_score_gemma":0.000017076105,"threshold_uncertainty_score":0.3270464},"labels":[],"label_agreement":null},{"id":"W2128602454","doi":"10.1142/s0218213014600215","title":"A Mixture Model-Based Combination Approach for Outlier Detection","year":2014,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence Tools","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Outlier; Computer science; Anomaly detection; Pattern recognition (psychology); Artificial intelligence; Data mining; Identification (biology); Local outlier factor","score_opus":0.04797987691904263,"score_gpt":0.31312310688875405,"score_spread":0.2651432299697114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128602454","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":[0.0017648811,0.000008653069,0.99570864,0.0013187933,0.00045695092,0.00021349337,0.0000050312433,0.000066903725,0.0004566658],"genre_scores_gemma":[0.75031424,0.0000033924798,0.24907616,0.00029095754,0.00023116682,0.000037147594,0.000003887775,0.000007642018,0.000035432455],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986359,0.000041673902,0.0005755926,0.00020114737,0.00040957986,0.00013612163],"domain_scores_gemma":[0.99794984,0.00015580191,0.0004517847,0.0002010603,0.001171982,0.000069525966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068095356,0.000113494,0.00014698514,0.00027628583,0.00011042992,0.00031981635,0.0009925101,0.000088633074,0.000006793965],"category_scores_gemma":[0.00024538985,0.000104800616,0.00019302893,0.00020116904,0.000044340726,0.0006073545,0.00004492256,0.000172469,0.000006985729],"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":[0.00006088118,0.00022970446,0.0000056292733,0.0000047143485,0.00002498654,5.304569e-7,0.00010364735,0.05616777,0.009717348,0.20022292,0.000118582226,0.73334324],"study_design_scores_gemma":[0.00005491007,0.00017684446,0.0000132012365,0.000008852054,0.0000068873364,0.000014307959,0.00001958459,0.76065934,0.15638551,0.081152126,0.0014193148,0.00008909983],"about_ca_topic_score_codex":0.0000031313346,"about_ca_topic_score_gemma":0.0000022184156,"teacher_disagreement_score":0.74854934,"about_ca_system_score_codex":0.00010006742,"about_ca_system_score_gemma":0.000071783,"threshold_uncertainty_score":0.42736453},"labels":[],"label_agreement":null},{"id":"W2130439336","doi":"10.1109/crv.2012.42","title":"Real-Time Semantics-Based Detection of Suspicious Activities in Public Spaces","year":2012,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"McGill University","funders":"","keywords":"Computer science; Semantics (computer science); Object (grammar); Fainting; Core (optical fiber); Motion (physics); Object detection; Cognitive neuroscience of visual object recognition; Artificial intelligence; Computer vision; Pattern recognition (psychology); Programming language","score_opus":0.015259920275286526,"score_gpt":0.24802708821052888,"score_spread":0.23276716793524235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130439336","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":[0.38234398,0.000008848533,0.61206806,0.0004391781,0.00003565944,0.00011734912,4.8153606e-7,0.00027299146,0.0047134655],"genre_scores_gemma":[0.9802646,0.000008887326,0.019320587,0.000025954521,0.00002147297,0.000036360394,4.566609e-7,0.0000047879284,0.00031690733],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994104,0.000031258307,0.0001445991,0.00012178781,0.0001086262,0.00018333794],"domain_scores_gemma":[0.99950135,0.000056126784,0.00007944971,0.00028500825,0.000032134514,0.000045908055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002040094,0.00006754559,0.00009865763,0.000198821,0.000050221162,0.000042039355,0.00020898815,0.00005046322,0.000025549167],"category_scores_gemma":[0.000009550955,0.00006227108,0.000037184815,0.0004367985,0.000034644603,0.00046478555,0.000052579093,0.000055691067,0.000014895562],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008941702,0.00057934644,0.013637191,0.000042308635,0.000014456345,7.696237e-7,0.00048567925,0.000044520104,0.74354124,0.17744717,0.00025084082,0.06394751],"study_design_scores_gemma":[0.00013749472,0.0000957579,0.010754712,0.000007317524,0.0000028336763,0.0000043436926,0.00007367046,0.032811772,0.9530317,0.0009899249,0.0019364738,0.000153962],"about_ca_topic_score_codex":0.00020842193,"about_ca_topic_score_gemma":0.000050690378,"teacher_disagreement_score":0.5979206,"about_ca_system_score_codex":0.000036216716,"about_ca_system_score_gemma":0.000027334132,"threshold_uncertainty_score":0.25393412},"labels":[],"label_agreement":null},{"id":"W2130656576","doi":"10.1109/icde.2011.5767850","title":"Outlier detection on uncertain data: Objects, instances, and inferences","year":2011,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; National Center for Atmospheric Research","keywords":"Outlier; Anomaly detection; Computer science; Object (grammar); Inference; Data mining; Set (abstract data type); Artificial intelligence; Computation; Uncertain data; Bayesian probability; Data set; Pattern recognition (psychology); Algorithm","score_opus":0.11932463509749472,"score_gpt":0.2802241295384721,"score_spread":0.16089949444097737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130656576","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":[0.0061968956,0.00002753936,0.94420904,0.000095786556,0.00007114976,0.00013040939,0.0000023862435,0.00038732018,0.04887948],"genre_scores_gemma":[0.9546176,0.000067107234,0.04466692,0.00022847766,0.000020387648,0.000028134427,0.0000013225039,0.0000030363185,0.00036702273],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993561,0.0000172637,0.000108039836,0.0003287448,0.000087511624,0.000102331294],"domain_scores_gemma":[0.999262,0.000021168426,0.000044389144,0.00060018967,0.000026826347,0.000045466208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012314647,0.000073499745,0.00006327649,0.00006410038,0.00013032503,0.00006629579,0.0004950063,0.00004034514,0.000024387553],"category_scores_gemma":[0.000010339761,0.000058257494,0.000010263829,0.00022285596,0.000038883783,0.00045491057,0.00019809112,0.00007480069,0.000026228052],"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":[0.000006298348,0.00007084862,0.00073351176,0.000005458938,0.0000090796275,0.0000014935941,0.00044946975,0.0000010342409,0.00035932157,0.25160095,0.0004902057,0.7462723],"study_design_scores_gemma":[0.0011743954,0.0025516506,0.07516957,0.000090841466,0.000040724946,0.00011211012,0.0013300534,0.15106617,0.32952654,0.23567182,0.2012271,0.0020390227],"about_ca_topic_score_codex":0.0002597276,"about_ca_topic_score_gemma":0.0002861639,"teacher_disagreement_score":0.9484207,"about_ca_system_score_codex":0.000011849505,"about_ca_system_score_gemma":0.000018805842,"threshold_uncertainty_score":0.23756716},"labels":[],"label_agreement":null},{"id":"W2131967083","doi":"10.1109/tkde.2010.262","title":"Resilient Identity Crime Detection","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","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":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Advantage Forensics (Canada)","funders":"Australian Research Council; University of Warwick","keywords":"Computer science; Identity (music); Computer security","score_opus":0.03861372655258806,"score_gpt":0.271454297643489,"score_spread":0.23284057109090095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131967083","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":[0.0019796735,0.000100119585,0.9960957,0.000009815875,0.00029854133,0.000109141656,0.000014723442,0.0004789632,0.00091332564],"genre_scores_gemma":[0.9766067,0.00008955969,0.023092115,0.000013414907,0.000027864013,0.00004050105,0.0000014885076,0.000009695036,0.00011866219],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928826,0.0000106185835,0.00014119594,0.00035299172,0.000070898306,0.0001360549],"domain_scores_gemma":[0.999044,0.00002297818,0.00002246993,0.0007990946,0.00003204959,0.00007938431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013022251,0.00010495356,0.00008112096,0.00015373089,0.00016281924,0.000056087745,0.00051871414,0.000053010463,0.000016961694],"category_scores_gemma":[0.0000023332575,0.00010927106,0.000028100423,0.0003197784,0.00001597481,0.0009551146,0.00001648774,0.00014884358,0.00005900265],"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":[0.000029567365,0.0009117267,0.000018310642,0.00014656028,0.00012138459,0.000010847765,0.001756719,0.0008294694,0.045830663,0.032735284,0.00063993933,0.91696954],"study_design_scores_gemma":[0.00030866274,0.00021724099,0.0020929978,0.000047751848,0.000045396057,0.00006636703,0.000030231295,0.4991866,0.4824637,0.0007846566,0.0142455185,0.00051086606],"about_ca_topic_score_codex":0.000035660163,"about_ca_topic_score_gemma":0.000030152729,"teacher_disagreement_score":0.974627,"about_ca_system_score_codex":0.000023963157,"about_ca_system_score_gemma":0.000011873614,"threshold_uncertainty_score":0.44559446},"labels":[],"label_agreement":null},{"id":"W2132557112","doi":"10.1109/iembs.2009.5333502","title":"Detecting changes in motion characteristics during sports training","year":2009,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Japan Society for the Promotion of Science; IRT Foundation","keywords":"Movement (music); Computer science; Artificial intelligence; Cluster analysis; Hidden Markov model; Divergence (linguistics); Motion (physics); Segmentation; Training (meteorology); Representation (politics); Measure (data warehouse); Pattern recognition (psychology); Human motion; Motion capture; Computer vision; Machine learning; Data mining","score_opus":0.018226821080741928,"score_gpt":0.24105139581394366,"score_spread":0.22282457473320175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132557112","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":[0.45801085,0.0000028617903,0.5385193,0.00060872035,0.000029371558,0.00008195126,1.5196345e-7,0.00037092392,0.0023758265],"genre_scores_gemma":[0.9763475,0.0000094427505,0.02328293,0.00015831337,0.000046970104,0.000013169416,4.2054026e-7,0.0000028541112,0.00013836776],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9994306,0.000007854278,0.0001338326,0.00019039732,0.00008532182,0.00015201242],"domain_scores_gemma":[0.99969876,0.000007818484,0.000057518948,0.0001859226,0.000017511287,0.000032448967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012685111,0.00006289708,0.000076384575,0.000119816796,0.00008678747,0.000049137103,0.00016281978,0.00003678844,0.00001191729],"category_scores_gemma":[0.0000095527375,0.00006398586,0.00001793966,0.000302019,0.000005146294,0.0001606246,0.000027142838,0.00008350827,0.000003205843],"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":[8.826549e-7,0.000019911116,0.00080915506,0.0000025230545,4.8667357e-7,0.0000067109977,0.00044069844,0.000004045794,0.017515715,0.0074084233,0.000001988601,0.97378945],"study_design_scores_gemma":[0.00017095193,0.00007880156,0.85164595,0.00003935542,0.0000018323202,0.00006963526,0.00015135345,0.023056488,0.11909978,0.0049226237,0.0004773252,0.00028588518],"about_ca_topic_score_codex":0.000005482687,"about_ca_topic_score_gemma":0.000011373344,"teacher_disagreement_score":0.9735036,"about_ca_system_score_codex":0.000029262745,"about_ca_system_score_gemma":0.00000650846,"threshold_uncertainty_score":0.26092678},"labels":[],"label_agreement":null},{"id":"W2132987460","doi":"10.1186/s40537-014-0011-y","title":"Contextual anomaly detection framework for big sensor data","year":2015,"lang":"en","type":"article","venue":"Journal Of Big Data","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":141,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Anomaly detection; Context (archaeology); Toolbox; Data mining; Detector; Cluster analysis; Task (project management); False positive paradox; Anomaly (physics); Volume (thermodynamics); Big data; Process (computing); Content (measure theory); Artificial intelligence; Machine learning; Pattern recognition (psychology)","score_opus":0.2992592294446529,"score_gpt":0.3639286034466015,"score_spread":0.06466937400194861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132987460","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":[0.0018064053,0.00016941299,0.9949761,0.0013932035,0.0011647812,0.00014703354,0.00019545968,0.000062293,0.00008528905],"genre_scores_gemma":[0.5638476,0.000055612392,0.43325475,0.00034477064,0.0023403766,0.0000060823504,0.000042849733,0.000013224968,0.000094739546],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998812,0.00004149844,0.00041279875,0.00031028708,0.00026704965,0.00015636208],"domain_scores_gemma":[0.9966733,0.00015341755,0.00045581278,0.002237383,0.00031591207,0.00016416825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010827829,0.00009640427,0.00017771042,0.00012903556,0.00010679373,0.00017686811,0.0032898379,0.00009120114,0.0000014790382],"category_scores_gemma":[0.0005621223,0.00008166174,0.000043587923,0.00031398048,0.00003579898,0.00088165235,0.0009480381,0.00022551793,0.0000121269595],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000596751,0.00011507964,0.00009564529,0.000009381334,0.00005352578,0.000009450145,0.000086048894,0.000008339404,0.0013619355,0.0042735925,0.038577374,0.9553499],"study_design_scores_gemma":[0.0007531003,0.000668313,0.00037809362,0.00005234512,0.00005685269,0.00056889903,0.00018206604,0.03737661,0.00660379,0.019777065,0.9333214,0.00026144797],"about_ca_topic_score_codex":0.000024262356,"about_ca_topic_score_gemma":0.00002729499,"teacher_disagreement_score":0.9550885,"about_ca_system_score_codex":0.000038641476,"about_ca_system_score_gemma":0.00017972337,"threshold_uncertainty_score":0.6113392},"labels":[],"label_agreement":null},{"id":"W2134535492","doi":"10.1093/rpd/ncm346","title":"Are portable personnel portal monitors too sensitive?","year":2007,"lang":"en","type":"article","venue":"Radiation Protection Dosimetry","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Health Canada","funders":"","keywords":"Doors; ALARM; Electromagnetic shielding; Computer science; False alarm; Real-time computing; Environmental science; Simulation; Engineering; Operating system; Electrical engineering; Artificial intelligence","score_opus":0.013085451767046305,"score_gpt":0.24677375670408308,"score_spread":0.23368830493703677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134535492","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":[0.07152887,0.000045554105,0.92445886,0.0003482491,0.00033488884,0.0004957304,0.0000029444006,0.00086397666,0.0019209505],"genre_scores_gemma":[0.98904854,0.000009227543,0.00924592,0.00032705034,0.00026001383,0.00007464927,0.0000037019965,0.00001597958,0.0010148917],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986345,0.00003777452,0.00032664504,0.00042456997,0.00029251273,0.00028398755],"domain_scores_gemma":[0.9988876,0.000034100856,0.00041840793,0.00040395086,0.00012388219,0.00013205459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005475999,0.00015621714,0.00013739259,0.00035866932,0.00045225405,0.00012902348,0.0002241889,0.00015529987,0.000036684363],"category_scores_gemma":[0.000062898995,0.00016828031,0.000099574165,0.0012129043,0.00003108321,0.0006456989,0.00005009152,0.0002407267,0.00009999346],"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":[0.00007736649,0.00070949766,0.017749328,0.000080322134,0.0001782406,0.00011013177,0.0015863304,0.00043770482,0.08818719,0.058305588,0.0071157687,0.8254625],"study_design_scores_gemma":[0.0005231027,0.00016473678,0.1556195,0.000022048416,0.000021103986,0.00022280481,0.0009268316,0.023196751,0.78670603,0.0010460623,0.030840019,0.0007110138],"about_ca_topic_score_codex":0.00008514836,"about_ca_topic_score_gemma":0.0000061317623,"teacher_disagreement_score":0.9175197,"about_ca_system_score_codex":0.00013448294,"about_ca_system_score_gemma":0.000031744516,"threshold_uncertainty_score":0.68622714},"labels":[],"label_agreement":null},{"id":"W2135915282","doi":"10.1007/11731139_66","title":"A Nonparametric Outlier Detection for Effectively Discovering Top-N Outliers from Engineering Data","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Outlier; Anomaly detection; Computer science; Nonparametric statistics; Data mining; Artificial intelligence; Pattern recognition (psychology); Statistics; Mathematics","score_opus":0.015691901354641755,"score_gpt":0.24531839966522867,"score_spread":0.2296264983105869,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135915282","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002613555,0.0001652005,0.9968516,0.000109175,0.0009270357,0.0008924651,0.00007535325,0.00041443555,0.000303355],"genre_scores_gemma":[0.3507829,0.000013612707,0.64809614,0.0001763798,0.0005880253,0.0000999902,0.000039034734,0.000052657426,0.00015125332],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967172,0.000012616726,0.0004469718,0.001827608,0.0004960879,0.00049947377],"domain_scores_gemma":[0.9967983,0.0006568994,0.0002615772,0.0020438628,0.00012808345,0.00011127458],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00054251,0.00045328477,0.00043034073,0.0009246281,0.0002765073,0.00060220255,0.0033067188,0.0003003462,0.0000030159129],"category_scores_gemma":[0.00012310273,0.00045127905,0.00013137049,0.0008840976,0.00016370922,0.0008149208,0.0014572416,0.0005490439,0.000011026504],"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":[0.0000069097864,0.000030490473,0.00003892895,0.000034596662,0.00002150484,0.000006682596,0.00009715256,0.059664644,0.0013623228,0.0021868718,0.000038135124,0.93651175],"study_design_scores_gemma":[0.0002085596,0.00012477962,0.00031546503,0.00011467744,0.000017180306,0.000011194283,8.56197e-8,0.9640335,0.009815433,0.020297665,0.00448598,0.0005755198],"about_ca_topic_score_codex":0.00019219435,"about_ca_topic_score_gemma":0.00008571655,"teacher_disagreement_score":0.9359362,"about_ca_system_score_codex":0.00040217827,"about_ca_system_score_gemma":0.00015345562,"threshold_uncertainty_score":0.9997939},"labels":[],"label_agreement":null},{"id":"W2136091149","doi":"10.1109/icpr.2006.273","title":"Anomaly Detection for Video Surveillance Applications","year":2006,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Anomaly detection; Computer science; Computer vision; Remote sensing; Artificial intelligence; Geology","score_opus":0.005986557614134556,"score_gpt":0.22675486539073858,"score_spread":0.22076830777660403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136091149","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":[0.00081653846,0.000027227812,0.9887595,0.00038452222,0.00003343555,0.00057468156,0.0000041313892,0.0008597692,0.008540189],"genre_scores_gemma":[0.8430282,0.0000020568682,0.15377028,0.00015885456,0.00011217761,0.0015900921,0.000003694658,0.0000068468735,0.0013278273],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.999312,0.000009125461,0.00016996094,0.00028913227,0.000072366725,0.00014744961],"domain_scores_gemma":[0.99933076,0.00005611469,0.000067411085,0.00041441416,0.0001011946,0.0000300829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011835527,0.0000763597,0.000072315284,0.000065543994,0.00023287695,0.000085723295,0.00032417782,0.000037150276,0.000007955094],"category_scores_gemma":[0.000003651222,0.00007427469,0.00006488035,0.00038622596,0.000020006411,0.00017375508,0.00004110199,0.000037108428,0.000038132228],"study_design_candidate":"theoretical_or_conceptual","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":[0.000003066073,0.00009237326,0.0007084583,0.000010841675,0.0000052749447,1.4442146e-7,0.0000048374827,0.0000729072,0.022344619,0.8563118,0.003821277,0.11662441],"study_design_scores_gemma":[0.00021223907,0.00007601939,0.008369928,0.000001335797,0.000002977367,0.000016423803,0.0000044970625,0.043678634,0.15400684,0.06330808,0.7300538,0.00026924064],"about_ca_topic_score_codex":0.00013221058,"about_ca_topic_score_gemma":0.00016728665,"teacher_disagreement_score":0.8422116,"about_ca_system_score_codex":0.000030793282,"about_ca_system_score_gemma":0.000015685153,"threshold_uncertainty_score":0.30288342},"labels":[],"label_agreement":null},{"id":"W2136322450","doi":"10.1007/11558590_52","title":"Robot Security and Failure Detection Using Bayesian Fusion","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Correctness; Fuse (electrical); Computer science; Bayesian probability; Sensor fusion; Artificial intelligence; Robot; Formalism (music); Data mining; Machine learning; Engineering; Algorithm","score_opus":0.011331376757613055,"score_gpt":0.24071784395063042,"score_spread":0.22938646719301736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136322450","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":[0.00035243752,0.00016017946,0.99736726,0.00057364255,0.0003122723,0.00035907366,0.000002169457,0.00025879144,0.00061414763],"genre_scores_gemma":[0.5510228,0.00004471003,0.44814527,0.00035657495,0.0003303656,0.000008253452,9.667955e-7,0.00001807722,0.00007294105],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99758327,0.000021159094,0.00035900687,0.0011935272,0.00046175483,0.0003813082],"domain_scores_gemma":[0.9985336,0.000087926805,0.00023155582,0.0008585285,0.00014021508,0.00014817677],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039375992,0.00036409625,0.00030348139,0.00060221914,0.00047829,0.00042407357,0.0011081654,0.00035192815,0.000020093046],"category_scores_gemma":[0.000015587013,0.000357173,0.00008253248,0.00052682025,0.0003673192,0.0005717962,0.00087075506,0.000654329,0.000010162846],"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":[0.0000018384704,0.000014689505,0.000014009195,0.000016517497,0.0000035357298,0.000007830953,0.00026698076,0.00389809,0.0023969053,0.0055377455,0.0000030423473,0.9878388],"study_design_scores_gemma":[0.00010473517,0.000094772484,0.00005496056,0.00012110669,0.000007291664,0.0001898139,2.1316161e-7,0.8864354,0.010171041,0.09881891,0.0035220536,0.0004796942],"about_ca_topic_score_codex":0.0000409552,"about_ca_topic_score_gemma":0.00024086685,"teacher_disagreement_score":0.9873591,"about_ca_system_score_codex":0.00026483374,"about_ca_system_score_gemma":0.00015047172,"threshold_uncertainty_score":0.999888},"labels":[],"label_agreement":null},{"id":"W2137841518","doi":"10.1109/icassp.2013.6638319","title":"Incorporating covariance information in one class support vector classification","year":2013,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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; Toronto Metropolitan University","funders":"","keywords":"Computer science; Benchmark (surveying); Support vector machine; Class (philosophy); Covariance; Covariance matrix; Artificial intelligence; Variance (accounting); Machine learning; Principal component analysis; Pattern recognition (psychology); Data mining; Algorithm; Mathematics; Statistics","score_opus":0.02587497480596195,"score_gpt":0.23741527202749205,"score_spread":0.2115402972215301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137841518","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":[0.0050422656,0.0000011010394,0.972451,0.0034444197,0.000037265087,0.000360186,6.4174804e-7,0.00033617346,0.018326988],"genre_scores_gemma":[0.8406439,0.0000025110664,0.15824278,0.00058110955,0.000015097552,0.0002853018,0.000007859702,0.0000024387732,0.00021901458],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992766,0.000018261133,0.0003082074,0.00014474674,0.00012912667,0.00012305121],"domain_scores_gemma":[0.99934554,0.000022617047,0.00014230264,0.0003339717,0.000111929876,0.000043660486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016339165,0.00006636291,0.000073698524,0.000119072436,0.00007155681,0.00018707904,0.00032490774,0.000058729307,0.000102120415],"category_scores_gemma":[0.000018190738,0.00006608894,0.000019121622,0.00052559836,0.000019404062,0.0022085474,0.00006977288,0.00009720565,0.00069693197],"study_design_candidate":"theoretical_or_conceptual","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":[7.805115e-7,0.00004884112,0.00086009264,0.000007722063,0.0000019903841,1.2108697e-7,0.00013045163,0.000044673,0.005944818,0.90303725,0.0024492457,0.08747399],"study_design_scores_gemma":[0.00035055165,0.0001232715,0.16704737,0.000016909275,0.0000020638927,0.000008503456,0.00013741161,0.76117057,0.0141263055,0.03885684,0.01779365,0.00036655035],"about_ca_topic_score_codex":0.0001870125,"about_ca_topic_score_gemma":0.000016395263,"teacher_disagreement_score":0.86418045,"about_ca_system_score_codex":0.00006607,"about_ca_system_score_gemma":0.00005077514,"threshold_uncertainty_score":0.8957883},"labels":[],"label_agreement":null},{"id":"W2137960471","doi":"10.1109/icdm.2006.159","title":"The PDD Framework for Detecting Categories of Peculiar Data","year":2006,"lang":"en","type":"article","venue":"Proceedings","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Data set; Set (abstract data type); Outlier; Oracle; Data mining; Extensibility; Data structure; Information retrieval; Artificial intelligence","score_opus":0.027368850258737355,"score_gpt":0.28545601390856057,"score_spread":0.2580871636498232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137960471","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":[0.0036234953,0.000095929514,0.99310434,0.0011684166,0.000048913233,0.0002148213,0.0000036412087,0.00019540057,0.001545024],"genre_scores_gemma":[0.7388476,0.0000109572375,0.26078406,0.00002817532,0.000104921535,0.000088833774,0.0000010782652,0.000005438394,0.00012895263],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999376,0.000001374726,0.0001661929,0.00021934885,0.000097519776,0.00013958027],"domain_scores_gemma":[0.9992817,0.000103866376,0.00013828566,0.0003051556,0.00015467315,0.000016342947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026477224,0.000059846436,0.00006568737,0.000023646788,0.00035718983,0.00014038081,0.0010938203,0.00004106189,6.71667e-7],"category_scores_gemma":[0.000110392815,0.0000443032,0.000027545264,0.0002797768,0.000050331793,0.00026660124,0.0002620511,0.00007447291,0.0000017830961],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001699886,0.000008718289,0.00018056275,0.000012950483,0.000002683659,1.8284625e-8,0.00006734078,4.774066e-7,0.0014365044,0.97628254,0.0019428402,0.020063695],"study_design_scores_gemma":[0.000062068786,0.00005787091,0.0004837166,0.000015736809,0.0000084600815,0.0000062618956,0.00016820076,0.02060637,0.11581917,0.73554546,0.12709805,0.00012863612],"about_ca_topic_score_codex":0.000039269882,"about_ca_topic_score_gemma":0.000003841223,"teacher_disagreement_score":0.73522407,"about_ca_system_score_codex":0.000010314249,"about_ca_system_score_gemma":0.00001611132,"threshold_uncertainty_score":0.27472514},"labels":[],"label_agreement":null},{"id":"W2139902660","doi":"10.1016/j.ins.2012.02.017","title":"A survey of techniques for incremental learning of HMM parameters","year":2012,"lang":"en","type":"article","venue":"Information Sciences","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":105,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer science; Machine learning; Hidden Markov model; Artificial intelligence; Benchmarking; Data mining","score_opus":0.054959383096668535,"score_gpt":0.3229061009650656,"score_spread":0.2679467178683971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139902660","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":[0.06332458,0.000011236018,0.9347175,0.00003914323,0.00003276444,0.00019349618,0.0000059766653,0.00007438086,0.0016009724],"genre_scores_gemma":[0.8682544,0.000004362784,0.13165008,0.00003821891,0.0000030922338,0.00004042087,0.0000029754776,6.892077e-7,0.0000057381408],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99935204,0.000025566147,0.00028534496,0.000050260074,0.000178261,0.00010850754],"domain_scores_gemma":[0.99930924,0.000120570876,0.00029393594,0.000101875936,0.00014864832,0.000025734922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015119172,0.000040932682,0.00007531373,0.00015136821,0.00011121915,0.000035451947,0.00035214348,0.000024253439,0.000002975396],"category_scores_gemma":[0.00011486904,0.00003465636,0.000029674571,0.00053900445,0.00011355255,0.001958759,0.00006539197,0.000029018463,0.0000024444214],"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":[0.000013451596,0.000114584196,0.1036612,0.0000874846,0.00001629436,7.193099e-9,0.0040971003,0.00026616466,0.0072614052,0.1719878,0.001074342,0.7114202],"study_design_scores_gemma":[0.00016379278,0.00059216394,0.09433095,0.000030084415,0.0000048595434,0.0000042672054,0.00058815844,0.08698376,0.8049587,0.001286765,0.010823563,0.0002329254],"about_ca_topic_score_codex":0.0002414296,"about_ca_topic_score_gemma":0.000002596285,"teacher_disagreement_score":0.80492985,"about_ca_system_score_codex":0.000010587014,"about_ca_system_score_gemma":0.000033232307,"threshold_uncertainty_score":0.1420053},"labels":[],"label_agreement":null},{"id":"W2140254487","doi":"10.3141/2019-08","title":"Automated Analysis of Road Safety with Video Data","year":2007,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":146,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Cluster analysis; Collision; Data mining; Heuristic; Traffic conflict; Hidden Markov model; Real-time computing; Artificial intelligence; Traffic congestion; Computer security; Floating car data; Transport engineering; Engineering","score_opus":0.08943039015637477,"score_gpt":0.40893624589701966,"score_spread":0.3195058557406449,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140254487","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":[0.5142746,0.00007099505,0.48270953,0.0018004437,0.000091083086,0.0006151025,0.00009761496,0.00011726986,0.00022338067],"genre_scores_gemma":[0.9657622,0.00031201064,0.03356612,0.000030600844,0.000045877074,0.000018622677,0.000042232496,0.000022586095,0.00019977191],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9932146,0.0005457354,0.0016061255,0.00052562426,0.0034428968,0.0006650149],"domain_scores_gemma":[0.9924415,0.0008886406,0.00073436607,0.0016472116,0.003975129,0.00031320663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009010596,0.00018877679,0.0004981396,0.0022346468,0.0005180289,0.00011281882,0.0036671143,0.00014559842,0.00008913255],"category_scores_gemma":[0.000096492135,0.00013147201,0.0003037687,0.010759383,0.0005432834,0.0010651905,0.000030355184,0.0012682149,0.0000052140567],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006035057,0.0021963294,0.6322551,0.0005125303,0.0059599727,0.00041318854,0.00892152,0.015968274,0.022445794,0.08299715,0.014042143,0.20825295],"study_design_scores_gemma":[0.000639359,0.00047927562,0.973386,0.00010561987,0.00020126363,7.5189877e-7,0.00048889755,0.012228741,0.0035585412,0.00063054304,0.008127751,0.00015329383],"about_ca_topic_score_codex":0.0074792784,"about_ca_topic_score_gemma":0.04572453,"teacher_disagreement_score":0.4514876,"about_ca_system_score_codex":0.00015093814,"about_ca_system_score_gemma":0.0005677253,"threshold_uncertainty_score":0.99913},"labels":[],"label_agreement":null},{"id":"W2140755893","doi":"10.1109/ideas.2007.12","title":"An EffectiveMulti-Layer Model for Controlling the Quality of Data","year":2007,"lang":"en","type":"article","venue":"International Database Engineering and Applications Symposium","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Agriculture Food and Rural Development; University of Manitoba","funders":"","keywords":"Computer science; Data mining; Layer (electronics); Data quality; Data modeling; Data set; Set (abstract data type); Data consistency; Consistency (knowledge bases); Data access layer; Quality (philosophy); Data model (GIS); Database; Artificial intelligence; Engineering","score_opus":0.04502782927028701,"score_gpt":0.354366736678551,"score_spread":0.309338907408264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140755893","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":[0.0017692492,0.00004533087,0.9965068,0.00047841304,0.000038207043,0.00049620925,0.00046227456,0.00013025446,0.000073277195],"genre_scores_gemma":[0.76989806,0.000029881014,0.22936335,0.00006228603,0.0001024288,0.00031586134,0.00019300709,0.000007729698,0.000027396165],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991562,0.000008964339,0.00027739594,0.00031149012,0.00013648662,0.00010946868],"domain_scores_gemma":[0.99844754,0.000351949,0.00010377758,0.00090688566,0.00013863269,0.00005120732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093891064,0.00008534157,0.00009148393,0.00007294996,0.0001130616,0.000054240834,0.0010796769,0.0000410257,5.87265e-7],"category_scores_gemma":[0.000026283557,0.00007128095,0.000028868395,0.00012770126,0.00002975697,0.00044916425,0.00018773582,0.00010026232,8.2491283e-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":[0.000015579672,0.0001349491,0.00008119433,0.000032668522,0.000041642692,7.9992795e-8,0.00007258871,0.024247512,0.28785145,0.6679872,0.00009759627,0.019437518],"study_design_scores_gemma":[0.0001846584,0.0000108501945,0.00025677175,0.0000067415194,0.000007080238,0.0000025608726,0.000008186558,0.98151255,0.00995213,0.0005924146,0.0073820823,0.00008395383],"about_ca_topic_score_codex":0.000031147723,"about_ca_topic_score_gemma":0.0000066962198,"teacher_disagreement_score":0.9572651,"about_ca_system_score_codex":0.00001810374,"about_ca_system_score_gemma":0.0000155481,"threshold_uncertainty_score":0.29067528},"labels":[],"label_agreement":null},{"id":"W2142047467","doi":"10.1017/s026988891300043x","title":"One-class classification: taxonomy of study and review of techniques","year":2014,"lang":"en","type":"article","venue":"The Knowledge Engineering Review","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":599,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Taxonomy (biology); Computer science; Class (philosophy); Artificial intelligence; Novelty; One-class classification; Machine learning; Novelty detection; Open research; Focus (optics); Field (mathematics); Data science; Classifier (UML); Mathematics","score_opus":0.036975583930530416,"score_gpt":0.2757778478196157,"score_spread":0.23880226388908526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142047467","genre_codex":"methods","genre_gemma":"review","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":[0.00011731063,0.24707003,0.74647486,0.000868318,0.000036965797,0.0018212313,7.4961144e-7,0.0002410961,0.0033694217],"genre_scores_gemma":[0.43184632,0.4951124,0.06976023,0.0003931996,0.00011198105,0.002513235,0.0000015663085,0.000030736326,0.00023032638],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999208,0.00006838575,0.0003957568,0.00016377974,0.00008809374,0.00007598716],"domain_scores_gemma":[0.9988235,0.00009442253,0.00017669384,0.00074268796,0.00013286978,0.000029796327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010103182,0.00009397303,0.00032616904,0.000042238516,0.000048453665,0.000006049466,0.00053235836,0.000020431742,0.0000057823936],"category_scores_gemma":[0.000095707976,0.00007088943,0.000059478392,0.00046350356,0.000031609296,0.00006630291,0.00019386521,0.00008852603,0.00000480814],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.945158e-7,0.00017022759,0.000042792803,0.024284868,0.000026917796,4.1647155e-8,0.000078288045,3.0394878e-7,0.0006283068,0.07608179,0.0012951646,0.897391],"study_design_scores_gemma":[0.00007985232,0.00022903572,0.0017482777,0.032660652,0.00012804929,0.000009022103,0.0000052761893,0.0068553607,0.0042595454,0.00023381945,0.9535403,0.00025080473],"about_ca_topic_score_codex":0.0000018576172,"about_ca_topic_score_gemma":3.482997e-7,"teacher_disagreement_score":0.9522451,"about_ca_system_score_codex":0.000013558189,"about_ca_system_score_gemma":0.000017570179,"threshold_uncertainty_score":0.2890787},"labels":[],"label_agreement":null},{"id":"W2144182447","doi":"10.1145/335191.335388","title":"LOF","year":2000,"lang":"en","type":"article","venue":"ACM SIGMOD Record","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5181,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Outlier; Local outlier factor; Computer science; Object (grammar); Anomaly detection; Degree (music); Data mining; Property (philosophy); Binary number; Theoretical computer science; Artificial intelligence; Mathematics","score_opus":0.012959734804963171,"score_gpt":0.24427321013530048,"score_spread":0.2313134753303373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144182447","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":[0.051220167,0.000041619143,0.8993725,0.0025405057,0.00011671523,0.00017801233,0.0000011654089,0.0010153602,0.045513913],"genre_scores_gemma":[0.7039994,0.00012917821,0.27095243,0.0009854356,0.00012937495,0.000096807,0.0000013275637,0.00001114469,0.023694886],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994681,0.000011960719,0.000107241445,0.00021277429,0.00007423,0.00012572341],"domain_scores_gemma":[0.99906695,0.000028951557,0.00002400522,0.0008137341,0.000018985565,0.000047368685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000038045364,0.000060359966,0.00006145999,0.0000322863,0.00009083017,0.00004875777,0.0008472514,0.000038775674,0.0005769607],"category_scores_gemma":[0.000009114733,0.00005594824,0.0000438223,0.00024839863,0.00001657201,0.00017773302,0.00009139064,0.00006965944,0.0006258794],"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":[9.1335613e-7,0.0000172974,0.00010742597,7.3080275e-7,0.0000019325485,7.9034965e-7,0.000016281067,0.000002387615,0.00040851478,0.0052488013,0.00884214,0.9853528],"study_design_scores_gemma":[0.000095799005,0.00009326492,0.0015497927,0.0000046520486,0.000002373166,0.000017899063,0.0000036858446,0.0042837546,0.0070996243,0.056204032,0.93046886,0.00017627934],"about_ca_topic_score_codex":0.00004216504,"about_ca_topic_score_gemma":0.0000057202133,"teacher_disagreement_score":0.9851765,"about_ca_system_score_codex":0.000013217473,"about_ca_system_score_gemma":0.0000122415695,"threshold_uncertainty_score":0.8044622},"labels":[],"label_agreement":null},{"id":"W2148812200","doi":"10.1109/cisda.2009.5356547","title":"Tracking a moving hypothesis for visual data with explicit switch detection","year":2009,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Benchmark (surveying); Artificial intelligence; Computer vision; Identification (biology); Video tracking; Digital video; Tracking (education); Video processing; Real-time computing; Frame (networking)","score_opus":0.04809818656169099,"score_gpt":0.2963183059189782,"score_spread":0.2482201193572872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148812200","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":[0.012602702,0.000007028896,0.98523134,0.0005784792,0.00001418099,0.00028767722,0.0000011495292,0.0005793141,0.00069815025],"genre_scores_gemma":[0.7780069,0.0000028937602,0.22144492,0.00029903976,0.00004608401,0.000055509114,0.000001300088,0.0000053969784,0.00013794497],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992024,0.000007764608,0.00013213728,0.00039050187,0.000106296364,0.00016093043],"domain_scores_gemma":[0.9991392,0.000065111395,0.000060440936,0.0006360605,0.000055581142,0.0000436089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014353175,0.00008814115,0.00008438669,0.000070583636,0.00021424748,0.00015786625,0.00061958854,0.000038669707,0.0000049787122],"category_scores_gemma":[0.00001726094,0.000071621136,0.000026345502,0.00030690047,0.0000066794946,0.0006470069,0.00006880547,0.000053301173,0.0000056800936],"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":[0.0000074926024,0.000050317016,0.000015799631,0.0000026752127,0.000004966474,3.7978697e-7,0.000047929192,0.00001299285,0.019391164,0.008537321,0.00011202787,0.97181696],"study_design_scores_gemma":[0.00032966005,0.00074214325,0.0036844548,0.000018062552,0.00001746157,0.00004942071,0.000112850124,0.59056735,0.38789514,0.0091605885,0.0070414683,0.00038142267],"about_ca_topic_score_codex":0.000020267746,"about_ca_topic_score_gemma":0.000026728092,"teacher_disagreement_score":0.9714355,"about_ca_system_score_codex":0.000022459175,"about_ca_system_score_gemma":0.000018648641,"threshold_uncertainty_score":0.29206252},"labels":[],"label_agreement":null},{"id":"W2150504213","doi":"10.1109/iwfhr.2004.95","title":"Speeding Up the Decision Making of Support Vector Classifiers","year":2004,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":12,"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","funders":"","keywords":"Benchmark (surveying); MNIST database; Computer science; Speedup; Probabilistic logic; Artificial intelligence; Context (archaeology); Machine learning; Pairwise comparison; Modular design; Support vector machine; Class (philosophy); Process (computing); Data mining; Pattern recognition (psychology); Artificial neural network; Parallel computing","score_opus":0.026758748940628362,"score_gpt":0.29641852017579184,"score_spread":0.2696597712351635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150504213","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":[0.008367689,0.0000051851302,0.981038,0.0011423286,0.00011016249,0.00008920337,3.232776e-7,0.00013994495,0.009107176],"genre_scores_gemma":[0.8793282,0.0000042786082,0.12019334,0.00022665228,0.000020323178,0.000007920595,9.246288e-8,0.0000026725397,0.00021654372],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994902,0.00000449241,0.00014257291,0.0001357162,0.00013394273,0.000093065435],"domain_scores_gemma":[0.99953234,0.00003804503,0.000060601953,0.00031254877,0.000039297985,0.000017158145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014398659,0.000046738678,0.000054682652,0.000042645315,0.00010812763,0.000041538264,0.00046592645,0.00002749531,0.00004462245],"category_scores_gemma":[0.000012937966,0.000030293555,0.000048127393,0.00029876028,0.000032241405,0.00013023507,0.000113961156,0.00005854513,0.000024308854],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000025487757,0.000020798157,0.00011590796,0.0000024465792,0.0000048633665,8.501149e-7,0.0002972251,0.00009205917,0.004534621,0.85052955,0.0030267348,0.1413724],"study_design_scores_gemma":[0.0009227906,0.00055397674,0.02241456,0.00014797109,0.000024532661,0.00016615074,0.00083232136,0.025308326,0.43264937,0.4092132,0.10705966,0.00070714304],"about_ca_topic_score_codex":0.000010990727,"about_ca_topic_score_gemma":0.0000041350218,"teacher_disagreement_score":0.8709605,"about_ca_system_score_codex":0.000029295003,"about_ca_system_score_gemma":0.00003274383,"threshold_uncertainty_score":0.12353353},"labels":[],"label_agreement":null},{"id":"W2151967264","doi":"","title":"A proposal for statistical outlier detection in relational structures","year":2014,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Outlier; Computer science; Generative model; Anomaly detection; Statistical model; Artificial intelligence; Feature (linguistics); Generative grammar; Relational database; Data modeling; Data mining; Pattern recognition (psychology); Machine learning; Database","score_opus":0.011665393312049594,"score_gpt":0.2651262843284641,"score_spread":0.2534608910164145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151967264","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":[0.001250618,0.0000011809723,0.99641764,0.0005509178,0.00004269696,0.00023417862,0.0000024327865,0.00015857644,0.0013417762],"genre_scores_gemma":[0.55654216,1.9703158e-7,0.44312763,0.00007067812,0.000025491001,0.000090328205,0.0000017781215,0.0000023057034,0.00013943302],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995104,0.000018356397,0.00012285048,0.00017791962,0.00007671204,0.00009375874],"domain_scores_gemma":[0.99968904,0.00008223312,0.000026713056,0.00013837281,0.000035794026,0.000027825203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013527145,0.000047183923,0.000051329393,0.00006199114,0.00006996907,0.000034149252,0.00012128588,0.00004114768,0.000018863582],"category_scores_gemma":[0.00003965824,0.00004027234,0.00001842668,0.00012350082,0.000017477236,0.000114673196,0.000027940172,0.00005497583,0.0000086982],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000022751258,0.000009067515,0.00009747678,0.0000017090472,7.263727e-7,3.4950826e-8,0.000012532781,0.000021761447,0.00045646576,0.9394189,0.00013704464,0.059841976],"study_design_scores_gemma":[0.00016156168,0.00008974663,0.010468659,0.0000010187265,0.0000010952294,0.0000042606607,0.0000028284937,0.36017117,0.008127941,0.6077837,0.013109317,0.00007873408],"about_ca_topic_score_codex":0.000020985466,"about_ca_topic_score_gemma":0.00008274137,"teacher_disagreement_score":0.55529153,"about_ca_system_score_codex":0.000022929042,"about_ca_system_score_gemma":0.000026058928,"threshold_uncertainty_score":0.16422585},"labels":[],"label_agreement":null},{"id":"W2152499371","doi":"10.1109/icdm.2006.6","title":"A Novel Method for Detecting Outlying Subspaces in High-dimensional Databases Using Genetic Algorithm","year":2006,"lang":"en","type":"article","venue":"Proceedings","topic":"Anomaly Detection Techniques and Applications","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":"Saint Mary's University; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Linear subspace; Computer science; Algorithm; Data mining; Database; Online analytical processing; Artificial intelligence; Pattern recognition (psychology); Data warehouse; Mathematics","score_opus":0.0389545296141228,"score_gpt":0.31030444506372773,"score_spread":0.2713499154496049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152499371","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05436529,0.000049702914,0.9448123,0.00014379174,0.000045090954,0.00031170892,0.000006836616,0.00021771723,0.000047578375],"genre_scores_gemma":[0.16094925,0.0000010841021,0.8387126,0.00006397067,0.00009976975,0.00013089486,0.0000016029888,0.000012218524,0.000028625574],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892694,0.0000037136822,0.00024370303,0.0004208799,0.00014460369,0.00026015533],"domain_scores_gemma":[0.9995188,0.000068756126,0.00013626297,0.00010968423,0.00013058708,0.00003590591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030562424,0.00012277935,0.0001380726,0.00018331787,0.00022220831,0.00011360653,0.00026344034,0.000041965664,0.0000015957469],"category_scores_gemma":[0.00003061733,0.00012594827,0.00004365148,0.00051754137,0.000017814453,0.00037541531,0.00011371382,0.00009349878,0.0000011658001],"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":[0.000007997927,0.00018356017,0.0019899604,0.00007499807,0.000011966263,0.0000018450271,0.00020249335,0.0013893294,0.70736885,0.041393287,0.00020535247,0.24717036],"study_design_scores_gemma":[0.0002483844,0.000027238526,0.0016675564,0.00003319172,0.0000075395924,0.000068759815,0.000030653257,0.8741021,0.1186254,0.0043777563,0.0006232762,0.00018809814],"about_ca_topic_score_codex":0.0011040857,"about_ca_topic_score_gemma":0.00001777376,"teacher_disagreement_score":0.8727128,"about_ca_system_score_codex":0.00006286439,"about_ca_system_score_gemma":0.000029451956,"threshold_uncertainty_score":0.51360214},"labels":[],"label_agreement":null},{"id":"W2153313896","doi":"10.1609/aimag.v34i1.2435","title":"Statistical Anomaly Detection for Train Fleets","year":2013,"lang":"en","type":"article","venue":"AI Magazine","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bombardier (Canada)","funders":"","keywords":"Anomaly detection; Train; Anomaly (physics); Computer science; Bayesian probability; Data mining; Parametric statistics; Event (particle physics); Artificial intelligence; Statistics; Mathematics; Geography","score_opus":0.009203911163737468,"score_gpt":0.2552243410683807,"score_spread":0.24602042990464323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153313896","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":[0.0034335984,0.000009381124,0.99137,0.0025917734,0.00007199323,0.00047346766,0.000007734003,0.0003766118,0.0016653896],"genre_scores_gemma":[0.829542,0.0000024695516,0.16782989,0.00082097214,0.00006766643,0.0005709376,0.0000049789414,0.000008617819,0.0011525126],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999303,0.000014512211,0.00016057029,0.0002534681,0.00008486886,0.00018359143],"domain_scores_gemma":[0.99942493,0.00006328354,0.000041115098,0.000292399,0.00010272217,0.000075564356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009128759,0.00008451101,0.00008752799,0.000060596914,0.00011813731,0.000112928465,0.0002529229,0.00004929463,0.00010894233],"category_scores_gemma":[0.000023064375,0.00007922197,0.00004192763,0.00019837853,0.000029190156,0.000302075,0.00004735584,0.00007019124,0.00044527807],"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":[0.0000040635096,0.00007735909,0.000059722774,0.000013777797,0.000009919615,9.0373675e-7,0.000046410878,0.0000061681108,0.050139807,0.15175879,0.03477642,0.76310664],"study_design_scores_gemma":[0.00077808724,0.00085738476,0.046094704,0.000009582578,0.000018342853,0.00006275446,0.00001278495,0.30915093,0.072590634,0.17847793,0.3913655,0.0005813723],"about_ca_topic_score_codex":0.000031937376,"about_ca_topic_score_gemma":0.000016147593,"teacher_disagreement_score":0.82610834,"about_ca_system_score_codex":0.000023996901,"about_ca_system_score_gemma":0.000016603435,"threshold_uncertainty_score":0.5723297},"labels":[],"label_agreement":null},{"id":"W2153487386","doi":"10.1109/iscc.2005.155","title":"WCOND-Mine: Algorithm for Detecting Web Content Outliers from Web Documents","year":2005,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":17,"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":"Outlier; Computer science; Web mining; Domain (mathematical analysis); Data mining; Web content; Web analytics; Competitor analysis; Information retrieval; Web service; Web page; World Wide Web; Web intelligence; Web modeling; Artificial intelligence; Mathematics","score_opus":0.02507518883317332,"score_gpt":0.2719936284313389,"score_spread":0.24691843959816556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153487386","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013707861,0.000043839245,0.9808326,0.0012675362,0.00012830635,0.0004266295,0.000018447074,0.00068600033,0.0028888213],"genre_scores_gemma":[0.34998503,0.000013936694,0.6453323,0.0007927225,0.00015764074,0.00024884962,0.0000038424823,0.000010547489,0.0034551367],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989506,0.000012879083,0.00026115586,0.00038997253,0.00014023948,0.0002451808],"domain_scores_gemma":[0.999223,0.00007740075,0.000096609176,0.00041703242,0.00008617337,0.000099793295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013815599,0.00012867023,0.00013047864,0.00007281564,0.00021130517,0.00015169592,0.00051447685,0.00006567213,0.000081782586],"category_scores_gemma":[0.000012868186,0.00011668274,0.00010281555,0.00018011885,0.000022812848,0.0003669554,0.0001326544,0.000087573855,0.00008888894],"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":[0.000001603682,0.0000464224,0.00009669312,0.000001366104,0.00002221008,4.006612e-7,0.000060560113,0.000008607103,0.009106793,0.004275705,0.0035271049,0.9828525],"study_design_scores_gemma":[0.0006895145,0.0000986741,0.00012846456,0.00000790821,0.0000116095425,0.000005298328,0.00010744041,0.68979883,0.087615296,0.0016681821,0.21958993,0.00027884878],"about_ca_topic_score_codex":0.00009355127,"about_ca_topic_score_gemma":0.000079909536,"teacher_disagreement_score":0.9825737,"about_ca_system_score_codex":0.00007467445,"about_ca_system_score_gemma":0.00003975173,"threshold_uncertainty_score":0.47581843},"labels":[],"label_agreement":null},{"id":"W2157261706","doi":"10.1109/tpami.2007.1145","title":"Value-Directed Human Behavior Analysis from Video Using Partially Observable Markov Decision Processes","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Partially observable Markov decision process; Computer science; Artificial intelligence; Markov decision process; Machine learning; Context (archaeology); Maximization; Markov process; Markov chain; Markov model; Mathematical optimization; Mathematics","score_opus":0.0319513845255916,"score_gpt":0.3118484687945718,"score_spread":0.2798970842689802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157261706","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":[0.11190192,0.000077016855,0.8873183,0.000043206066,0.000058779224,0.00018999874,0.000052892665,0.00032961107,0.000028293649],"genre_scores_gemma":[0.95373094,0.0001246637,0.04578578,0.00016958965,0.000023051463,0.000050408595,0.000016288719,0.000014909596,0.00008438092],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99767977,0.00006327912,0.0006928089,0.00084021734,0.0003997479,0.00032419147],"domain_scores_gemma":[0.99829155,0.00026087678,0.00022968097,0.00080195774,0.00021765943,0.00019826787],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042105967,0.00028562365,0.0004586602,0.0010631529,0.00060112984,0.00025813977,0.00060019334,0.00012251426,0.0002429568],"category_scores_gemma":[0.0000076122283,0.00026534643,0.000403617,0.005475329,0.000068842026,0.0003394466,0.00001404344,0.00025796882,0.000010524059],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028218998,0.00067244173,0.016477393,0.000014301976,0.0019023936,0.000021713182,0.0002760235,0.037981823,0.0075654765,0.000078288045,0.0000067911096,0.93497515],"study_design_scores_gemma":[0.00011658517,0.00013078122,0.031006787,0.00003086237,0.005455363,0.0000068580516,0.00004815249,0.4752973,0.48656914,0.0006539368,0.00010402761,0.00058021053],"about_ca_topic_score_codex":0.008909135,"about_ca_topic_score_gemma":0.013989911,"teacher_disagreement_score":0.9343949,"about_ca_system_score_codex":0.00007193334,"about_ca_system_score_gemma":0.000032910983,"threshold_uncertainty_score":0.99997985},"labels":[],"label_agreement":null},{"id":"W2159323669","doi":"","title":"Inferring State Sequences for Non-linear Systems with Embedded Hidden Markov Models","year":2003,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Hidden Markov model; Sequence (biology); Hidden semi-Markov model; Discretization; Computer science; Forward algorithm; Markov chain; State space; Algorithm; State (computer science); Markov process; Markov model; Variable-order Markov model; Dynamic programming; Mathematics; Artificial intelligence; Machine learning; Statistics","score_opus":0.024856138944886287,"score_gpt":0.2634598090419021,"score_spread":0.2386036700970158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159323669","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":[0.00938539,0.00007226496,0.9858512,0.000059113845,0.00021403434,0.0010474669,0.000008849531,0.0005244661,0.0028371846],"genre_scores_gemma":[0.96474653,0.000005105309,0.033962436,0.00009328313,0.00003755761,0.00078355294,0.000010403052,0.000011937169,0.00034916817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855185,0.000037191254,0.0005947542,0.00021718876,0.00031102885,0.00028797728],"domain_scores_gemma":[0.9985942,0.00003606422,0.00049385347,0.00031756787,0.00046808895,0.00009021418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003898641,0.00018685653,0.00021983728,0.00017351445,0.0004181646,0.0010151631,0.00039242927,0.00007191289,5.824095e-7],"category_scores_gemma":[0.000015692282,0.0001480255,0.000040806157,0.0004870526,0.000034318156,0.0052453578,0.000031416977,0.00011540757,0.000009452253],"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":[0.00008239162,0.000109254,0.0004592944,0.0047201184,0.00009909837,0.000004719165,0.011937226,0.7108535,0.0011644238,0.12608987,0.002325328,0.14215474],"study_design_scores_gemma":[0.00024691405,0.000092845214,0.000010114519,0.0001292102,0.000005482332,0.00008347337,0.0004618935,0.99440366,0.0010247839,0.00040596697,0.0029179032,0.00021777063],"about_ca_topic_score_codex":0.00006369426,"about_ca_topic_score_gemma":0.0000016381844,"teacher_disagreement_score":0.9553612,"about_ca_system_score_codex":0.000073676456,"about_ca_system_score_gemma":0.00015230611,"threshold_uncertainty_score":0.97892416},"labels":[],"label_agreement":null},{"id":"W2162541874","doi":"10.1109/ijcnn.2006.246960","title":"Clustering Vehicle Trajectories with Hidden Markov Models Application to Automated Traffic Safety Analysis","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Cluster analysis; Computer science; Hidden Markov model; Heuristic; Markov chain; Data mining; Traffic analysis; Markov process; Machine learning; Artificial intelligence; Computer security","score_opus":0.01987366171158034,"score_gpt":0.24700673331215373,"score_spread":0.2271330716005734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162541874","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":[0.16518204,0.000007024016,0.8197406,0.0067069298,0.00013956193,0.00064324593,0.000015510026,0.0014392775,0.0061258087],"genre_scores_gemma":[0.9832015,0.000008648681,0.0152138565,0.000502719,0.00030445022,0.00027196333,0.00001612326,0.000017187185,0.00046357082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982271,0.000017897351,0.0004134858,0.0005435901,0.0004720716,0.00032580775],"domain_scores_gemma":[0.99895,0.000040080162,0.00024035067,0.00028651557,0.00040275746,0.00008031542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023739808,0.00023860896,0.00023132315,0.00020101435,0.00032237975,0.00040713488,0.001001097,0.000066525135,0.0000151856275],"category_scores_gemma":[0.0000037703853,0.00017576572,0.00010931347,0.0013674325,0.000063651976,0.00044930849,0.000111997564,0.0002084966,0.000020354635],"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":[0.0001435513,0.00009556849,0.00028513052,0.0000071892655,0.00014185924,0.0000018930747,0.0002283257,0.87344116,0.0027192277,0.08992083,0.0050528967,0.027962385],"study_design_scores_gemma":[0.00014543216,0.00009941203,0.0041301656,0.000024366973,0.00003881826,0.0000137251145,0.000022876682,0.99189144,0.0007904983,0.0021849107,0.0004309436,0.0002274275],"about_ca_topic_score_codex":0.00015632044,"about_ca_topic_score_gemma":0.0001277334,"teacher_disagreement_score":0.81801945,"about_ca_system_score_codex":0.00012359828,"about_ca_system_score_gemma":0.000029567394,"threshold_uncertainty_score":0.7167518},"labels":[],"label_agreement":null},{"id":"W2164393601","doi":"","title":"A novel measure for data stream anomaly detection in a bio-surveillance system","year":2011,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Anomaly Detection Techniques and Applications","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":"Defence Research and Development Canada","funders":"","keywords":"Anomaly detection; Measure (data warehouse); Computer science; Notice; Constant false alarm rate; Data mining; False alarm; Anomaly (physics); Interval (graph theory); Artificial intelligence; Mathematics","score_opus":0.08155373264300304,"score_gpt":0.28181412324190797,"score_spread":0.20026039059890494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164393601","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":[0.0048544547,0.000002474276,0.9720863,0.00031811887,0.00037188534,0.00049228425,0.00010086933,0.00029242536,0.021481192],"genre_scores_gemma":[0.9757689,0.000008461397,0.023705821,0.000109844055,0.00003575333,0.00020968833,0.000116736286,0.0000048007873,0.000039973565],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988075,0.000019153731,0.00044762657,0.00027454188,0.0003033229,0.00014783915],"domain_scores_gemma":[0.9986067,0.00003114126,0.00028296886,0.00060553156,0.00042448737,0.000049177568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004148412,0.00013200943,0.000114471026,0.0003469812,0.00011659653,0.00016357265,0.0012299815,0.00009500558,0.000027657099],"category_scores_gemma":[0.000060627335,0.00012584806,0.000039772243,0.0002914256,0.00002054436,0.0018635467,0.00023248886,0.000114603055,0.00006303368],"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":[0.00015160194,0.00017065716,0.0005836096,0.000039471866,0.000020382115,6.8186085e-7,0.00074616395,0.00002891542,0.0049701827,0.66048527,0.00026530257,0.33253777],"study_design_scores_gemma":[0.00087610853,0.0002091615,0.009481155,0.00011688129,0.000003136456,0.000029880488,0.00029639233,0.96959865,0.009034392,0.0012887319,0.0087774815,0.0002880217],"about_ca_topic_score_codex":0.0002929094,"about_ca_topic_score_gemma":0.00018395891,"teacher_disagreement_score":0.9709145,"about_ca_system_score_codex":0.00013734795,"about_ca_system_score_gemma":0.00007666761,"threshold_uncertainty_score":0.51319355},"labels":[],"label_agreement":null},{"id":"W2165315096","doi":"","title":"Ship movement anomaly detection using specialized distance measures","year":2015,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":16,"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":"Anomaly detection; Trajectory; Cluster analysis; Computer science; Anomaly (physics); Set (abstract data type); Point (geometry); Division (mathematics); Movement (music); Domain (mathematical analysis); Work (physics); Longitude; Shore; Latitude; Geodesy; Real-time computing; Data mining; Artificial intelligence; Geography; Geology; Engineering; Mathematics; Geometry","score_opus":0.07999069881559094,"score_gpt":0.3042374426532906,"score_spread":0.22424674383769966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165315096","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":[0.01607509,0.0000043222262,0.9364756,0.00094535126,0.0006762583,0.00024330732,0.00001014157,0.0002768904,0.045293055],"genre_scores_gemma":[0.9880906,0.000018357385,0.010785703,0.00070172583,0.00010874562,0.000055803524,0.000020443731,0.000004681225,0.00021394846],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985419,0.00003709327,0.00040631476,0.00018877862,0.0006801283,0.00014580402],"domain_scores_gemma":[0.99859375,0.00001478336,0.0002818697,0.00030678135,0.00069116853,0.000111672874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003155337,0.00013904997,0.00010338887,0.00026724586,0.00016446183,0.00040855727,0.0005943925,0.00007389768,0.00008056041],"category_scores_gemma":[0.000064324704,0.00013357156,0.000056396882,0.00028270137,0.000029946961,0.0018268938,0.00013931107,0.00012822483,0.00018008809],"study_design_candidate":"theoretical_or_conceptual","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":[0.00009086409,0.00007097035,0.00022900332,0.0000048951374,0.00001593149,0.0000010660968,0.0006790023,0.0008004633,0.00483574,0.85212356,0.00055584946,0.14059263],"study_design_scores_gemma":[0.0010107404,0.00023585366,0.0017068295,0.00006396349,0.0000066071752,0.000016536378,0.00036942566,0.793109,0.03780331,0.044193037,0.12104772,0.00043698915],"about_ca_topic_score_codex":0.00012526376,"about_ca_topic_score_gemma":0.000026760434,"teacher_disagreement_score":0.9720155,"about_ca_system_score_codex":0.00031285983,"about_ca_system_score_gemma":0.000117077565,"threshold_uncertainty_score":0.544689},"labels":[],"label_agreement":null},{"id":"W2165324305","doi":"","title":"Fast and Scalable Training of Semi-Supervised CRFs with Application to Activity Recognition","year":2007,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"CRFS; Computer science; Boosting (machine learning); Conditional random field; Machine learning; Artificial intelligence; Feature selection; Inference; Scalability; Entropy (arrow of time); Activity recognition; Conditional entropy; Pattern recognition (psychology); Supervised learning; Feature engineering; Principle of maximum entropy; Deep learning; Artificial neural network","score_opus":0.02070623672826344,"score_gpt":0.2502963455457478,"score_spread":0.22959010881748437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165324305","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":[0.20435822,0.0000016914211,0.7902329,0.00021735879,0.0000041389426,0.00020703873,0.0000011040344,0.00013696475,0.004840567],"genre_scores_gemma":[0.80063796,0.0000019939969,0.19911186,0.000098645585,0.0000104091105,0.00004136999,8.775268e-7,0.000003435218,0.000093465576],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99947226,0.000007045558,0.00010396496,0.00021407177,0.00009240599,0.000110239904],"domain_scores_gemma":[0.9995695,0.000032640037,0.00004799225,0.00020544058,0.00007479532,0.00006967909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021616016,0.00005875777,0.00007684929,0.000078420235,0.00007320879,0.000025238927,0.000120575154,0.000033844357,0.000006888133],"category_scores_gemma":[0.000003851179,0.000050883373,0.000012690458,0.00042251384,0.00002254207,0.00020313059,0.000042118547,0.000045044184,0.0000062772606],"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":[0.000009629858,0.000030924268,0.00020094044,0.0000058486366,0.0000027242272,1.2626006e-7,0.0002639041,0.000010481966,0.06305385,0.0029947506,0.000026258573,0.9334006],"study_design_scores_gemma":[0.00034329298,0.00042254952,0.023868607,0.00003196468,0.000009274392,0.000028897974,0.00035511507,0.05051068,0.919298,0.00310001,0.0017329545,0.0002986899],"about_ca_topic_score_codex":0.00007627719,"about_ca_topic_score_gemma":0.000048451955,"teacher_disagreement_score":0.9331019,"about_ca_system_score_codex":0.000014738561,"about_ca_system_score_gemma":0.000014282291,"threshold_uncertainty_score":0.20749637},"labels":[],"label_agreement":null},{"id":"W2165816458","doi":"10.1007/0-387-68919-2_19","title":"Signal Processing Based on Hidden Markov Models for Extracting Small Channel Currents","year":2007,"lang":"en","type":"book-chapter","venue":"Biological and medical physics series","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Hidden Markov model; Signal processing; Channel (broadcasting); Computer science; Markov chain; SIGNAL (programming language); Markov model; Pattern recognition (psychology); Speech recognition; Artificial intelligence; Machine learning; Telecommunications","score_opus":0.09709567164933193,"score_gpt":0.289786348478053,"score_spread":0.1926906768287211,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165816458","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":[0.000019792491,0.00017753268,0.9692002,0.00044563264,0.00006258488,0.00033752993,0.00002489579,0.00017232016,0.029559521],"genre_scores_gemma":[0.55895615,0.002302138,0.3543007,0.008387997,0.007178906,0.0012678042,0.0006916417,0.0002132851,0.0667014],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986777,0.000011187007,0.00027103268,0.00052754226,0.0002601152,0.00025243423],"domain_scores_gemma":[0.9991761,0.00017948599,0.00017907265,0.00017019274,0.0000939621,0.0002012253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025016908,0.0002608705,0.00029081892,0.000040973042,0.0002489854,0.00006455818,0.0003675363,0.0004231744,0.000032443742],"category_scores_gemma":[0.000020481426,0.00018710097,0.00012224041,0.000043376895,0.00020927859,0.00011871819,0.00015034126,0.00036963198,0.0000037941336],"study_design_candidate":"theoretical_or_conceptual","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":[0.000031905525,0.000053612912,9.740671e-7,0.000043991648,0.000006199934,0.00000322613,0.000014355265,0.0000032265334,0.000011954403,0.14161128,0.00008709354,0.8581322],"study_design_scores_gemma":[0.00030574997,0.0008468191,0.000010520831,0.0004706685,0.00001862821,0.0000122444635,0.000007886545,0.18155721,0.0005089489,0.762579,0.053072292,0.0006100426],"about_ca_topic_score_codex":0.0000012411886,"about_ca_topic_score_gemma":6.0461105e-7,"teacher_disagreement_score":0.85752213,"about_ca_system_score_codex":0.0000225194,"about_ca_system_score_gemma":0.00008097208,"threshold_uncertainty_score":0.76297563},"labels":[],"label_agreement":null},{"id":"W2170932168","doi":"10.1016/j.cviu.2013.06.007","title":"An on-line, real-time learning method for detecting anomalies in videos using spatio-temporal compositions","year":2013,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":169,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Codebook; Artificial intelligence; Background subtraction; Computer vision; Video tracking; Probabilistic logic; Line (geometry); Pattern recognition (psychology); Tracking (education); Video processing; Pixel; Mathematics","score_opus":0.04988297504113703,"score_gpt":0.345789801825941,"score_spread":0.29590682678480396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170932168","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040300634,0.0000053444114,0.95830375,0.00032339434,0.00005220194,0.0005021304,0.0000017634742,0.00033265734,0.00017814348],"genre_scores_gemma":[0.47238567,0.000004590188,0.52742255,0.000089047375,0.00004293362,0.000024316196,0.000006543271,0.000011640108,0.000012718811],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866074,0.00013529895,0.00032336728,0.0004904716,0.00012472818,0.00026536145],"domain_scores_gemma":[0.9991032,0.000290337,0.00014405658,0.00027232003,0.00007882949,0.000111256115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003966063,0.00018078057,0.00021824501,0.00034622106,0.0006662147,0.00067644805,0.00025158803,0.000072355986,0.000014858001],"category_scores_gemma":[0.000011068168,0.00017560861,0.000063348874,0.0003533033,0.00004740967,0.0010112809,0.00013322683,0.00017154074,0.000006506964],"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":[0.00012007744,0.00071828126,0.0020947356,0.00019629633,0.00007436352,0.00002578824,0.004040177,0.028660696,0.43590924,0.17225496,0.0011552776,0.3547501],"study_design_scores_gemma":[0.0003321022,0.00054852426,0.00031727948,0.00007784569,0.000004312466,0.000022225904,0.00018140835,0.98590136,0.0026931718,0.009626096,0.00008192747,0.00021375943],"about_ca_topic_score_codex":0.00015506214,"about_ca_topic_score_gemma":0.000009325069,"teacher_disagreement_score":0.95724064,"about_ca_system_score_codex":0.00015980673,"about_ca_system_score_gemma":0.000020046231,"threshold_uncertainty_score":0.7161111},"labels":[],"label_agreement":null},{"id":"W2170986035","doi":"10.1109/imtc.2008.4547148","title":"Learning Multi-Sensor Confidence using Difference of Opinions","year":2008,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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 Ottawa","funders":"","keywords":"Computer science; Task (project management); Overhead (engineering); Low Confidence; Machine learning; Artificial intelligence; Confidence interval; Real-time computing; Engineering; Statistics; Mathematics","score_opus":0.07715386729343089,"score_gpt":0.30527464899406404,"score_spread":0.22812078170063316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170986035","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":[0.052470483,0.00001185165,0.94627154,0.00007170562,0.000025103249,0.000077158875,6.103133e-7,0.00022297395,0.00084858376],"genre_scores_gemma":[0.72368765,0.000021054522,0.27530995,0.000027681079,0.0000068346894,0.000006212228,3.3166077e-7,0.0000024659576,0.00093783956],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944043,0.000021450174,0.00015769042,0.00017719118,0.000097445554,0.000105763706],"domain_scores_gemma":[0.99949324,0.000044634337,0.00007992038,0.00026109794,0.000079259175,0.00004185113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000047899084,0.000060164286,0.00009027717,0.000055214416,0.00019920287,0.000012712524,0.0003190466,0.000031884094,0.000017591577],"category_scores_gemma":[0.000020728272,0.000054579417,0.000040165847,0.00025395278,0.00007500654,0.00010614635,0.00010511309,0.000087772685,0.000010115215],"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":[0.0000056053264,0.00046646674,0.03610158,0.000038639224,0.000034520708,0.000013383629,0.002500727,0.0039733183,0.34550112,0.5808447,0.00026053505,0.03025942],"study_design_scores_gemma":[0.00019879016,0.000116922565,0.03221754,0.000027628212,0.0000032567557,0.00013781629,0.00010394929,0.8448035,0.12008453,0.0007454023,0.0013199047,0.00024074933],"about_ca_topic_score_codex":0.00016135223,"about_ca_topic_score_gemma":0.0000020432217,"teacher_disagreement_score":0.8408302,"about_ca_system_score_codex":0.000010696127,"about_ca_system_score_gemma":0.000036259153,"threshold_uncertainty_score":0.2225684},"labels":[],"label_agreement":null},{"id":"W2180566385","doi":"10.1145/2733381","title":"Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection","year":2015,"lang":"en","type":"article","venue":"ACM Transactions on Knowledge Discovery from Data","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":857,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Cluster analysis; Computer science; Visualization; Outlier; Anomaly detection; Data mining; Hierarchy; Hierarchical clustering; Pattern recognition (psychology); CURE data clustering algorithm; Mathematics; Artificial intelligence; Correlation clustering","score_opus":0.09123092616616002,"score_gpt":0.34674055329636305,"score_spread":0.25550962713020303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2180566385","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":[0.0028126193,0.00013982081,0.99430025,0.00033434745,0.00033503308,0.0004047899,0.0012741553,0.00034260095,0.0000563591],"genre_scores_gemma":[0.8109892,0.00006770592,0.1868007,0.00012715303,0.00013535263,0.00012516971,0.0014515695,0.000030536055,0.00027259148],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983726,0.000049497638,0.0002716901,0.00095925434,0.00015042735,0.00019650072],"domain_scores_gemma":[0.99550456,0.00029294696,0.000078260215,0.003856342,0.00011648104,0.00015141336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032451277,0.00018488643,0.00018450592,0.00011841388,0.00036828665,0.00039129797,0.0024180699,0.00010381401,0.0000056187178],"category_scores_gemma":[0.0001649733,0.00018104671,0.00003250396,0.00030550046,0.00008700784,0.0023790856,0.0005098539,0.00015196647,0.000025779138],"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":[0.00030573053,0.0018055422,0.0005945723,0.00010529674,0.0003625728,0.00000400909,0.0017706957,0.00028852772,0.002929705,0.009771018,0.016538572,0.9655238],"study_design_scores_gemma":[0.00068030355,0.00019127945,0.0007163838,0.000032662057,0.00010315328,0.000017179287,0.0000833254,0.9418531,0.009978435,0.017274806,0.028692147,0.0003772346],"about_ca_topic_score_codex":0.0001832148,"about_ca_topic_score_gemma":0.0007949124,"teacher_disagreement_score":0.96514654,"about_ca_system_score_codex":0.00005027113,"about_ca_system_score_gemma":0.000101262085,"threshold_uncertainty_score":0.73828703},"labels":[],"label_agreement":null},{"id":"W2186966566","doi":"10.19026/rjaset.8.986","title":"Rough K-means Outlier Factor Based on Entropy Computation","year":2014,"lang":"en","type":"article","venue":"Research Journal of Applied Sciences Engineering and Technology","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Local outlier factor; Outlier; Anomaly detection; Pattern recognition (psychology); Cluster analysis; Computer science; Entropy (arrow of time); Data mining; Computation; Artificial intelligence; Cluster (spacecraft); Mathematics; Algorithm","score_opus":0.02406324781370703,"score_gpt":0.3020962834098534,"score_spread":0.27803303559614634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2186966566","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":[0.03486533,0.000022483755,0.96188104,0.002089742,0.000047274138,0.00008989754,3.843802e-7,0.00013299337,0.000870853],"genre_scores_gemma":[0.8988228,0.000013721711,0.10108211,0.000026640548,0.000034422264,0.000009470839,7.988042e-8,0.000004104399,0.000006642033],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988822,0.0000209333,0.00019965807,0.00020631011,0.00043018287,0.00026071776],"domain_scores_gemma":[0.99930006,0.00021451908,0.000084305684,0.00016655619,0.00015601287,0.00007857136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010828465,0.000079297206,0.00013211597,0.0010139495,0.00022920156,0.000114837814,0.00063143997,0.000080238875,0.000002168403],"category_scores_gemma":[0.00009335731,0.000063652544,0.000025043131,0.0013384994,0.00020971747,0.000106363055,0.000087493405,0.00041471823,0.00000474619],"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":[0.0000069652674,0.00006121079,0.00008842981,0.00001330847,0.0000073895962,0.000002822776,0.000065087195,0.05439222,0.021995958,0.80128855,0.00016421887,0.12191387],"study_design_scores_gemma":[0.00020604393,0.0006460804,0.00015273743,0.000022935421,0.0000012167816,0.000021247532,0.000026992588,0.9546843,0.02123674,0.015670983,0.007240018,0.00009067377],"about_ca_topic_score_codex":0.0000010129926,"about_ca_topic_score_gemma":1.5512957e-7,"teacher_disagreement_score":0.9002921,"about_ca_system_score_codex":0.000039014983,"about_ca_system_score_gemma":0.000056844437,"threshold_uncertainty_score":0.25956756},"labels":[],"label_agreement":null},{"id":"W2201330910","doi":"10.1109/iros.2015.7353858","title":"Kernel density estimation for target trajectory prediction","year":2015,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Trajectory; Computer science; Kernel (algebra); Cluster analysis; Similarity (geometry); Kernel density estimation; Probabilistic logic; Artificial intelligence; Set (abstract data type); Data mining; Similarity measure; Tracking (education); Machine learning; Measure (data warehouse); Pattern recognition (psychology); Mathematics; Statistics; Image (mathematics)","score_opus":0.028634649141698446,"score_gpt":0.26343475523007714,"score_spread":0.2348001060883787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2201330910","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031833511,0.0000068479376,0.99301195,0.00037955053,0.00010638505,0.00024899846,0.000002783901,0.0006892845,0.0023708649],"genre_scores_gemma":[0.4825236,6.312589e-7,0.5166266,0.00010703108,0.000035312,0.000096564814,0.0000050314407,0.0000027984395,0.00060245424],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955344,0.000009043905,0.00010267076,0.00016428238,0.000088298395,0.00008224958],"domain_scores_gemma":[0.99956226,0.00001664505,0.000036426558,0.00021125954,0.000107937216,0.0000654549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017500823,0.00004844865,0.000049240505,0.000038927054,0.00007831405,0.00004212404,0.00016131258,0.0000389079,0.0000042130105],"category_scores_gemma":[0.000021150083,0.000044989825,0.000031465956,0.0001235443,0.000011642903,0.00027602835,0.00003022368,0.00003144463,0.000021506059],"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":[0.0000236243,0.00025906644,0.0017834278,0.000024187622,0.00002387145,7.39374e-7,0.00070417667,0.004637636,0.0035598231,0.68698865,0.15271671,0.14927812],"study_design_scores_gemma":[0.00015965464,0.00009806834,0.0016359589,0.0000015136457,0.000002779899,0.0000066429484,0.000015307767,0.91234857,0.024060711,0.048158705,0.013438126,0.000073990625],"about_ca_topic_score_codex":0.000017844968,"about_ca_topic_score_gemma":0.0000026312725,"teacher_disagreement_score":0.9077109,"about_ca_system_score_codex":0.000043920485,"about_ca_system_score_gemma":0.000039660856,"threshold_uncertainty_score":0.18346319},"labels":[],"label_agreement":null},{"id":"W2202703817","doi":"10.1109/iccv.2015.462","title":"Learning Ensembles of Potential Functions for Structured Prediction with Latent Variables","year":2015,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Latent variable; Computer science; Artificial intelligence; Machine learning","score_opus":0.014590880288494427,"score_gpt":0.21939927434177198,"score_spread":0.20480839405327755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2202703817","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":[0.011848652,0.0000075176977,0.98644805,0.00017259885,0.00007034501,0.00020548332,0.0000052194996,0.0003053765,0.00093676406],"genre_scores_gemma":[0.8253153,0.0000019003951,0.17336828,0.000013255848,0.00003408971,0.00006444099,0.0000072173707,0.000004404548,0.0011911305],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950117,0.000012877198,0.00012534988,0.00016091032,0.00011077417,0.00008891732],"domain_scores_gemma":[0.9994871,0.000014746456,0.00007421712,0.00016835709,0.00020543803,0.00005016431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010471311,0.000057790607,0.00007195465,0.00005749639,0.000105861,0.000036932415,0.00013292478,0.00003891313,0.000007388806],"category_scores_gemma":[0.000009382884,0.000043955522,0.000029800212,0.00018868542,0.000020715386,0.00018051271,0.000035668643,0.00004801267,0.0000015442923],"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":[0.00027670237,0.00040136228,0.0074300226,0.000086295666,0.00026483208,0.000001979092,0.0013408103,0.1685206,0.08329623,0.57918376,0.024048103,0.13514933],"study_design_scores_gemma":[0.0017417454,0.0031083995,0.005652815,0.000028186892,0.000087035165,0.00009857482,0.0005976478,0.82185334,0.09785,0.0291454,0.039436042,0.00040080096],"about_ca_topic_score_codex":0.00004113181,"about_ca_topic_score_gemma":0.0000063394323,"teacher_disagreement_score":0.8134666,"about_ca_system_score_codex":0.000017047942,"about_ca_system_score_gemma":0.000049014994,"threshold_uncertainty_score":0.17924541},"labels":[],"label_agreement":null},{"id":"W2205283494","doi":"10.1109/bibm.2015.7359925","title":"Data integration in machine learning","year":2015,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor; National Research Council Canada","funders":"","keywords":"Computer science; Perspective (graphical); Key (lock); Machine learning; Data science; Artificial intelligence; Data integration; Data mining","score_opus":0.09112065495317836,"score_gpt":0.3178927914041724,"score_spread":0.22677213645099406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2205283494","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":[0.0008095496,0.000018543404,0.98552954,0.0008182516,0.000018505083,0.00004480338,6.930926e-7,0.00022888172,0.012531203],"genre_scores_gemma":[0.83101094,0.000006063496,0.16768561,0.000106906795,0.00001172023,0.000009068277,0.000014864532,0.0000017777636,0.0011530698],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996607,0.000016119655,0.00007368893,0.00014249362,0.000057317968,0.000049682083],"domain_scores_gemma":[0.9995547,0.000008812378,0.000018243125,0.0003704915,0.000019888948,0.00002789835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022115593,0.00002801737,0.000030672985,0.00004564824,0.000020661126,0.00004131759,0.0004845546,0.000015875403,0.000007667562],"category_scores_gemma":[0.000027844671,0.000023248347,0.0000045524102,0.00023123136,0.0000052730315,0.0003420509,0.00023203733,0.00007504071,0.000040984352],"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":[0.0000019351296,0.00006160548,0.00301985,0.0000010767069,0.0000014165184,0.0000019469057,0.00025705868,0.00015751013,0.0007287755,0.41720077,0.006830379,0.57173765],"study_design_scores_gemma":[0.00006644663,0.000025554724,0.0003995563,0.000002018746,3.0501553e-7,0.000003955145,0.000029726903,0.92160136,0.0014697742,0.0049522524,0.07140098,0.00004806995],"about_ca_topic_score_codex":0.00017529269,"about_ca_topic_score_gemma":0.00017118371,"teacher_disagreement_score":0.9214439,"about_ca_system_score_codex":0.000014990525,"about_ca_system_score_gemma":0.000014898089,"threshold_uncertainty_score":0.094804004},"labels":[],"label_agreement":null},{"id":"W2212891330","doi":"10.1007/s41060-018-0161-7","title":"Spectral ranking and unsupervised feature selection for point, collective, and contextual anomaly detection","year":2018,"lang":"en","type":"article","venue":"International Journal of Data Science and Analytics","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Anomaly detection; Feature selection; Ranking (information retrieval); Artificial intelligence; Pattern recognition (psychology); Feature (linguistics); Computer science; Point (geometry); Anomaly (physics); Selection (genetic algorithm); Machine learning; Data mining; Mathematics; Physics; Linguistics","score_opus":0.033771978519901054,"score_gpt":0.31927220439122783,"score_spread":0.2855002258713268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2212891330","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":[0.15893254,0.00007176547,0.8386636,0.0018984467,0.00015944323,0.00010519536,0.000019533236,0.00001934759,0.0001301594],"genre_scores_gemma":[0.952659,0.00012724106,0.046685025,0.00020889261,0.00026046915,0.0000016334072,0.000001355666,0.0000031696852,0.000053180058],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905896,0.000012927212,0.00019757645,0.0002760756,0.00033341313,0.00012106235],"domain_scores_gemma":[0.99841833,0.000070294736,0.00018979279,0.0001388578,0.0010984157,0.000084331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081816857,0.00007703187,0.000106908876,0.0003209407,0.00036885773,0.00046369433,0.00069253694,0.000035524386,0.0000020020996],"category_scores_gemma":[0.0001697432,0.00006614563,0.000019636069,0.00046294744,0.00035482013,0.0021504671,0.00024236538,0.00010423585,2.51276e-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":[0.00037281355,0.00021310375,0.013751173,0.00002781986,0.00031246105,0.0000114165205,0.0017353187,0.000015252851,0.208609,0.054741494,0.0040208017,0.7161893],"study_design_scores_gemma":[0.0017753153,0.0014974717,0.025293192,0.00007842285,0.00007850025,0.0018978092,0.00041659072,0.8852018,0.050723035,0.015352244,0.017335713,0.000349915],"about_ca_topic_score_codex":0.000016352084,"about_ca_topic_score_gemma":0.000053253214,"teacher_disagreement_score":0.88518655,"about_ca_system_score_codex":0.00007183621,"about_ca_system_score_gemma":0.00018529338,"threshold_uncertainty_score":0.4471415},"labels":[],"label_agreement":null},{"id":"W2237802582","doi":"","title":"Système de classification à deux niveaux de décision combinant approche par modélisation et machines à vecteurs de support","year":2005,"lang":"fr","type":"article","venue":"DSpace (Centre National De La Recherche Scientifique)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Support vector machine; Computer science; Outlier; Benchmark (surveying); Artificial intelligence; Linear discriminant analysis; Discriminative model; Probabilistic logic; Pattern recognition (psychology); Machine learning; Anomaly detection; Data mining","score_opus":0.15124172944120554,"score_gpt":0.3769578415229432,"score_spread":0.22571611208173767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2237802582","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":[0.023344941,0.00041522778,0.90487784,0.056030087,0.00026327037,0.0007391388,0.00006461081,0.0004451328,0.013819733],"genre_scores_gemma":[0.49468485,0.00080643344,0.4717182,0.0017493714,0.00029857102,0.00019605512,0.00013827195,0.000060467268,0.03034778],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9930541,0.003568765,0.00058169506,0.00098829,0.0008959597,0.00091118435],"domain_scores_gemma":[0.9957119,0.0016828174,0.0004524894,0.0005816727,0.0011132612,0.00045786455],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.015034926,0.0003862511,0.00028164385,0.0004053582,0.00055891596,0.0008328974,0.0010006638,0.0010223383,0.0001633325],"category_scores_gemma":[0.002626818,0.00043219817,0.00023851672,0.0016687805,0.00037669114,0.0010148621,0.00022332296,0.0012317129,0.00018643095],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053263317,0.0014081972,0.0037575457,0.00020458279,0.000067970264,0.000011162409,0.0124306185,0.010855582,0.07955641,0.69579136,0.09397438,0.10188889],"study_design_scores_gemma":[0.0004788087,0.000040287054,0.01806184,0.00018290535,0.000048903305,0.00019143238,0.00019800164,0.7312166,0.02660746,0.112535626,0.10998603,0.00045211517],"about_ca_topic_score_codex":0.00057878497,"about_ca_topic_score_gemma":0.00022428812,"teacher_disagreement_score":0.720361,"about_ca_system_score_codex":0.006044298,"about_ca_system_score_gemma":0.0038004676,"threshold_uncertainty_score":0.99981296},"labels":[],"label_agreement":null},{"id":"W2246496281","doi":"10.4271/2007-01-1156","title":"Wavelet-based Non-parametric Estimation of Injury Risk Functions","year":2007,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Dynamics (Canada)","funders":"","keywords":"Wavelet; Estimation; Computer science; Parametric statistics; Artificial intelligence; Semiparametric model; Pattern recognition (psychology); Mathematics; Statistics; Engineering","score_opus":0.008730080090645437,"score_gpt":0.25795576264349757,"score_spread":0.24922568255285213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2246496281","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":[0.6789715,0.0003639087,0.21125318,0.007515858,0.0010369185,0.0058868546,0.0004493741,0.016784664,0.077737704],"genre_scores_gemma":[0.87764335,0.000075414435,0.12084769,0.0007402629,0.000070092385,0.00030775485,0.000028492475,0.00005733804,0.00022963839],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9954909,0.00011078841,0.0014063249,0.0012222357,0.00096421933,0.00080553576],"domain_scores_gemma":[0.995596,0.0008065895,0.0006293381,0.0023387363,0.00024913248,0.00038021436],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014317614,0.0005959661,0.00071666157,0.000816261,0.000605724,0.000119518074,0.0017013439,0.00074396894,0.00017235697],"category_scores_gemma":[0.0007605246,0.00054666627,0.00053696503,0.003834742,0.000744788,0.0006422277,0.00042757264,0.0011998276,0.00014792175],"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":[0.00014977332,0.00070896896,0.00014441648,0.000035371457,0.000030381101,0.000008708647,0.000012417143,0.0001211138,0.8338502,0.033379517,0.0034732185,0.12808591],"study_design_scores_gemma":[0.00046616432,0.0019638203,0.9653497,0.00009254939,0.00006941555,0.000034985886,0.00002597396,0.00003110886,0.005009586,0.0055814963,0.020709006,0.00066624087],"about_ca_topic_score_codex":0.00011034758,"about_ca_topic_score_gemma":0.0065507055,"teacher_disagreement_score":0.96520525,"about_ca_system_score_codex":0.00032242178,"about_ca_system_score_gemma":0.00016229485,"threshold_uncertainty_score":0.99969846},"labels":[],"label_agreement":null},{"id":"W2250162459","doi":"10.1109/ssci.2015.224","title":"Model-Based Outlier Detection for Object-Relational Data","year":2015,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Science Council","keywords":"Computer science; Outlier; Probabilistic logic; Object (grammar); Bayesian network; Metric (unit); Data mining; Representation (politics); Artificial intelligence; Statistical model; Relational database; Statistical relational learning; Anomaly detection; Data modeling; Relational model; Population; Graphical model; Pattern recognition (psychology); Database","score_opus":0.15000706937082126,"score_gpt":0.3214185302820415,"score_spread":0.17141146091122023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2250162459","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":[0.00020063526,0.000007948954,0.9948751,0.0007635503,0.0000696274,0.00025090136,0.000012953943,0.00044416563,0.0033751582],"genre_scores_gemma":[0.53697693,2.4249155e-7,0.46170098,0.00026576023,0.000035684006,0.000094304334,0.000019182105,0.00000436357,0.0009025338],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993365,0.000008909433,0.00012507691,0.000294538,0.00013198475,0.00010302377],"domain_scores_gemma":[0.9989805,0.0000343355,0.00004399691,0.0007352107,0.0001326281,0.000073279225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025071113,0.000061766565,0.000053964173,0.00005857237,0.00010726463,0.000055507302,0.0005570391,0.00004832135,0.0000046732825],"category_scores_gemma":[0.000030965537,0.000056619563,0.000027508577,0.00017766582,0.000015370353,0.0003989568,0.0001303209,0.000046204426,0.000029721055],"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":[0.00003397302,0.00023939899,0.00015397511,0.000011788574,0.000022908363,3.3965856e-7,0.00012092669,0.04371643,0.0027937854,0.6276075,0.048175655,0.2771233],"study_design_scores_gemma":[0.00018071417,0.000039259692,0.000032869913,8.1849106e-7,0.0000029280754,0.0000012976835,0.0000038854128,0.94836026,0.0053628176,0.017947089,0.027990319,0.00007770802],"about_ca_topic_score_codex":0.0000131644265,"about_ca_topic_score_gemma":0.000021266977,"teacher_disagreement_score":0.90464383,"about_ca_system_score_codex":0.000037459788,"about_ca_system_score_gemma":0.00012012539,"threshold_uncertainty_score":0.23088787},"labels":[],"label_agreement":null},{"id":"W2278925444","doi":"10.7939/r3b23s","title":"A Synthetic Data Generator for Clustering and Outlier Analysis","year":2006,"lang":"en","type":"article","venue":"","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":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Cluster analysis; Outlier; Data mining; Anomaly detection; Generator (circuit theory); Pattern recognition (psychology); Artificial intelligence","score_opus":0.025875321116831412,"score_gpt":0.2705733718957009,"score_spread":0.2446980507788695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2278925444","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":[0.0018443284,0.000028628243,0.99621725,0.00040419458,0.000010680107,0.00010718038,0.000009619762,0.00016100165,0.0012171154],"genre_scores_gemma":[0.6091137,0.000002943941,0.3899019,0.00009100654,0.00002749259,0.00004066046,0.0000074115405,0.0000024868336,0.0008124268],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995072,0.0000048282027,0.00009248498,0.00027832334,0.000040452735,0.0000767298],"domain_scores_gemma":[0.9992637,0.000020623484,0.00002450856,0.00064673484,0.00002288579,0.000021563557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009915296,0.000042873722,0.00006595371,0.00006867352,0.00009251308,0.000117250675,0.000350167,0.000018697277,0.000010048162],"category_scores_gemma":[0.0000034801935,0.000036705573,0.000024959052,0.00026796912,0.000011517371,0.00015976936,0.00023732107,0.000015118646,0.000002211171],"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":[0.000008166076,0.00027284474,0.0030789385,0.000050116407,0.000409412,0.0000025065654,0.00011798364,0.0014974969,0.015515969,0.6163901,0.058941804,0.30371466],"study_design_scores_gemma":[0.000039086757,0.000009380594,0.0006338111,5.500191e-7,0.000036695983,0.0000020177938,0.0000027869498,0.9741563,0.0019498015,0.0014665471,0.021637654,0.00006534775],"about_ca_topic_score_codex":0.000082189006,"about_ca_topic_score_gemma":0.000113408576,"teacher_disagreement_score":0.9726588,"about_ca_system_score_codex":0.000005452948,"about_ca_system_score_gemma":0.0000055848864,"threshold_uncertainty_score":0.14968097},"labels":[],"label_agreement":null},{"id":"W2282861635","doi":"10.1007/s10618-015-0444-8","title":"On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study","year":2016,"lang":"en","type":"article","venue":"Data Mining and Knowledge Discovery","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":732,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Fundação de Amparo à Pesquisa do Estado de São Paulo; Teknologi og Produktion, Det Frie Forskningsråd","keywords":"Outlier; Anomaly detection; Benchmark (surveying); Computer science; Data mining; Artificial intelligence; Ground truth; Pattern recognition (psychology); Set (abstract data type); Machine learning","score_opus":0.15532021060375845,"score_gpt":0.3896393330565236,"score_spread":0.23431912245276515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2282861635","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":[0.84402204,0.00026912132,0.15417027,0.0003045741,0.00007680342,0.00041296842,0.00038885296,0.000064109336,0.00029126898],"genre_scores_gemma":[0.999271,0.000024462595,0.0004891072,0.000034730823,0.000041135987,0.000050797065,0.00004261375,0.0000051368543,0.000041041556],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9988352,0.00024928947,0.00016580295,0.0004354054,0.00022192458,0.00009239586],"domain_scores_gemma":[0.9983501,0.00022755458,0.000060221166,0.0012438695,0.00007414759,0.000044128337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015663095,0.000092258204,0.00010001161,0.000058021076,0.00019806977,0.00012245418,0.0005494495,0.000032564876,0.0000047265403],"category_scores_gemma":[0.00016278597,0.000050841867,0.000011150069,0.00017263467,0.000076680684,0.00080750836,0.00044193817,0.000046861172,0.000002973164],"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":[0.000017455483,0.0003473192,0.0027366749,0.000004705718,0.00002704191,3.6150016e-7,0.0010970075,3.3966037e-7,0.0009355545,0.0013399029,0.0029599823,0.99053365],"study_design_scores_gemma":[0.01149496,0.008421325,0.4109178,0.0007451616,0.0011799956,0.00012260865,0.017010277,0.40508255,0.032457434,0.017510092,0.09202111,0.003036718],"about_ca_topic_score_codex":0.000018213275,"about_ca_topic_score_gemma":0.00014948842,"teacher_disagreement_score":0.9874969,"about_ca_system_score_codex":0.000014355243,"about_ca_system_score_gemma":0.000057386278,"threshold_uncertainty_score":0.20732713},"labels":[],"label_agreement":null},{"id":"W2289757941","doi":"10.1007/s10115-016-0929-9","title":"Detecting emerging and evolving novelties with locally adaptive density ratio estimation","year":2016,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Novelty; Robustness (evolution); Kernel density estimation; Benchmark (surveying); Kernel (algebra); Artificial intelligence; Set (abstract data type); Density estimation; Focus (optics); Machine learning; Data mining; Pattern recognition (psychology); Mathematics; Statistics","score_opus":0.009207441277482483,"score_gpt":0.21909077333278848,"score_spread":0.209883332055306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2289757941","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":[0.012821802,0.00014275186,0.9838319,0.000093766816,0.0000573458,0.0002454325,0.0000012341907,0.00021147839,0.0025943033],"genre_scores_gemma":[0.98784447,0.000025517407,0.011913378,0.000022643026,0.00002664704,0.000060083137,7.597321e-7,0.0000032955459,0.000103221],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993898,0.000023829693,0.00025104103,0.0001250136,0.00009952482,0.00011073523],"domain_scores_gemma":[0.9992981,0.000066217304,0.0001642147,0.0001550892,0.00026328058,0.00005309509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024598988,0.000095247044,0.00010415095,0.00012678574,0.00030417703,0.00024070076,0.000090721696,0.000044827102,0.0000010414802],"category_scores_gemma":[0.00002679946,0.00006336424,0.000011829648,0.00019446618,0.000038449187,0.0038912122,0.000076416036,0.000045677654,0.000020365795],"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":[0.000013816813,0.000013225504,0.001171433,0.00011707153,0.000024594032,3.3755467e-7,0.0058690626,0.00008406947,0.0011798262,0.18549751,0.00028442946,0.80574465],"study_design_scores_gemma":[0.00048709076,0.00018690314,0.004772989,0.00029898548,0.000009055792,0.00010856303,0.0008380011,0.98105085,0.004867945,0.00035706317,0.006738772,0.00028377486],"about_ca_topic_score_codex":0.000026878884,"about_ca_topic_score_gemma":0.000010203429,"teacher_disagreement_score":0.9809668,"about_ca_system_score_codex":0.000041586278,"about_ca_system_score_gemma":0.000033715412,"threshold_uncertainty_score":0.28210348},"labels":[],"label_agreement":null},{"id":"W2290793703","doi":"10.1109/ms.2016.31","title":"A Deep-Intelligence Framework for Online Video Processing","year":2016,"lang":"en","type":"article","venue":"IEEE Software","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":41,"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; Cloud computing; Scalability; Big data; Deep learning; Architecture; Distributed computing; Computer architecture; Artificial intelligence; Database; Data mining; Operating system","score_opus":0.028500305236466746,"score_gpt":0.306971460595694,"score_spread":0.2784711553592273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2290793703","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006677016,0.00008742744,0.9966824,0.0013160823,0.000118320415,0.0002366342,0.000009305697,0.00086881046,0.000013313396],"genre_scores_gemma":[0.20922975,0.000018122315,0.78984773,0.00038778127,0.00012663932,0.00016820776,7.044992e-7,0.000010988179,0.00021007245],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99917394,0.000008499624,0.00017108835,0.0003310169,0.000109394605,0.00020606308],"domain_scores_gemma":[0.9990854,0.00020686603,0.000086010434,0.00041305748,0.00014044662,0.000068182235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007939669,0.00009817109,0.00009447053,0.000050348408,0.00027535384,0.000052431325,0.00058020244,0.00008321021,0.000011219003],"category_scores_gemma":[0.00013195642,0.000070187445,0.000067755434,0.0002698493,0.000040523028,0.0002515759,0.0000781881,0.00006660016,0.000027873151],"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":[0.000002378665,0.000034897657,0.00006127402,0.000012740822,0.0000025748172,5.0877924e-7,0.0000813686,0.0000118471535,0.00033738694,0.019852323,0.00022058502,0.9793821],"study_design_scores_gemma":[0.0001566123,0.00021745553,0.00038765723,0.000322107,0.000011495052,0.00002620555,0.000023423232,0.021758905,0.0785851,0.83217293,0.0658094,0.0005287011],"about_ca_topic_score_codex":0.0000027599278,"about_ca_topic_score_gemma":0.0000043732994,"teacher_disagreement_score":0.9788534,"about_ca_system_score_codex":0.000035562116,"about_ca_system_score_gemma":0.000045309178,"threshold_uncertainty_score":0.28621608},"labels":[],"label_agreement":null},{"id":"W2291754627","doi":"10.11575/prism/426","title":"Web content outlier mining: motivation, framework, and algorithms","year":2006,"lang":"en","type":"article","venue":"PRISM (University of Calgary)","topic":"Anomaly Detection Techniques and Applications","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 Calgary","funders":"","keywords":"Outlier; Computer science; Data mining; Web mining; Information retrieval; Web page; Artificial intelligence; World Wide Web","score_opus":0.01777883724610908,"score_gpt":0.19770894063287184,"score_spread":0.17993010338676277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2291754627","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08525475,0.00004399924,0.90995926,0.0010778867,0.000022952345,0.00009645897,1.9970126e-7,0.00012476434,0.003419739],"genre_scores_gemma":[0.33387476,0.000028231807,0.6634251,0.00006109337,0.000009085392,6.962986e-7,0.0000016018244,0.0000033362694,0.002596069],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951077,0.000015452193,0.00008019628,0.00019154043,0.000108692366,0.00009336345],"domain_scores_gemma":[0.9995471,0.00004736783,0.000085732936,0.00021370024,0.00006341559,0.00004262981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007091076,0.000060677325,0.00009616506,0.00008095952,0.00013542923,0.000018736133,0.0002566766,0.000069986854,0.000016439823],"category_scores_gemma":[0.00000858515,0.00007183358,0.000038333874,0.00016149353,0.000080767604,0.00019617779,0.00013500186,0.00007219295,0.0000051373413],"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":[0.000009468217,0.00019720772,0.015171934,0.00001991487,0.00003175971,0.0000135071,0.0010553967,0.0000014779877,0.0034432039,0.38339803,0.008542476,0.58811563],"study_design_scores_gemma":[0.0010972852,0.00023908206,0.37584895,0.00006281976,0.000041550018,0.000022351953,0.00023097808,0.4239763,0.0042711776,0.049344294,0.1442663,0.0005989091],"about_ca_topic_score_codex":0.0002633787,"about_ca_topic_score_gemma":0.0000025363922,"teacher_disagreement_score":0.5875167,"about_ca_system_score_codex":0.00001772685,"about_ca_system_score_gemma":0.000019675168,"threshold_uncertainty_score":0.2929288},"labels":[],"label_agreement":null},{"id":"W2294616603","doi":"","title":"Locating Anomalies Using Bayesian Factorizations and Masks","year":2011,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Categorical variable; Computer science; Anomaly detection; Bayesian network; Set (abstract data type); Bayesian probability; Independence (probability theory); Conditional independence; Data mining; Anomaly (physics); Data set; Artificial intelligence; Domain (mathematical analysis); Pattern recognition (psychology); Factorization; Conditional probability; Machine learning; Algorithm; Mathematics; Statistics","score_opus":0.055736035925228994,"score_gpt":0.2467660425957984,"score_spread":0.1910300066705694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294616603","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":[0.00886369,0.000014288403,0.97771174,0.00005665164,0.00002219205,0.00006957151,4.4477434e-7,0.00029267333,0.01296875],"genre_scores_gemma":[0.649578,0.0000020350187,0.35020208,0.000052623698,0.000008428921,0.0000063948874,1.8154218e-7,0.000002636752,0.00014760946],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996041,0.000008456644,0.00009717963,0.00015709993,0.00004364579,0.000089482724],"domain_scores_gemma":[0.99967587,0.000009126713,0.000035699577,0.00020495591,0.00003142409,0.000042953176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000041204858,0.000053593943,0.00004559128,0.000051906012,0.00018012863,0.000054368924,0.00016282623,0.00002754669,0.00004024268],"category_scores_gemma":[0.00000401746,0.000049130835,0.000013393757,0.00020489877,0.000028019718,0.00025360013,0.00009421988,0.000034286106,0.0000038734574],"study_design_candidate":"theoretical_or_conceptual","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":[4.3470556e-7,0.000034523116,0.010340021,0.0000071161044,0.0000074066324,9.203589e-7,0.0011574674,0.0000064419287,0.004167491,0.952363,0.000044810757,0.031870376],"study_design_scores_gemma":[0.00024441976,0.00017723025,0.040035866,0.00002506966,0.000028510576,0.0001303606,0.0006596519,0.7101322,0.15713312,0.08615894,0.0044042985,0.00087036344],"about_ca_topic_score_codex":0.00013283527,"about_ca_topic_score_gemma":0.0000076126885,"teacher_disagreement_score":0.8662041,"about_ca_system_score_codex":0.000009306047,"about_ca_system_score_gemma":0.00001377123,"threshold_uncertainty_score":0.20034973},"labels":[],"label_agreement":null},{"id":"W2303159270","doi":"10.1117/1.jei.25.5.051204","title":"Large-scale machine learning and evaluation platform for real-time traffic surveillance","year":2016,"lang":"en","type":"article","venue":"Journal of Electronic Imaging","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Intelligent Mechatronic Systems (Canada); Christie (Canada)","funders":"National Research Council Canada","keywords":"Computer science; Cloud computing; Artificial intelligence; Machine learning; Process (computing); AdaBoost; Scale (ratio); Data mining; Ground truth; Real-time computing; Support vector machine","score_opus":0.007063352452191428,"score_gpt":0.26759813514738096,"score_spread":0.26053478269518954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2303159270","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":[0.13632986,0.00064988685,0.86058617,0.0019923837,0.00003503542,0.00015523379,0.0000012685629,0.00006446507,0.00018570106],"genre_scores_gemma":[0.98962575,0.00042078708,0.00961894,0.000027294547,0.000069393165,0.000011797896,6.457148e-7,0.000008229417,0.00021718285],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918634,0.000033223758,0.00023452888,0.00013217946,0.00016735555,0.00024637376],"domain_scores_gemma":[0.9992569,0.00011605234,0.00026860993,0.000096751224,0.00021920174,0.000042458083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014796577,0.000070977374,0.00012045872,0.000097169956,0.00014983531,0.000052107127,0.00017438708,0.000019603045,0.000013786368],"category_scores_gemma":[0.000040566803,0.000050280094,0.000059776045,0.00012048277,0.000015288357,0.00039946588,0.000029426199,0.00011329673,0.0000019632446],"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":[0.000053336073,0.00007364175,0.0016370174,0.00001306336,0.000041546176,0.0000014401355,0.00042858723,0.00024234761,0.07496993,0.0063067055,0.0008704581,0.91536194],"study_design_scores_gemma":[0.0016511041,0.00038730438,0.0009520224,0.000045322493,0.000022632848,0.00032929008,0.00003347644,0.93857366,0.0063208295,0.008083209,0.043396316,0.00020483581],"about_ca_topic_score_codex":0.0000015562983,"about_ca_topic_score_gemma":0.0000060379884,"teacher_disagreement_score":0.9383313,"about_ca_system_score_codex":0.00013817982,"about_ca_system_score_gemma":0.000107050684,"threshold_uncertainty_score":0.20503627},"labels":[],"label_agreement":null},{"id":"W2313497603","doi":"10.5596/c16-005","title":"Book Review: The Accidental Data Scientist","year":2016,"lang":"fr","type":"article","venue":"Journal of the Canadian Health Libraries Association / Journal de l Association de bilbiothèques de la santé du Canada","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Accidental; Computer science; Data science; Physics","score_opus":0.00907314713897691,"score_gpt":0.2658654826380152,"score_spread":0.2567923354990383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2313497603","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":[0.00024045465,0.50344,0.0073637995,0.48501202,0.002708635,0.00032667205,0.00049605366,0.000021830267,0.00039049084],"genre_scores_gemma":[0.01559881,0.65086323,0.0032057192,0.30150348,0.0044380883,0.000015657743,0.0000114438735,0.00006139175,0.024302162],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9909357,0.0040456364,0.0016735877,0.0003118344,0.001615348,0.0014178865],"domain_scores_gemma":[0.9874868,0.0029874444,0.006165323,0.00082671875,0.0011820483,0.0013517124],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.014736025,0.00030390124,0.0005602761,0.00021155161,0.0028589945,0.0027975757,0.0038113438,0.00040816388,0.00063974987],"category_scores_gemma":[0.007551507,0.00020022354,0.0003407943,0.0010547749,0.00015878308,0.003503821,0.00035664692,0.001553297,0.0000075878224],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.0000050061212,0.00002694848,0.017745266,0.00004852531,0.00017391553,0.000045473873,0.0002971891,0.000012764221,0.0000065405584,0.002350515,0.9718811,0.0074067367],"study_design_scores_gemma":[0.0003575583,0.000061107625,0.0282179,0.0010327734,0.00010906513,0.0019975118,0.000064449414,0.00041658714,0.00006347742,0.00251298,0.96494246,0.0002241172],"about_ca_topic_score_codex":0.24287473,"about_ca_topic_score_gemma":0.46599862,"teacher_disagreement_score":0.22312388,"about_ca_system_score_codex":0.04044279,"about_ca_system_score_gemma":0.06304176,"threshold_uncertainty_score":0.99843913},"labels":[],"label_agreement":null},{"id":"W2333568743","doi":"10.1016/j.jfds.2016.03.001","title":"Auto insurance fraud detection using unsupervised spectral ranking for anomaly","year":2016,"lang":"en","type":"article","venue":"The Journal of Finance and Data Science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":113,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Ranking (information retrieval); Pattern recognition (psychology); Anomaly detection; Computer science; Spectral clustering; Data mining; Outlier; Laplacian matrix; Rank (graph theory); Artificial intelligence; Categorical variable; Similarity (geometry); Ranking SVM; Mathematics; Machine learning; Cluster analysis; Graph; Theoretical computer science","score_opus":0.04567491940861173,"score_gpt":0.3052709651566672,"score_spread":0.25959604574805545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2333568743","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":[0.36861238,0.00015225702,0.6305982,0.00044758656,0.0000817084,0.00007076423,0.000009424985,0.000012838342,0.00001484425],"genre_scores_gemma":[0.943011,0.0004087977,0.05640476,0.000081615304,0.00007636134,0.000001730157,5.8249938e-8,0.0000027797507,0.000012937029],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99914575,0.000020890117,0.00023164997,0.0002079707,0.00020929817,0.00018444067],"domain_scores_gemma":[0.99883544,0.00010607998,0.00025528896,0.00061105884,0.0001532982,0.00003882502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016170158,0.000068910245,0.00010063747,0.000095859104,0.00052481046,0.000103769584,0.001809547,0.00002086351,0.0000010240584],"category_scores_gemma":[0.00007860635,0.000037648348,0.000024522016,0.0005483102,0.00026953287,0.0025347231,0.00024996835,0.00007038962,8.3132903e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003817562,0.000024321434,0.00086884404,0.000006546131,0.000004628955,0.0000013065244,0.00017904706,0.00004400243,0.5044346,0.008847532,0.000042968604,0.48550805],"study_design_scores_gemma":[0.0018107559,0.0009722893,0.1831984,0.0003718423,0.000047140766,0.0014030155,0.00009423672,0.29307935,0.46667042,0.02805566,0.02369092,0.0006059645],"about_ca_topic_score_codex":0.000015435016,"about_ca_topic_score_gemma":0.0000057781203,"teacher_disagreement_score":0.5743986,"about_ca_system_score_codex":0.000034146364,"about_ca_system_score_gemma":0.00013423184,"threshold_uncertainty_score":0.40364707},"labels":[],"label_agreement":null},{"id":"W2336468187","doi":"10.1080/01691864.2016.1174370","title":"Special issue on machine learning and data engineering in robotics","year":2016,"lang":"en","type":"article","venue":"Advanced Robotics","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Robotics; Computer science; Constructive; Robot; Machine learning; Cognitive robotics; Field (mathematics); Data science; Mathematics","score_opus":0.014834309804696732,"score_gpt":0.26942713767124876,"score_spread":0.254592827866552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2336468187","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021399374,0.0000348349,0.99715215,0.0015430293,0.00014285746,0.00009712376,0.0000025695165,0.00017949448,0.000633973],"genre_scores_gemma":[0.11292843,0.00073203613,0.8834455,0.00014096274,0.0011772763,0.000012544542,0.0000070533124,0.000027027456,0.0015291859],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992834,0.000011425743,0.00014305176,0.00031263573,0.00008828853,0.00016120893],"domain_scores_gemma":[0.9993217,0.00010061069,0.0000448447,0.00046711555,0.000016723878,0.000048979455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009003631,0.000093991956,0.00010019886,0.000072413655,0.000058632788,0.000030144764,0.00041826535,0.000039006973,0.000006884063],"category_scores_gemma":[0.00006731649,0.0000753506,0.000010700273,0.00019763614,0.000018194345,0.00027886493,0.00032292024,0.000132401,0.000021165884],"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":[0.000006336435,0.000054256372,0.0011134302,0.000010630032,0.000005768692,0.000010377241,0.000045076944,0.22927769,0.0014755533,0.08468715,0.00036834503,0.6829454],"study_design_scores_gemma":[0.0007999182,0.0002562699,0.0016475315,0.000113089554,0.000005753567,0.000021019418,0.0000075399525,0.6800495,0.0027912767,0.0021551435,0.31172302,0.00042998974],"about_ca_topic_score_codex":0.0000027042431,"about_ca_topic_score_gemma":0.000008896135,"teacher_disagreement_score":0.6825154,"about_ca_system_score_codex":0.000031255342,"about_ca_system_score_gemma":0.000008574175,"threshold_uncertainty_score":0.30727082},"labels":[],"label_agreement":null},{"id":"W2338654955","doi":"10.14288/1.0102975","title":"Crime scene investigation: Identifying victims and perpetrators using new forensic science methods","year":2011,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Forensic science; Crime scene; Criminology; Forensic psychology; Psychology; History; Archaeology","score_opus":0.04683761550174438,"score_gpt":0.2515190849047406,"score_spread":0.20468146940299625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2338654955","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":[0.47689068,0.000038392187,0.52223945,0.000013902983,0.00004862458,0.00007928932,0.0000014367514,0.000095045056,0.00059315475],"genre_scores_gemma":[0.5699726,0.000013980197,0.429916,0.000023002489,0.000007564001,1.740941e-7,1.9532042e-7,0.0000029340567,0.00006356556],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99912983,0.00003199114,0.00009914301,0.0004055068,0.00016519717,0.00016832617],"domain_scores_gemma":[0.9992172,0.000015001015,0.00011314298,0.00031159085,0.00016449073,0.00017853739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003384481,0.00003488959,0.00012095665,0.000093709896,0.00056435826,0.00020067704,0.00054849393,0.000049774568,0.000020450014],"category_scores_gemma":[0.000016348158,0.00011367092,0.00004284136,0.00090848026,0.0005513859,0.0011337567,0.00031316918,0.00007915553,0.000002526222],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.198892e-7,0.000016604381,0.0036090582,0.000014413113,0.000007141575,0.000008361555,0.00084925466,6.271445e-7,0.008145755,0.00036196774,0.00012517133,0.98686105],"study_design_scores_gemma":[0.00015536699,0.000040395047,0.9784078,0.00005360372,0.000016499702,0.00010870115,0.0004291125,0.010340276,0.001088585,0.009141215,0.000060706516,0.0001577598],"about_ca_topic_score_codex":0.047215357,"about_ca_topic_score_gemma":0.0036281229,"teacher_disagreement_score":0.9867033,"about_ca_system_score_codex":0.000053227992,"about_ca_system_score_gemma":0.00017184165,"threshold_uncertainty_score":0.95912933},"labels":[],"label_agreement":null},{"id":"W2347998252","doi":"","title":"Research on Human Abnormal Behavior Detection Based on Optical Flow Energy","year":2014,"lang":"en","type":"article","venue":"Computer & Network","topic":"Anomaly Detection Techniques and Applications","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":"Université du Québec","funders":"","keywords":"Computer science; Optical flow; Energy (signal processing); Flow (mathematics); Artificial intelligence; False positive rate; Pattern recognition (psychology); Computer vision; Algorithm; Image (mathematics); Statistics; Mathematics","score_opus":0.04398392015748898,"score_gpt":0.32738807223933036,"score_spread":0.2834041520818414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2347998252","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":[0.0035072037,0.0000042071442,0.99076223,0.00024309783,0.000408451,0.00016987228,4.3521496e-7,0.0005344223,0.0043700957],"genre_scores_gemma":[0.90755284,0.0000023804114,0.08958576,0.0009151036,0.0015853335,0.00022339674,0.000004877734,0.000018445355,0.000111892194],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803054,0.0002263423,0.00023774449,0.0005831335,0.0004400623,0.00048219878],"domain_scores_gemma":[0.99846566,0.00024016536,0.000055155895,0.0009542346,0.00012245495,0.00016232536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000689676,0.00016776919,0.00015470074,0.00030033008,0.00073067873,0.0002314059,0.0008081628,0.0001385698,0.000016609827],"category_scores_gemma":[0.0000038031983,0.00016156278,0.00009887405,0.0010445112,0.000078173856,0.00010693124,0.00023451645,0.00040703372,0.00009220984],"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":[0.000012263259,0.00022112681,0.000056543864,0.000002941573,0.000004048772,0.0000058326905,0.000007972851,0.04816464,0.00011447889,0.11147435,0.01136106,0.8285747],"study_design_scores_gemma":[0.00019122178,0.0012119447,0.0041992655,0.000028382892,0.000003566498,0.000006446452,4.495387e-7,0.94320345,0.0052977633,0.0024920572,0.043167837,0.00019759874],"about_ca_topic_score_codex":0.000018806799,"about_ca_topic_score_gemma":0.000012301651,"teacher_disagreement_score":0.9040456,"about_ca_system_score_codex":0.00006484781,"about_ca_system_score_gemma":0.000018007879,"threshold_uncertainty_score":0.65883386},"labels":[],"label_agreement":null},{"id":"W2352375856","doi":"","title":"Outlier Pattern Research of Multivariate Time Series","year":2012,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CAE (Canada)","funders":"","keywords":"Multivariate statistics; Outlier; Anomaly detection; Series (stratigraphy); Principal component analysis; Pattern recognition (psychology); Dimensionality reduction; Time series; Curse of dimensionality; k-nearest neighbors algorithm; Computer science; Multivariate analysis; Artificial intelligence; Data mining; Mathematics; Algorithm; Machine learning","score_opus":0.05606491059138639,"score_gpt":0.353354400143195,"score_spread":0.2972894895518086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2352375856","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":[0.006213324,0.00001328185,0.9737225,0.00066930975,0.000028803586,0.00011761079,0.000001164853,0.00016007273,0.019073896],"genre_scores_gemma":[0.93177485,0.0000028892443,0.059045814,0.000044679935,0.000035059817,0.000031809912,5.0683917e-7,0.000003556255,0.00906084],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999441,0.00003990658,0.00010353331,0.00009951888,0.00014309026,0.00017292688],"domain_scores_gemma":[0.9994589,0.000036551188,0.000025827248,0.00033682666,0.00009158561,0.000050283674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004100388,0.000037697682,0.000055495268,0.00006328923,0.00007429329,0.00002188447,0.00031306208,0.000029701761,0.00017619887],"category_scores_gemma":[0.000009556296,0.000030413537,0.00002348337,0.00025286654,0.0000413169,0.00033803997,0.00020040956,0.000068630376,0.00029451647],"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":[0.000004430486,0.0003782329,0.005772028,0.000018620045,0.000021273516,5.421008e-7,0.001661913,0.0000029337227,0.06930305,0.6682201,0.013578607,0.24103828],"study_design_scores_gemma":[0.000216474,0.00022562996,0.034170344,0.000015591702,0.000003800583,0.000018273278,0.000092715396,0.0129116755,0.6860606,0.011154264,0.25481308,0.00031753635],"about_ca_topic_score_codex":0.00009790594,"about_ca_topic_score_gemma":0.000001509849,"teacher_disagreement_score":0.92556155,"about_ca_system_score_codex":0.000010278381,"about_ca_system_score_gemma":0.000010111944,"threshold_uncertainty_score":0.3785512},"labels":[],"label_agreement":null},{"id":"W2401417306","doi":"","title":"Automatic Behavior Analysis and Understanding of Collision Processes Using Video Sensors","year":2015,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Political science; Humanities; Philosophy","score_opus":0.036291863782639516,"score_gpt":0.2728496100035356,"score_spread":0.23655774622089606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2401417306","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":[0.34646064,0.00014955207,0.65232867,0.0002321151,0.000014253999,0.00032450914,0.0000073401084,0.00043183216,0.000051100946],"genre_scores_gemma":[0.70611095,0.00003626804,0.29359344,0.00006990611,0.000010920739,0.00012959614,0.0000022634606,0.000012878667,0.00003376078],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985033,0.00006970578,0.00042607656,0.00038194473,0.00032692935,0.00029204204],"domain_scores_gemma":[0.9985068,0.00009052869,0.0003149767,0.00063760614,0.00020909567,0.00024099174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005045536,0.00019017718,0.0003359199,0.00077237823,0.00020326875,0.00017781147,0.00043259512,0.00014434263,0.000003960496],"category_scores_gemma":[0.00011410079,0.00018759395,0.00009896735,0.0028032342,0.00009552818,0.0004895726,0.00024461388,0.0001349328,9.62518e-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":[0.0001049961,0.002033451,0.44671646,0.0007170488,0.0010910889,0.00014338014,0.006373924,0.03846894,0.06485796,0.37327963,0.0015824671,0.06463065],"study_design_scores_gemma":[0.00021536648,0.00012624002,0.0090329,0.00003992481,0.0002476277,0.00008355676,0.00031663768,0.94055146,0.04358762,0.005429784,0.000079340694,0.00028955127],"about_ca_topic_score_codex":0.0026324368,"about_ca_topic_score_gemma":0.00047047768,"teacher_disagreement_score":0.9020825,"about_ca_system_score_codex":0.00039365876,"about_ca_system_score_gemma":0.00024542646,"threshold_uncertainty_score":0.7649859},"labels":[],"label_agreement":null},{"id":"W2404978658","doi":"10.1061/9780784479827.092","title":"Project Related Entities Tracking on Construction Sites by Particle Filtering","year":2016,"lang":"en","type":"article","venue":"Construction Research Congress 2016","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Concordia University","funders":"","keywords":"Tracking (education); Particle filter; Computer vision; Computer science; Video tracking; Artificial intelligence; Object (grammar); Tracking system; Track (disk drive); Object detection; Window (computing); Pattern recognition (psychology); Filter (signal processing)","score_opus":0.06019269248268175,"score_gpt":0.34743266675587287,"score_spread":0.28723997427319115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2404978658","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":[0.47655496,0.00029066997,0.5006304,0.006566315,0.0012354418,0.0014660566,0.00008259418,0.002044069,0.0111295],"genre_scores_gemma":[0.9853163,0.00032806554,0.008703911,0.000033870667,0.00008140063,0.00025542124,0.0000041283647,0.00001971073,0.0052572098],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978608,0.00024499674,0.0003464169,0.0005603523,0.0005122648,0.00047515522],"domain_scores_gemma":[0.9984028,0.0003068975,0.00013181938,0.00060232653,0.00043949578,0.00011669326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065670157,0.00015205474,0.00015267031,0.0002904903,0.00058809115,0.00032570466,0.0005056619,0.000120799166,0.00024298648],"category_scores_gemma":[0.00019301576,0.000112285685,0.00007093519,0.0006562682,0.00077118556,0.0009232436,0.000184586,0.00025456445,0.00023777607],"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":[0.00002873452,0.000058194095,0.0019093857,0.000015562773,0.0000340514,0.0000053981703,0.00010257437,0.0000011665526,0.20985754,0.21736792,0.023855451,0.546764],"study_design_scores_gemma":[0.0009897412,0.00037459683,0.00053023704,0.0002558866,0.000006695017,0.00019997089,0.00034483153,0.0016776352,0.8628136,0.020783726,0.11157597,0.0004471355],"about_ca_topic_score_codex":0.000031448842,"about_ca_topic_score_gemma":0.0000034974105,"teacher_disagreement_score":0.652956,"about_ca_system_score_codex":0.00014849915,"about_ca_system_score_gemma":0.00012436965,"threshold_uncertainty_score":0.45788774},"labels":[],"label_agreement":null},{"id":"W2415000644","doi":"","title":"Learning Nonstationary Models of Normal Network Traffic for Detecting Novel Attacks, Edmonton, Alberta","year":2002,"lang":"en","type":"article","venue":"Knowledge Discovery and Data Mining","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Computer network; Artificial intelligence; Computer security","score_opus":0.058735039018205946,"score_gpt":0.2885419494280673,"score_spread":0.22980691040986137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2415000644","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":[0.037547104,0.00059614,0.95865256,0.000065850785,0.00007741287,0.0001966051,0.00003979007,0.00008104001,0.0027434747],"genre_scores_gemma":[0.8586611,0.000060910883,0.14003716,0.000022400258,0.000101397185,0.00004759181,0.00006315168,0.000012104325,0.0009941577],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888647,0.000023764296,0.00031445842,0.0004605864,0.000078576166,0.00023613691],"domain_scores_gemma":[0.99874824,0.0004504133,0.0001685377,0.0005157032,0.00006271359,0.000054368713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030933833,0.00013015821,0.0001777713,0.000064265965,0.0003904895,0.00012706337,0.00059553876,0.000060431794,0.000004980109],"category_scores_gemma":[0.000071430346,0.00013246312,0.00004493815,0.0002963407,0.00005016511,0.0020675461,0.00047047267,0.00010772689,0.0000023927464],"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":[0.000046623627,0.0005140578,0.0009387345,0.00024505256,0.00011748409,0.0000015719718,0.0101585975,0.07103395,0.001563563,0.052713204,0.008106056,0.8545611],"study_design_scores_gemma":[0.00024190223,0.0000992568,0.000121509234,0.000057492918,0.000015637985,0.000014052928,0.0002282768,0.9931043,0.0001777582,0.00018152663,0.0055904468,0.00016783923],"about_ca_topic_score_codex":0.000018893319,"about_ca_topic_score_gemma":0.00009162656,"teacher_disagreement_score":0.9220703,"about_ca_system_score_codex":0.000011719989,"about_ca_system_score_gemma":0.00002650796,"threshold_uncertainty_score":0.54016894},"labels":[],"label_agreement":null},{"id":"W2431654164","doi":"10.1109/icmcis.2016.7496546","title":"Complex event processing for content-based text, image, and video retrieval","year":2016,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Computer science; Automatic summarization; Data science; Exploit; Analytics; Workflow; Sensemaking; Event (particle physics); Information retrieval; Knowledge management; Computer security; Database","score_opus":0.04756599143939939,"score_gpt":0.30039350127477893,"score_spread":0.25282750983537955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2431654164","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":[0.0022567362,0.000016726155,0.99125403,0.0053837006,0.000013151231,0.00028138922,0.0000037602167,0.00028945788,0.0005010472],"genre_scores_gemma":[0.66931367,0.0000023809923,0.32911515,0.00046490575,0.000018110639,0.000050753024,5.446149e-7,0.000004607378,0.0010298936],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994352,0.000007985107,0.00013300906,0.00022733642,0.00007436146,0.00012212845],"domain_scores_gemma":[0.9995194,0.00006239377,0.000056999274,0.00019009094,0.00011785149,0.000053240114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012125902,0.00006418698,0.00007286247,0.000039013954,0.00014131793,0.000079186764,0.0001897794,0.000025445854,0.000020522326],"category_scores_gemma":[0.000022198552,0.000040840856,0.00003389971,0.00011125505,0.000052481,0.00020804118,0.000050169638,0.000019265002,0.000005463415],"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":[0.000025167206,0.00006842095,0.00013987304,0.000027465658,0.000004696787,4.256568e-7,0.00001982739,4.0661047e-7,0.42205134,0.057244834,0.0040200166,0.51639754],"study_design_scores_gemma":[0.0018081495,0.00045726652,0.0048462404,0.00007558741,0.00001216015,0.000018241151,0.00003447708,0.16555208,0.7311778,0.010103967,0.0854546,0.00045944387],"about_ca_topic_score_codex":0.000005543618,"about_ca_topic_score_gemma":0.0000018965794,"teacher_disagreement_score":0.6670569,"about_ca_system_score_codex":0.00002170149,"about_ca_system_score_gemma":0.00003082127,"threshold_uncertainty_score":0.16654418},"labels":[],"label_agreement":null},{"id":"W2461433156","doi":"10.1109/noms.2016.7502857","title":"Automated anomaly detection and root cause analysis in virtualized cloud infrastructures","year":2016,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Cloud computing; Anomaly detection; Computer science; Root cause analysis; Root (linguistics); Anomaly (physics); Operating system; Data mining; Reliability engineering; Engineering","score_opus":0.0070125087997242675,"score_gpt":0.2512590044370706,"score_spread":0.24424649563734635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2461433156","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":[0.37338468,0.000008461102,0.6252397,0.00020354013,0.000022354114,0.00007990488,0.0000011563571,0.00076229527,0.0002979205],"genre_scores_gemma":[0.9867968,0.00001301433,0.012852182,0.00006845204,0.000014568204,0.000039513343,4.5047642e-7,0.0000044859844,0.00021056434],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9991669,0.000045333618,0.00021669774,0.00032584628,0.0000973148,0.00014787407],"domain_scores_gemma":[0.99941975,0.000054099844,0.000069294234,0.00035761937,0.00003793396,0.00006131106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012889618,0.00009954989,0.00014642162,0.0003928419,0.0000787916,0.000072226736,0.00021753204,0.00007215044,0.0000489148],"category_scores_gemma":[0.000018089533,0.00006693913,0.00005011341,0.0012827318,0.00003898347,0.00027544197,0.00010695377,0.000046406836,0.000009272364],"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":[0.00005364909,0.00015589985,0.1393285,0.000014446616,0.0005168118,0.000017931829,0.0005605557,0.00018530444,0.27619243,0.15113181,0.0011911227,0.43065155],"study_design_scores_gemma":[0.00057861995,0.00010318767,0.75491786,0.000007133036,0.000056880002,0.00001988282,0.000013274422,0.15935114,0.075204775,0.007854083,0.0015851398,0.00030802446],"about_ca_topic_score_codex":0.00030438858,"about_ca_topic_score_gemma":0.001078269,"teacher_disagreement_score":0.6155894,"about_ca_system_score_codex":0.000043229014,"about_ca_system_score_gemma":0.0000131161905,"threshold_uncertainty_score":0.27296984},"labels":[],"label_agreement":null},{"id":"W2468446116","doi":"10.5281/zenodo.51472","title":"Dataset For Anomaly Detection Using Inter-Arrival Curves For Real-Time Systems","year":2016,"lang":"en","type":"dataset","venue":"INFM-OAR (INFN Catania)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Anomaly detection; Anomaly (physics); Arrival time; Computer science; Data mining; Real-time computing; Engineering; Physics; Transport engineering","score_opus":0.03234106427838934,"score_gpt":0.30979061805190566,"score_spread":0.2774495537735163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2468446116","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000021240456,0.00012500463,0.39930388,0.000105659965,0.0005974602,0.0017318997,0.5978929,0.00021387276,0.000008093987],"genre_scores_gemma":[0.00018622239,0.00043484636,0.011017114,0.0002571187,0.0009618053,0.0021499153,0.9846727,0.000070369184,0.00024990155],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99684346,0.000088948145,0.000901371,0.0011909932,0.00035107645,0.00062415603],"domain_scores_gemma":[0.99568003,0.00037071912,0.0008742593,0.0025145828,0.00035603083,0.0002043506],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00072997983,0.00055730407,0.00067811506,0.00037272525,0.00050828856,0.00040716928,0.0022209436,0.0004916864,0.000025985386],"category_scores_gemma":[0.00014363782,0.0005183179,0.000283876,0.00038523928,0.00011025617,0.00074529985,0.00055211183,0.00026181023,0.00013652125],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023063234,0.000046617522,7.073268e-7,0.0005442269,0.00006234132,0.0000022781014,0.000005474461,0.0000046873997,0.0026377973,0.00012814178,0.99395066,0.002593981],"study_design_scores_gemma":[0.00035506272,0.0003360154,0.0000028143384,0.0005167495,0.00013935893,0.00009836758,0.0000050018216,0.008023346,0.0025179964,0.00023075427,0.9870968,0.00067774934],"about_ca_topic_score_codex":0.0011342934,"about_ca_topic_score_gemma":0.00011296461,"teacher_disagreement_score":0.38828677,"about_ca_system_score_codex":0.00035505075,"about_ca_system_score_gemma":0.0002509977,"threshold_uncertainty_score":0.99972683},"labels":[],"label_agreement":null},{"id":"W2481230467","doi":"10.1007/978-3-319-41501-7_1","title":"Adaptation Approaches in Unsupervised Learning: A Survey of the State-of-the-Art and Future Directions","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Novelty; Adaptation (eye); Unsupervised learning; Cluster analysis; Novelty detection; Artificial intelligence; Process (computing); Machine learning; Streaming data; Resource (disambiguation); Data science; Data mining","score_opus":0.034467529019190056,"score_gpt":0.23382533305983347,"score_spread":0.1993578040406434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2481230467","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":[0.0012624679,0.00027979744,0.9962186,0.00085410406,0.00030253673,0.00040622798,0.000007187074,0.00004071028,0.0006283387],"genre_scores_gemma":[0.93817735,0.00025947485,0.060442984,0.00012724454,0.000093337665,0.00003598571,0.0000016344555,0.000021751583,0.0008402448],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984398,0.00010128067,0.00036026965,0.0005446541,0.00036946207,0.00018452985],"domain_scores_gemma":[0.9984849,0.0002860197,0.00031456535,0.00071749656,0.00016121946,0.00003578438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006982157,0.00019024544,0.00024107596,0.00026424968,0.00018090938,0.000062340776,0.0013089166,0.00012683208,0.000002602961],"category_scores_gemma":[0.000054873,0.0001150874,0.00007160423,0.00092665816,0.00056877936,0.00018265861,0.00060572533,0.00041830816,9.736195e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000033622691,0.000020577287,0.001325106,0.00001859996,0.000004487409,2.5114005e-7,0.0009148206,0.00392629,0.000114639086,0.0072561903,0.000003728325,0.9864119],"study_design_scores_gemma":[0.00052168587,0.00025740344,0.1388526,0.0006927319,0.000014657173,0.000038947503,0.0000024334943,0.6920373,0.009480327,0.14569034,0.011576628,0.0008349878],"about_ca_topic_score_codex":0.00010027173,"about_ca_topic_score_gemma":0.00079090707,"teacher_disagreement_score":0.985577,"about_ca_system_score_codex":0.000073490955,"about_ca_system_score_gemma":0.00024276949,"threshold_uncertainty_score":0.4693128},"labels":[],"label_agreement":null},{"id":"W2496887120","doi":"10.1111/coin.12097","title":"Bagged Subspaces for Unsupervised Outlier Detection","year":2016,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":27,"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","funders":"Consejo Nacional de Ciencia y Tecnología","keywords":"Anomaly detection; Outlier; Computer science; Linear subspace; Pattern recognition (psychology); Artificial intelligence; Ensemble learning; Data mining; Curse of dimensionality; Local outlier factor; Machine learning; Mathematics","score_opus":0.028698933474689207,"score_gpt":0.2844621595874893,"score_spread":0.2557632261128001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2496887120","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":[0.0024036262,0.00003458321,0.99450964,0.0019500277,0.00015079054,0.00031019084,0.000007278097,0.00038085764,0.00025299768],"genre_scores_gemma":[0.8484064,0.0000123959535,0.15070102,0.00019134342,0.000059265654,0.00020092976,0.0000020586006,0.000008134035,0.00041846593],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99905986,0.000023319419,0.0002279963,0.00035416227,0.00016287605,0.00017178497],"domain_scores_gemma":[0.998949,0.00037251896,0.000082926446,0.00025402507,0.00027263758,0.000068899026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016719545,0.000108109554,0.000092013805,0.00010523512,0.00020667426,0.00007636187,0.00048094543,0.000052461342,0.00003052675],"category_scores_gemma":[0.000057599136,0.000081601116,0.00008209296,0.00030307882,0.00005916602,0.00032950667,0.00006963554,0.00004284291,0.00012315811],"study_design_candidate":"theoretical_or_conceptual","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":[0.000011070169,0.0000491736,0.000079737765,0.0000071772047,0.000013284649,4.2387427e-7,0.00010160899,0.001932819,0.004862418,0.30140048,0.00037594774,0.69116586],"study_design_scores_gemma":[0.00018365745,0.0002027942,0.0015819132,0.000029784487,0.0000065283825,0.000019230885,0.000028589897,0.31130704,0.18554594,0.48164532,0.01910807,0.00034112736],"about_ca_topic_score_codex":0.0000053686886,"about_ca_topic_score_gemma":0.0000054306292,"teacher_disagreement_score":0.84600276,"about_ca_system_score_codex":0.00005552285,"about_ca_system_score_gemma":0.00004316976,"threshold_uncertainty_score":0.33275968},"labels":[],"label_agreement":null},{"id":"W2499081054","doi":"10.1007/978-3-319-33681-7_3","title":"Automated Pedestrians Data Collection Using Computer Vision","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pedestrian; Walkability; Computer science; Matching (statistics); Schema crosswalk; Transport engineering; Data collection; Gait; Vulnerability (computing); Human–computer interaction; Engineering; Built environment; Computer security; Physical medicine and rehabilitation; Medicine","score_opus":0.04236451303108773,"score_gpt":0.28595437464398415,"score_spread":0.24358986161289642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2499081054","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006484245,0.00009473609,0.99665093,0.00087521895,0.00044737835,0.0005841436,0.00008901119,0.00035466178,0.00083905016],"genre_scores_gemma":[0.0048173903,0.00008589375,0.9946596,0.00011112722,0.00017590527,0.000015922298,0.000037312882,0.000017560411,0.00007929468],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986712,0.000013179895,0.0006184589,0.0002551723,0.00023484413,0.00020718317],"domain_scores_gemma":[0.9977839,0.00021574927,0.00055623625,0.0012169853,0.00017949172,0.000047656176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036692808,0.00026090146,0.00031368286,0.0002425028,0.0012596204,0.0002781021,0.0029251163,0.00023434749,9.3165767e-7],"category_scores_gemma":[0.000021603479,0.00019808646,0.00013418317,0.0003183639,0.00032505998,0.00063629186,0.0017841004,0.00026676827,8.5790714e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004946777,0.000054781583,0.000005157267,0.00029142151,0.00020552249,2.478721e-7,0.00086555094,0.68136185,0.0001793392,0.1764612,0.0014158532,0.1391541],"study_design_scores_gemma":[0.00013602123,0.00007101495,0.000017342583,0.00018461877,0.000031961055,0.000024586165,5.92265e-7,0.97138876,0.00008162964,0.00096299575,0.026860073,0.00024038061],"about_ca_topic_score_codex":0.000028702145,"about_ca_topic_score_gemma":0.000025896099,"teacher_disagreement_score":0.2900269,"about_ca_system_score_codex":0.00009755183,"about_ca_system_score_gemma":0.0002123072,"threshold_uncertainty_score":0.9688109},"labels":[],"label_agreement":null},{"id":"W2508613594","doi":"10.1109/ecrts.2016.22","title":"Anomaly Detection Using Inter-Arrival Curves for Real-Time Systems","year":2016,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Anomaly detection; Computer science; Kernel (algebra); Arrival time; Anomaly (physics); Time of arrival; Exploit; TRACE (psycholinguistics); Real-time computing; Aerospace; Data mining; Event (particle physics); Mathematics; Engineering; Operating system","score_opus":0.022456209274512733,"score_gpt":0.269774464169794,"score_spread":0.24731825489528128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2508613594","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":[0.0127660455,0.00002415397,0.9841081,0.00037403795,0.00014278294,0.00041490278,0.0000043095342,0.0006944065,0.0014712947],"genre_scores_gemma":[0.94026875,0.000049136488,0.055653725,0.00007075312,0.00010707294,0.00019101225,5.1334666e-7,0.000012532587,0.0036464788],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992276,0.000025432239,0.00020553835,0.00028716307,0.000085833795,0.00016842724],"domain_scores_gemma":[0.99927884,0.00007208219,0.00009365516,0.0003927846,0.000106441556,0.0000562087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019433191,0.00009184813,0.00011132862,0.00007832903,0.00013790216,0.0000713795,0.00033430284,0.000050039722,0.000018222587],"category_scores_gemma":[0.000015727193,0.00006275422,0.00007231257,0.00018468742,0.000024655732,0.00037195726,0.00008263667,0.000024268751,0.00004210382],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010018176,0.000056003813,0.00014901826,0.00007434762,0.000026114041,7.1198696e-7,0.000026158514,0.000008780185,0.84364945,0.04668571,0.0030144972,0.1062992],"study_design_scores_gemma":[0.00064510707,0.00062172895,0.0006725712,0.0004615279,0.000034772012,0.00014981974,0.000025543799,0.44465736,0.5027053,0.005582852,0.043672796,0.00077067467],"about_ca_topic_score_codex":0.00015850073,"about_ca_topic_score_gemma":0.0000061940236,"teacher_disagreement_score":0.92845434,"about_ca_system_score_codex":0.000071724084,"about_ca_system_score_gemma":0.000022605642,"threshold_uncertainty_score":0.2559043},"labels":[],"label_agreement":null},{"id":"W2514146696","doi":"10.1016/j.clinbiochem.2015.07.046","title":"Assessment of the equivalency for Troponin I as either negative or positive by Alere Triage and Abbott i-STAT relative to Siemens Dimension Vista","year":2015,"lang":"en","type":"article","venue":"Clinical Biochemistry","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Alberta Health Services","funders":"","keywords":"Semi-supervised learning; Computer science; Supervised learning; Artificial intelligence; Machine learning; Dimension (graph theory); Benchmark (surveying); Pattern recognition (psychology); Mathematics; Artificial neural network","score_opus":0.04775782156211933,"score_gpt":0.40575507142254996,"score_spread":0.35799724986043063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2514146696","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":[0.42954195,0.00007249956,0.56051654,0.004818039,0.000114771756,0.00119233,0.00016274626,0.00009250261,0.0034886717],"genre_scores_gemma":[0.9509059,0.000015890335,0.044847596,0.0005088846,0.000042144078,0.00010688486,0.000006235425,0.000009685545,0.0035567374],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987382,0.000081651975,0.00041308932,0.00044459905,0.00017554249,0.00014688342],"domain_scores_gemma":[0.99829197,0.0006052,0.00024361556,0.00048688942,0.00019438332,0.00017795831],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042597982,0.00012534307,0.00021211526,0.000013176048,0.0001061338,0.000032840595,0.00038625783,0.00012740365,0.000011334629],"category_scores_gemma":[0.0005775859,0.00008294614,0.00010644184,0.0002127957,0.00017332537,0.000080939695,0.00032832831,0.00017187315,0.0000025724914],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013886704,0.0033263683,0.007871789,0.0002110425,0.0006152668,0.000012842652,0.0026227853,0.000019737192,0.6492149,0.08491402,0.1909799,0.058822636],"study_design_scores_gemma":[0.0026134658,0.0028092058,0.0068278764,0.00017348121,0.000079456164,0.000012137981,0.0005517968,0.0035429783,0.95991755,0.015505297,0.007480379,0.00048638848],"about_ca_topic_score_codex":0.000011246722,"about_ca_topic_score_gemma":7.9064273e-7,"teacher_disagreement_score":0.52136403,"about_ca_system_score_codex":0.00005241998,"about_ca_system_score_gemma":0.0001968014,"threshold_uncertainty_score":0.33824456},"labels":[],"label_agreement":null},{"id":"W2520420489","doi":"10.1002/atr.1406","title":"Classification and speed estimation of vehicles via tire detection using single‐element piezoelectric sensor","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Oklahoma Department of Transportation","keywords":"Piezoelectric sensor; Classifier (UML); Diagonal; Computer science; Engineering; Process (computing); Artificial intelligence; Automotive engineering; Real-time computing; Piezoelectricity; Electrical engineering","score_opus":0.017955535011277494,"score_gpt":0.26128213781042375,"score_spread":0.24332660279914625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2520420489","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":[0.4773518,0.000024636125,0.52238405,0.00010888229,0.000035647456,0.00007308549,0.0000010019877,0.000017871991,0.000003002784],"genre_scores_gemma":[0.90769625,0.00006549001,0.09218884,0.000008752266,0.00002590835,0.0000022903141,9.228966e-7,0.0000060950942,0.0000054758193],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990545,0.000023750237,0.0005014803,0.00013131932,0.00020252072,0.00008642685],"domain_scores_gemma":[0.9988168,0.00004584575,0.000722295,0.00011709714,0.0002540965,0.000043861673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016064345,0.00008003941,0.00013038005,0.00020112343,0.00006773694,0.000015206405,0.000096880474,0.00004555815,0.0000015159882],"category_scores_gemma":[0.000014243727,0.000062953375,0.000052815478,0.00032126484,0.00002851632,0.00073153124,0.0000024532044,0.00005643909,4.3820032e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018607547,0.000028770814,0.00021760064,0.000008783306,0.000005668166,4.4955016e-7,0.000086334745,0.0018285867,0.5276496,0.0003324524,3.7641618e-7,0.46982276],"study_design_scores_gemma":[0.0009878234,0.0007805774,0.11642526,0.00013737039,0.000062851126,0.00005968217,0.000098289114,0.14683676,0.73005074,0.0042297225,0.00014196632,0.0001889696],"about_ca_topic_score_codex":0.000004460561,"about_ca_topic_score_gemma":0.000004202749,"teacher_disagreement_score":0.4696338,"about_ca_system_score_codex":0.00008413167,"about_ca_system_score_gemma":0.000024600922,"threshold_uncertainty_score":0.2567164},"labels":[],"label_agreement":null},{"id":"W2523180702","doi":"10.1007/978-3-319-46349-0_33","title":"IDA 2016 Industrial Challenge: Using Machine Learning for Predicting Failures","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Classifier (UML); Random forest; Baseline (sea); Component (thermodynamics); Data mining","score_opus":0.03910668136540076,"score_gpt":0.2736425585978755,"score_spread":0.23453587723247474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2523180702","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000036455156,0.00030280944,0.9954504,0.0012255231,0.0007667008,0.0006959823,0.000010980387,0.00040520725,0.0011059511],"genre_scores_gemma":[0.1824194,0.00014018048,0.81212837,0.00042404604,0.0030079926,0.000105904335,0.000007174093,0.00009921015,0.001667726],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997087,0.00002934211,0.0005058836,0.0012887688,0.0005145269,0.0005745015],"domain_scores_gemma":[0.99789774,0.00048207806,0.00043859213,0.0008163276,0.00022596477,0.00013930471],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00079378014,0.0004217785,0.00040656189,0.0006017157,0.0006309504,0.0003613046,0.0020094875,0.0004402242,0.00001618127],"category_scores_gemma":[0.00011839958,0.00034600563,0.00015968732,0.00033511713,0.0003420066,0.00051545794,0.0009439156,0.0007829635,0.000010126618],"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":[0.000007805484,0.000017771936,0.00006616743,0.000021537904,0.0000137191355,0.0000059094637,0.00017318182,0.004680146,0.00064391294,0.031145096,0.000017367052,0.96320736],"study_design_scores_gemma":[0.00046263935,0.0003371242,0.0000049776318,0.00056882104,0.000013220171,0.000045537843,1.8375763e-7,0.8546178,0.0038555195,0.113823086,0.025580766,0.00069032435],"about_ca_topic_score_codex":0.0000410231,"about_ca_topic_score_gemma":0.00003474155,"teacher_disagreement_score":0.9625171,"about_ca_system_score_codex":0.00027375977,"about_ca_system_score_gemma":0.00042226573,"threshold_uncertainty_score":0.9998992},"labels":[],"label_agreement":null},{"id":"W252840763","doi":"","title":"Maritime Anomaly Detection: Domain Introduction and Review of Selected Literature","year":2011,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":48,"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":"Domain (mathematical analysis); Anomaly detection; Field (mathematics); Computer science; Subject (documents); Data science; Anomaly (physics); Data mining; World Wide Web","score_opus":0.0067309841151265756,"score_gpt":0.2044348861986381,"score_spread":0.19770390208351152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W252840763","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020175762,0.003623192,0.9861832,0.0010813655,0.000058798676,0.00027452747,0.0000013871285,0.00034181878,0.006418128],"genre_scores_gemma":[0.46725413,0.007918681,0.522084,0.0008689378,0.00028188666,0.00014378688,0.0000065002746,0.000014104061,0.0014279617],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993863,0.000033519547,0.00018651607,0.00023508805,0.000074343836,0.00008422239],"domain_scores_gemma":[0.9993488,0.000007642234,0.00007830475,0.0003252245,0.00020138557,0.00003863823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015279798,0.00007372531,0.0001111755,0.000073220785,0.00006394857,0.000022569193,0.00016811532,0.000049191658,0.00010344795],"category_scores_gemma":[0.000013615481,0.00006372694,0.000030256031,0.00090899743,0.000031786334,0.000259693,0.000064709144,0.00008298055,0.000005683641],"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":[0.000023083045,0.00026877303,0.0006330133,0.0021900937,0.00006140317,0.0000056740523,0.00078228686,8.889263e-8,0.042677682,0.44008234,0.014654671,0.4986209],"study_design_scores_gemma":[0.0005844607,0.0012547432,0.04580197,0.001712199,0.00006421905,0.001359913,0.000044366614,0.0018537779,0.64130723,0.053200696,0.2518053,0.0010110891],"about_ca_topic_score_codex":0.000018863646,"about_ca_topic_score_gemma":0.0000050930494,"teacher_disagreement_score":0.5986296,"about_ca_system_score_codex":0.000011841926,"about_ca_system_score_gemma":0.000013546542,"threshold_uncertainty_score":0.25987092},"labels":[],"label_agreement":null},{"id":"W2528898308","doi":"10.1007/s12652-016-0415-y","title":"Feature-level combination of skeleton joints and body parts for accurate aggressive and agitated behavior recognition","year":2016,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto","funders":"","keywords":"Skeleton (computer programming); Computational intelligence; Feature (linguistics); Computer science; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.06550070766336202,"score_gpt":0.3103618895993951,"score_spread":0.24486118193603307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2528898308","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":[0.4594474,0.00010593761,0.5399373,0.00025628327,0.000060121514,0.00016753768,0.0000036180368,0.00001404966,0.00000775394],"genre_scores_gemma":[0.9737899,0.00040492028,0.025683999,0.00004388291,0.00003581303,0.000006839644,0.0000011322855,0.000005541415,0.00002799359],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991678,0.00003242797,0.0003742274,0.00017716378,0.00012759703,0.00012077366],"domain_scores_gemma":[0.9982329,0.00018191035,0.0008952386,0.0000918591,0.0005327411,0.00006535955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037295162,0.00009974522,0.00019509488,0.00015551805,0.00018070026,0.00009231239,0.00014250462,0.000051301064,0.0000020205523],"category_scores_gemma":[0.00008206453,0.00007307923,0.000046513465,0.000105292274,0.000079897654,0.0003479144,0.000088841196,0.00008652548,4.2185061e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049579732,0.00014976309,0.00071378326,0.000049712522,0.000036246558,0.0000062656136,0.0008334246,0.0000024512153,0.029997464,0.01087951,0.00020334695,0.95707846],"study_design_scores_gemma":[0.0027625842,0.004395716,0.049297243,0.0023859579,0.00020786893,0.001013278,0.0006455623,0.028246447,0.8163916,0.09247537,0.0013980141,0.00078038493],"about_ca_topic_score_codex":0.0000047262965,"about_ca_topic_score_gemma":0.000001612211,"teacher_disagreement_score":0.95629805,"about_ca_system_score_codex":0.000020090627,"about_ca_system_score_gemma":0.000019037223,"threshold_uncertainty_score":0.29800847},"labels":[],"label_agreement":null},{"id":"W2529760013","doi":"10.1109/wacv.2018.00188","title":"Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Leverage (statistics); Convolutional neural network; Abnormality; Artificial intelligence; Optical flow; Event (particle physics); Feature (linguistics); Pattern recognition (psychology); Computer vision; Machine learning; Image (mathematics)","score_opus":0.014072595284633083,"score_gpt":0.29180920997678433,"score_spread":0.2777366146921513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2529760013","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":[0.026237512,0.000020868474,0.9715419,0.00018404689,0.000091863025,0.0011496311,0.000008997328,0.0004664981,0.0002987269],"genre_scores_gemma":[0.91365427,0.000025696883,0.08372877,0.00007365392,0.00013294297,0.00218592,0.00007802146,0.000012261757,0.000108435306],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817353,0.000055355325,0.00047038475,0.000919661,0.00016468455,0.00021638404],"domain_scores_gemma":[0.9984821,0.000030171048,0.00028514868,0.000944424,0.00016674121,0.00009138008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005913434,0.00021662364,0.00027052072,0.00058791257,0.0001758157,0.00020368405,0.0005738376,0.00032265915,0.0000053156546],"category_scores_gemma":[0.0000075205594,0.00022071695,0.0001639708,0.0007149845,0.000040466326,0.00033441492,0.00037594573,0.00023114713,0.0000111960935],"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":[0.000040485644,0.0004934133,0.0028765618,0.00013645564,0.0002629888,5.559882e-7,0.000564906,0.01306581,0.003130263,0.034401838,0.00015031428,0.94487643],"study_design_scores_gemma":[0.00014553695,0.000120814715,0.020907396,0.000009071745,0.000096826254,0.0000031376073,0.0000148755025,0.94280344,0.012645424,0.021648925,0.0013020854,0.00030249203],"about_ca_topic_score_codex":0.00054964155,"about_ca_topic_score_gemma":0.00092611095,"teacher_disagreement_score":0.94457394,"about_ca_system_score_codex":0.00014203346,"about_ca_system_score_gemma":0.000045551522,"threshold_uncertainty_score":0.9000576},"labels":[],"label_agreement":null},{"id":"W2535548825","doi":"10.1109/icdsc.2013.6778240","title":"Multi-view support vector machines for distributed activity recognition","year":2013,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Support vector machine; Activity recognition; Artificial intelligence; Wireless sensor network; Pattern recognition (psychology); Machine learning; Data mining; Computer network","score_opus":0.036374324487654756,"score_gpt":0.2866376854795173,"score_spread":0.2502633609918625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2535548825","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":[0.006591803,0.000003615414,0.99018675,0.0015711833,0.000052118492,0.00062996306,0.000033935532,0.00053574244,0.0003949096],"genre_scores_gemma":[0.63982004,0.000007714994,0.35785863,0.00024906447,0.000034599885,0.0011015258,0.00004020638,0.0000063809916,0.000881816],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946827,0.000012904409,0.000109911605,0.0002208605,0.000055056924,0.00013300886],"domain_scores_gemma":[0.9994826,0.00003937768,0.000053819545,0.00025150814,0.00011578196,0.000056921042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007378276,0.000077041164,0.0000830525,0.000029706664,0.000120571254,0.00009536839,0.0002295192,0.00004191956,0.00022707591],"category_scores_gemma":[0.000016649117,0.00006403211,0.00006212214,0.00016989143,0.000014082814,0.00040165556,0.000057673762,0.00004565158,0.00021913684],"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":[0.0000021175192,0.00018699771,0.00009688904,0.000021375477,0.0000101386995,1.7581618e-7,0.000019802263,7.96324e-7,0.021644488,0.004770281,0.013917721,0.95932925],"study_design_scores_gemma":[0.0009387407,0.00044309936,0.052513767,0.000018894794,0.000021618664,0.000028101811,0.000012800227,0.5863447,0.24145961,0.02790112,0.089518175,0.0007993368],"about_ca_topic_score_codex":0.00012593991,"about_ca_topic_score_gemma":0.000013804287,"teacher_disagreement_score":0.9585299,"about_ca_system_score_codex":0.000020813799,"about_ca_system_score_gemma":0.000015871356,"threshold_uncertainty_score":0.2816634},"labels":[],"label_agreement":null},{"id":"W2542314080","doi":"10.1109/aipr.2008.4906460","title":"Exploitation of massive numbers of simple events","year":2008,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Lockheed Martin (Canada)","funders":"","keywords":"Computer science; Event (particle physics); Simple (philosophy); Visualization; Data mining; Image (mathematics); Volume (thermodynamics); Data type; Data structure; Data science; Artificial intelligence","score_opus":0.0222547487096781,"score_gpt":0.26165769734517547,"score_spread":0.23940294863549738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2542314080","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":[0.13918032,0.0000027232973,0.8561103,0.000088715096,0.000010494109,0.00006525703,8.827858e-7,0.00005597481,0.004485367],"genre_scores_gemma":[0.9250594,0.000010090161,0.07454981,0.000024142173,0.0000033324511,0.000013369602,6.471957e-7,0.0000015520336,0.00033769786],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996506,0.000007666011,0.00013022916,0.000079864396,0.00008733505,0.00004429172],"domain_scores_gemma":[0.99962866,0.00001820778,0.000085304455,0.00017983509,0.00007086693,0.00001710957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000031101023,0.00002878939,0.000055526074,0.000037363425,0.000030559244,0.0000012056055,0.00016837814,0.000017088123,0.0000309058],"category_scores_gemma":[0.0000056288713,0.000026499189,0.00003242773,0.00020222711,0.000021197899,0.00012455639,0.000035713558,0.000016902233,0.000006249856],"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":[0.000010324656,0.00045993543,0.019677162,0.000043230462,0.00004325137,0.0000021969004,0.0028277074,0.00036771945,0.062798575,0.8497755,0.014989463,0.04900494],"study_design_scores_gemma":[0.00026937193,0.00019490243,0.030498395,0.000008847549,0.000004247478,0.0000118427315,0.00021201484,0.012014929,0.9075143,0.04603056,0.0030825008,0.00015810588],"about_ca_topic_score_codex":0.00006791893,"about_ca_topic_score_gemma":0.0000020420994,"teacher_disagreement_score":0.8447157,"about_ca_system_score_codex":0.000006844088,"about_ca_system_score_gemma":0.000015914018,"threshold_uncertainty_score":0.10806055},"labels":[],"label_agreement":null},{"id":"W2544119733","doi":"10.1109/tic-sth.2009.5444523","title":"Auto-calibration of Support Vector Machines for detecting disease outbreaks","year":2009,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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 Guelph","funders":"","keywords":"Outbreak; Support vector machine; Hyperplane; Computer science; Kernel (algebra); Telehealth; Data set; Data mining; Set (abstract data type); Calibration; Relation (database); Artificial intelligence; Machine learning; Statistics; Mathematics; Medicine; Health care; Telemedicine","score_opus":0.014164456922603555,"score_gpt":0.27112923635197667,"score_spread":0.2569647794293731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2544119733","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":[0.0034046082,0.000006073593,0.9932365,0.0015098304,0.00003463074,0.00024949902,0.0000046052114,0.00038070182,0.0011735965],"genre_scores_gemma":[0.8919239,0.0000010085173,0.107246,0.00028708606,0.000036161196,0.000043798955,0.000003189601,0.000003280207,0.00045556593],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999457,0.000008010116,0.00017993861,0.00017773734,0.00007828778,0.00009901511],"domain_scores_gemma":[0.99951124,0.000030539424,0.000079082514,0.0002691637,0.00004682929,0.000063136555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000089965855,0.00006362358,0.000076253076,0.000053021133,0.00008886573,0.000040390212,0.00023456055,0.00002558935,0.000019779536],"category_scores_gemma":[0.000023536071,0.0000553149,0.00006945207,0.00016755513,0.000010118003,0.00023596741,0.000027273943,0.000029399984,0.0000018480679],"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":[0.000020751746,0.00015549855,0.0005055003,0.000024688816,0.00000762136,7.0164236e-7,0.00013120918,0.00006156833,0.037951156,0.5397726,0.0015856681,0.419783],"study_design_scores_gemma":[0.0002175367,0.00032776227,0.012818913,0.0000075621306,0.000011559697,0.0000036376607,0.000008083572,0.7967983,0.1272394,0.059126407,0.0032404116,0.00020041899],"about_ca_topic_score_codex":0.000008158996,"about_ca_topic_score_gemma":0.00000195602,"teacher_disagreement_score":0.8885193,"about_ca_system_score_codex":0.000009787281,"about_ca_system_score_gemma":0.000027420516,"threshold_uncertainty_score":0.22556762},"labels":[],"label_agreement":null},{"id":"W2549542439","doi":"10.1109/isi.2016.7745453","title":"Activity monitoring using topic models","year":2016,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Categorical variable; Computer science; Data mining; Set (abstract data type); Window (computing); Data set; Dimensionality reduction; Task (project management); Pattern recognition (psychology); Support vector machine; Artificial intelligence; Machine learning","score_opus":0.07190127207603408,"score_gpt":0.29463795907377194,"score_spread":0.22273668699773785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2549542439","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":[0.10445202,0.0000034094626,0.89143825,0.00036165104,0.000048769576,0.00003860106,1.3183715e-7,0.00028047958,0.0033766825],"genre_scores_gemma":[0.87605053,0.000005663066,0.12295803,0.000013907146,0.000038697908,0.000008712836,3.822026e-9,0.000001899756,0.0009225337],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996684,0.000006466794,0.000046844732,0.00013510179,0.000057956862,0.0000851935],"domain_scores_gemma":[0.9996574,0.000014796307,0.00001959063,0.000258696,0.00002067091,0.000028872582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003822888,0.000037418235,0.00003605462,0.00002586518,0.00007080638,0.000028371669,0.0002040595,0.000021683052,0.000008502833],"category_scores_gemma":[0.0000015461356,0.00002421867,0.000022283795,0.000096571064,0.000008641242,0.0004609393,0.000087070955,0.000020242196,0.000011001728],"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":[5.56612e-7,0.00002160242,0.00063996064,0.0000013528644,0.0000032516955,5.1856006e-7,0.000022835002,0.00004172035,0.19117007,0.23981884,0.00004315104,0.5682362],"study_design_scores_gemma":[0.00009019462,0.000020891752,0.001845473,0.0000128364,0.0000016080483,0.0000075552616,0.0000031043585,0.121214665,0.81528574,0.05998377,0.0013865954,0.00014755514],"about_ca_topic_score_codex":0.000034583612,"about_ca_topic_score_gemma":4.4639754e-7,"teacher_disagreement_score":0.7715985,"about_ca_system_score_codex":0.000032086195,"about_ca_system_score_gemma":0.00001088618,"threshold_uncertainty_score":0.09876087},"labels":[],"label_agreement":null},{"id":"W2557372423","doi":"10.1109/iemcon.2016.7746271","title":"An overview of path tortuosity measures for tracking and monitoring","year":2016,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Simon Fraser University","funders":"","keywords":"Tortuosity; Measure (data warehouse); Computer science; Path (computing); Set (abstract data type); Tracking (education); Data mining; Engineering","score_opus":0.10493863629197944,"score_gpt":0.34541798023080267,"score_spread":0.24047934393882323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2557372423","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":[0.036529433,0.00012873113,0.9628933,0.00013763859,0.00002048521,0.00009016606,0.0000012741727,0.00010300264,0.0000959506],"genre_scores_gemma":[0.914533,0.00012988376,0.08523733,0.0000124296275,0.000022575272,0.000023169701,3.4574906e-8,0.000001919716,0.000039662915],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99968666,0.000006953058,0.00007901961,0.00011991153,0.000051347433,0.00005612529],"domain_scores_gemma":[0.9996707,0.000028661516,0.000033580483,0.00018416563,0.00005333871,0.000029574758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011848729,0.00003330451,0.000052446747,0.000016683176,0.000050908828,0.000019310377,0.00014406932,0.000018222669,0.000001639931],"category_scores_gemma":[0.0000059010445,0.000021639973,0.000019921164,0.000048074304,0.000011648258,0.00023259346,0.000025488047,0.000009790331,2.6863677e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010597296,0.000019935203,0.0041229604,0.0000097909015,0.0000024935223,4.289092e-8,0.0000410278,1.826514e-7,0.083452016,0.11377808,0.000028403707,0.798544],"study_design_scores_gemma":[0.00017587493,0.00019189066,0.05623753,0.000057691996,0.000005200823,0.0000033575368,0.000017868766,0.0012721209,0.91833705,0.018624827,0.0049401917,0.000136385],"about_ca_topic_score_codex":0.000015698863,"about_ca_topic_score_gemma":0.0000018199736,"teacher_disagreement_score":0.87800354,"about_ca_system_score_codex":0.000006904934,"about_ca_system_score_gemma":0.0000067183364,"threshold_uncertainty_score":0.08824525},"labels":[],"label_agreement":null},{"id":"W2562370852","doi":"10.1109/dsaa.2016.8","title":"On the Evaluation of Outlier Detection and One-Class Classification Methods","year":2016,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":48,"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":"","keywords":"Anomaly detection; Class (philosophy); Computer science; Outlier; Artificial intelligence; One-class classification; Pattern recognition (psychology); Data mining; Machine learning; Support vector machine","score_opus":0.12792077506755628,"score_gpt":0.3818108824604719,"score_spread":0.2538901073929156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2562370852","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":[0.01947334,0.000007402742,0.9685897,0.004181647,0.000033918033,0.00026553348,4.1070916e-7,0.00009657265,0.0073514455],"genre_scores_gemma":[0.953232,0.0000118571525,0.046258643,0.00010436907,0.000011168494,0.00013999682,7.895804e-8,0.0000030237627,0.00023888913],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99919283,0.00020330456,0.00014231577,0.0001857522,0.00021418741,0.000061590414],"domain_scores_gemma":[0.99901706,0.00027866612,0.00009921339,0.00040478984,0.00017942025,0.000020866977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015013461,0.000050490456,0.000053278764,0.00005928594,0.00009486862,0.000024342164,0.00017808679,0.00004049781,0.00004588727],"category_scores_gemma":[0.00012385058,0.000026438263,0.000024201574,0.00019667788,0.000046221026,0.0001383848,0.000040837647,0.000035933954,0.0000132716705],"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":[9.458039e-7,0.000011181842,0.0000075570824,5.0206626e-7,0.0000025633963,1.6071666e-9,0.000015642354,5.635775e-7,0.10500064,0.34155834,0.000037071393,0.553365],"study_design_scores_gemma":[0.00018252071,0.0001117689,0.017004859,0.00001176165,0.000017618302,0.0000018071129,0.000027423328,0.12576428,0.62524396,0.22959502,0.0019482346,0.000090700545],"about_ca_topic_score_codex":0.00000719981,"about_ca_topic_score_gemma":0.000005575102,"teacher_disagreement_score":0.9337586,"about_ca_system_score_codex":0.00004056036,"about_ca_system_score_gemma":0.000022946011,"threshold_uncertainty_score":0.10781211},"labels":[],"label_agreement":null},{"id":"W2564529810","doi":"10.1109/crv.2016.14","title":"Computer Vision-Based Detection of Violent Individual Actions Witnessed by Crowds","year":2016,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Crowds; Computer science; Upload; Reliability (semiconductor); Artificial intelligence; Computer vision; Exploit; Crowd psychology; Action (physics); Crowd simulation; Human–computer interaction; Motion (physics); Amateur; Computer security; Machine learning; World Wide Web","score_opus":0.012646509587546281,"score_gpt":0.2577765590610312,"score_spread":0.2451300494734849,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2564529810","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":[0.021273972,0.0000069501516,0.97664416,0.0010839074,0.00009904963,0.0001588553,0.000011090769,0.000407033,0.00031500155],"genre_scores_gemma":[0.9638782,0.00000525723,0.03561565,0.00020030557,0.000029952915,0.00006334917,0.0000017152654,0.000006674851,0.0001988626],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99911785,0.000031179905,0.0002306819,0.00027939747,0.00020076016,0.0001401074],"domain_scores_gemma":[0.9991821,0.00009268039,0.00011602334,0.0004380182,0.00010455099,0.000066625864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012816829,0.00009971059,0.000105339466,0.00011926666,0.0001298099,0.000044360797,0.000413776,0.00007105969,0.000057318834],"category_scores_gemma":[0.0000049780556,0.00006825017,0.0000735159,0.00035510265,0.00006617841,0.00026389092,0.00009059102,0.000052742755,0.000030123407],"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":[0.000007381882,0.00021631607,0.00012907897,0.000006074687,0.00001606299,2.9572357e-7,0.000017664026,0.000026007529,0.44655854,0.010270572,0.0040027066,0.53874934],"study_design_scores_gemma":[0.00035721558,0.00034922396,0.0028378237,0.000024326244,0.0000070575443,0.0000044522258,0.0000038718103,0.017606612,0.9616869,0.0011183688,0.015836785,0.0001673468],"about_ca_topic_score_codex":0.00003262548,"about_ca_topic_score_gemma":0.0000065494523,"teacher_disagreement_score":0.94260424,"about_ca_system_score_codex":0.000035977595,"about_ca_system_score_gemma":0.000034808032,"threshold_uncertainty_score":0.2783161},"labels":[],"label_agreement":null},{"id":"W2572309888","doi":"","title":"Propositionalization for Unsupervised Outlier Detection in Multi-Relational Data.","year":2016,"lang":"en","type":"article","venue":"The Florida AI Research Society","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Anomaly detection; Outlier; Artificial intelligence; Pattern recognition (psychology); Data mining","score_opus":0.20858490883839576,"score_gpt":0.416416922956498,"score_spread":0.20783201411810226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2572309888","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":[0.0025273957,0.000027023136,0.984856,0.01139355,0.00007726665,0.0008988871,0.00003661419,0.00013257377,0.000050666233],"genre_scores_gemma":[0.9304666,0.00009320067,0.06607044,0.00036780728,0.00028310393,0.0012106876,0.000044420838,0.000018446302,0.001445296],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985467,0.00013368342,0.00019766066,0.00040251395,0.0004371085,0.0002822893],"domain_scores_gemma":[0.99835426,0.0003965171,0.000039121198,0.00077654433,0.000385407,0.000048126723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020742482,0.0000792074,0.00007100993,0.00006556517,0.0005874028,0.00009223368,0.0009752111,0.000082789964,0.000017817949],"category_scores_gemma":[0.00012397021,0.00004815913,0.00006744128,0.0007196962,0.00014686913,0.0006816786,0.00037247347,0.00019604938,0.000036638063],"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":[0.0001091917,0.000610718,0.002186373,0.00009114601,0.00012291066,0.0000012532361,0.0029244123,0.0003722385,0.12692948,0.5189866,0.056450106,0.2912156],"study_design_scores_gemma":[0.0014470498,0.00013470321,0.011357748,0.00004786384,0.000005919673,0.000006639431,0.000111877394,0.846819,0.027143888,0.04162199,0.07104548,0.00025785476],"about_ca_topic_score_codex":0.000040708797,"about_ca_topic_score_gemma":0.00003641202,"teacher_disagreement_score":0.9279392,"about_ca_system_score_codex":0.00018808397,"about_ca_system_score_gemma":0.00013800673,"threshold_uncertainty_score":0.45178866},"labels":[],"label_agreement":null},{"id":"W2573118261","doi":"","title":"Branch Line Analysis of Faults and Fractures","year":2016,"lang":"en","type":"article","venue":"50th U.S. Rock Mechanics/Geomechanics Symposium","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"ConocoPhillips (Canada)","funders":"","keywords":"Geology; Line (geometry); Forensic engineering; Engineering; Mathematics; Geometry","score_opus":0.008914010773033193,"score_gpt":0.24295843076067486,"score_spread":0.23404441998764167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2573118261","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":[0.0060035093,0.00016304234,0.9908512,0.0017427407,0.00020394067,0.0003446807,0.000072917384,0.0004137719,0.00020416082],"genre_scores_gemma":[0.991668,0.0006591404,0.006710972,0.00035243222,0.00006414974,0.00008815635,0.000009853153,0.000029331812,0.0004179714],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99760485,0.00006464831,0.00062613754,0.00084446435,0.0004177088,0.00044216771],"domain_scores_gemma":[0.99767345,0.00015409102,0.00042661492,0.0012282742,0.0002973748,0.00022017721],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039724782,0.0003279408,0.0005647152,0.0007226374,0.00023373697,0.000059296424,0.0010004838,0.00026144474,0.00007793233],"category_scores_gemma":[0.000043031123,0.0002511208,0.0002688819,0.0019412091,0.000009370309,0.0004132861,0.0005955168,0.00016746276,0.000026016121],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022795066,0.00015747124,0.00002745811,0.00003049571,0.00043322617,0.000002872182,0.00019840212,0.0008703079,0.49683338,0.48013726,0.00021834149,0.021067964],"study_design_scores_gemma":[0.00068618637,0.0005272057,0.00006897747,0.00006346345,0.00048452782,0.000024870516,0.000023255048,0.428542,0.44259644,0.11784802,0.008436373,0.0006986961],"about_ca_topic_score_codex":0.000040481493,"about_ca_topic_score_gemma":0.000033825745,"teacher_disagreement_score":0.9856645,"about_ca_system_score_codex":0.000068709705,"about_ca_system_score_gemma":0.000069757225,"threshold_uncertainty_score":0.9999941},"labels":[],"label_agreement":null},{"id":"W2583684061","doi":"10.1109/bigdata.2016.7840763","title":"Hidden Markov based anomaly detection for water supply systems","year":2016,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"SCADA; Anomaly detection; Computer science; Hidden Markov model; Water supply; Vulnerability (computing); Hierarchy; Data mining; Risk analysis (engineering); Computer security; Artificial intelligence; Engineering; Business","score_opus":0.01004811144963864,"score_gpt":0.2214728399075394,"score_spread":0.21142472845790078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2583684061","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":[0.007282203,0.0000069340294,0.9882364,0.0020275395,0.00015998453,0.0004720091,0.000005310877,0.0006504824,0.0011590818],"genre_scores_gemma":[0.93052834,0.0000018738809,0.06387084,0.00013701885,0.00007372104,0.0006144443,0.0000012554184,0.00000935925,0.004763162],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991881,0.000021363125,0.00017551119,0.0003008469,0.000098990284,0.00021516239],"domain_scores_gemma":[0.9993046,0.000063771884,0.00003849477,0.00043710932,0.000096073,0.00005996329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018907008,0.00009288524,0.00008923847,0.00008923594,0.00014706569,0.00010046064,0.00035281447,0.00006368543,0.000050044207],"category_scores_gemma":[0.0000061119326,0.00005012108,0.000073306896,0.00010541437,0.000019365918,0.00026647103,0.000053319087,0.000025065245,0.00009748132],"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":[0.000021521839,0.000064335276,0.00036495845,0.000029745943,0.000016025055,9.607224e-7,0.000025266085,0.0000033526103,0.43294153,0.038413726,0.0051655825,0.52295303],"study_design_scores_gemma":[0.00033924132,0.00016384796,0.0005643244,0.00001155528,0.000004739639,0.000010014305,0.0000051490665,0.03380935,0.8790311,0.001576191,0.084299855,0.00018463562],"about_ca_topic_score_codex":0.000050879265,"about_ca_topic_score_gemma":0.000011388306,"teacher_disagreement_score":0.92436564,"about_ca_system_score_codex":0.000051715146,"about_ca_system_score_gemma":0.000015948877,"threshold_uncertainty_score":0.20438784},"labels":[],"label_agreement":null},{"id":"W2585917148","doi":"10.7287/peerj.preprints.2670v1","title":"Signature-based detection of behavioural deviations in flight simulators - Experiments on FlightGear and JSBSim","year":2016,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Crew; Computer science; Flight simulator; Signature (topology); Simulation; Event (particle physics); Real-time computing; Reliability engineering; Engineering; Aeronautics","score_opus":0.022534023519725624,"score_gpt":0.2591032350340215,"score_spread":0.23656921151429589,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2585917148","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":[0.3564678,0.000014948587,0.6425082,0.00035537165,0.000033367392,0.00017037007,0.0000015412976,0.000119308046,0.0003290665],"genre_scores_gemma":[0.99457693,0.0000033451736,0.005116256,0.00010454212,0.000006927696,0.000053445827,3.5480687e-7,0.000004977646,0.00013324083],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993462,0.000024784931,0.00018243978,0.00022156996,0.000119181466,0.000105828236],"domain_scores_gemma":[0.99952996,0.00006561402,0.00006616205,0.00025544714,0.00003902501,0.00004381993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007653758,0.00008134789,0.00008740262,0.0001778015,0.00006147539,0.00001820771,0.00015949606,0.00006690099,0.000016824464],"category_scores_gemma":[0.0000101507885,0.000055858964,0.000032170086,0.00027481,0.00002944415,0.0001731506,0.000036521127,0.000049070393,0.0000069741163],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035527082,0.0004880095,0.012051951,0.000009821391,0.0000129159625,0.0000024673702,0.0002080525,0.000114874,0.78657836,0.101562485,0.000119978315,0.098815575],"study_design_scores_gemma":[0.00035932596,0.00015056272,0.015867697,0.000018025547,0.0000020209695,6.59201e-7,0.0000058487863,0.0067965183,0.97539574,0.00079926336,0.00050476444,0.00009954783],"about_ca_topic_score_codex":0.000032864544,"about_ca_topic_score_gemma":0.000018771801,"teacher_disagreement_score":0.6381091,"about_ca_system_score_codex":0.00004640739,"about_ca_system_score_gemma":0.000015572381,"threshold_uncertainty_score":0.22778624},"labels":[],"label_agreement":null},{"id":"W2589440298","doi":"10.1038/srep43167","title":"Robust High-dimensional Bioinformatics Data Streams Mining by ODR-ioVFDT","year":2017,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Fundo para o Desenvolvimento das Ciências e da Tecnologia; Universidade de Macau","keywords":"Computer science; Data mining; Outlier; Data stream mining; Identification (biology); Decision tree; Anomaly detection; Set (abstract data type); Machine learning; Artificial intelligence","score_opus":0.04870537857117124,"score_gpt":0.27216969148588654,"score_spread":0.2234643129147153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2589440298","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":[0.07290014,0.0000612228,0.91375226,0.0014814362,0.0054784548,0.00039815228,0.000043189746,0.0005931824,0.0052919425],"genre_scores_gemma":[0.6710982,0.0000035618714,0.32198423,0.00008244864,0.00007063033,0.000024857829,0.00023343523,0.000011307233,0.0064913547],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99797994,0.000011629833,0.00045468067,0.000785785,0.0005011882,0.00026679045],"domain_scores_gemma":[0.9930115,0.000019823723,0.00064780813,0.00604864,0.00013768424,0.00013457102],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0011140766,0.00012701747,0.00013513413,0.000088584005,0.0018249842,0.0019442344,0.0020007212,0.00006337139,0.000040104038],"category_scores_gemma":[0.00010979391,0.00011328683,0.000042303007,0.00020418322,0.00025973533,0.0016541794,0.0015805099,0.00009537894,0.000047902544],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011353297,0.00011879536,0.00089269987,0.000014298155,0.000019811072,0.000077380275,0.0001413647,0.00016142151,0.0035332716,0.0030925535,0.81396604,0.17798124],"study_design_scores_gemma":[0.00020968617,0.000058551665,0.0014044759,0.00007271565,0.00002490014,0.00061756076,0.000057282432,0.55665165,0.04082204,0.013265578,0.38609624,0.00071929453],"about_ca_topic_score_codex":0.00008942382,"about_ca_topic_score_gemma":0.000014188728,"teacher_disagreement_score":0.59819806,"about_ca_system_score_codex":0.000027001211,"about_ca_system_score_gemma":0.00012715474,"threshold_uncertainty_score":0.9994745},"labels":[],"label_agreement":null},{"id":"W2592340788","doi":"10.24963/ijcai.2017/497","title":"Deep Forest: Towards An Alternative to Deep Neural Networks","year":2017,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":900,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Novelis (Canada)","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Deep neural networks; Deep learning; Artificial neural network; Machine learning; Process (computing); Contrast (vision); Decision tree; Scale (ratio); Range (aeronautics); Training set; Training (meteorology); Engineering","score_opus":0.02349395584424253,"score_gpt":0.30188753147887787,"score_spread":0.27839357563463535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592340788","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":[0.0073753833,0.0000061181113,0.9798175,0.001581401,0.00014845452,0.0001935119,2.7015403e-7,0.00031952315,0.010557827],"genre_scores_gemma":[0.89729244,0.0000030819244,0.10129199,0.0008263561,0.00014423355,0.0000788352,7.097738e-7,0.000006348358,0.00035601744],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923885,0.000014253714,0.00011613135,0.00031698914,0.00011420004,0.000199602],"domain_scores_gemma":[0.99860674,0.00000950966,0.000074912736,0.0010743245,0.00007361527,0.0001609103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000094403214,0.00009392325,0.00008522365,0.000042473286,0.0004905269,0.00046859292,0.0015964241,0.000041455463,0.000025795936],"category_scores_gemma":[0.000013148249,0.00008060162,0.000044843742,0.00008199137,0.000033952398,0.000619127,0.00042157454,0.000085536136,0.000028450975],"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":[0.000003354879,0.00004059411,0.000974833,8.615774e-7,0.000006572957,0.000004801013,0.00019844635,0.011512922,0.000034055553,0.22180532,0.00027975187,0.7651385],"study_design_scores_gemma":[0.000055273547,0.00011232929,0.015861467,0.0000010599699,0.0000014991692,0.0000068998634,0.0000087882345,0.9735043,0.0007462307,0.008009483,0.001574138,0.00011853498],"about_ca_topic_score_codex":0.00039027588,"about_ca_topic_score_gemma":0.00052908977,"teacher_disagreement_score":0.96199137,"about_ca_system_score_codex":0.000021938073,"about_ca_system_score_gemma":0.0000070631627,"threshold_uncertainty_score":0.45186523},"labels":[],"label_agreement":null},{"id":"W2594434602","doi":"10.1016/j.enbuild.2017.02.058","title":"An ensemble learning framework for anomaly detection in building energy consumption","year":2017,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":251,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Anomaly detection; Sliding window protocol; Anomaly (physics); Majority rule; Computer science; Classifier (UML); Data mining; Energy consumption; Voting; Artificial intelligence; Machine learning; Engineering; Window (computing); Operating system","score_opus":0.01616124286566902,"score_gpt":0.2885836997072087,"score_spread":0.2724224568415397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2594434602","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":[0.18932219,0.00007447469,0.810064,0.00014683031,0.00007941684,0.00004228249,3.781148e-7,0.00016214635,0.00010827418],"genre_scores_gemma":[0.8476811,0.00015213361,0.15177321,0.000097954515,0.00008978018,0.00010332715,9.926113e-7,0.00000985636,0.000091615584],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991196,0.000027780561,0.00016290417,0.00040333444,0.00007615254,0.0002102635],"domain_scores_gemma":[0.99924934,0.00007400089,0.00017505918,0.00039270706,0.000038288283,0.00007062919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022442915,0.0001162689,0.00012473635,0.00013208526,0.00091902283,0.00037622728,0.00040340354,0.00014803367,0.0000032926441],"category_scores_gemma":[0.000046224734,0.00012386004,0.00004116588,0.00008052785,0.00005352049,0.000703829,0.00010241792,0.000108612156,4.1606626e-7],"study_design_candidate":"theoretical_or_conceptual","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":[0.000009448908,0.00001953301,0.0009528971,0.0000045960755,0.0000033810243,7.0569246e-7,0.000039554034,0.00009305017,0.059659712,0.62829894,0.0000062750632,0.31091192],"study_design_scores_gemma":[0.00041815828,0.00036863916,0.00983516,0.000078648656,0.000009967218,0.00003346177,0.000024168556,0.13764723,0.5567805,0.22183248,0.0725062,0.00046533864],"about_ca_topic_score_codex":0.0005164793,"about_ca_topic_score_gemma":0.00010931747,"teacher_disagreement_score":0.65835893,"about_ca_system_score_codex":0.00003093587,"about_ca_system_score_gemma":0.000011191887,"threshold_uncertainty_score":0.70684737},"labels":[],"label_agreement":null},{"id":"W2605703804","doi":"","title":"Keystone rescue techniques","year":2004,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Keystone species; Biology","score_opus":0.10363478090037884,"score_gpt":0.35601805817508964,"score_spread":0.25238327727471077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605703804","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":[0.0010205697,0.000007001769,0.9583215,0.006407953,0.00008679027,0.00023210878,0.0000050662334,0.00062837143,0.03329064],"genre_scores_gemma":[0.9229295,0.000025056042,0.07585459,0.0007292086,0.0000969701,0.00013213548,0.0000038779813,0.0000074455947,0.00022117053],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984673,0.000027841563,0.00033729424,0.00043286177,0.00052962394,0.00020503665],"domain_scores_gemma":[0.9988904,0.000040993844,0.00008436984,0.00034413632,0.00055352406,0.00008657542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031910455,0.00014402931,0.000117629395,0.000212649,0.0002493033,0.00019091406,0.0007994949,0.000099997524,0.00010999439],"category_scores_gemma":[0.00010028473,0.00014192708,0.00006572117,0.0005918137,0.00010864623,0.00031018452,0.000097441276,0.0002256491,0.0005892319],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000042470406,0.000086460735,8.3947094e-7,0.0000012399448,0.0000024186168,0.0000013793108,0.000049946488,0.0003020706,0.0037527578,0.89908075,0.0000642884,0.09665362],"study_design_scores_gemma":[0.000009645849,0.000105329564,0.000037650974,0.000018460549,9.2617864e-7,0.0000070915403,0.000020806347,0.007784896,0.35370895,0.6366261,0.0015360996,0.00014400146],"about_ca_topic_score_codex":0.00006372756,"about_ca_topic_score_gemma":0.000036632126,"teacher_disagreement_score":0.921909,"about_ca_system_score_codex":0.00016369032,"about_ca_system_score_gemma":0.00025385135,"threshold_uncertainty_score":0.757358},"labels":[],"label_agreement":null},{"id":"W26074071","doi":"10.1016/j.celrep.2015.04.061","title":"Machine learning approaches to network anomaly detection","year":2007,"lang":"en","type":"article","venue":"USENIX workshop on Tackling computer systems problems with machine learning techniques","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":131,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Institute of General Medical Sciences; Howard Hughes Medical Institute","keywords":"Anomaly detection; Computer science; Machine learning; Kernel (algebra); Artificial intelligence; Anomaly (physics); Block (permutation group theory); Data mining","score_opus":0.03239857703943934,"score_gpt":0.2381682372014419,"score_spread":0.20576966016200254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W26074071","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":[0.0023941507,0.00037633418,0.9843871,0.0005489136,0.00043493923,0.0019700804,0.0000018718616,0.008473693,0.0014129198],"genre_scores_gemma":[0.6390744,0.000046325928,0.35815534,0.00023032488,0.0009890781,0.00039066598,0.00002161021,0.00012715108,0.0009651046],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99499077,0.00043631607,0.0010777141,0.0015832531,0.0007444901,0.0011674863],"domain_scores_gemma":[0.9969526,0.00047857434,0.00075079,0.0011986411,0.00022325513,0.00039613145],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0030729366,0.00078593637,0.000742066,0.0008468066,0.0012692873,0.0009195099,0.0013900708,0.00039346775,0.00000405668],"category_scores_gemma":[0.000053215255,0.00068610965,0.00019741216,0.0024281235,0.00007571506,0.0004941906,0.0006226154,0.0020283486,0.000046687415],"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":[0.00019057632,0.00032244693,0.0095063215,0.00022242604,0.00016749826,0.00006788571,0.0012794575,0.58543205,0.0011296893,0.02536673,0.0004680639,0.3758469],"study_design_scores_gemma":[0.0003971278,0.0021226325,0.00039061834,0.0010436178,0.000031686384,0.00032203636,0.00003287514,0.8024523,0.005449722,0.00035760377,0.18607135,0.0013283897],"about_ca_topic_score_codex":0.00042631198,"about_ca_topic_score_gemma":0.00017839238,"teacher_disagreement_score":0.63668025,"about_ca_system_score_codex":0.00027259314,"about_ca_system_score_gemma":0.000047653095,"threshold_uncertainty_score":0.999559},"labels":[],"label_agreement":null},{"id":"W2608660467","doi":"","title":"Evaluation of Internal Leak Detection Techniques","year":2015,"lang":"en","type":"article","venue":"PSIG Annual Meeting","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"TransCanada (Canada)","funders":"","keywords":"Leak; Leak detection; Computer science; Risk analysis (engineering); Business; Engineering","score_opus":0.044679181878038425,"score_gpt":0.31917667725898397,"score_spread":0.27449749538094553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2608660467","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":[0.035840176,0.00007499931,0.9460198,0.0001166252,0.00011229616,0.0002148147,0.0000016754694,0.0004251578,0.017194439],"genre_scores_gemma":[0.96672094,0.0000024043698,0.033030678,0.000038338003,0.000081255064,0.00007774755,3.9446144e-7,0.000005866768,0.00004238499],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988142,0.00012566401,0.000236146,0.00019829728,0.0005140551,0.00011159869],"domain_scores_gemma":[0.9985492,0.000028364853,0.00017119742,0.0002490456,0.00094583054,0.000056309524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024327226,0.00007272328,0.00008777078,0.00011419615,0.00006300181,0.000033736123,0.00031230974,0.00005210886,0.0000033542271],"category_scores_gemma":[0.00024583863,0.000071095965,0.00004091008,0.00030686005,0.000026212621,0.00033782088,0.00011982058,0.000089035166,0.000010351183],"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":[0.0000048938145,0.00004774775,0.00021528921,0.0000056796525,0.000010790755,2.898876e-7,0.00090181874,0.00013158211,0.022041662,0.0029097903,0.00041137228,0.9733191],"study_design_scores_gemma":[0.0001858849,0.00025260096,0.00035658246,0.000046587258,0.00002587529,0.000022213291,0.00044657904,0.09980031,0.8759049,0.015125962,0.0076679443,0.00016457075],"about_ca_topic_score_codex":0.00017449897,"about_ca_topic_score_gemma":0.000046595203,"teacher_disagreement_score":0.9731545,"about_ca_system_score_codex":0.00009280375,"about_ca_system_score_gemma":0.00006285933,"threshold_uncertainty_score":0.28992093},"labels":[],"label_agreement":null},{"id":"W2609731728","doi":"10.1109/access.2017.2696365","title":"Machine Learning With Big Data: Challenges and Approaches","year":2017,"lang":"en","type":"article","venue":"IEEE Access","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1022,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Big data; Computer science; Data science; Artificial intelligence; Machine learning; Context (archaeology); Variety (cybernetics); Field (mathematics); Process (computing); Domain (mathematical analysis); Grand Challenges; Set (abstract data type); Data mining","score_opus":0.30558686274291835,"score_gpt":0.32949277619839307,"score_spread":0.023905913455474714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2609731728","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":[0.010249262,0.0011354309,0.9758743,0.002599366,0.00008822647,0.00014479792,0.000002914641,0.0002775511,0.009628154],"genre_scores_gemma":[0.99190694,0.00086655124,0.0068925316,0.000029037385,0.00009733059,0.000027187722,0.0000015161762,0.000005623161,0.00017328035],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994412,0.00001233431,0.00005801384,0.00031876587,0.00007689354,0.00009280398],"domain_scores_gemma":[0.9987119,0.000016580272,0.000095541676,0.0011215582,0.00001641945,0.000038045473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013134224,0.00006617471,0.00007292853,0.000029990277,0.00043321113,0.0005191361,0.0018233235,0.00002653622,6.892549e-7],"category_scores_gemma":[0.000008353911,0.000050330877,0.00000686581,0.000032769563,0.00006269486,0.0009854877,0.0006488181,0.00009980653,0.0000027542535],"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":[0.0000018428116,0.000016952252,0.0013921413,0.0000112487205,0.000008066855,0.0000021120554,0.00008153314,0.000008828647,0.000032775748,0.009304626,0.00004914861,0.98909074],"study_design_scores_gemma":[0.0013831754,0.0005265417,0.25055763,0.00013301983,0.00005916715,0.00023936637,0.00013793442,0.39989668,0.023225773,0.021201536,0.30113488,0.0015042732],"about_ca_topic_score_codex":0.00008148922,"about_ca_topic_score_gemma":0.0000910068,"teacher_disagreement_score":0.98758644,"about_ca_system_score_codex":0.000003551781,"about_ca_system_score_gemma":0.000010229847,"threshold_uncertainty_score":0.50060415},"labels":[],"label_agreement":null},{"id":"W2610606565","doi":"10.1117/12.2262037","title":"Deep learning on temporal-spectral data for anomaly detection","year":2017,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Anomaly Detection Techniques and Applications","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":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Anomaly detection; Computer science; Deep learning; Anomaly (physics); Artificial intelligence; Time series; Pipeline (software); Pattern recognition (psychology); Artificial neural network; Series (stratigraphy); Data modeling; Machine learning; Geology","score_opus":0.020493157885133187,"score_gpt":0.2598653978385564,"score_spread":0.23937223995342322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2610606565","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":[0.950444,0.0000279502,0.043919686,0.0029202653,0.00020806151,0.000699094,0.000026329852,0.0002279331,0.001526724],"genre_scores_gemma":[0.718323,0.00004131376,0.28069717,0.00005775817,0.00036630328,0.0002527849,0.000008383777,0.000034330562,0.00021896942],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820226,1.2875066e-8,0.0004728459,0.0005740991,0.00042772203,0.00032302857],"domain_scores_gemma":[0.99802566,0.000108365035,0.0005974589,0.00026007165,0.000914166,0.00009430878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006857902,0.00023906298,0.00027589314,0.00008801707,0.00046196836,0.0004234456,0.0033151633,0.00015712187,0.0000026780137],"category_scores_gemma":[0.00060108025,0.00020933643,0.00039129925,0.00015951358,0.00015547652,0.0011845105,0.0004996336,0.00030371654,0.0000015689499],"study_design_candidate":"theoretical_or_conceptual","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":[0.00005460307,0.00010196083,0.00031373533,0.00014860951,0.00017006882,4.3629843e-8,0.00006783677,0.00009466446,0.256402,0.7346143,0.0010220809,0.007010124],"study_design_scores_gemma":[0.00095651246,0.0008810212,0.003086057,0.00015476519,0.00008718257,0.000017626593,0.00026740183,0.6699678,0.29550362,0.0069170375,0.021673622,0.00048739652],"about_ca_topic_score_codex":0.000019222878,"about_ca_topic_score_gemma":6.1213973e-7,"teacher_disagreement_score":0.72769725,"about_ca_system_score_codex":0.00010674589,"about_ca_system_score_gemma":0.000021862368,"threshold_uncertainty_score":0.8536492},"labels":[],"label_agreement":null},{"id":"W2613852406","doi":"10.1145/3041021.3054929","title":"On Recognizing Abnormal Human Behaviours by Data Stream Mining with Misclassified Recalls","year":2017,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Squatting position; Computer science; Wearable computer; Sitting; Falling (accident); Enabling; Artificial intelligence; Class (philosophy); Machine learning; Psychology; Medicine","score_opus":0.07562221435511085,"score_gpt":0.3209373256169884,"score_spread":0.24531511126187755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2613852406","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":[0.19622912,0.000006976067,0.7351327,0.0019743699,0.00007016083,0.00029368856,0.000054555927,0.0006928248,0.06554558],"genre_scores_gemma":[0.90790313,0.000003010179,0.08781725,0.0001784203,0.000030081132,0.000027462296,0.000044388715,0.000011346684,0.0039849244],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99900013,0.000013959245,0.0001442082,0.0004906543,0.00015970992,0.00019131889],"domain_scores_gemma":[0.9973545,0.000025003616,0.00016647427,0.0023323519,0.000037242342,0.00008442992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017270214,0.00011392916,0.00009991505,0.000039770453,0.0009076235,0.0004980812,0.002164383,0.000055182692,0.000036651163],"category_scores_gemma":[0.0000124816015,0.000091400405,0.000019408955,0.000058385827,0.00006356558,0.0006523814,0.00046449056,0.00012068515,0.000016033595],"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":[0.000026137652,0.0006237213,0.010388013,0.000010452514,0.00006200252,0.000029885288,0.00023260544,0.000005630684,0.007337706,0.15005492,0.22057068,0.6106582],"study_design_scores_gemma":[0.006602401,0.0070193205,0.1896329,0.0009119916,0.00026899957,0.00031942147,0.0007074809,0.11164611,0.52607113,0.014528479,0.13560945,0.006682335],"about_ca_topic_score_codex":0.00024957393,"about_ca_topic_score_gemma":0.00014092388,"teacher_disagreement_score":0.711674,"about_ca_system_score_codex":0.000019478714,"about_ca_system_score_gemma":0.000022829536,"threshold_uncertainty_score":0.6980798},"labels":[],"label_agreement":null},{"id":"W2615402609","doi":"","title":"Frustration: a generic mechanism to improve autonomy in robotics.","year":2013,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"NeuroDevNet","funders":"","keywords":"Novelty; Computer science; Mechanism (biology); Autonomy; Focus (optics); Robot; Artificial intelligence; Human–computer interaction; Simple (philosophy); Novelty detection; Controller (irrigation); Robotics; Architecture; Distributed computing; Machine learning; Psychology","score_opus":0.014052394475866717,"score_gpt":0.22371776508322644,"score_spread":0.20966537060735974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2615402609","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007035865,0.00009100742,0.9523316,0.023072055,0.0002050956,0.0010706707,0.000012455941,0.0005692727,0.015611964],"genre_scores_gemma":[0.338861,0.00006823631,0.65133035,0.00030738127,0.000029247316,0.0009393751,0.00004873964,0.000030292507,0.008385378],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99638176,0.0011677733,0.0006325049,0.0011109764,0.0003205327,0.00038648097],"domain_scores_gemma":[0.99504197,0.0002721897,0.00039225238,0.002914247,0.0011347001,0.00024462907],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0020653438,0.00033873913,0.0003419319,0.000352629,0.00023752791,0.00079211855,0.0024465963,0.0003291059,0.00006910637],"category_scores_gemma":[0.00019096686,0.0003834046,0.00015974784,0.000764895,0.000063396015,0.00027201205,0.0023919602,0.0006270423,0.00015919915],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000010265268,0.0002618184,0.000060861676,0.00004015133,0.000017284632,0.0000021538622,0.0026493107,0.00040805322,0.004325848,0.9211296,0.0009148068,0.0701891],"study_design_scores_gemma":[0.0005595698,0.0000029331277,0.0023206323,0.0009074347,0.00002967344,0.000019282143,0.0000860699,0.4666143,0.21498394,0.28026,0.032680716,0.0015354303],"about_ca_topic_score_codex":0.0015163614,"about_ca_topic_score_gemma":0.00055151136,"teacher_disagreement_score":0.64086956,"about_ca_system_score_codex":0.00029274254,"about_ca_system_score_gemma":0.00050262234,"threshold_uncertainty_score":0.9998618},"labels":[],"label_agreement":null},{"id":"W2615609169","doi":"10.1016/j.inffus.2017.04.005","title":"Big data fusion in Internet of Things","year":2017,"lang":"en","type":"article","venue":"Information Fusion","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"National Key Research and Development Program of China; Higher Education Discipline Innovation Project; Academy of Finland; National Natural Science Foundation of China","keywords":"Computer science; Machine learning; Artificial intelligence; Sensor fusion; Big data; Field (mathematics); Raw data; Probabilistic logic; Deep learning; Data mining","score_opus":0.04435548282382823,"score_gpt":0.2797193089688706,"score_spread":0.23536382614504237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2615609169","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":[0.032983705,0.0000053596177,0.9521476,0.00068831,0.00018668191,0.00015305297,0.0000036387316,0.000091840164,0.013739808],"genre_scores_gemma":[0.9805189,0.000034271656,0.019114656,0.00017796151,0.000015356125,0.000008503258,0.000029692459,0.0000015636389,0.000099105244],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993868,0.000008679696,0.00028481177,0.00009993107,0.0001465281,0.00007325707],"domain_scores_gemma":[0.99833226,0.00001237336,0.00032721035,0.0012430762,0.00006185011,0.000023210223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030499647,0.00005169433,0.000069742084,0.00013347747,0.00012180485,0.00015406894,0.0015669825,0.000049865517,0.0000126817595],"category_scores_gemma":[0.00005844925,0.000046383222,0.000016519807,0.00010118083,0.000026517322,0.0037053495,0.0012542577,0.00007506172,0.000052925167],"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":[0.0000038400153,0.000018284836,0.0006102023,0.000012479626,9.433741e-7,2.0002405e-7,0.0008273601,0.000002764457,0.00074917433,0.015058679,0.0018593794,0.9808567],"study_design_scores_gemma":[0.0005617127,0.00009252705,0.044121195,0.0001375604,0.0000030168433,0.00001078838,0.00008000848,0.6792853,0.027047906,0.005192277,0.24322696,0.00024074726],"about_ca_topic_score_codex":0.00043629418,"about_ca_topic_score_gemma":0.000012576049,"teacher_disagreement_score":0.980616,"about_ca_system_score_codex":0.00001644128,"about_ca_system_score_gemma":0.000022133478,"threshold_uncertainty_score":0.29118693},"labels":[],"label_agreement":null},{"id":"W2624924254","doi":"10.1016/j.ymssp.2017.06.003","title":"Graph-based structural change detection for rotating machinery monitoring","year":2017,"lang":"en","type":"article","venue":"Mechanical Systems and Signal Processing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China; Shandong University","keywords":"Autoregressive integrated moving average; Martingale (probability theory); Computer science; Change detection; Autoregressive model; Graph; Algorithm; Autoregressive–moving-average model; Data mining; Time series; Artificial intelligence; Machine learning; Mathematics; Theoretical computer science; Statistics","score_opus":0.04648469636521532,"score_gpt":0.3024014566226259,"score_spread":0.2559167602574106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2624924254","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":[0.04932636,0.00021256407,0.9495551,0.00012739099,0.00016571212,0.00039615587,0.0000020297714,0.00019383384,0.000020863246],"genre_scores_gemma":[0.9662417,0.0000015282604,0.03298777,0.000020881735,0.00034382462,0.00037979768,5.3011405e-7,0.000010515722,0.000013455493],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911773,0.00002212155,0.00021065104,0.00032656587,0.00013698007,0.0001859531],"domain_scores_gemma":[0.9993116,0.000036651858,0.000252311,0.00023298248,0.00008680157,0.00007964773],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00029933234,0.00011855688,0.00015070876,0.000052399275,0.0016568783,0.00088185136,0.0003375272,0.00007972135,4.517424e-7],"category_scores_gemma":[0.000018447634,0.00010085729,0.000053569867,0.00007737308,0.000023278451,0.0005430213,0.00008102128,0.00010696567,4.2206202e-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":[0.000009255906,0.000008774143,0.0005227912,0.0001774464,0.000005349872,0.000001079648,0.000050392133,0.000031554773,0.027500309,0.0062880907,0.0000010242221,0.9654039],"study_design_scores_gemma":[0.00021252382,0.00010954227,0.0012038619,0.00013743501,0.0000075544776,0.000012582368,0.000017844799,0.9723297,0.021525925,0.0041580806,0.000117086194,0.00016787785],"about_ca_topic_score_codex":0.00014781782,"about_ca_topic_score_gemma":0.000004497948,"teacher_disagreement_score":0.97229815,"about_ca_system_score_codex":0.000018992934,"about_ca_system_score_gemma":0.000014033195,"threshold_uncertainty_score":0.99964285},"labels":[],"label_agreement":null},{"id":"W2673124289","doi":"10.1299/jsmermd.2010._1a2-g26_1","title":"1A2-G26 Crystal Ball : Toward a Robot that Sees and Prepares for Your Future","year":2010,"lang":"en","type":"article","venue":"The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Motion (physics); Computer science; Artificial intelligence; Hidden Markov model; Hierarchy; Motion analysis; Motion capture; Markov chain; Tree (set theory); Computer vision; Machine learning; Mathematics","score_opus":0.025583232647417468,"score_gpt":0.2629644424371804,"score_spread":0.23738120978976296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2673124289","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":[0.17895798,0.0004221912,0.78308743,0.031682026,0.0005134115,0.0023521716,0.00012334995,0.00055015204,0.0023112504],"genre_scores_gemma":[0.9340923,0.00045141295,0.06474798,0.00016620764,0.0001228306,0.00010314778,0.0000029590226,0.00001843383,0.0002947629],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986347,0.000005891892,0.00026590948,0.00049166766,0.00024402613,0.0003577828],"domain_scores_gemma":[0.9987622,0.000055601886,0.00025757018,0.00025321936,0.00052762567,0.00014380123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039263934,0.00025362778,0.00028918064,0.00010573019,0.0003145828,0.00032436228,0.0007704409,0.00017500255,0.0000054811744],"category_scores_gemma":[0.000030746527,0.00018811962,0.000087985754,0.00016694934,0.00015541514,0.0003976352,0.00037186028,0.0003545424,0.000001158606],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028093707,0.00005476121,0.00006458928,0.000062763975,0.000025465768,1.0704391e-7,0.0018116339,0.000051803996,0.011505869,0.9799129,0.0004040588,0.0060779597],"study_design_scores_gemma":[0.0023945568,0.00447088,0.001987389,0.00026752564,0.00026659292,0.00018292948,0.019834707,0.2228559,0.06584381,0.6547333,0.025115788,0.0020466517],"about_ca_topic_score_codex":0.000015423377,"about_ca_topic_score_gemma":0.000012016185,"teacher_disagreement_score":0.7551343,"about_ca_system_score_codex":0.000014696469,"about_ca_system_score_gemma":0.00008262282,"threshold_uncertainty_score":0.76712954},"labels":[],"label_agreement":null},{"id":"W2734963162","doi":"10.3390/aerospace4030036","title":"Time Series Analysis Methods and Applications for Flight Data. By Jianye Zhang and Peng Zhang. Springer: Berlin, Heidelberg, Germany, 2017; pp. 1–240; ISBN: 978-3-662-53430-4","year":2017,"lang":"en","type":"article","venue":"Aerospace","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Zhàng; Series (stratigraphy); History; Geology; Archaeology; China","score_opus":0.023324826525246166,"score_gpt":0.33777473103993266,"score_spread":0.3144499045146865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2734963162","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014222879,0.001715507,0.9878509,0.006764679,0.00004326549,0.0007616428,0.00013435983,0.00032101272,0.0009863537],"genre_scores_gemma":[0.025177909,0.0024858485,0.9551103,0.00031480344,0.00021264862,0.0009810206,0.00012235095,0.000045068307,0.01555004],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982156,0.000053271382,0.0002880355,0.0009539163,0.00015665517,0.0003325223],"domain_scores_gemma":[0.9963869,0.00015466835,0.0003525435,0.0027867523,0.00012975371,0.00018939916],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00065505126,0.0002483332,0.00037404365,0.00009043975,0.0014101169,0.0007473057,0.0015063039,0.00014169724,0.000014644807],"category_scores_gemma":[0.000048480055,0.00024600685,0.00009417557,0.00033219546,0.00025315484,0.0012628601,0.0012004771,0.0001383922,0.000023334707],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060941147,0.0004957268,0.0041411133,0.0002968088,0.0018124599,0.000008575517,0.0015479982,0.00004893477,0.1541549,0.13942242,0.35265467,0.34535548],"study_design_scores_gemma":[0.0002959831,0.000084884945,0.0027386711,0.000013686678,0.00036470502,0.00002491165,0.000048699996,0.030367833,0.022919338,0.0022572821,0.94031155,0.0005724826],"about_ca_topic_score_codex":0.00018546208,"about_ca_topic_score_gemma":0.00012692138,"teacher_disagreement_score":0.58765686,"about_ca_system_score_codex":0.000029050452,"about_ca_system_score_gemma":0.000032538184,"threshold_uncertainty_score":0.9999992},"labels":[],"label_agreement":null},{"id":"W2742708112","doi":"10.1109/isit.2017.8006974","title":"Information-theoretic limits of subspace clustering","year":2017,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Kootenay Association for Science & Technology","funders":"","keywords":"Cluster analysis; Linear subspace; Limit (mathematics); Computer science; Subspace topology; Correlation clustering; Pattern recognition (psychology); Artificial intelligence; Data mining; Mathematics","score_opus":0.015562342519139152,"score_gpt":0.26545679658794186,"score_spread":0.2498944540688027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2742708112","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":[0.0039822813,0.0000020370928,0.9160504,0.0008702287,0.000033027147,0.000062581035,4.0256475e-7,0.00011003857,0.078889035],"genre_scores_gemma":[0.9429371,0.0000087911585,0.056583792,0.00007290266,0.0000067090273,0.000011185637,1.9625247e-7,0.0000011512982,0.00037822136],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997182,0.000003507046,0.00010473335,0.00004710859,0.00006537979,0.000061110186],"domain_scores_gemma":[0.9991701,0.000011246576,0.00012091381,0.00061887037,0.000054699984,0.000024160401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007676538,0.00003484435,0.000046349658,0.000034512363,0.00017965658,0.0001299399,0.000632274,0.000021344757,0.000016229511],"category_scores_gemma":[0.000019741725,0.000030457484,0.000024158206,0.00004113005,0.00004084534,0.0007628544,0.0001470133,0.000028278668,0.000037343023],"study_design_candidate":"theoretical_or_conceptual","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":[7.075259e-7,0.0000073351966,0.00025648647,0.000009049069,0.0000022096333,7.311595e-8,0.00024148397,0.000015785614,0.00043252076,0.94561386,0.00035274847,0.053067748],"study_design_scores_gemma":[0.0005957719,0.00023854699,0.044912357,0.00006073478,0.000010955354,0.000037021244,0.00019532313,0.38000983,0.42160994,0.06612171,0.08562044,0.0005873869],"about_ca_topic_score_codex":0.000031521744,"about_ca_topic_score_gemma":0.000005838127,"teacher_disagreement_score":0.9389548,"about_ca_system_score_codex":0.0000056118497,"about_ca_system_score_gemma":0.000010766607,"threshold_uncertainty_score":0.13817914},"labels":[],"label_agreement":null},{"id":"W2749627657","doi":"10.23977/jeis.2016.11004","title":"The Analysis Meteorological Satellite Software Based on Principal Component","year":2016,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Software; Computer science; Principal component analysis; Software sizing; Software construction; Component-based software engineering; Component (thermodynamics); Set (abstract data type); Software system; Real-time computing; Data mining; Operating system; Artificial intelligence; Programming language","score_opus":0.008145108320536087,"score_gpt":0.2445882592382693,"score_spread":0.2364431509177332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2749627657","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":[0.02714009,0.00007103843,0.9691186,0.0032397418,0.000034714136,0.000053275577,6.304683e-7,0.000020589629,0.00032132625],"genre_scores_gemma":[0.9867095,0.0005888061,0.012213293,0.00046521795,0.00000941688,0.0000033076674,1.0845088e-7,6.906266e-7,0.000009705314],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989656,0.000020968842,0.00033117709,0.00007615097,0.0004305374,0.00017553515],"domain_scores_gemma":[0.99885184,0.00017153058,0.00035589677,0.00021460322,0.00032534613,0.000080764075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015255262,0.00005467048,0.00008730305,0.00028338566,0.0004025007,0.00026440137,0.00061548443,0.00002196538,0.0000026787566],"category_scores_gemma":[0.00013170266,0.000024731264,0.00006951661,0.001040774,0.00015174398,0.0018886764,0.00005942924,0.00009497327,0.0000036212346],"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":[0.000025395728,0.00002551803,0.00065273716,0.000001321355,0.000019750589,3.281654e-7,0.000062518615,0.00059147476,0.0015882484,0.37238297,0.00005107018,0.6245987],"study_design_scores_gemma":[0.00074890855,0.0018686508,0.12772427,0.000024611058,0.00006576888,0.00006889959,0.000030674077,0.2760954,0.021454353,0.012223676,0.55937225,0.0003225425],"about_ca_topic_score_codex":4.6207438e-7,"about_ca_topic_score_gemma":6.560602e-7,"teacher_disagreement_score":0.95956933,"about_ca_system_score_codex":0.000083226645,"about_ca_system_score_gemma":0.00017076504,"threshold_uncertainty_score":0.30957508},"labels":[],"label_agreement":null},{"id":"W2750624071","doi":"10.1109/tsmc.2017.2741558","title":"Efficient and Rapid Machine Learning Algorithms for Big Data and Dynamic Varying Systems","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":30,"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":"Computer science; Machine learning; Artificial intelligence; Big data; Convergence (economics); Algorithm; Computational intelligence; Data mining","score_opus":0.04525507197249201,"score_gpt":0.2742761598752964,"score_spread":0.2290210879028044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2750624071","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":[0.007795792,0.0034356536,0.9846467,0.00011604445,0.001907685,0.0013759831,0.00017251275,0.00026511008,0.0002845258],"genre_scores_gemma":[0.9958794,0.00067532173,0.0011774907,0.000009541415,0.00014598815,0.00035513367,0.000007664885,0.000035303347,0.0017141652],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99796337,0.00011791562,0.000497972,0.0008415968,0.0002598255,0.00031934716],"domain_scores_gemma":[0.9977841,0.00015668725,0.00037511598,0.0013846639,0.00010106437,0.0001983957],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007305219,0.00028778004,0.00041231333,0.00018860448,0.0016077445,0.0015752728,0.00071530795,0.00015972249,3.5112612e-7],"category_scores_gemma":[0.000007999562,0.00026705704,0.00004588664,0.00010219974,0.00013823813,0.00017155314,0.00004397546,0.00025091908,0.000003993323],"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":[0.00016046688,0.00088801334,0.00053392956,0.006590737,0.0011088896,0.000051442836,0.0041054212,0.12321867,0.009200151,0.02704217,0.00072605786,0.82637405],"study_design_scores_gemma":[0.00054535404,0.00020334015,0.00007213391,0.00024979986,0.0000496631,0.00024885402,0.00023093288,0.98708856,0.00009879306,0.000016793227,0.010889968,0.0003057984],"about_ca_topic_score_codex":0.0009772687,"about_ca_topic_score_gemma":0.000026538502,"teacher_disagreement_score":0.9880836,"about_ca_system_score_codex":0.000048086,"about_ca_system_score_gemma":0.000026308298,"threshold_uncertainty_score":0.9999782},"labels":[],"label_agreement":null},{"id":"W2753798143","doi":"","title":"Deep Sets","year":2017,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":318,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Anomaly detection; Computer science; Invariant (physics); Statistic; Population; Outlier; Artificial intelligence; Permutation (music); Machine learning; Data mining; Pattern recognition (psychology); Theoretical computer science; Mathematics; Statistics","score_opus":0.02175104945544126,"score_gpt":0.28259626304558094,"score_spread":0.2608452135901397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2753798143","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":[0.001724631,0.000036731617,0.97727203,0.00079056923,0.00028495866,0.00018959575,8.5793477e-7,0.0005227535,0.019177893],"genre_scores_gemma":[0.9945038,0.000002895382,0.004980181,0.00019612182,0.000042180218,0.000079319565,0.0000027610529,0.0000029137095,0.00018985094],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933064,0.00000994133,0.00026123543,0.000098633085,0.00016988724,0.00012966522],"domain_scores_gemma":[0.9988355,0.000006158542,0.0004160778,0.00053870195,0.0001530313,0.000050522085],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00013476466,0.00007732134,0.000080375365,0.0000656129,0.0009969203,0.002350276,0.00079888833,0.000049650225,0.0000020180642],"category_scores_gemma":[0.00002392816,0.00006753228,0.000027294109,0.00008236348,0.00003050415,0.006075746,0.00011306034,0.000079337486,0.00010000892],"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":[0.0000020553023,0.000012733192,0.00035801702,0.00013836999,0.0000038879607,8.510228e-7,0.0007411142,0.0008659214,0.00012736615,0.060194872,0.0019805927,0.93557423],"study_design_scores_gemma":[0.000089549605,0.000017223643,0.0013610151,0.000022637476,0.0000014358044,0.00004229907,0.00003779191,0.9630685,0.00045012325,0.00053829775,0.034260172,0.0001109576],"about_ca_topic_score_codex":0.000033536548,"about_ca_topic_score_gemma":0.0000010011228,"teacher_disagreement_score":0.99277914,"about_ca_system_score_codex":0.000024239778,"about_ca_system_score_gemma":0.000024997791,"threshold_uncertainty_score":0.99868536},"labels":[],"label_agreement":null},{"id":"W2754085840","doi":"10.1109/ichi.2017.75","title":"Provider-Consumer Anomaly Detection for Healthcare Systems","year":2017,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Anomaly detection; Task (project management); Government (linguistics); Health care; Anomaly (physics); Computer science; Quality (philosophy); Work (physics); Function (biology); Data mining; Phase (matter); Business","score_opus":0.03498777777455201,"score_gpt":0.3064840716967706,"score_spread":0.2714962939222186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2754085840","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":[0.002924647,0.000066397195,0.99203753,0.0019970029,0.00027902142,0.00072311144,0.000003204835,0.00048809123,0.0014810142],"genre_scores_gemma":[0.96753544,0.000009242887,0.029596359,0.00012212973,0.00008061903,0.0006820287,6.2239536e-7,0.000007172464,0.0019664005],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992876,0.000012494393,0.00015891713,0.00029196165,0.00008184948,0.00016715571],"domain_scores_gemma":[0.9986693,0.000023978979,0.00015712313,0.00094764197,0.00013458452,0.00006736725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015474563,0.00007954287,0.00009497205,0.000047856603,0.00094637054,0.00047795806,0.00063753483,0.00006478489,0.0000019683284],"category_scores_gemma":[0.000024213625,0.000070385104,0.000058035333,0.00004946083,0.000027218677,0.0004541703,0.00009795013,0.000054939133,0.00002488534],"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":[0.000010051783,0.00005690218,0.0014964744,0.00013056386,0.00002206707,0.0000010324103,0.00011716591,0.000007871501,0.005214121,0.46555635,0.002933319,0.52445406],"study_design_scores_gemma":[0.0010017367,0.00078560424,0.019670434,0.00006360303,0.000023677634,0.00011232745,0.00013702792,0.33258212,0.14581802,0.026849464,0.4719945,0.00096151175],"about_ca_topic_score_codex":0.0008577682,"about_ca_topic_score_gemma":0.00022211854,"teacher_disagreement_score":0.96461076,"about_ca_system_score_codex":0.000035363802,"about_ca_system_score_gemma":0.000035706707,"threshold_uncertainty_score":0.7278813},"labels":[],"label_agreement":null},{"id":"W2754104704","doi":"10.1145/3131893","title":"TrailSense","year":2017,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Cluster analysis; Generalizability theory; Artificial intelligence; Crowdsensing; Climb; Data mining; Machine learning; Pattern recognition (psychology); Computer security; Statistics; Mathematics; Engineering","score_opus":0.014321975747081595,"score_gpt":0.27182965950396837,"score_spread":0.25750768375688676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2754104704","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":[0.9798316,0.00012791672,0.00073948596,0.006901832,0.00013324386,0.0005328996,0.0000035311325,0.00086056325,0.010868979],"genre_scores_gemma":[0.99380463,0.00022151577,0.0052077086,0.000046179568,0.0000140509765,0.00025343508,2.8456551e-8,0.0000073852357,0.0004450782],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992064,0.0000026766063,0.0001594307,0.00032791603,0.00013133622,0.00017218737],"domain_scores_gemma":[0.99823207,0.00005923864,0.00036245256,0.0011778203,0.00015045065,0.000017980281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012260707,0.0001311393,0.00016508657,0.00009362234,0.0006081258,0.00025848905,0.0038803166,0.00009893281,0.0000021101266],"category_scores_gemma":[0.00062832027,0.000088462046,0.00007363529,0.00012101015,0.0003232486,0.0005676601,0.002495087,0.00026774735,0.000005175004],"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":[0.000060387378,0.00024053769,0.0025419577,0.00006862522,0.00006703751,0.0000013086495,0.000584189,0.000003024186,0.2508265,0.14944658,0.005168565,0.59099126],"study_design_scores_gemma":[0.00009701146,0.00030502354,0.002638271,0.00011228555,0.000006383961,0.000020884358,0.00068239897,0.00020908944,0.890917,0.09993116,0.0049599186,0.0001205463],"about_ca_topic_score_codex":0.000025784391,"about_ca_topic_score_gemma":0.0000014818952,"teacher_disagreement_score":0.6400905,"about_ca_system_score_codex":0.000026544532,"about_ca_system_score_gemma":0.000010483145,"threshold_uncertainty_score":0.7210658},"labels":[],"label_agreement":null},{"id":"W2758890981","doi":"10.1109/jiot.2017.2756025","title":"Recursive Principal Component Analysis-Based Data Outlier Detection and Sensor Data Aggregation in IoT Systems","year":2017,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":162,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Principal component analysis; Wireless sensor network; Data mining; Outlier; Anomaly detection; Data aggregator; Redundancy (engineering); Data redundancy; Cluster analysis; Software deployment; Real-time computing; Machine learning; Artificial intelligence; Computer network; Database","score_opus":0.06396428269156561,"score_gpt":0.323417098243368,"score_spread":0.2594528155518024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2758890981","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":[0.2824605,0.00005870454,0.7166444,0.00032084942,0.00029519995,0.00012366453,0.000012239129,0.000028143386,0.00005628917],"genre_scores_gemma":[0.9791365,0.000022852793,0.020638935,0.00003918395,0.00007578328,0.0000044400203,0.0000075142193,0.0000060055922,0.00006878493],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986442,0.00008568094,0.00045781166,0.00040065852,0.00027212405,0.00013958126],"domain_scores_gemma":[0.9969709,0.000053011503,0.00094007823,0.0018221646,0.00013636188,0.00007748559],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011548228,0.00010876847,0.00022899466,0.00031660168,0.00019223151,0.0005678953,0.0026675889,0.000072259325,0.000002993711],"category_scores_gemma":[0.00010241525,0.00009919173,0.000045212666,0.00015428777,0.00007564231,0.00082280696,0.000593689,0.00031630116,0.0000023204934],"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":[0.0007423856,0.0018847486,0.12090584,0.0005067319,0.0035458747,0.00037157684,0.007510521,0.018009135,0.0793642,0.0079226475,0.0046230173,0.75461334],"study_design_scores_gemma":[0.00027120806,0.00007100538,0.007829662,0.00010789959,0.000070456976,0.00010102281,0.000039380702,0.98155504,0.00837425,0.00019215317,0.0012701729,0.000117720025],"about_ca_topic_score_codex":0.001106369,"about_ca_topic_score_gemma":0.00013013682,"teacher_disagreement_score":0.9635459,"about_ca_system_score_codex":0.00006746578,"about_ca_system_score_gemma":0.000037133406,"threshold_uncertainty_score":0.54762274},"labels":[],"label_agreement":null},{"id":"W2760865832","doi":"10.1109/ihtc.2017.8058198","title":"Enhancing autonomous access hole detection","year":2017,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Toronto Metropolitan University","funders":"","keywords":"Rubble; Work (physics); Computer science; Computer security; Forensic engineering; Architectural engineering; Engineering; Civil engineering; Mechanical engineering","score_opus":0.024340159391877836,"score_gpt":0.3084300069073474,"score_spread":0.28408984751546956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2760865832","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":[0.011410367,0.0000036351266,0.95878065,0.0006254191,0.00011915134,0.00009073656,1.8302589e-7,0.000557145,0.028412733],"genre_scores_gemma":[0.9722337,0.000004219493,0.025615165,0.00014040456,0.00005093683,0.00005024312,1.0868709e-7,0.0000036961767,0.0019014939],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995049,0.0000060140064,0.00009773955,0.00021094715,0.00006766915,0.000112727634],"domain_scores_gemma":[0.9989542,0.000008944658,0.00009636859,0.000860251,0.00003751317,0.00004274997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009804845,0.00005576602,0.000053806183,0.000041227413,0.0007594087,0.00080610276,0.0011862768,0.0000367068,0.000026303056],"category_scores_gemma":[0.000014407492,0.000051452564,0.000035047622,0.0000586622,0.00002441772,0.0009938446,0.0003705219,0.00006156375,0.00008537709],"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":[0.0000012895543,0.00004149366,0.00033166166,0.0000058336936,0.0000073389015,0.0000023018752,0.00006369605,0.000019146026,0.06656665,0.089197494,0.0003649219,0.84339815],"study_design_scores_gemma":[0.000058876147,0.000026766455,0.010520446,0.0000030970746,0.0000015851086,0.0000066955195,0.0000036808124,0.021736626,0.9447648,0.0064758114,0.016285127,0.000116501134],"about_ca_topic_score_codex":0.0002068071,"about_ca_topic_score_gemma":0.00013328962,"teacher_disagreement_score":0.96082336,"about_ca_system_score_codex":0.000025475118,"about_ca_system_score_gemma":0.000017770986,"threshold_uncertainty_score":0.77732676},"labels":[],"label_agreement":null},{"id":"W2765152351","doi":"10.4000/books.septentrion.16066","title":"Abnormal behaviors Modeling and detection. Some of the approaches developed in the CAnADA project","year":2011,"lang":"fr","type":"book-chapter","venue":"Presses universitaires du Septentrion eBooks","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Computer science; Data science","score_opus":0.05530516199651471,"score_gpt":0.20342102604218776,"score_spread":0.14811586404567306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765152351","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":[0.21612403,0.0045261825,0.42636237,0.0044946657,0.0015306908,0.01118605,0.00028120217,0.00047371298,0.33502108],"genre_scores_gemma":[0.9960165,0.00021811477,0.00061013707,0.000074069976,0.000042221727,0.00003790059,0.0000032908079,0.000021054197,0.0029767363],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99827594,0.00013683629,0.00042289402,0.0004912737,0.0003818025,0.00029126785],"domain_scores_gemma":[0.99867433,0.000104580446,0.00037625624,0.0006123185,0.0001741418,0.00005839571],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025159464,0.0003397415,0.00028866503,0.00019203544,0.00060897623,0.00007552692,0.001220025,0.0002381891,0.000016302945],"category_scores_gemma":[0.00001363051,0.00025153076,0.00014454029,0.00011790548,0.00034569195,0.00030813887,0.0006788925,0.0005154998,9.296161e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047641,0.000074712865,0.0005450287,0.000120683886,0.00010583338,0.000040487947,0.0030102467,0.0007710274,0.00012211633,0.91472757,0.00011744877,0.080317184],"study_design_scores_gemma":[0.0041000405,0.001249939,0.012918155,0.0022335947,0.0028042304,0.0016196718,0.008671916,0.29311228,0.03150522,0.08150024,0.55436957,0.005915154],"about_ca_topic_score_codex":0.15127635,"about_ca_topic_score_gemma":0.11192312,"teacher_disagreement_score":0.83322734,"about_ca_system_score_codex":0.00018565614,"about_ca_system_score_gemma":0.00072126166,"threshold_uncertainty_score":0.9999937},"labels":[],"label_agreement":null},{"id":"W2767547957","doi":"10.1016/j.inffus.2017.10.006","title":"A survey on deep learning for big data","year":2017,"lang":"en","type":"article","venue":"Information Fusion","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1216,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Big data; Deep learning; Computer science; Artificial intelligence; Data science; Variety (cybernetics); Machine learning; Unsupervised learning; Data mining","score_opus":0.08845215427397193,"score_gpt":0.3135410329470981,"score_spread":0.22508887867312619,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767547957","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":[0.0022727435,0.0000025489053,0.99247944,0.00043264375,0.00024051142,0.00021661901,0.000014950727,0.00018008026,0.0041604624],"genre_scores_gemma":[0.98871446,0.000022084934,0.010384676,0.0002765452,0.00008577301,0.00004541391,0.00024403413,0.0000031711118,0.0002238681],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99951404,0.000014150532,0.00015667659,0.00011217156,0.00011374323,0.00008921794],"domain_scores_gemma":[0.9984855,0.00005794801,0.00022501378,0.0010960214,0.00010388079,0.000031627813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046890017,0.000053368858,0.000052043913,0.00006575342,0.0008799537,0.00043339274,0.001140822,0.000041665015,0.0000036620308],"category_scores_gemma":[0.0002683053,0.000048197027,0.000018341136,0.00005956331,0.00001418912,0.001531517,0.00043423384,0.00006919035,0.00011571195],"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":[0.0000053252843,0.000007956607,0.0003458189,0.0000037558939,0.0000014048442,2.978056e-8,0.00008047302,0.000059689195,0.000033485274,0.0038957268,0.003515007,0.9920513],"study_design_scores_gemma":[0.00022770607,0.00009664108,0.092484914,0.000010887306,0.0000014441957,0.0000011138145,0.000009964308,0.4337376,0.00088294444,0.00066879235,0.47176015,0.000117814925],"about_ca_topic_score_codex":0.00010532788,"about_ca_topic_score_gemma":0.000036541594,"teacher_disagreement_score":0.9919335,"about_ca_system_score_codex":0.000014111293,"about_ca_system_score_gemma":0.000016330514,"threshold_uncertainty_score":0.67679816},"labels":[],"label_agreement":null},{"id":"W2768641377","doi":"10.1109/ithings-greencom-cpscom-smartdata.2017.158","title":"Deep Neural Networks with Confidence Sampling for Electrical Anomaly Detection","year":2017,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Anomaly detection; Computer science; Energy consumption; Anomaly (physics); Artificial intelligence; Deep learning; Data mining; Artificial neural network; Metric (unit); Energy (signal processing); Convolutional neural network; Machine learning; Engineering; Statistics; Mathematics","score_opus":0.024899030054146346,"score_gpt":0.2832867212124336,"score_spread":0.25838769115828725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768641377","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":[0.0030727542,0.000017149476,0.9951044,0.00037996474,0.00006117755,0.0003184001,1.8179861e-7,0.00035269975,0.00069329527],"genre_scores_gemma":[0.8529268,0.000003889644,0.14653666,0.00014806411,0.0000810972,0.00016497189,3.9450546e-7,0.0000067251804,0.00013140727],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923325,0.000008413037,0.00012642398,0.0003198351,0.000088204746,0.00022388666],"domain_scores_gemma":[0.99898493,0.00006430272,0.00012941711,0.00064942223,0.000105057086,0.00006685182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011242622,0.00009482899,0.0000957586,0.00004080152,0.0008994613,0.00044323548,0.00068870006,0.000059055707,0.0000043513023],"category_scores_gemma":[0.000023345066,0.000076517696,0.000048945953,0.00010757419,0.000048221198,0.00043053922,0.000081930884,0.00010311568,0.0000027370281],"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":[0.000048738584,0.00005409977,0.0014202477,0.000006351534,0.000020687532,0.0000020214661,0.000032135762,0.004409288,0.0026217778,0.19484729,0.00008383065,0.79645354],"study_design_scores_gemma":[0.00013587216,0.00022508405,0.004637221,0.0000020754796,0.000004989391,0.000030305731,0.0000025522443,0.98541784,0.006582315,0.002003476,0.00083099556,0.0001272691],"about_ca_topic_score_codex":0.00007789898,"about_ca_topic_score_gemma":0.00012417794,"teacher_disagreement_score":0.9810085,"about_ca_system_score_codex":0.000023974713,"about_ca_system_score_gemma":0.0000122409765,"threshold_uncertainty_score":0.691802},"labels":[],"label_agreement":null},{"id":"W2768695633","doi":"10.3138/cjccj.2017-0022.r1","title":"Will Rogers Is Jenksing Police Response Times","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Criminology and Criminal Justice/La Revue canadienne de criminologie et de justice pénale","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Benchmarking; Jurisdiction; Context (archaeology); Computer science; Law enforcement; Response time; Emergency response; Psychology; Operations research; Criminology; Data science; Computer security; Political science; Law; Engineering; Medical emergency; Business; Medicine; Geography","score_opus":0.0797773688464547,"score_gpt":0.3187902193614349,"score_spread":0.23901285051498022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768695633","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":[0.8874993,0.0006778778,0.09223241,0.015395056,0.000457694,0.00015463836,0.000025047917,0.0000684899,0.0034894987],"genre_scores_gemma":[0.9700876,0.0003938677,0.025090689,0.0035961922,0.0002618389,0.000017602013,0.0000014075175,0.000031852127,0.00051897654],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99694985,0.0003902567,0.0006223281,0.000535731,0.00010848105,0.0013933702],"domain_scores_gemma":[0.9951739,0.0008509134,0.0007813953,0.0010692073,0.00051444594,0.0016101502],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0017810257,0.0003701483,0.00051491347,0.00072138757,0.0016105105,0.00057156885,0.0022352333,0.00050299364,0.0000666001],"category_scores_gemma":[0.0024349503,0.00039907024,0.0001859105,0.000101384496,0.0009640109,0.0011914318,0.00018283445,0.001045672,0.000012183061],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006280256,0.00013847824,0.0015934323,0.0015173662,0.00032827895,0.0064282552,0.073321156,0.0002950631,0.0067285523,0.78730583,0.009880831,0.11183476],"study_design_scores_gemma":[0.0052588256,0.0062755486,0.2601992,0.0019342768,0.023688002,0.13597882,0.19360915,0.015268027,0.022855965,0.12126318,0.20814651,0.0055225],"about_ca_topic_score_codex":0.01617826,"about_ca_topic_score_gemma":0.014444613,"teacher_disagreement_score":0.6660426,"about_ca_system_score_codex":0.0007333682,"about_ca_system_score_gemma":0.0033770183,"threshold_uncertainty_score":0.9998461},"labels":[],"label_agreement":null},{"id":"W2770146469","doi":"10.3390/jsan6040026","title":"Big Sensed Data Meets Deep Learning for Smarter Health Care in Smart Cities","year":2017,"lang":"en","type":"article","venue":"Journal of Sensor and Actuator Networks","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":58,"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 Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Deep learning; Smart city; Wearable computer; Data science; Data acquisition; Artificial intelligence; Internet of Things; Big data; Wearable technology; Machine learning; Human–computer interaction; Data mining; World Wide Web; Embedded system","score_opus":0.031164035700135997,"score_gpt":0.29729793776334384,"score_spread":0.26613390206320786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770146469","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":[0.05478233,0.0011151019,0.93979496,0.0035574415,0.00034388193,0.00021616256,0.000004271255,0.000037305774,0.00014852341],"genre_scores_gemma":[0.9697863,0.0005175525,0.029000543,0.00026614545,0.00036793898,0.0000036817707,0.000002835319,0.000008502361,0.000046530924],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991659,0.00003785328,0.00031360835,0.00018290461,0.00010627599,0.00019344798],"domain_scores_gemma":[0.9987321,0.00009029528,0.0004967885,0.00047142268,0.0001094934,0.00009992275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040592568,0.00009377654,0.00021744819,0.00008421347,0.00047159544,0.00030755613,0.00058934797,0.00006172937,0.0000013690546],"category_scores_gemma":[0.000045534103,0.0000791663,0.000050214036,0.000050183608,0.000043743043,0.00038499344,0.00017368805,0.0002501788,3.2005025e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047847294,0.00002908777,0.0031438323,0.00003559816,0.00003090456,0.000013932102,0.0012249016,0.00041112798,0.000083933104,0.0006175058,0.001795839,0.9925655],"study_design_scores_gemma":[0.0026134944,0.00139214,0.050167713,0.00035437848,0.000042845964,0.00044303204,0.002020193,0.63442993,0.00049790175,0.0010719394,0.30630782,0.0006586302],"about_ca_topic_score_codex":0.00002529049,"about_ca_topic_score_gemma":0.00007880841,"teacher_disagreement_score":0.9919069,"about_ca_system_score_codex":0.000031044994,"about_ca_system_score_gemma":0.00004529882,"threshold_uncertainty_score":0.36271787},"labels":[],"label_agreement":null},{"id":"W2773752798","doi":"10.1109/icdmw.2017.109","title":"Finding Suspicious Activities in Financial Transactions and Distributed Ledgers","year":2017,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Ledger; Money laundering; Order (exchange); Ranking (information retrieval); Financial transaction; Payment; Popularity; Cryptocurrency; Domain (mathematical analysis); Computer security; Finance; Business; Artificial intelligence; World Wide Web; Database transaction","score_opus":0.015872901162711422,"score_gpt":0.2647130117016127,"score_spread":0.2488401105389013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2773752798","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":[0.09600938,0.0000058511564,0.9012269,0.0008678385,0.000041283318,0.00007655043,0.0000036086549,0.00011063308,0.0016579696],"genre_scores_gemma":[0.9922472,0.000015469792,0.007216945,0.000033479893,0.000011172095,0.00003696753,4.5565244e-7,0.000002064285,0.00043622518],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9996147,0.000006468481,0.00007720722,0.00015230234,0.000042466756,0.0001068213],"domain_scores_gemma":[0.9996179,0.00002020022,0.000040122257,0.00028296042,0.000009232064,0.000029599358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006110889,0.000052034186,0.000065006345,0.000051301744,0.00046270812,0.0001734918,0.00026361304,0.000039061677,0.000010414197],"category_scores_gemma":[0.00001122983,0.000050749422,0.000020898715,0.00006471443,0.00005483497,0.00040071385,0.000034392888,0.00007877807,0.0000021770152],"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":[0.000011481048,0.00019268793,0.011808157,0.00002173966,0.0000119419365,0.000017043374,0.0010446898,0.000100917605,0.0049691186,0.29640606,0.0011303844,0.68428576],"study_design_scores_gemma":[0.0016371693,0.00028371916,0.6916256,0.00007116304,0.000017662102,0.00008499875,0.00045091193,0.11131271,0.11709939,0.049083475,0.027153308,0.0011799182],"about_ca_topic_score_codex":0.00016176993,"about_ca_topic_score_gemma":0.00022239304,"teacher_disagreement_score":0.89623785,"about_ca_system_score_codex":0.000022653094,"about_ca_system_score_gemma":0.000022069638,"threshold_uncertainty_score":0.35588235},"labels":[],"label_agreement":null},{"id":"W2774018923","doi":"","title":"Stochastic Segmentation Trees for Multiple Ground Truths.","year":2017,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence","score_opus":0.07325594585640749,"score_gpt":0.3439437889214061,"score_spread":0.2706878430649986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774018923","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":[0.016922804,0.000011938357,0.98099434,0.0009067465,0.00024022571,0.0006031186,0.000009579409,0.00013692661,0.0001743032],"genre_scores_gemma":[0.96798646,0.000006296223,0.031356566,0.00007684956,0.00008498259,0.0004062926,0.000005111831,0.000007684404,0.00006976427],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988641,0.000021063246,0.00034199303,0.0003901855,0.00013538344,0.0002472725],"domain_scores_gemma":[0.998695,0.00022402754,0.0001921226,0.0007329,0.00009808676,0.000057825266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029520591,0.0001240843,0.000133785,0.00010948266,0.00058842526,0.00042082462,0.0010461872,0.00006664318,0.00001385698],"category_scores_gemma":[0.00024527352,0.00012457104,0.00007299185,0.00014088342,0.0001205256,0.0004218952,0.00009216493,0.000096311174,0.000036447833],"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":[0.00003508853,0.00014532018,0.00013780469,0.000007942359,0.00000655699,0.0000013661452,0.0006691084,0.028620262,0.008066508,0.3453508,0.000066759436,0.61689246],"study_design_scores_gemma":[0.00004841452,0.000095327305,0.00065037573,0.000017135411,0.0000033504134,0.0000017068512,0.00022097213,0.7997108,0.031795878,0.1670067,0.00027428145,0.00017505321],"about_ca_topic_score_codex":0.0006498058,"about_ca_topic_score_gemma":0.0012939487,"teacher_disagreement_score":0.95106363,"about_ca_system_score_codex":0.000093142946,"about_ca_system_score_gemma":0.00003933222,"threshold_uncertainty_score":0.50798595},"labels":[],"label_agreement":null},{"id":"W2774424149","doi":"10.1145/3148055.3148076","title":"An Imputation-based Augmented Anomaly Detection from Large Traces of Operating System Events","year":2017,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Computer science; Debugging; Real-time operating system; Embedded system; Embedded operating system; Software; Operating system; Anomaly detection; Real-time computing","score_opus":0.011078262860423129,"score_gpt":0.2796311382725046,"score_spread":0.26855287541208145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774424149","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":[0.356966,0.0000038454777,0.6421772,0.000054191012,0.000054296113,0.00013663057,0.000008853054,0.00023907064,0.0003599089],"genre_scores_gemma":[0.93789315,4.787997e-7,0.061940808,0.00002875912,0.000031051422,0.000056197816,0.0000055996034,0.0000061391543,0.000037802674],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991592,0.00004303023,0.0002478243,0.0002823042,0.00014738855,0.00012024427],"domain_scores_gemma":[0.9987653,0.000027418915,0.00027478836,0.0007645163,0.00011303012,0.00005497819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020408006,0.00008793549,0.00011561561,0.00006112295,0.00061832054,0.00017700507,0.0006697527,0.000055784123,0.000014168434],"category_scores_gemma":[0.0000128677475,0.00008067418,0.00005027415,0.00008817394,0.000017076321,0.0006089552,0.000056396646,0.00005345488,0.00000900125],"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":[0.00003544238,0.0011032181,0.06192089,0.00013480287,0.00012267417,0.00000721574,0.00088451954,0.003574343,0.59431165,0.06187692,0.00008452964,0.2759438],"study_design_scores_gemma":[0.00023693088,0.0000880672,0.03446874,0.000018656252,0.0000053339813,9.3003047e-7,0.00007961676,0.7038367,0.2609511,0.00018195808,0.0000448878,0.000087096705],"about_ca_topic_score_codex":0.0010302672,"about_ca_topic_score_gemma":0.00014519674,"teacher_disagreement_score":0.70026237,"about_ca_system_score_codex":0.000038210765,"about_ca_system_score_gemma":0.000032578617,"threshold_uncertainty_score":0.4755684},"labels":[],"label_agreement":null},{"id":"W2775068609","doi":"10.1109/smc.2017.8122940","title":"CARTS: Constraint-based analytics from real-time system monitoring","year":2017,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Ontario Tech University","funders":"","keywords":"Computer science; Intersection (aeronautics); Real-time computing; Analytics; Constraint (computer-aided design); Time constraint; Data mining; Engineering","score_opus":0.023035290889349393,"score_gpt":0.26919510835049837,"score_spread":0.24615981746114898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2775068609","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":[0.01675898,0.0000042550864,0.9379793,0.00039084273,0.00012421224,0.00011326081,0.000007994064,0.00088320347,0.043738],"genre_scores_gemma":[0.86806613,0.0000027947146,0.13123672,0.000015705222,0.00009080191,0.00002115426,0.0000010746766,0.0000048557754,0.0005607967],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993282,0.000012557334,0.00015242933,0.00025239898,0.0001231912,0.00013124169],"domain_scores_gemma":[0.9984869,0.00004160375,0.00013106807,0.0011954565,0.00006779641,0.000077216275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009985716,0.000082855455,0.00011149601,0.000037812944,0.00044974955,0.0003941968,0.0008311713,0.00005212861,0.000020039754],"category_scores_gemma":[0.000009982714,0.00007611537,0.000056485333,0.00005469806,0.000056805755,0.0001978417,0.00011433032,0.000057230714,0.000116803785],"study_design_candidate":"theoretical_or_conceptual","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":[0.000010497921,0.0002018714,0.03167651,0.00006636454,0.00015073938,0.0000815602,0.00024859706,0.00040005043,0.11055043,0.6839242,0.005821217,0.16686797],"study_design_scores_gemma":[0.00044080295,0.00007787715,0.018989813,0.00012508577,0.000032826963,0.000009762204,0.00015801437,0.68015224,0.2931129,0.0015444611,0.004819228,0.0005370087],"about_ca_topic_score_codex":0.00094496866,"about_ca_topic_score_gemma":0.0000048133124,"teacher_disagreement_score":0.8513071,"about_ca_system_score_codex":0.000056401084,"about_ca_system_score_gemma":0.000043631528,"threshold_uncertainty_score":0.3801249},"labels":[],"label_agreement":null},{"id":"W2782981605","doi":"10.1016/j.jvcir.2018.01.002","title":"Early event detection based on dynamic images of surveillance videos","year":2018,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; McGill University; Nvidia","keywords":"Computer science; Event (particle physics); Artificial intelligence; Set (abstract data type); Spotting; CLIPS; Computer vision; Detector; Pattern recognition (psychology)","score_opus":0.012166063302968181,"score_gpt":0.34896424195507647,"score_spread":0.3367981786521083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782981605","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":[0.14113863,0.00006154283,0.8576719,0.00060703803,0.000043655888,0.000117243,0.0000012174828,0.000029985014,0.000328788],"genre_scores_gemma":[0.9647757,0.00019418787,0.034901977,0.000059811315,0.000026394511,0.0000072754324,0.0000018680801,0.000005912019,0.000026917807],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989013,0.00023040245,0.00043839033,0.00012324376,0.00023284365,0.000073787254],"domain_scores_gemma":[0.99806696,0.00017716545,0.0006580916,0.00044192607,0.0006091037,0.00004675823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056481647,0.00007653681,0.00013970284,0.00021363284,0.00014989411,0.000082754,0.0003482794,0.000039247883,0.0000071218547],"category_scores_gemma":[0.00010970344,0.00007021964,0.000071662,0.00035847878,0.00013077719,0.0005318372,0.000073720534,0.00013721835,0.000004487772],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021309959,0.0005726546,0.002696021,0.000034851593,0.0000416309,0.0000014287659,0.0006396482,0.000104385916,0.56108063,0.0012508295,0.00033869263,0.4330261],"study_design_scores_gemma":[0.0010476216,0.002566144,0.2262954,0.000115458155,0.000019587307,0.000049914644,0.00017730896,0.3057859,0.460077,0.0029154231,0.0007243404,0.00022593923],"about_ca_topic_score_codex":0.00003107883,"about_ca_topic_score_gemma":0.0000063215984,"teacher_disagreement_score":0.823637,"about_ca_system_score_codex":0.00003785718,"about_ca_system_score_gemma":0.00002933952,"threshold_uncertainty_score":0.2863474},"labels":[],"label_agreement":null},{"id":"W2790894931","doi":"10.1109/jiot.2018.2818113","title":"Agile IoT for Critical Infrastructure Resilience: Cross-Modal Sensing As Part of a Situational Awareness Approach","year":2018,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Carleton University","keywords":"Critical infrastructure; Situation awareness; Agile software development; Resilience (materials science); Computer science; Internet of Things; Critical infrastructure protection; Middleware (distributed applications); Computer security; Systems engineering; Distributed computing; Engineering","score_opus":0.019519892942692036,"score_gpt":0.3357473388367771,"score_spread":0.3162274458940851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790894931","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":[0.19135016,0.00001003346,0.80717,0.0001955322,0.00038843387,0.00011373113,0.0000040580626,0.000035619367,0.00073245965],"genre_scores_gemma":[0.7338756,0.0000012854453,0.26552713,0.00014390056,0.00027727088,0.0000054218244,6.7421865e-7,0.0000065600034,0.00016217634],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986636,0.000035553512,0.00049635116,0.00024304759,0.0003628443,0.0001985992],"domain_scores_gemma":[0.9983026,0.00013136724,0.00034035638,0.000244116,0.0008900906,0.000091516114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051034725,0.00011318952,0.00018913504,0.0001265499,0.00018913945,0.00016880102,0.0006997171,0.000105470404,0.00003565029],"category_scores_gemma":[0.00021327363,0.00010062096,0.00013889707,0.00018055143,0.00036877327,0.00040616756,0.00010611674,0.00023601695,0.0000028865607],"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":[0.00079281756,0.0009554394,0.005535554,0.0005514939,0.00037146843,0.000022004615,0.020532819,0.0036830578,0.29462358,0.44565013,0.051569812,0.17571181],"study_design_scores_gemma":[0.00043984162,0.0007132111,0.0013489175,0.00019506844,0.000021101196,0.0011614875,0.00010764653,0.3867639,0.47578594,0.1296875,0.0035157604,0.00025961164],"about_ca_topic_score_codex":0.000030819723,"about_ca_topic_score_gemma":6.963265e-7,"teacher_disagreement_score":0.5425254,"about_ca_system_score_codex":0.000045374625,"about_ca_system_score_gemma":0.00017559997,"threshold_uncertainty_score":0.41032034},"labels":[],"label_agreement":null},{"id":"W2791180278","doi":"10.1145/3018896.3018950","title":"Subspace selection in high-dimensional big data using genetic algorithm in apache spark","year":2017,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Linear subspace; Outlier; Subspace topology; Computer science; Data mining; Curse of dimensionality; Data point; Anomaly detection; Clustering high-dimensional data; SPARK (programming language); Population; Scale (ratio); Selection (genetic algorithm); Algorithm; Pattern recognition (psychology); Artificial intelligence; Mathematics; Cluster analysis","score_opus":0.08433379621789451,"score_gpt":0.307438046025988,"score_spread":0.2231042498080935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791180278","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":[0.100666165,0.000016952083,0.89828634,0.00050443807,0.000091959606,0.00013843703,0.00000231628,0.000088785404,0.00020460566],"genre_scores_gemma":[0.5586249,0.000006689007,0.44111845,0.00004987212,0.000055709763,0.000009904498,0.0000018151106,0.0000042265888,0.00012839084],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990869,0.000027743523,0.0001644061,0.00041738056,0.0001296517,0.00017388604],"domain_scores_gemma":[0.99890476,0.00001673513,0.00009118952,0.0009170496,0.00003006365,0.000040216266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023162225,0.00008341788,0.00009674841,0.0001370966,0.00021207647,0.00015772975,0.0009890835,0.0000614486,0.000009640302],"category_scores_gemma":[0.000015759964,0.000081676946,0.0000137898505,0.00026725495,0.000033931476,0.00039730844,0.00055293465,0.0001247731,0.000012911199],"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":[0.000005167743,0.00026441485,0.027283872,0.000008038049,0.000010339654,0.000020290128,0.00007469756,0.0024356241,0.010538829,0.009142156,0.0011671574,0.9490494],"study_design_scores_gemma":[0.00016040416,0.00001633483,0.12811841,0.000009196904,0.0000015897767,0.000021470914,0.0000032266846,0.865331,0.0040261336,0.00156047,0.0006349328,0.000116824354],"about_ca_topic_score_codex":0.0042874995,"about_ca_topic_score_gemma":0.0012217918,"teacher_disagreement_score":0.9489326,"about_ca_system_score_codex":0.00006455028,"about_ca_system_score_gemma":0.00006358215,"threshold_uncertainty_score":0.64814454},"labels":[],"label_agreement":null},{"id":"W2793289516","doi":"10.1109/tr.2017.2787138","title":"Anomaly Detection Techniques Based on Kappa-Pruned Ensembles","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":31,"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":"Detector; Pruning; Anomaly detection; Computer science; Pattern recognition (psychology); Measure (data warehouse); Algorithm; Constant false alarm rate; Base (topology); Mathematics; Data mining; Artificial intelligence","score_opus":0.00998604196558932,"score_gpt":0.24645213904147445,"score_spread":0.23646609707588512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793289516","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":[0.018971473,0.0000018248085,0.9731564,0.00088388205,0.0003286123,0.0005891061,0.0000124945855,0.0020903782,0.0039658216],"genre_scores_gemma":[0.94902986,0.000006426021,0.049644064,0.0006295622,0.000077259676,0.0003998858,8.48766e-7,0.000016507045,0.0001955938],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998095,0.0001281874,0.0003649556,0.00078777666,0.00032114674,0.00030293316],"domain_scores_gemma":[0.9978516,0.00016936245,0.00011085749,0.0014883291,0.00025089923,0.00012896853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043930963,0.00024977373,0.0001989025,0.0002883688,0.00065708003,0.000104099556,0.0005603933,0.00020281678,0.00006969231],"category_scores_gemma":[0.000014247929,0.00023800292,0.00020508362,0.0008936666,0.0002096161,0.0002823325,0.000003053815,0.0003384602,0.00011669612],"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":[0.00023105364,0.0017998151,0.00004942245,0.000040201765,0.00002453177,0.0000026505998,0.00014264791,0.0015654576,0.06003971,0.0016601499,0.0005391976,0.9339052],"study_design_scores_gemma":[0.00016622969,0.0012242936,0.0007473833,0.00002155193,0.000012035781,0.000005216633,0.000006838597,0.08250695,0.9056999,0.0019395871,0.0073943697,0.0002756255],"about_ca_topic_score_codex":0.0001261675,"about_ca_topic_score_gemma":0.0000683518,"teacher_disagreement_score":0.9336295,"about_ca_system_score_codex":0.00020539945,"about_ca_system_score_gemma":0.00007291496,"threshold_uncertainty_score":0.97054774},"labels":[],"label_agreement":null},{"id":"W2804054616","doi":"10.1504/ijes.2018.10013359","title":"Exploration and application of the value of big data based on data-driven techniques for the hydraulic internet of things","year":2018,"lang":"en","type":"article","venue":"International Journal of Embedded Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Big data; Warning system; Computer science; The Internet; Diversification (marketing strategy); Data science; Data mining; World Wide Web; Business","score_opus":0.06735651137505713,"score_gpt":0.3276325890754084,"score_spread":0.2602760777003512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804054616","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":[0.0010948577,0.000047333247,0.9963415,0.0014490379,0.00041061256,0.00040006317,0.000052430136,0.000014714347,0.0001894492],"genre_scores_gemma":[0.97959363,0.000021385255,0.01994088,0.00011904902,0.00026170578,0.000025811341,0.000011832139,0.0000062156246,0.000019513776],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986112,0.000080489655,0.0006388226,0.00017667428,0.00043764492,0.000055144832],"domain_scores_gemma":[0.9964294,0.00037185784,0.0013654127,0.0009997907,0.0008137102,0.000019856456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011579497,0.00007562925,0.00016103215,0.00013238114,0.000043951237,0.000048816746,0.003193548,0.000045347962,7.737731e-7],"category_scores_gemma":[0.00017151117,0.000045784593,0.000054344266,0.00016506945,0.00013163278,0.0005100309,0.00040329818,0.0000865097,3.128962e-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":[0.00050057407,0.00088421005,0.0012983717,0.00030655516,0.00082353543,0.0000012400085,0.0041525164,0.0052698874,0.08533341,0.31676212,0.02394086,0.5607267],"study_design_scores_gemma":[0.00017644695,0.00020565947,0.00014090173,0.00018490976,0.000023733299,0.00001772112,0.00008643138,0.9386274,0.048889518,0.0017932317,0.009806367,0.00004764525],"about_ca_topic_score_codex":0.0001408593,"about_ca_topic_score_gemma":0.000006420626,"teacher_disagreement_score":0.97849876,"about_ca_system_score_codex":0.000025891084,"about_ca_system_score_gemma":0.00007385121,"threshold_uncertainty_score":0.593446},"labels":[],"label_agreement":null},{"id":"W2805055069","doi":"","title":"Ensemble-Based Anomaly Detetction using Cooperative Learning.","year":2017,"lang":"en","type":"article","venue":"Knowledge Discovery and Data Mining","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Ensemble learning; Anomaly detection; Artificial intelligence; Anomaly (physics); Machine learning; Physics","score_opus":0.08496144598796657,"score_gpt":0.35238607384059706,"score_spread":0.2674246278526305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805055069","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":[0.1503478,0.00013705117,0.8462742,0.00013451734,0.000112682275,0.000103174694,0.000015069673,0.00010991879,0.00276556],"genre_scores_gemma":[0.95403546,0.000016312753,0.044976212,0.000032559674,0.00008828813,0.000013677561,0.000029960673,0.0000085361835,0.0007990067],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999057,0.000048453992,0.00013683461,0.0005200126,0.000071566334,0.00016611218],"domain_scores_gemma":[0.9984095,0.000071574796,0.000156403,0.0012415143,0.00006173069,0.000059247202],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00027158868,0.00012418295,0.00012970113,0.00006543552,0.0014592428,0.0011066602,0.0009558623,0.000057005123,0.000003376184],"category_scores_gemma":[0.00011370678,0.00011876519,0.000024665535,0.00009974228,0.000103842336,0.0028428128,0.00087401364,0.00012461028,0.0000102173835],"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":[0.00007540927,0.00043460602,0.03677882,0.00012734554,0.000104000916,0.00003263261,0.0024066274,0.00049401534,0.04457005,0.040765665,0.0035279905,0.87068284],"study_design_scores_gemma":[0.0003683861,0.000125366,0.0057131224,0.00007856391,0.000023792361,0.000022775888,0.00016798249,0.9554408,0.015072134,0.00014565955,0.022510545,0.0003308476],"about_ca_topic_score_codex":0.000045821696,"about_ca_topic_score_gemma":0.000058595568,"teacher_disagreement_score":0.9549468,"about_ca_system_score_codex":0.000021482463,"about_ca_system_score_gemma":0.00010110144,"threshold_uncertainty_score":0.99993026},"labels":[],"label_agreement":null},{"id":"W2805573979","doi":"","title":"Convolutional Neural Network for Automatic Detection of Sociomoral Reasoning Level.","year":2017,"lang":"en","type":"article","venue":"Espace ÉTS (ETS)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Artificial neural network; Machine learning; Pattern recognition (psychology)","score_opus":0.030657443630151163,"score_gpt":0.29057989125734146,"score_spread":0.2599224476271903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805573979","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":[0.06591263,0.000040703882,0.931606,0.0013457591,0.00027124508,0.00030315106,0.000008468618,0.00023907196,0.00027297167],"genre_scores_gemma":[0.88511825,0.0000029726768,0.113929264,0.000052924992,0.00016719835,0.000116520256,0.0000020119037,0.000008945486,0.00060190674],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915916,0.000025796433,0.0001866412,0.00025127223,0.00015026766,0.00022686206],"domain_scores_gemma":[0.9988253,0.000069265545,0.0003352836,0.00059485174,0.00011823268,0.000057035842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027221514,0.000104569226,0.00014866613,0.00004496927,0.00081243314,0.00013350254,0.0005875415,0.00007664605,0.000009146281],"category_scores_gemma":[0.000077304285,0.000105981904,0.00011099055,0.000106950865,0.00009727892,0.0003692945,0.00014988941,0.0000956997,0.00001029969],"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":[0.00006091672,0.00020151206,0.0069727898,0.0001373644,0.00012253739,0.0000039399188,0.00088769826,0.002944436,0.026344968,0.53444785,0.014143746,0.41373223],"study_design_scores_gemma":[0.00040377906,0.00015870508,0.07475484,0.000040969848,0.000016866146,0.000020521695,0.00002618685,0.8924931,0.015453299,0.010135884,0.00625639,0.00023943685],"about_ca_topic_score_codex":0.00006291052,"about_ca_topic_score_gemma":0.00005451749,"teacher_disagreement_score":0.88954866,"about_ca_system_score_codex":0.00004822203,"about_ca_system_score_gemma":0.000046733632,"threshold_uncertainty_score":0.62486607},"labels":[],"label_agreement":null},{"id":"W2805630085","doi":"","title":"Recognizing Human Interactions Using Group Feature Relevance in Multinomial Kernel Logistic Regression.","year":2018,"lang":"en","type":"article","venue":"The Florida AI Research Society","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec","funders":"","keywords":"Multinomial logistic regression; Logistic regression; Kernel (algebra); Computer science; Artificial intelligence; Relevance (law); Feature (linguistics); Group (periodic table); Multinomial distribution; Machine learning; Pattern recognition (psychology); Statistics; Mathematics; Combinatorics","score_opus":0.1671893198132866,"score_gpt":0.4502641022920779,"score_spread":0.2830747824787913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805630085","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":[0.21677606,0.00016142582,0.7724644,0.007010661,0.0006436447,0.0009285885,0.00000733364,0.00047519428,0.0015326808],"genre_scores_gemma":[0.945345,0.000070633076,0.052214086,0.00030012793,0.0009313798,0.00012530375,0.00000259123,0.00001730939,0.0009935631],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981357,0.00024117314,0.00022706554,0.00048339504,0.00041310873,0.0004995437],"domain_scores_gemma":[0.9982468,0.00044608297,0.00008149178,0.0008151863,0.00032523178,0.000085191066],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0016295895,0.00013535934,0.00013626921,0.00008890902,0.0016735097,0.0002484484,0.0011458874,0.000110595094,0.000025809688],"category_scores_gemma":[0.00021606211,0.00009800133,0.00012704059,0.0012864746,0.00048322775,0.0004316585,0.00068866415,0.0012714115,0.00005795019],"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":[0.00011735448,0.0006548259,0.0034219893,0.00014134424,0.00013560017,0.000032688666,0.023714868,0.00022926166,0.45602918,0.071048036,0.30553648,0.13893835],"study_design_scores_gemma":[0.0010053602,0.0003897335,0.010381268,0.0005448263,0.000015114915,0.00009104865,0.0019911677,0.79671097,0.035278935,0.041088335,0.111739956,0.00076330884],"about_ca_topic_score_codex":0.00059221464,"about_ca_topic_score_gemma":0.00016629451,"teacher_disagreement_score":0.79648167,"about_ca_system_score_codex":0.00033148727,"about_ca_system_score_gemma":0.000075783886,"threshold_uncertainty_score":0.99962616},"labels":[],"label_agreement":null},{"id":"W2808673810","doi":"10.2352/issn.2470-1173.2018.09.iriacv-239","title":"Accumulated Relative Density Outlier Detection For Large Scale Traffic Data","year":2018,"lang":"en","type":"article","venue":"Electronic Imaging","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":12,"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":"Outlier; Anomaly detection; Local outlier factor; Data set; Mathematics; Data point; Statistics; Principal component analysis; Point (geometry); Scale (ratio); Set (abstract data type); Dimension (graph theory); Pattern recognition (psychology); Computer science; Data mining; Artificial intelligence; Geography; Cartography; Geometry; Combinatorics","score_opus":0.01717890585864737,"score_gpt":0.2951430971756584,"score_spread":0.277964191317011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808673810","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":[0.021586718,0.000069475536,0.9762813,0.00072982424,0.00009559088,0.0003484651,0.000005868093,0.0006123437,0.00027041783],"genre_scores_gemma":[0.981018,0.0000068613695,0.018215004,0.00022523622,0.0001405895,0.000046522127,0.000016101425,0.000014183076,0.00031747902],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987088,0.000030659572,0.00016769048,0.00052326795,0.00010277455,0.00046681825],"domain_scores_gemma":[0.9988283,0.00004187629,0.00009129229,0.00085897447,0.00013589258,0.000043695636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044492565,0.0001106868,0.00010063105,0.00008332764,0.00052942464,0.000100784724,0.00071997923,0.00004215962,0.0000095992045],"category_scores_gemma":[0.00002588422,0.000113887116,0.00004516131,0.0003986201,0.000037600785,0.0008085964,0.00024396136,0.00017854596,0.00004478226],"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":[0.00007171466,0.00032806137,0.0004004547,0.000021539588,0.00012192056,0.0000021070616,0.0018840244,0.000047406167,0.039915904,0.07070215,0.008249942,0.8782548],"study_design_scores_gemma":[0.00030475404,0.000090358684,0.00031361068,0.0000055369287,0.000016788712,0.000022896687,0.000023166353,0.8796047,0.043145817,0.006293226,0.06999476,0.000184409],"about_ca_topic_score_codex":0.000011616044,"about_ca_topic_score_gemma":0.0001358849,"teacher_disagreement_score":0.9594313,"about_ca_system_score_codex":0.0001336817,"about_ca_system_score_gemma":0.00008127329,"threshold_uncertainty_score":0.46441817},"labels":[],"label_agreement":null},{"id":"W2810520500","doi":"10.1007/978-3-319-93659-8_104","title":"Motion Processing and Big Data","year":2018,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Variety (cybernetics); Motion (physics); Component (thermodynamics); Big data; Tracking (education); Volume (thermodynamics); Instrumentation (computer programming); Match moving; Human motion; Data science; Artificial intelligence; Data mining","score_opus":0.05639615271055136,"score_gpt":0.3004619585639189,"score_spread":0.2440658058533675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810520500","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":[0.000053906126,0.018121567,0.9557593,0.0000285895,0.0004223758,0.00029352063,0.0000062254626,0.00014687204,0.02516768],"genre_scores_gemma":[0.8654136,0.023825338,0.06438922,0.00021734537,0.003129101,0.000057629844,0.00008238422,0.00013097962,0.042754404],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985103,0.000015025997,0.0004427213,0.00071904686,0.0001487825,0.00016410061],"domain_scores_gemma":[0.99889755,0.000055327808,0.00032382482,0.00059827627,0.000071887276,0.000053158947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034346673,0.00020711949,0.00026115333,0.00015325751,0.00018553769,0.00023390395,0.00056365074,0.00012771065,0.0000021661992],"category_scores_gemma":[0.000008301577,0.00019702339,0.000018576737,0.00007253155,0.00009512299,0.0003698888,0.00076300534,0.00019522748,0.0000060066614],"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":[7.86727e-7,0.000005179655,0.00004024853,0.00015046593,0.000004227738,0.0000020368077,0.00013000907,0.000036953046,0.000002072414,0.08921969,0.000049602313,0.9103587],"study_design_scores_gemma":[0.00007147327,0.00007972696,0.000022937942,0.0016562244,0.0000116165875,0.00011818028,0.000092359565,0.36247623,0.000051352567,0.029124817,0.605825,0.0004700666],"about_ca_topic_score_codex":0.000022914583,"about_ca_topic_score_gemma":0.000015829897,"teacher_disagreement_score":0.9098887,"about_ca_system_score_codex":0.000035247514,"about_ca_system_score_gemma":0.000020611196,"threshold_uncertainty_score":0.80343807},"labels":[],"label_agreement":null},{"id":"W2823573924","doi":"10.1109/mdm.2018.00024","title":"Tensor Methods for Group Pattern Discovery of Pedestrian Trajectories","year":2018,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cluster analysis; Trajectory; Group (periodic table); Data mining; Tensor (intrinsic definition); Artificial intelligence; Mathematics","score_opus":0.030508819793614677,"score_gpt":0.3519156673488131,"score_spread":0.3214068475551984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2823573924","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004018656,0.0000099732915,0.9940903,0.00028588434,0.00009635086,0.00020774621,0.0000041016347,0.00016069706,0.0011262542],"genre_scores_gemma":[0.4626335,0.000002796557,0.53669167,0.00008578477,0.00006887176,0.00006647791,6.9076816e-7,0.0000034695158,0.0004467489],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999478,0.000022228045,0.00015296348,0.00018446296,0.000047256326,0.00011509373],"domain_scores_gemma":[0.99940807,0.00009813354,0.000064859334,0.00032621433,0.000076613505,0.00002613274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020245177,0.000059915783,0.00009509937,0.000042157426,0.000083116036,0.000053254695,0.0003324696,0.00003323332,0.000011388791],"category_scores_gemma":[0.000016587775,0.00004720218,0.00006675994,0.00018072297,0.000062987245,0.00025853672,0.00006229195,0.000024893414,0.000002875333],"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":[0.0000060811885,0.00007396402,0.000782176,0.0000211631,0.000014847926,7.1807335e-8,0.00019303156,1.4286786e-7,0.03065625,0.17858516,0.0011350628,0.7885321],"study_design_scores_gemma":[0.0003258543,0.0009974701,0.007847327,0.000011628267,0.000015003299,0.0000071596996,0.00009295901,0.017806374,0.8488881,0.05872182,0.06499592,0.00029039895],"about_ca_topic_score_codex":0.00010574103,"about_ca_topic_score_gemma":0.000028988738,"teacher_disagreement_score":0.8182318,"about_ca_system_score_codex":0.000009205152,"about_ca_system_score_gemma":0.000016161046,"threshold_uncertainty_score":0.19248489},"labels":[],"label_agreement":null},{"id":"W28470321","doi":"10.1039/c7dt01039g","title":"Test-retest reliability of self-reports among prison inmates","year":2006,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Prison; Test (biology); Reliability (semiconductor); Psychology; Social psychology; Clinical psychology; Criminology; Power (physics)","score_opus":0.0035123638113836353,"score_gpt":0.20835614381699616,"score_spread":0.2048437800056125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W28470321","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":[0.4977177,0.000009859915,0.48565567,0.00016063153,0.0000290493,0.0002158471,7.177127e-7,0.0009393006,0.015271207],"genre_scores_gemma":[0.8601308,0.0000019959057,0.13934742,0.000018815506,0.000015087829,0.000037662652,8.3823375e-7,0.0000035761445,0.00044380606],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991828,0.0000103858465,0.00031311368,0.0002560903,0.00012477646,0.000112798225],"domain_scores_gemma":[0.99901766,0.00007999998,0.00015784115,0.0006001975,0.00011374149,0.000030548646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018195382,0.00007293887,0.00009918125,0.000044858585,0.00006223314,0.00003397274,0.00023437737,0.000050710398,0.000020251939],"category_scores_gemma":[0.000029306131,0.000061766885,0.000049467788,0.0003350911,0.000044376124,0.00022250896,0.00010350436,0.00005443418,0.0000070631827],"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":[5.265383e-7,0.00056330883,0.93374556,0.000031819276,0.0000043798755,0.0000043852715,0.000079383324,0.00007797609,0.008147621,0.048949797,0.004637775,0.0037574477],"study_design_scores_gemma":[0.00007691177,0.00014408732,0.4995673,0.000011803823,0.000008862521,0.000027194505,0.000012413923,0.024675682,0.42948994,0.037660886,0.008067594,0.0002573132],"about_ca_topic_score_codex":0.00051543704,"about_ca_topic_score_gemma":0.000018147028,"teacher_disagreement_score":0.4341783,"about_ca_system_score_codex":0.000022908218,"about_ca_system_score_gemma":0.000023201113,"threshold_uncertainty_score":0.25187802},"labels":[],"label_agreement":null},{"id":"W2884161042","doi":"10.1109/itsc.2018.8569519","title":"Unlimited Road-scene Synthetic Annotation (URSA) Dataset","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Annotation; Ground truth; Synthetic data; Limiting; Artificial intelligence; Segmentation; Convolutional neural network; Machine learning","score_opus":0.028271880551065866,"score_gpt":0.29384291420052017,"score_spread":0.2655710336494543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884161042","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011629597,0.000032164462,0.9909112,0.0016838842,0.00029036563,0.00043652687,0.0003925796,0.0010133638,0.004076998],"genre_scores_gemma":[0.415529,0.00014485938,0.5766747,0.0015325149,0.00040201022,0.00058933155,0.0034393102,0.00004415955,0.0016441091],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983899,0.00005380672,0.00031673926,0.0007857578,0.0002376151,0.00021619971],"domain_scores_gemma":[0.997573,0.000029289871,0.00021736829,0.0019277581,0.00015632724,0.00009620985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022710187,0.00022076881,0.00019179547,0.00018204574,0.0001598005,0.00028691613,0.0014467888,0.00023070612,0.00014055612],"category_scores_gemma":[0.000022411145,0.00020971854,0.00008043336,0.0003249577,0.00007325448,0.00018879795,0.0013991174,0.00027808725,0.00039097315],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007937607,0.00042626218,0.0000918486,0.00013141887,0.00011118034,0.0000079918555,0.00026431022,0.00025761058,0.0015813037,0.08314093,0.4548938,0.4590854],"study_design_scores_gemma":[0.000254927,0.00021900785,0.0037463317,0.00014832336,0.00006967622,0.00004735973,0.000023014105,0.5834885,0.03388447,0.064568095,0.31216577,0.0013845176],"about_ca_topic_score_codex":0.00017779782,"about_ca_topic_score_gemma":0.000011665494,"teacher_disagreement_score":0.5832309,"about_ca_system_score_codex":0.00006346292,"about_ca_system_score_gemma":0.00008464516,"threshold_uncertainty_score":0.8552073},"labels":[],"label_agreement":null},{"id":"W2885826197","doi":"10.11159/mvml18.3","title":"Putting Machine Vision and Machine Learning at Work in HumanMonitoring Applications","year":2018,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Human–machine system; Machine vision; Work (physics); Artificial intelligence; Human–computer interaction; Machine learning; Computer vision; Engineering","score_opus":0.006841503600306367,"score_gpt":0.22111290399575678,"score_spread":0.21427140039545042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885826197","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":[0.7840138,0.0021802993,0.20966059,0.00084722805,0.00082477427,0.0011177718,0.0000012779748,0.0005351289,0.0008191036],"genre_scores_gemma":[0.99359465,0.000032719854,0.0059432266,0.000015771277,0.00009617629,0.000041776606,4.122028e-8,0.0000057408593,0.00026990662],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989968,0.0000051856646,0.0002043957,0.00038177267,0.0001964028,0.0002154416],"domain_scores_gemma":[0.99954784,0.00006539704,0.00010645761,0.00011091039,0.00008933176,0.00008003868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040485463,0.00011844853,0.00015203241,0.00026229044,0.00037733116,0.00024961238,0.00040853323,0.000027621221,1.3490518e-7],"category_scores_gemma":[0.000015325622,0.00008722812,0.000017018088,0.001381645,0.00014457945,0.0001977806,0.00043682024,0.00019139875,3.1437793e-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":[0.000037236427,0.00015297992,0.09103758,0.0003432137,0.000028349872,0.0000014066376,0.0007362086,0.0022009928,0.037637427,0.48414963,0.00025851702,0.38341644],"study_design_scores_gemma":[0.00011831124,0.00012430036,0.011418507,0.00018980568,0.000002628027,0.000023577393,0.0000020429266,0.9804665,0.0036853633,0.00011501604,0.003727384,0.00012659338],"about_ca_topic_score_codex":0.000021197671,"about_ca_topic_score_gemma":0.0000016841157,"teacher_disagreement_score":0.97826546,"about_ca_system_score_codex":0.000040818897,"about_ca_system_score_gemma":0.000007200279,"threshold_uncertainty_score":0.35570595},"labels":[],"label_agreement":null},{"id":"W2886206145","doi":"10.1109/tnnls.2018.2855699","title":"Variational Bayesian Learning of Generalized Dirichlet-Based Hidden Markov Models Applied to Unusual Events Detection","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":43,"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":"Dirichlet distribution; Marginal likelihood; Hidden Markov model; Mixture model; Posterior probability; Computer science; Bayesian probability; Computation; Expectation–maximization algorithm; Artificial intelligence; Markov chain Monte Carlo; Context (archaeology); Mathematics; Machine learning; Applied mathematics; Pattern recognition (psychology); Algorithm; Mathematical optimization; Maximum likelihood; Statistics","score_opus":0.01188397169366679,"score_gpt":0.23078210556071602,"score_spread":0.21889813386704923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886206145","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":[0.019759852,0.000021270143,0.9787523,0.000101146215,0.00038860898,0.00043162706,0.0000019945016,0.00035328866,0.00018992547],"genre_scores_gemma":[0.98948485,0.00000818367,0.009676337,0.0000934519,0.00018091252,0.00020191226,0.000002475649,0.000024528166,0.0003273475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983336,0.00025680862,0.00039565793,0.00046772236,0.00027710348,0.00026915586],"domain_scores_gemma":[0.99913514,0.00012747184,0.00020949698,0.0002535683,0.00013587013,0.00013847419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038282917,0.0002006995,0.00024927952,0.00024491537,0.00074642396,0.000104485815,0.0002421474,0.0001393467,0.000009413701],"category_scores_gemma":[0.00000407768,0.00019829969,0.000087401386,0.0006892674,0.000044124543,0.00017846312,0.00000634116,0.00045264195,0.000004720501],"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":[0.0000548812,0.000039985283,0.00002367141,0.000011793329,0.000021256,3.5389104e-7,0.00010927231,0.93558717,0.0014212538,0.0010907302,0.000015380654,0.06162427],"study_design_scores_gemma":[0.00032159768,0.0004307564,0.00018368289,0.00003058931,0.000016773722,0.000009978145,0.00003131978,0.9967435,0.0016464926,0.00006108706,0.00032456705,0.00019963773],"about_ca_topic_score_codex":0.00014318712,"about_ca_topic_score_gemma":0.000012346234,"teacher_disagreement_score":0.969725,"about_ca_system_score_codex":0.000050434996,"about_ca_system_score_gemma":0.000022199449,"threshold_uncertainty_score":0.8086427},"labels":[],"label_agreement":null},{"id":"W2886248458","doi":"10.1155/2018/7191549","title":"An Improved Robust Principal Component Analysis Model for Anomalies Detection of Subway Passenger Flow","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Funding Project for Academic Human Resources Development in Institutions of Higher Learning under the Jurisdiction of Beijing Municipality; Beijing Municipal Education Commission","keywords":"Punctuality; Principal component analysis; Beijing; Anomaly detection; Computer science; Flow (mathematics); Data mining; Anomaly (physics); Operations research; Transport engineering; Engineering; Artificial intelligence; Geography; Mathematics","score_opus":0.014969910281846575,"score_gpt":0.2675625172839604,"score_spread":0.25259260700211383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886248458","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":[0.3319289,0.000013132286,0.6677823,0.00004139059,0.00005934419,0.00013126178,0.000010662053,0.000029644792,0.0000033712356],"genre_scores_gemma":[0.653168,0.000013766085,0.3467219,0.000012347266,0.000047881607,0.000015939806,0.000007082346,0.000005435965,0.000007649424],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989345,0.000017088683,0.00057688175,0.00017685915,0.00017674062,0.00011791114],"domain_scores_gemma":[0.99829227,0.00002668457,0.0006462488,0.00023676005,0.00072864565,0.00006941758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023423183,0.000099492194,0.0002308765,0.00028144763,0.00011913703,0.000023697965,0.0002526555,0.000056933295,0.0000021845772],"category_scores_gemma":[0.0000060446137,0.000091515954,0.00021539631,0.0005173316,0.000041490206,0.0007644437,0.000002601238,0.000078075806,1.7781207e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015395848,0.00015680182,0.00024878074,0.000017669903,0.00010364236,4.756232e-7,0.0011577888,0.6378214,0.3283324,0.0016207638,0.0000015451974,0.030384837],"study_design_scores_gemma":[0.00035900148,0.00058843277,0.02411133,0.000006861718,0.00014432227,0.0000021209278,0.000066713896,0.85749924,0.11578884,0.0012472324,0.00009181867,0.000094066185],"about_ca_topic_score_codex":0.0000054854977,"about_ca_topic_score_gemma":0.00018535779,"teacher_disagreement_score":0.3212391,"about_ca_system_score_codex":0.000037773876,"about_ca_system_score_gemma":0.00004092656,"threshold_uncertainty_score":0.37319124},"labels":[],"label_agreement":null},{"id":"W2886346447","doi":"10.1109/qrs.2018.00051","title":"Performance Analysis Using Automatic Grouping","year":2018,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Polytechnique Montréal","funders":"","keywords":"Computer science","score_opus":0.02167340803188958,"score_gpt":0.27629366614124984,"score_spread":0.2546202581093603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886346447","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":[0.2805995,0.0000015216364,0.7139777,0.000042657946,0.000016993923,0.000032910317,5.914962e-8,0.00035570032,0.0049729473],"genre_scores_gemma":[0.71411777,0.0000010295734,0.28559306,0.000103751685,0.000025086174,0.0000049238038,1.3164248e-7,0.0000015081446,0.00015272974],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999553,0.000007481833,0.00010747557,0.00015342428,0.00007661319,0.000101998],"domain_scores_gemma":[0.9995329,0.0000077392315,0.000040272917,0.00034247583,0.00004744441,0.000029210172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009255202,0.000045198114,0.000065840715,0.00014402454,0.00019857023,0.00006269603,0.00029169262,0.0000195227,0.00008960701],"category_scores_gemma":[0.0000019832316,0.000039346905,0.000046159163,0.0013250837,0.00002893802,0.00025312076,0.000085740736,0.000026142507,0.000060768038],"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":[0.0000016146599,0.0001470155,0.0327774,0.000034642657,0.000442171,0.0000017046732,0.0012095529,0.0007500575,0.01735981,0.19816403,0.0008234136,0.7482886],"study_design_scores_gemma":[0.000014365765,0.000020966578,0.007346663,0.0000018592172,0.00001942339,0.0000031814536,0.00000513251,0.98478436,0.007173931,0.00023322667,0.00033792778,0.000058930127],"about_ca_topic_score_codex":0.000025573736,"about_ca_topic_score_gemma":0.0000043269943,"teacher_disagreement_score":0.98403436,"about_ca_system_score_codex":0.000020929761,"about_ca_system_score_gemma":0.000010596785,"threshold_uncertainty_score":0.16045202},"labels":[],"label_agreement":null},{"id":"W2887330313","doi":"","title":"Neural Message Passing for Jet Physics","year":2017,"lang":"en","type":"article","venue":"Open Repository and Bibliography (University of Liège)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Energy Research Scientific Computing Center; Tencent; Canadian Institute for Advanced Research; Nvidia; National Science Foundation","keywords":"Physics; Jet (fluid); Computer science; Mechanics","score_opus":0.030454662094405397,"score_gpt":0.26330178887934946,"score_spread":0.23284712678494407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887330313","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":[0.0838673,0.000061183906,0.90293616,0.0011483488,0.00013285723,0.0004150674,0.000009983904,0.000085218715,0.011343912],"genre_scores_gemma":[0.95744634,0.00009694259,0.042100426,0.000023776223,0.000028622988,0.0000017556762,7.67379e-7,0.0000026423706,0.00029875827],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995269,0.000017990415,0.00006297715,0.0002292389,0.000070597074,0.00009228748],"domain_scores_gemma":[0.9991405,0.00002521663,0.00020353611,0.00050003297,0.00007612767,0.0000545778],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00011408914,0.0000662126,0.00011681786,0.0005576595,0.0016311647,0.00051483,0.0011439251,0.000042250773,0.0000016285621],"category_scores_gemma":[1.05896305e-7,0.00007105726,0.00010596085,0.00096928526,0.00015333634,0.0013221479,0.0005108596,0.00004952485,2.8261255e-7],"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":[0.00016629578,0.00054849917,0.67909473,0.00018912672,0.00030499318,0.00005120877,0.00095202663,0.000011483078,0.04522548,0.1650969,0.056545734,0.05181351],"study_design_scores_gemma":[0.0019825983,0.0007472457,0.8915799,0.00013490203,0.00015263385,0.000054246353,0.0005442384,0.023029469,0.04413849,0.018113859,0.01864874,0.0008736717],"about_ca_topic_score_codex":0.000556507,"about_ca_topic_score_gemma":0.0000034007455,"teacher_disagreement_score":0.873579,"about_ca_system_score_codex":0.0000022053162,"about_ca_system_score_gemma":0.000010804622,"threshold_uncertainty_score":0.9996686},"labels":[],"label_agreement":null},{"id":"W288980890","doi":"10.1137/1.9781611973440.95","title":"Discriminative Density-ratio Estimation","year":2014,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Discriminative model; Matching (statistics); Computer science; Covariate; Density estimation; Artificial intelligence; Class (philosophy); Regression; Focus (optics); Machine learning; Pattern recognition (psychology); Mathematics; Statistics; Estimator","score_opus":0.018263534251393227,"score_gpt":0.28047591596567434,"score_spread":0.26221238171428113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W288980890","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":[0.0008488197,0.000006203602,0.9832936,0.0016047555,0.00015559046,0.00030218967,0.0000015228267,0.00085319125,0.012934131],"genre_scores_gemma":[0.62437415,0.000004950386,0.3740886,0.00022822258,0.000041742478,0.00013875277,0.000012163732,0.0000056445083,0.0011058018],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902415,0.000041252024,0.0002049543,0.0004541625,0.00016012696,0.000115359275],"domain_scores_gemma":[0.99878734,0.000039374165,0.00015969286,0.0008328775,0.00012784942,0.00005286626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015540695,0.00015076344,0.00015431746,0.00009733275,0.00013183498,0.0002424989,0.0007018589,0.00014897822,0.000019994503],"category_scores_gemma":[0.000022048469,0.00013685366,0.00008154534,0.00011584207,0.000032341253,0.00015108769,0.00094400573,0.00026135484,0.000111139525],"study_design_candidate":"theoretical_or_conceptual","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":[4.7006682e-7,0.00003055653,0.000010219282,0.000024968493,0.000009463023,5.1181974e-7,0.00019332378,0.0012844725,0.00011059904,0.90311635,0.0034156556,0.09180339],"study_design_scores_gemma":[0.000028689783,0.000021414266,0.0005489156,0.000016870506,0.0000075784146,0.0000035837165,0.000005784174,0.78720164,0.012985375,0.19768882,0.0013015198,0.00018979373],"about_ca_topic_score_codex":0.00006337898,"about_ca_topic_score_gemma":0.000009773312,"teacher_disagreement_score":0.78591716,"about_ca_system_score_codex":0.000056727953,"about_ca_system_score_gemma":0.000051972132,"threshold_uncertainty_score":0.55807304},"labels":[],"label_agreement":null},{"id":"W2890491818","doi":"10.1109/icra.2018.8460840","title":"Deep Learning a Quadrotor Dynamic Model for Multi-Step Prediction","year":2018,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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 Waterloo","funders":"","keywords":"Initialization; Computer science; Artificial neural network; Artificial intelligence; Recurrent neural network; Deep learning; Motion (physics); Exploit; Black box; State (computer science); Machine learning; Algorithm","score_opus":0.0288259425513264,"score_gpt":0.30044844984822994,"score_spread":0.2716225072969035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890491818","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012158485,0.000004842204,0.99660176,0.00012364304,0.000048418406,0.00040896438,0.0000013962695,0.0008950841,0.0007000596],"genre_scores_gemma":[0.44511282,0.0000025864206,0.550719,0.00007151424,0.000025255718,0.000290031,0.0000015638096,0.000005323748,0.003771899],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940425,0.0000091486845,0.00012897361,0.0002507033,0.00006833844,0.00013858687],"domain_scores_gemma":[0.99954563,0.000014321448,0.000048775924,0.00023914597,0.00010773094,0.000044363904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010660117,0.000066989385,0.000058425303,0.0000552866,0.0002718826,0.000056254088,0.00025978245,0.000051840158,0.000011211907],"category_scores_gemma":[0.000012158448,0.000062006555,0.000048646307,0.00014604161,0.000029201481,0.00020406119,0.00006574142,0.000060521044,0.000037149148],"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":[0.00002766964,0.00033378747,0.0007603971,0.00003772583,0.00004080973,2.818244e-7,0.0016897337,0.011287323,0.0122496765,0.20513323,0.002321334,0.76611805],"study_design_scores_gemma":[0.00013357573,0.00012278928,0.00038838224,0.0000023209152,0.000002397733,0.0000025743298,0.000016904625,0.99534595,0.00063815806,0.001014455,0.0022602673,0.000072209965],"about_ca_topic_score_codex":0.000015771697,"about_ca_topic_score_gemma":0.000057291887,"teacher_disagreement_score":0.9840586,"about_ca_system_score_codex":0.000037291673,"about_ca_system_score_gemma":0.000015606376,"threshold_uncertainty_score":0.2528554},"labels":[],"label_agreement":null},{"id":"W2890576388","doi":"10.1007/s41666-019-00061-4","title":"DeepFall: Non-Invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders","year":2019,"lang":"en","type":"preprint","venue":"Journal of Healthcare Informatics Research","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Health Network; Toronto Rehabilitation Institute; University of Toronto","funders":"","keywords":"Autoencoder; Computer science; Artificial intelligence; Anomaly detection; Convolutional neural network; Pattern recognition (psychology); Modalities; Deep learning; Anomaly (physics); Perspective (graphical); Machine learning","score_opus":0.052966584355553674,"score_gpt":0.3612514232270916,"score_spread":0.3082848388715379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890576388","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":[0.024427855,0.00019078807,0.9704174,0.0027866217,0.00041860988,0.001071411,0.000014737985,0.000082280356,0.0005902999],"genre_scores_gemma":[0.83350307,0.00046191647,0.16541398,0.00021715737,0.00019540837,0.00008668505,0.000018727274,0.000022763514,0.00008030119],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99506134,0.00024977967,0.0015838734,0.00025846786,0.0022292477,0.00061728666],"domain_scores_gemma":[0.99184144,0.00037523918,0.0016897027,0.0009263595,0.004783735,0.00038352679],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0028443371,0.0002869056,0.00053377694,0.0013050585,0.00045139977,0.00046612733,0.0018775063,0.00045477104,0.0000092641185],"category_scores_gemma":[0.00014666951,0.00023725524,0.00022217544,0.00082157925,0.00018395219,0.00090815115,0.0009319459,0.003474946,0.000060878785],"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":[0.0020748596,0.0017900016,0.03212875,0.024002781,0.0021728303,0.00039440495,0.065910615,0.34812889,0.00029857171,0.056008667,0.024196407,0.44289324],"study_design_scores_gemma":[0.0011056025,0.003336316,0.0032907382,0.0013777453,0.000028264021,0.00092576887,0.003033785,0.95606947,0.0018400014,0.016176112,0.012083398,0.0007327863],"about_ca_topic_score_codex":0.0007498077,"about_ca_topic_score_gemma":0.001227535,"teacher_disagreement_score":0.80907524,"about_ca_system_score_codex":0.0011557781,"about_ca_system_score_gemma":0.004745226,"threshold_uncertainty_score":0.99882406},"labels":[],"label_agreement":null},{"id":"W2890739803","doi":"10.1109/tpami.2017.2757489","title":"Ghost Numbers","year":2017,"lang":"en","type":"letter","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Partition (number theory); Set (abstract data type); Image (mathematics); Pattern recognition (psychology); Machine learning; Algorithm; Mathematics","score_opus":0.021380227084035607,"score_gpt":0.27967285770368183,"score_spread":0.25829263061964625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890739803","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":[0.0000063437155,0.00005812357,0.9044184,0.094148204,0.0002561696,0.00019005642,0.00012422894,0.00026087,0.0005375881],"genre_scores_gemma":[0.7702371,0.0015025818,0.00949668,0.2080635,0.00049616466,0.00033064053,0.000058308106,0.00007474212,0.0097403],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99770725,0.00007461133,0.0004619547,0.0010161371,0.00038809248,0.000351969],"domain_scores_gemma":[0.9973938,0.00015237273,0.00034844968,0.0018905081,0.0001008276,0.00011405892],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017259047,0.00043464062,0.0005475958,0.00073623203,0.00066901493,0.0005304408,0.0015222307,0.00042744426,0.0002612691],"category_scores_gemma":[0.0000037881207,0.00039943762,0.00059462595,0.00063161703,0.0001460649,0.00024792398,0.000014334872,0.0014831859,0.00014174206],"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":[0.0000024193878,0.00008567098,0.000035490917,0.000043119144,0.0009953442,0.000093516115,0.000118408556,0.00039733297,0.000016845417,0.00014858835,0.021762373,0.9763009],"study_design_scores_gemma":[0.00021845613,0.00058661425,0.0003248422,0.000295847,0.004713707,0.0002850599,0.00003646651,0.14495431,0.136808,0.008131136,0.6997713,0.003874261],"about_ca_topic_score_codex":0.0022694862,"about_ca_topic_score_gemma":0.00040413116,"teacher_disagreement_score":0.97242665,"about_ca_system_score_codex":0.00005698631,"about_ca_system_score_gemma":0.00003453255,"threshold_uncertainty_score":0.99984574},"labels":[],"label_agreement":null},{"id":"W2890953374","doi":"10.1007/978-3-030-03801-4_65","title":"Road User Abnormal Trajectory Detection Using a Deep Autoencoder","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Autoencoder; Computer science; Focus (optics); Artificial intelligence; Deep learning; Anomaly detection; Trajectory; Outlier; Computer vision; Pattern recognition (psychology); Machine learning","score_opus":0.01761473281827005,"score_gpt":0.2520296678389761,"score_spread":0.23441493502070607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890953374","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00050807645,0.0001353512,0.99586433,0.00008471664,0.00091674225,0.0004414321,0.000002412106,0.00052989426,0.0015170317],"genre_scores_gemma":[0.14490823,0.00002609547,0.85294175,0.0006860036,0.00087146554,0.000032467353,0.000001852552,0.00005344557,0.00047865318],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966104,0.00003204228,0.0005286313,0.0014956349,0.0007163069,0.00061700685],"domain_scores_gemma":[0.9976787,0.00009330821,0.00033606202,0.0014116241,0.000306206,0.00017407446],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00058331643,0.00049458264,0.0003920044,0.00090613624,0.0005496699,0.00048762592,0.002284787,0.00043565774,0.000096180505],"category_scores_gemma":[0.000022142802,0.00048327036,0.0001771614,0.0007201291,0.00068016513,0.0008040135,0.00083081005,0.00072067714,0.00009307532],"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":[0.00000648447,0.000038826573,0.000020271482,0.00002465464,0.000015627453,0.000025728225,0.0006215684,0.026143553,0.001502373,0.0035313962,0.000013007956,0.9680565],"study_design_scores_gemma":[0.000121518235,0.00017438532,0.0001347947,0.00008959676,0.0000105933095,0.00017561569,1.9275218e-7,0.9506492,0.008283179,0.036634155,0.0031106623,0.00061613275],"about_ca_topic_score_codex":0.00007184226,"about_ca_topic_score_gemma":0.00013515068,"teacher_disagreement_score":0.96744037,"about_ca_system_score_codex":0.00047809893,"about_ca_system_score_gemma":0.00036950805,"threshold_uncertainty_score":0.9997619},"labels":[],"label_agreement":null},{"id":"W2894423278","doi":"10.1145/3110218","title":"Dependable Deep Computation Model for Feature Learning on Big Data in Cyber-Physical Systems","year":2018,"lang":"en","type":"article","venue":"ACM Transactions on Cyber-Physical Systems","topic":"Anomaly Detection Techniques and Applications","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":"St. Francis Xavier University","funders":"","keywords":"Deep learning; Computer science; Big data; Artificial intelligence; Computation; Feature (linguistics); Data modeling; Feature learning; Machine learning; Autoencoder; Cyber-physical system; Data mining; Algorithm; Database","score_opus":0.05205405559164946,"score_gpt":0.30541126428534515,"score_spread":0.2533572086936957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2894423278","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":[0.019013232,0.000022520611,0.97740763,0.00048146356,0.0006028793,0.001284589,0.000105377294,0.000602378,0.00047994507],"genre_scores_gemma":[0.98842317,0.0000052407167,0.009133111,0.000058248723,0.0007537386,0.00068495696,0.000072080424,0.000051038216,0.0008183909],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972066,0.00017335001,0.00042619152,0.0011462756,0.00052406866,0.00052350655],"domain_scores_gemma":[0.99705476,0.00053220225,0.00022307127,0.0017975993,0.00021456499,0.0001778268],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00035106158,0.0003657466,0.0005140134,0.00026126183,0.00068737933,0.00037213575,0.0016357,0.00020085067,7.8525426e-7],"category_scores_gemma":[0.00003906959,0.00033968384,0.00017265067,0.0009384291,0.000094471,0.0005817287,0.000072643794,0.0006276238,0.00012476],"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":[0.00012217945,0.002000279,0.0000293087,0.0001485221,0.00014257345,0.0000063736034,0.0017105605,0.74050045,0.004088505,0.035261158,0.0011358568,0.21485423],"study_design_scores_gemma":[0.00047893162,0.00052394293,0.000067089306,0.00009709501,0.000030868123,0.000013241929,0.00010529878,0.9921931,0.0013419569,0.0020738065,0.0027029694,0.00037171613],"about_ca_topic_score_codex":0.0005107847,"about_ca_topic_score_gemma":0.00010860579,"teacher_disagreement_score":0.96940994,"about_ca_system_score_codex":0.0002172738,"about_ca_system_score_gemma":0.00008031414,"threshold_uncertainty_score":0.9999055},"labels":[],"label_agreement":null},{"id":"W2895303702","doi":"10.1007/s00180-018-0843-6","title":"Package mTEXO for testing the presence of outliers in exponential samples","year":2018,"lang":"en","type":"article","venue":"Computational Statistics","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Ministry of Science and Technology, Taiwan","keywords":"Outlier; Exponential function; Mathematics; Statistics; R package; Computer science; Mathematical analysis","score_opus":0.05998396423568078,"score_gpt":0.3081988046849449,"score_spread":0.24821484044926415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895303702","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037110911,0.000008125644,0.9955169,0.00016468178,0.00007179137,0.00024332119,0.00011166652,0.000043415777,0.00012901849],"genre_scores_gemma":[0.4940053,4.9201043e-7,0.5058579,0.000043516895,0.000033733766,0.000033222,0.000008095586,0.0000027577341,0.000014988016],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933535,0.000025897669,0.00022355416,0.00015926118,0.00014470691,0.0001112133],"domain_scores_gemma":[0.9982398,0.0011712187,0.00011968589,0.0001619548,0.00028428854,0.000023020017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018253613,0.00005801233,0.00007321987,0.000047084162,0.00015735617,0.000041259747,0.00036224668,0.000020100137,0.0000051004777],"category_scores_gemma":[0.0002743406,0.000048804442,0.000017974942,0.0002841364,0.00015017111,0.00007075836,0.000083414045,0.000045036715,0.00000430417],"study_design_candidate":"theoretical_or_conceptual","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":[0.00000805546,0.000064928216,0.0010754137,0.000020339,0.000008837729,6.5870086e-7,0.0006111378,0.004420168,0.00092181424,0.91390735,0.0046825474,0.07427875],"study_design_scores_gemma":[0.00014327304,0.00011267336,0.026143225,0.0000110151905,0.0000031076218,0.0000030087672,0.000027456164,0.6551932,0.001047793,0.31566304,0.0015743321,0.000077885],"about_ca_topic_score_codex":0.000037904665,"about_ca_topic_score_gemma":0.000012784006,"teacher_disagreement_score":0.65077305,"about_ca_system_score_codex":0.000017613205,"about_ca_system_score_gemma":0.000063084684,"threshold_uncertainty_score":0.19901875},"labels":[],"label_agreement":null},{"id":"W2901801304","doi":"10.1016/j.knosys.2018.11.011","title":"A hierarchical memory network-based approach to uncertain streaming data","year":2018,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Changjiang Scholar Program of Chinese Ministry of Education; China Scholarship Council; Science and Technology Commission of Shanghai Municipality; Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Forgetting; Computer science; Outlier; Artificial intelligence; Streaming data; Term (time); Memory model; Data mining; Machine learning; Shared memory","score_opus":0.057031543272017544,"score_gpt":0.30351011396684957,"score_spread":0.24647857069483203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901801304","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":[0.00076596986,0.00017006477,0.9771162,0.0003084862,0.00049424963,0.00086128776,0.000025675157,0.0008561374,0.019401923],"genre_scores_gemma":[0.8710635,4.879563e-7,0.12611808,0.0002667505,0.0011579426,0.00043133312,0.000056248336,0.000029469318,0.0008761977],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974951,0.00026331202,0.0004024954,0.0010209776,0.0002977366,0.00052037585],"domain_scores_gemma":[0.9963137,0.00020232207,0.00013245898,0.0028005347,0.000251031,0.00029999026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010335639,0.00025207916,0.00030245061,0.0002098598,0.0004694277,0.00029142888,0.0026013502,0.00014069899,0.000008277182],"category_scores_gemma":[0.000054041095,0.00023151482,0.000076146076,0.001388616,0.00012827895,0.00019216376,0.0004842571,0.00021489015,0.0002829107],"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":[0.00014987143,0.0035676672,0.0013206889,0.00088402885,0.00017912764,0.000022534623,0.0015883177,0.05137462,0.0030305032,0.21303618,0.40378654,0.32105994],"study_design_scores_gemma":[0.00026964777,0.00015456598,0.00009482022,0.00009543691,0.000009600211,0.0000067263923,0.00002615818,0.8934549,0.00092191354,0.00011570952,0.10455363,0.00029684987],"about_ca_topic_score_codex":0.00014292117,"about_ca_topic_score_gemma":0.0000393261,"teacher_disagreement_score":0.87029755,"about_ca_system_score_codex":0.0001294377,"about_ca_system_score_gemma":0.00039218442,"threshold_uncertainty_score":0.94409},"labels":[],"label_agreement":null},{"id":"W2902025192","doi":"10.29007/xwfw","title":"Road User Abnormal Trajectory Detection using a Deep Autoencoder","year":2018,"lang":"en","type":"paratext","venue":"EasyChair preprint","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Institut de Valorisation des Données","keywords":"Autoencoder; Focus (optics); Computer science; Artificial intelligence; Deep learning; Anomaly detection; Trajectory; Outlier; Computer vision; Pattern recognition (psychology); Machine learning","score_opus":0.020193309876833297,"score_gpt":0.2780743541197892,"score_spread":0.2578810442429559,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902025192","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":[0.0011331656,0.00017710842,0.97054446,0.00006370523,0.0016916496,0.0007778736,0.000007965584,0.0007293932,0.024874704],"genre_scores_gemma":[0.72837925,0.00014039723,0.22406092,0.00033259514,0.0017885711,0.00083368813,0.00001822117,0.000120659104,0.0443257],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972627,0.00012661764,0.00054135907,0.0012266231,0.0003700954,0.0004726106],"domain_scores_gemma":[0.9973697,0.000020878471,0.0004212301,0.0017825871,0.00022868445,0.00017689352],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003621856,0.000429115,0.00036514894,0.0003074515,0.00042086837,0.00025871614,0.0013328715,0.0005091224,0.0012527659],"category_scores_gemma":[0.000011596116,0.00044867033,0.00029399872,0.00042373876,0.000113083945,0.00038102482,0.0006793415,0.0006024799,0.006600506],"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":[0.0001207759,0.0012810461,0.000122062775,0.0005335548,0.00068081194,0.000033962984,0.004628619,0.35583314,0.019992964,0.006926559,0.050123945,0.55972254],"study_design_scores_gemma":[0.00024449982,0.00017501581,0.00091026444,0.00010205093,0.0000125457445,0.0001588649,0.000025899762,0.67389554,0.023572478,0.0008239566,0.29907498,0.0010039202],"about_ca_topic_score_codex":0.00035399926,"about_ca_topic_score_gemma":0.000047614845,"teacher_disagreement_score":0.7464835,"about_ca_system_score_codex":0.0003055129,"about_ca_system_score_gemma":0.00020785572,"threshold_uncertainty_score":0.9997965},"labels":[],"label_agreement":null},{"id":"W2902415114","doi":"10.1137/1.9781611975673.66","title":"LSCP: Locally Selective Combination in Parallel Outlier Ensembles","year":2019,"lang":"en","type":"book-chapter","venue":"Society for Industrial and Applied Mathematics eBooks","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":139,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"PricewaterhouseCoopers (Canada); University of Toronto","funders":"","keywords":"Outlier; Linear subspace; Anomaly detection; Computer science; Base (topology); Task (project management); Stability (learning theory); Artificial intelligence; Pattern recognition (psychology); Data mining; Local outlier factor; Feature (linguistics); Machine learning; Mathematics; Engineering","score_opus":0.0393636122034942,"score_gpt":0.24020444092177468,"score_spread":0.20084082871828046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902415114","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012341818,0.00003486858,0.5951776,0.000214138,0.00011294174,0.0035016853,0.000042488264,0.00026951073,0.40052333],"genre_scores_gemma":[0.025492363,0.00012486656,0.37217864,0.0006879969,0.0005886266,0.0017569939,0.00009178943,0.00022436111,0.59885436],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985196,0.0000036179333,0.0004919677,0.00050802145,0.00022596127,0.00025085674],"domain_scores_gemma":[0.99890476,0.00018828758,0.00037498775,0.00037652362,0.000089029534,0.00006642373],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033211545,0.00033363907,0.0004838174,0.00007636698,0.00017307237,0.00012566766,0.0003730584,0.0007717228,0.000004331727],"category_scores_gemma":[0.0000066469115,0.00032288762,0.00028869807,0.000040010622,0.00009804989,0.000049289025,0.00018668575,0.000510933,0.000012879588],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000070736205,0.000031386884,2.548909e-7,0.00007008681,0.000054984557,1.2828754e-7,0.0005842725,0.0000051822135,0.00012580754,0.9764934,0.0023082474,0.020319179],"study_design_scores_gemma":[0.00165214,0.00017149892,7.676065e-7,0.00015147134,0.00006612382,0.000005059227,0.00013103038,0.0043840967,0.0013885592,0.94066423,0.050805036,0.0005799827],"about_ca_topic_score_codex":0.0000033169763,"about_ca_topic_score_gemma":0.0000041486874,"teacher_disagreement_score":0.22299895,"about_ca_system_score_codex":0.00010740244,"about_ca_system_score_gemma":0.00017351715,"threshold_uncertainty_score":0.99992234},"labels":[],"label_agreement":null},{"id":"W2904290903","doi":"10.48550/arxiv.1812.07410","title":"An Improved Deep Belief Network Model for Road Safety Analyses","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence","score_opus":0.10070326643532959,"score_gpt":0.2499663142212201,"score_spread":0.1492630477858905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904290903","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":[0.006837083,0.000025912732,0.9908739,0.000052865085,0.00014902336,0.00060088036,0.000028995137,0.00064848235,0.00078290945],"genre_scores_gemma":[0.8839686,0.00009079361,0.114580974,0.00014078537,0.0002120399,0.00001345343,0.000036463523,0.000020492722,0.00093640015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981107,0.000053166274,0.00024004166,0.0011760008,0.000053757623,0.00036632462],"domain_scores_gemma":[0.9974655,0.000037933794,0.00029757555,0.001726165,0.00029153223,0.0001813198],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024010966,0.00027950684,0.00029869284,0.00014443918,0.00045068495,0.00013114545,0.001980289,0.0003197882,0.000012297025],"category_scores_gemma":[0.000008377116,0.0003184327,0.00028645407,0.00051740505,0.00010131089,0.00034050297,0.0009314643,0.00027456263,0.000014223205],"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":[0.00003474335,0.00009072544,0.00006524619,0.000019879599,0.00007150822,0.0000024614396,0.00007365552,0.91798604,0.00012240259,0.07660648,0.0003776718,0.004549201],"study_design_scores_gemma":[0.00015923861,0.00009521266,0.00013103453,0.000012928422,0.000065324784,8.581466e-7,0.000007609525,0.90061396,0.00020129907,0.0979148,0.00046842007,0.00032931418],"about_ca_topic_score_codex":0.000116500465,"about_ca_topic_score_gemma":0.00010916024,"teacher_disagreement_score":0.8771315,"about_ca_system_score_codex":0.00015042689,"about_ca_system_score_gemma":0.0001505366,"threshold_uncertainty_score":0.99992675},"labels":[],"label_agreement":null},{"id":"W2910857484","doi":"10.48550/arxiv.1901.08680","title":"Multi-objective training of Generative Adversarial Networks with multiple discriminators","year":2019,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Institut National de la Recherche Scientifique; McGill University; Université du Québec","funders":"","keywords":"Discriminator; Maximization; Computer science; Adversarial system; Gradient descent; Mathematical optimization; Generative grammar; Minification; Generator (circuit theory); Multi-objective optimization; Leverage (statistics); Artificial intelligence; Machine learning; Algorithm; Mathematics; Artificial neural network","score_opus":0.043523412042016714,"score_gpt":0.17943603438791716,"score_spread":0.13591262234590046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910857484","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":[0.28181303,0.000003187573,0.7172129,0.000011001994,0.000051891526,0.00018776442,0.0000021200733,0.00009458918,0.00062351185],"genre_scores_gemma":[0.97602934,0.000006378358,0.023436068,0.000025865647,0.000018113924,0.0000016887047,0.0000018172751,0.000007467593,0.0004732585],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924463,0.00004163611,0.00009592914,0.0004056723,0.000044591863,0.00016756672],"domain_scores_gemma":[0.99928087,0.00006394805,0.00012650702,0.00037092456,0.00009464478,0.00006307678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007371281,0.00011380829,0.00014879301,0.00009137293,0.000102000966,0.000017138598,0.00040402124,0.00006271206,0.000012563956],"category_scores_gemma":[0.000005906609,0.000107776235,0.00006685329,0.00057128945,0.00007895906,0.00035224427,0.000117052965,0.00012052588,0.000009660078],"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":[0.000182622,0.00031454256,0.039938163,0.000018762477,0.00017579707,0.00003713614,0.0038999289,0.5394032,0.0035638064,0.40781268,0.00006822342,0.0045851194],"study_design_scores_gemma":[0.000816962,0.00020369537,0.0058902283,0.0000138877285,0.000017338114,0.0000029262596,0.00078417116,0.9880658,0.0033563382,0.00054787606,0.0001155134,0.00018527127],"about_ca_topic_score_codex":0.00009249015,"about_ca_topic_score_gemma":0.000051269348,"teacher_disagreement_score":0.6942163,"about_ca_system_score_codex":0.000054282333,"about_ca_system_score_gemma":0.00005037749,"threshold_uncertainty_score":0.43949875},"labels":[],"label_agreement":null},{"id":"W2911153497","doi":"10.1109/rtcsa.2018.00035","title":"Hierarchical Attention-Based Anomaly Detection Model for Embedded Operating Systems","year":2018,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":26,"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":"Computer science; Anomaly detection; Executable; Debugging; Bespoke; Fault detection and isolation; Embedded operating system; A priori and a posteriori; Software system; Kernel (algebra); Software; Real-time operating system; Set (abstract data type); TRACE (psycholinguistics); Embedded system; Real-time computing; Data mining; Operating system; Artificial intelligence; Programming language","score_opus":0.02409379219822335,"score_gpt":0.2805511547473958,"score_spread":0.25645736254917245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911153497","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":[0.009493157,0.0000039904885,0.98707175,0.00058018416,0.00010556431,0.00047025643,0.0000027237238,0.0006547953,0.0016176016],"genre_scores_gemma":[0.72914106,2.9402938e-7,0.2691525,0.00035537666,0.00007382459,0.00035420805,0.0000018835628,0.000007530623,0.0009133003],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991252,0.000030925203,0.00022102849,0.0003337772,0.00011613365,0.0001729286],"domain_scores_gemma":[0.9992462,0.00005310893,0.00006335058,0.00036640183,0.00020842785,0.0000625088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027059377,0.00009456195,0.0000936219,0.00008747771,0.00037343954,0.00022516359,0.00030434964,0.000064620144,0.0000048989828],"category_scores_gemma":[0.000017860135,0.00008481498,0.00007331183,0.00023352596,0.00004045249,0.00024545778,0.000050418384,0.00006965157,0.000020356567],"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":[0.000019544157,0.00020460933,0.00014215945,0.000050929695,0.000026373664,4.806439e-7,0.00029990636,0.017253458,0.1424726,0.8000704,0.0019272261,0.037532307],"study_design_scores_gemma":[0.0001573864,0.00014136975,0.00008587237,0.000006776505,0.000003040695,0.0000037475374,0.000010950416,0.9811122,0.015993377,0.0017300702,0.0006410644,0.000114133414],"about_ca_topic_score_codex":0.000039031074,"about_ca_topic_score_gemma":0.000025057856,"teacher_disagreement_score":0.9638588,"about_ca_system_score_codex":0.000035475743,"about_ca_system_score_gemma":0.00006220163,"threshold_uncertainty_score":0.34586546},"labels":[],"label_agreement":null},{"id":"W2912183823","doi":"10.5210/fm.v24i2.8237","title":"Down the deep rabbit hole: Untangling deep learning from machine learning and artificial intelligence","year":2019,"lang":"en","type":"article","venue":"First Monday","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Deep learning; Artificial intelligence; Context (archaeology); Computer science; Machine learning; History","score_opus":0.012278418433392621,"score_gpt":0.23040662983519308,"score_spread":0.21812821140180047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912183823","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":[0.14381573,0.0005164153,0.85352516,0.0011095828,0.00010715126,0.00018280657,0.0000011595802,0.00032480687,0.0004171756],"genre_scores_gemma":[0.98719645,0.00013802707,0.012046167,0.00009883958,0.0000755384,0.000033707696,0.0000064101837,0.000013101099,0.00039175746],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989457,0.00006044134,0.00021819079,0.00039746636,0.00016616711,0.00021199191],"domain_scores_gemma":[0.9991244,0.00022964875,0.000120051984,0.0004234223,0.000042879576,0.000059563743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002532673,0.00013557952,0.00013008142,0.000058250633,0.000590604,0.00025057842,0.00061756396,0.000053092273,0.00017635293],"category_scores_gemma":[0.00006227273,0.00010630254,0.000051344105,0.00034220365,0.00005438487,0.00019589569,0.0004067123,0.00046949042,0.00022945901],"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":[0.000025278889,0.0000841218,0.017962962,0.000025102854,0.000055272667,0.000016243857,0.022895915,0.033565566,0.0046417774,0.028095096,0.00008120159,0.8925515],"study_design_scores_gemma":[0.00004268637,0.0000785509,0.00091129803,0.000017526101,0.0000070547376,0.000009522595,0.00019660617,0.861907,0.007682097,0.005357887,0.12359384,0.00019591917],"about_ca_topic_score_codex":0.00019271822,"about_ca_topic_score_gemma":0.000030578343,"teacher_disagreement_score":0.89235556,"about_ca_system_score_codex":0.000027137174,"about_ca_system_score_gemma":0.000009913368,"threshold_uncertainty_score":0.4542508},"labels":[],"label_agreement":null},{"id":"W2912978515","doi":"","title":"DFNAnalyzer: A Web-Based Application for Discrete Fracture Network Analysis","year":2018,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Computer science; Fracture (geology); World Wide Web; Geology","score_opus":0.006502668218953674,"score_gpt":0.2663710128813928,"score_spread":0.25986834466243913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912978515","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":[0.00020529146,0.000012775768,0.99417675,0.002597537,0.00002256793,0.0003836371,0.0000039222805,0.00056777394,0.0020297617],"genre_scores_gemma":[0.73313856,0.0000016468925,0.26433793,0.0015314512,0.0001811886,0.00040015788,0.0000147672345,0.0000054913344,0.00038883937],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991035,0.000015476238,0.0001851971,0.000385616,0.00011215187,0.00019810049],"domain_scores_gemma":[0.998874,0.000056234276,0.0001101987,0.00073545874,0.0001556631,0.000068423804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018964897,0.00010029251,0.00013991409,0.00012354551,0.00029200036,0.00008673856,0.0005128379,0.0000715391,0.000056438388],"category_scores_gemma":[0.000005797213,0.00008261908,0.00019372693,0.001673661,0.00004441411,0.00012481761,0.000048411275,0.00005017289,0.000037050093],"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":[0.000054250606,0.00025335888,0.009514946,0.000027767815,0.000867224,5.156477e-7,0.00014184373,0.024000822,0.0034067587,0.6767936,0.13311633,0.1518226],"study_design_scores_gemma":[0.00009616109,0.00005812738,0.0012050478,0.0000010808805,0.00007843013,2.2883637e-7,0.0000017176064,0.77067745,0.0035541719,0.0067529297,0.2174617,0.00011291689],"about_ca_topic_score_codex":0.000040078787,"about_ca_topic_score_gemma":0.00010970368,"teacher_disagreement_score":0.7466767,"about_ca_system_score_codex":0.000023810804,"about_ca_system_score_gemma":0.00003959657,"threshold_uncertainty_score":0.33691084},"labels":[],"label_agreement":null},{"id":"W2913690110","doi":"10.2991/ijcis.2018.25905178","title":"Spontaneous Concept Learning with Deep Autoencoder","year":2018,"lang":"en","type":"article","venue":"International Journal of Computational Intelligence Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Solana Networks (Canada)","funders":"","keywords":"Autoencoder; Artificial intelligence; Computer science; Deep learning; Machine learning","score_opus":0.012463206846108023,"score_gpt":0.2834297972876058,"score_spread":0.2709665904414978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913690110","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":[0.0020361908,0.00012533538,0.9946244,0.000574063,0.00084330497,0.00010806875,0.0000017560493,0.00009665427,0.0015902717],"genre_scores_gemma":[0.8811218,0.000012801708,0.11789067,0.000114388575,0.0005925317,0.0000071666086,0.0000025901732,0.000009929265,0.00024811414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807495,0.00007974766,0.00062555296,0.0002096314,0.00085688254,0.00015320728],"domain_scores_gemma":[0.99609745,0.0002067085,0.000641359,0.00014189219,0.0028106123,0.000101965976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032782476,0.00013688803,0.0001812848,0.00028058,0.000137707,0.00031520065,0.0012054485,0.000053645006,0.00006454682],"category_scores_gemma":[0.00004516213,0.00011108024,0.00008715856,0.00026150717,0.00015336639,0.0005273019,0.00008716435,0.0002364518,0.00008548692],"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":[0.000051022744,0.00007907673,0.000103744766,0.0000035412713,0.00015834603,0.0003492277,0.00092700456,0.76098275,0.00006184627,0.19477548,0.000349239,0.04215875],"study_design_scores_gemma":[0.00017043739,0.0008113881,0.00015711269,0.00010295438,0.00001008236,0.018017244,0.00031555028,0.94221497,0.0014668652,0.01849674,0.018013574,0.00022310225],"about_ca_topic_score_codex":0.000023609799,"about_ca_topic_score_gemma":0.000002492111,"teacher_disagreement_score":0.8790856,"about_ca_system_score_codex":0.00013149026,"about_ca_system_score_gemma":0.00017413925,"threshold_uncertainty_score":0.45297205},"labels":[],"label_agreement":null},{"id":"W2914095169","doi":"10.1016/j.neunet.2019.01.015","title":"Deep divergence-based approach to clustering","year":2019,"lang":"en","type":"article","venue":"Neural Networks","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Norges Forskningsråd; Nvidia","keywords":"Cluster analysis; Computer science; Discriminative model; Artificial intelligence; Deep learning; Divergence (linguistics); Machine learning; Regularization (linguistics); Partition (number theory); Artificial neural network; Clustering high-dimensional data; Field (mathematics); Pattern recognition (psychology); Data mining; Mathematics","score_opus":0.013910210144021443,"score_gpt":0.22482269667860352,"score_spread":0.21091248653458208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914095169","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":[0.0050094645,0.00001996239,0.9897718,0.0002829345,0.0002230856,0.0002892189,1.7860992e-7,0.00035298328,0.0040503815],"genre_scores_gemma":[0.9311742,0.000001728989,0.06718307,0.0012479738,0.00007562264,0.00006492658,0.0000014765145,0.0000068394716,0.00024413584],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992458,0.000017758673,0.00011514041,0.00030718476,0.00010776954,0.00020630035],"domain_scores_gemma":[0.9993836,0.000020181453,0.000035512763,0.0004461594,0.000027322249,0.000087200395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006226899,0.000087301676,0.00008467057,0.000044882847,0.00009200467,0.00007972754,0.00057018467,0.000049889317,0.00001796045],"category_scores_gemma":[0.0000018129566,0.000080637124,0.000051227154,0.0004158501,0.000008851824,0.00012199714,0.00020445966,0.000117009484,0.00006409066],"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":[0.0000035663422,0.000026204934,0.0009843579,0.0000040448354,0.0000020284997,4.7094827e-7,0.000027784796,0.9367516,0.00011904579,0.004051709,0.00068085594,0.057348326],"study_design_scores_gemma":[0.00005703437,0.000047610825,0.001069883,0.0000027611588,0.0000010550292,0.0000028937227,0.0000028161817,0.9957304,0.00014732464,0.00007125515,0.0027619516,0.00010504077],"about_ca_topic_score_codex":0.00001230829,"about_ca_topic_score_gemma":0.0000022972383,"teacher_disagreement_score":0.92616475,"about_ca_system_score_codex":0.000019571891,"about_ca_system_score_gemma":0.000005281977,"threshold_uncertainty_score":0.32882866},"labels":[],"label_agreement":null},{"id":"W2915058771","doi":"10.1007/978-3-030-04506-7_3","title":"Descriptive, Predictive, and Prescriptive Analytics","year":2019,"lang":"en","type":"book-chapter","venue":"SpringerBriefs in health care management and economics","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Predictive analytics; Analytics; Computer science; Data science; Data analysis; Business intelligence; Descriptive statistics; Artificial intelligence; Key (lock); Machine learning; Data mining; Mathematics; Statistics","score_opus":0.014629455132736627,"score_gpt":0.2216422629171623,"score_spread":0.20701280778442566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2915058771","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00085250946,0.008674222,0.15306899,0.0012199167,0.00069368305,0.0035824096,0.000086918604,0.00039552755,0.83142585],"genre_scores_gemma":[0.11139669,0.1532204,0.14239356,0.0045397403,0.00045934072,0.000438277,0.00009763401,0.00028838075,0.58716595],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984085,0.000011513762,0.0004739476,0.0007582436,0.000071585026,0.00027623476],"domain_scores_gemma":[0.9989869,0.00002512924,0.0002895912,0.0005426515,0.00003682877,0.00011887732],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018948465,0.0002778337,0.00038936155,0.00033354003,0.00012826276,0.00014940905,0.00033443727,0.00015593259,0.0000079688425],"category_scores_gemma":[0.0000017924714,0.00033163218,0.00005588536,0.00004456952,0.000078674086,0.00022709701,0.00071506185,0.00027700065,0.0000118366415],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000084200865,0.0000062136146,0.00036903907,0.00043461865,0.000046515597,0.0000026631722,0.00046840895,0.00007491096,4.6226962e-8,0.949078,0.0004927467,0.049018368],"study_design_scores_gemma":[0.0004895375,0.00033246898,0.0043665846,0.00037795768,0.000034045388,0.0000063124535,0.0003371029,0.013235316,0.0000055783808,0.038223896,0.94190985,0.0006813777],"about_ca_topic_score_codex":0.000075214986,"about_ca_topic_score_gemma":0.000104168954,"teacher_disagreement_score":0.9414171,"about_ca_system_score_codex":0.00041637794,"about_ca_system_score_gemma":0.000074162665,"threshold_uncertainty_score":0.9999136},"labels":[],"label_agreement":null},{"id":"W2915952428","doi":"10.5281/zenodo.17619","title":"ProbabilisticNetwork.jl: 0.3.0","year":2015,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science","score_opus":0.058032705377300396,"score_gpt":0.25489494271943736,"score_spread":0.19686223734213695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2915952428","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":[0.0007198662,0.000031664556,0.83697563,0.0012654254,0.00006799167,0.00030688176,0.000012414125,0.0024412973,0.15817885],"genre_scores_gemma":[0.9632193,0.000027181832,0.033287782,0.00037529608,0.00024387056,2.6008595e-7,0.00021186766,0.00072325853,0.0019111732],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989162,0.00012212164,0.0001601139,0.00032927564,0.00024358786,0.00022867187],"domain_scores_gemma":[0.99864393,0.000010802976,0.00006455416,0.0005898457,0.00048933853,0.00020154407],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005264985,0.000083928564,0.000077604534,0.000103746585,0.0010261884,0.00071386877,0.0014276184,0.0000416724,0.00058698124],"category_scores_gemma":[0.00020550816,0.000086106345,0.000031536743,0.0006478294,0.00007868461,0.00029233267,0.0011230235,0.00014233388,0.0044650654],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008670893,0.00011417791,0.0000019357924,0.000010817559,0.000010526667,0.000004520668,0.0005545612,0.00013598384,0.00045465652,0.30141217,0.41545746,0.2818345],"study_design_scores_gemma":[0.0001433468,0.00013669865,0.00006309843,0.0000051440247,0.0000023706543,0.000075243384,0.00003986107,0.005745117,0.00035154022,0.007025936,0.98630255,0.00010909559],"about_ca_topic_score_codex":0.0000069666594,"about_ca_topic_score_gemma":8.5879414e-8,"teacher_disagreement_score":0.96249944,"about_ca_system_score_codex":0.00010421788,"about_ca_system_score_gemma":0.0000046205882,"threshold_uncertainty_score":0.99631006},"labels":[],"label_agreement":null},{"id":"W2916075447","doi":"10.5281/zenodo.16578","title":"ProbabilisticNetwork.jl: 0.1.0","year":2015,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science","score_opus":0.0577910096644441,"score_gpt":0.25457662372418616,"score_spread":0.19678561405974204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2916075447","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":[0.00070074736,0.000031999843,0.83572096,0.0012764842,0.00006732453,0.00030756317,0.0000126720015,0.0024442326,0.15943803],"genre_scores_gemma":[0.9628547,0.000027616727,0.03346795,0.00037970385,0.00024956942,2.6631957e-7,0.00022204565,0.0007404744,0.0020577104],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99891716,0.00012199099,0.00015996023,0.0003289939,0.00024349139,0.00022840225],"domain_scores_gemma":[0.99864286,0.00001079577,0.00006450686,0.0005895766,0.0004908929,0.0002013518],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005250411,0.00008386513,0.00007751949,0.0001035836,0.0010245112,0.0007124,0.0014255181,0.000041649033,0.0006080191],"category_scores_gemma":[0.00020633469,0.00008604845,0.000031309475,0.0006505648,0.00007857256,0.0002919493,0.0011215414,0.00014247233,0.004578862],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000085559595,0.00011312898,0.0000018910282,0.000010659128,0.000010464609,0.0000044746166,0.00054474466,0.00013024475,0.00044707776,0.28764537,0.43396744,0.27711594],"study_design_scores_gemma":[0.00014291338,0.00013475807,0.0000611665,0.0000051170355,0.0000023678317,0.00007494063,0.00003916575,0.0058810604,0.00034716693,0.0069026514,0.98629993,0.0001087332],"about_ca_topic_score_codex":0.000006864755,"about_ca_topic_score_gemma":8.640245e-8,"teacher_disagreement_score":0.9621539,"about_ca_system_score_codex":0.00010386062,"about_ca_system_score_gemma":0.0000046374616,"threshold_uncertainty_score":0.9961962},"labels":[],"label_agreement":null},{"id":"W2916903355","doi":"10.28991/esj-2019-01165","title":"Deep Learning Research: Scientometric Assessment of Global Publications Output during 2004 -17","year":2019,"lang":"en","type":"article","venue":"Emerging Science Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":13,"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":"Citation impact; Citation; China; Science Citation Index; Library science; Political science; Index (typography); Period (music); Regional science; Geography; Computer science","score_opus":0.048787174966725085,"score_gpt":0.39991327890057654,"score_spread":0.35112610393385146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2916903355","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":[0.38764617,0.00009186018,0.59683585,0.0015197672,0.00028338577,0.0001732354,4.3190988e-7,0.00013871082,0.013310572],"genre_scores_gemma":[0.94036144,0.000028681974,0.058146905,0.00001915439,0.00005362293,0.000010851779,1.5971428e-7,0.00000420693,0.0013749914],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99667,0.000077765646,0.00039601675,0.00045296663,0.0018004167,0.0006028512],"domain_scores_gemma":[0.997675,0.000052973057,0.00031429064,0.00056817505,0.0011276412,0.00026193226],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0050094626,0.000095757125,0.00013490678,0.0018304668,0.0017584777,0.0008268378,0.0023913267,0.000036848218,0.00008806804],"category_scores_gemma":[0.00025382126,0.00008836651,0.00008299612,0.0181943,0.00034709295,0.0017640322,0.0005764637,0.0005829762,0.00003513765],"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":[0.0000040140617,0.00041869463,0.5039976,0.000035792767,0.00002878689,0.00000567682,0.0009091815,0.035535127,0.05069164,0.31639266,0.0015519367,0.090428844],"study_design_scores_gemma":[0.00032125687,0.00022305269,0.7687283,0.000057045185,0.0000043697078,0.00026260276,0.00040508376,0.21459073,0.0031249477,0.0041203205,0.007896586,0.00026567618],"about_ca_topic_score_codex":0.000018152597,"about_ca_topic_score_gemma":0.0000018753295,"teacher_disagreement_score":0.55271524,"about_ca_system_score_codex":0.0006218768,"about_ca_system_score_gemma":0.0006923773,"threshold_uncertainty_score":0.9995411},"labels":[],"label_agreement":null},{"id":"W2917397300","doi":"10.1142/s0218001419400196","title":"Failure Modeling of a Propulsion Subsystem: Unsupervised and Semi-Supervised Approaches to Anomaly Detection","year":2019,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"National Research Council Canada","funders":"","keywords":"Anomaly detection; Turbocharger; Support vector machine; Computer science; Cluster analysis; Artificial intelligence; Machine learning; Random forest; Process (computing); Supervised learning; Data mining; Artificial neural network; Engineering; Turbine","score_opus":0.1347624027680912,"score_gpt":0.2705682918042429,"score_spread":0.1358058890361517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917397300","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":[0.4724599,0.000020469151,0.5265968,0.0006128364,0.0001302562,0.00013213445,0.000005008962,0.000014237426,0.000028377877],"genre_scores_gemma":[0.9903669,0.000045837893,0.009340711,0.00011761978,0.00009948194,0.0000117729,0.000002574672,0.0000073280307,0.0000077685345],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987328,0.00005200093,0.0005876792,0.00022718555,0.00029848318,0.00010187311],"domain_scores_gemma":[0.99905324,0.00004462138,0.0002564108,0.000118961565,0.00043537252,0.00009139172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032807092,0.00011128719,0.00017396254,0.0003195502,0.00004929824,0.0001354933,0.00032562006,0.00006460699,0.000029102805],"category_scores_gemma":[0.000026770413,0.00010039364,0.000069386,0.00019259934,0.000025458858,0.0004459373,0.00010028136,0.00013634266,0.000019649535],"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":[0.000058909798,0.000095638796,0.00084817666,0.000039633152,0.000038429695,0.0000039882193,0.0008856333,0.0006490079,0.04547309,0.0007629028,0.0000026691903,0.9511419],"study_design_scores_gemma":[0.00014290174,0.000471879,0.00039307727,0.00034588555,0.000018262848,0.00025400164,0.0010949217,0.7507623,0.23136571,0.014835117,0.00007498551,0.00024096313],"about_ca_topic_score_codex":0.000048387323,"about_ca_topic_score_gemma":0.000031916978,"teacher_disagreement_score":0.950901,"about_ca_system_score_codex":0.000028677434,"about_ca_system_score_gemma":0.000022794098,"threshold_uncertainty_score":0.40939337},"labels":[],"label_agreement":null},{"id":"W2918865870","doi":"","title":"Towards international standards for evaluating machine learning.","year":2019,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Toronto Rehabilitation Institute","funders":"","keywords":"Computer science","score_opus":0.13433544121054686,"score_gpt":0.41564346603083413,"score_spread":0.2813080248202873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2918865870","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":[0.0013560936,0.000010105924,0.9560742,0.0035211202,0.0003957706,0.00045003265,0.000068747264,0.00023388454,0.03789008],"genre_scores_gemma":[0.95468885,0.000017971252,0.043758404,0.00039209373,0.000097945776,0.00016381835,0.0000313881,0.000009902954,0.00083962036],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99793595,0.000046095392,0.0003579776,0.00046721112,0.0010005853,0.00019220421],"domain_scores_gemma":[0.99769515,0.00017404919,0.00016528771,0.00021785847,0.001685662,0.00006197292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010775682,0.00014045798,0.00013296117,0.00018111724,0.0001980799,0.00026248777,0.00080277293,0.00007796086,0.00063830405],"category_scores_gemma":[0.00039471657,0.00013858336,0.00009191778,0.00029383443,0.000041256862,0.00028310873,0.00011160248,0.0002235453,0.00025825636],"study_design_candidate":"theoretical_or_conceptual","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":[0.000025810645,0.0000412703,0.000017311875,0.0000032308678,0.0000073792557,1.185167e-7,0.00005108702,0.0012234931,0.0019412063,0.7321508,0.000082792045,0.26445553],"study_design_scores_gemma":[0.00003814025,0.0002923552,0.000053938827,0.0000158642,0.0000017622052,0.0000021595717,0.00003273449,0.70933723,0.02709944,0.24762535,0.015352264,0.00014874118],"about_ca_topic_score_codex":0.000019545178,"about_ca_topic_score_gemma":0.00000955731,"teacher_disagreement_score":0.9533328,"about_ca_system_score_codex":0.00020191421,"about_ca_system_score_gemma":0.00044173995,"threshold_uncertainty_score":0.69889814},"labels":[],"label_agreement":null},{"id":"W2919609384","doi":"10.48550/arxiv.1903.00102","title":"A detailed comparative study of open source deep learning frameworks","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":11,"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":"Implementation; Benchmark (surveying); Computer science; Artificial intelligence; Open source; Deep learning; Machine learning; Data science; Software engineering; Programming language; Software","score_opus":0.08405114499820342,"score_gpt":0.243754385431879,"score_spread":0.15970324043367556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2919609384","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":[0.39120552,0.000010472691,0.60595363,0.000011468775,0.000049612147,0.0007639001,8.756892e-7,0.00015998412,0.0018445518],"genre_scores_gemma":[0.9941601,0.00002092236,0.003687774,0.000024313656,0.000015374782,0.0000095351115,0.0000034338632,0.000012509982,0.002065997],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983493,0.0002355976,0.00023612472,0.00090515066,0.00008305783,0.00019074719],"domain_scores_gemma":[0.9978307,0.00013379508,0.00049894315,0.001250269,0.00020495654,0.00008133991],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022886544,0.00022647578,0.000455102,0.00019527915,0.00019553407,0.00018361745,0.003295211,0.00036009427,0.0000328472],"category_scores_gemma":[0.000012728618,0.00025770772,0.00012385946,0.00067701045,0.000059983864,0.0002990091,0.005305366,0.0013828655,0.000053949112],"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":[0.000042712967,0.0007380004,0.022314645,0.000035087367,0.00020681659,0.000016229224,0.0040751323,0.9198016,0.000032620883,0.05087973,0.00010152739,0.0017558726],"study_design_scores_gemma":[0.0005882609,0.00039483246,0.004689151,0.00006461578,0.00007281282,0.0000015948852,0.0028707297,0.97980624,0.00023036447,0.010106569,0.000747789,0.0004270451],"about_ca_topic_score_codex":0.00025516126,"about_ca_topic_score_gemma":0.000105666855,"teacher_disagreement_score":0.6029546,"about_ca_system_score_codex":0.00009160703,"about_ca_system_score_gemma":0.00007612309,"threshold_uncertainty_score":0.99998754},"labels":[],"label_agreement":null},{"id":"W2919816726","doi":"10.1109/cspis.2018.8642713","title":"Data Analytics Methods for Anomaly Detection: Evolution and Recommendations","year":2018,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Western University","funders":"","keywords":"Anomaly detection; Computer science; Big data; Anomaly (physics); Support vector machine; Data mining; Analytics; Artificial intelligence; Artificial neural network; Machine learning; Data science","score_opus":0.09630863901501527,"score_gpt":0.40950644176371126,"score_spread":0.313197802748696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2919816726","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008204932,0.0000283606,0.99530524,0.0024254376,0.00011944019,0.00021991767,0.000014299947,0.00031554748,0.0014896906],"genre_scores_gemma":[0.15278848,0.000013104145,0.84630615,0.00017015307,0.00010885472,0.000052134415,0.000008792363,0.0000044609465,0.0005479049],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992852,0.00003510987,0.00016107093,0.0003619581,0.00004009208,0.0001165251],"domain_scores_gemma":[0.99880606,0.00010563135,0.000062479216,0.00082849147,0.0001444134,0.000052912863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048439388,0.00006710086,0.000071783674,0.00009147665,0.00037525044,0.000105677835,0.00050548377,0.000046369518,0.000020099527],"category_scores_gemma":[0.00005293466,0.00006355116,0.000020653455,0.0003982378,0.000058661688,0.00048506365,0.0003166852,0.00004380791,0.0000087454855],"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":[0.000002258336,0.0000237283,0.000041650015,0.0000035145229,0.0000132548585,2.2466446e-8,0.000022565382,4.222599e-7,0.0033495051,0.19258699,0.004646948,0.79930913],"study_design_scores_gemma":[0.00009044135,0.0001377844,0.00055501494,0.0000021237477,0.0000127439,0.0000135283935,0.000022750035,0.6134442,0.01404489,0.02998649,0.3415797,0.00011032933],"about_ca_topic_score_codex":0.000030400872,"about_ca_topic_score_gemma":0.00007402429,"teacher_disagreement_score":0.7991988,"about_ca_system_score_codex":0.000028983966,"about_ca_system_score_gemma":0.00002505425,"threshold_uncertainty_score":0.2886161},"labels":[],"label_agreement":null},{"id":"W2924996394","doi":"10.1109/crv.2019.00017","title":"Adversarially Learned Abnormal Trajectory Classifier","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Institut de Valorisation des Données","keywords":"Discriminator; Autoencoder; Discriminative model; Artificial intelligence; Computer science; Classifier (UML); Trajectory; Binary classification; Pattern recognition (psychology); Adversarial system; Deep learning; Generative adversarial network; Event (particle physics); Machine learning; Detector; Support vector machine","score_opus":0.03150289045242371,"score_gpt":0.2717169280692984,"score_spread":0.2402140376168747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2924996394","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":[0.00069274235,0.000034366305,0.88104653,0.0009953714,0.0006979214,0.00042233107,0.0000042374622,0.0010817122,0.1150248],"genre_scores_gemma":[0.764805,0.000096983196,0.19707295,0.00094403914,0.00030997483,0.00023240475,0.000018129738,0.00003139895,0.0364891],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984643,0.000046864163,0.00029220263,0.0007102564,0.0002430904,0.00024331006],"domain_scores_gemma":[0.9981845,0.000038799575,0.00017339906,0.0014230376,0.00009383002,0.00008640764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002107292,0.00022410463,0.00023995014,0.00014154572,0.00009383837,0.00021932549,0.0015304104,0.00038001037,0.0002156198],"category_scores_gemma":[0.000007968145,0.00021115082,0.00021324639,0.00016520565,0.00003693491,0.0001868162,0.0013795898,0.0006459676,0.00050518103],"study_design_candidate":"theoretical_or_conceptual","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":[0.000024437684,0.0003165218,0.0004969507,0.00018591185,0.00018600759,0.000014396833,0.00056178524,0.0063041826,0.0020920697,0.6999824,0.07151026,0.21832505],"study_design_scores_gemma":[0.0008152441,0.00029800987,0.008881393,0.000101251615,0.000060414342,0.00004166199,0.000073067225,0.31963614,0.015969714,0.05693937,0.59490484,0.0022788758],"about_ca_topic_score_codex":0.00009438729,"about_ca_topic_score_gemma":0.000010800025,"teacher_disagreement_score":0.7641123,"about_ca_system_score_codex":0.00008252067,"about_ca_system_score_gemma":0.00030534586,"threshold_uncertainty_score":0.8610481},"labels":[],"label_agreement":null},{"id":"W2934016713","doi":"10.5281/zenodo.2649605","title":"A network abstraction of multi-vessel trajectory data for detecting anomalies","year":2019,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":30,"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":"European Commission","keywords":"Trajectory; Computer science; Abstraction; Artificial intelligence; Data mining; Physics","score_opus":0.0670796913027739,"score_gpt":0.2775150462926529,"score_spread":0.21043535498987898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2934016713","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":[0.011746955,0.000054405104,0.9801054,0.00020861524,0.00010503854,0.00070542697,0.000106711675,0.00091344124,0.00605398],"genre_scores_gemma":[0.9505888,0.000024723035,0.048100114,0.00006318262,0.000109254725,2.588395e-7,0.00033977808,0.00034492076,0.00042892084],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99886876,0.00008013204,0.0002417776,0.0004246217,0.00016056119,0.00022416742],"domain_scores_gemma":[0.99842876,0.000046193545,0.00018070462,0.0009778511,0.00030752167,0.000058956655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066240225,0.00009000903,0.00011685444,0.00010984382,0.0008772937,0.00028866524,0.0016965345,0.000052643496,0.00038951155],"category_scores_gemma":[0.00013651795,0.00009782411,0.00003886092,0.00044202968,0.000047503483,0.0005074967,0.0010104596,0.00013336015,0.00040976855],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000101935286,0.00048156016,0.000048468806,0.0003577705,0.00010941743,0.0000020008497,0.0011483408,0.00244254,0.090929836,0.04457796,0.1009041,0.75889605],"study_design_scores_gemma":[0.00041976135,0.00026415646,0.0012706836,0.000029583856,0.000009916539,0.000039013114,0.00015651711,0.06690455,0.0047958787,0.0005189794,0.92539805,0.00019292955],"about_ca_topic_score_codex":0.000012330118,"about_ca_topic_score_gemma":3.765603e-7,"teacher_disagreement_score":0.9388419,"about_ca_system_score_codex":0.00004463208,"about_ca_system_score_gemma":0.000004669897,"threshold_uncertainty_score":0.67475224},"labels":[],"label_agreement":null},{"id":"W2939458062","doi":"10.22215/etd/2011-06991","title":"Investigation of a novel software based laboratory jammer architecture","year":2011,"lang":"en","type":"dissertation","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Library and Archives Canada","funders":"","keywords":"Computer science; Architecture; Software; Humanities; Operating system; Art; Visual arts","score_opus":0.02059824764133245,"score_gpt":0.24052910986444453,"score_spread":0.2199308622231121,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2939458062","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052405503,0.00004236508,0.9906669,0.00006175969,0.00011914802,0.00028934947,0.000024224819,0.00044076084,0.0031149425],"genre_scores_gemma":[0.060808606,0.0000073824717,0.93345207,0.0005690724,0.000052642397,0.00028927473,0.0002778908,0.00003842395,0.0045046206],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99900156,0.000020352074,0.00029445477,0.00035948397,0.00020077474,0.00012335189],"domain_scores_gemma":[0.9986501,0.00003658855,0.00032303832,0.0006044071,0.0003158547,0.00007001811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010278954,0.00018165342,0.00018776984,0.00024178872,0.000070428316,0.000029034738,0.000598221,0.0002561694,0.000063931635],"category_scores_gemma":[0.000022752958,0.00016867109,0.00009427001,0.0005614063,0.000033216413,0.00011341828,0.000029327086,0.00021976318,0.0000110503415],"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":[0.000119702025,0.0007369866,0.0017718421,0.0021040337,0.00025529062,0.0000053163453,0.0062109893,0.00020814553,0.26723903,0.43021503,0.019103775,0.27202985],"study_design_scores_gemma":[0.00042321146,0.00018642106,0.005924312,0.00023129646,0.000049115948,0.0000022653205,0.000071689035,0.0035709215,0.9669957,0.010687725,0.011094595,0.00076274405],"about_ca_topic_score_codex":0.00010212851,"about_ca_topic_score_gemma":0.00008135724,"teacher_disagreement_score":0.6997567,"about_ca_system_score_codex":0.000018446479,"about_ca_system_score_gemma":0.0003079347,"threshold_uncertainty_score":0.68782073},"labels":[],"label_agreement":null},{"id":"W2940979366","doi":"10.1016/j.inffus.2018.11.002","title":"Data fusion in cyber-physical-social systems: State-of-the-art and perspectives","year":2018,"lang":"en","type":"article","venue":"Information Fusion","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":125,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Cyber-physical system; Sensor fusion; Space (punctuation); Representation (politics); Resource (disambiguation); Cyber Space; Data science; Data mining; Fusion; Artificial intelligence; The Internet; World Wide Web","score_opus":0.021804196020372604,"score_gpt":0.26755679923892217,"score_spread":0.24575260321854958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2940979366","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":[0.4950159,0.000027027836,0.49615252,0.0013457536,0.00018128472,0.00057611923,0.00005471296,0.00016948546,0.006477204],"genre_scores_gemma":[0.9982739,0.000016877397,0.0015212571,0.000048943308,0.00004096168,0.000014461105,0.000009442785,0.0000018880024,0.00007223102],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993972,0.0000279869,0.0002117342,0.00011429017,0.00016830442,0.00008047634],"domain_scores_gemma":[0.9992824,0.000019131296,0.00015855617,0.0004017357,0.00011895807,0.000019215382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018482096,0.00005778611,0.00007647693,0.000088522545,0.00018925591,0.000075713586,0.00045777997,0.00002877317,0.0000031433917],"category_scores_gemma":[0.000020579624,0.00004423099,0.000015791893,0.00039812457,0.000074228345,0.0012867618,0.00057523354,0.000066768676,0.000030448091],"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":[0.000038064944,0.00023478338,0.0010012516,0.00011013229,0.00001423317,3.4299168e-7,0.054800496,0.00006501048,0.0073519424,0.1760317,0.021455124,0.7388969],"study_design_scores_gemma":[0.0007000479,0.00023682766,0.07260472,0.00012449165,0.000009033789,0.000017267988,0.0021837384,0.6457889,0.008693943,0.005308272,0.2639425,0.0003902366],"about_ca_topic_score_codex":0.000058529567,"about_ca_topic_score_gemma":0.000017646393,"teacher_disagreement_score":0.7385067,"about_ca_system_score_codex":0.000028748265,"about_ca_system_score_gemma":0.000024759474,"threshold_uncertainty_score":0.18036874},"labels":[],"label_agreement":null},{"id":"W2944313419","doi":"10.1109/access.2019.2915641","title":"Performance Evaluation of Techniques for Identifying Abnormal Energy Consumption in Buildings","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Networks of Centres of Excellence of Canada; BC Hydro; Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Energy consumption; Computer science; Consumption (sociology); Engineering; Electrical engineering","score_opus":0.06747585709710031,"score_gpt":0.36442873975576523,"score_spread":0.29695288265866493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944313419","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":[0.5000933,0.000016936116,0.49910358,0.000019772591,0.00006855332,0.00026807646,7.789849e-7,0.000072591465,0.0003564121],"genre_scores_gemma":[0.98405004,0.000044637913,0.015416588,0.000044565622,0.000021420632,0.00038373924,0.0000018259136,0.000005188349,0.000032022912],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992211,0.000022473349,0.00022642051,0.00020670661,0.00020963883,0.00011365274],"domain_scores_gemma":[0.9993548,0.000032868113,0.00015319118,0.00026973133,0.00017341814,0.000015984053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006192082,0.0000640395,0.00009654093,0.00018549051,0.000043078606,0.00006557556,0.00054003904,0.000053110907,0.00001589845],"category_scores_gemma":[0.000006486435,0.000066408844,0.000034305107,0.00027509985,0.000016026677,0.0010543949,0.00007038202,0.000043264525,0.0000032403161],"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":[0.000020768199,0.0001004064,0.04791645,0.00015321172,0.000011028481,1.3352394e-7,0.00016589445,0.0009710149,0.09556563,0.026380764,0.00033058715,0.8283841],"study_design_scores_gemma":[0.00021371763,0.00004882063,0.019104784,0.000054126904,0.000006000149,0.000002422911,0.0000032783396,0.24275395,0.73477596,0.0022490423,0.00068180705,0.000106091786],"about_ca_topic_score_codex":0.00006724015,"about_ca_topic_score_gemma":0.000010722108,"teacher_disagreement_score":0.828278,"about_ca_system_score_codex":0.000057105466,"about_ca_system_score_gemma":0.00003101597,"threshold_uncertainty_score":0.27080742},"labels":[],"label_agreement":null},{"id":"W2946106399","doi":"10.1007/978-3-030-18305-9_55","title":"Compression Improves Image Classification Accuracy","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Image compression; Computer science; JPEG; Artificial intelligence; Compression (physics); Convolutional neural network; Compression ratio; Singular value decomposition; Pattern recognition (psychology); Texture compression; Data compression; Lossless compression; Data compression ratio; Image (mathematics); Computer vision; Image processing","score_opus":0.019292802609735118,"score_gpt":0.2734313471444804,"score_spread":0.2541385445347453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946106399","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000056769015,0.00012563492,0.98751074,0.001058394,0.0006979865,0.0006535532,0.000004687982,0.0004190346,0.009473172],"genre_scores_gemma":[0.22368969,0.00013278573,0.7725304,0.001287918,0.00041094638,0.00004740806,0.000014597116,0.000051868676,0.0018343801],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99695766,0.000022768994,0.00049375504,0.0014475187,0.00064582494,0.00043244837],"domain_scores_gemma":[0.9968641,0.00034833205,0.00044419317,0.0019466506,0.0002725029,0.00012422235],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043518853,0.0004078605,0.00036879478,0.00058792945,0.00029501825,0.0006075135,0.0031213914,0.00032174503,0.000026515216],"category_scores_gemma":[0.000052469546,0.00036713944,0.00013368759,0.0005183233,0.0004278266,0.00091585924,0.0011546472,0.00071494887,0.0002011118],"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":[0.0000026431733,0.000027246704,0.000009625947,0.00002772838,0.000004100457,0.0000047756457,0.000118217904,0.00037825762,0.016779367,0.06387158,0.000110874644,0.9186656],"study_design_scores_gemma":[0.00021570419,0.00017743223,0.0006685608,0.00027543283,0.000008039424,0.000044391454,2.2275e-7,0.79222995,0.02081404,0.1666563,0.018060643,0.00084929704],"about_ca_topic_score_codex":0.00001473898,"about_ca_topic_score_gemma":0.000007588222,"teacher_disagreement_score":0.9178163,"about_ca_system_score_codex":0.0002448239,"about_ca_system_score_gemma":0.00042853333,"threshold_uncertainty_score":0.99987805},"labels":[],"label_agreement":null},{"id":"W2946553955","doi":"10.1016/j.procs.2019.04.117","title":"Spatio-temporal Anomaly Detection in Intelligent Transportation Systems","year":2019,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Anomaly detection; Data mining; Anomaly (physics); Intelligent transportation system; Scheme (mathematics); Temporal database; Data stream mining; Real-time computing","score_opus":0.009553193899687666,"score_gpt":0.22951632105431477,"score_spread":0.2199631271546271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946553955","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":[0.313403,0.000022119271,0.6852187,0.0000812657,0.00048029487,0.0004182325,6.8850755e-7,0.00023684133,0.0001388963],"genre_scores_gemma":[0.9584652,0.000007490069,0.0412435,0.00007096662,0.000059821297,0.00010055127,0.000002198847,0.000006329428,0.000043916683],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826264,0.000018952584,0.00034955732,0.0006645156,0.000399199,0.0003051464],"domain_scores_gemma":[0.9990989,0.000026843534,0.00014020385,0.00045698386,0.00017855916,0.000098522076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050873146,0.00013521101,0.00014467374,0.00040310159,0.0001286333,0.00029584122,0.0009819211,0.000054111842,0.000004672843],"category_scores_gemma":[0.000005921143,0.00013312556,0.00004071663,0.002016599,0.00007786134,0.0012135516,0.00007698689,0.00013880088,0.0000985138],"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":[0.00003515538,0.0005188578,0.1630756,0.0002832688,0.000014052895,0.000022508384,0.005528952,0.022921551,0.018558234,0.16746691,0.00014880992,0.6214261],"study_design_scores_gemma":[0.00014378977,0.00017280136,0.0436598,0.00003244499,0.0000014142147,0.000021955337,0.000022239594,0.92842335,0.024623854,0.0009322748,0.0017259923,0.00024006722],"about_ca_topic_score_codex":0.0001832135,"about_ca_topic_score_gemma":0.00008506305,"teacher_disagreement_score":0.90550184,"about_ca_system_score_codex":0.00013738859,"about_ca_system_score_gemma":0.00015112752,"threshold_uncertainty_score":0.5428703},"labels":[],"label_agreement":null},{"id":"W2947114727","doi":"10.23977/cpcs.2017.21001","title":"An improved outlier delection algorithm K-LOF based on density","year":2017,"lang":"en","type":"article","venue":"Computing Performance and Communication systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Anomaly detection; Cluster analysis; Outlier; Computer science; Algorithm; Data set; Data mining; Local outlier factor; CURE data clustering algorithm; Set (abstract data type); Canopy clustering algorithm; Correlation clustering; Artificial intelligence","score_opus":0.017405435147687366,"score_gpt":0.2747611372145496,"score_spread":0.2573557020668622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947114727","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":[0.14440916,0.00004066535,0.8535241,0.00023578216,0.00012802631,0.0002466419,7.98893e-7,0.00033116428,0.0010836796],"genre_scores_gemma":[0.96047056,0.000047514284,0.03916657,0.00012963916,0.00005887967,0.00003698609,0.000005630917,0.0000086639075,0.00007555078],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990871,0.0000864257,0.00023847008,0.00029700645,0.00013077779,0.00016024252],"domain_scores_gemma":[0.9969964,0.000039727263,0.00032381585,0.002406867,0.00015946607,0.000073710646],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00057425885,0.00012383968,0.00014066762,0.000083081664,0.002272036,0.00071816286,0.0012029757,0.0000770789,6.9711075e-7],"category_scores_gemma":[0.000009258441,0.00011984802,0.000030747804,0.00010125632,0.00007091289,0.0005504116,0.00021586503,0.00018954767,0.000010700483],"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":[0.000010717026,0.00016636768,0.009875752,0.000034718905,0.000013106653,3.4517e-7,0.00038937497,0.0020852315,0.00080345286,0.0058024465,0.00025167863,0.9805668],"study_design_scores_gemma":[0.00017545429,0.00014437847,0.023522532,0.00005060914,0.0000031899826,0.000009492885,0.00002698178,0.97320116,0.0010177526,0.000054218366,0.0016515314,0.00014267518],"about_ca_topic_score_codex":0.00015321412,"about_ca_topic_score_gemma":0.0000050601493,"teacher_disagreement_score":0.9804241,"about_ca_system_score_codex":0.000043274198,"about_ca_system_score_gemma":0.000029953382,"threshold_uncertainty_score":0.9990269},"labels":[],"label_agreement":null},{"id":"W2947705258","doi":"10.48550/arxiv.1905.13147","title":"Anomaly Detection in Images","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Anomaly detection; Anomaly (physics); Geology; Computer vision; Artificial intelligence; Computer science; Physics","score_opus":0.04017590732217693,"score_gpt":0.18200820393581107,"score_spread":0.14183229661363414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947705258","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":[0.16569012,0.000022004819,0.82932436,0.000062911,0.0001877736,0.00029812372,0.0000046699115,0.00036516765,0.004044865],"genre_scores_gemma":[0.99569124,0.000105109175,0.0021321105,0.000048414917,0.000031280033,0.0000044876806,0.000003073351,0.000011247958,0.001973061],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867666,0.000060856244,0.00015857485,0.0008416265,0.000049200396,0.00021309809],"domain_scores_gemma":[0.99861294,0.00003595692,0.00016442136,0.0010525069,0.00007284975,0.000061319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014415244,0.00018812022,0.00019725386,0.0003500745,0.00007629469,0.00008844868,0.0010914715,0.0002473111,0.000015591702],"category_scores_gemma":[0.000007701747,0.0002324883,0.00013275348,0.0006739896,0.00004212985,0.000302818,0.0010263006,0.00047332153,0.00014849358],"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":[0.000086635526,0.0007503972,0.035281938,0.00037205228,0.0001597757,0.0004437264,0.00045967664,0.39834806,0.0061873463,0.49715862,0.001335355,0.059416432],"study_design_scores_gemma":[0.00045405852,0.000106385065,0.024517715,0.00007767469,0.000027926291,0.000011607403,0.000048261518,0.8779434,0.016313568,0.07673774,0.0029490914,0.0008125763],"about_ca_topic_score_codex":0.00035968414,"about_ca_topic_score_gemma":0.00009090397,"teacher_disagreement_score":0.8300011,"about_ca_system_score_codex":0.00021099494,"about_ca_system_score_gemma":0.000080406215,"threshold_uncertainty_score":0.94805974},"labels":[],"label_agreement":null},{"id":"W2950134958","doi":"10.22215/etd/2018-13228","title":"Real-Time Outlier (Anomaly) Detection Over Data Streams","year":2018,"lang":"en","type":"dissertation","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Data stream mining; Outlier; Anomaly detection; Computer science; Data mining; Sliding window protocol; STREAMS; Data stream; Task (project management); Artificial intelligence; Window (computing); Engineering","score_opus":0.016164004942346988,"score_gpt":0.29124777208698077,"score_spread":0.2750837671446338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950134958","genre_codex":"methods","genre_gemma":"other","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":[0.038894057,0.00004822622,0.6581871,0.0001029306,0.0011858055,0.0010755629,0.00009497188,0.0036258649,0.29678547],"genre_scores_gemma":[0.17586526,0.0005413767,0.26218006,0.00026400847,0.0017747488,0.0005743662,0.0052850307,0.000231513,0.55328363],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99783456,0.00003351236,0.00040682568,0.0011039224,0.00033876565,0.00028243367],"domain_scores_gemma":[0.9963722,0.000032408672,0.0003111219,0.0029961227,0.00017828982,0.00010982717],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00021847652,0.0003138173,0.00026589626,0.0002309619,0.00027717868,0.00028985782,0.0023340161,0.00039373207,0.00073035684],"category_scores_gemma":[0.000018814471,0.00029762756,0.00010076549,0.0005342352,0.00003075901,0.0006935386,0.0003514743,0.00022579744,0.0010750704],"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":[0.000037894803,0.00025094452,0.000045389446,0.000073861695,0.00014852294,0.0000061269825,0.00031227258,9.766096e-7,0.039248135,0.012309251,0.078909196,0.8686574],"study_design_scores_gemma":[0.00065175735,0.0007132265,0.009774267,0.000151669,0.00023756828,0.000042336942,0.00020806101,0.19440064,0.277487,0.010428561,0.50303066,0.0028742836],"about_ca_topic_score_codex":0.0007413256,"about_ca_topic_score_gemma":0.0007017653,"teacher_disagreement_score":0.86578315,"about_ca_system_score_codex":0.00007933812,"about_ca_system_score_gemma":0.0001173116,"threshold_uncertainty_score":0.9999476},"labels":[],"label_agreement":null},{"id":"W2952429811","doi":"10.3390/s19122804","title":"Low-Power Distributed Data Flow Anomaly-Monitoring Technology for Industrial Internet of Things","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Anomaly Detection Techniques and Applications","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":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Anomaly detection; Correctness; Overhead (engineering); Context (archaeology); Data flow diagram; Wireless sensor network; Real-time computing; Big data; Distributed computing; Internet of Things; Data mining; Embedded system; Database; Computer network","score_opus":0.029838047239415615,"score_gpt":0.2707657416357896,"score_spread":0.240927694396374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952429811","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":[0.47524273,0.000011983907,0.5228185,0.0004692943,0.00038217095,0.00040966866,0.00005537504,0.00033086503,0.000279414],"genre_scores_gemma":[0.94723713,0.0000027450783,0.052354917,0.000014657124,0.000048288894,0.000020855281,0.000024977095,0.000007480053,0.0002889701],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99910355,0.000012033423,0.00023060598,0.0003748084,0.00010327197,0.00017572004],"domain_scores_gemma":[0.99861026,0.000056251483,0.00013695395,0.0010770783,0.00008621633,0.00003323708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016036264,0.00009527225,0.00015815148,0.000121135556,0.000038327802,0.00003581567,0.0012056073,0.00015542447,0.000012931661],"category_scores_gemma":[0.00005410673,0.00009171971,0.000043146436,0.00042581072,0.00004227466,0.0002429197,0.0004834886,0.00014764449,0.000022659322],"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":[0.00026650043,0.0010547691,0.063116066,0.00023699408,0.00048415526,0.000016129807,0.002276238,0.00082214054,0.11922495,0.23896267,0.024348062,0.54919136],"study_design_scores_gemma":[0.0010820742,0.0005155698,0.0008121615,0.00012415196,0.00002177032,0.000022798704,0.00024645732,0.2678173,0.631434,0.0031806706,0.0942617,0.00048131996],"about_ca_topic_score_codex":0.000021691378,"about_ca_topic_score_gemma":5.0386416e-7,"teacher_disagreement_score":0.54871,"about_ca_system_score_codex":0.000026794978,"about_ca_system_score_gemma":0.000030387484,"threshold_uncertainty_score":0.37402213},"labels":[],"label_agreement":null},{"id":"W2952433032","doi":"","title":"Deep Sets","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":471,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Statistic; Invariant (physics); Permutation (music); Anomaly detection; Population; Outlier; Computer science; Artificial intelligence; Mathematics; Statistics","score_opus":0.07431587457308048,"score_gpt":0.206285731045744,"score_spread":0.13196985647266352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952433032","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":[0.00871789,0.000024198216,0.97105616,0.00019482226,0.00022445737,0.00020280242,0.000004525352,0.0005753686,0.018999768],"genre_scores_gemma":[0.9886915,0.00014409659,0.008010768,0.00007007907,0.000044530592,0.0000028583743,0.0000054535626,0.000010033366,0.0030206451],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988315,0.00003317605,0.00010564809,0.0007797108,0.000049083897,0.00020088487],"domain_scores_gemma":[0.9974304,0.000022338869,0.00024341837,0.0020909072,0.0000992292,0.000113699905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010404234,0.00018147197,0.00017441754,0.0001260757,0.00035849505,0.00019431021,0.0025554476,0.00023246394,0.000026484417],"category_scores_gemma":[0.000010770687,0.0002156603,0.00016490217,0.00015209042,0.00008329803,0.0002598942,0.0020905803,0.00038049422,0.00017896674],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000047269223,0.00007764153,0.0006554891,0.00003379874,0.00005177792,0.00012643763,0.00008536235,0.01778135,0.000028211325,0.9664919,0.0015127768,0.013150554],"study_design_scores_gemma":[0.000121867175,0.000024284944,0.0010530944,0.000027444323,0.000024927172,0.0000065456943,0.0000076538545,0.6880576,0.00033933463,0.30108082,0.008884305,0.00037208985],"about_ca_topic_score_codex":0.0000941126,"about_ca_topic_score_gemma":0.000026042393,"teacher_disagreement_score":0.9799737,"about_ca_system_score_codex":0.000096887896,"about_ca_system_score_gemma":0.00008660874,"threshold_uncertainty_score":0.87943715},"labels":[],"label_agreement":null},{"id":"W2953298990","doi":"10.48550/arxiv.1809.00957","title":"Road User Abnormal Trajectory Detection using a Deep Autoencoder","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","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":"Polytechnique Montréal","funders":"","keywords":"Autoencoder; Focus (optics); Computer science; Artificial intelligence; Deep learning; Anomaly detection; Trajectory; Outlier; Computer vision; Pattern recognition (psychology); Machine learning","score_opus":0.05912866758396525,"score_gpt":0.2004498826492755,"score_spread":0.14132121506531026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953298990","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":[0.21419623,0.00001855589,0.7835023,0.000012960744,0.00030017382,0.00026115766,0.0000038661724,0.0006890706,0.0010156934],"genre_scores_gemma":[0.9730043,0.00004071066,0.026025783,0.000057897614,0.00016610623,0.000004414373,0.0000031398101,0.000022269754,0.0006754157],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823064,0.00009388469,0.0002165637,0.0010353052,0.00009285488,0.00033077804],"domain_scores_gemma":[0.99816364,0.000021821232,0.00027465,0.0011952264,0.00019990426,0.00014474746],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018326852,0.00029079284,0.00023438437,0.000328941,0.00037600278,0.00014153591,0.0012443188,0.00038518742,0.00006474914],"category_scores_gemma":[0.000007474094,0.00035177785,0.0002371153,0.00058520393,0.00013048318,0.00047392343,0.001097856,0.00050460687,0.00010819374],"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":[0.000120013654,0.00066937093,0.003446372,0.00025376267,0.0004969854,0.000310631,0.0011347348,0.8556618,0.0038643037,0.097048916,0.000523121,0.036469974],"study_design_scores_gemma":[0.00014881766,0.000056165605,0.0015034517,0.000024414701,0.00004955411,0.000019577055,0.000021126167,0.9851106,0.0023337458,0.009002504,0.0013135476,0.00041651682],"about_ca_topic_score_codex":0.0004098962,"about_ca_topic_score_gemma":0.00008188449,"teacher_disagreement_score":0.758808,"about_ca_system_score_codex":0.00033557456,"about_ca_system_score_gemma":0.00014988848,"threshold_uncertainty_score":0.9998934},"labels":[],"label_agreement":null},{"id":"W2954404573","doi":"10.22260/isarc2019/0069","title":"Automated Detection of Urban Flooding from News","year":2019,"lang":"en","type":"article","venue":"Proceedings of the ... ISARC","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"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":"Flooding (psychology); Computer science","score_opus":0.007769918990503912,"score_gpt":0.21462781491245284,"score_spread":0.20685789592194892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954404573","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":[0.97995883,0.000026613654,0.014134474,0.00035298738,0.00012852755,0.00027665828,0.0000027894123,0.00047932917,0.004639801],"genre_scores_gemma":[0.9899895,0.0000052032983,0.009700837,0.000039134058,0.000029151026,0.000015854956,1.7971942e-7,0.0000068730346,0.00021325776],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99929893,0.0000034363757,0.00021734534,0.00020237316,0.0001715787,0.000106315616],"domain_scores_gemma":[0.99936044,0.000021177262,0.00026775233,0.00020967687,0.000116390234,0.00002457123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000103005994,0.00007722828,0.00012747478,0.000063857864,0.00006172518,0.00003365124,0.00074588653,0.000054716213,0.000009599004],"category_scores_gemma":[0.000018867706,0.000059422095,0.00008398347,0.00046952697,0.00003188842,0.00026321947,0.00023640523,0.00008735426,0.000014848868],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004531917,0.000030463865,0.006524932,0.00002421356,0.000013025801,1.1128989e-8,0.0003467221,0.000005220323,0.97172946,0.015915088,0.0006054325,0.004800908],"study_design_scores_gemma":[0.000113807226,0.000053659798,0.0070318817,0.00003923621,0.000008296892,0.0000019483607,0.00007806099,0.033469312,0.95183593,0.0057557006,0.0015309138,0.00008127229],"about_ca_topic_score_codex":0.00016058654,"about_ca_topic_score_gemma":0.000001742405,"teacher_disagreement_score":0.033464093,"about_ca_system_score_codex":0.000024540523,"about_ca_system_score_gemma":0.0000124023745,"threshold_uncertainty_score":0.24231626},"labels":[],"label_agreement":null},{"id":"W2958658808","doi":"10.1002/stc.2404","title":"Real‐time anomaly detection with Bayesian dynamic linear models","year":2019,"lang":"en","type":"article","venue":"Structural Control and Health Monitoring","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Hydro-Québec","keywords":"Anomaly detection; Bayesian probability; Anomaly (physics); Computer science; Artificial intelligence; Pattern recognition (psychology); Physics","score_opus":0.00942687170045171,"score_gpt":0.2725016903912342,"score_spread":0.2630748186907825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2958658808","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":[0.5192796,0.00013090648,0.4785019,0.0007338089,0.00016921257,0.00053877314,0.0000032727035,0.00040153085,0.00024100646],"genre_scores_gemma":[0.97925305,0.000062935644,0.020330818,0.00005720296,0.000102625425,0.000034277564,8.77271e-7,0.000011300225,0.00014693629],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990016,0.000028536488,0.00020363688,0.00034647703,0.0001399376,0.00027981302],"domain_scores_gemma":[0.99936706,0.000020615471,0.00012870351,0.00029129905,0.00005795371,0.00013438727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013047823,0.00013747577,0.00020211423,0.00007735416,0.00031548925,0.000078616285,0.00018290806,0.000055376742,0.0000026640796],"category_scores_gemma":[8.859393e-7,0.00010998713,0.00003080433,0.00018048089,0.000019597612,0.00042962944,0.00003958518,0.00015508117,0.0000065001354],"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":[0.00024668226,0.000032713928,0.02973132,0.00034593267,0.0001327368,0.000008494909,0.0015156285,0.008473755,0.07025944,0.023561414,0.00001401647,0.8656779],"study_design_scores_gemma":[0.0006088953,0.0004311898,0.053042077,0.000033148135,0.0000050469275,0.00003481461,0.000027908942,0.94192386,0.00075221487,0.0028743052,0.00007126436,0.00019528836],"about_ca_topic_score_codex":0.00051819644,"about_ca_topic_score_gemma":0.00002012626,"teacher_disagreement_score":0.9334501,"about_ca_system_score_codex":0.000094788375,"about_ca_system_score_gemma":0.000051704872,"threshold_uncertainty_score":0.4485145},"labels":[],"label_agreement":null},{"id":"W2960634780","doi":"10.48550/arxiv.1907.06312","title":"Exploring Deep Anomaly Detection Methods Based on Capsule Net","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","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":"Brock University; Queen's University; University of Ottawa","funders":"","keywords":"Artificial intelligence; Computer science; Autoencoder; Pattern recognition (psychology); Benchmark (surveying); Deep learning; Classifier (UML); Anomaly detection; Anomaly (physics); Cartography","score_opus":0.17680086827457359,"score_gpt":0.23243690184047933,"score_spread":0.05563603356590574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2960634780","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":[0.045824736,0.000011629029,0.94851804,0.000059831917,0.0005357777,0.000418173,0.0000045183388,0.0007770384,0.0038502298],"genre_scores_gemma":[0.9498474,0.000056687473,0.04929369,0.00013136669,0.00006852022,0.000019325333,0.000005840018,0.000023818162,0.0005533648],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99802077,0.00020560568,0.00020113288,0.0011836921,0.00008936346,0.00029941075],"domain_scores_gemma":[0.99765605,0.00010351947,0.00025492412,0.0017309202,0.00012291581,0.00013168501],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00035252256,0.00030344859,0.00027970594,0.0004356698,0.00022481773,0.00012817756,0.0012907537,0.00024483778,0.000025625237],"category_scores_gemma":[0.000020492891,0.00036281516,0.00025337006,0.0007876325,0.000046630208,0.00040665537,0.0007562083,0.0006127974,0.00013689288],"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":[0.000029415756,0.00016538521,0.00023866803,0.00008058795,0.00005565564,0.000037965227,0.000102709004,0.8837099,0.0017146867,0.06391293,0.000057451347,0.04989464],"study_design_scores_gemma":[0.00018684032,0.00011722146,0.001023295,0.000034848024,0.000030531344,0.00000197875,0.00002204566,0.96882766,0.021218931,0.006081344,0.002057422,0.00039785975],"about_ca_topic_score_codex":0.00018763305,"about_ca_topic_score_gemma":0.000019784047,"teacher_disagreement_score":0.90402263,"about_ca_system_score_codex":0.00030240417,"about_ca_system_score_gemma":0.00009665595,"threshold_uncertainty_score":0.9998824},"labels":[],"label_agreement":null},{"id":"W2963111876","doi":"10.1109/wacv.2018.00188","title":"Plug-and-play CNN for crowd motion analysis: An application in abnormal event detection","year":2018,"lang":"en","type":"article","venue":"Institutional Research Information System (Università degli Studi di Trento)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":197,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Leverage (statistics); Convolutional neural network; Artificial intelligence; Abnormality; Optical flow; Pattern recognition (psychology); Feature (linguistics); Event (particle physics); Feature extraction; Machine learning; Computer vision; Image (mathematics)","score_opus":0.040076827010339926,"score_gpt":0.33095104712453555,"score_spread":0.2908742201141956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963111876","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":[0.061496273,0.000014504663,0.93399984,0.00017687527,0.000091815185,0.00097434875,0.000027553884,0.00022512219,0.002993697],"genre_scores_gemma":[0.9959668,0.0000090741105,0.003327819,0.00002331601,0.000090321984,0.000428788,0.00009581824,0.0000041974245,0.000053863372],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979625,0.0001348357,0.00045791556,0.0003801049,0.00070655195,0.00035813308],"domain_scores_gemma":[0.99814,0.00007579333,0.00020537052,0.00043231316,0.0009829169,0.00016364233],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0015207753,0.00014992329,0.00019754648,0.0018593345,0.0013873569,0.00024859258,0.0005287912,0.00013231095,0.0000037131306],"category_scores_gemma":[0.00003697565,0.00015899904,0.00009823204,0.0029931548,0.00020994469,0.0041485187,0.00021563223,0.00018836246,0.000084288935],"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":[0.00016325369,0.0002053647,0.0050012553,0.0001375577,0.00021485724,0.0000023387531,0.0032983401,0.0047731465,0.0003483286,0.6930285,0.00012078,0.29270625],"study_design_scores_gemma":[0.0011484805,0.00039782465,0.110986784,0.000042146505,0.0000522332,0.000024037166,0.0025236432,0.86729264,0.0010815761,0.0005600703,0.015593186,0.0002973494],"about_ca_topic_score_codex":0.0004839789,"about_ca_topic_score_gemma":0.0005089257,"teacher_disagreement_score":0.93447053,"about_ca_system_score_codex":0.0008524342,"about_ca_system_score_gemma":0.00017041723,"threshold_uncertainty_score":0.9999127},"labels":[],"label_agreement":null},{"id":"W2963341152","doi":"10.1007/978-3-030-11018-5_37","title":"Adversarial network compression","year":2019,"lang":"en","type":"book-chapter","venue":"IRIS Research product catalog (Sapienza University of Rome)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Bundesministerium für Wirtschaft und Energie; Ministerio de Economía y Competitividad; Canadian Institute for Advanced Research","keywords":"Discriminator; Computer science; Adversarial system; Regularization (linguistics); Artificial intelligence; Artificial neural network; Deep neural networks; Compression (physics); Scheme (mathematics); Machine learning; Mathematics","score_opus":0.0441552592918066,"score_gpt":0.2908001374468965,"score_spread":0.24664487815508987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963341152","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00033888157,0.0020856005,0.28132826,0.0030119338,0.0009964501,0.0040388675,0.00026895577,0.00090756186,0.7070235],"genre_scores_gemma":[0.023911634,0.0012995948,0.030147238,0.0000453272,0.0007142513,0.0000031596824,0.00024577338,0.00006888549,0.9435641],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969687,0.00011210763,0.0002742802,0.001089289,0.0010138428,0.00054177846],"domain_scores_gemma":[0.9964648,0.00016715543,0.00031761898,0.0020558794,0.0008032467,0.00019133402],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009965297,0.00027691727,0.0004718488,0.00051120785,0.00059515395,0.00006147733,0.002726898,0.00033968533,0.0002981166],"category_scores_gemma":[0.000034530207,0.0003145364,0.00022798471,0.00037245482,0.0005631949,0.00041876256,0.002207723,0.0011246668,0.0007758433],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013771697,0.00014708856,0.00004484954,0.00022941003,0.00019222798,0.0000786235,0.00066142844,0.00026884984,0.001032553,0.44058862,0.4974009,0.059217725],"study_design_scores_gemma":[0.00030091844,0.00023425416,0.00013871414,0.00017750644,0.000015146044,0.00001263807,0.000025529695,0.00043099915,0.00040602568,0.012385931,0.98553574,0.00033658827],"about_ca_topic_score_codex":0.00034911506,"about_ca_topic_score_gemma":0.000038461545,"teacher_disagreement_score":0.48813486,"about_ca_system_score_codex":0.00024489735,"about_ca_system_score_gemma":0.0005626979,"threshold_uncertainty_score":0.9999307},"labels":[],"label_agreement":null},{"id":"W2963471079","doi":"10.1007/s00500-019-04238-2","title":"A soft computing model based on asymmetric Gaussian mixtures and Bayesian inference","year":2019,"lang":"en","type":"article","venue":"Soft Computing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Concordia University; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Gibbs sampling; Markov chain Monte Carlo; Computer science; Artificial intelligence; Bayesian inference; Gaussian process; Dimensionality reduction; Mixture model; Bayesian probability; Machine learning; Reversible-jump Markov chain Monte Carlo; Pattern recognition (psychology); Gaussian; Algorithm","score_opus":0.011286004912766393,"score_gpt":0.259670205688664,"score_spread":0.24838420077589762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963471079","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":[0.01908649,0.00003794386,0.9751607,0.00045687603,0.000095164534,0.0002922272,0.0000015174775,0.00069464685,0.004174423],"genre_scores_gemma":[0.8216899,0.000001266483,0.17724329,0.00092971075,0.000051495164,0.0000041673934,0.0000018968451,0.000015295733,0.00006297521],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831533,0.000053867938,0.00031100094,0.0006584323,0.00027565114,0.00038569927],"domain_scores_gemma":[0.9985669,0.00042963162,0.00019043923,0.00060061325,0.000077278906,0.00013512342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036368967,0.00022573837,0.00023491125,0.00032458044,0.00037079846,0.0002754244,0.00062583305,0.00010617149,0.0000048090114],"category_scores_gemma":[0.00006550324,0.00021924858,0.00007769061,0.00087335694,0.00004271119,0.00016365957,0.00034334118,0.0003165847,0.00003288007],"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":[0.000009054791,0.00015377738,0.010665405,0.00009223969,0.000018094224,0.000005249159,0.0005294181,0.18852045,0.00094098586,0.08670619,0.00033045467,0.7120287],"study_design_scores_gemma":[0.00021911971,0.00010090887,0.0022624806,0.00007504378,0.0000037928735,0.000006534415,0.000011011604,0.9924186,0.0008164391,0.0036287173,0.00019461015,0.00026275025],"about_ca_topic_score_codex":0.000026210659,"about_ca_topic_score_gemma":0.0000012939257,"teacher_disagreement_score":0.80389816,"about_ca_system_score_codex":0.000048380924,"about_ca_system_score_gemma":0.000081794606,"threshold_uncertainty_score":0.8940698},"labels":[],"label_agreement":null},{"id":"W2964212410","doi":"","title":"On calibration of modern neural networks","year":2017,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1185,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Normalization (sociology); Computer science; Artificial neural network; Correctness; Calibration; Scaling; Deep neural networks; Artificial intelligence; Machine learning; Simple (philosophy); Deep learning; Pattern recognition (psychology); Data mining; Algorithm; Mathematics; Statistics","score_opus":0.035265833212912136,"score_gpt":0.31205347794228316,"score_spread":0.276787644729371,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964212410","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":[0.017135227,0.0000054129164,0.96183634,0.0025252865,0.00017285343,0.00006941536,0.0000025506122,0.00012073932,0.018132204],"genre_scores_gemma":[0.9968289,0.000012213824,0.0022180995,0.00016559508,0.000055708868,0.000015960752,0.000008767826,0.0000060258903,0.0006887113],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928427,0.00003303991,0.00015839183,0.00022286964,0.00021118498,0.00009026213],"domain_scores_gemma":[0.99918073,0.000042732292,0.0002688504,0.00037144346,0.00010209167,0.000034159802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011206497,0.00008732355,0.00008597664,0.000078252524,0.00026787244,0.00024645482,0.00093637925,0.000043513533,0.00007741734],"category_scores_gemma":[0.000059705897,0.000080864316,0.0000492567,0.000033559456,0.000038516897,0.0002955308,0.0001487578,0.0002706188,0.000006331112],"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":[0.00001625396,0.00003500897,0.0015913155,0.0000012305046,0.0000091963,0.0000015976904,0.000038132082,0.0375682,0.00069517584,0.8622808,0.00003506221,0.097728044],"study_design_scores_gemma":[0.000113787864,0.00011953512,0.0026206446,0.000016320982,0.0000011520626,0.0000023650427,0.000002183393,0.9769416,0.0008270384,0.019078016,0.00020296227,0.000074383344],"about_ca_topic_score_codex":0.00006325945,"about_ca_topic_score_gemma":0.000009126119,"teacher_disagreement_score":0.9796937,"about_ca_system_score_codex":0.000018980978,"about_ca_system_score_gemma":0.000013365007,"threshold_uncertainty_score":0.3297551},"labels":[],"label_agreement":null},{"id":"W2965110031","doi":"10.1109/crv.2019.00030","title":"Traffic Risk Assessment: A Two-Stream Approach Using Dynamic-Attention","year":2019,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer 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":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Focus (optics); Convolutional neural network; Categorical variable; Artificial intelligence; Recurrent neural network; Frame (networking); Object detection; Implementation; Object (grammar); Frame rate; Sequence (biology); Computer vision; Artificial neural network; Machine learning; Pattern recognition (psychology)","score_opus":0.009792322182666246,"score_gpt":0.2791888751739031,"score_spread":0.2693965529912369,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965110031","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":[0.20111054,0.000005479416,0.7835321,0.000050340226,0.000046809222,0.00024272825,0.0000015719443,0.00044213937,0.01456832],"genre_scores_gemma":[0.65948683,0.000004787166,0.3397082,0.000030246338,0.000008952727,0.00002079618,0.0000032162854,0.0000052292953,0.0007317428],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991447,0.000044883305,0.0001587805,0.00035145134,0.00014251801,0.00015769668],"domain_scores_gemma":[0.99928975,0.000017421446,0.00010235909,0.00049971626,0.000045030363,0.00004573353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021374316,0.00009491284,0.00009643133,0.00008920561,0.0001352371,0.00014423045,0.0003598669,0.000044119344,0.000038315455],"category_scores_gemma":[0.0000016601698,0.00008538533,0.00007756068,0.000375214,0.0000151708355,0.00036707678,0.00009811822,0.00012473177,0.00006418111],"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":[0.000004661187,0.0009116304,0.0094030835,0.000048281527,0.00008316608,0.0000016496053,0.0001893982,0.030161994,0.026784383,0.34967172,0.00030203437,0.582438],"study_design_scores_gemma":[0.00015270273,0.000031332766,0.0027654436,0.0000033528438,0.000006746272,0.000013190487,0.000037686776,0.9955167,0.00017660928,0.00081008463,0.00036769232,0.00011843701],"about_ca_topic_score_codex":0.00004594922,"about_ca_topic_score_gemma":0.0000032431476,"teacher_disagreement_score":0.96535474,"about_ca_system_score_codex":0.00006811125,"about_ca_system_score_gemma":0.00003106989,"threshold_uncertainty_score":0.34819126},"labels":[],"label_agreement":null},{"id":"W2966841869","doi":"10.1109/mwscas48704.2020.9184548","title":"Configurable FPGA-Based Outlier Detection for Time Series Data","year":2020,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Carleton University","funders":"","keywords":"Computer science; Field-programmable gate array; Stratix; Outlier; Verilog; Anomaly detection; Series (stratigraphy); Time series; Algorithm; Real-time computing; Embedded system; Data mining; Artificial intelligence; Machine learning","score_opus":0.0401627452096999,"score_gpt":0.26305893857044527,"score_spread":0.22289619336074537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966841869","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":[0.00008705788,0.000006214219,0.9878271,0.007818811,0.00002826704,0.00031588555,0.00002165787,0.0008184134,0.0030765894],"genre_scores_gemma":[0.57048875,0.0000038674484,0.4195522,0.0050609456,0.00015511595,0.00026305785,0.00005848984,0.000020329058,0.0043972377],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934113,0.000010052741,0.00012253654,0.00033611414,0.00007522862,0.00011493228],"domain_scores_gemma":[0.99921125,0.000031413274,0.000043681615,0.0005870086,0.000058614885,0.00006804652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008940166,0.00007024467,0.00007882447,0.000022828623,0.00014839451,0.00010900841,0.0006910491,0.000037680424,0.000087957116],"category_scores_gemma":[0.000025536277,0.00006448889,0.00003094453,0.00021881795,0.000019137906,0.00044198913,0.000115119794,0.00004493174,0.00015036263],"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":[0.00011162107,0.00019150655,0.00003547264,0.0001136987,0.00006002123,0.0000024230433,0.00022648308,0.000440453,0.24369678,0.10290971,0.16084707,0.49136478],"study_design_scores_gemma":[0.00010685728,0.00011856821,0.000012663711,0.0000012036617,0.0000034982163,0.0000013309524,0.00000544302,0.3975552,0.30090025,0.00073857163,0.30046886,0.000087531356],"about_ca_topic_score_codex":0.000010793588,"about_ca_topic_score_gemma":0.000004265272,"teacher_disagreement_score":0.57040167,"about_ca_system_score_codex":0.000009583904,"about_ca_system_score_gemma":0.000033895387,"threshold_uncertainty_score":0.26297808},"labels":[],"label_agreement":null},{"id":"W2968225932","doi":"10.1109/iccv.2019.00136","title":"Anomaly Detection in Video Sequence With Appearance-Motion Correspondence","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Benchmark (surveying); Anomaly detection; Convolutional neural network; Frame (networking); Computer vision; Motion (physics); Encoder; Sequence (biology); Pattern recognition (psychology); Object (grammar); Translation (biology); Geography","score_opus":0.02054405363282799,"score_gpt":0.26389697268701234,"score_spread":0.24335291905418435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968225932","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":[0.05164552,0.00006114232,0.9434149,0.00031289348,0.000251498,0.00085923955,0.0000029535802,0.00068553427,0.002766304],"genre_scores_gemma":[0.9673867,0.000050940278,0.03086189,0.00016182993,0.000041524196,0.00038204686,0.0000033907036,0.000017339624,0.0010943388],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979261,0.00008003597,0.0003576018,0.0010211566,0.0003256774,0.00028943497],"domain_scores_gemma":[0.9981615,0.00003456935,0.00026594062,0.0013287006,0.00014224759,0.00006704584],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032921074,0.00027981433,0.00027618647,0.00033950747,0.00008711279,0.00023530098,0.0011138971,0.00029000946,0.000019465566],"category_scores_gemma":[0.00001381145,0.00025201123,0.00007846993,0.00076657464,0.00006085908,0.000455066,0.0005996039,0.0006826227,0.00016891565],"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":[0.00025653554,0.0005484861,0.0165097,0.0006630865,0.00007288944,0.00008488017,0.0011974538,0.055684257,0.034623645,0.054681607,0.00041939065,0.83525807],"study_design_scores_gemma":[0.0006200092,0.00048988604,0.049528256,0.0007254728,0.000018404464,0.0001726682,0.000059028836,0.8253304,0.10376188,0.014791642,0.0030072413,0.0014951143],"about_ca_topic_score_codex":0.00071221846,"about_ca_topic_score_gemma":0.0004057133,"teacher_disagreement_score":0.9157412,"about_ca_system_score_codex":0.0002370015,"about_ca_system_score_gemma":0.00017448726,"threshold_uncertainty_score":0.9999932},"labels":[],"label_agreement":null},{"id":"W2968336449","doi":"10.1007/978-3-030-27520-4_1","title":"Detecting the Onset of Machine Failure Using Anomaly Detection Methods","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Anomaly detection; Closing (real estate); Robot; Artificial intelligence; Torque; Robotic arm; Point (geometry); Key (lock); Fault detection and isolation; Machine learning; Computer security; Actuator","score_opus":0.022837100018044003,"score_gpt":0.3005863306353197,"score_spread":0.2777492306172757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968336449","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005319536,0.00023479604,0.9972039,0.0002670089,0.00054906704,0.00054791325,0.000005057238,0.00017196461,0.0004883559],"genre_scores_gemma":[0.39793247,0.000009639632,0.6016424,0.00020608676,0.00009747571,0.000007619409,6.373092e-7,0.000021368685,0.00008231306],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99737227,0.00009369113,0.0005797773,0.0010285644,0.0005256048,0.0004000837],"domain_scores_gemma":[0.99678624,0.00064455724,0.0006026688,0.0016438208,0.00025131236,0.000071382594],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015493889,0.0003874285,0.00044307538,0.00064301456,0.00041827417,0.0002462137,0.0025573277,0.00029894948,0.000013142079],"category_scores_gemma":[0.000081282946,0.00029798728,0.00019259832,0.0010165414,0.0004743302,0.00036267255,0.0010664364,0.0008682116,0.000008356412],"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":[0.0000034837599,0.00001251174,0.000041468833,0.000030439825,0.0000127585445,0.0000034364361,0.00021508816,0.012127257,0.00911633,0.003881251,0.0000015829563,0.9745544],"study_design_scores_gemma":[0.00009422026,0.00014653077,0.000090036105,0.00012926242,0.000015894453,0.00020522007,4.6577756e-7,0.8620007,0.097424604,0.03828663,0.0012173363,0.00038906754],"about_ca_topic_score_codex":0.00012807133,"about_ca_topic_score_gemma":0.00018500962,"teacher_disagreement_score":0.9741653,"about_ca_system_score_codex":0.00022553455,"about_ca_system_score_gemma":0.00034018364,"threshold_uncertainty_score":0.99994725},"labels":[],"label_agreement":null},{"id":"W2968848515","doi":"10.1109/ic2e.2019.00024","title":"Host Hypervisor Trace Mining for Virtual Machine Workload Characterization","year":2019,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Polytechnique Montréal","funders":"","keywords":"Computer science; Hypervisor; Virtual machine; Workload; Operating system; Cluster analysis; Host (biology); Tracing; Temporal isolation among virtual machines; Distributed computing; Cloud computing; Virtualization; Artificial intelligence","score_opus":0.01075376449059177,"score_gpt":0.22897652413575806,"score_spread":0.2182227596451663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968848515","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":[0.11519884,0.00001204923,0.8818527,0.00094284065,0.00010483128,0.00034023222,0.0000051635393,0.00034312654,0.0012002408],"genre_scores_gemma":[0.90640366,0.000011992873,0.08458447,0.00047404744,0.000046598972,0.000120037366,0.000009942038,0.000009147252,0.008340085],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994305,0.000008195984,0.0001345323,0.00024033326,0.00006201899,0.00012441413],"domain_scores_gemma":[0.999526,0.00003903737,0.000059627117,0.00029452422,0.00004386487,0.000036938603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008697248,0.000074765194,0.00008304171,0.00005177071,0.00008556617,0.00008272861,0.00030805863,0.000044287925,0.00007081897],"category_scores_gemma":[0.0000050493595,0.000067832436,0.000046050533,0.00021300431,0.0000073470064,0.00028139332,0.000059873048,0.00003932031,0.00009043983],"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":[0.0000134281145,0.000103555525,0.0012729025,0.000013582027,0.00001202321,2.4948437e-7,0.0002905952,0.000013995298,0.26576596,0.14998704,0.0004184026,0.58210826],"study_design_scores_gemma":[0.00083833834,0.00068303983,0.020539785,0.000039542607,0.000010686322,0.000018096343,0.00010236301,0.68894196,0.11493818,0.0008207235,0.17244467,0.000622611],"about_ca_topic_score_codex":0.0000059567024,"about_ca_topic_score_gemma":0.0000023403634,"teacher_disagreement_score":0.7972682,"about_ca_system_score_codex":0.000013125791,"about_ca_system_score_gemma":0.000013454338,"threshold_uncertainty_score":0.27661264},"labels":[],"label_agreement":null},{"id":"W2968923568","doi":"10.1609/aaai.v34i04.5712","title":"Detecting Semantic Anomalies","year":2020,"lang":"en","type":"preprint","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Compute Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Generalization; Relevance (law); Task (project management); Context (archaeology); Anomaly detection; Set (abstract data type); Artificial intelligence; Object (grammar); Natural language processing; Machine learning; Information retrieval; Data science; Programming language; Epistemology","score_opus":0.10565315845507382,"score_gpt":0.3098998095140946,"score_spread":0.2042466510590208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968923568","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":[0.078770146,0.00005993628,0.8727182,0.021771451,0.0009473327,0.0016711743,0.000019083118,0.0011086799,0.02293396],"genre_scores_gemma":[0.98404574,0.00005395805,0.015218273,0.00026495778,0.00012475431,0.00013107684,5.1630536e-7,0.000020120306,0.00014058227],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99766135,0.000017146642,0.000704679,0.00085104705,0.00046021238,0.0003055534],"domain_scores_gemma":[0.9978381,0.0000669325,0.0008222619,0.00057237124,0.0005989506,0.000101381454],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032370945,0.00034084579,0.00039341324,0.00014385377,0.00028487714,0.00049774814,0.0037077924,0.00022554556,0.00003175565],"category_scores_gemma":[0.00027112584,0.00027735057,0.00026067955,0.00066255324,0.00022581256,0.00019570182,0.0025302733,0.0009058228,0.00008278502],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000125899915,0.00006454757,0.00005352874,0.00014165118,0.000021850552,4.0007757e-7,0.000772881,0.000052147487,0.030706573,0.8997338,0.00015141377,0.06828864],"study_design_scores_gemma":[0.000005740292,0.000081602986,0.000061138606,0.00024145286,0.000014714978,0.0000033650344,0.0001611836,0.0776983,0.52042,0.40095502,0.00012685616,0.00023057789],"about_ca_topic_score_codex":0.00006364026,"about_ca_topic_score_gemma":0.0000067085484,"teacher_disagreement_score":0.90527564,"about_ca_system_score_codex":0.000057727273,"about_ca_system_score_gemma":0.00014721492,"threshold_uncertainty_score":0.9999679},"labels":[],"label_agreement":null},{"id":"W2969609970","doi":"10.48550/arxiv.1809.04729","title":"A Less Biased Evaluation of Out-of-distribution Sample Detectors","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Outlier; Computer science; Sample (material); Artificial intelligence; Set (abstract data type); Machine learning; Population; Scheme (mathematics); Data mining; Mathematics","score_opus":0.1871613230210542,"score_gpt":0.24610366101325556,"score_spread":0.05894233799220136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969609970","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":[0.39428386,0.000008274114,0.60497963,0.000008910922,0.00013490106,0.00025289413,0.00007395736,0.000095438285,0.00016211618],"genre_scores_gemma":[0.9960495,0.000019283829,0.003793172,0.0000044701883,0.000031559513,0.0000047806093,0.00005631333,0.0000068211884,0.00003408734],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876714,0.000142894,0.0002457441,0.0005438598,0.00016146716,0.00013889684],"domain_scores_gemma":[0.9975529,0.00008841166,0.00051241944,0.0009830636,0.0008043525,0.0000588712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005801377,0.00015196716,0.00022391422,0.00016160971,0.00008526845,0.000020903613,0.0008902142,0.00020715657,0.00002529267],"category_scores_gemma":[0.00007047929,0.00017999126,0.00017212042,0.0005446345,0.00013450984,0.00013583484,0.00058704306,0.00016554016,0.0000073036795],"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":[0.0001911911,0.0015540386,0.013951026,0.0007540859,0.0006827553,0.000012563554,0.0015537055,0.17827411,0.0065822587,0.6366433,0.002298289,0.15750268],"study_design_scores_gemma":[0.0003729186,0.00012059375,0.0026067102,0.00008782791,0.00016871227,9.810553e-7,0.000077166995,0.86493015,0.039536618,0.091532946,0.00026250858,0.00030287774],"about_ca_topic_score_codex":0.0003505501,"about_ca_topic_score_gemma":0.00006285939,"teacher_disagreement_score":0.686656,"about_ca_system_score_codex":0.00019934785,"about_ca_system_score_gemma":0.00024540725,"threshold_uncertainty_score":0.73398304},"labels":[],"label_agreement":null},{"id":"W2969766398","doi":"10.1016/j.neucom.2020.04.103","title":"Calibration of deep probabilistic models with decoupled bayesian neural networks","year":2020,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ministerio de Ciencia, Innovación y Universidades; Canadian Institute for Advanced Research; Universitat Politècnica de València; Nvidia","keywords":"Computer science; Calibration; Artificial neural network; Flexibility (engineering); Deep neural networks; Bayesian probability; Probabilistic logic; Artificial intelligence; Machine learning; Limiting; Bayesian network; Statistics; Mathematics","score_opus":0.016169737697604325,"score_gpt":0.21842770237851084,"score_spread":0.2022579646809065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969766398","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":[0.01259818,0.000011960334,0.9857626,0.00074213004,0.000020859607,0.00028632671,2.5482473e-7,0.00038929185,0.00018841743],"genre_scores_gemma":[0.9111711,8.486014e-7,0.08816248,0.0005734532,0.00006238278,0.000016648732,0.0000011082044,0.000010538691,0.0000014707014],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991189,0.00003387279,0.00023674995,0.0003230591,0.00012950799,0.00015790937],"domain_scores_gemma":[0.99941623,0.00005802536,0.00014915055,0.00022923276,0.00006047389,0.000086877866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000052540025,0.00010248714,0.0001301425,0.000030078128,0.000111194626,0.000065047934,0.00038205463,0.000034314053,0.0000022967174],"category_scores_gemma":[0.000009788442,0.00009148014,0.000038502632,0.0004978905,0.00002680107,0.00025030485,0.00013064618,0.0001315314,3.8054745e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000055954406,0.000017836159,0.00014860234,0.000016016776,0.0000030251506,0.0000024645435,0.0001675253,0.9507594,0.00027798937,0.017867364,0.000014356866,0.030719813],"study_design_scores_gemma":[0.00010365677,0.0001494866,0.00012387166,0.000005963479,0.000004175388,0.000010365599,0.000005306548,0.9976538,0.0005628511,0.0012745257,0.000015489515,0.00009053436],"about_ca_topic_score_codex":0.000006718336,"about_ca_topic_score_gemma":0.0000027051112,"teacher_disagreement_score":0.89857286,"about_ca_system_score_codex":0.000007948315,"about_ca_system_score_gemma":0.000015722828,"threshold_uncertainty_score":0.3730452},"labels":[],"label_agreement":null},{"id":"W2972153360","doi":"10.48550/arxiv.1909.02168","title":"Future Frame Prediction Using Convolutional VRNN for Anomaly Detection","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Autoencoder; Computer science; Anomaly detection; Benchmark (surveying); Artificial intelligence; Frame (networking); Generative grammar; Generative model; Machine learning; Anomaly (physics); Pattern recognition (psychology); Deep learning","score_opus":0.059036402447474816,"score_gpt":0.19408796814595355,"score_spread":0.13505156569847873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972153360","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":[0.09692609,0.000043223998,0.90027726,0.00007200485,0.0010641152,0.0007619838,0.00008829196,0.00053646363,0.0002305548],"genre_scores_gemma":[0.98348796,0.00005693359,0.015240094,0.00006351497,0.00045316457,0.0000119143415,0.000034039407,0.000019287274,0.0006331156],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984406,0.000048870323,0.00020759305,0.00096522574,0.000083034654,0.00025468244],"domain_scores_gemma":[0.9984226,0.00004832711,0.00030038922,0.00085608196,0.00027932634,0.000093251656],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016257381,0.00024409976,0.00022303693,0.00025689023,0.00032868507,0.00010513309,0.0007826081,0.00049982074,0.0000132274145],"category_scores_gemma":[0.000008765679,0.00030148472,0.0002675624,0.0004472617,0.000058648606,0.00038930113,0.00054342276,0.00043600102,0.000025475252],"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":[0.00011787464,0.00026524058,0.0037356908,0.0002831422,0.00026389677,0.0000120938175,0.00015640404,0.48038408,0.0056922124,0.5026477,0.001157308,0.0052843634],"study_design_scores_gemma":[0.0002695803,0.00008547088,0.0016598362,0.00002914361,0.000060933602,0.000010095988,0.000032160013,0.96095926,0.0015146703,0.02816667,0.006911972,0.00030019757],"about_ca_topic_score_codex":0.0000973085,"about_ca_topic_score_gemma":0.000012900217,"teacher_disagreement_score":0.8865619,"about_ca_system_score_codex":0.0003977442,"about_ca_system_score_gemma":0.00019245838,"threshold_uncertainty_score":0.99994373},"labels":[],"label_agreement":null},{"id":"W2972463432","doi":"10.1109/i2mtc.2019.8826819","title":"Operation Status Tracking for Legacy Manufacturing Systems via Vibration Analysis","year":2019,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":13,"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":"","keywords":"Interrupt; Vibration; Computer science; Process (computing); Fault (geology); Track (disk drive); Real-time computing; Reliability engineering; Engineering; Embedded system; Operating system","score_opus":0.012620308261090184,"score_gpt":0.25299416296299404,"score_spread":0.24037385470190387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972463432","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":[0.02354655,0.000013369333,0.9740763,0.00017003532,0.00009707814,0.00042076345,0.0000016265948,0.0003052647,0.0013690476],"genre_scores_gemma":[0.93687075,0.000004099662,0.061965078,0.00008572376,0.00003181117,0.00010434947,0.000018161823,0.000004933228,0.0009150821],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992665,0.000016929414,0.00019133421,0.0002795056,0.000103443315,0.00014226793],"domain_scores_gemma":[0.999425,0.000034126915,0.00007200323,0.00035428497,0.00007581931,0.000038772938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001135078,0.00007057943,0.000109132554,0.00013214417,0.0001277606,0.0006259859,0.00016371359,0.000042642478,0.00003231117],"category_scores_gemma":[0.000002469135,0.00006514466,0.00008344145,0.00028766796,0.000003744598,0.0013157703,0.000034072153,0.00003847759,0.00004943221],"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":[0.000013400554,0.00015043314,0.001881775,0.00011166782,0.00041966347,0.000001008961,0.0005138338,0.1290034,0.101053156,0.55903715,0.00048479353,0.2073297],"study_design_scores_gemma":[0.000096207565,0.000032583772,0.0020618604,0.0000018853954,0.000027742934,0.0000020081948,0.00004737956,0.8215867,0.170467,0.00062990165,0.004937087,0.0001096047],"about_ca_topic_score_codex":0.00008863022,"about_ca_topic_score_gemma":0.000020651814,"teacher_disagreement_score":0.91332424,"about_ca_system_score_codex":0.000046839792,"about_ca_system_score_gemma":0.000021934904,"threshold_uncertainty_score":0.6036396},"labels":[],"label_agreement":null},{"id":"W2975052829","doi":"10.1016/j.future.2019.09.038","title":"A multi-stage anomaly detection scheme for augmenting the security in IoT-enabled applications","year":2019,"lang":"en","type":"article","venue":"Future Generation Computer Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":110,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; École de Technologie Supérieure; Université du Québec à Montréal","funders":"Ministry of Electronics and Information technology","keywords":"DBSCAN; Computer science; Data mining; Anomaly detection; Cluster analysis; Outlier; k-nearest neighbors algorithm; Artificial intelligence; Canopy clustering algorithm; Fuzzy clustering","score_opus":0.0220517241116298,"score_gpt":0.2604915366134877,"score_spread":0.23843981250185786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2975052829","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":[0.0135172885,0.00023454671,0.9776157,0.0004533008,0.0049008336,0.0028897903,0.000008997497,0.0003157464,0.000063782616],"genre_scores_gemma":[0.6830198,0.000015489064,0.28917024,0.0004642205,0.020223761,0.005706588,0.00004741759,0.000037450427,0.0013150238],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984352,0.000109940054,0.00045049642,0.00056621945,0.00018228928,0.00025586074],"domain_scores_gemma":[0.9986902,0.000052652766,0.0002477824,0.0007705085,0.00018792739,0.00005095413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005618356,0.00017921558,0.00019118107,0.00014911771,0.000388393,0.0004366524,0.0005959663,0.00013495219,0.0000041090293],"category_scores_gemma":[0.000002890572,0.00014788295,0.00009867228,0.00062038633,0.000013784078,0.00026941986,0.00012745224,0.0001764533,0.000036966714],"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":[0.000049505445,0.0012668057,0.007120551,0.0011243853,0.00029450498,0.0000047855015,0.0062572816,0.062020313,0.29948902,0.4445349,0.05040083,0.12743711],"study_design_scores_gemma":[0.00032688316,0.000042054126,0.0003562851,0.000008635047,0.0000027134954,0.0000078129715,0.000057440124,0.76130354,0.002816338,0.000017679238,0.23491204,0.0001485849],"about_ca_topic_score_codex":0.000068529844,"about_ca_topic_score_gemma":0.00013797985,"teacher_disagreement_score":0.69928324,"about_ca_system_score_codex":0.000111319445,"about_ca_system_score_gemma":0.00004662274,"threshold_uncertainty_score":0.60304916},"labels":[],"label_agreement":null},{"id":"W2975711528","doi":"10.18280/ria.330201","title":"Terrorism Prediction Using Artificial Neural Network","year":2019,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Terrorism; Computer science; Artificial intelligence; Machine learning; Political science","score_opus":0.041455735520354565,"score_gpt":0.27518755690850094,"score_spread":0.23373182138814638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2975711528","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":[0.090125784,0.00005353703,0.9058424,0.0003591717,0.0007305189,0.00033794937,0.0000021500723,0.00039111596,0.0021573508],"genre_scores_gemma":[0.9760606,0.0000102594095,0.022687376,0.00014214464,0.0002977041,0.000028694143,0.0000029984735,0.000013366804,0.0007568334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860525,0.000039009414,0.0004002437,0.0004689671,0.00015056763,0.00033596196],"domain_scores_gemma":[0.99893117,0.000047069265,0.00013029733,0.00074474985,0.000066957895,0.00007972842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026115167,0.00013851063,0.00014881427,0.00008199448,0.00023853942,0.00013407868,0.0005580323,0.000091734844,0.00015149012],"category_scores_gemma":[0.0000103766215,0.00014385895,0.000104035265,0.0007169343,0.00003885713,0.00037057296,0.00015796488,0.00019101142,0.00060436915],"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":[0.00001085097,0.00014341707,0.0016195867,0.000025955444,0.000015343638,0.0000055697437,0.00046802108,0.59260714,0.027904304,0.2603611,0.0007373311,0.1161014],"study_design_scores_gemma":[0.00001052041,0.00008206216,0.00010711773,0.00001998347,0.000004390996,0.000031750424,0.00007638081,0.9513256,0.030287081,0.0126860095,0.005221978,0.00014714408],"about_ca_topic_score_codex":0.000027345317,"about_ca_topic_score_gemma":0.0000026714242,"teacher_disagreement_score":0.8859348,"about_ca_system_score_codex":0.000055399178,"about_ca_system_score_gemma":0.000023703149,"threshold_uncertainty_score":0.77681446},"labels":[],"label_agreement":null},{"id":"W2976040440","doi":"10.48550/arxiv.1909.13165","title":"Relational Graph Learning for Crowd Navigation","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crowds; ENCODE; Reinforcement learning; Computer science; Graph; Artificial intelligence; Representation (politics); Machine learning; Feature learning; Convolutional neural network; Baseline (sea); Exploit; Theoretical computer science; Computer security","score_opus":0.0738761143946521,"score_gpt":0.19959949112950004,"score_spread":0.12572337673484796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2976040440","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":[0.038260188,0.000015576748,0.9587848,0.00011342227,0.00019338261,0.00046713036,0.0000075455273,0.00043451926,0.001723443],"genre_scores_gemma":[0.97984815,0.000030985226,0.015631046,0.000036049878,0.00005365663,0.00000947411,0.000068199224,0.000012325968,0.00431012],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892026,0.000040644798,0.00014246597,0.0006771807,0.000057007634,0.00016245329],"domain_scores_gemma":[0.9988723,0.00009547866,0.00023712941,0.00055379776,0.00018359235,0.000057720346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016420169,0.00015215791,0.00014390424,0.00016537301,0.00024466962,0.000073824005,0.00065956457,0.00023410632,0.000011771886],"category_scores_gemma":[0.000013710418,0.00019044528,0.00019746914,0.00036438176,0.00003910106,0.0002616548,0.00047971282,0.0004093826,0.000059687136],"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":[0.0000062726554,0.00002255349,0.0011624774,0.000033521486,0.000022332644,0.0000021247506,0.000040962375,0.32575372,0.000056414527,0.6717979,0.00032534686,0.0007764121],"study_design_scores_gemma":[0.00019390752,0.000050592156,0.0012648517,0.000043128915,0.000024358722,0.0000023813548,0.000015844855,0.8026174,0.0003379523,0.18703803,0.0081385765,0.0002729707],"about_ca_topic_score_codex":0.000024955174,"about_ca_topic_score_gemma":0.00000164249,"teacher_disagreement_score":0.94315374,"about_ca_system_score_codex":0.00010830152,"about_ca_system_score_gemma":0.00009711384,"threshold_uncertainty_score":0.77661335},"labels":[],"label_agreement":null},{"id":"W2981507930","doi":"10.3390/s19214664","title":"Augmenting Deep Learning Performance in an Evidential Multiple Classifier System","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Safran Electronics (Canada)","funders":"Qatar National Research Fund; Agence Nationale de la Recherche","keywords":"Artificial intelligence; Machine learning; Computer science; Classifier (UML); Exploit; Leverage (statistics); Ensemble learning; Artificial neural network; Crowds","score_opus":0.01188563110383821,"score_gpt":0.23161418725759758,"score_spread":0.21972855615375936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981507930","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":[0.94412184,0.000009342139,0.053368423,0.00002766178,0.00011176392,0.00017202648,1.1254912e-7,0.0003407366,0.001848104],"genre_scores_gemma":[0.9928538,0.000004050352,0.0064213295,0.000014579374,0.0000372138,0.000020033869,9.296052e-7,0.000007670524,0.0006403695],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992038,0.00005107183,0.00017062585,0.00026794476,0.00012390444,0.00018263297],"domain_scores_gemma":[0.9995309,0.000027070158,0.0000755704,0.00029369848,0.000032819076,0.00003993953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019466312,0.00007376342,0.00008975382,0.00009630899,0.00011143404,0.000089604626,0.00027664917,0.000048883125,0.0000099484305],"category_scores_gemma":[0.000006826503,0.00007453269,0.00003114924,0.00026332773,0.000010475772,0.00034647048,0.000081771635,0.00015727642,0.0001825571],"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":[0.00002065899,0.00011430493,0.7325652,0.00019694072,0.000015542504,0.000012346839,0.002288296,0.052630953,0.023440767,0.018507695,0.000019156334,0.17018813],"study_design_scores_gemma":[0.0001294043,0.00004983447,0.043329183,0.000031972486,0.0000012271637,0.000008185072,0.00028821317,0.9504249,0.0040711374,0.000007705723,0.0015513607,0.00010685804],"about_ca_topic_score_codex":0.00002803221,"about_ca_topic_score_gemma":0.000018713752,"teacher_disagreement_score":0.89779395,"about_ca_system_score_codex":0.00005975266,"about_ca_system_score_gemma":0.0000091471675,"threshold_uncertainty_score":0.3039355},"labels":[],"label_agreement":null},{"id":"W2981902149","doi":"10.48550/arxiv.1910.10307","title":"Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Output","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Layer (electronics); Artificial neural network; Distribution (mathematics); Computer science; Deep neural networks; Artificial intelligence; Mathematics; Materials science; Nanotechnology","score_opus":0.09811232229998548,"score_gpt":0.22440718222980946,"score_spread":0.12629485992982398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981902149","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":[0.45019704,0.00000957168,0.5491955,0.0000081746775,0.00021407845,0.00019922531,0.000005475809,0.000115989424,0.000054929762],"genre_scores_gemma":[0.9973463,0.00001336149,0.002458695,0.000029630226,0.000057417008,0.0000016497672,0.000021948012,0.000014929571,0.000056068595],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983878,0.00012680703,0.00028248533,0.00082304544,0.00007373818,0.00030609028],"domain_scores_gemma":[0.9983424,0.000047202797,0.0003817284,0.0009998044,0.00013339182,0.00009544961],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023473952,0.00023209189,0.00028679383,0.00021216381,0.00011932864,0.00008501476,0.0010670808,0.0003476856,0.00000356241],"category_scores_gemma":[0.00001166829,0.00029105693,0.00013411304,0.0006017469,0.000056419252,0.00044061977,0.0010680434,0.0006398763,0.0000060582447],"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":[0.000010868449,0.000070502145,0.010700358,0.000021239579,0.000013634644,0.00001752124,0.00012512301,0.98169065,0.00013591643,0.004529138,0.0000036396937,0.0026813964],"study_design_scores_gemma":[0.00015746783,0.000058972528,0.004247492,0.000034678844,0.000019551817,0.0000019696026,0.000021573984,0.99248725,0.000592742,0.0020733553,0.000026255939,0.0002786683],"about_ca_topic_score_codex":0.00037499456,"about_ca_topic_score_gemma":0.00011812768,"teacher_disagreement_score":0.54714924,"about_ca_system_score_codex":0.00024650613,"about_ca_system_score_gemma":0.000056367342,"threshold_uncertainty_score":0.99995416},"labels":[],"label_agreement":null},{"id":"W2987004716","doi":"10.22564/16cisbgf2019.015","title":"A residual dictionary learning method for footprint removal from seismic data","year":2019,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"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":"Universidad Nacional de La Plata; Consejo Nacional de Investigaciones Científicas y Técnicas; Nova Scotia Department of Energy; Royal Society; U.S. Department of Energy","keywords":"Residual; Footprint; Computer science; Artificial intelligence; Dictionary learning; Geology; Algorithm; Sparse approximation","score_opus":0.034122211661182685,"score_gpt":0.31663018586657027,"score_spread":0.2825079742053876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2987004716","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034847672,0.000020423726,0.9898673,0.0020769367,0.00009054401,0.00030261694,0.0000135101145,0.0005711008,0.003572748],"genre_scores_gemma":[0.07584166,0.0000068230993,0.91740113,0.00026857882,0.00009120262,0.000047767637,0.00005369975,0.0000083828745,0.0062807514],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990716,0.000038943468,0.00015162362,0.0004975227,0.00011232001,0.00012799435],"domain_scores_gemma":[0.99869007,0.00018789039,0.00006147641,0.0009799869,0.00004117438,0.000039427458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000317269,0.00007392008,0.00009340939,0.0000471173,0.00012850981,0.000072573974,0.0009191308,0.000051195104,0.00004137263],"category_scores_gemma":[0.000019493089,0.000068251655,0.000036186946,0.00014572764,0.000008453473,0.00020094238,0.0006282788,0.000118227494,0.00007329683],"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":[0.000020208889,0.00007054123,0.0004061339,0.000010176326,0.000050589442,0.0000021361836,0.00014759961,0.0013225508,0.016818022,0.15290335,0.011557564,0.8166911],"study_design_scores_gemma":[0.000111229965,0.000051360385,0.00041862205,0.0000033510273,0.000003954764,0.0000123600885,0.000028446027,0.63321763,0.0039391844,0.012846798,0.34927824,0.000088800545],"about_ca_topic_score_codex":0.00028692023,"about_ca_topic_score_gemma":0.0000067159153,"teacher_disagreement_score":0.81660235,"about_ca_system_score_codex":0.000020360003,"about_ca_system_score_gemma":0.000036000405,"threshold_uncertainty_score":0.2783222},"labels":[],"label_agreement":null},{"id":"W2989667869","doi":"","title":"Multivariate Triangular Quantile Maps for Novelty Detection","year":2019,"lang":"en","type":"article","venue":"NPARC","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Novelty detection; Novelty; Hyperparameter; Quantile; Univariate; Artificial intelligence; Machine learning; Gradient descent; Exploit; Density estimation; Artificial neural network; Multivariate statistics; Pattern recognition (psychology); Mathematics; Statistics","score_opus":0.01564936860355491,"score_gpt":0.26231736353977025,"score_spread":0.24666799493621533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2989667869","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":[0.012698827,0.0000042644906,0.9817251,0.00036369282,0.00019897001,0.0005461511,0.000007186549,0.0003442886,0.0041115414],"genre_scores_gemma":[0.83576,0.0000016192961,0.16265136,0.00011334558,0.000048479527,0.00017204869,0.0000024440196,0.00000769535,0.001242994],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993461,0.0000139250105,0.00013572862,0.00026684333,0.00008871439,0.00014872356],"domain_scores_gemma":[0.99937856,0.00004729776,0.000068055866,0.0004058338,0.00006127753,0.000038993818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016676764,0.00007318152,0.00009272059,0.00006021394,0.00010059387,0.00005752221,0.00030197378,0.000059306898,0.000051880957],"category_scores_gemma":[0.000015158812,0.00006965966,0.00007865116,0.0002087752,0.000009462559,0.00017819855,0.00005868382,0.000061919505,0.00014830692],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016079735,0.000057473255,0.00003933186,0.000013826602,0.000010183765,2.4105228e-7,0.00007440551,0.000015665082,0.65779054,0.19433573,0.001037188,0.14660934],"study_design_scores_gemma":[0.0009231345,0.00027000494,0.0009965325,0.000009534841,0.0000075973117,0.000009514325,0.000019113093,0.20705487,0.50819445,0.07972514,0.20251817,0.00027194648],"about_ca_topic_score_codex":0.000015645672,"about_ca_topic_score_gemma":0.0000029700268,"teacher_disagreement_score":0.82306117,"about_ca_system_score_codex":0.000027501857,"about_ca_system_score_gemma":0.000017031734,"threshold_uncertainty_score":0.28406385},"labels":[],"label_agreement":null},{"id":"W2990140997","doi":"10.1109/smc.2019.8914322","title":"A Neural Word Embedding Approach to System Trace Reconstruction","year":2019,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; TRACE (psycholinguistics); Data mining; Word embedding; Word (group theory); Intrusion detection system; Embedding; Hidden Markov model; Noise (video); Anomaly detection; Event (particle physics); Benchmark (surveying); Artificial intelligence; Algorithm","score_opus":0.012715240361192285,"score_gpt":0.23721750830852673,"score_spread":0.22450226794733444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990140997","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":[0.06138291,0.000004774747,0.8827179,0.00017928591,0.00014202978,0.0003116821,2.856446e-7,0.00078414386,0.054476976],"genre_scores_gemma":[0.7408524,2.9078495e-7,0.25732538,0.00006727118,0.000024934097,0.00006160219,1.8792329e-7,0.000003811248,0.0016641027],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993343,0.000015908152,0.00013445223,0.0002946349,0.00009091597,0.00012979168],"domain_scores_gemma":[0.9994682,0.000011640474,0.000039623545,0.00038361555,0.00003354069,0.000063398315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000101536556,0.00006841093,0.0000837072,0.00008205722,0.00007612385,0.00010445664,0.0003255735,0.000037121234,0.000011393389],"category_scores_gemma":[0.0000018389131,0.000060829665,0.00004102086,0.00040188766,0.000005904918,0.00025679756,0.000075915515,0.00006725634,0.00019865809],"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":[0.000003873493,0.000042168675,0.0003495817,0.000039857823,0.000008022312,3.8539497e-7,0.00026848412,0.0015751247,0.0046595326,0.50918376,0.0009711083,0.48289812],"study_design_scores_gemma":[0.000083077815,0.000043337914,0.00050655816,0.000014250053,0.0000020133502,0.00018990124,0.0003823337,0.98996294,0.004367204,0.00020136392,0.004076608,0.00017040818],"about_ca_topic_score_codex":0.00001854075,"about_ca_topic_score_gemma":5.418516e-7,"teacher_disagreement_score":0.9883878,"about_ca_system_score_codex":0.00004869993,"about_ca_system_score_gemma":0.000009538353,"threshold_uncertainty_score":0.2553414},"labels":[],"label_agreement":null},{"id":"W2991506670","doi":"10.1109/avss.2019.8909850","title":"Future Frame Prediction Using Convolutional VRNN for Anomaly Detection","year":2019,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":125,"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 Manitoba","funders":"","keywords":"Autoencoder; Computer science; Anomaly detection; Benchmark (surveying); Artificial intelligence; Frame (networking); Generative grammar; Machine learning; Generative model; Anomaly (physics); Pattern recognition (psychology); Deep learning","score_opus":0.010780598075322131,"score_gpt":0.2385440194694902,"score_spread":0.22776342139416805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991506670","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":[0.05605614,0.000022656212,0.94137734,0.00027193848,0.00048678785,0.00047351993,0.000008619078,0.00050234253,0.0008006783],"genre_scores_gemma":[0.8561994,0.0000038596063,0.14253736,0.00018175475,0.00030100712,0.000084068066,0.0000045479846,0.0000072986522,0.00068069546],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999264,0.000011562745,0.00015845435,0.00029787773,0.00011979276,0.00014832927],"domain_scores_gemma":[0.999444,0.00002465683,0.00007026493,0.0002981759,0.00011904352,0.00004387241],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000112223206,0.00008499064,0.00007998712,0.000084876076,0.00017944424,0.000068170346,0.00021500772,0.000106450185,0.00005053498],"category_scores_gemma":[0.0000037400778,0.00008126148,0.00007779535,0.00025844504,0.000014503019,0.00040127384,0.000049384424,0.0000803267,0.000043083233],"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":[0.00003464582,0.00015753809,0.0043448615,0.00004919266,0.00005129915,3.0949406e-7,0.00012340331,0.0010340106,0.30750343,0.6145023,0.002317739,0.069881305],"study_design_scores_gemma":[0.00029413062,0.00019865678,0.0063995947,0.0000057534885,0.000008044181,0.000032364867,0.00003968236,0.86233085,0.046352185,0.00781274,0.07634693,0.0001790874],"about_ca_topic_score_codex":0.000029731713,"about_ca_topic_score_gemma":0.00000428887,"teacher_disagreement_score":0.86129683,"about_ca_system_score_codex":0.00007636241,"about_ca_system_score_gemma":0.000037744445,"threshold_uncertainty_score":0.3313747},"labels":[],"label_agreement":null},{"id":"W2995175434","doi":"10.1109/iemcon.2019.8936183","title":"User Modeling via Anomaly Detection Techniques for User Authentication","year":2019,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Western University","funders":"","keywords":"Anomaly detection; Computer science; Intrusion detection system; User modeling; Data mining; Filter (signal processing); Variety (cybernetics); Authentication (law); Big data; Data modeling; Real-time computing; User interface; Artificial intelligence; Database; Computer security","score_opus":0.011587310714151346,"score_gpt":0.2521150418193131,"score_spread":0.24052773110516176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995175434","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":[0.0242243,0.000006298217,0.9718809,0.0003283927,0.000081499646,0.0008508801,9.3358017e-7,0.0012215099,0.0014052886],"genre_scores_gemma":[0.7470115,0.0000035716141,0.25019425,0.00015758479,0.000035811645,0.00036131934,0.0000020136417,0.000011277291,0.0022226742],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990963,0.000013479057,0.00022118124,0.00037654396,0.000115377305,0.00017713057],"domain_scores_gemma":[0.99912685,0.000026922715,0.000073571406,0.00057388947,0.00015399023,0.00004479675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019480755,0.00010906818,0.00010167787,0.00012869548,0.0001365739,0.00010930276,0.0003980881,0.000095134106,0.000029409766],"category_scores_gemma":[0.0000060361026,0.00010184965,0.00008754559,0.00027824618,0.00001006005,0.00053685537,0.00008371081,0.000070147114,0.000091563634],"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":[0.000019119914,0.0001655644,0.00048817633,0.00004722576,0.000029128132,1.6942096e-7,0.0001642905,0.0003997394,0.40288457,0.32132033,0.00047886866,0.27400282],"study_design_scores_gemma":[0.00007381037,0.00009069472,0.00010710534,0.0000047012563,0.000004896966,0.000004683738,0.000008648888,0.72801363,0.24907364,0.009233459,0.013239578,0.00014517109],"about_ca_topic_score_codex":0.000055836812,"about_ca_topic_score_gemma":0.000011598432,"teacher_disagreement_score":0.72761387,"about_ca_system_score_codex":0.00005082537,"about_ca_system_score_gemma":0.000017231463,"threshold_uncertainty_score":0.4153308},"labels":[],"label_agreement":null},{"id":"W2995201943","doi":"10.1016/j.inffus.2019.12.001","title":"A survey on machine learning for data fusion","year":2019,"lang":"en","type":"article","venue":"Information Fusion","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":650,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Postdoctoral Program for Innovative Talents; Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project; China Postdoctoral Science Foundation; Academy of Finland; National Natural Science Foundation of China","keywords":"Computer science; Machine learning; Artificial intelligence; Sensor fusion; Raw data; Field (mathematics); Probabilistic logic; Deep learning; Fusion","score_opus":0.03328903191420649,"score_gpt":0.2784212690193559,"score_spread":0.2451322371051494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995201943","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":[0.013487461,0.0000068473437,0.98261046,0.00035058754,0.00015362441,0.000514835,0.000048323654,0.00032744205,0.002500426],"genre_scores_gemma":[0.97893775,0.000035921763,0.017739588,0.00071107567,0.000026367868,0.000043853644,0.0017487037,0.0000063587518,0.00075036933],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930686,0.000028321738,0.00022432038,0.00015798463,0.0001715713,0.00011097027],"domain_scores_gemma":[0.9989254,0.00010348175,0.00015171918,0.0006776583,0.0001061923,0.000035582852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005607481,0.000075616284,0.000075549666,0.00011761947,0.00019191513,0.00012519222,0.0006410157,0.000054410273,0.00004684211],"category_scores_gemma":[0.000074965545,0.00006603776,0.000024698184,0.0002541265,0.0000056860727,0.0013027834,0.00036271353,0.00010259167,0.0005308225],"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":[0.000053241663,0.000054165113,0.0030507387,0.00003332735,0.000005706281,7.548376e-8,0.00034828414,0.0006882759,0.00091638824,0.021282475,0.0150377555,0.9585296],"study_design_scores_gemma":[0.00020249831,0.0001275348,0.007001905,0.000009875127,8.3506734e-7,9.636863e-7,0.0000059893414,0.7046803,0.0006440033,0.00015659307,0.28708556,0.000083936815],"about_ca_topic_score_codex":0.0001305732,"about_ca_topic_score_gemma":0.000013280575,"teacher_disagreement_score":0.9654503,"about_ca_system_score_codex":0.000023296674,"about_ca_system_score_gemma":0.000023748316,"threshold_uncertainty_score":0.6822826},"labels":[],"label_agreement":null},{"id":"W2995914069","doi":"10.5121/csit.2020.100515","title":"Oversampling Log Messages using A Sequence Generative Adversarial Network for Anomaly Detection and Classification","year":2020,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Oversampling; Anomaly detection; Autoencoder; Sequence (biology); Anomaly (physics); Pattern recognition (psychology); Adversarial system; Data modeling","score_opus":0.10559295528431267,"score_gpt":0.30958650415026506,"score_spread":0.2039935488659524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995914069","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":[0.010607617,0.000031104188,0.9873658,0.0010708881,0.00006694234,0.00038828127,0.0000037186076,0.0002934307,0.00017222739],"genre_scores_gemma":[0.6317533,0.00001130486,0.36756122,0.0004132715,0.00018665059,0.00005128603,0.0000015789411,0.0000050749723,0.000016286918],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992145,0.000024020692,0.00016274163,0.0003734883,0.000076688506,0.00014861327],"domain_scores_gemma":[0.99953455,0.00005968524,0.000102151585,0.0001525278,0.00007713686,0.00007394866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011506102,0.00009541019,0.00009988136,0.000027834985,0.0003287743,0.00012788331,0.00016559062,0.0000650963,0.0000036824665],"category_scores_gemma":[0.00002439162,0.00009433847,0.00004058158,0.0003021868,0.000034730176,0.0003699987,0.000074321106,0.00006798406,0.0000019250103],"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":[0.000056107554,0.000026275162,0.00047232042,0.00003086646,0.000043606775,9.796397e-7,0.000655594,0.006863722,0.68294376,0.2251269,0.00057428563,0.08320557],"study_design_scores_gemma":[0.00016208102,0.00009898517,0.00039188788,0.0000037827624,0.000010618165,0.000005334236,0.00004045695,0.96625024,0.026164506,0.0037339807,0.003009637,0.00012851215],"about_ca_topic_score_codex":0.000031400676,"about_ca_topic_score_gemma":0.000008881214,"teacher_disagreement_score":0.95938647,"about_ca_system_score_codex":0.00004244312,"about_ca_system_score_gemma":0.000034538152,"threshold_uncertainty_score":0.38470113},"labels":[],"label_agreement":null},{"id":"W2996611057","doi":"10.1016/j.neunet.2019.12.001","title":"Minimum variance-embedded deep kernel regularized least squares method for one-class classification and its applications to biomedical data","year":2019,"lang":"en","type":"article","venue":"Neural Networks","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Department of Science and Technology, Ministry of Science and Technology, India; Northern California Institute for Research and Education; DoD Alzheimer's Disease Neuroimaging Initiative; BioClinica; Biogen; Pfizer; Novartis Pharmaceuticals Corporation; Eli Lilly and Company; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Council of Scientific and Industrial Research, India; University of Southern California; Bristol-Myers Squibb; Alzheimer's Drug Discovery Foundation","keywords":"Artificial intelligence; Computer science; Classifier (UML); Pattern recognition (psychology); Machine learning; Embedding; Kernel (algebra); Benchmark (surveying); Deep learning; Test set; Data mining; Mathematics","score_opus":0.04872986741370013,"score_gpt":0.3274939656895831,"score_spread":0.278764098275883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996611057","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":[0.0008387382,0.00009067007,0.991575,0.004951536,0.00014873069,0.0018625008,0.000029287012,0.00032909558,0.00017442502],"genre_scores_gemma":[0.5078943,0.000038168233,0.4886,0.0011059681,0.00035729658,0.001293887,0.00016678123,0.000027337996,0.00051628886],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981467,0.00007495944,0.0003471662,0.0009068763,0.00020494024,0.0003193191],"domain_scores_gemma":[0.997808,0.00031142967,0.00015667519,0.0013853294,0.00012800781,0.00021058363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039686947,0.00017014162,0.0002302916,0.00009781142,0.00024222833,0.00017529426,0.0012988189,0.00017740071,0.000015059976],"category_scores_gemma":[0.00003905365,0.00016806825,0.000051081643,0.0006448017,0.00003792179,0.00036039288,0.00049995893,0.0001889249,0.000028244429],"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":[0.00010569404,0.0003422373,0.000103940976,0.000090249116,0.0000701239,7.9561215e-7,0.00019197348,0.0019871516,0.037239667,0.26386037,0.009386079,0.6866217],"study_design_scores_gemma":[0.00032392459,0.00010149415,0.00068880845,0.000010716869,0.000017715676,0.000009836593,0.00001901206,0.96132594,0.0002191817,0.0016537267,0.03543892,0.00019071663],"about_ca_topic_score_codex":0.0000066250586,"about_ca_topic_score_gemma":0.0000057975026,"teacher_disagreement_score":0.9593388,"about_ca_system_score_codex":0.000030047842,"about_ca_system_score_gemma":0.000029606153,"threshold_uncertainty_score":0.6853624},"labels":[],"label_agreement":null},{"id":"W2996780607","doi":"10.4018/jdm.2020010104","title":"A Service Architecture Using Machine Learning to Contextualize Anomaly Detection","year":2019,"lang":"en","type":"article","venue":"Journal of Database Management","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"University of Ontario Institute of Technology","keywords":"Computer science; Anomaly detection; Context (archaeology); Outlier; Service (business); Set (abstract data type); Dashboard; Feature (linguistics); Architecture; Data mining; Artificial intelligence; Anomaly (physics); Machine learning; Data science","score_opus":0.015408573620899046,"score_gpt":0.2594359076302085,"score_spread":0.24402733400930948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996780607","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":[0.13330713,0.00004918779,0.8651918,0.00064543635,0.00012509509,0.00025142045,0.000003976407,0.000057645353,0.00036829154],"genre_scores_gemma":[0.7489868,0.000024934783,0.24961975,0.0010279837,0.00006606668,0.0000075706535,0.0000023067128,0.000011580041,0.00025303278],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99900717,0.00005516766,0.00031238273,0.00020035637,0.00027205536,0.00015286577],"domain_scores_gemma":[0.99909604,0.000023135366,0.00027471114,0.00038854862,0.00012005587,0.00009753014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040312536,0.00010800808,0.0001498804,0.0003189144,0.00010140898,0.00009890934,0.0005413196,0.000022605807,0.00003500259],"category_scores_gemma":[0.000008834355,0.00009531292,0.0000687156,0.00062841165,0.000005071493,0.000333906,0.00039423286,0.00022044741,0.000049739403],"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":[0.0003392463,0.00034373446,0.0027433126,0.0005876029,0.0005773272,0.0002487982,0.0018814722,0.024983373,0.31467205,0.023013262,0.0011783115,0.6294315],"study_design_scores_gemma":[0.0021693069,0.0012864904,0.0026445666,0.00048845663,0.00016201977,0.0008999544,0.0009209927,0.31924787,0.047065996,0.0010591199,0.6231826,0.00087263575],"about_ca_topic_score_codex":0.000120883175,"about_ca_topic_score_gemma":0.00003679383,"teacher_disagreement_score":0.6285589,"about_ca_system_score_codex":0.00007223354,"about_ca_system_score_gemma":0.000013749791,"threshold_uncertainty_score":0.38867483},"labels":[],"label_agreement":null},{"id":"W2996890197","doi":"10.32920/22734350.v1","title":"Deep Clustering with a Dynamic Autoencoder: From Reconstruction towards Centroids Construction","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor; Toronto Metropolitan University","funders":"","keywords":"Autoencoder; Cluster analysis; Benchmark (surveying); Artificial intelligence; Unsupervised learning; Computer science; Centroid; Deep learning; Function (biology); Process (computing); Machine learning; Pattern recognition (psychology); Data mining; Geography; Cartography","score_opus":0.014863722538617428,"score_gpt":0.24362376121723114,"score_spread":0.22876003867861372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996890197","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028614567,0.000032532527,0.98913944,0.0011339433,0.001029048,0.0005082669,0.000022928598,0.0036103022,0.0016620773],"genre_scores_gemma":[0.08408723,0.00018470004,0.91456795,0.00007197698,0.00011622446,0.00033477368,0.00006127261,0.000038530954,0.0005373368],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99793005,0.00004812538,0.00041723883,0.0010278645,0.0002859692,0.00029076557],"domain_scores_gemma":[0.99829245,0.00003243599,0.0003163339,0.0011047148,0.0001434337,0.00011062907],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001191991,0.00031791645,0.00031491322,0.00024947725,0.00022099,0.0003988737,0.00080991193,0.00031615703,0.000063732215],"category_scores_gemma":[0.000008372598,0.00029172943,0.0001306215,0.00040309574,0.00011236173,0.00028193562,0.0010519326,0.00055347005,0.000067365014],"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":[0.000020107382,0.000046610785,0.0005202934,0.00007139995,0.00017757028,0.000012827882,0.00032687964,0.024482934,0.00027377947,0.011805936,0.00020446446,0.9620572],"study_design_scores_gemma":[0.00015177585,0.000044545595,0.0017304394,0.00010115627,0.000024778594,0.00010513405,0.0000960147,0.96343154,0.0005144446,0.03308531,0.0003412704,0.00037359007],"about_ca_topic_score_codex":0.0012743613,"about_ca_topic_score_gemma":0.0008422856,"teacher_disagreement_score":0.96168363,"about_ca_system_score_codex":0.00028233396,"about_ca_system_score_gemma":0.0001871349,"threshold_uncertainty_score":0.9999535},"labels":[],"label_agreement":null},{"id":"W2997167991","doi":"10.1609/aaai.v34i04.5712","title":"Detecting Semantic Anomalies","year":2020,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Compute Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Generalization; Relevance (law); Context (archaeology); Task (project management); Anomaly detection; Set (abstract data type); Artificial intelligence; Natural language processing; Object (grammar); Machine learning; Data science; Information retrieval; Programming language; Epistemology","score_opus":0.023175811355098677,"score_gpt":0.2364524755749352,"score_spread":0.21327666421983654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997167991","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":[0.007257508,0.000011324652,0.9740163,0.0052249725,0.000015451213,0.00006105533,1.2060359e-7,0.00095917255,0.01245408],"genre_scores_gemma":[0.895409,0.00000236621,0.102758475,0.0016169876,0.000033997043,0.00001211618,8.55309e-8,0.0000028017669,0.00016418095],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995958,0.0000072587713,0.000084204206,0.00016760538,0.00005883332,0.0000862612],"domain_scores_gemma":[0.999721,0.000015074611,0.000024455267,0.00016403986,0.000019009965,0.000056435478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003426519,0.000044792898,0.000047889003,0.000017859107,0.000092586146,0.00006797212,0.00030577285,0.000017976201,0.000039647522],"category_scores_gemma":[0.000011568615,0.000039997947,0.000028808336,0.00028305003,0.000009758489,0.00014979724,0.00011692856,0.00004770005,0.00013472949],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000022408271,0.00003411249,0.0015565801,0.000025297826,0.000015303764,0.000008147881,0.0009928298,0.00005017011,0.042792022,0.633154,0.005485965,0.31588337],"study_design_scores_gemma":[0.00018488851,0.00024072129,0.0022567501,0.0000066341504,0.0000059849754,0.00003680108,0.00016398274,0.4598306,0.42861566,0.010807796,0.09740546,0.00044468604],"about_ca_topic_score_codex":0.000008719092,"about_ca_topic_score_gemma":0.0000013455401,"teacher_disagreement_score":0.88815147,"about_ca_system_score_codex":0.0000046972914,"about_ca_system_score_gemma":0.000008070295,"threshold_uncertainty_score":0.173172},"labels":[],"label_agreement":null},{"id":"W2998421476","doi":"10.1609/aaai.v34i04.6154","title":"CD-UAP: Class Discriminative Universal Adversarial Perturbation","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Discriminative model; Adversarial system; Computer science; Perturbation (astronomy); Benchmark (surveying); Artificial intelligence","score_opus":0.07347077682599268,"score_gpt":0.2773857039367226,"score_spread":0.20391492711072995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998421476","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":[0.022428172,0.000007308977,0.88604474,0.047707748,0.00029546765,0.00071675197,0.000010888697,0.00037910123,0.04240981],"genre_scores_gemma":[0.9947759,0.0000140422335,0.0042700735,0.00054384256,0.00008995204,0.000023341925,5.208475e-7,0.0000075546304,0.00027475125],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987588,0.000016389586,0.00031280756,0.00040925774,0.00031776438,0.00018501155],"domain_scores_gemma":[0.9989525,0.00004802468,0.00028153652,0.00017817438,0.00044599402,0.00009378156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016188281,0.0001528091,0.00015808044,0.00006143869,0.00021003993,0.00014221008,0.0014210155,0.00007483038,0.00005318418],"category_scores_gemma":[0.00018006958,0.000119587625,0.00010583091,0.0006546553,0.00019535417,0.00045219206,0.0003196608,0.0002430193,0.000064099615],"study_design_candidate":"theoretical_or_conceptual","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":[0.000024896912,0.0000445548,0.000012710463,0.0000092312275,0.000006320602,1.3321062e-7,0.0015721652,0.000019631694,0.024975233,0.9383988,0.0003228653,0.03461351],"study_design_scores_gemma":[0.000033701763,0.0003452396,0.000120866825,0.00004739632,0.000015198834,0.0000016470719,0.0013655936,0.17040691,0.6409893,0.18492317,0.0015295431,0.0002214177],"about_ca_topic_score_codex":0.000018531655,"about_ca_topic_score_gemma":0.000002324541,"teacher_disagreement_score":0.97234774,"about_ca_system_score_codex":0.000049963513,"about_ca_system_score_gemma":0.0000714619,"threshold_uncertainty_score":0.4876642},"labels":[],"label_agreement":null},{"id":"W2998481686","doi":"10.1299/jsmermd.2019.2a1-g03","title":"Anomaly Detection Based on Deep Learning Using Skeleton Information for Prevention of Industrial Accident","year":2019,"lang":"en","type":"article","venue":"The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Conestoga Meat Packers (Canada)","funders":"","keywords":"Autoencoder; Anomaly detection; Computer science; Accident (philosophy); Skeleton (computer programming); Artificial intelligence; Deep learning; Anomaly (physics); Pattern recognition (psychology); Machine learning","score_opus":0.025179072457183514,"score_gpt":0.2582045130124924,"score_spread":0.2330254405553089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998481686","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":[0.24393818,0.0000075548014,0.75448745,0.00032222012,0.00008397829,0.0008106468,0.000003379949,0.000059092305,0.0002875117],"genre_scores_gemma":[0.9870469,0.000028546285,0.012763834,0.00003960593,0.00002724281,0.00003777369,0.0000041197814,0.000008808385,0.00004318668],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892765,0.000013106959,0.00038221836,0.0002202517,0.00025917188,0.0001976113],"domain_scores_gemma":[0.99856985,0.000060968472,0.00058155286,0.00015311054,0.00058823236,0.000046274043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004858589,0.00015111388,0.0002161164,0.00021252113,0.0001672373,0.00011602589,0.00035551807,0.00013614848,0.0000039403876],"category_scores_gemma":[0.000052906948,0.00012657684,0.00009166224,0.00028147834,0.000034742952,0.0005801248,0.000097342854,0.00023324633,0.0000022244978],"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":[0.0002630015,0.0001484249,0.00045274853,0.00013212378,0.000040484123,2.4766473e-8,0.0009129932,0.074714065,0.022559851,0.7994314,0.000017234164,0.10132768],"study_design_scores_gemma":[0.0007945579,0.0025310498,0.00014114467,0.00014265742,0.000035593468,0.0000021044814,0.00083360175,0.9206448,0.0620876,0.012368477,0.00022695541,0.00019143747],"about_ca_topic_score_codex":0.000019363462,"about_ca_topic_score_gemma":0.000002030723,"teacher_disagreement_score":0.84593076,"about_ca_system_score_codex":0.000059171245,"about_ca_system_score_gemma":0.00006867871,"threshold_uncertainty_score":0.5161654},"labels":[],"label_agreement":null},{"id":"W2998868794","doi":"10.48550/arxiv.2001.04433","title":"Towards Automated Swimming Analytics Using Deep Neural Networks","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Analytics; Data collection; Computer science; Tracking (education); Work (physics); Data analysis; Data science; Scale (ratio); Artificial intelligence; Data mining; Engineering; Cartography; Geography","score_opus":0.0952943346761203,"score_gpt":0.2202412368902031,"score_spread":0.12494690221408282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998868794","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":[0.022069963,0.00003073925,0.9744421,0.00015263303,0.00023407317,0.00023247373,0.000004168817,0.0022887313,0.0005450734],"genre_scores_gemma":[0.97731054,0.000038998984,0.022277648,0.00017337498,0.000100978024,0.0000011024003,0.000009940432,0.000019797642,0.00006761665],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839973,0.00006846378,0.00022434702,0.0009310116,0.00007227116,0.00030419728],"domain_scores_gemma":[0.99846005,0.000028897251,0.00027798332,0.00091227505,0.00013391413,0.00018688336],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010171547,0.00027370037,0.0002905085,0.00018066213,0.0002474395,0.00018914256,0.0015120823,0.00028616888,0.000010916724],"category_scores_gemma":[0.000012003216,0.00033538303,0.000229163,0.001127726,0.000067633744,0.00024488874,0.0020009724,0.0006013566,0.000012454593],"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":[0.000003963241,0.000024496503,0.00023140784,0.000017682421,0.000044424374,0.0000880341,0.00004836652,0.96205276,0.00003052676,0.035672456,0.000060552353,0.0017253503],"study_design_scores_gemma":[0.00009327817,0.00002376823,0.00030266948,0.000017173901,0.00006633199,0.0000078380435,0.000020287673,0.9921061,0.00010175571,0.0067946967,0.0001317213,0.00033439274],"about_ca_topic_score_codex":0.00017608353,"about_ca_topic_score_gemma":0.0000117065365,"teacher_disagreement_score":0.9552406,"about_ca_system_score_codex":0.0002152516,"about_ca_system_score_gemma":0.00009063631,"threshold_uncertainty_score":0.9999098},"labels":[],"label_agreement":null},{"id":"W2999250053","doi":"10.48550/arxiv.2001.03674","title":"Semi-supervised Anomaly Detection using AutoEncoders","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Anomaly detection; Artificial intelligence; Computer science; Pattern recognition (psychology); Segmentation; Outlier; Residual; Task (project management); Process (computing); Anomaly (physics); Automation; Computer vision; Engineering; Algorithm","score_opus":0.10267824503926161,"score_gpt":0.20068394159784234,"score_spread":0.09800569655858073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999250053","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":[0.11721035,0.000017109161,0.87989295,0.00015540973,0.00022089753,0.00032464557,0.000008426347,0.0010675031,0.0011027359],"genre_scores_gemma":[0.97451067,0.000049622686,0.024900971,0.00016633651,0.000086105414,0.0000032252624,0.000005437354,0.00002216984,0.00025545357],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817044,0.000085119806,0.0002150178,0.0011639245,0.00008712017,0.00027837028],"domain_scores_gemma":[0.998374,0.00003132535,0.00023796069,0.0010368886,0.00012718524,0.00019263753],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010910297,0.00029168843,0.00026903223,0.00022988529,0.00030002857,0.00015606763,0.0013914518,0.00031633835,0.000023980532],"category_scores_gemma":[0.000012792534,0.00037055527,0.00025620157,0.000950714,0.000076008466,0.0003698765,0.0013884684,0.00057617587,0.000061268205],"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":[0.000054368582,0.00022424334,0.0017473586,0.00023014609,0.00026399418,0.0002985016,0.0006236052,0.8309184,0.010623077,0.14701895,0.00035860963,0.0076387753],"study_design_scores_gemma":[0.00014978102,0.000043294505,0.00031605476,0.000025462992,0.0000483701,0.000008725186,0.00003968341,0.9716811,0.0028903675,0.023673566,0.0007448827,0.00037867768],"about_ca_topic_score_codex":0.00035519837,"about_ca_topic_score_gemma":0.000031302538,"teacher_disagreement_score":0.85730034,"about_ca_system_score_codex":0.00029635392,"about_ca_system_score_gemma":0.00017636907,"threshold_uncertainty_score":0.99987465},"labels":[],"label_agreement":null},{"id":"W3003706366","doi":"10.1007/978-3-030-51310-8_10","title":"Enhancement of Short Text Clustering by Iterative Classification","year":2020,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Cluster analysis; CURE data clustering algorithm; Pattern recognition (psychology); Correlation clustering; Computer science; Single-linkage clustering; Outlier; Artificial intelligence; Canopy clustering algorithm; Data mining; Hierarchical clustering","score_opus":0.02989990342643889,"score_gpt":0.29519910866141097,"score_spread":0.2652992052349721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003706366","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":[0.0036960235,0.00011291035,0.99289566,0.0021007569,0.0004123544,0.0005377645,0.000007665535,0.00016824523,0.00006861635],"genre_scores_gemma":[0.6125999,0.000025715084,0.38690507,0.00032365392,0.00005691784,0.00007635384,0.00000545138,0.000006016673,8.921083e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99735355,0.000055758705,0.0005282643,0.0012416493,0.00051872025,0.00030205128],"domain_scores_gemma":[0.99825424,0.00010885811,0.0002576392,0.0010725202,0.00020433226,0.000102420956],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004095982,0.00026812422,0.00032849822,0.00026726373,0.00014390198,0.00031841485,0.0025303776,0.00016180541,0.00000539035],"category_scores_gemma":[0.00003841922,0.00026079646,0.00008532619,0.001327305,0.00027905105,0.00029420754,0.0025025099,0.0005438286,0.000005357556],"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":[0.0000047926014,0.00012593684,0.0002109544,0.00008983101,0.000011140945,0.0000023778043,0.001816895,0.014612947,0.11721818,0.0012780717,0.000110239336,0.86451864],"study_design_scores_gemma":[0.000041026924,0.000084177576,0.0003133944,0.000086091146,0.0000024662233,0.0000027630454,5.689473e-7,0.76033396,0.23039788,0.008401724,0.00012561084,0.00021035406],"about_ca_topic_score_codex":0.000034856017,"about_ca_topic_score_gemma":0.000010454446,"teacher_disagreement_score":0.8643083,"about_ca_system_score_codex":0.00021045012,"about_ca_system_score_gemma":0.0002457025,"threshold_uncertainty_score":0.99998444},"labels":[],"label_agreement":null},{"id":"W3005469911","doi":"10.1038/s41592-019-0702-6","title":"Markov models — training and evaluation of hidden Markov models","year":2020,"lang":"en","type":"article","venue":"Nature Methods","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre","funders":"","keywords":"Markov model; Hidden Markov model; Computer science; Markov chain; Maximum-entropy Markov model; Variable-order Markov model; Machine learning; Artificial intelligence; Computational biology; Biology","score_opus":0.1105233762431157,"score_gpt":0.39980136430660157,"score_spread":0.28927798806348587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005469911","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001955421,0.0009722074,0.9866561,0.0017485846,0.00005317844,0.0002774822,0.0000035773248,0.00015759635,0.008175815],"genre_scores_gemma":[0.41688427,0.000024197896,0.5826475,0.0003531044,0.000031869247,0.00003262721,9.927451e-7,0.000005533632,0.000019869802],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986304,0.0003430603,0.0002134223,0.0003543896,0.00033449856,0.00012421604],"domain_scores_gemma":[0.99913293,0.00013578385,0.00011833864,0.00031051316,0.0002155223,0.00008693717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018920918,0.00010519398,0.00018337835,0.00006837205,0.000072555486,0.000040102506,0.00037312775,0.00020232542,0.000011711698],"category_scores_gemma":[0.00009458953,0.00009820405,0.000057855417,0.0004552688,0.000031977193,0.00037481036,0.00014906866,0.00033217034,4.3243952e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000035351816,0.000008175297,0.0000020560426,0.000010477189,0.000010125631,1.7575664e-7,0.00095222,0.00047343184,0.0029355418,0.05599062,0.00023418677,0.93937945],"study_design_scores_gemma":[0.00013079542,0.000034794433,0.0000621258,0.0000069085704,0.000021099782,0.00000420054,0.000042616648,0.8440585,0.0078158025,0.14711206,0.00062156224,0.0000895178],"about_ca_topic_score_codex":0.0000048329284,"about_ca_topic_score_gemma":5.259415e-7,"teacher_disagreement_score":0.9392899,"about_ca_system_score_codex":0.000022190234,"about_ca_system_score_gemma":0.00006974313,"threshold_uncertainty_score":0.4004645},"labels":[],"label_agreement":null},{"id":"W3005592562","doi":"10.1109/vast47406.2019.8986935","title":"UofC-Bayes: A Bayesian Approach to Visualizing Uncertainty in Likert Scales","year":2019,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Visualization; Computer science; Bayesian probability; Likert scale; Dashboard; Data visualization; Bayes' theorem; Data mining; Data science; Machine learning; Information visualization; Artificial intelligence; Statistics","score_opus":0.011558071153828169,"score_gpt":0.26678096729285394,"score_spread":0.25522289613902577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005592562","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":[0.016619505,0.000008871528,0.9291017,0.00057827926,0.000033062046,0.0004090616,5.1488496e-7,0.00038024868,0.052868728],"genre_scores_gemma":[0.82955724,0.000002681509,0.16834249,0.0008606514,0.000016834412,0.00011707213,0.0000011592524,0.000006778291,0.0010951118],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989886,0.00002626532,0.0001945665,0.00042517163,0.00014229536,0.00022307827],"domain_scores_gemma":[0.9993039,0.000028902588,0.000031555646,0.00051513285,0.000030996875,0.000089535955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019875889,0.00010323282,0.00012937907,0.00016826078,0.000055781213,0.0001014997,0.0005911234,0.0000568415,0.000024053881],"category_scores_gemma":[0.0000062384756,0.00009102665,0.000047397098,0.0007740221,0.0000128739075,0.00020325104,0.00018962468,0.000092938164,0.00018453706],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000071961554,0.00031854206,0.0043118764,0.000029859495,0.000008607961,0.0000011786369,0.0009281432,0.0018491326,0.00470146,0.91877544,0.0029024354,0.066166125],"study_design_scores_gemma":[0.00040189284,0.00021947347,0.007536971,0.000045579953,0.0000024797662,0.000021492224,0.0005278452,0.9239855,0.0071513862,0.012242568,0.0471696,0.00069522107],"about_ca_topic_score_codex":0.00023846923,"about_ca_topic_score_gemma":0.000047696463,"teacher_disagreement_score":0.92213637,"about_ca_system_score_codex":0.000059712856,"about_ca_system_score_gemma":0.000024688781,"threshold_uncertainty_score":0.37119594},"labels":[],"label_agreement":null},{"id":"W3006123093","doi":"10.1016/j.knosys.2020.105659","title":"K-Means-based isolation forest","year":2020,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":172,"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":"Narodowe Centrum Nauki","keywords":"Computer science; Anomaly detection; Data mining; Tree (set theory); Isolation (microbiology); Cluster analysis; Anomaly (physics); Decision tree; Node (physics); Imperfect; Contrast (vision); Machine learning; Artificial intelligence; Mathematics; Engineering","score_opus":0.02771375491932919,"score_gpt":0.25437524051692634,"score_spread":0.22666148559759713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006123093","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":[0.0007947864,0.00021802762,0.9887634,0.0018276521,0.00024696338,0.00050164486,0.0000058971054,0.0011639398,0.006477695],"genre_scores_gemma":[0.986496,8.768746e-7,0.01236005,0.00043035042,0.00025281898,0.00024374375,0.000011465701,0.000018837249,0.00018585638],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871624,0.00009878989,0.00033819315,0.0004411384,0.0001826704,0.00022300007],"domain_scores_gemma":[0.9988382,0.000097314005,0.00014269631,0.0005395214,0.00018987201,0.00019238662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020480236,0.00016595943,0.00019026696,0.000106602594,0.00020516494,0.00019256957,0.0006383691,0.00010083924,0.000016999382],"category_scores_gemma":[0.000030547042,0.00016079382,0.000114218856,0.000805896,0.00003336522,0.00018090734,0.00005029636,0.00013305075,0.0004888824],"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":[0.00012620873,0.0015616013,0.022317972,0.0020412768,0.00013300945,0.00003882144,0.0028100226,0.13758495,0.03821192,0.5750825,0.13414398,0.08594778],"study_design_scores_gemma":[0.00029277592,0.00013796872,0.00033870176,0.000033284938,0.0000055118985,0.0000010830522,0.000013147307,0.9025069,0.003900865,0.00010338779,0.09248053,0.0001858249],"about_ca_topic_score_codex":0.000025964277,"about_ca_topic_score_gemma":0.000013313778,"teacher_disagreement_score":0.9857012,"about_ca_system_score_codex":0.00007271917,"about_ca_system_score_gemma":0.000175992,"threshold_uncertainty_score":0.6556981},"labels":[],"label_agreement":null},{"id":"W3007271490","doi":"10.1109/icmla.2019.00285","title":"Pattern and Anomaly Localization in Complex and Dynamic Data","year":2019,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"University of Ottawa","funders":"","keywords":"Computer science; Pattern recognition (psychology); Anomaly detection; Artificial intelligence; Generalization; Convolutional neural network; Outlier; Image (mathematics); Segmentation; Representation (politics); Filter (signal processing); External Data Representation; Series (stratigraphy); Computer vision; Mathematics","score_opus":0.02126072701281245,"score_gpt":0.27017648749144674,"score_spread":0.24891576047863428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007271490","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":[0.060743555,0.000020287654,0.9374807,0.000529183,0.00000797188,0.00012594378,0.0000026247717,0.00008102705,0.0010087313],"genre_scores_gemma":[0.98713195,0.000029326835,0.012320813,0.0003703829,0.000002284561,0.0000044431663,0.000013548592,0.0000024020871,0.00012484546],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956024,0.000009676555,0.000082806204,0.00024405665,0.000041040243,0.00006221086],"domain_scores_gemma":[0.9995255,0.000012083332,0.000020861577,0.00041077394,0.000009409166,0.000021407785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007172958,0.00004111529,0.000051916104,0.000046229194,0.000026629396,0.00005925358,0.00024191785,0.000021330508,0.000028198603],"category_scores_gemma":[0.0000014195589,0.00003777733,0.0000031055276,0.00013029446,0.000015470849,0.00026448868,0.00033548108,0.000029120589,0.000011628861],"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":[0.0000021896476,0.00007517304,0.22542657,0.000046003952,0.0000065852573,0.0000021087849,0.00021666731,0.00004920946,0.0027545786,0.04807283,0.0014931398,0.7218549],"study_design_scores_gemma":[0.000084004576,0.000018080917,0.08744358,0.0000028278323,5.3468904e-7,0.0000065520603,0.000011455149,0.9077158,0.00005297729,0.00075769174,0.0038510845,0.00005538987],"about_ca_topic_score_codex":0.00013836594,"about_ca_topic_score_gemma":0.00013871354,"teacher_disagreement_score":0.9263884,"about_ca_system_score_codex":0.0000068310283,"about_ca_system_score_gemma":0.0000050269186,"threshold_uncertainty_score":0.15405148},"labels":[],"label_agreement":null},{"id":"W3007613960","doi":"10.1109/bigdata47090.2019.9006260","title":"VidAnomaly: LSTM-Autoencoder-Based Adversarial Learning for One-Class Video Classification With Multiple Dynamic Images","year":2019,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"McMaster University","funders":"","keywords":"Autoencoder; Artificial intelligence; Computer science; Pattern recognition (psychology); Feature learning; Deep learning; Encoder; Representation (politics); Computer vision; Machine learning","score_opus":0.01238634715211213,"score_gpt":0.24109792899574314,"score_spread":0.22871158184363102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007613960","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":[0.011509595,0.000006613492,0.98193294,0.0015379863,0.00007624095,0.00093294174,0.0000039943006,0.0009203869,0.0030793268],"genre_scores_gemma":[0.731332,0.000002089713,0.26599786,0.00016141252,0.00002315862,0.00029639964,0.000016670596,0.000016795659,0.0021536383],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867165,0.000038954677,0.00023737265,0.0005757423,0.0002099132,0.0002663814],"domain_scores_gemma":[0.9987681,0.00021611221,0.00017953382,0.00059193786,0.00017079279,0.000073556505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020093986,0.00016426337,0.00017339163,0.00013509176,0.00024279261,0.00016623343,0.0005197086,0.00009408824,0.00004588308],"category_scores_gemma":[0.000029519551,0.00014560274,0.0000927053,0.00035684023,0.000045707,0.0004354438,0.00006519129,0.0001587915,0.00010075006],"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":[0.00068382977,0.0014190748,0.03771026,0.00033238746,0.00023510428,0.0000034429474,0.0005222996,0.05591358,0.46857885,0.2255799,0.0031477262,0.20587355],"study_design_scores_gemma":[0.0008701253,0.00036749477,0.0066309203,0.000016041691,0.000011619677,0.0000022181825,0.000047090907,0.9609465,0.018553771,0.00046940066,0.011843474,0.000241373],"about_ca_topic_score_codex":0.000046122423,"about_ca_topic_score_gemma":0.00003546185,"teacher_disagreement_score":0.9050329,"about_ca_system_score_codex":0.00010211191,"about_ca_system_score_gemma":0.00011444783,"threshold_uncertainty_score":0.5937507},"labels":[],"label_agreement":null},{"id":"W3007670733","doi":"10.3390/s20051261","title":"Designing a Streaming Algorithm for Outlier Detection in Data Mining—An Incrementa Approach","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":34,"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":"Computer science; Data stream mining; Sliding window protocol; Anomaly detection; Streaming data; Outlier; Data mining; Context (archaeology); Streaming algorithm; Concept drift; Scalability; Big data; Algorithm; Database; Artificial intelligence; Window (computing)","score_opus":0.07941839438265871,"score_gpt":0.2957602859902442,"score_spread":0.21634189160758546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007670733","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01544641,0.0000075644625,0.9834572,0.00020171357,0.000024974976,0.0003734156,0.000011970569,0.0002710385,0.00020570446],"genre_scores_gemma":[0.35320708,0.0000019804581,0.6464679,0.00014658541,0.000066598885,0.000057821988,0.000018420655,0.0000094314855,0.000024188957],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898195,0.000042083873,0.00018618083,0.00050850265,0.00010675955,0.00017450843],"domain_scores_gemma":[0.999291,0.000037561353,0.00006981772,0.00049558794,0.000027801794,0.000078248806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023821222,0.000094760166,0.00010468789,0.00007236259,0.00012193949,0.00008701751,0.0006270742,0.000051312156,0.000001571833],"category_scores_gemma":[0.000027040755,0.00010060086,0.000023505354,0.00037590432,0.000015859247,0.00033881408,0.00019995017,0.00008803011,0.0000034082855],"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":[0.0000048314396,0.00005779896,0.00006967371,0.000010308921,0.000008612204,0.0000014900617,0.0011114428,0.00033471495,0.007364892,0.00023406709,0.00013119177,0.990671],"study_design_scores_gemma":[0.00017725983,0.00008638266,0.00008556604,0.00000374402,0.00000456779,0.0000043732507,0.00054583425,0.9738771,0.022540934,0.000075126794,0.0024761136,0.00012302556],"about_ca_topic_score_codex":0.00005513297,"about_ca_topic_score_gemma":0.000014440648,"teacher_disagreement_score":0.99054796,"about_ca_system_score_codex":0.000030815692,"about_ca_system_score_gemma":0.000017351975,"threshold_uncertainty_score":0.41023842},"labels":[],"label_agreement":null},{"id":"W3008072996","doi":"10.1016/j.jbiomech.2020.109684","title":"Adversarial autoencoder for visualization and classification of human activity: Application to a low-cost commercial force plate","year":2020,"lang":"en","type":"article","venue":"Journal of Biomechanics","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Tokyo University of Agriculture and Technology","keywords":"Autoencoder; Dimensionality reduction; Artificial intelligence; Computer science; Visualization; Machine learning; Deep learning; Representation (politics); Similarity (geometry); Wearable computer; Feature learning; Wearable technology; Pattern recognition (psychology)","score_opus":0.03574354985869459,"score_gpt":0.31921552045297275,"score_spread":0.28347197059427814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008072996","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":[0.02009645,0.0000053947815,0.97765243,0.0016636414,0.000058690963,0.00046256543,0.0000096714775,0.000034795736,0.00001634782],"genre_scores_gemma":[0.9738571,0.000010312789,0.025714163,0.0002492441,0.00011597155,0.000034528646,0.0000033642173,0.00000818646,0.000007116592],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925333,0.00002099753,0.00032844127,0.00015348641,0.0001577703,0.00008598299],"domain_scores_gemma":[0.9989872,0.00003131664,0.0005022152,0.00013641617,0.00024135121,0.00010151104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022915355,0.00007473307,0.00015943116,0.00013429801,0.00010450178,0.00004107901,0.00026564146,0.000071854854,0.0000011671887],"category_scores_gemma":[0.000028723674,0.000074126045,0.000062049796,0.00042630782,0.000012328833,0.00026274705,0.00006467659,0.00006677063,0.0000011179727],"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":[0.00006840945,0.00007200802,0.0000054537445,0.000035284273,0.000014095711,1.5745763e-7,0.00049429666,0.000024346109,0.67220944,0.05202904,0.0004476621,0.2745998],"study_design_scores_gemma":[0.000489387,0.00062125653,0.00016284654,0.000018188499,0.00002182707,0.000005297017,0.00003825258,0.806446,0.18253721,0.004416376,0.005136211,0.00010712059],"about_ca_topic_score_codex":0.000003735783,"about_ca_topic_score_gemma":0.0000026138443,"teacher_disagreement_score":0.9537607,"about_ca_system_score_codex":0.000039717397,"about_ca_system_score_gemma":0.000042561274,"threshold_uncertainty_score":0.30227724},"labels":[],"label_agreement":null},{"id":"W3009393055","doi":"10.1007/s10463-022-00828-4","title":"On the rate of convergence of image classifiers based on convolutional neural networks","year":2022,"lang":"en","type":"article","venue":"Annals of the Institute of Statistical Mathematics","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Convolutional neural network; Pattern recognition (psychology); Curse of dimensionality; Artificial intelligence; Rate of convergence; Convergence (economics); Artificial neural network; Dimension (graph theory); Image (mathematics); Contextual image classification; Computer science; Mathematics; Algorithm; Machine learning","score_opus":0.048064738232478206,"score_gpt":0.2954215988966239,"score_spread":0.2473568606641457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009393055","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":[0.014329356,0.000006011903,0.9816763,0.0027503653,0.00012002128,0.00024048536,0.00017019462,0.0000143201305,0.0006929567],"genre_scores_gemma":[0.96420926,0.000004399226,0.035242643,0.00047668492,0.000005394959,0.000028654807,0.000002353686,0.0000044056947,0.000026208263],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891615,0.00008630401,0.0004418192,0.00012155356,0.00032861836,0.000105561056],"domain_scores_gemma":[0.9980331,0.0007913526,0.0004577276,0.00052658055,0.00016217813,0.00002905943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004878993,0.000085340595,0.00019697058,0.000042991065,0.00013395051,0.000006331554,0.000816344,0.000023431943,0.00006845327],"category_scores_gemma":[0.00025440764,0.000055310404,0.00010020766,0.00032344286,0.00055895303,0.000049975566,0.0001931284,0.00015374506,7.716528e-7],"study_design_candidate":"theoretical_or_conceptual","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":[0.00001700633,0.00020009417,0.0000052147575,0.000044270026,0.000012418726,4.3308717e-7,0.00003446641,0.022664886,0.0007127192,0.97314,0.0028684007,0.0003001069],"study_design_scores_gemma":[0.000082465,0.0002355357,0.00027886545,0.000038095088,0.000009411996,0.0000015128179,0.00002123407,0.8295255,0.012623456,0.15694852,0.0001796256,0.000055772605],"about_ca_topic_score_codex":0.000020523325,"about_ca_topic_score_gemma":6.585861e-7,"teacher_disagreement_score":0.9498799,"about_ca_system_score_codex":0.000010054061,"about_ca_system_score_gemma":0.0000687349,"threshold_uncertainty_score":0.22554927},"labels":[],"label_agreement":null},{"id":"W3011249019","doi":"10.1109/access.2020.2980942","title":"Analysis of Dimensionality Reduction Techniques on Big Data","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":869,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Dimensionality reduction; Machine learning; Artificial intelligence; Computer science; Random forest; Naive Bayes classifier; Principal component analysis; Linear discriminant analysis; Decision tree; Support vector machine; Data mining","score_opus":0.18461252891629262,"score_gpt":0.3704329397476505,"score_spread":0.1858204108313579,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011249019","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":[0.023205452,0.000009847308,0.9730942,0.0023066597,0.000079081,0.00012732197,0.00002878555,0.00034307764,0.0008055795],"genre_scores_gemma":[0.9906361,0.000017829907,0.008779062,0.0004127462,0.000097538614,0.000019168574,0.000018028246,0.0000035152607,0.000016007412],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991386,0.00002849342,0.00019684012,0.00038602177,0.00017726299,0.000072801704],"domain_scores_gemma":[0.9987114,0.000025234935,0.00013271405,0.0010054056,0.00007478876,0.0000504692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014031588,0.000065900815,0.00014390438,0.00013852306,0.00006914612,0.000058722813,0.001552158,0.000038546084,0.000009522806],"category_scores_gemma":[0.000015651449,0.000059854436,0.000056138673,0.0018141231,0.000031210555,0.00038506716,0.00035029644,0.00007385146,0.0000045983807],"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":[0.000036275516,0.00037149325,0.0018115581,0.00003764938,0.00051440054,0.0000032037497,0.00019686502,0.0006753759,0.07902527,0.036512755,0.025042187,0.855773],"study_design_scores_gemma":[0.00007177637,0.00013542217,0.007781823,0.000012094815,0.00019143577,0.000002068989,0.000009878144,0.088481575,0.8816137,0.002758411,0.018695038,0.00024680808],"about_ca_topic_score_codex":0.000094598,"about_ca_topic_score_gemma":0.0000035838532,"teacher_disagreement_score":0.96743065,"about_ca_system_score_codex":0.00001200241,"about_ca_system_score_gemma":0.00002168308,"threshold_uncertainty_score":0.28843215},"labels":[],"label_agreement":null},{"id":"W3011983636","doi":"10.23919/fusion43075.2019.9011363","title":"Online Video Anomaly Detection Methodology With Highly Descriptive Feature Sets","year":2019,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Histogram; Anomaly detection; Feature (linguistics); Pattern recognition (psychology); Optical flow; Artificial intelligence; Entropy (arrow of time); Component (thermodynamics); Feature vector; Feature extraction; k-nearest neighbors algorithm; Orientation (vector space); Data mining; Computer vision; Image (mathematics); Mathematics","score_opus":0.030060918260066852,"score_gpt":0.2750690948679381,"score_spread":0.24500817660787128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011983636","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":[0.121365815,0.00002096244,0.87322026,0.0012320174,0.00010218784,0.00029510612,0.000002845689,0.0006217149,0.0031390723],"genre_scores_gemma":[0.5791444,0.000004675557,0.41709933,0.00041287064,0.000022662498,0.00003311565,0.0000023591385,0.000007759085,0.003272806],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999001,0.00008661448,0.00013224203,0.00044834698,0.00013057468,0.00020121217],"domain_scores_gemma":[0.9990822,0.00008418604,0.000090637455,0.0005533187,0.00012286287,0.00006680902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018104796,0.00013758207,0.0001630246,0.0001668144,0.00009953271,0.00006315558,0.00040866734,0.00011712989,0.000038577113],"category_scores_gemma":[0.000009569173,0.0001027944,0.000051448922,0.00071712607,0.00003295248,0.00035287917,0.000110308225,0.00019856857,0.000100021236],"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":[0.0001506125,0.00052879576,0.0037663528,0.00003451661,0.00016613812,0.00001939163,0.00094363134,0.00022905588,0.23221816,0.2787016,0.004516104,0.47872564],"study_design_scores_gemma":[0.0014371313,0.003246504,0.06357017,0.00004268452,0.000051824973,0.0006089336,0.0005904458,0.07150435,0.6981443,0.01327084,0.14624138,0.0012914406],"about_ca_topic_score_codex":0.00012157161,"about_ca_topic_score_gemma":0.00017804412,"teacher_disagreement_score":0.47743422,"about_ca_system_score_codex":0.000056978588,"about_ca_system_score_gemma":0.000038464346,"threshold_uncertainty_score":0.4191834},"labels":[],"label_agreement":null},{"id":"W3012365277","doi":"10.1007/978-3-030-38557-6_13","title":"Privacy Preserving Abnormality Detection: A Deep Learning Approach","year":2020,"lang":"en","type":"book-chapter","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Deep learning; Convolutional neural network; Computer science; Abnormality; Artificial intelligence; Machine learning; Deep neural networks; Competence (human resources); Transfer of learning; Data science; Medicine; Psychology","score_opus":0.025057218828163967,"score_gpt":0.2321273251799406,"score_spread":0.20707010635177664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012365277","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[7.781476e-7,0.00006183219,0.5284906,0.0001661912,0.000029936191,0.0002189132,5.0880266e-7,0.0010614698,0.46996978],"genre_scores_gemma":[0.066761985,0.00016558146,0.22809663,0.00055958406,0.00050442683,0.00024111781,0.00001859463,0.00009084626,0.70356125],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982921,0.000025871681,0.0003722555,0.0007865856,0.00031164772,0.00021158018],"domain_scores_gemma":[0.99857634,0.000036580386,0.00025682643,0.0008616957,0.00011117886,0.00015739528],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016912389,0.00030304954,0.00028713333,0.00011223283,0.00035517136,0.00024047813,0.0012941768,0.0002940842,0.00019758266],"category_scores_gemma":[0.00001673818,0.0003020934,0.00021294072,0.00013572343,0.000043235657,0.00033109463,0.0010576274,0.0008254785,0.00019750398],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000035920139,0.000019044823,0.000004545592,0.0000773247,0.00004961754,0.0000071074705,0.00018411441,0.000080096106,0.00006347498,0.8696703,0.0009147195,0.12892607],"study_design_scores_gemma":[0.00009214166,0.0001129349,0.000031710988,0.000019687,0.00002228688,0.00008331396,0.000008929324,0.13158835,0.0008254276,0.031471815,0.83517355,0.0005698737],"about_ca_topic_score_codex":0.000022291058,"about_ca_topic_score_gemma":0.0000043833593,"teacher_disagreement_score":0.8381985,"about_ca_system_score_codex":0.00007831369,"about_ca_system_score_gemma":0.0000391481,"threshold_uncertainty_score":0.99994314},"labels":[],"label_agreement":null},{"id":"W3012962091","doi":"","title":"Simple Continual Learning Strategies for Safer Classifers.","year":2020,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"SAFER; Simple (philosophy); Computer science; Risk analysis (engineering); Computer security; Business","score_opus":0.1673988167164265,"score_gpt":0.3643801908165334,"score_spread":0.19698137410010688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012962091","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":[0.0008083271,0.000005056265,0.98177856,0.0067497753,0.000054633834,0.00028323906,0.000014121055,0.0003285565,0.00997774],"genre_scores_gemma":[0.98604715,0.0000064522255,0.012047206,0.0014823398,0.00015527157,0.00013054471,0.000013612433,0.000008091204,0.00010934709],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986837,0.000037991522,0.00031412745,0.00041819442,0.00034660893,0.0001993629],"domain_scores_gemma":[0.99885464,0.0001900123,0.00011838271,0.00012275597,0.0006085719,0.00010565751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020700868,0.00013231099,0.0001342027,0.000070007,0.00028294153,0.00035185253,0.0005408273,0.000078473386,0.00014100353],"category_scores_gemma":[0.00022550797,0.00013387534,0.00007614413,0.00034251195,0.000072481154,0.00035860107,0.00006846457,0.00022325442,0.00019166361],"study_design_candidate":"theoretical_or_conceptual","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":[0.000018685247,0.00002644699,0.000007240275,0.0000035665914,0.0000046266696,3.1902127e-7,0.00019909514,0.0012859685,0.0032600958,0.9405762,0.0005594362,0.054058306],"study_design_scores_gemma":[0.000024301728,0.00027880503,0.00007698972,0.000005541066,0.0000023066602,8.408826e-7,0.00048337283,0.5454111,0.03008191,0.4071272,0.016325174,0.00018242415],"about_ca_topic_score_codex":0.00000838351,"about_ca_topic_score_gemma":0.000009462474,"teacher_disagreement_score":0.9852388,"about_ca_system_score_codex":0.00003901788,"about_ca_system_score_gemma":0.0002679172,"threshold_uncertainty_score":0.54592776},"labels":[],"label_agreement":null},{"id":"W3014347301","doi":"10.1109/igarss39084.2020.9323375","title":"Graph-Based Micro-Seismic Signal Classification with an Optimised Feature Space","year":2020,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"York University","funders":"","keywords":"Overfitting; Pattern recognition (psychology); Feature vector; Computer science; Artificial intelligence; Graph; Feature selection; Classifier (UML); Wavelet; Dimensionality reduction; Theoretical computer science; Artificial neural network","score_opus":0.020625153346589292,"score_gpt":0.23286303302556158,"score_spread":0.21223787967897229,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014347301","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":[0.0048057786,0.000008259186,0.9676478,0.025056973,0.000007918978,0.0002386823,0.0000022298352,0.00084658497,0.0013857883],"genre_scores_gemma":[0.59163445,0.0000016386865,0.40561852,0.0024887635,0.00002583835,0.000043840428,0.0000069758476,0.000007410806,0.0001725314],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991966,0.000026768796,0.000094384515,0.00040372985,0.00013845476,0.00014006314],"domain_scores_gemma":[0.99928933,0.000014949773,0.00007058132,0.0003882418,0.00007818949,0.0001587197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000048997015,0.00011519163,0.00008846618,0.00005336317,0.00014032112,0.00013283118,0.00049898506,0.00006519802,0.00003391905],"category_scores_gemma":[0.0000017292025,0.000092303664,0.00004195352,0.0006185985,0.00004183653,0.0002585137,0.000029687684,0.00013411428,0.000027033202],"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":[0.0002271671,0.0006909092,0.0013615958,0.00007121692,0.00006635841,0.000021725633,0.0010603196,0.013471734,0.6062354,0.25672898,0.05299142,0.06707321],"study_design_scores_gemma":[0.00033341305,0.00042612126,0.0011076997,0.0000053322638,0.000007616246,0.000006536544,0.00006257004,0.83386314,0.1493774,0.00051790796,0.014047999,0.00024428006],"about_ca_topic_score_codex":0.000012020784,"about_ca_topic_score_gemma":0.000002065472,"teacher_disagreement_score":0.82039136,"about_ca_system_score_codex":0.000017124425,"about_ca_system_score_gemma":0.0000645536,"threshold_uncertainty_score":0.37640342},"labels":[],"label_agreement":null},{"id":"W3014453666","doi":"10.2478/jaiscr-2020-0006","title":"Combining Classifiers for Foreign Pattern Rejection","year":2020,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence and Soft Computing Research","topic":"Anomaly Detection Techniques and Applications","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 Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Set (abstract data type); Simple (philosophy); Data mining; Pattern recognition (psychology); Architecture; Artificial intelligence; Garbage; Machine learning; Geography","score_opus":0.2224048076925108,"score_gpt":0.4049159527529628,"score_spread":0.18251114506045202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014453666","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":[0.01901039,0.000064908534,0.97770905,0.0027868394,0.00008607927,0.00016758748,5.31645e-7,0.000044985834,0.00012963646],"genre_scores_gemma":[0.9711017,0.000029388188,0.02829413,0.00023324294,0.00032008157,0.0000037933178,2.563611e-7,0.0000069104462,0.0000105113595],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99865043,0.00010367553,0.00047146468,0.00020893317,0.00032153766,0.00024394899],"domain_scores_gemma":[0.99827063,0.00065704033,0.00019168567,0.00012067751,0.000601997,0.00015799799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020157956,0.000073935895,0.00015626477,0.00017531788,0.00045607035,0.0002686463,0.00047121357,0.000060159444,0.0000037725194],"category_scores_gemma":[0.00039174128,0.0000668853,0.000084351515,0.00052488915,0.000097330776,0.0002239463,0.00015167284,0.00041982537,0.000005175484],"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":[0.000033659264,0.00003607627,0.00016559426,0.000020941141,0.000013530872,0.00000425292,0.0013332729,0.0004163467,0.0020331445,0.075590745,0.0005005401,0.9198519],"study_design_scores_gemma":[0.000053355663,0.0014236663,0.00009369615,0.000057531866,0.000005916548,0.00004278277,0.0021745123,0.8243391,0.033559326,0.13582355,0.0022985588,0.00012802654],"about_ca_topic_score_codex":0.000008677178,"about_ca_topic_score_gemma":0.0000010595775,"teacher_disagreement_score":0.9520913,"about_ca_system_score_codex":0.000031549236,"about_ca_system_score_gemma":0.00008062818,"threshold_uncertainty_score":0.35077706},"labels":[],"label_agreement":null},{"id":"W3014626338","doi":"10.2478/jaiscr-2020-0009","title":"A New Approach to Detection of Changes in Multidimensional Patterns","year":2020,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence and Soft Computing Research","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Change detection; Computer science; Metadata; Focus (optics); Noise (video); Probabilistic logic; Variety (cybernetics); Data mining; Multivariate statistics; Kernel (algebra); The Internet; Artificial intelligence; Pattern recognition (psychology); Machine learning; Image (mathematics); Mathematics; World Wide Web","score_opus":0.17254129633715437,"score_gpt":0.38634640547980986,"score_spread":0.2138051091426555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014626338","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":[0.16731706,0.00003241987,0.8308356,0.0016359851,0.000030765725,0.00010869651,3.0035153e-7,0.000011887587,0.00002731022],"genre_scores_gemma":[0.93126345,0.000022476317,0.0684636,0.00009191062,0.00014785641,0.0000013959725,7.750648e-8,0.0000037115221,0.0000055419778],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987764,0.00008958371,0.00039111185,0.00019180714,0.00037551427,0.00017562986],"domain_scores_gemma":[0.9990647,0.00021730637,0.00012354158,0.00010973728,0.00030847496,0.00017623765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011249627,0.00006153341,0.00015828965,0.00032236442,0.00009330184,0.000057461246,0.0003619266,0.000044782722,0.0000040248306],"category_scores_gemma":[0.00020492221,0.000055370318,0.000037910497,0.0008810117,0.000032902906,0.000100947604,0.00022257458,0.00034609257,0.000005166244],"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":[0.000044820634,0.00009222418,0.0003867061,0.00002495137,0.0000064724222,0.0000044937674,0.0037663192,0.0025150578,0.049324732,0.006765078,0.00006134133,0.9370078],"study_design_scores_gemma":[0.000056414836,0.0009842924,0.0017973564,0.00010636146,0.0000026746052,0.000041138286,0.0014332115,0.5792009,0.41038436,0.0052776462,0.0005795567,0.00013609706],"about_ca_topic_score_codex":0.00011204571,"about_ca_topic_score_gemma":0.000023967788,"teacher_disagreement_score":0.9368717,"about_ca_system_score_codex":0.00002530227,"about_ca_system_score_gemma":0.000077688295,"threshold_uncertainty_score":0.22579361},"labels":[],"label_agreement":null},{"id":"W3015809448","doi":"10.1007/978-3-030-47358-7_41","title":"Challenges in Vessel Behavior and Anomaly Detection: From Classical Machine Learning to Deep Learning","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Anomaly detection; Artificial intelligence; Deep learning; Machine learning","score_opus":0.0273795102785721,"score_gpt":0.2546712898518961,"score_spread":0.22729177957332397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015809448","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":[0.0011631214,0.0012005611,0.99357355,0.0022634377,0.00021822767,0.00039401394,0.0000017793601,0.00034943948,0.00083584915],"genre_scores_gemma":[0.83461964,0.00036856718,0.16415529,0.00040616398,0.00025916693,0.000071924915,0.0000031139477,0.000033146123,0.00008296715],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970038,0.000056381086,0.00041398054,0.0016792086,0.00045815305,0.00038848646],"domain_scores_gemma":[0.9986586,0.0002862221,0.00017759221,0.0005558952,0.000085008105,0.00023666894],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00035567975,0.0003967193,0.00044099474,0.0005825299,0.00030226866,0.00034645628,0.0012548344,0.00029806947,0.000010424145],"category_scores_gemma":[0.00007608629,0.00040637617,0.00007321373,0.00060571736,0.00022327265,0.00031965956,0.0011897815,0.001517214,0.000026347405],"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":[0.0000048683833,0.000017683304,0.00025816934,0.0000090928315,0.0000032268072,0.000041334224,0.0006845565,0.0035072202,0.0006773883,0.002303621,5.38767e-7,0.9924923],"study_design_scores_gemma":[0.0003323631,0.0007828654,0.0070801713,0.00022509795,0.000016283404,0.00009713372,0.0000029882824,0.9486837,0.003948498,0.022876117,0.014897878,0.001056939],"about_ca_topic_score_codex":0.00007089943,"about_ca_topic_score_gemma":0.00056487636,"teacher_disagreement_score":0.99143535,"about_ca_system_score_codex":0.00018074813,"about_ca_system_score_gemma":0.00007961377,"threshold_uncertainty_score":0.9998388},"labels":[],"label_agreement":null},{"id":"W3015945207","doi":"10.1109/smartworld-uic-atc-scalcom-iop-sci.2019.00063","title":"Real-time Outlier Detection Over Streaming Data","year":2019,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Sliding window protocol; Computer science; Outlier; Anomaly detection; Streaming data; Correctness; Concept drift; Data mining; Context (archaeology); Data stream mining; Streaming algorithm; Data point; Task (project management); Window (computing); Artificial intelligence; Algorithm; Mathematics","score_opus":0.014859512390164462,"score_gpt":0.26200888012352247,"score_spread":0.247149367733358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015945207","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":[0.088415116,0.0000023608231,0.86682534,0.00013423518,0.000078069854,0.00019553039,0.0000036126614,0.0008106097,0.04353513],"genre_scores_gemma":[0.9258101,0.000012431722,0.059681326,0.000084052495,0.00004439612,0.000014566026,0.00000766655,0.000008470845,0.014337006],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992982,0.000011935334,0.000099117955,0.00035989305,0.0001120352,0.00011882859],"domain_scores_gemma":[0.9985064,0.000024673871,0.000045693196,0.0013613515,0.000024372846,0.000037464924],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000115664574,0.00006679992,0.00006701104,0.000052322575,0.00006874325,0.000088732886,0.00076090585,0.000043004216,0.00028985692],"category_scores_gemma":[0.00000403741,0.000059461967,0.000022142254,0.0002267839,0.000008617849,0.0005650973,0.0004225589,0.00005761309,0.0009818805],"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":[0.000004633294,0.00013571154,0.0013752396,0.000011672504,0.000025729261,0.0000015385652,0.00008633511,0.00002524301,0.27395666,0.06121321,0.007867851,0.65529615],"study_design_scores_gemma":[0.00025463212,0.0001170898,0.017254602,0.000009560112,0.000008271284,0.000017978473,0.000025502388,0.8124312,0.08016957,0.0023538957,0.08695867,0.00039903444],"about_ca_topic_score_codex":0.0001760393,"about_ca_topic_score_gemma":0.000008529214,"teacher_disagreement_score":0.83739495,"about_ca_system_score_codex":0.00002347746,"about_ca_system_score_gemma":0.000014885634,"threshold_uncertainty_score":0.999796},"labels":[],"label_agreement":null},{"id":"W3015946821","doi":"10.48550/arxiv.2004.04391","title":"Anomaly Detection with SDAE","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Autoencoder; Artificial intelligence; Deep learning; Anomaly detection; Preprocessor; Computer science; Pattern recognition (psychology); Data pre-processing; Anomaly (physics); Data mining; Machine learning","score_opus":0.0557094429099231,"score_gpt":0.1712829598481255,"score_spread":0.11557351693820239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015946821","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":[0.058434445,0.000011172306,0.93651867,0.00021391758,0.00009290914,0.00029156168,0.000005510127,0.0009852296,0.003446585],"genre_scores_gemma":[0.99146295,0.00004029315,0.007588525,0.00013206109,0.00006350317,0.0000055682362,0.000004409093,0.000016180604,0.00068653456],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859375,0.000045615157,0.00012673104,0.00096879946,0.00006728818,0.00019784413],"domain_scores_gemma":[0.99858177,0.00002343879,0.00019419445,0.00094109785,0.00011199136,0.00014750175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006562527,0.000226553,0.00019948064,0.00014765811,0.0001869211,0.00011768969,0.0011312122,0.00019514652,0.000015569334],"category_scores_gemma":[0.0000053355466,0.00024212152,0.00012416084,0.0007679062,0.00006825764,0.00025138832,0.00090449286,0.00047841002,0.000096747804],"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":[0.0001815508,0.00037095265,0.0036148147,0.0002596257,0.00041013715,0.0007187741,0.00051251985,0.08070905,0.0041878074,0.87803996,0.0012193763,0.02977546],"study_design_scores_gemma":[0.000500591,0.0004194893,0.0041767084,0.00006754865,0.00011614465,0.000035522895,0.00006346675,0.88438296,0.014304164,0.08481797,0.010041516,0.0010739363],"about_ca_topic_score_codex":0.00014790296,"about_ca_topic_score_gemma":0.00006378085,"teacher_disagreement_score":0.93302846,"about_ca_system_score_codex":0.00012951692,"about_ca_system_score_gemma":0.00010564668,"threshold_uncertainty_score":0.9873429},"labels":[],"label_agreement":null},{"id":"W3017121050","doi":"10.1109/icpr48806.2021.9412632","title":"Motion and Region Aware Adversarial Learning for Fall Detection with Thermal Imaging","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Health Network","funders":"","keywords":"Computer science; Optical flow; Artificial intelligence; Adversarial system; Computer vision; Discriminator; Anomaly detection; Differential privacy; Deep learning; Trajectory; Image (mathematics); Algorithm; Detector; Telecommunications","score_opus":0.012176111823513344,"score_gpt":0.23135625110321287,"score_spread":0.21918013927969954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3017121050","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":[0.021792324,0.000034641973,0.97624856,0.00037750596,0.0001299431,0.0004787736,4.5505973e-7,0.0005761788,0.00036161894],"genre_scores_gemma":[0.95087665,0.000021268672,0.04840939,0.000057383186,0.00012561487,0.00026705558,0.000012647122,0.000015093673,0.00021491978],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897796,0.000039856324,0.00015176593,0.00057686644,0.00010871796,0.00014482618],"domain_scores_gemma":[0.9992715,0.00003164865,0.00015611995,0.00033167418,0.00016399764,0.000045064833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011910259,0.00015713958,0.0001402056,0.00008909807,0.0002715336,0.0003331317,0.0002077587,0.00011976773,0.000002130733],"category_scores_gemma":[0.000009731495,0.00014236195,0.00006922289,0.00011727932,0.000028051547,0.00025975317,0.00037526636,0.00031747698,5.8474683e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003583568,0.000048694546,0.0015384357,0.0001328588,0.0000451243,0.000007002334,0.0005902122,0.005786494,0.0035877703,0.0028123942,0.000044811375,0.98537034],"study_design_scores_gemma":[0.00040293188,0.00012254284,0.002411019,0.000078801546,0.000037128088,0.000083829334,0.0002895356,0.97468287,0.018696854,0.0012487031,0.0015201787,0.00042561727],"about_ca_topic_score_codex":0.00022070955,"about_ca_topic_score_gemma":0.00006258467,"teacher_disagreement_score":0.98494476,"about_ca_system_score_codex":0.0000572423,"about_ca_system_score_gemma":0.000042695945,"threshold_uncertainty_score":0.58053523},"labels":[],"label_agreement":null},{"id":"W3017927394","doi":"10.1117/12.2558889","title":"A deep learning based methodology for video anomaly detection in crowded scenes","year":2020,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Anomaly detection; Artificial intelligence; Deep learning; Unsupervised learning; Autoencoder; Pattern recognition (psychology); Block (permutation group theory); Process (computing); Trajectory; Encoder; Supervised learning; Machine learning; Computer vision; Artificial neural network; Mathematics","score_opus":0.06895003938961532,"score_gpt":0.3090635816675826,"score_spread":0.24011354227796727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3017927394","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":[0.005231537,0.000018210554,0.99151427,0.002101264,0.000029402787,0.00028727492,1.8390602e-7,0.0005243168,0.0002935448],"genre_scores_gemma":[0.60357016,0.0000011287123,0.39515147,0.0010492811,0.000024061215,0.00016980295,4.741277e-7,0.000005086026,0.000028543733],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991594,0.0001051381,0.00018588763,0.00033142054,0.000057836834,0.00016029424],"domain_scores_gemma":[0.99942946,0.0002383098,0.00006606347,0.00015337982,0.000052640597,0.000060135815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002924413,0.00008134634,0.00012650732,0.00009713439,0.000112239206,0.000050014864,0.00027355674,0.000057966776,0.00001968853],"category_scores_gemma":[0.00016165012,0.0000803955,0.00006513791,0.0005263284,0.000017406543,0.00015191833,0.000056782872,0.00011135199,0.00001163983],"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":[0.00011544472,0.000105354295,0.0019285202,0.00007529625,0.000018169883,0.000005149869,0.00066408596,0.010669363,0.224172,0.04289357,0.00019059963,0.71916246],"study_design_scores_gemma":[0.00022546253,0.00019451312,0.0008591389,0.0000018258006,0.0000022903696,0.000002435128,0.000027619388,0.78051126,0.2083284,0.0014193144,0.008330716,0.00009701819],"about_ca_topic_score_codex":0.00006375481,"about_ca_topic_score_gemma":0.00009145907,"teacher_disagreement_score":0.7698419,"about_ca_system_score_codex":0.000024729861,"about_ca_system_score_gemma":0.000022373808,"threshold_uncertainty_score":0.32784334},"labels":[],"label_agreement":null},{"id":"W3020644008","doi":"10.1109/cvprw50498.2020.00047","title":"Climate Adaptation: Reliably Predicting from Imbalanced Satellite Data","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"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":"Institute for Catastrophic Loss Reduction; Indian Institute of Science","keywords":"Computer science; Robustness (evolution); Artificial intelligence; Machine learning; Deep learning; Adaptation (eye); Data science; Data mining","score_opus":0.0800582694112559,"score_gpt":0.2965323343236615,"score_spread":0.2164740649124056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3020644008","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00032383445,0.0002647724,0.98301405,0.00368584,0.000351117,0.00041020094,0.00032671395,0.0023055787,0.009317872],"genre_scores_gemma":[0.24265666,0.0013331028,0.7535251,0.0008151805,0.00040573912,0.00012691409,0.00096744444,0.00002437024,0.00014548958],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99764466,0.000042996006,0.0004699656,0.001332183,0.00027384047,0.00023637668],"domain_scores_gemma":[0.9963764,0.00008269857,0.0003189834,0.0030067468,0.000090834255,0.00012433142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020239266,0.00023493166,0.0002673429,0.00005163138,0.0001652739,0.0004150022,0.0033639495,0.00020522598,0.000042798278],"category_scores_gemma":[0.00003416677,0.00023363616,0.00007434303,0.00026233812,0.000027405113,0.00036678254,0.006909718,0.00052748574,0.00015364452],"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":[0.000033038163,0.00017143747,0.001963618,0.00023801027,0.00022124917,0.000034480403,0.0020757879,0.0016419608,0.002146317,0.14082567,0.020138377,0.8305101],"study_design_scores_gemma":[0.000100142934,0.00002569089,0.0016560596,0.00008474784,0.00002373763,0.0000023625387,0.000057082674,0.93321633,0.0014488859,0.025203448,0.037827563,0.00035395916],"about_ca_topic_score_codex":0.0003602329,"about_ca_topic_score_gemma":0.000028721961,"teacher_disagreement_score":0.93157434,"about_ca_system_score_codex":0.00003794049,"about_ca_system_score_gemma":0.00010576081,"threshold_uncertainty_score":0.9527406},"labels":[],"label_agreement":null},{"id":"W3022222530","doi":"","title":"One-Shot Scene-Specific Crowd Counting.","year":2019,"lang":"en","type":"article","venue":"British Machine Vision Conference","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Shot (pellet); Computer science; Computer vision; Computer graphics (images); Artificial intelligence","score_opus":0.03211394440543816,"score_gpt":0.27616253086785353,"score_spread":0.24404858646241537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022222530","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":[0.043640714,0.0004898186,0.9274382,0.0010240168,0.00028286796,0.00047051118,0.000020654841,0.0008810519,0.025752159],"genre_scores_gemma":[0.9643102,0.00047643977,0.03278358,0.00042841112,0.000053098636,0.00003601362,0.00001143524,0.0000151933455,0.001885625],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984004,0.00003664356,0.0003053449,0.0006367059,0.00033917677,0.00028176003],"domain_scores_gemma":[0.9987467,0.000045266668,0.00012703538,0.00075351837,0.0002161559,0.00011133605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022189782,0.00014131302,0.0002036356,0.00009037086,0.00020316709,0.0007811704,0.0010153108,0.00009043661,0.0008359254],"category_scores_gemma":[0.000012528116,0.00018311696,0.00007870496,0.00043499496,0.00005249669,0.0005019656,0.0003470991,0.00022704169,0.0006778528],"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":[0.0000047475414,0.00016173608,0.0010722616,0.000019648394,0.0000067129986,0.000008542281,0.00003717684,0.000008649679,0.013175187,0.09151585,0.005463941,0.88852555],"study_design_scores_gemma":[0.0012195219,0.0005302562,0.093593255,0.00044471736,0.000009273804,0.00028157153,0.000024077939,0.19716926,0.016690413,0.016822325,0.6718593,0.0013559947],"about_ca_topic_score_codex":0.00032512634,"about_ca_topic_score_gemma":0.000051811927,"teacher_disagreement_score":0.9206695,"about_ca_system_score_codex":0.00003165031,"about_ca_system_score_gemma":0.000057969715,"threshold_uncertainty_score":0.9152797},"labels":[],"label_agreement":null},{"id":"W3022318665","doi":"","title":"A deep unsupervised representation learning approach for effective cyber-physical attack detection and identification on highly imbalanced data.","year":2019,"lang":"en","type":"article","venue":"Conference of the Centre for Advanced Studies on Collaborative Research","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Identification (biology); Artificial intelligence; Unsupervised learning; Feature learning; Representation (politics); Machine learning; Deep learning; Cyber-physical system; Pattern recognition (psychology)","score_opus":0.0900765946049906,"score_gpt":0.40932393107881104,"score_spread":0.3192473364738204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022318665","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":[0.2912743,0.00014278392,0.69631565,0.0017183959,0.00023066688,0.009638113,0.00009767975,0.00012409192,0.00045830384],"genre_scores_gemma":[0.99153763,0.0001115963,0.006392647,0.000011959296,0.000038194252,0.0013251811,0.000022992865,0.000012919538,0.00054688624],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99797803,0.00034735337,0.00024789842,0.0007772605,0.00038400668,0.0002654674],"domain_scores_gemma":[0.99535626,0.0013659998,0.00024518766,0.00082481577,0.002162819,0.000044925993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000703504,0.00015649578,0.00028614103,0.00012313717,0.0006132277,0.00010367651,0.00069528504,0.000053167605,4.527581e-7],"category_scores_gemma":[0.0010777771,0.000117773896,0.00005744005,0.0010268341,0.00020148169,0.0003729509,0.00036821252,0.00023971811,0.0000034252803],"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":[0.0023498635,0.0009518185,0.00054487993,0.0007676728,0.0005029815,2.9832327e-7,0.012677595,0.03621416,0.4059658,0.17330459,0.000647854,0.3660725],"study_design_scores_gemma":[0.0014282591,0.0013058637,0.0011418104,0.0001096326,0.000018862118,3.6884947e-7,0.0061479574,0.62750673,0.35441607,0.0060808356,0.0016274347,0.00021616103],"about_ca_topic_score_codex":0.0000027344038,"about_ca_topic_score_gemma":0.000014631373,"teacher_disagreement_score":0.7002633,"about_ca_system_score_codex":0.00014539972,"about_ca_system_score_gemma":0.00006754876,"threshold_uncertainty_score":0.480268},"labels":[],"label_agreement":null},{"id":"W3022677227","doi":"","title":"Hybrid Deep Network for Anomaly Detection.","year":2019,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Anomaly detection; Benchmark (surveying); Artificial intelligence; Deep learning; Convolutional neural network; Frame (networking); Recurrent neural network; Anomaly (physics); Pattern recognition (psychology); Perspective (graphical); Autoencoder; Machine learning; Artificial neural network","score_opus":0.02501309035513013,"score_gpt":0.1635033675480604,"score_spread":0.13849027719293028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022677227","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":[0.12576622,0.0000120736995,0.87130076,0.00004154442,0.00014518833,0.000307885,0.0000018763878,0.00034802288,0.0020764228],"genre_scores_gemma":[0.98680186,0.000011989018,0.010414865,0.00013808486,0.000064262866,0.0000046466107,0.0000016124709,0.000008512965,0.0025541894],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991865,0.000019115556,0.000092725815,0.00044112536,0.00003067296,0.00022984699],"domain_scores_gemma":[0.9991743,0.00006027158,0.000077735844,0.00053489575,0.00007960544,0.00007318057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010443713,0.000098793316,0.000102892176,0.00006967924,0.0002011342,0.000045951878,0.0005491078,0.000046898727,0.000030220659],"category_scores_gemma":[0.000004619184,0.00011410865,0.000110607296,0.00044638533,0.000024354054,0.0003372156,0.00011897857,0.00007775445,0.00017843048],"study_design_candidate":"theoretical_or_conceptual","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":[0.000030105683,0.00006265581,0.0032901624,0.000015511712,0.00003664136,0.000009702082,0.000022754479,0.042121496,0.0010608499,0.93873566,0.0009651643,0.0136493165],"study_design_scores_gemma":[0.00036501154,0.00017584542,0.0015418169,0.0000053425047,0.000014636515,0.000011547199,0.000011455199,0.8882419,0.005842103,0.07574544,0.027786044,0.0002588558],"about_ca_topic_score_codex":0.000021891836,"about_ca_topic_score_gemma":0.00001243736,"teacher_disagreement_score":0.8629902,"about_ca_system_score_codex":0.0000591804,"about_ca_system_score_gemma":0.000021530914,"threshold_uncertainty_score":0.46532154},"labels":[],"label_agreement":null},{"id":"W3022787740","doi":"10.1016/j.chaos.2020.109864","title":"Time series forecasting of COVID-19 transmission in Canada using LSTM networks","year":2020,"lang":"en","type":"article","venue":"Chaos Solitons & Fractals","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1098,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Regina","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Outbreak; Pandemic; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Transmission (telecommunications); Deep learning; Long short term memory; Artificial intelligence; Recurrent neural network; China; Artificial neural network; Geography; 2019-20 coronavirus outbreak; Computer science; Demography; Operations research; Virology; Telecommunications; Medicine; Infectious disease (medical specialty); Mathematics; Sociology","score_opus":0.040508720273706715,"score_gpt":0.25726993730914044,"score_spread":0.21676121703543372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022787740","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":[0.058722034,0.00013416045,0.9377604,0.0027540235,0.000035142883,0.00023529209,0.000008279705,0.00010213466,0.00024851048],"genre_scores_gemma":[0.9807224,0.000015593747,0.018286316,0.00086496497,0.000056105288,0.00001849631,0.0000034073712,0.000009908583,0.000022795051],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900085,0.000040386618,0.00032468367,0.00026358137,0.0001437828,0.0002267141],"domain_scores_gemma":[0.9992722,0.000089324836,0.00014834308,0.00022292699,0.000036925114,0.00023027734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000115103314,0.000112421716,0.00020436804,0.000048910937,0.00012373817,0.0000248812,0.00037921593,0.000057942405,0.00004504569],"category_scores_gemma":[0.000043482614,0.00011430246,0.00004863513,0.0005432482,0.000040381714,0.00024903682,0.00009571705,0.00014295572,0.0000014362566],"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":[0.00018070613,0.00027415663,0.0073244995,0.00079683185,0.00013479267,0.0002995859,0.0140028745,0.564416,0.10156691,0.014208779,0.012451647,0.2843432],"study_design_scores_gemma":[0.000094229006,0.00003427695,0.00010548494,0.000026048743,0.00000424507,0.00001913761,0.00010598443,0.98225415,0.009612665,0.0003887953,0.0072167297,0.00013824007],"about_ca_topic_score_codex":0.12312237,"about_ca_topic_score_gemma":0.013030769,"teacher_disagreement_score":0.92200035,"about_ca_system_score_codex":0.00025300623,"about_ca_system_score_gemma":0.001025808,"threshold_uncertainty_score":0.88271683},"labels":[],"label_agreement":null},{"id":"W3025790545","doi":"10.48550/arxiv.2005.05744","title":"Deep Learning: Our Miraculous Year 1990-1991","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"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":"Quarter (Canadian coin); Deep learning; History; Artificial intelligence; Classics; Art history; Computer science; Archaeology","score_opus":0.061421340617305614,"score_gpt":0.19902042741933898,"score_spread":0.13759908680203337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3025790545","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":[0.012314977,0.000026697622,0.98076063,0.00068041903,0.00017216112,0.00026510566,0.0000037569694,0.0011972087,0.0045790155],"genre_scores_gemma":[0.9881888,0.00014371004,0.008940853,0.00014704945,0.000105380605,0.0000036258614,0.00001815199,0.000020422613,0.002432032],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984181,0.00008165584,0.00016141121,0.0010044711,0.000082001396,0.00025234107],"domain_scores_gemma":[0.998612,0.000023052988,0.00022036233,0.0008576381,0.00009432706,0.00019264316],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009473385,0.0002427221,0.0002416004,0.00014890925,0.00021659322,0.00012610141,0.0016163718,0.00026034997,0.000019958417],"category_scores_gemma":[0.00002009675,0.00029514605,0.00023365435,0.00061380374,0.000035159108,0.00017148729,0.0017947234,0.00082828506,0.00047160647],"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":[0.000034800534,0.00022808777,0.0025664265,0.0001337592,0.00019856043,0.0004553053,0.00064163277,0.10620666,0.00033596548,0.8747948,0.004949307,0.009454694],"study_design_scores_gemma":[0.00035488865,0.00016038277,0.0014598825,0.00004072905,0.000078133235,0.000011515684,0.00029719647,0.86713004,0.00051152083,0.07364119,0.055423897,0.00089059776],"about_ca_topic_score_codex":0.00013599262,"about_ca_topic_score_gemma":0.00001216573,"teacher_disagreement_score":0.97587377,"about_ca_system_score_codex":0.00012244309,"about_ca_system_score_gemma":0.00006365695,"threshold_uncertainty_score":0.99995005},"labels":[],"label_agreement":null},{"id":"W3026085784","doi":"10.1109/icii.2019.00064","title":"LSTM-based Approach to Monitor Operator Situation Awareness via HMI State Prediction","year":2019,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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 Tech University; Ontario Power Generation","funders":"","keywords":"Situation awareness; Operator (biology); Computer science; Situation analysis; Recurrent neural network; Task (project management); Artificial intelligence; Machine learning; Aviation accident; State (computer science); Artificial neural network; Aviation; Engineering; Systems engineering","score_opus":0.01142490090125119,"score_gpt":0.24174881054152755,"score_spread":0.23032390964027635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3026085784","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":[0.08453626,0.0000027336275,0.9114692,0.00027617507,0.00013675846,0.0006027725,0.0000052870037,0.000670366,0.0023004122],"genre_scores_gemma":[0.87500376,9.826188e-7,0.12312773,0.00038377295,0.000039600873,0.00025979907,0.000010582706,0.000008707073,0.0011650429],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990365,0.00002726523,0.00018347103,0.0003944452,0.00020201494,0.00015634127],"domain_scores_gemma":[0.9991801,0.000016142218,0.000046044257,0.00053191057,0.00012950503,0.00009633962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014671536,0.00010471617,0.000095765354,0.000114628194,0.0001255343,0.00013202995,0.00038895072,0.000050323564,0.000022140268],"category_scores_gemma":[0.000003601105,0.000094276984,0.000039280592,0.00050293095,0.000008734409,0.00034082122,0.000076671364,0.0000678508,0.0002810136],"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":[0.00008429192,0.0019288603,0.021576023,0.00017093196,0.000081043545,0.0000017820134,0.0019073128,0.13134958,0.29450572,0.08391809,0.012618051,0.4518583],"study_design_scores_gemma":[0.00020455889,0.00015256008,0.00643489,0.0000062226236,0.0000030042281,0.0000022221052,0.000019304458,0.86633605,0.12148181,0.0005131665,0.004656268,0.00018993305],"about_ca_topic_score_codex":0.00009007673,"about_ca_topic_score_gemma":0.0000027142696,"teacher_disagreement_score":0.7904675,"about_ca_system_score_codex":0.00006433265,"about_ca_system_score_gemma":0.000047052352,"threshold_uncertainty_score":0.38445038},"labels":[],"label_agreement":null},{"id":"W3027679662","doi":"10.1177/1550147720920478","title":"An adaptive method based on contextual anomaly detection in Internet of Things through wireless sensor networks","year":2020,"lang":"en","type":"article","venue":"International Journal of Distributed Sensor Networks","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Anomaly detection; Anomaly (physics); Wireless sensor network; Data mining; The Internet; Wireless; Computer network; Telecommunications; World Wide Web","score_opus":0.019870255151798473,"score_gpt":0.2831473270530145,"score_spread":0.263277071901216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3027679662","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":[0.022970371,0.000030990166,0.9750605,0.0011941012,0.0003524318,0.00017860721,0.000021820895,0.00008095373,0.00011021906],"genre_scores_gemma":[0.9303473,0.000023636401,0.06807719,0.0011858059,0.0003143623,0.000008029377,0.000022507611,0.000016937413,0.0000042277343],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976798,0.00028596143,0.00091507536,0.00036163666,0.00052059133,0.00023692587],"domain_scores_gemma":[0.9975246,0.0003410678,0.0009614688,0.00024815617,0.0007698461,0.00015485573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043632672,0.00022619822,0.0003952275,0.0001684692,0.000048341786,0.00012310606,0.0011374556,0.00019244631,0.000019164441],"category_scores_gemma":[0.00007090168,0.00021404705,0.00022373116,0.0005835242,0.00006795901,0.00068193674,0.000091910704,0.00070469023,0.0000018504289],"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":[0.0014760969,0.0004891885,0.0008650803,0.000006484418,0.00020097387,0.00015402056,0.0006396224,0.9080109,0.0028026197,0.0067993165,0.0004532298,0.078102455],"study_design_scores_gemma":[0.0008395047,0.0010309851,0.0012359621,0.00009082041,0.000016982503,0.000069571775,0.00014760417,0.9859781,0.009763262,0.00020165338,0.0004392544,0.00018629641],"about_ca_topic_score_codex":0.00010051893,"about_ca_topic_score_gemma":0.000008788694,"teacher_disagreement_score":0.90737695,"about_ca_system_score_codex":0.0001738905,"about_ca_system_score_gemma":0.00005860134,"threshold_uncertainty_score":0.8728585},"labels":[],"label_agreement":null},{"id":"W3030491320","doi":"10.1007/978-3-030-47358-7_23","title":"Anomaly Detection and Prototype Selection Using Polyhedron Curvature","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Anomaly detection; Computer science; Curvature; Vertex (graph theory); Polyhedron; CAD; Anomaly (physics); Kernel (algebra); Artificial intelligence; Pattern recognition (psychology); Projection (relational algebra); Algorithm; Mathematics; Theoretical computer science; Graph; Combinatorics; Geometry; Physics","score_opus":0.017055655212748908,"score_gpt":0.24948962499013608,"score_spread":0.23243396977738717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3030491320","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":[0.00026582569,0.00020523813,0.99686295,0.000491508,0.0003003736,0.00080737536,0.000002140348,0.00039303993,0.00067152525],"genre_scores_gemma":[0.56882316,0.000029501029,0.4299431,0.0006460855,0.00040109712,0.00003648455,0.0000013641031,0.000033730383,0.00008546233],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975782,0.000023286218,0.00034146715,0.0012841977,0.00043303275,0.00033979162],"domain_scores_gemma":[0.99883634,0.00006455638,0.0002537621,0.0005305567,0.00016405358,0.00015070417],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002753632,0.00037400686,0.00031296955,0.00050998176,0.0004087968,0.00042615912,0.000915853,0.00035754766,0.000007807128],"category_scores_gemma":[0.000025513069,0.0003719919,0.000076475,0.0008875159,0.00028415947,0.0005599834,0.00055564096,0.00079166744,0.000009426425],"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":[0.000017222803,0.000022515673,0.000120468016,0.00006376284,0.000014844983,0.000014113406,0.00024452826,0.0027757066,0.014238468,0.016911425,0.000006305485,0.9655706],"study_design_scores_gemma":[0.00013837162,0.0004599858,0.0002605486,0.00012799488,0.000014452108,0.00022089307,1.0848387e-7,0.8978084,0.024458462,0.072718054,0.003177531,0.0006152084],"about_ca_topic_score_codex":0.000050282906,"about_ca_topic_score_gemma":0.00006696563,"teacher_disagreement_score":0.96495545,"about_ca_system_score_codex":0.00025177837,"about_ca_system_score_gemma":0.00024668247,"threshold_uncertainty_score":0.9998732},"labels":[],"label_agreement":null},{"id":"W3032697887","doi":"10.1007/978-3-030-47358-7_39","title":"Exploring Deep Anomaly Detection Methods Based on Capsule Net","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University; Queen's University; University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Autoencoder; Pattern recognition (psychology); Benchmark (surveying); Classifier (UML); Anomaly detection; Deep learning; ENCODE; Anomaly (physics); Cartography","score_opus":0.06106865309463925,"score_gpt":0.2897956188595806,"score_spread":0.22872696576494136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3032697887","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000022696979,0.000060429906,0.99341303,0.00088228955,0.0008446118,0.0004399889,0.000002376105,0.0006388529,0.0036957152],"genre_scores_gemma":[0.19485031,0.000020541374,0.8026041,0.0019704276,0.00036022288,0.0000959026,0.000002305702,0.000038094156,0.000058132286],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967017,0.00006392917,0.00047170534,0.0016617043,0.00063718157,0.00046375804],"domain_scores_gemma":[0.9976548,0.00029955513,0.00027473015,0.0013965042,0.0001567715,0.00021766937],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007105604,0.00048380214,0.00042792805,0.0008071391,0.00039416066,0.00042922967,0.002243535,0.00023389643,0.00001792732],"category_scores_gemma":[0.00007512267,0.00047542754,0.00017864325,0.001093463,0.00027443032,0.0005859268,0.00060703483,0.00089489867,0.000057421577],"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":[0.0000035514418,0.000016245069,0.0000017790478,0.000014102809,0.0000037824382,0.000014213991,0.0001048745,0.025600629,0.0014478413,0.008558211,0.0000031443676,0.9642316],"study_design_scores_gemma":[0.000115754076,0.00033117936,0.000064919586,0.00008325535,0.0000065322506,0.000017751307,1.288916e-7,0.89755416,0.065377265,0.031966105,0.0039780955,0.00050482916],"about_ca_topic_score_codex":0.000027194103,"about_ca_topic_score_gemma":0.000026728729,"teacher_disagreement_score":0.9637268,"about_ca_system_score_codex":0.0003391708,"about_ca_system_score_gemma":0.00021753613,"threshold_uncertainty_score":0.99976975},"labels":[],"label_agreement":null},{"id":"W3033835801","doi":"10.1016/j.neucom.2020.05.078","title":"Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications","year":2020,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":313,"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":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; University of California, San Diego; Universidad de Granada; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; Comunidad de Madrid; University of Southern California; Eli Lilly and Company; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; Alzheimer's Association; Michael J. Fox Foundation for Parkinson's Research","keywords":"Artificial intelligence; Field (mathematics); Computer science; Robotics; Applications of artificial intelligence; Data science; Artificial Intelligence System; Robot","score_opus":0.0473477852512321,"score_gpt":0.3485424178329651,"score_spread":0.30119463258173296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033835801","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":[0.017897306,0.00014903922,0.9759074,0.0053463127,0.000059051676,0.00023967445,0.000004217949,0.00017841536,0.000218604],"genre_scores_gemma":[0.9755641,0.000011689945,0.023804275,0.00037969043,0.00020981515,0.000019151035,0.000004163246,0.000005535858,0.0000015287321],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855214,0.00004248833,0.00036519615,0.00067123765,0.00018925493,0.00017966959],"domain_scores_gemma":[0.9991974,0.00019515607,0.00014488347,0.00033357932,0.000044021766,0.00008495492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004084203,0.000113755574,0.00012416876,0.00014086372,0.000523085,0.00035500832,0.0010775103,0.000025399908,6.0203706e-7],"category_scores_gemma":[0.00004366566,0.000093751594,0.000013466897,0.0017317042,0.00031751028,0.0007819366,0.001067445,0.00028610983,0.0000029438177],"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":[0.0000017377982,0.000008978476,0.00039071497,0.000005533065,0.0000010937678,7.1337496e-7,0.0003863818,0.00046087697,0.00038069303,0.056505978,0.000007246281,0.94185007],"study_design_scores_gemma":[0.000023019158,0.00004538807,0.0036492704,0.000010557434,0.0000046946743,0.000015869115,0.00013709188,0.9807264,0.00210779,0.011099232,0.002031003,0.00014967938],"about_ca_topic_score_codex":0.000006242945,"about_ca_topic_score_gemma":0.000013811303,"teacher_disagreement_score":0.98026556,"about_ca_system_score_codex":0.000011427303,"about_ca_system_score_gemma":0.000038963186,"threshold_uncertainty_score":0.40232},"labels":[],"label_agreement":null},{"id":"W3034355320","doi":"10.1109/phm-besancon49106.2020.00046","title":"Deep Variational Autoencoder: An Efficient Tool for PHM Frameworks","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro-Québec","funders":"","keywords":"Autoencoder; Interpretability; Computer science; Visualization; Artificial intelligence; Dimensionality reduction; Classifier (UML); Machine learning; Prognostics; Data mining; Dimension (graph theory); Deep learning; Pattern recognition (psychology); Mathematics","score_opus":0.020304444371091242,"score_gpt":0.2859199891385841,"score_spread":0.26561554476749283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034355320","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000050026687,0.000013812335,0.9912376,0.00484257,0.00032816647,0.0009248975,0.000023137538,0.0013467853,0.0012330383],"genre_scores_gemma":[0.05850787,0.000005429872,0.9381665,0.0016880477,0.0002847405,0.0010835424,0.00007022652,0.000016856538,0.00017679998],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983196,0.000030973897,0.00033879175,0.0008485834,0.0002502449,0.00021175954],"domain_scores_gemma":[0.9984936,0.00010268749,0.00017848899,0.0009109173,0.0001934549,0.00012086761],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020049029,0.00021142987,0.00020891322,0.000079897305,0.00020593758,0.000363931,0.0012543378,0.0005245574,0.00007806423],"category_scores_gemma":[0.000039972972,0.00020715065,0.00017998894,0.00019912662,0.000024029121,0.00009431214,0.0008568185,0.00064848183,0.000031584343],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000028247923,0.00010705043,0.000003954434,0.000023230861,0.000018617344,4.5713173e-7,0.00021148036,0.11419095,0.000024167366,0.86509436,0.00141094,0.018911941],"study_design_scores_gemma":[0.00006339399,0.000053873246,0.00018711454,0.000008306059,0.000008806906,0.0000021355042,0.0000053802933,0.8535586,0.00016362613,0.14071634,0.005002881,0.0002295407],"about_ca_topic_score_codex":0.000020023299,"about_ca_topic_score_gemma":0.0000031920056,"teacher_disagreement_score":0.73936766,"about_ca_system_score_codex":0.00007656244,"about_ca_system_score_gemma":0.00017936145,"threshold_uncertainty_score":0.84473586},"labels":[],"label_agreement":null},{"id":"W3034495876","doi":"10.1049/iet-ipr.2019.1663","title":"video compression based on sphere‐rotated frame prediction","year":2020,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"National Natural Science Foundation of China","keywords":"Compression (physics); Computer science; Frame (networking); Data compression; Computer vision; Artificial intelligence; Materials science; Telecommunications; Composite material","score_opus":0.016195403135500514,"score_gpt":0.25434400217511716,"score_spread":0.23814859903961663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034495876","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":[0.0017088794,0.00002984679,0.9907496,0.0032679874,0.00003776537,0.00019546208,0.000004674806,0.001304036,0.0027017351],"genre_scores_gemma":[0.7773092,0.0000021280914,0.22027504,0.0022330857,0.00007926827,0.00005424908,0.0000056038157,0.000012822795,0.000028643584],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989863,0.000030773324,0.00019904091,0.0003950989,0.00022390047,0.000164859],"domain_scores_gemma":[0.9993975,0.000024454328,0.00011111041,0.00025570372,0.00009753151,0.000113680006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007888659,0.00012317348,0.00010895383,0.000041795036,0.00026309394,0.00028525697,0.00039187458,0.00006448642,0.000041626743],"category_scores_gemma":[0.000028143739,0.00011415958,0.000045119596,0.00049229263,0.00003744316,0.0005250039,0.00007759128,0.00020870306,0.00006686405],"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":[0.000065853186,0.00029247178,0.00062333304,0.00021488508,0.000008669717,0.00001594993,0.00076123356,0.001996067,0.38419384,0.0010051529,0.013855139,0.5969674],"study_design_scores_gemma":[0.00018809168,0.00011467517,0.00041627168,0.00007170376,0.000003837932,0.000002452362,0.000015485655,0.8941987,0.09779578,0.00073121395,0.0063389884,0.00012278682],"about_ca_topic_score_codex":0.0000046434925,"about_ca_topic_score_gemma":1.6061892e-7,"teacher_disagreement_score":0.8922027,"about_ca_system_score_codex":0.000026839733,"about_ca_system_score_gemma":0.000059148984,"threshold_uncertainty_score":0.46552926},"labels":[],"label_agreement":null},{"id":"W3035097696","doi":"10.24963/ijcai.2020/296","title":"Handling Black Swan Events in Deep Learning with Diversely Extrapolated Neural Networks","year":2020,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Artificial intelligence; Hindsight bias; Deep learning; Artificial neural network; Set (abstract data type); Machine learning; Function (biology); Interpretability; Recurrent neural network","score_opus":0.01508044470271067,"score_gpt":0.21808565098580868,"score_spread":0.203005206283098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035097696","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":[0.07505494,0.000013881831,0.92301434,0.0006495154,0.000011066685,0.0001216471,5.418246e-8,0.00038756808,0.0007469905],"genre_scores_gemma":[0.9864411,0.000010499937,0.013042415,0.00038164316,0.000023357396,0.000010284854,0.0000010648196,0.00000594604,0.00008369755],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934864,0.00002710927,0.00012171269,0.0002498967,0.00008948881,0.00016316249],"domain_scores_gemma":[0.9997134,0.000017716451,0.000047290905,0.00011808668,0.000024832954,0.00007866242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000056152883,0.00007992103,0.0000827224,0.000037504,0.00010163447,0.00003998642,0.00028643815,0.000038348993,0.00002096424],"category_scores_gemma":[0.000004520684,0.000067663634,0.000025016423,0.0005764123,0.000018231896,0.0002146957,0.000103403014,0.00021179453,0.00001270848],"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":[0.000076240496,0.00013721365,0.1269748,0.000019958841,0.000042275547,0.000041520005,0.0034865483,0.6834873,0.0013490963,0.017533636,0.00024371217,0.16660766],"study_design_scores_gemma":[0.00017069596,0.00010623984,0.005603807,0.0000046834507,0.0000018399447,0.000002745453,0.00006043679,0.99319494,0.00028044908,0.00005702809,0.00041863008,0.00009849864],"about_ca_topic_score_codex":0.000042697346,"about_ca_topic_score_gemma":0.000020994168,"teacher_disagreement_score":0.91138613,"about_ca_system_score_codex":0.00001418049,"about_ca_system_score_gemma":0.0000072177227,"threshold_uncertainty_score":0.2759243},"labels":[],"label_agreement":null},{"id":"W3035353183","doi":"","title":"Small-GAN: Speeding up GAN Training using Core-Sets","year":2020,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Anomaly Detection Techniques and Applications","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":"Université de Montréal; University of Toronto","funders":"","keywords":"Computer science; Selection (genetic algorithm); Training (meteorology); Cache; Core (optical fiber); Fraction (chemistry); Machine learning; Artificial intelligence; Generative grammar; Training set; Parallel computing","score_opus":0.27406164920131143,"score_gpt":0.3543970622550278,"score_spread":0.08033541305371639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035353183","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":[0.050028786,0.000013478565,0.91672367,0.006381056,0.00030693613,0.00013283973,0.000005979194,0.0005628659,0.025844399],"genre_scores_gemma":[0.97009826,0.000016570346,0.028260114,0.001066625,0.00018466268,0.000010752158,0.000013497867,0.000016518925,0.00033297966],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987141,0.000039899853,0.00027004743,0.00045381783,0.00030761614,0.00021451087],"domain_scores_gemma":[0.99929196,0.000059124828,0.0001956775,0.00016314186,0.00015234397,0.00013775454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017819492,0.00017216461,0.00015853357,0.00012672789,0.00024274405,0.00029445667,0.000884045,0.000061764906,0.00027544016],"category_scores_gemma":[0.00013649219,0.00017758449,0.00008126043,0.00026465804,0.000033974804,0.00024824444,0.00014597009,0.00053687685,0.00008333612],"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":[0.000043959288,0.000049698556,0.0038136027,0.000014514402,0.000073741685,0.000034589884,0.0041036783,0.011817816,0.05594751,0.7353928,0.00015617917,0.18855189],"study_design_scores_gemma":[0.00019692672,0.00011701489,0.00033047333,0.00003871782,0.0000042467514,0.000025898184,0.00016147939,0.9843449,0.0029163035,0.0026110401,0.009047042,0.00020595164],"about_ca_topic_score_codex":0.00006671909,"about_ca_topic_score_gemma":0.0000053910717,"teacher_disagreement_score":0.9725271,"about_ca_system_score_codex":0.000060863127,"about_ca_system_score_gemma":0.00006736373,"threshold_uncertainty_score":0.72416854},"labels":[],"label_agreement":null},{"id":"W3035622304","doi":"10.1145/3381028","title":"Outlier Detection","year":2020,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":319,"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 Ottawa","funders":"Canada Research Chairs","keywords":"Computer science; Anomaly detection; Outlier; Data mining; Big data; Data science; Data stream mining; Artificial intelligence","score_opus":0.06577665279930664,"score_gpt":0.34038940564783293,"score_spread":0.27461275284852626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035622304","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.6288932e-7,0.44710958,0.5511427,0.000041133637,0.00023830064,0.00028491157,0.000003065875,0.00089067174,0.00028950468],"genre_scores_gemma":[0.00022662133,0.9598336,0.03924007,0.000071734044,0.00035608007,0.000056577686,0.000016064534,0.000050167295,0.00014904777],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972703,0.00079232577,0.00059768005,0.0008151168,0.00022163894,0.0003029717],"domain_scores_gemma":[0.9973045,0.00048387173,0.00045103682,0.0015517478,0.00007993002,0.00012894136],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011619527,0.00037276023,0.0009309642,0.00017697815,0.000285943,0.0002545097,0.0025872681,0.0002605623,0.000008844302],"category_scores_gemma":[0.00018813959,0.00034472253,0.0004301082,0.0010784316,0.000031609503,0.00012526265,0.0014026047,0.00056912226,0.0006853089],"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":[4.6917307e-8,0.000010658486,8.2421434e-7,0.00036596932,0.000024466513,0.0000038919407,0.000015558133,7.731769e-7,3.32392e-7,0.0005380255,0.00025088494,0.9987886],"study_design_scores_gemma":[0.00003053078,0.00003846661,0.000016145283,0.00042722377,0.000041145955,0.000053972155,8.9888727e-7,0.0019798162,0.000012400655,0.0004421303,0.9966011,0.0003561851],"about_ca_topic_score_codex":0.000033386696,"about_ca_topic_score_gemma":0.000004357315,"teacher_disagreement_score":0.9984324,"about_ca_system_score_codex":0.00011378781,"about_ca_system_score_gemma":0.00013080552,"threshold_uncertainty_score":0.99990046},"labels":[],"label_agreement":null},{"id":"W3036001641","doi":"10.1007/978-3-030-50347-5_14","title":"2D Bidirectional Gated Recurrent Unit Convolutional Neural Networks for End-to-End Violence Detection in Videos","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Convolutional neural network; Artificial intelligence; End-to-end principle; Frame (networking); Computer vision; Deep learning; Action recognition; Pattern recognition (psychology); Feature extraction; Computer network","score_opus":0.02282298543659758,"score_gpt":0.2601818844942797,"score_spread":0.23735889905768212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036001641","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":[0.0001852954,0.00017970495,0.99550444,0.0011776391,0.0013216449,0.0010851988,0.000022783728,0.00034612027,0.00017719936],"genre_scores_gemma":[0.81532913,0.00005804483,0.18179683,0.0017378638,0.00072112336,0.00022203232,0.00002517902,0.000039035884,0.000070769805],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965074,0.00004416461,0.00063631305,0.001616665,0.0006314837,0.000563994],"domain_scores_gemma":[0.9980449,0.00045001996,0.00027635822,0.0006455784,0.00034167818,0.00024146054],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005585971,0.00045608153,0.0004295998,0.00087284815,0.0003736896,0.00028360795,0.0017102967,0.0003169551,0.000023252647],"category_scores_gemma":[0.00008701107,0.00047656888,0.00016023248,0.001546675,0.00033748566,0.00038576027,0.00065306254,0.00088866975,0.000016885533],"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":[0.000026620917,0.000030542087,0.000036530462,0.0000159715,0.0000064092274,0.000006844884,0.00006573292,0.08633596,0.00029145827,0.011381724,0.000022429398,0.9017798],"study_design_scores_gemma":[0.00021102773,0.00034244792,0.00044280727,0.0002012225,0.000006009736,0.00004610269,1.7843622e-7,0.9514794,0.0024312828,0.040302385,0.004033463,0.00050372],"about_ca_topic_score_codex":0.00005809465,"about_ca_topic_score_gemma":0.00027859438,"teacher_disagreement_score":0.90127605,"about_ca_system_score_codex":0.0004467503,"about_ca_system_score_gemma":0.00031930415,"threshold_uncertainty_score":0.9997686},"labels":[],"label_agreement":null},{"id":"W3036778324","doi":"10.1007/978-3-030-50516-5_13","title":"Anomaly Detection for Images Using Auto-encoder Based Sparse Representation","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Anomaly detection; Pattern recognition (psychology); Artificial intelligence; Sparse approximation; Encoding (memory); Representation (politics); Encoder; Autoencoder; Anomaly (physics); Computer vision; Deep learning","score_opus":0.03831875270723427,"score_gpt":0.2887622803448364,"score_spread":0.25044352763760214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036778324","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000043988814,0.00006190552,0.996634,0.0008786606,0.0005343093,0.0008836273,0.000011265348,0.0004481489,0.00050410547],"genre_scores_gemma":[0.22310695,0.0000062138524,0.77541965,0.00093653926,0.00033484233,0.000060995968,0.000005795087,0.000032792443,0.00009623727],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99712604,0.000026326652,0.00047783263,0.0014822525,0.000506919,0.00038064524],"domain_scores_gemma":[0.9979304,0.00027163437,0.00036905456,0.0009870183,0.00030416713,0.00013770656],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003955171,0.00037682374,0.00036499382,0.00059123675,0.0004287698,0.0004932199,0.0014548209,0.00025984747,0.000009844225],"category_scores_gemma":[0.00007643938,0.0003862442,0.00018977401,0.0007768404,0.00030208076,0.0006131147,0.00040466743,0.00041562266,0.0000118113885],"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":[0.000019780064,0.000040878946,0.000039462426,0.00006992171,0.00001450347,0.00001813041,0.00017447739,0.061657686,0.0202638,0.010065139,0.000043824894,0.9075924],"study_design_scores_gemma":[0.0001650386,0.00014681967,0.000051854837,0.000067606146,0.000012214419,0.000021506105,1.11255424e-7,0.8675701,0.066456616,0.063994125,0.0011328388,0.0003811565],"about_ca_topic_score_codex":0.000043557047,"about_ca_topic_score_gemma":0.000027475578,"teacher_disagreement_score":0.90721124,"about_ca_system_score_codex":0.00029694318,"about_ca_system_score_gemma":0.00037737007,"threshold_uncertainty_score":0.999859},"labels":[],"label_agreement":null},{"id":"W3039137796","doi":"10.1145/3394053","title":"Internal Evaluation of Unsupervised Outlier Detection","year":2020,"lang":"en","type":"article","venue":"ACM Transactions on Knowledge Discovery from Data","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Outlier; Anomaly detection; Computer science; Cluster analysis; Data mining; Artificial intelligence; Domain (mathematical analysis); Pattern recognition (psychology); Binary number; Mathematics","score_opus":0.1100711224031969,"score_gpt":0.32755916439882915,"score_spread":0.21748804199563226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3039137796","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":[0.019308383,0.00010768334,0.9780458,0.0007170492,0.00023641532,0.00032099013,0.0005167158,0.00019983476,0.0005471258],"genre_scores_gemma":[0.98686296,0.00003857337,0.012630235,0.00013122067,0.00008374235,0.000073489,0.00010662593,0.000014288283,0.000058851678],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984031,0.00012483388,0.0003400802,0.00064221915,0.00035486656,0.00013495058],"domain_scores_gemma":[0.9973517,0.00012403564,0.00011194914,0.0021407101,0.00018103488,0.0000905405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029692875,0.0001534508,0.00017394463,0.000100673344,0.00013929866,0.000116203904,0.002318597,0.00007887397,0.00012245901],"category_scores_gemma":[0.000075098935,0.00015156652,0.00009180734,0.00056323764,0.00004761198,0.0015288091,0.00013279935,0.00020976523,0.00012115627],"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":[0.00005553005,0.00044508235,0.000058062946,0.000017328774,0.00011862043,5.2802733e-7,0.0008607258,0.00045662586,0.022800341,0.00047335736,0.0005580741,0.9741557],"study_design_scores_gemma":[0.001273032,0.00034590828,0.0023815536,0.000062015235,0.00025793072,0.0000037230036,0.00023207815,0.6556272,0.32453197,0.0061374228,0.008700502,0.00044664342],"about_ca_topic_score_codex":0.0001415572,"about_ca_topic_score_gemma":0.00010752028,"teacher_disagreement_score":0.9737091,"about_ca_system_score_codex":0.00006210299,"about_ca_system_score_gemma":0.00011596031,"threshold_uncertainty_score":0.61807036},"labels":[],"label_agreement":null},{"id":"W3040197085","doi":"10.1016/j.icte.2020.06.003","title":"Unsupervised log message anomaly detection","year":2020,"lang":"en","type":"article","venue":"ICT Express","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":116,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Autoencoder; Anomaly detection; Anomaly (physics); Isolation (microbiology); Computer science; Artificial intelligence; Data mining; Pattern recognition (psychology); Feature (linguistics); Deep learning; Bioinformatics; Biology","score_opus":0.019497205972440982,"score_gpt":0.22906776017103608,"score_spread":0.2095705541985951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3040197085","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":[0.023016876,0.000049037197,0.97024554,0.0015296756,0.00006061279,0.00017883685,0.000002996922,0.00095165905,0.0039647426],"genre_scores_gemma":[0.9806872,0.000011217486,0.018000424,0.00095819816,0.00010029286,0.00009085869,0.0000011843853,0.000010036203,0.00014059273],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99916756,0.00003283755,0.00015914161,0.00033690254,0.00014062137,0.0001629209],"domain_scores_gemma":[0.99934584,0.000023390487,0.000056092613,0.00040739038,0.000044944245,0.0001223721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000059786347,0.000104309314,0.00010485097,0.00004006264,0.00014492375,0.00011097486,0.0006374295,0.000063439,0.0000434147],"category_scores_gemma":[0.0000124316375,0.00010334017,0.00006664043,0.00038428415,0.00002388186,0.00033715877,0.00017381826,0.00011727449,0.000117902346],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023746034,0.00012219332,0.00046163882,0.000043494885,0.000031493673,0.000019890054,0.0017678231,0.00016379678,0.75308585,0.030059623,0.0045404118,0.20968002],"study_design_scores_gemma":[0.0003399178,0.0001944547,0.0020177022,0.000009093803,0.000008623241,0.000013371253,0.0000519643,0.07923293,0.7255469,0.0018703334,0.19035634,0.00035838876],"about_ca_topic_score_codex":0.000027261656,"about_ca_topic_score_gemma":0.000002089061,"teacher_disagreement_score":0.95767033,"about_ca_system_score_codex":0.000016907376,"about_ca_system_score_gemma":0.000014491139,"threshold_uncertainty_score":0.42140898},"labels":[],"label_agreement":null},{"id":"W3041107669","doi":"10.1016/j.patcog.2020.107355","title":"Abnormal event detection in surveillance videos based on low-rank and compact coefficient dictionary learning","year":2020,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Beijing Municipal Natural Science Foundation; Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Guizhou Province; National Natural Science Foundation of China","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Histogram; Norm (philosophy); Background subtraction; Projection (relational algebra); Matrix norm; Computer vision; Subtraction; Frame (networking); Feature vector; Rank (graph theory); Mathematics; Algorithm; Pixel; Image (mathematics)","score_opus":0.01601153955025432,"score_gpt":0.23434137155868326,"score_spread":0.21832983200842893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041107669","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":[0.24486591,0.00000833516,0.7534591,0.000986168,0.000046510017,0.00020338404,0.000006407997,0.00022597302,0.00019822386],"genre_scores_gemma":[0.9985912,0.00001742487,0.00036701365,0.00088242826,0.000054102755,0.000053103122,0.000022454205,0.000008827221,0.000003443201],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903136,0.00010987042,0.00020730215,0.0003397809,0.00016409952,0.00014759228],"domain_scores_gemma":[0.9995833,0.00008431024,0.00009501051,0.00011226719,0.000044776418,0.00008035118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018429897,0.000110156325,0.000107209074,0.000111765316,0.00014076183,0.00006223628,0.00010759467,0.000051705094,0.00002521304],"category_scores_gemma":[0.000025458781,0.000116751726,0.00004020808,0.00033393965,0.00001889023,0.00015320495,0.00003123185,0.00022163455,0.00005653369],"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":[0.00004080974,0.00013609679,0.018000718,0.000040828603,0.0000045971037,0.0000072622893,0.00022176307,0.008232333,0.0024142219,0.000009314729,0.0000425864,0.97084945],"study_design_scores_gemma":[0.00037588316,0.00031782966,0.08889131,0.000046286823,0.0000019436038,0.000006730426,0.00002165761,0.8986811,0.011016126,0.000066710774,0.00041241324,0.00016201902],"about_ca_topic_score_codex":0.00005514077,"about_ca_topic_score_gemma":0.00001676018,"teacher_disagreement_score":0.97068745,"about_ca_system_score_codex":0.00005293276,"about_ca_system_score_gemma":0.000012155598,"threshold_uncertainty_score":0.4760997},"labels":[],"label_agreement":null},{"id":"W3042773043","doi":"10.1109/jsyst.2020.3004805","title":"A Learning-Aided Generic Framework for Fault Detection and Recovery of Inertial Sensors in Automated Driving Systems","year":2020,"lang":"en","type":"article","venue":"IEEE Systems Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Fault detection and isolation; Convolutional neural network; Computer science; Fault (geology); Inertial measurement unit; Artificial intelligence; Artificial neural network; Fault injection; Machine learning; State (computer science); Control engineering; Engineering; Real-time computing; Algorithm; Software","score_opus":0.020094062862995685,"score_gpt":0.2606669203438352,"score_spread":0.2405728574808395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042773043","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":[0.37377277,0.00010862265,0.62511784,0.00008782058,0.00045015107,0.00024827648,0.0000010018784,0.000203282,0.000010250491],"genre_scores_gemma":[0.9909589,0.00004858417,0.008473175,0.000018541723,0.00040045273,0.000064414715,2.2833358e-7,0.000013733502,0.000021944743],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986895,0.00016014106,0.0005592591,0.00023123757,0.00017552561,0.00018436438],"domain_scores_gemma":[0.9990179,0.00012356858,0.00047076095,0.00013001738,0.00014256677,0.00011517659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004177743,0.000116182164,0.00026770137,0.00016673615,0.00016626426,0.0002005538,0.00021410885,0.00013562143,5.185723e-7],"category_scores_gemma":[0.00013452032,0.00010912358,0.00007069071,0.00048677513,0.00001914838,0.0002466905,0.000032059557,0.00031976507,0.0000017589435],"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":[0.00008554836,0.00005923553,0.0015754177,0.00042913127,0.0000945722,0.00002132415,0.0019224465,0.69590306,0.28339666,0.0013134001,0.00055956515,0.014639662],"study_design_scores_gemma":[0.00023850017,0.00035243336,0.00050700374,0.00017287827,0.0000072457815,0.0003013305,0.00015561364,0.9914933,0.005766655,0.00010596381,0.00076724106,0.00013184568],"about_ca_topic_score_codex":0.00006699557,"about_ca_topic_score_gemma":0.0000029012617,"teacher_disagreement_score":0.6171862,"about_ca_system_score_codex":0.00006658129,"about_ca_system_score_gemma":0.00004208508,"threshold_uncertainty_score":0.44499305},"labels":[],"label_agreement":null},{"id":"W3042886277","doi":"10.48550/arxiv.2007.07843","title":"Few-shot Scene-adaptive Anomaly Detection","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Anomaly detection; Artificial intelligence; Shot (pellet); Generalization; Computer vision; Machine learning; Mathematics","score_opus":0.11562808880726747,"score_gpt":0.19719246209889987,"score_spread":0.08156437329163241,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042886277","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":[0.029795641,0.00002398288,0.9627541,0.0002571773,0.0002516291,0.00041017594,0.000013754395,0.0011270575,0.005366494],"genre_scores_gemma":[0.9907276,0.00008387907,0.008108987,0.00018412435,0.00011396652,0.000007690707,0.0000064130704,0.000021106049,0.00074623915],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804276,0.00008944509,0.0002060765,0.0012897756,0.000091455404,0.00028051317],"domain_scores_gemma":[0.9982055,0.00004122893,0.00027972818,0.0011008697,0.0001655276,0.00020715526],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000112583184,0.00030870168,0.0002807309,0.00021879816,0.00028196364,0.00014076578,0.0015575734,0.00032166284,0.000027428552],"category_scores_gemma":[0.0000132188725,0.0003818335,0.00026324348,0.0009258335,0.00009098543,0.00032605245,0.0016389246,0.0006741462,0.0001967661],"study_design_candidate":"theoretical_or_conceptual","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":[0.00014413105,0.00035593548,0.0010943374,0.00013964366,0.00034544224,0.00041678682,0.0004955565,0.037291773,0.0056600817,0.9211254,0.0015086133,0.03142231],"study_design_scores_gemma":[0.00028002853,0.00021080887,0.0019024519,0.000041249237,0.000075376345,0.000012918533,0.000062477244,0.8752024,0.013651045,0.10355722,0.0042467345,0.000757249],"about_ca_topic_score_codex":0.00023557394,"about_ca_topic_score_gemma":0.000061454644,"teacher_disagreement_score":0.96093196,"about_ca_system_score_codex":0.0002589714,"about_ca_system_score_gemma":0.0001393443,"threshold_uncertainty_score":0.9998634},"labels":[],"label_agreement":null},{"id":"W3043191018","doi":"10.1007/s11042-020-09232-7","title":"Artificial intelligence in deep learning algorithms for multimedia analysis","year":2020,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Multimedia; Deep learning; Machine learning; Algorithm","score_opus":0.05039430584973119,"score_gpt":0.30440973544027394,"score_spread":0.25401542959054274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043191018","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00043509156,0.000088614186,0.99641293,0.0018130009,0.000012558406,0.0008409528,0.00002341142,0.00024996063,0.00012347502],"genre_scores_gemma":[0.42678103,0.00009829431,0.5700657,0.0002605918,0.00015627085,0.0025395418,0.00006168846,0.000011080902,0.000025800911],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876595,0.000022700859,0.00035966904,0.0005226501,0.000110948284,0.0002180959],"domain_scores_gemma":[0.9991085,0.00029276588,0.00010464229,0.0002518144,0.00007474892,0.00016754633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015648107,0.00013189118,0.00020950373,0.00014670637,0.00021387122,0.00015093917,0.00039558404,0.00008086663,0.000016940056],"category_scores_gemma":[0.000073830895,0.00013574841,0.00009940021,0.0014977271,0.000062598076,0.00021512726,0.00011178051,0.00017574134,0.000030196405],"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":[0.0000025874801,0.000047196954,0.00028714494,0.0000066602315,0.000021771286,2.8513733e-7,0.00047943715,0.00088320964,0.00068041356,0.012637043,0.000011845413,0.9849424],"study_design_scores_gemma":[0.000054063366,0.000039846007,0.0016173101,0.0000017523527,0.000034832537,6.163069e-7,0.00012680754,0.98047256,0.0036264316,0.002409067,0.011450078,0.00016664072],"about_ca_topic_score_codex":0.000024530296,"about_ca_topic_score_gemma":0.000026020647,"teacher_disagreement_score":0.9847758,"about_ca_system_score_codex":0.000019934621,"about_ca_system_score_gemma":0.000018485658,"threshold_uncertainty_score":0.5535659},"labels":[],"label_agreement":null},{"id":"W3045231911","doi":"10.1109/iccicc46617.2019.9146048","title":"Sparse spatiotemporal feature learning for pipeline anomaly detection","year":2019,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Pipeline (software); Anomaly detection; Computer science; Feature (linguistics); Noise (video); Embedding; Pipeline transport; Data mining; Pattern recognition (psychology); Anomaly (physics); Feature vector; Artificial intelligence; Machine learning; Engineering; Image (mathematics)","score_opus":0.010407564482731033,"score_gpt":0.2403134888220055,"score_spread":0.22990592433927448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045231911","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":[0.020349616,0.000012702751,0.9737437,0.0008593275,0.00011848793,0.00043242556,6.1590714e-7,0.00064033165,0.0038428379],"genre_scores_gemma":[0.88327074,0.0000033998176,0.09646992,0.00016789677,0.00006878114,0.00007611838,0.000004064643,0.000008441105,0.019930637],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932677,0.000015072745,0.00012147021,0.00029512914,0.00009139842,0.00015014254],"domain_scores_gemma":[0.9994519,0.000033344586,0.00007925572,0.00030250606,0.000089303,0.0000437393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014281204,0.00008901308,0.00009309817,0.000076708006,0.00013324962,0.00008390845,0.00025290734,0.00008042707,0.00003727741],"category_scores_gemma":[0.0000120748255,0.00008108326,0.00007370813,0.00026378527,0.000008983227,0.0002699911,0.000061757724,0.00012593594,0.00011569376],"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":[0.00004703332,0.00015374756,0.008097377,0.00006236997,0.000026042026,0.0000012620434,0.00020549815,0.001267773,0.08136951,0.114931464,0.0111571895,0.78268075],"study_design_scores_gemma":[0.00032765025,0.00029572792,0.0028234564,0.0000062440554,0.000004789142,0.000013692797,0.000026656287,0.58441347,0.13636312,0.0025136839,0.27296898,0.00024256021],"about_ca_topic_score_codex":0.00003752134,"about_ca_topic_score_gemma":0.000023574337,"teacher_disagreement_score":0.87727374,"about_ca_system_score_codex":0.000027345277,"about_ca_system_score_gemma":0.00001876245,"threshold_uncertainty_score":0.33064792},"labels":[],"label_agreement":null},{"id":"W3045302321","doi":"10.48550/arxiv.2007.13703","title":"From Sound Representation to Model Robustness","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Robustness (evolution); Computer science; Spectrogram; Pattern recognition (psychology); Artificial intelligence; Mel-frequency cepstrum; Speech recognition; Convolutional neural network; Residual; Short-time Fourier transform; Discrete wavelet transform; Feature extraction; Wavelet; Fourier transform; Wavelet transform; Mathematics; Algorithm","score_opus":0.14154795927917282,"score_gpt":0.22885025879988602,"score_spread":0.0873022995207132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045302321","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":[0.090517305,0.000004764862,0.90638417,0.00076918205,0.00011686423,0.00031910933,0.000028954764,0.0006056373,0.0012540317],"genre_scores_gemma":[0.94255435,0.000017623932,0.056174435,0.0002378778,0.00009430791,0.000006910279,0.000024017172,0.000013362465,0.00087708526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843025,0.00004186085,0.00015499971,0.0011315835,0.00007495489,0.00016633762],"domain_scores_gemma":[0.9983545,0.00003648355,0.00014443991,0.0011633022,0.00011355708,0.0001877354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000053562224,0.0001880555,0.00020039576,0.00012944214,0.00013580124,0.00014572385,0.0014707306,0.00017776592,0.000016506372],"category_scores_gemma":[0.000014044074,0.00023922964,0.00013720471,0.00062898174,0.00003040346,0.00024521377,0.0018412152,0.0003168846,0.000094744966],"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":[0.000007645148,0.0000240315,0.000064351065,0.000006047354,0.000020058807,0.00001690968,0.00018376888,0.9082599,0.00010723094,0.08938701,0.0013349742,0.0005880409],"study_design_scores_gemma":[0.00006798098,0.000012045521,0.00012120261,0.000010260649,0.000018816063,4.181546e-7,0.00003154697,0.77023137,0.00047031906,0.22866218,0.0001721584,0.00020169794],"about_ca_topic_score_codex":0.00042561788,"about_ca_topic_score_gemma":0.000027302905,"teacher_disagreement_score":0.8520371,"about_ca_system_score_codex":0.00012690782,"about_ca_system_score_gemma":0.000088569046,"threshold_uncertainty_score":0.9755502},"labels":[],"label_agreement":null},{"id":"W3046556377","doi":"10.23977/jaip.2020.030106","title":"A Co-word Analysis of the Applications of Machine Learning in China","year":2020,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Realization (probability); Word (group theory); Computer science; Natural language processing; Statistical analysis; Machine learning; Moment (physics); Linguistics; Statistics; Mathematics","score_opus":0.03615764190301387,"score_gpt":0.3415110604561321,"score_spread":0.30535341855311826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046556377","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":[0.008495443,0.00009082591,0.98327583,0.0074947053,0.000021983644,0.00012523099,0.0000021972032,0.000012955788,0.00048082139],"genre_scores_gemma":[0.97288555,0.00011033613,0.02677799,0.00017397375,0.00003227592,0.0000060333346,2.76747e-7,0.0000033058914,0.000010250988],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985857,0.00015422408,0.0007749503,0.00012202257,0.0002804751,0.00008261201],"domain_scores_gemma":[0.99776894,0.00034798382,0.0013165819,0.00022787706,0.00028782443,0.000050806888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007686944,0.00006480722,0.0002266172,0.00023558279,0.00007269248,0.000032710715,0.00074676354,0.00003709316,0.000026974329],"category_scores_gemma":[0.00064312463,0.00004946545,0.0001836035,0.0032373287,0.00006491578,0.0003898439,0.00006897802,0.00039566157,0.0000033763686],"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":[0.00020623344,0.0010183642,0.005605438,0.000041810057,0.0005827786,0.000007436946,0.009245368,0.09470446,0.048215583,0.15720846,0.000090459,0.68307364],"study_design_scores_gemma":[0.000051953746,0.00044907074,0.0046705264,0.000035442907,0.00053805363,0.00003908019,0.001720875,0.64451236,0.31723475,0.009079872,0.021466536,0.00020145155],"about_ca_topic_score_codex":0.00010855034,"about_ca_topic_score_gemma":0.000022675977,"teacher_disagreement_score":0.9643901,"about_ca_system_score_codex":0.000022415232,"about_ca_system_score_gemma":0.00008290164,"threshold_uncertainty_score":0.20171425},"labels":[],"label_agreement":null},{"id":"W3047647804","doi":"10.1061/9780784483213.024","title":"Finding Big Leaks with Big Data: Case Studies from an Internet-of-Things Leak Detection Platform","year":2020,"lang":"en","type":"article","venue":"Pipelines 2020","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Genomics","funders":"","keywords":"Big data; Leak; Internet of Things; Computer science; Leak detection; Computer security; Embedded system; Data mining; Engineering","score_opus":0.1429447404586784,"score_gpt":0.3235493404215648,"score_spread":0.18060459996288641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047647804","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":[0.08126633,0.00017364313,0.9165492,0.0011896066,0.00019752083,0.00016803498,0.0000141842065,0.00038079554,0.000060694718],"genre_scores_gemma":[0.9666111,0.00005078263,0.03200756,0.0007031015,0.0005150355,0.000027251484,0.000020444126,0.000015714557,0.00004902431],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987665,0.00002074266,0.0003196336,0.0005750677,0.00016756963,0.0001504906],"domain_scores_gemma":[0.9988592,0.00007972195,0.00019787822,0.0006322378,0.00012440089,0.000106515676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012886485,0.00016122247,0.00022312162,0.000057998546,0.00013438199,0.000093923714,0.00075367425,0.00005999759,0.0000043406912],"category_scores_gemma":[0.000067467605,0.00013291849,0.00003738071,0.0005485333,0.0000616927,0.0007343515,0.0005651567,0.00018832194,0.00001728849],"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":[0.000055130335,0.00006338488,0.00029458627,0.00005519069,0.00009761435,0.00019034008,0.004646822,0.0000547602,0.0064729475,0.0005072257,0.0013931274,0.98616886],"study_design_scores_gemma":[0.0006847847,0.0009280426,0.00014150047,0.000088239474,0.00010326817,0.00056743436,0.001838051,0.8261463,0.13768712,0.0016944564,0.029449094,0.00067172153],"about_ca_topic_score_codex":0.00074241037,"about_ca_topic_score_gemma":0.00076312665,"teacher_disagreement_score":0.9854972,"about_ca_system_score_codex":0.000024337642,"about_ca_system_score_gemma":0.000031504398,"threshold_uncertainty_score":0.54202586},"labels":[],"label_agreement":null},{"id":"W3048804154","doi":"10.3390/electronics9081295","title":"The k-means Algorithm: A Comprehensive Survey and Performance Evaluation","year":2020,"lang":"en","type":"article","venue":"Electronics","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1609,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Cluster analysis; Computer science; Data mining; Algorithm; Convergence (economics); Initialization; Centroid; Outlier; Popularity; Variety (cybernetics); k-means clustering; Machine learning; Artificial intelligence","score_opus":0.029516166267777817,"score_gpt":0.2715012711448277,"score_spread":0.24198510487704988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048804154","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":[0.048418157,0.0031535733,0.9444315,0.003216174,0.00003137473,0.00031917047,0.0000019473412,0.00017924217,0.00024884974],"genre_scores_gemma":[0.9819476,0.0028839214,0.01413246,0.00082195,0.000049950744,0.00009120392,0.0000060886096,0.000007731754,0.000059110065],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935716,0.000069267175,0.00009322174,0.00017606789,0.00015373601,0.00015053715],"domain_scores_gemma":[0.9995152,0.00007415744,0.00004462172,0.0001878575,0.00013500845,0.0000431529],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002765835,0.00005889338,0.000052134477,0.000010087228,0.00030854103,0.00009015835,0.00027676352,0.000023459968,0.0000019280387],"category_scores_gemma":[0.000017396682,0.00004554056,0.000014992197,0.00027174488,0.000028802711,0.0001147281,0.0000758186,0.00012248573,0.000011920259],"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":[0.0000029282965,0.000005180999,0.00022316803,0.0000013653209,0.00000676003,6.687285e-8,0.00011260767,0.000037347272,0.00015649461,0.0045140623,0.00082251814,0.9941175],"study_design_scores_gemma":[0.00008906996,0.00018343993,0.012817097,8.9165064e-7,0.000002821601,0.0000040340587,0.0000076069327,0.895909,0.0013809284,0.00071985484,0.08881411,0.00007112938],"about_ca_topic_score_codex":0.0000044502017,"about_ca_topic_score_gemma":0.000014884791,"teacher_disagreement_score":0.9940464,"about_ca_system_score_codex":0.000031492815,"about_ca_system_score_gemma":0.000079260615,"threshold_uncertainty_score":0.23730792},"labels":[],"label_agreement":null},{"id":"W3048896729","doi":"10.5121/ijaia.2020.11405","title":"Log Message Anomaly Detection with Oversampling","year":2020,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence & Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Oversampling; Anomaly detection; Anomaly (physics); Computer science; Artificial intelligence; Physics; Computer network; Bandwidth (computing); Condensed matter physics","score_opus":0.0389444143972389,"score_gpt":0.302322601934411,"score_spread":0.2633781875371721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048896729","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":[0.0032751134,0.00004351657,0.99164873,0.0039717425,0.00012180563,0.00026962528,0.0000058863325,0.00012492474,0.00053863664],"genre_scores_gemma":[0.9098792,0.000047164107,0.08888455,0.0006010348,0.0004666365,0.00009541931,0.0000018804901,0.000011404417,0.000012721958],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984556,0.000025403298,0.0006173432,0.0002734812,0.00047767174,0.00015051459],"domain_scores_gemma":[0.9981331,0.00008817855,0.00052478694,0.00024219276,0.0008469279,0.0001648047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001929326,0.00013468131,0.00015695048,0.00018269308,0.00014981437,0.00024878272,0.001366777,0.000060462997,0.000049978167],"category_scores_gemma":[0.000042304255,0.000120563076,0.00012124898,0.00062513095,0.0000830388,0.0006211761,0.000114192655,0.0002687136,0.00007568383],"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":[0.00010665227,0.00023592889,0.00021507365,0.000006606256,0.00013440882,0.000017821872,0.00060093025,0.004338018,0.05648951,0.25827444,0.00010356537,0.67947704],"study_design_scores_gemma":[0.00018786802,0.0009891063,0.0005071473,0.000058152455,0.000073633586,0.0005159279,0.00084451,0.094424926,0.69732183,0.06725648,0.13714369,0.00067674107],"about_ca_topic_score_codex":0.00002265443,"about_ca_topic_score_gemma":0.000016227039,"teacher_disagreement_score":0.90660405,"about_ca_system_score_codex":0.00008519561,"about_ca_system_score_gemma":0.00010206025,"threshold_uncertainty_score":0.49164194},"labels":[],"label_agreement":null},{"id":"W3080145627","doi":"10.1007/978-981-15-6695-0_6","title":"Lightweight Classifier-Based Outlier Detection Algorithms from Multivariate Data Stream","year":2020,"lang":"en","type":"book-chapter","venue":"Springer tracts in nature-inspired computing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Anomaly detection; Outlier; Computer science; Local outlier factor; Classifier (UML); Artificial intelligence; Data mining; Preprocessor; Mahalanobis distance; Pattern recognition (psychology); One-class classification; Machine learning","score_opus":0.036495893396721296,"score_gpt":0.27960324577539625,"score_spread":0.24310735237867495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3080145627","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":[0.00032064356,0.000766644,0.95608205,0.00091175287,0.0016742332,0.0010083714,0.00017997214,0.0020900771,0.036966246],"genre_scores_gemma":[0.7366345,0.000046986785,0.2582505,0.0010036995,0.0012706695,0.000028900033,0.0003121854,0.00018061613,0.0022719773],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951652,0.00007005093,0.0010795621,0.0024173865,0.0007073256,0.0005604534],"domain_scores_gemma":[0.9955292,0.00036183064,0.0009005375,0.0027840342,0.00015823163,0.00026620645],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0004242897,0.0007980217,0.000787893,0.00051412894,0.00035298738,0.00042712814,0.0034432341,0.0015465056,0.000021921114],"category_scores_gemma":[0.000089588,0.00085121626,0.00024351173,0.0003996688,0.00008261827,0.00050886365,0.0014202894,0.0036381844,0.00010080531],"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":[0.00005693963,0.000306592,0.00009081927,0.00011099048,0.00027645595,0.00030983207,0.0002655233,0.0007915146,0.002818347,0.08758654,0.00044612086,0.90694034],"study_design_scores_gemma":[0.0010216008,0.00014318698,0.0018263025,0.00063923385,0.0000943688,0.0000104172605,0.0000067244914,0.789864,0.015904572,0.0120143145,0.17687115,0.0016041521],"about_ca_topic_score_codex":0.00020958092,"about_ca_topic_score_gemma":0.000119273835,"teacher_disagreement_score":0.9053362,"about_ca_system_score_codex":0.0003570015,"about_ca_system_score_gemma":0.00025035575,"threshold_uncertainty_score":0.9997497},"labels":[],"label_agreement":null},{"id":"W3081193447","doi":"10.1145/3324884.3416559","title":"Hybrid deep neural networks to infer state models of black-box systems","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","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 Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Black box; Deep learning; Set (abstract data type); Code (set theory); Artificial intelligence; Source code; Convolutional neural network; Univariate; Artificial neural network; Inference; Machine learning; Data mining; White box; Multivariate statistics; Programming language","score_opus":0.02705330652425838,"score_gpt":0.25586800033618884,"score_spread":0.22881469381193045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081193447","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":[0.0017529605,0.000062891435,0.99261767,0.0008649912,0.00025781558,0.0008329309,0.000013983767,0.00069304707,0.002903686],"genre_scores_gemma":[0.9503182,0.000036159567,0.048531275,0.0004495192,0.00008850888,0.00022485522,0.000008251085,0.000019329884,0.00032388573],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983178,0.00004846908,0.00051259366,0.00064884423,0.00023649976,0.00023580683],"domain_scores_gemma":[0.998285,0.000033574383,0.0002286021,0.0010675263,0.00019326604,0.00019201184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013499614,0.00023227374,0.0003658337,0.000112003036,0.00005136158,0.00020738681,0.0013473512,0.0000997607,0.000008021427],"category_scores_gemma":[0.0000053323197,0.00021876922,0.00014345271,0.0002592011,0.00003464652,0.00015097718,0.0021134482,0.00038424,0.000019887382],"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":[0.0000025597612,0.00001747394,0.000006184625,0.000044183704,0.000017958795,0.0000022963081,0.00011011569,0.9481547,0.000034609988,0.042538173,0.0018253659,0.007246386],"study_design_scores_gemma":[0.00003659526,0.000049716084,0.000021892449,0.00002057663,0.000006625037,0.000004202157,0.000005344251,0.9768633,0.0008140864,0.021459427,0.0004952156,0.00022296894],"about_ca_topic_score_codex":0.00032593915,"about_ca_topic_score_gemma":0.000006704747,"teacher_disagreement_score":0.94856524,"about_ca_system_score_codex":0.00004545995,"about_ca_system_score_gemma":0.000043803295,"threshold_uncertainty_score":0.892115},"labels":[],"label_agreement":null},{"id":"W3082309692","doi":"10.3390/info11090426","title":"Exploring Neural Network Hidden Layer Activity Using Vector Fields","year":2020,"lang":"en","type":"article","venue":"Information","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Artificial neural network; Computer science; Projection (relational algebra); Representation (politics); Artificial intelligence; Debugging; Layer (electronics); Task (project management); Field (mathematics); Machine learning; Pattern recognition (psychology); Algorithm; Engineering; Mathematics","score_opus":0.14296523704079261,"score_gpt":0.2721613201989134,"score_spread":0.1291960831581208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082309692","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":[0.19431064,0.0000022913516,0.8032142,0.0013505773,0.000093654584,0.00008995784,5.8843153e-7,0.00028992552,0.0006482083],"genre_scores_gemma":[0.9787209,0.000003685656,0.020252397,0.00084742234,0.00014279815,0.00002669091,0.0000011801371,0.000001992887,0.0000029240691],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960256,0.000010182834,0.00012013202,0.00006883674,0.00009325776,0.00010502337],"domain_scores_gemma":[0.9996929,0.000012880067,0.000074245625,0.00013940384,0.00003239481,0.000048157966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004839578,0.00005296171,0.000052341828,0.0000229867,0.0001237692,0.00011706524,0.00019194657,0.000028666998,0.0000077832765],"category_scores_gemma":[0.000009270791,0.00005318146,0.000030009951,0.00028630707,0.000005606293,0.0032806918,0.00009270474,0.00009161471,0.00004298201],"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":[0.000017522849,0.000016269072,0.00039255279,0.000040023868,0.000014369628,0.0000011616087,0.0039929594,0.028314909,0.002402336,0.03539506,0.0028325748,0.92658025],"study_design_scores_gemma":[0.00006218757,0.000037177586,0.0025577776,0.000004159314,0.0000021301073,0.0000035314708,0.000019143945,0.9781265,0.0079935705,0.00024228804,0.010852566,0.00009893557],"about_ca_topic_score_codex":0.000026660162,"about_ca_topic_score_gemma":7.3293194e-7,"teacher_disagreement_score":0.94981164,"about_ca_system_score_codex":0.000020975524,"about_ca_system_score_gemma":0.00001347934,"threshold_uncertainty_score":0.23784223},"labels":[],"label_agreement":null},{"id":"W3083031547","doi":"10.1016/j.enbuild.2020.110445","title":"Cluster analysis-based anomaly detection in building automation systems","year":2020,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Resources Canada; National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Anomaly detection; Cluster (spacecraft); Automation; Computer science; Anomaly (physics); Engineering; Data mining; Data science; Operating system; Physics; Mechanical engineering","score_opus":0.008587907301939661,"score_gpt":0.2228632657908328,"score_spread":0.21427535848889312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083031547","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":[0.14594051,0.00006766803,0.8524809,0.0010125559,0.000031485724,0.000060308776,5.4700655e-7,0.0002851525,0.00012085254],"genre_scores_gemma":[0.9791908,0.000008997619,0.019939201,0.00073062483,0.000045789748,0.00005406084,0.0000016220675,0.000005413286,0.00002345059],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991544,0.000041176558,0.00021571806,0.00034186043,0.000110941124,0.00013593936],"domain_scores_gemma":[0.9995963,0.00003879866,0.00009738895,0.000160281,0.000032161468,0.00007506184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014828816,0.00009889666,0.00014745371,0.00025981647,0.000115870615,0.000151595,0.00019928518,0.00007626,0.0000029108028],"category_scores_gemma":[0.000012738293,0.000098177516,0.0000569944,0.0014610658,0.000017984923,0.00029813938,0.00006332045,0.00006815396,0.0000011877025],"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":[0.000062634004,0.0001511837,0.008521379,0.000115433904,0.00021479066,0.000015804368,0.00076355395,0.15282072,0.13647869,0.4985125,0.00051070936,0.20183258],"study_design_scores_gemma":[0.00015056922,0.000053022977,0.002237923,0.000007854566,0.000019840547,0.0000027490235,0.000008983555,0.9602206,0.027654575,0.00033835645,0.009176989,0.00012854522],"about_ca_topic_score_codex":0.00030956333,"about_ca_topic_score_gemma":0.000037789774,"teacher_disagreement_score":0.83325034,"about_ca_system_score_codex":0.0000333797,"about_ca_system_score_gemma":0.0000129119,"threshold_uncertainty_score":0.40035626},"labels":[],"label_agreement":null},{"id":"W3084688615","doi":"10.1109/tnse.2020.3022869","title":"FAST-ODT: A Lightweight Outlier Detection Scheme for Categorical Data Sets","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Categorical variable; Anomaly detection; Computer science; Outlier; Data mining; Data set; Tree (set theory); Pattern recognition (psychology); Artificial intelligence; Intrusion detection system; Local outlier factor; Machine learning; Mathematics","score_opus":0.029524753623604766,"score_gpt":0.24527070003992052,"score_spread":0.21574594641631575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3084688615","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":[0.0011815855,0.00003938035,0.99668956,0.0010425106,0.00029643485,0.00025140814,0.000006786366,0.0004377788,0.00005453725],"genre_scores_gemma":[0.90904516,0.000040481584,0.09035582,0.00029336032,0.00013402452,0.00009995325,8.9071733e-7,0.000010926765,0.000019394241],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987155,0.000005351836,0.00016606234,0.0005682158,0.00022654244,0.00031831936],"domain_scores_gemma":[0.9991769,0.000049582683,0.0000332433,0.00045752717,0.00007764404,0.0002050998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002771315,0.00012982967,0.000115476214,0.0000926308,0.00050603173,0.00018618713,0.0007546309,0.000056264922,0.0000026156263],"category_scores_gemma":[0.000010169626,0.00012530352,0.000034142373,0.0012922059,0.000057304962,0.00074710674,0.0000180124,0.00017978258,0.00000823275],"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":[0.000035648685,0.00012182473,0.000008622275,0.00007960427,0.00004825805,0.0000046900514,0.00065885205,0.21518984,0.06290886,0.010914146,0.00214639,0.70788324],"study_design_scores_gemma":[0.00010411731,0.00010520886,0.000030033396,0.000006440326,0.0000073882798,0.000011096387,0.000007380953,0.9693084,0.015407624,0.00010429634,0.014749842,0.00015819994],"about_ca_topic_score_codex":0.000005196127,"about_ca_topic_score_gemma":0.000003069109,"teacher_disagreement_score":0.90786356,"about_ca_system_score_codex":0.000042796863,"about_ca_system_score_gemma":0.00006236388,"threshold_uncertainty_score":0.51097286},"labels":[],"label_agreement":null},{"id":"W3085364681","doi":"10.14778/3415478.3415562","title":"Data collection and quality challenges for deep learning","year":2020,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":145,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Machine learning; Artificial intelligence; Feature engineering; Deep learning; Data collection; Big data; Data science; Software; Data mining","score_opus":0.12845824892835148,"score_gpt":0.3110977474383105,"score_spread":0.182639498509959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3085364681","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":[0.038945284,0.001535561,0.8831534,0.06631579,0.00012736954,0.0021987313,0.000014846895,0.00067745853,0.0070315828],"genre_scores_gemma":[0.9614473,0.00042233357,0.037741568,0.00015732815,0.000039222483,0.00010763241,6.8974833e-7,0.000004764426,0.00007916247],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999429,0.000004350586,0.00013658358,0.00024973066,0.000102142505,0.00007821957],"domain_scores_gemma":[0.99960136,0.000030645457,0.00013961899,0.00011841736,0.00007515113,0.000034799104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024573467,0.000054332108,0.000082374514,0.000015867787,0.00016029565,0.000039649574,0.00058924255,0.000022265722,9.3283114e-7],"category_scores_gemma":[0.00009299544,0.00004134062,0.000025023679,0.00014894363,0.000023223844,0.00018352339,0.0005234082,0.0000624755,4.671265e-7],"study_design_candidate":"theoretical_or_conceptual","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":[0.000047985137,0.00011978023,0.001822355,0.0005275249,0.000067034794,2.937659e-8,0.0042955074,0.0000143875795,0.07864595,0.62925315,0.0025350556,0.2826712],"study_design_scores_gemma":[0.0013275876,0.0009906711,0.013023881,0.00007684271,0.00006673175,0.000012986558,0.002236707,0.2485432,0.42419934,0.031442072,0.27752557,0.00055440573],"about_ca_topic_score_codex":0.0000089119185,"about_ca_topic_score_gemma":0.0000017197472,"teacher_disagreement_score":0.92250204,"about_ca_system_score_codex":0.000012818247,"about_ca_system_score_gemma":0.000006592554,"threshold_uncertainty_score":0.16858216},"labels":[],"label_agreement":null},{"id":"W3086241558","doi":"10.1007/s10687-020-00393-0","title":"Extreme value theory for anomaly detection – the GPD classifier","year":2020,"lang":"en","type":"article","venue":"Extremes","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Université de Genève; University of Toronto; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Extreme value theory; Univariate; Classifier (UML); Anomaly detection; Intuition; Mathematics; Artificial intelligence; Extreme learning machine; Machine learning; Pattern recognition (psychology); Computer science; Algorithm; Multivariate statistics; Data mining; Statistics","score_opus":0.057156399253130964,"score_gpt":0.2509169956157298,"score_spread":0.19376059636259882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3086241558","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":[0.0011049638,0.00016740654,0.98733866,0.00788064,0.000111929505,0.0004015799,0.0000029817202,0.0005005312,0.0024912786],"genre_scores_gemma":[0.9728156,0.000015903659,0.023451172,0.0021529105,0.0002700183,0.0002913727,0.0000011105316,0.000013696253,0.0009882707],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991346,0.00006124682,0.00017107278,0.00032962547,0.00012614664,0.00017730717],"domain_scores_gemma":[0.99922585,0.00015126154,0.00008373465,0.00040891973,0.00006037769,0.00006985101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002386778,0.00011144874,0.0000941438,0.000031647858,0.0003495897,0.00012246903,0.0006340138,0.000056203102,0.00002863982],"category_scores_gemma":[0.000057994097,0.00007960864,0.00011885162,0.00032704664,0.00005223083,0.00021449289,0.00010835477,0.00010645089,0.000041520936],"study_design_candidate":"theoretical_or_conceptual","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":[0.000031391475,0.000035385874,0.00006627494,0.00001260643,0.000026244963,9.403131e-7,0.00061938335,0.000048673304,0.039287534,0.56900835,0.0069916374,0.3838716],"study_design_scores_gemma":[0.000284682,0.0002921287,0.0015663053,0.0000069439866,0.000024855959,0.000013117178,0.00018722676,0.20785168,0.111689515,0.09055539,0.587208,0.00032017918],"about_ca_topic_score_codex":0.0000065983368,"about_ca_topic_score_gemma":0.0000036827848,"teacher_disagreement_score":0.97171056,"about_ca_system_score_codex":0.000020689926,"about_ca_system_score_gemma":0.000027016404,"threshold_uncertainty_score":0.32463458},"labels":[],"label_agreement":null},{"id":"W3086697721","doi":"10.48550/arxiv.1905.05137","title":"Analyzing Adversarial Attacks Against Deep Learning for Intrusion\\n Detection in IoT Networks","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","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":"Carleton University","funders":"","keywords":"Computer science; Intrusion detection system; Adversarial system; Artificial intelligence; Deep learning; Internet of Things; Machine learning; Artificial neural network; Computer security; Resilience (materials science); Robustness (evolution); SAFER; Data mining","score_opus":0.03224449575092127,"score_gpt":0.19928459149966266,"score_spread":0.1670400957487414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3086697721","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":[0.08702255,0.00003128127,0.91112715,0.000035306253,0.00041221382,0.00061440846,0.0000013463251,0.00034409086,0.00041166728],"genre_scores_gemma":[0.9950859,0.0002017428,0.0040740604,0.000050139468,0.00015541173,0.000011233969,0.000016694952,0.0000208527,0.0003839415],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982744,0.00010066394,0.0002569568,0.0009856375,0.00005191992,0.00033045217],"domain_scores_gemma":[0.99861515,0.00013393204,0.00032858967,0.00070133596,0.00013414316,0.0000868591],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032410267,0.0002485872,0.00029250348,0.00041599353,0.0002657344,0.00011305165,0.0009442245,0.0004423692,0.0000062518016],"category_scores_gemma":[0.00003182668,0.00031457932,0.00023833911,0.0008032611,0.000041322375,0.00019307654,0.000977764,0.00086866185,0.00001581763],"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":[0.000026762302,0.000027897426,0.0013122555,0.000021909616,0.000022614182,0.0000063893754,0.00005113602,0.9680345,0.00007082051,0.0063141915,0.000013590407,0.024097927],"study_design_scores_gemma":[0.00038174045,0.000059990234,0.0006580637,0.000047359496,0.000024514307,9.862209e-7,0.000039234383,0.9926377,0.00027060544,0.0038895183,0.0016585381,0.0003317617],"about_ca_topic_score_codex":0.000102890765,"about_ca_topic_score_gemma":0.00017482959,"teacher_disagreement_score":0.90806335,"about_ca_system_score_codex":0.00036829105,"about_ca_system_score_gemma":0.00007044104,"threshold_uncertainty_score":0.9999306},"labels":[],"label_agreement":null},{"id":"W3088587179","doi":"10.3390/bdcc4040024","title":"Multi-Level Clustering-Based Outlier’s Detection (MCOD) Using Self-Organizing Maps","year":2020,"lang":"en","type":"article","venue":"Big Data and Cognitive Computing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Toronto Metropolitan University","funders":"","keywords":"Anomaly detection; Outlier; Computer science; Data mining; Cluster analysis; Artificial intelligence; Profitability index; Pattern recognition (psychology); Revenue; Machine learning","score_opus":0.19951076187945738,"score_gpt":0.31314471665542093,"score_spread":0.11363395477596355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088587179","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":[0.016606864,0.0000734422,0.98200226,0.00022668393,0.00011548002,0.00024440844,0.000075140364,0.000611897,0.00004382104],"genre_scores_gemma":[0.8136934,0.0000045566003,0.1851603,0.0009054682,0.00018413461,0.000003512848,0.000031489177,0.000014791881,0.0000023634993],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986278,0.000062644605,0.0002463557,0.000693247,0.00013818633,0.00023176707],"domain_scores_gemma":[0.9991344,0.000098183285,0.0001521147,0.00034265008,0.0001342015,0.00013842578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020267969,0.00016939806,0.00015807661,0.00007562737,0.000506109,0.00025208166,0.00056430756,0.000066124885,0.0000014560125],"category_scores_gemma":[0.00009404738,0.0001828463,0.000029382836,0.00048223182,0.000037458398,0.00029557914,0.0012213996,0.00018256035,0.000012388343],"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":[0.0000074320797,0.000055560944,0.00048246395,0.000045903013,0.000027090236,0.000008005973,0.00042809272,0.000052807194,0.011620836,0.000040561416,0.000026929845,0.9872043],"study_design_scores_gemma":[0.0004354739,0.00005886408,0.0017251513,0.000056231653,0.00002975281,0.000022306705,0.00014526802,0.9860061,0.010433057,0.000020680267,0.0008299914,0.00023716292],"about_ca_topic_score_codex":0.000029018845,"about_ca_topic_score_gemma":0.000022383301,"teacher_disagreement_score":0.98696715,"about_ca_system_score_codex":0.000023316594,"about_ca_system_score_gemma":0.000057893267,"threshold_uncertainty_score":0.74562556},"labels":[],"label_agreement":null},{"id":"W3088878551","doi":"10.1109/access.2020.3025530","title":"Design and Development of AD-CGAN: Conditional Generative Adversarial Networks for Anomaly Detection","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":19,"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":"Anomaly detection; Computer science; Anomaly (physics); Artificial intelligence; Pattern recognition (psychology); Generative grammar; Data mining; Class (philosophy)","score_opus":0.06074284376028855,"score_gpt":0.2960437699990981,"score_spread":0.23530092623880955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088878551","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":[0.0057116784,0.00002546108,0.9934107,0.00024403055,0.000085552994,0.00041366095,0.000003189327,0.00009119811,0.000014507038],"genre_scores_gemma":[0.75134695,0.0000044048193,0.24815272,0.00023449666,0.0000781249,0.00017052515,0.0000033835076,0.000004236355,0.0000051499987],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99937993,0.000022064894,0.000185427,0.00023446664,0.0000825196,0.00009561428],"domain_scores_gemma":[0.9995781,0.00006440874,0.000110873945,0.00009179903,0.00009697351,0.000057813515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009386634,0.0000763976,0.00009891178,0.000034604076,0.00017495913,0.00007183834,0.00028190395,0.000050683626,0.000004974561],"category_scores_gemma":[0.0000073559554,0.00007634338,0.000023166811,0.00017584149,0.00002970404,0.0003355699,0.000066122746,0.000049813523,0.0000011656989],"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":[0.0004318557,0.00024890076,0.00027139924,0.00012720226,0.00027737647,0.0000040193027,0.0036431162,0.08393562,0.21892373,0.013650955,0.0057811565,0.6727047],"study_design_scores_gemma":[0.00030374393,0.00011550818,0.0005222739,0.0000032459147,0.0000057305756,0.0000023730668,0.000008153912,0.471973,0.52321005,0.0011238499,0.0026160646,0.000116025854],"about_ca_topic_score_codex":0.0000025819973,"about_ca_topic_score_gemma":0.0000024771975,"teacher_disagreement_score":0.7456353,"about_ca_system_score_codex":0.000018475157,"about_ca_system_score_gemma":0.00006260056,"threshold_uncertainty_score":0.31131926},"labels":[],"label_agreement":null},{"id":"W3089047966","doi":"10.1109/tits.2020.3022612","title":"A Two-Phase Anomaly Detection Model for Secure Intelligent Transportation Ride-Hailing Trajectories","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Trajectory; Outlier; Anomaly detection; Computer science; Feature (linguistics); Artificial intelligence; Data mining; Algorithm","score_opus":0.044114763316685394,"score_gpt":0.2958136131561321,"score_spread":0.2516988498394467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089047966","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":[0.017068306,0.00009871855,0.9774195,0.00048627492,0.00082595326,0.0022523992,0.0005412398,0.0012745132,0.000033085347],"genre_scores_gemma":[0.9821929,0.00010994572,0.015032433,0.0002860474,0.00014582394,0.0019066922,0.000089123794,0.000072533614,0.00016450086],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965044,0.00007098538,0.0013675855,0.0010462082,0.0005495889,0.00046119088],"domain_scores_gemma":[0.99811107,0.00014401086,0.00039249644,0.00051789964,0.00047671766,0.0003578348],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028457597,0.00050000846,0.0004941884,0.0003394271,0.00057542574,0.00023263514,0.0005836598,0.00024272155,0.000023982937],"category_scores_gemma":[0.000005291639,0.0005361062,0.00051899866,0.0010695081,0.00006756195,0.00080598425,5.402741e-7,0.00041490418,0.000039473955],"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":[0.0004630611,0.0005929362,0.000014384789,0.0003395846,0.00018904993,0.000006852826,0.011496121,0.8994512,0.02423935,0.0155619485,0.00011705605,0.047528453],"study_design_scores_gemma":[0.0007783201,0.0005715594,0.000009827666,0.000054883207,0.00010666556,0.000004331522,0.00078852754,0.7159887,0.27874786,0.00039249597,0.0020943817,0.00046241647],"about_ca_topic_score_codex":0.00025951475,"about_ca_topic_score_gemma":0.00055104267,"teacher_disagreement_score":0.9651246,"about_ca_system_score_codex":0.00016927927,"about_ca_system_score_gemma":0.00013343758,"threshold_uncertainty_score":0.99970907},"labels":[],"label_agreement":null},{"id":"W3091250492","doi":"10.1109/camad50429.2020.9209295","title":"Machine Learning-Driven Event Characterization under Scarce Vehicular Sensing Data","year":2020,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Event (particle physics); Baseline (sea); Deep learning; Encoder; Artificial intelligence; Artificial neural network; Recurrent neural network; Representation (politics); Feature (linguistics); Machine learning; Autoencoder; Pipeline (software); Data modeling; Encoding (memory); Real-time computing; Data mining","score_opus":0.038310644899255245,"score_gpt":0.263008347071157,"score_spread":0.22469770217190177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091250492","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":[0.003402741,0.000009017216,0.9856416,0.0098239165,0.000022185668,0.00012085984,0.000004390063,0.0006112896,0.0003639988],"genre_scores_gemma":[0.9481829,0.0000258462,0.04940863,0.0018846695,0.0000653598,0.000002640255,0.00013314596,0.00000860397,0.00028823692],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924374,0.000036624475,0.0001354382,0.0003529031,0.00012744365,0.00010385908],"domain_scores_gemma":[0.99933094,0.000011675114,0.00006465602,0.00047999222,0.000037376733,0.00007537345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007459616,0.00007117188,0.000075540025,0.0000255677,0.00014039912,0.00009785589,0.000555259,0.00003321153,0.000026255264],"category_scores_gemma":[0.000011817478,0.000069222224,0.000024460925,0.00029342243,0.000013240241,0.0003282999,0.00052970974,0.0001175222,0.00007378152],"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":[0.000008270464,0.00009582995,0.0010901727,0.00003350692,0.00005840157,0.00001339678,0.0005643092,0.0054216413,0.48835185,0.09953178,0.00081608887,0.40401477],"study_design_scores_gemma":[0.000051980358,0.00002848908,0.0010457528,0.0000031291913,0.0000035601347,0.000006644555,0.000006285859,0.94472325,0.007060978,0.0001493213,0.046830434,0.000090191825],"about_ca_topic_score_codex":0.000021197546,"about_ca_topic_score_gemma":0.0000018483577,"teacher_disagreement_score":0.9447801,"about_ca_system_score_codex":0.000013699585,"about_ca_system_score_gemma":0.00001871775,"threshold_uncertainty_score":0.28228003},"labels":[],"label_agreement":null},{"id":"W3091846787","doi":"10.1007/978-3-030-58526-6_46","title":"On Diverse Asynchronous Activity Anticipation","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Anticipation (artificial intelligence); Inference; Naturalness; Softmax function; Artificial intelligence; Asynchronous communication; Machine learning; Simple (philosophy); Artificial neural network","score_opus":0.021874736966227108,"score_gpt":0.2618794578580614,"score_spread":0.24000472089183428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091846787","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":[0.00021291686,0.000018610941,0.99246424,0.0011759498,0.00043794682,0.0003673306,0.00000489394,0.00037490393,0.0049431846],"genre_scores_gemma":[0.8330685,0.000017764052,0.16507947,0.0014461452,0.00023302133,0.000018659299,0.0000023131977,0.000020110281,0.000114025446],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99766314,0.000019118717,0.0002390586,0.0011848465,0.0005806716,0.0003131388],"domain_scores_gemma":[0.99836606,0.00019500988,0.00022168268,0.0009675221,0.000109674176,0.00014006205],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023485739,0.00032610117,0.00029689976,0.0004008664,0.00029225447,0.00028536975,0.0017369854,0.00021371931,0.000024000805],"category_scores_gemma":[0.000040937164,0.00031385737,0.00010602822,0.0005103185,0.00029414537,0.0003758833,0.0007530586,0.00061975553,0.00013792665],"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":[0.0000049114237,0.00003280672,0.000008035339,0.000011657356,0.0000047173958,0.0000225123,0.00014229966,0.0049575334,0.00030004824,0.11323149,0.000037110232,0.88124686],"study_design_scores_gemma":[0.00018487313,0.00055643514,0.00034356624,0.0001412132,0.000008657589,0.000021892563,6.745195e-8,0.6913843,0.01197968,0.29307368,0.0016009591,0.00070469],"about_ca_topic_score_codex":0.000019768147,"about_ca_topic_score_gemma":0.000016650683,"teacher_disagreement_score":0.8805422,"about_ca_system_score_codex":0.0002791844,"about_ca_system_score_gemma":0.00023512899,"threshold_uncertainty_score":0.99993134},"labels":[],"label_agreement":null},{"id":"W3093752029","doi":"10.1007/s10994-022-06300-x","title":"Early anomaly detection in time series: a hierarchical approach for predicting critical health episodes","year":2023,"lang":"en","type":"article","venue":"Machine Learning","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Leverage (statistics); Computer science; Anomaly detection; Key (lock); Machine learning; Artificial intelligence; Data mining; Time series; Series (stratigraphy); Event (particle physics); Computer security","score_opus":0.014507948139910411,"score_gpt":0.2850247771733264,"score_spread":0.270516829033416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093752029","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":[0.05737827,0.000025996034,0.9387802,0.0017996884,0.000030345469,0.0003464387,0.000003819785,0.0013137117,0.00032155446],"genre_scores_gemma":[0.89148706,0.00000830816,0.10739201,0.000098714794,0.00007559325,0.00033659063,0.000014306579,0.00001949832,0.0005679406],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986002,0.0001424875,0.0002859561,0.00042174303,0.00016972655,0.00037988467],"domain_scores_gemma":[0.9993667,0.00022285763,0.00006959333,0.00021260498,0.00003346403,0.00009480425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088762975,0.0001181901,0.00017729752,0.00025617488,0.00046204735,0.00014568186,0.00033777647,0.0000733837,0.000004626888],"category_scores_gemma":[0.0002478633,0.00012197031,0.00006842593,0.0009196476,0.0000423028,0.0002880165,0.00017546973,0.0003818049,0.000020316344],"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":[0.0001856261,0.00053240574,0.11512301,0.00058238703,0.00005362944,0.000019237199,0.007098157,0.014680381,0.011715796,0.075211324,0.0004708263,0.7743272],"study_design_scores_gemma":[0.0001831957,0.00041767134,0.015687753,0.000016823255,0.0000017527881,0.000019624453,0.000030824373,0.97836995,0.0005104045,0.0024136477,0.0022104986,0.00013788012],"about_ca_topic_score_codex":0.00019987668,"about_ca_topic_score_gemma":0.0000164928,"teacher_disagreement_score":0.96368957,"about_ca_system_score_codex":0.00006147243,"about_ca_system_score_gemma":0.000032658878,"threshold_uncertainty_score":0.4973805},"labels":[],"label_agreement":null},{"id":"W3093789645","doi":"10.1109/icse-seip52600.2021.00024","title":"Anomaly Detection in a Large-Scale Cloud Platform","year":2021,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"IBM (Canada); Toronto Metropolitan University","funders":"","keywords":"Cloud computing; DevOps; Anomaly detection; IBM; Deep learning; Popularity; Service (business); Quality (philosophy)","score_opus":0.011015981610005275,"score_gpt":0.24056560352937778,"score_spread":0.2295496219193725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093789645","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":[0.08290579,0.000021894879,0.90274733,0.00035931283,0.000084889325,0.000074433374,7.4673585e-7,0.00030906673,0.01349656],"genre_scores_gemma":[0.944112,0.000012804381,0.05384384,0.00032769798,0.000039393806,0.000048984475,0.0000010849421,0.0000041640105,0.0016100131],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99932206,0.000011754006,0.0001519407,0.00026006982,0.00008799598,0.0001661861],"domain_scores_gemma":[0.99950814,0.000015883246,0.00002934946,0.0003591743,0.000045193407,0.00004224138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000117526026,0.00006262872,0.000075328855,0.00007031201,0.00008580332,0.000068455025,0.00020650798,0.00005682939,0.00007256058],"category_scores_gemma":[0.0000064903866,0.000062325635,0.00004469446,0.0006955521,0.000008274182,0.00025915095,0.00013352883,0.00009915881,0.00006658558],"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":[0.0000135582595,0.0009271094,0.008652543,0.000030865034,0.000021837068,0.0000810585,0.0014149157,0.00013603935,0.046283852,0.43470907,0.002145446,0.5055837],"study_design_scores_gemma":[0.00043356346,0.000075921635,0.01660531,0.000009880665,0.0000028198504,0.00011298758,0.00025937904,0.1399069,0.7375924,0.018761689,0.085933946,0.00030519412],"about_ca_topic_score_codex":0.000043578133,"about_ca_topic_score_gemma":0.0009397081,"teacher_disagreement_score":0.86120623,"about_ca_system_score_codex":0.00004326957,"about_ca_system_score_gemma":0.000033231816,"threshold_uncertainty_score":0.25415656},"labels":[],"label_agreement":null},{"id":"W3093823951","doi":"10.1016/j.patcog.2023.109960","title":"Graph fairing convolutional networks for anomaly detection","year":2023,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":false,"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":"Discriminative model; Computer science; Graph; Theoretical computer science; Anomaly detection; Convolution (computer science); Node (physics); Benchmark (surveying); Convolutional neural network; Algorithm; Pattern recognition (psychology); Artificial intelligence; Artificial neural network","score_opus":0.03351837487831204,"score_gpt":0.25610090580314115,"score_spread":0.2225825309248291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093823951","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":[0.03415153,0.000011068844,0.9636706,0.00028388534,0.00026313105,0.0003468485,0.0000170829,0.0010980621,0.00015775613],"genre_scores_gemma":[0.9930285,0.00002870451,0.0056237536,0.0001995357,0.00019813616,0.00074901053,0.00009094514,0.00001219707,0.000069173846],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991785,0.000022225851,0.00017838387,0.0003024843,0.00010264759,0.0002157257],"domain_scores_gemma":[0.9994978,0.00007822606,0.00008341078,0.0001811163,0.00011098032,0.00004848586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020460573,0.00009223999,0.00007792224,0.00018627437,0.00026950313,0.00008260929,0.00018247071,0.00007442588,0.000013743805],"category_scores_gemma":[0.000012289752,0.00010220421,0.00009458766,0.0005585878,0.000019170599,0.0002696926,0.00005639659,0.00008224732,0.00015418426],"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":[0.0000039324354,0.000020701862,0.000504178,0.000013128796,0.000011539887,8.027566e-7,0.000026066165,0.00016481365,0.0020473578,0.0003447814,0.00070506724,0.99615765],"study_design_scores_gemma":[0.00044875318,0.00017213797,0.024893532,0.000034663557,0.000016609436,0.000028643712,0.00003020169,0.90691173,0.02739485,0.035775978,0.003913822,0.00037905516],"about_ca_topic_score_codex":0.00002494497,"about_ca_topic_score_gemma":0.000020048397,"teacher_disagreement_score":0.99577856,"about_ca_system_score_codex":0.000033925644,"about_ca_system_score_gemma":0.0000094241095,"threshold_uncertainty_score":0.4167767},"labels":[],"label_agreement":null},{"id":"W3093926104","doi":"10.1145/3467981","title":"A Federated Learning Approach to Anomaly Detection in Smart Buildings","year":2021,"lang":"en","type":"preprint","venue":"ACM Transactions on Internet of Things","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Anomaly detection; Baseline (sea); Task (project management); Internet of Things; Machine learning; Convergence (economics); Building automation; Efficient energy use; Artificial intelligence; Federated learning; Computer security; Systems engineering; Engineering","score_opus":0.017958993568189027,"score_gpt":0.2524868078945012,"score_spread":0.23452781432631215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093926104","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":[0.114628114,0.000021410648,0.88264096,0.00040546007,0.00024927926,0.00045963688,0.0000018365671,0.0004285809,0.0011647248],"genre_scores_gemma":[0.8132674,0.00002175186,0.18546133,0.00022713306,0.000012989671,0.0002916811,0.000006419515,0.000026509928,0.0006847538],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977168,0.00011753617,0.00061692076,0.00096722256,0.00030638106,0.00027515253],"domain_scores_gemma":[0.9985146,0.00008078772,0.00027667976,0.00081629027,0.00020516623,0.000106509964],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037189442,0.00031775623,0.0004268621,0.0007153555,0.000144872,0.00036863313,0.0013515927,0.00037128493,0.000023800118],"category_scores_gemma":[0.0000615731,0.00036306848,0.00026374042,0.00089190237,0.000041443287,0.00037650825,0.00024800395,0.0014907519,0.000011610812],"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":[0.00017927296,0.0022534998,0.00024525006,0.0006012543,0.00040086528,0.000018009823,0.018296804,0.04886647,0.041077644,0.0018593997,0.00017555701,0.88602597],"study_design_scores_gemma":[0.000483218,0.00062606647,0.00083956576,0.00083808263,0.000058015466,0.00008081522,0.000607856,0.38520247,0.6053608,0.001953443,0.0028944702,0.0010551919],"about_ca_topic_score_codex":0.002872562,"about_ca_topic_score_gemma":0.000096863696,"teacher_disagreement_score":0.8849708,"about_ca_system_score_codex":0.00024397793,"about_ca_system_score_gemma":0.00008616196,"threshold_uncertainty_score":0.9998821},"labels":[],"label_agreement":null},{"id":"W3094528781","doi":"10.1109/mlsp49062.2020.9231832","title":"Fixing Bias in Reconstruction-Based Anomaly Detection with Lipschitz Discriminators","year":2020,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Anomaly detection; Lipschitz continuity; Anomaly (physics); Computer science; Artificial intelligence; Computer vision; Pattern recognition (psychology); Mathematics; Physics","score_opus":0.03132567353545581,"score_gpt":0.2285699888498959,"score_spread":0.1972443153144401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094528781","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":[0.20733553,0.0000037667166,0.78861976,0.0014403248,0.000028779303,0.00014699285,4.4203696e-7,0.000448448,0.0019759722],"genre_scores_gemma":[0.9214185,0.0000014187324,0.07792303,0.00052831037,0.000026385469,0.000060955645,4.8046996e-7,0.0000071880754,0.000033767716],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999195,0.000029209465,0.00018218442,0.00034057914,0.00011046782,0.00014259727],"domain_scores_gemma":[0.9995404,0.000037084992,0.00007102746,0.00022826513,0.000036373425,0.0000868406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000078537,0.00010198108,0.000100230274,0.00011638359,0.00010701884,0.00008691649,0.00025259645,0.00004583405,0.000026704402],"category_scores_gemma":[0.000016566943,0.00008499373,0.0000372723,0.0008810066,0.00003349259,0.00030951694,0.000039273647,0.00013551782,0.000018683935],"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":[0.00006620293,0.00014857204,0.062438246,0.00005713749,0.000020640447,0.000018983897,0.0007164489,0.001937719,0.014575985,0.028611641,0.00015661234,0.8912518],"study_design_scores_gemma":[0.0006425781,0.000570039,0.017904522,0.00003970918,0.000009044269,0.000051129922,0.00027035823,0.6722996,0.30347443,0.0012087991,0.0030227343,0.00050705223],"about_ca_topic_score_codex":0.00013361021,"about_ca_topic_score_gemma":0.0001752553,"teacher_disagreement_score":0.89074475,"about_ca_system_score_codex":0.0000376535,"about_ca_system_score_gemma":0.00004727849,"threshold_uncertainty_score":0.34659436},"labels":[],"label_agreement":null},{"id":"W3094726226","doi":"10.3390/app10217833","title":"Detection and Identification of Malicious Cyber-Attacks in Connected and Automated Vehicles’ Real-Time Sensors","year":2020,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Software deployment; Identification (biology); Real-time computing; Anomaly detection; Intelligent transportation system; Automation; Computer security; Data mining; Discrete wavelet transform; Wavelet; Artificial intelligence; Wavelet transform; Engineering; Transport engineering","score_opus":0.01286938115014578,"score_gpt":0.25064969379129104,"score_spread":0.23778031264114527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094726226","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":[0.9706929,0.000016441392,0.027192457,0.000703526,0.000010967001,0.00024520315,0.0000016841866,0.0004530973,0.00068374566],"genre_scores_gemma":[0.9953138,0.00003748316,0.004517632,0.00008069282,0.000007382355,0.0000308413,5.039191e-7,0.0000032290345,0.000008417468],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908245,0.00002769201,0.00025577223,0.00037090207,0.00014258506,0.000120577715],"domain_scores_gemma":[0.9995749,0.000063694955,0.00014054247,0.00013150257,0.000031458487,0.000057882145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000300973,0.00007648013,0.000119288634,0.00010900581,0.00014899847,0.000085412306,0.00022508856,0.00005059429,0.0000020706852],"category_scores_gemma":[0.000018836772,0.0000733299,0.000011482597,0.00096470857,0.00021343863,0.00017275843,0.00008874959,0.000056059675,0.000007115636],"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":[0.000003986301,0.000014115661,0.00037963595,0.000009814105,0.0000021024443,4.4327695e-7,0.00057743385,0.00009890245,0.97686887,0.0121986745,0.00003415482,0.009811894],"study_design_scores_gemma":[0.00019710652,0.0001050375,0.036737602,0.000006406641,0.0000043966184,0.000008706938,0.000175839,0.56315845,0.3973324,0.0020289149,0.000091404254,0.00015373512],"about_ca_topic_score_codex":0.00007284611,"about_ca_topic_score_gemma":0.000008460417,"teacher_disagreement_score":0.57953644,"about_ca_system_score_codex":0.000010523847,"about_ca_system_score_gemma":0.000018415363,"threshold_uncertainty_score":0.29903066},"labels":[],"label_agreement":null},{"id":"W3096190105","doi":"10.1007/978-3-030-61609-0_38","title":"Unsupervised Anomaly Detection with a GAN Augmented Autoencoder","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Autoencoder; Computer science; Anomaly detection; Artificial intelligence; Anomaly (physics); Pattern recognition (psychology); Artificial neural network; Physics; Condensed matter physics","score_opus":0.012412822279327683,"score_gpt":0.22099166406703585,"score_spread":0.20857884178770816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096190105","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":[0.00007130079,0.00007261739,0.9933166,0.0011122199,0.00025968582,0.0006030921,0.0000041416124,0.000764371,0.0037959905],"genre_scores_gemma":[0.53189754,0.00002070777,0.46558335,0.0018047827,0.0002587321,0.000062859785,0.0000045400566,0.000048593665,0.00031889096],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996711,0.00002419453,0.00043172672,0.0016198875,0.00074636756,0.00046683563],"domain_scores_gemma":[0.99795264,0.000106500054,0.00026136558,0.0012211179,0.00023422933,0.00022415425],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026911637,0.00049950177,0.00042101918,0.00058934116,0.00036846948,0.00047090356,0.0021710994,0.00026393135,0.000025463498],"category_scores_gemma":[0.000018137027,0.00043215023,0.00011634795,0.0011633666,0.00044115668,0.0005238883,0.00051627244,0.000748373,0.000055566004],"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":[0.000023966146,0.000056909863,0.00005155764,0.00004820252,0.000030648604,0.000092406975,0.00050771644,0.012849431,0.0030169238,0.016827647,0.000025861653,0.96646875],"study_design_scores_gemma":[0.0003466364,0.00067552674,0.0002210886,0.00017789146,0.000016652339,0.00014815602,2.9518486e-7,0.9302107,0.021020416,0.041682813,0.00465812,0.0008416824],"about_ca_topic_score_codex":0.00003751129,"about_ca_topic_score_gemma":0.0001235897,"teacher_disagreement_score":0.9656271,"about_ca_system_score_codex":0.0002899569,"about_ca_system_score_gemma":0.00039262828,"threshold_uncertainty_score":0.999813},"labels":[],"label_agreement":null},{"id":"W3096492506","doi":"","title":"Proportional hazards model under progressive Type-II censoring","year":2009,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Censoring (clinical trials); Proportional hazards model; Statistics; Mathematics; Econometrics","score_opus":0.017993630715250848,"score_gpt":0.25659387994727106,"score_spread":0.23860024923202022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096492506","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":[0.011733135,0.00044670692,0.94748783,0.012890962,0.00011867716,0.0006213014,0.000019449506,0.0009859524,0.025695981],"genre_scores_gemma":[0.5627739,0.00016444751,0.4296212,0.00021546472,0.000034429748,0.00018830287,0.00012447519,0.000030908333,0.0068468614],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99655116,0.0009031229,0.0005540184,0.0010464835,0.0005567604,0.00038845342],"domain_scores_gemma":[0.9939321,0.00019893775,0.0006076254,0.0025038328,0.0025616155,0.00019588797],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0020370951,0.00035652783,0.00031899288,0.00021379403,0.0007295035,0.0005143321,0.002136094,0.00033941687,0.000052720312],"category_scores_gemma":[0.000270057,0.00037401766,0.00021519775,0.0005515043,0.00016387423,0.00027065555,0.0022891145,0.0007463437,0.000028654798],"study_design_candidate":"theoretical_or_conceptual","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":[0.000004728804,0.0005942195,0.0000659986,0.000051573938,0.000046650413,0.000003923808,0.0014876929,0.003017978,0.0014643985,0.9093376,0.0018711698,0.082054056],"study_design_scores_gemma":[0.00027845067,0.0000018244458,0.0013745984,0.00082976854,0.00003490249,0.000034854627,0.00002272462,0.7600219,0.050086312,0.17980812,0.006725615,0.0007808744],"about_ca_topic_score_codex":0.00011048051,"about_ca_topic_score_gemma":0.000047933132,"teacher_disagreement_score":0.75700396,"about_ca_system_score_codex":0.00018838808,"about_ca_system_score_gemma":0.0006791283,"threshold_uncertainty_score":0.9998712},"labels":[],"label_agreement":null},{"id":"W3102100346","doi":"10.1016/j.inffus.2021.05.008","title":"A review of uncertainty quantification in deep learning: Techniques, applications and challenges","year":2021,"lang":"en","type":"review","venue":"Information Fusion","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2453,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; University of Waterloo","funders":"Australian Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Variety (cybernetics); Artificial intelligence; Field (mathematics); Deep learning; Machine learning; Reinforcement learning; Uncertainty quantification; Data science; Image processing; Image (mathematics)","score_opus":0.04813692263496225,"score_gpt":0.3210454838638859,"score_spread":0.2729085612289237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3102100346","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":[1.853547e-8,0.7968782,0.20034197,0.00019444652,0.000014443916,0.0011804504,0.0000044369617,0.00016514404,0.0012208635],"genre_scores_gemma":[0.0000022927918,0.9882359,0.009826958,0.00008618438,0.000014423139,0.0015919486,0.00021925212,0.00000779219,0.000015270662],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982324,0.00014100829,0.0010506948,0.0002644795,0.00018787934,0.00012354145],"domain_scores_gemma":[0.9980669,0.00010278509,0.00091610296,0.0006164937,0.00025054198,0.000047158068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006561139,0.00020260942,0.00069068855,0.00040527617,0.00009228644,0.00005360017,0.00044385108,0.00022560028,0.000009499238],"category_scores_gemma":[0.00007207475,0.00018517497,0.00014323808,0.0010030867,0.00003534815,0.0005597032,0.0002448105,0.00029799895,0.000019828005],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.3144667e-7,0.000016921915,9.89252e-8,0.03199238,0.0000035236435,7.483104e-8,0.000047303034,7.7537055e-7,3.5676993e-7,0.02512725,0.000054663935,0.94275653],"study_design_scores_gemma":[0.000025968016,0.00002140894,0.000001707128,0.02766418,0.000026360647,0.000023121112,0.00002121478,0.0004159667,0.000010356087,0.00013325065,0.97149324,0.00016319827],"about_ca_topic_score_codex":0.00001618304,"about_ca_topic_score_gemma":0.0000039173638,"teacher_disagreement_score":0.9714386,"about_ca_system_score_codex":0.000083524894,"about_ca_system_score_gemma":0.00011964368,"threshold_uncertainty_score":0.7551216},"labels":[],"label_agreement":null},{"id":"W3103821869","doi":"10.36001/phmconf.2020.v12i1.1136","title":"Automatic detection of rare observations during production tests using statistical models","year":2020,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Safran Electronics (Canada)","funders":"CHIST-ERA; Safran Aircraft Engines; Safran; Association Nationale de la Recherche et de la Technologie","keywords":"Interpretability; Computer science; Cluster analysis; Context (archaeology); Machine learning; Anomaly detection; Representation (politics); Artificial intelligence; Data mining; Feature (linguistics); Variable (mathematics); Pattern recognition (psychology); Mathematics","score_opus":0.0841597462602211,"score_gpt":0.27522073547337605,"score_spread":0.19106098921315495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3103821869","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":[0.41936347,0.0000056770796,0.5794841,0.0008389674,0.000031599742,0.00015671008,0.000017427103,0.00008515426,0.000016864475],"genre_scores_gemma":[0.94021785,0.0000070826454,0.05965501,0.000054340126,0.000026212725,0.000016262875,0.0000010170224,0.0000050643334,0.000017180115],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991952,0.000039596773,0.00025579406,0.0002095989,0.00019376707,0.000106038824],"domain_scores_gemma":[0.9990507,0.000030166106,0.00021085414,0.00030922866,0.00035769632,0.000041375326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008963896,0.000081313556,0.00012662342,0.000012606846,0.00019347071,0.000026414591,0.00041451052,0.00004994359,0.0000049476585],"category_scores_gemma":[0.00007447624,0.00006803143,0.000098582954,0.00045908304,0.00010015759,0.0004767791,0.00019174496,0.00012004583,7.259748e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009074,0.0001923334,0.00073484454,0.0004182497,0.00008863695,3.5325957e-7,0.02300578,0.007980322,0.8953275,0.052839804,0.00041729468,0.018985823],"study_design_scores_gemma":[0.00007359606,0.000043347794,0.007567657,0.000035069796,0.00001655345,0.000005192287,0.0005934785,0.8269634,0.15660313,0.007986203,0.000019042145,0.000093281415],"about_ca_topic_score_codex":0.000049835344,"about_ca_topic_score_gemma":0.000002887799,"teacher_disagreement_score":0.81898314,"about_ca_system_score_codex":0.000029763216,"about_ca_system_score_gemma":0.00008812726,"threshold_uncertainty_score":0.27742416},"labels":[],"label_agreement":null},{"id":"W3105826472","doi":"10.1007/978-3-030-68799-1_16","title":"Local Anomaly Detection in Videos Using Object-Centric Adversarial Learning","year":2021,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Artificial intelligence; Computer science; Discriminator; Adversarial system; Classifier (UML); Pattern recognition (psychology); Inference; Computer vision; Anomaly detection; Object (grammar); Object detection; Image (mathematics); Detector","score_opus":0.013895541371072883,"score_gpt":0.26036010055919495,"score_spread":0.24646455918812207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3105826472","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":[0.1280325,0.00016215566,0.86964405,0.00014063458,0.0012833962,0.00043036378,6.645445e-7,0.00029333855,0.000012921709],"genre_scores_gemma":[0.7234059,0.000019070818,0.27618778,0.0001586139,0.00017661713,0.000036067064,0.0000019837046,0.0000134008715,5.918973e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960275,0.00021489734,0.0006017292,0.0018149792,0.00064682844,0.000694025],"domain_scores_gemma":[0.9980556,0.00021118425,0.00031420193,0.0010626657,0.0002237526,0.00013260217],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009678869,0.00039175875,0.00044443647,0.001189936,0.00036434564,0.00089076534,0.0020901973,0.00040685688,0.0000067661995],"category_scores_gemma":[0.00014518356,0.00042609798,0.00015306138,0.0045189904,0.00030573033,0.00064633426,0.0031645494,0.0017387961,0.0000043279174],"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":[0.0000035546136,0.00005086649,0.00083969056,0.000020851185,0.0000030995059,0.000033297452,0.00045458303,0.59394497,0.0026801336,0.000039135957,2.1499999e-7,0.40192965],"study_design_scores_gemma":[0.00021084334,0.0000672688,0.0023532491,0.00015571155,0.000004944783,0.00006958546,0.0000030047377,0.94001305,0.05402982,0.002645202,0.00002831636,0.0004190266],"about_ca_topic_score_codex":0.0011821812,"about_ca_topic_score_gemma":0.0005524095,"teacher_disagreement_score":0.5953734,"about_ca_system_score_codex":0.0009928695,"about_ca_system_score_gemma":0.0009121909,"threshold_uncertainty_score":0.9998191},"labels":[],"label_agreement":null},{"id":"W3106636111","doi":"10.1109/ijcnn52387.2021.9533899","title":"Entropic Out-of-Distribution Detection","year":2021,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Université de Montréal; Concordia University","funders":"","keywords":"Softmax function; Hyperparameter; Outlier; Training set; Pattern recognition (psychology); Anomaly detection","score_opus":0.01182554384114572,"score_gpt":0.24284095281246912,"score_spread":0.2310154089713234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106636111","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":[0.0049823606,0.00001825641,0.99163383,0.00028359427,0.000111198366,0.00003912899,0.0000014617792,0.00020059885,0.00272957],"genre_scores_gemma":[0.9775335,0.000012712033,0.02167627,0.000048964164,0.000019991563,0.000013302452,0.0000038259227,0.0000014343418,0.00068999897],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996212,0.000013547881,0.00010368841,0.00012987046,0.000068673085,0.00006297819],"domain_scores_gemma":[0.9995825,0.000010992011,0.00003706935,0.00024931558,0.000098003635,0.000022117987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000036100406,0.000033750082,0.00004641869,0.000014753423,0.000050468163,0.000022836672,0.00011754201,0.000027983033,0.000037730762],"category_scores_gemma":[0.000007814429,0.000032788346,0.000038209346,0.00023151995,0.000010516103,0.00010107974,0.000057806123,0.00003540462,0.000028638235],"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":[0.0000010102294,0.000086948065,0.00013371946,0.0000078235835,0.000008922064,0.0000020322796,0.00006395704,0.000008502098,0.17563336,0.53058004,0.00093209086,0.29254162],"study_design_scores_gemma":[0.00004858441,0.000024972256,0.0012574092,0.0000018106037,0.000001954905,0.0000062640943,0.000013857111,0.006203281,0.9468882,0.005627657,0.03987822,0.0000477824],"about_ca_topic_score_codex":0.000009887519,"about_ca_topic_score_gemma":0.0000135098035,"teacher_disagreement_score":0.97255117,"about_ca_system_score_codex":0.000019723184,"about_ca_system_score_gemma":0.00002118081,"threshold_uncertainty_score":0.13370699},"labels":[],"label_agreement":null},{"id":"W3106791815","doi":"10.1109/dsaa49011.2020.00017","title":"Ensemble of Hierarchical Temporal Memory for Anomaly Detection","year":2020,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Research and Development; Science and Engineering Research Council","keywords":"Anomaly detection; Computer science; Univariate; Encoder; Anomaly (physics); Multivariate statistics; Artificial intelligence; Ensemble learning; Data mining; Pattern recognition (psychology); Machine learning","score_opus":0.024377575810313357,"score_gpt":0.25243615508237255,"score_spread":0.2280585792720592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106791815","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":[0.012774224,0.000006800751,0.9821138,0.0020992989,0.000025876227,0.00025879487,0.0000016166134,0.00029833973,0.0024212706],"genre_scores_gemma":[0.82351345,0.0000010912414,0.17583211,0.00038930174,0.000042048843,0.00006479429,6.922929e-7,0.000003899333,0.00015260924],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99945253,0.000011500055,0.00016451742,0.00020138062,0.00007738156,0.000092678885],"domain_scores_gemma":[0.9995991,0.000035782774,0.000056742974,0.00018466533,0.00005551076,0.00006819174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000070789596,0.000054284865,0.00009103813,0.00003464882,0.00006448234,0.000019596178,0.0002671784,0.000040492752,0.000010972766],"category_scores_gemma":[0.000016754402,0.00005093156,0.00007341337,0.00024789217,0.000023294702,0.00012267592,0.000067199166,0.0000521797,0.000007765876],"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":[0.000044753648,0.0001095319,0.00023088399,0.00006294123,0.000020559468,8.875801e-7,0.0003717264,0.00004429889,0.37880483,0.17773992,0.0029698892,0.43959975],"study_design_scores_gemma":[0.00017710621,0.0004060597,0.00031193745,0.0000016624325,0.000003551408,0.0000041064286,0.00001930192,0.11568556,0.86324006,0.007428771,0.012621521,0.00010035926],"about_ca_topic_score_codex":0.00002351309,"about_ca_topic_score_gemma":0.0000051453744,"teacher_disagreement_score":0.8107392,"about_ca_system_score_codex":0.000007824193,"about_ca_system_score_gemma":0.000023194601,"threshold_uncertainty_score":0.20769288},"labels":[],"label_agreement":null},{"id":"W3107019507","doi":"10.1002/asmb.2674","title":"Detecting systematic anomalies affecting systems when inputs are stationary time series","year":2022,"lang":"en","type":"article","venue":"Applied Stochastic Models in Business and Industry","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Series (stratigraphy); Anomaly (physics); Computer science; Anomaly detection; Autoregressive–moving-average model; Class (philosophy); Time series; Process (computing); Econometrics; Data mining; Mathematics; Artificial intelligence; Autoregressive model; Machine learning; Geology","score_opus":0.01754364299488242,"score_gpt":0.21368086327549218,"score_spread":0.19613722028060976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107019507","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":[0.06737337,0.00018734927,0.930252,0.00026161375,0.00010967212,0.0009211642,0.0000108043605,0.0002945315,0.0005894586],"genre_scores_gemma":[0.99368715,0.0000020821728,0.0043073012,0.00008629773,0.000047035683,0.0016410459,0.0000052828555,0.00002173682,0.00020206369],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841666,0.000075654796,0.00045054633,0.00047640622,0.00030511798,0.0002755907],"domain_scores_gemma":[0.99897397,0.0001565028,0.00033116483,0.00039143863,0.00008534015,0.00006160617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048726625,0.00020913254,0.0003622922,0.00024806833,0.0006810383,0.00019808639,0.00041506742,0.00014434395,0.000010699305],"category_scores_gemma":[0.000027275237,0.0002166626,0.000023596303,0.0007614024,0.000049797512,0.00046398482,0.0005506665,0.0005107059,0.000002965434],"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":[0.00002919876,0.00014254113,0.00010009486,0.002832432,0.000041892737,0.000019847059,0.0025806401,0.756137,0.00089215575,0.23494159,0.00015396943,0.002128601],"study_design_scores_gemma":[0.00051735115,0.00006654355,0.00057343044,0.0007772758,0.000027447477,0.0002742401,0.0035357776,0.96378845,0.00010143876,0.029665,0.000032943808,0.0006401046],"about_ca_topic_score_codex":0.00006376835,"about_ca_topic_score_gemma":0.0000031372176,"teacher_disagreement_score":0.92631376,"about_ca_system_score_codex":0.00011944909,"about_ca_system_score_gemma":0.000057509107,"threshold_uncertainty_score":0.8835245},"labels":[],"label_agreement":null},{"id":"W3108316614","doi":"10.3390/e22121363","title":"Functional Kernel Density Estimation: Point and Fourier Approaches to Time Series Anomaly Detection","year":2020,"lang":"en","type":"article","venue":"Entropy","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; International Air Transport Association","funders":"","keywords":"Reproducing kernel Hilbert space; Kernel density estimation; Mathematics; Kernel embedding of distributions; Series (stratigraphy); Anomaly detection; Kernel (algebra); Probability density function; Variable kernel density estimation; Kernel principal component analysis; Multivariate kernel density estimation; Kernel method; Probability distribution; Hilbert space; Algorithm; Computer science; Statistics; Artificial intelligence; Mathematical analysis; Support vector machine","score_opus":0.036205894001502766,"score_gpt":0.2009190637946315,"score_spread":0.16471316979312875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108316614","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":[0.0345208,0.000012976501,0.95332533,0.011289445,0.000034487148,0.00019095707,0.0000017217109,0.00036044593,0.00026383172],"genre_scores_gemma":[0.8101528,0.000001703933,0.18868601,0.0006776946,0.00011188662,0.000054285534,0.0000023680707,0.0000063566135,0.0003068586],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999288,0.000022064423,0.00012691412,0.0003143965,0.00013208856,0.00011653618],"domain_scores_gemma":[0.9995747,0.000015953257,0.000046316694,0.00019186483,0.00003259311,0.00013856433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007210665,0.00009470797,0.0000923512,0.00003928849,0.00019742432,0.00011951532,0.00013312745,0.000040355506,0.00004273733],"category_scores_gemma":[0.000024644645,0.000093739385,0.000034749566,0.00026217126,0.000029074352,0.00036073732,0.0001397137,0.00007725769,0.00021089529],"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":[0.00024681643,0.0002032293,0.0020207427,0.000067396875,0.0001145446,0.000017131395,0.0027711987,0.002982453,0.088344276,0.5810963,0.01871296,0.303423],"study_design_scores_gemma":[0.00036195444,0.0005192371,0.034691386,0.000007644891,0.000021284784,0.0001266272,0.000053355743,0.77319676,0.1576076,0.016314903,0.016639136,0.00046009265],"about_ca_topic_score_codex":0.0000071926484,"about_ca_topic_score_gemma":0.0000016632897,"teacher_disagreement_score":0.775632,"about_ca_system_score_codex":0.00002734638,"about_ca_system_score_gemma":0.000016356862,"threshold_uncertainty_score":0.38225812},"labels":[],"label_agreement":null},{"id":"W3111000109","doi":"10.1109/smc42975.2020.9282831","title":"Interactive Machine Learning for Data Exfiltration Detection: Active Learning with Human Expertise","year":2020,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Anomaly detection; Machine learning; Salient; Artificial intelligence; Exploit; Process (computing); Domain (mathematical analysis); Computer security","score_opus":0.05264542404194708,"score_gpt":0.3020139036644195,"score_spread":0.2493684796224724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111000109","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":[0.0028691182,0.00001094903,0.9933766,0.0011480502,0.000017050133,0.00034810838,0.0000021327744,0.0007508643,0.0014771426],"genre_scores_gemma":[0.9536849,0.0000054990974,0.04539522,0.00024478624,0.00008300085,0.00016300468,0.000041249066,0.000013124009,0.00036916218],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907374,0.00004265677,0.00014673393,0.0004933333,0.000111321446,0.0001322139],"domain_scores_gemma":[0.99933004,0.000059949307,0.00012476182,0.00031687433,0.00009159107,0.00007677313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000079091726,0.00011304322,0.0001069966,0.00004402157,0.0004927735,0.00014400754,0.0005158785,0.00003849295,0.000034576657],"category_scores_gemma":[0.00004749703,0.0000966897,0.000029374274,0.00027859205,0.00002287701,0.0009832125,0.00021236083,0.00025411329,0.000010914719],"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":[0.00019825422,0.00011563375,0.0003570435,0.00002640318,0.00009479152,0.000003607727,0.004647396,0.0011389958,0.14170323,0.010426784,0.00040699053,0.8408809],"study_design_scores_gemma":[0.00030305705,0.00083331735,0.00014256702,0.000007124999,0.00000911239,0.000011072992,0.00032483257,0.8114725,0.15726437,0.00015292614,0.029293338,0.00018579442],"about_ca_topic_score_codex":0.000077795245,"about_ca_topic_score_gemma":0.000056429104,"teacher_disagreement_score":0.95081586,"about_ca_system_score_codex":0.00003663126,"about_ca_system_score_gemma":0.000019518697,"threshold_uncertainty_score":0.39428917},"labels":[],"label_agreement":null},{"id":"W3111373706","doi":"10.3390/e23121690","title":"Perfect Density Models Cannot Guarantee Anomaly Detection","year":2021,"lang":"en","type":"article","venue":"Entropy","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Google (Canada)","funders":"","keywords":"Curse of dimensionality; Anomaly detection; Anomaly (physics); Computer science; Generative grammar; Estimation; Econometrics; Artificial intelligence; Machine learning; Mathematics; Economics; Physics","score_opus":0.01136590351285806,"score_gpt":0.21986220440424456,"score_spread":0.2084963008913865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111373706","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":[0.14913628,0.000078322286,0.8485527,0.00048204872,0.00012805931,0.000085855834,0.0000014244331,0.00035830095,0.0011769842],"genre_scores_gemma":[0.97487533,0.0000383734,0.024053665,0.0002587646,0.000077219345,0.00003599489,0.0000016288548,0.000007320463,0.0006516771],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99913,0.000047161764,0.00014105013,0.00034960097,0.00014654308,0.00018562918],"domain_scores_gemma":[0.9992389,0.000020022728,0.000051613424,0.00051781465,0.00012447158,0.000047140515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008616071,0.0000965922,0.000105612475,0.000050441166,0.00022314816,0.000106686864,0.00025796244,0.000056381454,0.000026841713],"category_scores_gemma":[0.00001180014,0.00009917151,0.00008862608,0.00038637253,0.000020736315,0.00028697768,0.00012521143,0.00012232759,0.000069364956],"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":[0.000013236065,0.000192015,0.00042061557,0.000012716016,0.00004215685,0.00007539482,0.00037454482,0.0009226902,0.27556375,0.61789423,0.0016754296,0.1028132],"study_design_scores_gemma":[0.00018887327,0.00006704054,0.0016003785,0.0000057467223,0.000009716999,0.00015142042,0.000020800704,0.26940167,0.6840423,0.034790695,0.009505933,0.00021544936],"about_ca_topic_score_codex":0.00010773414,"about_ca_topic_score_gemma":0.00006494219,"teacher_disagreement_score":0.8257391,"about_ca_system_score_codex":0.000067601024,"about_ca_system_score_gemma":0.000057321653,"threshold_uncertainty_score":0.4044097},"labels":[],"label_agreement":null},{"id":"W3111948375","doi":"10.1145/3432291.3432311","title":"Review on Machine learning and its application in Atmospheric science and Human Behavior Recognition","year":2020,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"McGill University","funders":"","keywords":"Machine learning; Artificial intelligence; Computer science; Field (mathematics)","score_opus":0.03163876775348806,"score_gpt":0.29775665078788593,"score_spread":0.2661178830343979,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111948375","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":[0.44944155,0.008315755,0.5129036,0.015246772,0.000029190369,0.0038785026,0.0000037246193,0.0015249032,0.008655989],"genre_scores_gemma":[0.98647547,0.0019218363,0.009484755,0.0018489141,0.000008490191,0.00022394824,0.0000017872751,0.0000037044822,0.00003107473],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937624,0.000015625252,0.0001182732,0.0003081821,0.00010235092,0.000079340025],"domain_scores_gemma":[0.99973434,0.000010408101,0.000048670994,0.00008516314,0.000055730714,0.00006568588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021635494,0.00005707447,0.00007127111,0.000014651599,0.00016726232,0.000043870466,0.00014216993,0.000017537826,0.0000091559605],"category_scores_gemma":[0.000028465129,0.000052562114,0.000007471523,0.0006847624,0.000033215176,0.00020610109,0.000096727934,0.0001001734,0.000013885266],"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":[0.0000014630664,0.000056296118,0.0021625918,0.00013390182,7.845207e-7,8.9285504e-7,0.0000793845,0.0000013416321,0.03148509,0.02412358,0.00008410894,0.94187057],"study_design_scores_gemma":[0.0015181534,0.0027553786,0.080593616,0.0013277754,0.00007018999,0.000117247626,0.00009198141,0.66611546,0.097279094,0.004995225,0.14318292,0.0019529366],"about_ca_topic_score_codex":0.000028161041,"about_ca_topic_score_gemma":0.000005580897,"teacher_disagreement_score":0.9399176,"about_ca_system_score_codex":0.000019062622,"about_ca_system_score_gemma":0.000011620608,"threshold_uncertainty_score":0.21434207},"labels":[],"label_agreement":null},{"id":"W3112155140","doi":"10.18280/ts.370501","title":"A Novel Spatio-Temporal Violence Classification Framework Based on Material Derivative and LSTM Neural Network","year":2020,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Confusion matrix; Artificial intelligence; Field (mathematics); Artificial neural network; Class (philosophy); Motion (physics); Machine learning; Action (physics); Confusion; Human behavior; Pattern recognition (psychology); Mathematics","score_opus":0.03103585091728862,"score_gpt":0.2483206109889262,"score_spread":0.21728476007163758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112155140","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":[0.050613306,0.000004707255,0.94366205,0.0049304543,0.00006211142,0.0003521097,0.00001438583,0.00027497156,0.000085907624],"genre_scores_gemma":[0.8842816,0.0000030345248,0.111727126,0.003572929,0.00026204003,0.00012314424,0.000019122772,0.000008885354,0.0000021302424],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988926,0.000042780004,0.0002462626,0.00041174944,0.0002163503,0.0001902647],"domain_scores_gemma":[0.999431,0.00006603464,0.0001384525,0.00019748052,0.000047960028,0.000119031756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011851906,0.0001488805,0.00012683972,0.000034460696,0.00019304361,0.00015638777,0.00031945185,0.00006614874,0.0000884837],"category_scores_gemma":[0.000010686948,0.00014262604,0.000040444032,0.00032716946,0.0000536121,0.0001805032,0.00006917538,0.00013651772,0.0000094674],"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":[0.0009474183,0.0010668946,0.014944624,0.00014818793,0.00008905773,0.000017254168,0.0024235803,0.034507114,0.06242039,0.56923157,0.0052813804,0.30892253],"study_design_scores_gemma":[0.0002876674,0.00046603274,0.017401427,0.000050158254,0.00000702989,0.0000019783763,0.000019106374,0.97292864,0.0045331013,0.002245058,0.0018469271,0.00021289787],"about_ca_topic_score_codex":0.000010822592,"about_ca_topic_score_gemma":0.0000013210109,"teacher_disagreement_score":0.9384215,"about_ca_system_score_codex":0.000026910346,"about_ca_system_score_gemma":0.00002895674,"threshold_uncertainty_score":0.5816121},"labels":[],"label_agreement":null},{"id":"W3112497262","doi":"10.1109/tnnls.2022.3218982","title":"Measuring Disentanglement: A Review of Metrics","year":2022,"lang":"en","type":"review","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Ubisoft (Canada)","funders":"","keywords":"Computer science; Measure (data warehouse); Representation (politics); Variation (astronomy); Artificial intelligence; Taxonomy (biology); Machine learning; Data science; Data mining","score_opus":0.06860897007677486,"score_gpt":0.2914445748819299,"score_spread":0.22283560480515502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112497262","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":[7.46396e-8,0.5352744,0.46372965,0.000010155143,0.00030530285,0.0004840534,0.0000041599487,0.00012264724,0.00006955561],"genre_scores_gemma":[0.0008464047,0.99780667,0.00020730673,0.00003638678,0.000060341605,0.0006509758,0.0000064841984,0.000032605887,0.00035283776],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99757826,0.0006017149,0.00076083356,0.00048629657,0.00034637479,0.00022652258],"domain_scores_gemma":[0.9983917,0.00039445522,0.00061321986,0.00047485792,0.000032213262,0.00009355112],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00065769337,0.0002991671,0.0010026882,0.00030488442,0.0005082988,0.00009172985,0.0005041219,0.0001131197,0.000026865468],"category_scores_gemma":[0.000007939559,0.00025828375,0.00042693593,0.00148228,0.000028813858,0.00011166586,0.000012406115,0.0011105294,0.0000021461133],"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":[5.773968e-7,0.000029316987,1.3873e-7,0.013083988,0.000051297236,0.00000238438,0.000008656588,0.007369855,3.656553e-8,0.000118774304,0.00008632829,0.97924864],"study_design_scores_gemma":[0.000043518685,0.00013386238,7.621269e-8,0.01395833,0.00021857684,0.00008958362,0.00000908389,0.08647475,3.652957e-7,0.0000010445601,0.89883935,0.00023147392],"about_ca_topic_score_codex":0.000033623764,"about_ca_topic_score_gemma":6.10897e-7,"teacher_disagreement_score":0.9790172,"about_ca_system_score_codex":0.00008747391,"about_ca_system_score_gemma":0.000033896093,"threshold_uncertainty_score":0.99998695},"labels":[],"label_agreement":null},{"id":"W3112882642","doi":"10.1109/smc42975.2020.9282938","title":"GWAD: Greedy Workflow Graph Anomaly Detection Framework for System Traces","year":2020,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"University of British Columbia","funders":"University of Waterloo","keywords":"Anomaly detection; Computer science; Workflow; TRACE (psycholinguistics); Data mining; Greedy algorithm; Graph; Event (particle physics); Distributed computing; Real-time computing; Theoretical computer science; Algorithm; Database","score_opus":0.022853206808513164,"score_gpt":0.24826780482236205,"score_spread":0.2254145980138489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112882642","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":[0.0019593535,0.000048067537,0.99211824,0.0022471761,0.0001312461,0.00049069413,0.0000029851824,0.0018287923,0.0011734464],"genre_scores_gemma":[0.6658445,0.0000038769936,0.33328986,0.00042603866,0.00013082089,0.00023232284,5.934684e-7,0.000008582119,0.00006343812],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900985,0.000020579391,0.00022708112,0.00041470796,0.00013149841,0.00019626654],"domain_scores_gemma":[0.9992699,0.000083464976,0.000095969765,0.00034459573,0.00007673798,0.00012934841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000110876186,0.00012536588,0.0001414245,0.00006632506,0.00026155682,0.00016212149,0.0005290402,0.00011653981,0.000010930371],"category_scores_gemma":[0.000021550675,0.00011454002,0.00013274071,0.0007290099,0.000022389666,0.00026883328,0.00006778022,0.00012382428,0.000040874605],"study_design_candidate":"theoretical_or_conceptual","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":[0.000018094644,0.00003841799,0.000136472,0.00007949617,0.000025416572,0.0000015756035,0.00024550295,0.00009356635,0.0028877014,0.805212,0.0010085207,0.19025323],"study_design_scores_gemma":[0.000644954,0.0013931415,0.0016520384,0.00009839228,0.00005798092,0.0000595067,0.0006255989,0.6048134,0.21708241,0.11063528,0.061778218,0.0011591087],"about_ca_topic_score_codex":0.000021588754,"about_ca_topic_score_gemma":0.000009527842,"teacher_disagreement_score":0.69457674,"about_ca_system_score_codex":0.000028743296,"about_ca_system_score_gemma":0.000016392944,"threshold_uncertainty_score":0.46708062},"labels":[],"label_agreement":null},{"id":"W3114976675","doi":"10.1109/isncc49221.2020.9297188","title":"Human Trait Analysis via Machine Learning Techniques for User Authentication","year":2020,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Machine learning; Hidden Markov model; Artificial intelligence; Identification (biology); Field (mathematics); Intrusion detection system; Data mining","score_opus":0.02094328384305394,"score_gpt":0.2803131589947183,"score_spread":0.2593698751516644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3114976675","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":[0.00085183093,0.000005496026,0.9934997,0.0031349296,0.000004042536,0.0002655187,0.0000024486967,0.0012707353,0.0009653308],"genre_scores_gemma":[0.7217194,0.0000012931375,0.2767489,0.0004503313,0.000026508202,0.00015760266,0.000012979685,0.000005762454,0.0008772719],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993241,0.000017660188,0.00017873429,0.00028624546,0.00008573532,0.000107551445],"domain_scores_gemma":[0.9995554,0.000017104603,0.00007803841,0.0002209879,0.00006302738,0.000065441876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011318837,0.00007720021,0.00011540768,0.00010436875,0.00021503917,0.00008199956,0.00037773873,0.000042796382,0.00007413374],"category_scores_gemma":[0.000009451038,0.00007092338,0.00013891913,0.00078486436,0.000015121804,0.0001628007,0.00006827102,0.00007797782,0.00000867619],"study_design_candidate":"theoretical_or_conceptual","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":[0.00000525399,0.00014259237,0.0016799489,0.000030942276,0.00028037705,5.514386e-7,0.0008486072,0.00010367321,0.20603454,0.6620053,0.0018471694,0.12702101],"study_design_scores_gemma":[0.00012295404,0.00029544122,0.002219145,0.0000021020378,0.00015550776,0.0000013882867,0.000018582172,0.6625313,0.22211824,0.008011232,0.104226895,0.00029721067],"about_ca_topic_score_codex":0.000041314197,"about_ca_topic_score_gemma":0.00001016293,"teacher_disagreement_score":0.7208675,"about_ca_system_score_codex":0.000013614994,"about_ca_system_score_gemma":0.000006028714,"threshold_uncertainty_score":0.28921714},"labels":[],"label_agreement":null},{"id":"W3115695223","doi":"","title":"Semi-supervised Anomaly Detection using AutoEncoders","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Anomaly detection; Artificial intelligence; Computer science; Segmentation; Pattern recognition (psychology); Anomaly (physics); Process (computing); Residual; Task (project management); Encoder; Computer vision; Engineering; Algorithm","score_opus":0.008693236621937717,"score_gpt":0.2616070934454122,"score_spread":0.2529138568234745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115695223","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":[0.20649415,0.00023437825,0.79232794,0.00024547748,0.000369471,0.00010782801,5.9528537e-7,0.000046224977,0.00017394553],"genre_scores_gemma":[0.95229745,0.000008456683,0.047446903,0.00011121897,0.00007407958,0.0000013816921,4.1418787e-7,0.000007227068,0.000052862913],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891585,0.00006601571,0.00044148485,0.00014237566,0.00032473882,0.00010954466],"domain_scores_gemma":[0.99899507,0.00008233131,0.0003790349,0.00012923584,0.00033279177,0.00008156372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039874972,0.0001002716,0.00018359222,0.00026073892,0.00013139815,0.00027545783,0.0002184196,0.000031309086,0.0000053895637],"category_scores_gemma":[0.000009407578,0.00008466223,0.000078489225,0.00026702433,0.000023581926,0.0007827556,0.000054704095,0.00012913621,0.000008514197],"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":[0.000060721384,0.00024384548,0.01727916,0.00021475299,0.00012897236,0.000026683038,0.0011949401,0.79006696,0.052092846,0.025535295,0.0012280128,0.11192782],"study_design_scores_gemma":[0.0003075237,0.00008859954,0.0053124786,0.00008059066,0.000006279158,0.0007454952,0.00012316075,0.98958236,0.00021656643,0.0016425995,0.0017958275,0.00009850163],"about_ca_topic_score_codex":0.000021597696,"about_ca_topic_score_gemma":1.6608135e-7,"teacher_disagreement_score":0.7458033,"about_ca_system_score_codex":0.00006229077,"about_ca_system_score_gemma":0.00006894091,"threshold_uncertainty_score":0.34524256},"labels":[],"label_agreement":null},{"id":"W3118300572","doi":"10.1016/j.knosys.2020.106733","title":"A new density-based subspace selection method using mutual information for high dimensional outlier detection","year":2021,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Subspace topology; Outlier; Anomaly detection; Local outlier factor; Curse of dimensionality; Redundancy (engineering); Computer science; Pattern recognition (psychology); Dimensionality reduction; Relevance (law); Data mining; Artificial intelligence; Selection (genetic algorithm)","score_opus":0.019845518038239664,"score_gpt":0.28487974477641514,"score_spread":0.2650342267381755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118300572","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":[0.0064801476,0.00007305444,0.99094075,0.00020956775,0.00088944635,0.00071978633,0.000009167937,0.0005802623,0.00009782051],"genre_scores_gemma":[0.6468641,2.9995246e-7,0.35215473,0.00010882883,0.00023395503,0.00018390518,0.000031347925,0.000016918153,0.00040587428],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983061,0.0002249338,0.00047254664,0.00043222518,0.0002694446,0.00029472777],"domain_scores_gemma":[0.997916,0.0002264605,0.00028084443,0.00044618713,0.00097479235,0.00015573013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058260927,0.00022032263,0.00026922018,0.00031043496,0.00050403073,0.00032558522,0.00023023254,0.00020231062,0.000009676264],"category_scores_gemma":[0.000080276965,0.00023250029,0.00015891381,0.0011554508,0.000016774145,0.00056928553,0.000052730324,0.00016394729,0.000055318167],"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":[0.00033921056,0.0007824368,0.0008013544,0.0011162058,0.00024902963,0.000008096095,0.0011254981,0.17160778,0.42558104,0.09492068,0.012631875,0.29083678],"study_design_scores_gemma":[0.00059146393,0.00009253052,0.0001048073,0.00004342169,0.000026122103,0.000029298562,0.000019271714,0.6993318,0.28791958,0.0002773914,0.011355638,0.00020867382],"about_ca_topic_score_codex":0.00029407468,"about_ca_topic_score_gemma":0.00017183418,"teacher_disagreement_score":0.640384,"about_ca_system_score_codex":0.00037535388,"about_ca_system_score_gemma":0.00088083616,"threshold_uncertainty_score":0.9481086},"labels":[],"label_agreement":null},{"id":"W3119320318","doi":"10.18280/ts.370609","title":"Multiple Linear Regression of Multi-class Images in Devices of Internet of Things","year":2020,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Government of Jiangsu Province","keywords":"Disjoint sets; Computer science; MNIST database; Convolutional neural network; Robustness (evolution); Artificial intelligence; Class (philosophy); Pattern recognition (psychology); Benchmark (surveying); Contextual image classification; Data mining; Artificial neural network; Machine learning; Image (mathematics); Mathematics","score_opus":0.029579010468508676,"score_gpt":0.26877901639544366,"score_spread":0.239200005926935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119320318","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":[0.18042919,0.000050916802,0.8187782,0.00040104924,0.000010501036,0.00018480707,0.0000055662763,0.000046574696,0.00009320029],"genre_scores_gemma":[0.9305571,0.000007936539,0.069298156,0.000097684,0.000008685053,0.000013165151,0.0000017638264,0.0000036865067,0.000011876285],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992002,0.000028710447,0.00037747587,0.00016686443,0.00015072405,0.00007597481],"domain_scores_gemma":[0.999474,0.000049677834,0.00024804642,0.00012540733,0.000069512695,0.00003334213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013551762,0.000073974035,0.00016731232,0.000066944696,0.000011559271,0.000006033901,0.0003961454,0.000032818458,0.000028754379],"category_scores_gemma":[0.000014333408,0.000060407,0.00005694886,0.0002420159,0.000045578698,0.00017774754,0.0001222256,0.000067038425,0.000001490426],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007435859,0.00069260225,0.01816627,0.00037262368,0.000032130225,0.0000029816638,0.0066492213,0.0006222868,0.9159967,0.004636193,0.0005627368,0.05219192],"study_design_scores_gemma":[0.00036388775,0.00016787356,0.0038020113,0.00007734641,0.0000030064866,3.9645616e-7,0.000067435045,0.35539487,0.63957137,0.000053226522,0.0004434245,0.00005516587],"about_ca_topic_score_codex":0.00010639844,"about_ca_topic_score_gemma":0.0000052212235,"teacher_disagreement_score":0.75012785,"about_ca_system_score_codex":0.000008284647,"about_ca_system_score_gemma":0.000016008751,"threshold_uncertainty_score":0.2463326},"labels":[],"label_agreement":null},{"id":"W3119618471","doi":"","title":"Behavioral model inference of black-box software using deep neural networks","year":2021,"lang":"en","type":"preprint","venue":"White Rose Research Online (University of Leeds, The University of Sheffield, University of York)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Black box; Inference; Process (computing); TRACE (psycholinguistics); Anomaly detection; Set (abstract data type); Software; Univariate; Artificial intelligence; Deep learning; Transfer of learning; Multivariate statistics; Machine learning; Artificial neural network; Time series; Data mining; Programming language","score_opus":0.08371361773198485,"score_gpt":0.3081357852991077,"score_spread":0.22442216756712283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119618471","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":[0.3189306,0.00013475155,0.6793947,0.0006363832,0.000041331987,0.0004062988,0.00014956399,0.00007842282,0.00022793496],"genre_scores_gemma":[0.8744816,0.000976869,0.12368682,0.000007803202,0.00001892547,2.3869374e-8,0.00007267512,0.0000141387245,0.0007411105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970057,0.00044736633,0.0002984029,0.0007961979,0.0009349938,0.0005173574],"domain_scores_gemma":[0.99519646,0.00030850276,0.0008024143,0.001661458,0.0017946074,0.00023656813],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000709003,0.0003243466,0.0008307711,0.0007576396,0.000837013,0.000032809276,0.0047475654,0.0006865438,0.00016068137],"category_scores_gemma":[0.000033934455,0.0004339398,0.00064455724,0.0014189505,0.0023300257,0.0005364822,0.00685582,0.0017676831,0.0000014940539],"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":[0.00063310075,0.0022809473,0.0039782478,0.0009690999,0.00060098193,0.00019637147,0.03397969,0.9163067,0.0023702292,0.0034004685,0.00094529043,0.034338888],"study_design_scores_gemma":[0.0007366701,0.00028174807,0.0015227641,0.0003626807,0.00022728219,0.0000060865323,0.030702906,0.96496636,0.00018112542,0.00033185116,0.0002833559,0.0003971819],"about_ca_topic_score_codex":0.0116104195,"about_ca_topic_score_gemma":0.003741521,"teacher_disagreement_score":0.5557079,"about_ca_system_score_codex":0.00025678534,"about_ca_system_score_gemma":0.00072611327,"threshold_uncertainty_score":0.99981123},"labels":[],"label_agreement":null},{"id":"W3119704657","doi":"10.1007/s10694-020-01055-0","title":"Hierarchical Temporal Memory Continuous Learning Algorithms for Fire State Determination","year":2021,"lang":"en","type":"article","venue":"Fire Technology","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Fire detection; Fire protection; Computer science; State (computer science); Engineering; Architectural engineering; Algorithm; Civil engineering","score_opus":0.013174743596133473,"score_gpt":0.2696740538817616,"score_spread":0.2564993102856281,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119704657","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":[0.052682467,0.00013510707,0.94030994,0.00468282,0.00009977612,0.00026726956,0.0000061933906,0.0016049844,0.0002114484],"genre_scores_gemma":[0.67492473,0.00003272047,0.32157576,0.00013362642,0.000042201635,0.00036638684,0.000018177772,0.0000151436525,0.0028912209],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895793,0.00003006589,0.00022772058,0.00042698788,0.00009918132,0.00025812074],"domain_scores_gemma":[0.9991806,0.000068756046,0.00011041064,0.00042619847,0.00017081643,0.00004320461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013099985,0.000115451185,0.00017467837,0.00010675426,0.00026309126,0.000058303627,0.00044699662,0.0001882742,0.000012921763],"category_scores_gemma":[0.00010594569,0.00012282401,0.00007230501,0.00058395945,0.00009124854,0.00013401758,0.0002454878,0.00029249903,0.000018380124],"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":[0.0000018123965,0.000038626244,0.00016958265,0.000008202257,0.0000058068317,0.000018567755,0.000056906792,0.0000068282884,0.0018296948,0.007864674,0.000418335,0.989581],"study_design_scores_gemma":[0.0007495101,0.00065086153,0.0005793961,0.000036649337,0.000014873142,0.00049238466,0.00016918585,0.5057947,0.16740735,0.10098153,0.2225643,0.0005592544],"about_ca_topic_score_codex":0.000009697054,"about_ca_topic_score_gemma":0.0000065696004,"teacher_disagreement_score":0.9890217,"about_ca_system_score_codex":0.000041845506,"about_ca_system_score_gemma":0.000072559575,"threshold_uncertainty_score":0.5008618},"labels":[],"label_agreement":null},{"id":"W3119935873","doi":"10.1007/s11227-020-03582-7","title":"SS-ITS: secure scalable intelligent transportation systems","year":2021,"lang":"en","type":"article","venue":"The Journal of Supercomputing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Computer science; Scalability; Intelligent transportation system; Reinforcement learning; Merge (version control); Distributed computing; Anomaly detection; Baseline (sea); Process (computing); Artificial intelligence; Database; Parallel computing; Operating system","score_opus":0.01773233525814767,"score_gpt":0.24458984945994575,"score_spread":0.2268575142017981,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119935873","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":[0.11435982,0.001291932,0.88280284,0.00084622897,0.00037794738,0.00006384661,0.0000010250446,0.00004949171,0.0002068889],"genre_scores_gemma":[0.98761135,0.00017193855,0.01176428,0.00011819271,0.00017199706,0.0000011563226,7.685463e-7,0.0000061056403,0.00015423207],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990528,0.00008728116,0.0004183536,0.00009268986,0.0002188234,0.0001300137],"domain_scores_gemma":[0.99909496,0.00009795466,0.00017100347,0.00021140456,0.0003722252,0.00005245922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066699274,0.00007300499,0.0001314115,0.00005103636,0.00019447485,0.000105000385,0.0004841181,0.000036895424,0.000010989713],"category_scores_gemma":[0.000015554593,0.000051323415,0.000080202575,0.00040698407,0.000015754948,0.00029105885,0.00003383979,0.00022388229,0.000009570731],"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":[0.00004636403,0.00078829855,0.0026666627,0.00039700372,0.00046107513,0.0002904794,0.028480846,0.16032259,0.17731078,0.42323348,0.01755198,0.18845043],"study_design_scores_gemma":[0.00068959245,0.00044317657,0.0039290683,0.0007701902,0.00015862552,0.0040333048,0.0077640302,0.55681354,0.33445305,0.006627698,0.083631486,0.0006862372],"about_ca_topic_score_codex":0.000016270185,"about_ca_topic_score_gemma":0.0000027890694,"teacher_disagreement_score":0.8732515,"about_ca_system_score_codex":0.000038962342,"about_ca_system_score_gemma":0.00007532317,"threshold_uncertainty_score":0.2092908},"labels":[],"label_agreement":null},{"id":"W3123412338","doi":"10.18280/isi.250603","title":"A Weight Based Feature Extraction Model on Multifaceted Multimedia Bigdata Using Convolutional Neural Network","year":2020,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Interpretability; Big data; Feature extraction; Feature (linguistics); Artificial intelligence; Feature selection; Data mining; Cluster analysis; Convolutional neural network; Task (project management); Machine learning; Component (thermodynamics); Pattern recognition (psychology); Engineering","score_opus":0.032261969904904216,"score_gpt":0.25788405246884916,"score_spread":0.22562208256394495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123412338","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":[0.0136911785,0.000023494735,0.98366237,0.0008454699,0.0001609398,0.00040845064,0.00003891163,0.00068058807,0.00048858713],"genre_scores_gemma":[0.7487839,0.000003977528,0.24937731,0.0015037782,0.00013127146,0.00006088187,0.00011743197,0.000008902766,0.000012575123],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875087,0.00004682263,0.00038450115,0.0002382791,0.00030362152,0.00027591755],"domain_scores_gemma":[0.99894494,0.00005844379,0.00031197834,0.00033405254,0.00020414183,0.00014644564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015955318,0.00019427077,0.00016041617,0.00012737485,0.0004548172,0.00025564615,0.00041366665,0.00015307905,0.00001484844],"category_scores_gemma":[0.00007781718,0.0001916702,0.000085051026,0.00070450053,0.00006552898,0.0027702518,0.00009374972,0.00026260052,0.00006119071],"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":[0.000080585756,0.00004654586,0.00024851214,0.00008285627,0.000023572284,0.0000018637307,0.0011651517,0.90523475,0.0028826797,0.008704072,0.0063043456,0.07522509],"study_design_scores_gemma":[0.00031554844,0.00006608728,0.00072155485,0.00003549383,0.000008258236,0.000011025401,0.000027665816,0.99270016,0.0023260717,0.0005554346,0.0030253634,0.00020733036],"about_ca_topic_score_codex":0.000019101217,"about_ca_topic_score_gemma":0.0000012748753,"teacher_disagreement_score":0.7350927,"about_ca_system_score_codex":0.00020035298,"about_ca_system_score_gemma":0.00011455995,"threshold_uncertainty_score":0.7816084},"labels":[],"label_agreement":null},{"id":"W3126140139","doi":"","title":"Uncertainty for deep image classifiers on out of distribution data.","year":2021,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Outlier; Calibration; Computer science; Benchmark (surveying); Range (aeronautics); Artificial intelligence; Data mining; Probability distribution; Machine learning; Statistics; Pattern recognition (psychology); Mathematics; Geography","score_opus":0.04642992248722309,"score_gpt":0.317586111422284,"score_spread":0.2711561889350609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126140139","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":[0.00040621203,0.0000055243145,0.99473757,0.0012718529,0.000052435902,0.000118319505,0.00009138375,0.00011088594,0.0032057944],"genre_scores_gemma":[0.71994096,0.000017325558,0.27795333,0.00028613314,0.000047167432,0.00007794886,0.0004502436,0.000005506784,0.0012213755],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994582,0.000011450261,0.00012133366,0.0002440373,0.00008008705,0.00008492188],"domain_scores_gemma":[0.999009,0.000054692984,0.000048685848,0.0007336936,0.00012452,0.000029393987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008833257,0.000045475823,0.00006765704,0.000012821123,0.00006609775,0.000033866767,0.0004059174,0.000030183854,0.000026878384],"category_scores_gemma":[0.000043128704,0.00004036658,0.000035474437,0.00014172161,0.000024980694,0.00014126582,0.00014756198,0.000036651618,0.000008798277],"study_design_candidate":"theoretical_or_conceptual","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":[0.000007984238,0.000210541,0.000016641843,0.000021776408,0.00001907146,0.0000010553503,0.000042249943,0.00004647296,0.026815046,0.8328112,0.03167048,0.10833748],"study_design_scores_gemma":[0.00022698521,0.00012523774,0.00024337608,0.00001028764,0.000010753291,0.0000022379938,0.00008436649,0.44007966,0.41130728,0.013921211,0.133834,0.00015458677],"about_ca_topic_score_codex":0.0000059785584,"about_ca_topic_score_gemma":0.00001422364,"teacher_disagreement_score":0.81889,"about_ca_system_score_codex":0.000024451403,"about_ca_system_score_gemma":0.000039581206,"threshold_uncertainty_score":0.16461015},"labels":[],"label_agreement":null},{"id":"W3126956208","doi":"","title":"Detection of abnormal driving situations using distributed representations and unsupervised learning.","year":2020,"lang":"en","type":"article","venue":"The European Symposium on Artificial Neural Networks","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Unsupervised learning; Artificial intelligence; Machine learning","score_opus":0.026518067377594736,"score_gpt":0.24653059305591843,"score_spread":0.2200125256783237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126956208","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":[0.16120969,0.000012131029,0.8346284,0.0030667728,0.000069924616,0.00021252084,0.0000022792615,0.0002674273,0.000530879],"genre_scores_gemma":[0.9987965,0.000017099603,0.0006364017,0.0002769399,0.00023450569,0.000006612502,0.0000059816293,0.000015692181,0.000010241355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868405,0.0004050376,0.00031260066,0.0002923954,0.00013974003,0.00016620419],"domain_scores_gemma":[0.9992644,0.00012164468,0.00017111284,0.00028617264,0.000066848734,0.000089777874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025432528,0.00011778836,0.0001117377,0.000042320975,0.00064448686,0.00013327856,0.00034132358,0.000028145267,0.0000030177616],"category_scores_gemma":[0.000038055496,0.00009889601,0.00006332735,0.00059617026,0.0000962002,0.00018877344,0.00020334171,0.0002741628,0.00000657132],"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":[0.000032083764,0.00006065321,0.00040238118,0.000006546598,0.0000216755,0.000005609951,0.0010255591,0.71786064,0.2109066,0.00619736,0.00009390669,0.06338697],"study_design_scores_gemma":[0.000067847504,0.0001235887,0.0027930285,0.000006611031,0.0000150779115,0.000008310933,0.00006268683,0.98904496,0.0074085733,0.00012664915,0.0002384533,0.00010419005],"about_ca_topic_score_codex":0.000016034837,"about_ca_topic_score_gemma":0.0000042295596,"teacher_disagreement_score":0.8375868,"about_ca_system_score_codex":0.00001208391,"about_ca_system_score_gemma":0.000006922649,"threshold_uncertainty_score":0.49569368},"labels":[],"label_agreement":null},{"id":"W3128446401","doi":"10.1109/tnnls.2021.3053563","title":"Abnormal Event Detection and Localization via Adversarial Event Prediction","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":97,"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; University of Regina","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; Ministry of Culture, Sports and Tourism","keywords":"Adversarial system; Computer science; Event (particle physics); Hyperparameter; Artificial intelligence; Machine learning; Discriminative model; Representation (politics); Feature learning; Anomaly detection; Pattern recognition (psychology)","score_opus":0.006499792144302944,"score_gpt":0.21124496630219297,"score_spread":0.20474517415789004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128446401","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":[0.011468173,0.00022677427,0.98657286,0.000086709,0.0010277372,0.0002399103,0.0000017806773,0.00034529084,0.00003075388],"genre_scores_gemma":[0.99890023,0.00018433941,0.00025741532,0.000053067964,0.00017716258,0.00009110569,0.0000031823279,0.000013371256,0.0003201345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987155,0.00022844254,0.0002931889,0.00040367251,0.00016962674,0.0001895511],"domain_scores_gemma":[0.99943775,0.000068129324,0.000113373404,0.00019118514,0.000087292465,0.00010226526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022350308,0.00014831942,0.0001483991,0.00008749265,0.00078875554,0.00019487602,0.000078065605,0.00013413941,0.0000055068836],"category_scores_gemma":[0.0000031616332,0.00014983813,0.00006267509,0.00036464233,0.000032541877,0.00031596847,0.0000052492987,0.00042364403,0.0000020150924],"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":[0.000012823271,0.00003355583,0.00006659964,0.000014527028,0.000016115257,0.0000030769195,0.00007827999,0.870144,0.00054637465,0.0001317076,0.000013136624,0.1289398],"study_design_scores_gemma":[0.00025098963,0.00022888747,0.00046310402,0.00003221352,0.000021312797,0.00021998209,0.000062643914,0.99579656,0.00092859636,0.000024494966,0.0018350785,0.00013613925],"about_ca_topic_score_codex":0.000094791496,"about_ca_topic_score_gemma":0.000022242983,"teacher_disagreement_score":0.98743206,"about_ca_system_score_codex":0.00004718527,"about_ca_system_score_gemma":0.000013240475,"threshold_uncertainty_score":0.6110222},"labels":[],"label_agreement":null},{"id":"W3132491413","doi":"10.1007/978-3-030-67658-2_37","title":"Parameterless Semi-supervised Anomaly Detection in Univariate Time Series","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Anomaly detection; Computer science; Univariate; Series (stratigraphy); Data mining; Time series; Cluster analysis; Anomaly (physics); Representation (politics); Process (computing); Pattern recognition (psychology); Artificial intelligence; Machine learning; Multivariate statistics","score_opus":0.01122024185259916,"score_gpt":0.2248268616107407,"score_spread":0.21360661975814155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132491413","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008707906,0.00016483037,0.99507076,0.0005417916,0.00043055532,0.00042295354,0.0000050870635,0.0003385566,0.0021547019],"genre_scores_gemma":[0.43226072,0.00013565537,0.5636028,0.0009897758,0.0003434017,0.00008989329,0.000013280394,0.00007002939,0.0024944523],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967227,0.000058235557,0.00058223447,0.0015490714,0.000544612,0.0005431757],"domain_scores_gemma":[0.99776363,0.0002020438,0.00024003022,0.0014432891,0.00021882044,0.00013216647],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005748207,0.00047159105,0.00052557804,0.0008891937,0.00025048986,0.00058084924,0.0020592066,0.00040936598,0.000045144465],"category_scores_gemma":[0.0000384749,0.00048682131,0.00014350038,0.0014068594,0.00036802018,0.0008180515,0.0009762337,0.00078740227,0.000051205036],"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":[0.000010672072,0.00006386722,0.00005088013,0.000043392236,0.000013584827,0.00014119,0.00046795767,0.008632686,0.006154975,0.021585017,0.000007724794,0.96282804],"study_design_scores_gemma":[0.00037842983,0.00031808697,0.000511966,0.00044395166,0.000012456201,0.00027785797,7.237039e-7,0.803163,0.054610837,0.13524482,0.00371236,0.0013255434],"about_ca_topic_score_codex":0.000075157426,"about_ca_topic_score_gemma":0.00024109942,"teacher_disagreement_score":0.9615025,"about_ca_system_score_codex":0.00039127725,"about_ca_system_score_gemma":0.00039032634,"threshold_uncertainty_score":0.99975836},"labels":[],"label_agreement":null},{"id":"W3132761010","doi":"10.1109/access.2021.3059519","title":"Multi-Modal Anomaly Detection by Using Audio and Visual Cues","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Higher Education Commision, Pakistan; Higher Education Commission, Pakistan","keywords":"Computer science; Anomaly detection; Centroid; Artificial intelligence; Mel-frequency cepstrum; Pattern recognition (psychology); Speech recognition; Anomaly (physics); Ground truth; Optical flow; Computer vision; Cepstrum; Feature extraction; Image (mathematics)","score_opus":0.03268652957786624,"score_gpt":0.33880803929181363,"score_spread":0.3061215097139474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132761010","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":[0.3959535,0.0000910705,0.6035452,0.00006640535,0.000092220595,0.000054829277,0.0000016028268,0.00014659614,0.00004857519],"genre_scores_gemma":[0.97867477,0.000029544384,0.020899061,0.0001767285,0.000059342965,0.000024047777,0.0000010325003,0.0000079268775,0.00012757407],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992433,0.000030006726,0.0001385262,0.00034197478,0.000101267105,0.00014489036],"domain_scores_gemma":[0.99956304,0.000020342139,0.00006613677,0.00020339307,0.00008539709,0.00006168439],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006815701,0.00009137636,0.000094941934,0.000054263513,0.00023146535,0.00036922737,0.00026413755,0.00006242731,0.000007173333],"category_scores_gemma":[0.0000079064575,0.000096160315,0.00003340874,0.00038595407,0.000032532946,0.00072628254,0.00016959773,0.00008820641,0.0000048443762],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031281174,0.00012207215,0.0029361793,0.000014653089,0.00001658815,0.000009654851,0.00007185442,0.00004148152,0.8469631,0.00015904027,0.000307387,0.14935483],"study_design_scores_gemma":[0.00014058096,0.000026478374,0.004139904,0.0000061250894,0.000006536726,0.00005597505,0.000010442621,0.19110632,0.80224705,0.00022185793,0.0018826606,0.00015609115],"about_ca_topic_score_codex":0.00016415546,"about_ca_topic_score_gemma":0.00005896297,"teacher_disagreement_score":0.58272123,"about_ca_system_score_codex":0.000032911572,"about_ca_system_score_gemma":0.000028208035,"threshold_uncertainty_score":0.3921304},"labels":[],"label_agreement":null},{"id":"W3133532155","doi":"10.3390/app11052187","title":"Anomaly Analysis of Alzheimer’s Disease in PET Images Using an Unsupervised Adversarial Deep Learning Model","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; BioClinica; F. Hoffmann-La Roche; University of Southern California; Biogen; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Artificial intelligence; Benchmark (surveying); Computer science; Pattern recognition (psychology); Anomaly detection; Anomaly (physics); Convolutional neural network; Image (mathematics); Discriminator; Pipeline (software); Deep learning; Neuroimaging; Autoencoder; Medicine; Cartography","score_opus":0.04128451734625569,"score_gpt":0.29893171217543135,"score_spread":0.25764719482917564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133532155","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":[0.37727872,0.00007092112,0.6215761,0.000061947954,0.0000141651435,0.00008087107,0.0000026916998,0.00007115242,0.0008434524],"genre_scores_gemma":[0.8559062,0.000011669197,0.1439816,0.00005318025,0.000010437909,0.000019426832,0.0000046458576,0.0000034573789,0.000009390791],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861443,0.00005269578,0.0002708128,0.0005468832,0.00029940595,0.00021576614],"domain_scores_gemma":[0.9992983,0.00003948632,0.000120171295,0.0003664905,0.00007078911,0.00010480928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040451006,0.00010811277,0.00022021185,0.00038458343,0.00027489572,0.00012539166,0.0005955242,0.000026356834,0.000020634106],"category_scores_gemma":[0.00001508979,0.0001059895,0.00009665786,0.0034546247,0.00017921938,0.0004395023,0.00019063309,0.0000940122,0.0000014678267],"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":[0.000006535094,0.00017440364,0.0068577807,0.0000040980267,0.00005737185,0.0000095244895,0.0005108165,0.8885104,0.04326862,0.052999858,0.0000024700937,0.0075980844],"study_design_scores_gemma":[0.000088887755,0.000015122695,0.007191726,0.000002709187,0.00010991396,9.046635e-7,0.00016123487,0.97833765,0.011012059,0.0029408094,0.0000093060835,0.00012967833],"about_ca_topic_score_codex":0.00012444476,"about_ca_topic_score_gemma":0.00005784696,"teacher_disagreement_score":0.4786275,"about_ca_system_score_codex":0.000020299549,"about_ca_system_score_gemma":0.00019164241,"threshold_uncertainty_score":0.43221262},"labels":[],"label_agreement":null},{"id":"W3133724915","doi":"10.1002/stc.2720","title":"Anomaly detection using state‐space models and reinforcement learning","year":2021,"lang":"en","type":"article","venue":"Structural Control and Health Monitoring","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Anomaly detection; Interpretability; Reinforcement learning; Computer science; Artificial intelligence; Univariate; Anomaly (physics); Bayesian probability; Machine learning; Data mining; Multivariate statistics","score_opus":0.03536080234147399,"score_gpt":0.31392497046154094,"score_spread":0.27856416812006696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133724915","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":[0.35668558,0.0008971478,0.641524,0.0005078179,0.00011855768,0.00012543668,4.3637203e-7,0.00010943625,0.00003157348],"genre_scores_gemma":[0.98706096,0.00025520223,0.012418141,0.000073157265,0.00010141187,0.000013442072,4.0102316e-7,0.0000062278928,0.00007102617],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913186,0.00004189836,0.00020084907,0.0002773908,0.00011228456,0.00023574264],"domain_scores_gemma":[0.99952364,0.000020422141,0.000114163944,0.00013634299,0.00007841883,0.00012701197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014599168,0.000104602674,0.00015158017,0.000051561296,0.0006598098,0.00015884702,0.0000668626,0.000034924487,7.8891605e-7],"category_scores_gemma":[0.0000051102456,0.00010149707,0.000022921615,0.0001480781,0.000019212399,0.0003810657,0.00008258805,0.00017308076,2.1843131e-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":[0.000042294025,0.0000103352495,0.023017533,0.00026884684,0.0000795179,0.000015726588,0.002878716,0.10479116,0.04851835,0.028478928,0.0000048336897,0.7918938],"study_design_scores_gemma":[0.00042794383,0.000114062,0.0192953,0.000032530934,0.0000054238217,0.0000709751,0.00012464062,0.9689406,0.0059835906,0.0046276744,0.00021891405,0.00015830794],"about_ca_topic_score_codex":0.00046901967,"about_ca_topic_score_gemma":0.000014897563,"teacher_disagreement_score":0.86414945,"about_ca_system_score_codex":0.000079983794,"about_ca_system_score_gemma":0.000059485803,"threshold_uncertainty_score":0.507479},"labels":[],"label_agreement":null},{"id":"W3135019605","doi":"10.1145/3448016.3452812","title":"PCOR: Private Contextual Outlier Release via Differentially Private Search","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Royal Bank of Canada","keywords":"Outlier; Anomaly detection; Context (archaeology); Computer science; Population; Metric (unit); Differential privacy; Data mining; Artificial intelligence; Geography; Engineering; Medicine","score_opus":0.024393399639940336,"score_gpt":0.27216580579033606,"score_spread":0.24777240615039572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135019605","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":[0.075536475,0.00008167979,0.9184083,0.0010786228,0.00032360337,0.00070786703,0.000010012283,0.0011966381,0.0026568132],"genre_scores_gemma":[0.8508308,0.00013308816,0.14410087,0.0004181789,0.00014334208,0.00028760475,0.000043482327,0.000036675174,0.004005951],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99707574,0.00013899492,0.00056472275,0.001253594,0.00050895545,0.000457988],"domain_scores_gemma":[0.99693596,0.000055383203,0.00020356633,0.0022909013,0.0002631226,0.00025106498],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002485455,0.00039437736,0.0004414985,0.00018011998,0.00023417207,0.00079516566,0.0021394063,0.00037602175,0.00033296988],"category_scores_gemma":[0.000020961146,0.00036963573,0.0003168514,0.0003446584,0.00009436658,0.00021509356,0.00511471,0.000992065,0.00015194582],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000320958,0.0010361967,0.00092210737,0.00032697927,0.0003813094,0.00011951263,0.0010485938,0.0004268561,0.026872074,0.49808386,0.0034820735,0.46726835],"study_design_scores_gemma":[0.0016770836,0.0005195359,0.02039306,0.0005166016,0.00016655424,0.0001736305,0.00014170805,0.40366846,0.3996687,0.06643545,0.10190405,0.004735156],"about_ca_topic_score_codex":0.0001827987,"about_ca_topic_score_gemma":0.000027463022,"teacher_disagreement_score":0.7752943,"about_ca_system_score_codex":0.000116911935,"about_ca_system_score_gemma":0.00020862912,"threshold_uncertainty_score":0.99987555},"labels":[],"label_agreement":null},{"id":"W3136637725","doi":"10.1016/j.jtcvs.2021.02.095","title":"Deus ex machina? Demystifying rather than deifying machine learning","year":2021,"lang":"en","type":"review","venue":"Journal of Thoracic and Cardiovascular Surgery","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CancerCare Manitoba; Research Institute in Oncology and Hematology; University of Manitoba","funders":"","keywords":"Philosophy; Epistemology","score_opus":0.056312741064523185,"score_gpt":0.3285863735667296,"score_spread":0.27227363250220643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136637725","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":[0.000015210737,0.62199265,0.3775294,0.000013803369,0.00021110874,0.000100644036,0.0000014122338,0.00005248999,0.00008323419],"genre_scores_gemma":[0.00052230567,0.9883553,0.010535152,0.000019508447,0.00035124554,0.000019728157,0.000005052257,0.000050458646,0.00014127053],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.996636,0.0009651064,0.0010329561,0.00046724637,0.0005861876,0.00031252447],"domain_scores_gemma":[0.9975387,0.00044139192,0.00091586995,0.00070000644,0.00020878427,0.00019526278],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0038042995,0.0004033523,0.0026232596,0.0004949502,0.00030753153,0.00035348718,0.0005697221,0.00028268163,0.00001236554],"category_scores_gemma":[0.00016056995,0.00032133202,0.004506359,0.00078126165,0.00004313431,0.0003656673,0.0003323815,0.0011167862,0.0000072294843],"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":[0.0000011345215,0.000015294856,0.000041507737,0.0013192816,0.0010548459,0.00023730996,0.000025603656,0.000020765045,3.938042e-7,0.00007972742,0.0000956979,0.99710846],"study_design_scores_gemma":[0.00002883751,0.000017584027,0.000011604273,0.004994372,0.0012038518,0.005826925,0.000017717679,0.00022121432,0.0000147058,0.000062985455,0.9872328,0.00036740996],"about_ca_topic_score_codex":0.00001545301,"about_ca_topic_score_gemma":5.3844695e-7,"teacher_disagreement_score":0.99674106,"about_ca_system_score_codex":0.00007627333,"about_ca_system_score_gemma":0.00032472084,"threshold_uncertainty_score":0.9999239},"labels":[],"label_agreement":null},{"id":"W3137295812","doi":"10.48550/arxiv.2103.11285","title":"Geo-Spatiotemporal Features and Shape-Based Prior Knowledge for Fine-grained Imbalanced Data Classification","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; McGill University; Canada Research Chairs; University of Toronto; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Class (philosophy); Artificial intelligence; Perception; Data science; Machine learning; Data mining; Pattern recognition (psychology)","score_opus":0.12454754859020065,"score_gpt":0.24525091782001304,"score_spread":0.1207033692298124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137295812","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":[0.042094473,0.00016017004,0.95553845,0.00054932886,0.00013693844,0.00071761344,0.000114788774,0.0003872994,0.0003009548],"genre_scores_gemma":[0.96177006,0.00007539605,0.03653475,0.000071530034,0.00006927476,0.000019869645,0.00061859464,0.000016491746,0.00082402915],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981396,0.000067934154,0.00019300832,0.0013346407,0.000054746703,0.00021007986],"domain_scores_gemma":[0.9972931,0.00012973258,0.0002679906,0.0019405329,0.00025872554,0.00010989401],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022217262,0.00023888565,0.00025571385,0.0001661534,0.00026982688,0.00022660465,0.0016232606,0.00028105307,0.000008957412],"category_scores_gemma":[0.000048074544,0.0002771175,0.00010853722,0.00043600373,0.00008744422,0.00031844797,0.0014592934,0.0003019756,0.000003823031],"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":[0.00030017048,0.0015887693,0.006540157,0.0016395554,0.00047425512,0.00008352849,0.000930648,0.0090790335,0.005345323,0.75476867,0.02424245,0.19500743],"study_design_scores_gemma":[0.00046172694,0.000045812187,0.009565372,0.000071732306,0.000050463856,0.0000025307452,0.000034829034,0.9809729,0.00081374217,0.0045815385,0.0030490938,0.0003502833],"about_ca_topic_score_codex":0.00005659281,"about_ca_topic_score_gemma":0.00014921385,"teacher_disagreement_score":0.97189385,"about_ca_system_score_codex":0.00009668347,"about_ca_system_score_gemma":0.00033373883,"threshold_uncertainty_score":0.9999681},"labels":[],"label_agreement":null},{"id":"W3137651173","doi":"10.3390/ijgi10030177","title":"Spatio-Temporal Visual Analysis for Urban Traffic Characters Based on Video Surveillance Camera Data","year":2021,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Anomaly Detection Techniques and Applications","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":"National Natural Science Foundation of China","keywords":"Visualization; Computer science; License; Computer vision; Artificial intelligence; Transport engineering; Data mining; Engineering","score_opus":0.015175737149275715,"score_gpt":0.29138277559739784,"score_spread":0.2762070384481221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137651173","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":[0.02272587,0.000014479852,0.9712874,0.0049113883,0.00057942903,0.0001345689,0.00016594722,0.00005717716,0.000123704],"genre_scores_gemma":[0.9704933,0.000021386562,0.026807182,0.0015391099,0.00019528873,0.00001472823,0.00089285395,0.000004727559,0.00003142692],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982388,0.000049578306,0.0007508617,0.00016493848,0.0006540284,0.00014178564],"domain_scores_gemma":[0.997048,0.00014866199,0.00088736403,0.00044033804,0.0013928362,0.0000827811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006480208,0.00012652969,0.00021287212,0.00062570494,0.00011744464,0.00044606824,0.001214489,0.000064185915,0.000036641864],"category_scores_gemma":[0.00018730675,0.00012216477,0.00021897498,0.00061625853,0.00002257596,0.003179252,0.00011128692,0.00015674613,0.000017768349],"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":[0.0010460275,0.001104398,0.03290232,0.00008902516,0.0028751297,0.00004838716,0.0017453288,0.15516457,0.00036250876,0.023838453,0.054223817,0.72660005],"study_design_scores_gemma":[0.00068899523,0.00014769046,0.015740165,0.00002369383,0.000044558146,0.00002930304,0.000052669224,0.86124176,0.0011024642,0.00009966855,0.12065719,0.00017182302],"about_ca_topic_score_codex":0.000017752796,"about_ca_topic_score_gemma":0.000018230066,"teacher_disagreement_score":0.94776744,"about_ca_system_score_codex":0.0001420423,"about_ca_system_score_gemma":0.00024800448,"threshold_uncertainty_score":0.49817348},"labels":[],"label_agreement":null},{"id":"W3139118796","doi":"10.3389/fnbot.2021.671519","title":"Editorial: Advances in Robots Trajectories Learning via Fast Neural Networks","year":2021,"lang":"en","type":"editorial","venue":"Frontiers in Neurorobotics","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Artificial intelligence; Robot; Artificial neural network; Machine learning","score_opus":0.004379168022704514,"score_gpt":0.2335529804798782,"score_spread":0.2291738124571737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3139118796","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000012062752,0.0012349276,0.49011743,0.00005555376,0.5081797,0.00016075841,0.0000025763823,0.00019119657,0.00005662276],"genre_scores_gemma":[0.000430465,0.0039969785,0.0540892,0.00003386465,0.9407755,0.00011983989,0.00008168917,0.00007736449,0.0003950645],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99653614,0.00028382023,0.00072795333,0.0010669462,0.000740362,0.00064479857],"domain_scores_gemma":[0.9981723,0.00035736847,0.0003770826,0.0007509077,0.00022473562,0.00011759327],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0003165277,0.00048197788,0.00073741394,0.00038749937,0.00017586253,0.0003167305,0.0014163542,0.0009573237,0.0000024044832],"category_scores_gemma":[0.00034714513,0.00055653916,0.00016617568,0.0014423067,0.00010607757,0.00068488915,0.00042336577,0.0030500884,0.0000016481001],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007590967,0.000046158755,0.0003609997,0.000029117522,0.000005126874,0.000027435457,0.000057731526,0.25919726,0.0000026240252,0.00005108113,0.7266812,0.013533688],"study_design_scores_gemma":[0.00023463047,0.000101812824,0.00003506479,0.00006522669,0.000010136693,0.00000230686,0.000030122332,0.29568186,0.000013097162,0.00040620173,0.70298624,0.00043328624],"about_ca_topic_score_codex":0.0000438502,"about_ca_topic_score_gemma":0.00010093277,"teacher_disagreement_score":0.43602824,"about_ca_system_score_codex":0.00025387222,"about_ca_system_score_gemma":0.00018468616,"threshold_uncertainty_score":0.9996886},"labels":[],"label_agreement":null},{"id":"W3140302147","doi":"10.1016/j.cag.2021.03.004","title":"ProSeCo: Visual analysis of class separation measures and dataset characteristics","year":2021,"lang":"en","type":"article","venue":"Computers & Graphics","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Österreichische Forschungsförderungsgesellschaft; NÖ Forschungs- und Bildungsges.m.b.H.; Deutsche Forschungsgemeinschaft","keywords":"Separation (statistics); Computer science; Skewness; Class (philosophy); Visualization; Dimensionality reduction; Artificial intelligence; Visual analytics; Pattern recognition (psychology); Data mining; Machine learning; Statistics; Mathematics","score_opus":0.01852406985079736,"score_gpt":0.3002955657759921,"score_spread":0.28177149592519474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3140302147","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":[0.052269932,0.000074536125,0.94698286,0.0002863115,0.000069609654,0.00008283042,0.0001327516,0.00007252744,0.000028616465],"genre_scores_gemma":[0.97438574,0.0001610141,0.024477633,0.0004515928,0.000024407143,0.000014076362,0.00046824795,0.000004840114,0.000012461228],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991003,0.000051032916,0.00025404053,0.00031407014,0.00017207785,0.00010845494],"domain_scores_gemma":[0.9991186,0.00006018532,0.00014530201,0.00043609782,0.0001789659,0.00006080314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015600715,0.000094732095,0.00021226886,0.00024756909,0.000111458874,0.00011654436,0.00024924538,0.00006030297,0.0000031202524],"category_scores_gemma":[0.000011634187,0.00009923772,0.00007800279,0.001594543,0.00006754291,0.0001738769,0.00019271388,0.00009075724,0.0000013147071],"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":[0.000023613153,0.0009101761,0.037596,0.00015150022,0.002187063,0.000044302746,0.0012048404,0.000374707,0.017570814,0.63944876,0.015207578,0.28528064],"study_design_scores_gemma":[0.00019377586,0.0001260888,0.121046714,0.00001777702,0.0003463054,0.000021107116,0.000018040959,0.814141,0.008676636,0.0019172474,0.0531652,0.00033015248],"about_ca_topic_score_codex":0.000010079089,"about_ca_topic_score_gemma":0.00001590034,"teacher_disagreement_score":0.92250526,"about_ca_system_score_codex":0.000009429449,"about_ca_system_score_gemma":0.00004066135,"threshold_uncertainty_score":0.40467966},"labels":[],"label_agreement":null},{"id":"W3143099543","doi":"10.22215/etd/2019-13838","title":"A Framework for Traffic Collision Prediction Using Historical Accident Information and Real-Time Sensor Data: A Case Study for the City of Ottawa","year":2019,"lang":"en","type":"dissertation","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Carleton University","funders":"","keywords":"Collision; Geospatial analysis; Computer science; Enforcement; Event (particle physics); Transport engineering; Accident (philosophy); Geography; Engineering; Computer security; Cartography","score_opus":0.048364520272355,"score_gpt":0.3371275167617943,"score_spread":0.2887629964894393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3143099543","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":[0.22208841,0.000025529764,0.7737238,0.00005583514,0.00027453594,0.0036047606,0.000083910985,0.0001222153,0.000020986612],"genre_scores_gemma":[0.63300097,0.000085152584,0.3639469,0.000034568893,0.00015832529,0.0010189706,0.0005079197,0.000027938455,0.0012192085],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998698,0.00002936733,0.0005757321,0.00035529656,0.00021713416,0.00012445178],"domain_scores_gemma":[0.9977017,0.0005112633,0.00055511773,0.0008598634,0.00033255504,0.000039500814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055404636,0.00016327472,0.00025784085,0.00014832238,0.0003578396,0.00014219701,0.0004598986,0.00022516765,0.0000025090271],"category_scores_gemma":[0.000106627675,0.00012458408,0.00007545727,0.00027570102,0.000010326018,0.0006846147,0.00009220681,0.00012796099,7.4831644e-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":[0.001917563,0.004847582,0.0044121845,0.0037132357,0.0016988863,0.000026203004,0.062343273,0.009024811,0.002630989,0.08500444,0.042070273,0.78231055],"study_design_scores_gemma":[0.0005783088,0.00081271783,0.0010705402,0.00007151659,0.0002829782,0.00011157415,0.0046736724,0.987152,0.0004137601,0.00039879826,0.0041553928,0.00027876106],"about_ca_topic_score_codex":0.0009909387,"about_ca_topic_score_gemma":0.00018267252,"teacher_disagreement_score":0.9781272,"about_ca_system_score_codex":0.00018310918,"about_ca_system_score_gemma":0.00011859137,"threshold_uncertainty_score":0.5080392},"labels":[],"label_agreement":null},{"id":"W3145506661","doi":"10.1007/978-3-030-10546-4_1","title":"Introduction to Machine Learning","year":2019,"lang":"en","type":"book-chapter","venue":"Springer briefs in electrical and computer engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1620,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.004177339516571247,"score_gpt":0.1790495292019016,"score_spread":0.17487218968533033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3145506661","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000103092505,0.0003690607,0.9944279,0.00071605557,0.00023430958,0.00025644095,6.571713e-7,0.00045340447,0.0034390371],"genre_scores_gemma":[0.088425756,0.0016821354,0.5726867,0.0019882577,0.009169838,0.00022934562,0.000038273665,0.0004280809,0.32535163],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99865115,0.0000064682317,0.0002712774,0.0006389009,0.00015914635,0.00027303363],"domain_scores_gemma":[0.9994188,0.00004068893,0.000058248475,0.000343891,0.000034327768,0.00010402195],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001200731,0.00025978612,0.00029146872,0.00040009606,0.000047923342,0.00011126422,0.0003481791,0.00016601314,0.00001376864],"category_scores_gemma":[0.0000071415384,0.00028208346,0.00006615955,0.00020443264,0.000008313387,0.000106429114,0.00034291347,0.0007048048,0.00006423353],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000046254654,0.000017921302,0.000044290366,0.00004249475,0.000027184216,0.000011495796,0.000046990128,0.013273567,0.0002647083,0.71739185,0.00081240304,0.26806247],"study_design_scores_gemma":[0.00007965491,0.00014247319,0.00027741338,0.000042430307,0.000004339505,0.00003434636,5.0746326e-8,0.46059203,0.00021283054,0.0009323907,0.53733265,0.00034939646],"about_ca_topic_score_codex":0.00001203171,"about_ca_topic_score_gemma":0.0000015306148,"teacher_disagreement_score":0.71645945,"about_ca_system_score_codex":0.000102542035,"about_ca_system_score_gemma":0.00002151413,"threshold_uncertainty_score":0.9999631},"labels":[],"label_agreement":null},{"id":"W3146830617","doi":"10.18280/ts.380109","title":"Abnormal Behavior Recognition in Classroom Pose Estimation of College Students Based on Spatiotemporal Representation Learning","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Benchmark (surveying); Class (philosophy); Artificial intelligence; Convolutional neural network; Cluster analysis; Enhanced Data Rates for GSM Evolution; Representation (politics); Frame (networking); Feature learning; Feature (linguistics); Pattern recognition (psychology); Machine learning; Deep learning","score_opus":0.0255216064441882,"score_gpt":0.29646576102311895,"score_spread":0.27094415457893073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3146830617","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":[0.5443359,0.0000026865182,0.45483467,0.00015540463,0.00003421594,0.0002908783,0.00000896348,0.00007285551,0.00026444343],"genre_scores_gemma":[0.96823126,0.0000026566095,0.031340472,0.000075774486,0.00002081046,0.00018088556,0.00009183588,0.0000063312987,0.000049974926],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872744,0.00012489384,0.0003671886,0.00027407936,0.0003879014,0.00011849147],"domain_scores_gemma":[0.99944115,0.00005854759,0.00017546416,0.00017301086,0.000114462644,0.000037384998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027568976,0.0000909385,0.00011632362,0.00016889152,0.00008557092,0.000050487724,0.00018124151,0.0000516097,0.00010907592],"category_scores_gemma":[0.000018288676,0.000103388265,0.000053617743,0.0005126522,0.000016801769,0.00028056974,0.00004454887,0.00012342719,0.000010083572],"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":[0.00019521537,0.004858037,0.16576281,0.0000896805,0.000034746496,0.00012029384,0.00092021143,0.07160143,0.030760039,0.0071532293,0.00046651674,0.7180378],"study_design_scores_gemma":[0.0012952603,0.0004702178,0.29895693,0.00008078668,0.000018077906,0.0000069985067,0.00011551428,0.56775874,0.13026454,0.00073969254,0.00009505167,0.00019820688],"about_ca_topic_score_codex":0.000038431772,"about_ca_topic_score_gemma":0.000017329616,"teacher_disagreement_score":0.7178396,"about_ca_system_score_codex":0.00007724291,"about_ca_system_score_gemma":0.000059678772,"threshold_uncertainty_score":0.4216051},"labels":[],"label_agreement":null},{"id":"W3149512852","doi":"10.35429/jtd.2019.11.3.22.37","title":"Detección de anomalías en redes de sensores inalámbricos","year":2019,"lang":"en","type":"article","venue":"Revista del Desarrollo Tecnologico","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Network for Business Sustainability","funders":"","keywords":"Computer science; Relevance (law); Wireless sensor network; Anomaly detection; Variety (cybernetics); Context (archaeology); Event (particle physics); Wireless; Artificial intelligence; Computer network; Telecommunications; Geography","score_opus":0.0070453619089715725,"score_gpt":0.2380977234223159,"score_spread":0.23105236151334432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3149512852","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":[0.41918126,0.00044098336,0.5752487,0.000765026,0.00003624434,0.00047112617,0.000004261765,0.0011064874,0.0027458777],"genre_scores_gemma":[0.8607888,0.00018119995,0.1369621,0.0006939667,0.000057005742,0.00011362306,0.0000019735912,0.000019063435,0.0011822626],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981257,0.0001791631,0.00035276116,0.00058992946,0.00019453578,0.00055792194],"domain_scores_gemma":[0.9983429,0.00016557558,0.00018228004,0.0010740387,0.0000940621,0.00014116944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058583845,0.00024615502,0.00030592064,0.0001867829,0.00018963544,0.00027958755,0.001264128,0.00024236004,0.000072920164],"category_scores_gemma":[0.000118045165,0.0002195911,0.00017195406,0.00065199996,0.000068175636,0.00026347916,0.00033005714,0.0003179295,0.0003416678],"study_design_candidate":"theoretical_or_conceptual","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":[0.000049585105,0.00033728196,0.11037752,0.00017121491,0.00010094553,0.00015028931,0.0003784234,0.00038340723,0.16562992,0.48459587,0.0055693416,0.23225622],"study_design_scores_gemma":[0.0016000441,0.0017794429,0.14466882,0.0002551233,0.00010782675,0.0026338906,0.0002813717,0.119543485,0.14820799,0.05785507,0.5204198,0.0026471587],"about_ca_topic_score_codex":0.000047732025,"about_ca_topic_score_gemma":0.000001931423,"teacher_disagreement_score":0.51485044,"about_ca_system_score_codex":0.00023566795,"about_ca_system_score_gemma":0.00012458565,"threshold_uncertainty_score":0.8954665},"labels":[],"label_agreement":null},{"id":"W3149955010","doi":"10.48550/arxiv.2103.14953","title":"OLED: One-Class Learned Encoder-Decoder Network with Adversarial Context Masking for Novelty Detection","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Novelty; Computer science; Novelty detection; Context (archaeology); Artificial intelligence; Masking (illustration); Pattern recognition (psychology); MNIST database; Autoencoder; Encoder; Computer vision; Deep learning","score_opus":0.08390783299909817,"score_gpt":0.19881529139394227,"score_spread":0.1149074583948441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3149955010","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":[0.022316167,0.00003200559,0.9745891,0.00016717218,0.00042900303,0.0007927863,0.000010541578,0.00056433486,0.0010988809],"genre_scores_gemma":[0.97030365,0.00006334732,0.02803544,0.00017601218,0.0002656899,0.000028291406,0.000023336343,0.00003199816,0.0010722145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977811,0.00009061399,0.00024998258,0.001342322,0.00011343454,0.00042252475],"domain_scores_gemma":[0.9978624,0.00013314518,0.00039396205,0.0011103889,0.00035875436,0.00014134924],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025117132,0.00033231115,0.00038706756,0.000153065,0.00048101333,0.00026113738,0.0010062205,0.00040599817,0.000023667542],"category_scores_gemma":[0.000020082141,0.00038801823,0.00027593254,0.000692628,0.00009201315,0.00039654569,0.0008835861,0.00059237017,0.000012199391],"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":[0.00044938605,0.0004068695,0.0006200357,0.00020263683,0.0006360372,0.00008343417,0.000554356,0.72788006,0.0007307601,0.22878489,0.00048998237,0.039161544],"study_design_scores_gemma":[0.0012842616,0.00024788556,0.00033203897,0.00017498716,0.00018900902,0.000014181021,0.00025515686,0.9626254,0.0041286,0.0226617,0.007236334,0.00085045095],"about_ca_topic_score_codex":0.0002333484,"about_ca_topic_score_gemma":0.0007964026,"teacher_disagreement_score":0.9479875,"about_ca_system_score_codex":0.00029542518,"about_ca_system_score_gemma":0.00026955883,"threshold_uncertainty_score":0.9998572},"labels":[],"label_agreement":null},{"id":"W3152110224","doi":"10.3390/s21072532","title":"Outlier Detection Transilience-Probabilistic Model for Wind Tunnels Based on Sensor Data","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Anomaly detection; Outlier; Data mining; Identification (biology); Computer science; Set (abstract data type); Data set; Anomaly (physics); Probabilistic logic; Statistical model; Local outlier factor; Artificial intelligence","score_opus":0.05592338187970599,"score_gpt":0.2884915138427008,"score_spread":0.23256813196299478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3152110224","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":[0.024042038,0.000005914816,0.9735333,0.0011073058,0.00010165162,0.00038248478,0.00007471106,0.00031478942,0.00043782732],"genre_scores_gemma":[0.88647825,0.0000032284922,0.112071045,0.00045132017,0.00005183978,0.000044230634,0.000020413976,0.000014798528,0.00086488633],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861836,0.00004103979,0.0002126184,0.0007063512,0.00019166569,0.00022993561],"domain_scores_gemma":[0.9981345,0.00013307566,0.00006041295,0.0014450462,0.00014304898,0.00008391597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018969098,0.00013802978,0.00013762403,0.000072022835,0.00022955079,0.00009558857,0.00054202916,0.000084017905,0.000009246974],"category_scores_gemma":[0.000099464174,0.00013639095,0.000080263686,0.0003686657,0.00004381178,0.00015962037,0.00007017403,0.000115981464,0.000019807774],"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":[0.000047820056,0.00034939699,0.000024151535,0.000063418156,0.000021971962,0.000012326988,0.00037455052,0.86877805,0.026352445,0.012736756,0.0006853492,0.090553775],"study_design_scores_gemma":[0.00019828376,0.00005995748,0.00007530658,0.000009007413,0.00001170265,0.000009014482,0.000026645126,0.969121,0.024987806,0.0030464907,0.0022916452,0.00016318199],"about_ca_topic_score_codex":0.0000062934823,"about_ca_topic_score_gemma":0.000015519672,"teacher_disagreement_score":0.8624362,"about_ca_system_score_codex":0.000043910037,"about_ca_system_score_gemma":0.00009412507,"threshold_uncertainty_score":0.55618614},"labels":[],"label_agreement":null},{"id":"W3152187070","doi":"10.3390/s21144805","title":"OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection","year":2021,"lang":"en","type":"preprint","venue":"Sensors","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Autoencoder; Anomaly detection; Deep learning; Latency (audio); Computer science; Software deployment; Low latency (capital markets); Artificial intelligence; Convolutional neural network; Real-time computing; Computer network; Telecommunications; Software engineering","score_opus":0.01895605087259754,"score_gpt":0.27069476755233673,"score_spread":0.2517387166797392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3152187070","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":[0.090145886,0.00017951785,0.9048903,0.00068983535,0.0007858397,0.0010758957,0.000022298509,0.0011605471,0.0010499071],"genre_scores_gemma":[0.9095179,0.000025956133,0.08836614,0.0005445587,0.0006445647,0.00026880682,0.00002702194,0.000056664463,0.00054838747],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99728304,0.00014012789,0.0004973413,0.0011920659,0.00033371113,0.00055372657],"domain_scores_gemma":[0.9974309,0.00032338136,0.0003867354,0.0014532894,0.00023236676,0.00017328102],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027150204,0.00047612176,0.00049811444,0.0002723183,0.00040696288,0.00043617998,0.0009828581,0.00041631918,0.000016243961],"category_scores_gemma":[0.000061739374,0.00047247813,0.00040891362,0.0004968705,0.000068118854,0.00003970976,0.00041286318,0.0008009076,0.000030305491],"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":[0.000020921821,0.00007337579,0.00004215085,0.000088916844,0.00009018562,0.000008104509,0.00026353027,0.97836804,0.00029481854,0.0010607993,0.0005209112,0.019168222],"study_design_scores_gemma":[0.00020281028,0.00021637583,0.0019339499,0.00011987539,0.0000708092,0.000036900972,0.000042013555,0.97530216,0.005169074,0.008751327,0.007486421,0.000668309],"about_ca_topic_score_codex":0.00014261133,"about_ca_topic_score_gemma":0.0002505883,"teacher_disagreement_score":0.819372,"about_ca_system_score_codex":0.00018781851,"about_ca_system_score_gemma":0.00013401764,"threshold_uncertainty_score":0.99977267},"labels":[],"label_agreement":null},{"id":"W3152537180","doi":"10.22215/etd/2021-14348","title":"Intruder Alert: Dimension Reduction and Density-Based Clustering for a Cybersecurity Application","year":2021,"lang":"en","type":"dissertation","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Principal component analysis; Dimensionality reduction; Cluster analysis; Centroid; Dimension (graph theory); Metric (unit); Heuristic; Computer science; Data mining; Reduction (mathematics); Artificial intelligence; Independent component analysis; Component (thermodynamics); Pattern recognition (psychology); Engineering; Mathematics","score_opus":0.009015318340336814,"score_gpt":0.2625727049539636,"score_spread":0.2535573866136268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3152537180","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":[0.0142797725,0.00009410502,0.98301774,0.00041112132,0.00024189474,0.000895463,0.0000033275,0.0004055245,0.00065107277],"genre_scores_gemma":[0.64072096,0.00009217485,0.35295197,0.00027399135,0.00019024975,0.001638568,0.0009420844,0.000046129968,0.0031438456],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871314,0.000024576837,0.00027337213,0.0006787502,0.0001508996,0.00015928442],"domain_scores_gemma":[0.9988867,0.000038602873,0.00020333192,0.0005109627,0.00029016615,0.0000702057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001498681,0.00019970266,0.00020969844,0.00014214346,0.0002949077,0.00015730875,0.00020854293,0.00026221457,0.0000068296263],"category_scores_gemma":[0.000013854532,0.00021170333,0.000093864044,0.0002806159,0.000018368455,0.0001800994,0.0000586841,0.0001605566,0.0000040585264],"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":[0.00013973216,0.0003907299,0.000044401833,0.0010952475,0.000105545325,0.0000030560684,0.0018383333,0.00020085627,0.16864015,0.1509948,0.0055860025,0.67096114],"study_design_scores_gemma":[0.0007696087,0.00020367015,0.0012469053,0.00021947913,0.00012241297,0.00005846125,0.00073172845,0.5318252,0.42694354,0.014846764,0.021848273,0.001183994],"about_ca_topic_score_codex":0.00009812097,"about_ca_topic_score_gemma":0.00025987806,"teacher_disagreement_score":0.66977715,"about_ca_system_score_codex":0.00006523093,"about_ca_system_score_gemma":0.00008501047,"threshold_uncertainty_score":0.8633011},"labels":[],"label_agreement":null},{"id":"W3152927204","doi":"10.2196/27172","title":"Anomaly Detection Algorithm for Real-World Data and Evidence in Clinical Research: Implementation, Evaluation, and Validation Study","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Data mining; Anomaly detection; Data quality; Cluster analysis; Data set; Algorithm; Audit; Process (computing); Machine learning; Artificial intelligence; Metric (unit)","score_opus":0.33701158427584943,"score_gpt":0.5574363388989187,"score_spread":0.22042475462306932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3152927204","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":[0.30424643,0.00004991227,0.69295263,0.0009050552,0.000066877416,0.0016350849,0.000007914817,0.00007301389,0.000063102125],"genre_scores_gemma":[0.89653593,0.00042486377,0.101805836,0.00021389502,0.00010406919,0.00083045405,0.000046571295,0.000006949523,0.00003143011],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973476,0.00035925367,0.0009242704,0.00031410527,0.0008588956,0.0001959087],"domain_scores_gemma":[0.997586,0.0008012615,0.00019795333,0.00069338066,0.00056854327,0.00015285145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009584437,0.000085671454,0.00016604515,0.00021605092,0.00022029747,0.00022471922,0.0004668335,0.000067814806,0.000014776562],"category_scores_gemma":[0.0007030754,0.00008228136,0.000017588514,0.0009910918,0.00012231879,0.0011720409,0.0006837694,0.00026818027,0.0000030403203],"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":[0.0000028978386,0.00010165222,0.010163386,0.000024894547,0.000008413553,9.705163e-7,0.0010135345,3.845427e-7,0.000004791066,0.0006099204,0.00047149052,0.98759764],"study_design_scores_gemma":[0.0011126284,0.00042573415,0.12793626,0.00007117315,0.000019524561,0.000018405159,0.004580598,0.85944384,0.000521271,0.0026759969,0.0030369773,0.00015756534],"about_ca_topic_score_codex":0.00012994032,"about_ca_topic_score_gemma":0.0016607913,"teacher_disagreement_score":0.9874401,"about_ca_system_score_codex":0.00006438257,"about_ca_system_score_gemma":0.00036230695,"threshold_uncertainty_score":0.33553365},"labels":[],"label_agreement":null},{"id":"W3157756127","doi":"10.1109/icmew53276.2021.9456010","title":"Swimmer Stroke Rate Estimation from Overhead Race Video","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Analytics; Overhead (engineering); Computer science; Competition (biology); Work (physics); Class (philosophy); Motion (physics); Race (biology); Artificial intelligence; Computer vision; Data science; Engineering","score_opus":0.017184188747643162,"score_gpt":0.2712326485835844,"score_spread":0.25404845983594126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157756127","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008351481,0.00011134853,0.98371214,0.0024788,0.00035399065,0.00030592261,0.00002409792,0.0007995299,0.0038626895],"genre_scores_gemma":[0.39984715,0.00010567418,0.5936423,0.0006584376,0.00009868206,0.00023893722,0.00009755907,0.000016148882,0.0052951286],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842066,0.00007112362,0.00033338985,0.0007903462,0.0002076672,0.00017682479],"domain_scores_gemma":[0.9980937,0.000094243114,0.00021119564,0.0013800566,0.00013776077,0.00008300262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017845619,0.00021841626,0.0002522813,0.00008582284,0.000121968165,0.0005884024,0.0008550241,0.0002494025,0.0002758623],"category_scores_gemma":[0.0000265815,0.0001920873,0.0001628396,0.00019104937,0.000023686107,0.00030437758,0.0013523983,0.0004246939,0.00012122171],"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":[0.000016050213,0.0005398924,0.00047926066,0.00013281408,0.00036870118,0.00003770956,0.0013123691,0.019233298,0.037418712,0.12542143,0.03235018,0.7826896],"study_design_scores_gemma":[0.00015776207,0.000023385124,0.0035542538,0.000082662176,0.000029267803,0.0000042848606,0.00004007866,0.8274349,0.13027452,0.025647711,0.012195167,0.0005560262],"about_ca_topic_score_codex":0.0009064396,"about_ca_topic_score_gemma":0.000041766907,"teacher_disagreement_score":0.80820155,"about_ca_system_score_codex":0.000082505,"about_ca_system_score_gemma":0.00014941584,"threshold_uncertainty_score":0.7833093},"labels":[],"label_agreement":null},{"id":"W3158435984","doi":"10.48550/arxiv.2104.12300","title":"ODDObjects: A Framework for Multiclass Unsupervised Anomaly Detection on Masked Objects","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Autoencoder; Anomaly detection; Artificial intelligence; Computer science; Pattern recognition (psychology); Anomaly (physics); Convolutional neural network; Object (grammar); Unsupervised learning; Deep learning","score_opus":0.06926371866493562,"score_gpt":0.20762889158383516,"score_spread":0.13836517291889955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158435984","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":[0.13852504,0.00002099721,0.85866874,0.00010897994,0.00041723516,0.000846473,0.000016178978,0.0007673725,0.000629008],"genre_scores_gemma":[0.9473835,0.00007733033,0.051640514,0.00021778168,0.00011713232,0.000046945133,0.000016875007,0.000033698492,0.00046618146],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976181,0.00011374623,0.00025635114,0.0015075437,0.00011029183,0.00039397366],"domain_scores_gemma":[0.99730057,0.00028604988,0.00027589104,0.0016565613,0.00030640824,0.00017450165],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018813799,0.0003780034,0.0003759699,0.00031786988,0.0004266285,0.0002681436,0.0012648194,0.0005952562,0.000012116744],"category_scores_gemma":[0.00009780418,0.00045840666,0.00041576606,0.00094527047,0.00006883789,0.00027137372,0.0008062262,0.0007064864,0.000024015764],"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":[0.00034884736,0.001321563,0.000631707,0.00062967767,0.00056431716,0.00029745023,0.0017714323,0.10178448,0.0046439986,0.85333174,0.00019571131,0.03447908],"study_design_scores_gemma":[0.00077195326,0.0003803898,0.0009853907,0.00026082734,0.00011015355,0.000009064685,0.00038100238,0.8700106,0.042641714,0.08237344,0.0010932226,0.0009822419],"about_ca_topic_score_codex":0.00011822266,"about_ca_topic_score_gemma":0.00011770126,"teacher_disagreement_score":0.8088585,"about_ca_system_score_codex":0.0003376637,"about_ca_system_score_gemma":0.00015918369,"threshold_uncertainty_score":0.9997868},"labels":[],"label_agreement":null},{"id":"W3158594148","doi":"10.1109/tfuzz.2021.3076265","title":"Detection and Classification of Anomalies in Large Datasets on the Basis of Information Granules","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":33,"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":"","keywords":"Anomaly detection; Computer science; Data mining; Outlier; Anomaly (physics); Novelty detection; Basis (linear algebra); Degree (music); Semantics (computer science); Pattern recognition (psychology); Artificial intelligence; Novelty; Mathematics","score_opus":0.017607457819498327,"score_gpt":0.2366671011083605,"score_spread":0.21905964328886218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158594148","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":[0.044785287,0.000031529536,0.9537678,0.00027531903,0.00013756896,0.0002844258,0.00017444423,0.000051864827,0.00049175735],"genre_scores_gemma":[0.9990712,0.000064925334,0.0006588781,0.000032621214,0.000006111567,0.00013673492,0.0000071872437,0.0000034689147,0.000018902001],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99910444,0.00010966382,0.0003671882,0.00015120368,0.00017680581,0.00009072473],"domain_scores_gemma":[0.9991268,0.0001286968,0.00017528629,0.0004501738,0.00009563485,0.000023425771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030027278,0.00008159736,0.0001298422,0.00020363907,0.00012378999,0.00004931243,0.00016717889,0.0000652668,0.0000031593408],"category_scores_gemma":[0.000008041591,0.00006712398,0.00004287668,0.0005939484,0.000037233895,0.00038191234,0.0000029875898,0.00011025242,0.0000066227826],"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":[0.00014997231,0.0015919221,0.0005652216,0.00083850446,0.00015948112,0.0000033279682,0.003396915,0.007896054,0.1400995,0.47945786,0.0006778302,0.36516342],"study_design_scores_gemma":[0.0007319629,0.00035192556,0.016139697,0.00026130502,0.000032911063,0.000043999316,0.0020663599,0.13825004,0.83547914,0.0015579936,0.0047697476,0.00031489914],"about_ca_topic_score_codex":0.00008832839,"about_ca_topic_score_gemma":0.00012975598,"teacher_disagreement_score":0.95428586,"about_ca_system_score_codex":0.000032023065,"about_ca_system_score_gemma":0.000027688047,"threshold_uncertainty_score":0.27372366},"labels":[],"label_agreement":null},{"id":"W3160297038","doi":"10.1109/icassp39728.2021.9413754","title":"Unified Clustering and Outlier Detection on Specialized Hardware","year":2021,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Cluster analysis; Computer science; Anomaly detection; Outlier; Data mining; Quadratic unconstrained binary optimization; Machine learning; Artificial intelligence","score_opus":0.019043904943283167,"score_gpt":0.2510750385282856,"score_spread":0.23203113358500244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160297038","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":[0.004978495,0.000009146674,0.9683337,0.0006811858,0.00008821944,0.000071230854,4.631151e-7,0.00032501758,0.025512502],"genre_scores_gemma":[0.9480601,0.000028012686,0.045898356,0.0005701634,0.00006731996,0.000026681799,7.721501e-7,0.0000056952344,0.0053428765],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99950457,0.000018016892,0.0000895675,0.00023079476,0.000073880954,0.00008320295],"domain_scores_gemma":[0.9996047,0.000017897406,0.000023616458,0.00026814427,0.000044388642,0.00004127423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000051473202,0.000057338515,0.00006189109,0.00004003636,0.00012986417,0.00011764968,0.0001023287,0.000035789628,0.000057306017],"category_scores_gemma":[0.000009494845,0.0000540957,0.000025223588,0.00020602952,0.000010978517,0.00010706584,0.000111594585,0.00006038807,0.000027378997],"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":[0.000011372022,0.000077867815,0.00006229743,0.000013127106,0.000016000728,0.00001618357,0.00021661595,0.000046376,0.038065553,0.204644,0.0013250957,0.7555055],"study_design_scores_gemma":[0.0004577709,0.00012754323,0.0042989207,0.000016447591,0.0000063083744,0.00008425169,0.00009899323,0.05428685,0.74307936,0.009079308,0.18814605,0.00031818144],"about_ca_topic_score_codex":0.0000122639,"about_ca_topic_score_gemma":0.000049886425,"teacher_disagreement_score":0.9430816,"about_ca_system_score_codex":0.000019396002,"about_ca_system_score_gemma":0.000012072125,"threshold_uncertainty_score":0.22059587},"labels":[],"label_agreement":null},{"id":"W3160648873","doi":"10.32473/flairs.v34i1.128479","title":"Performance Metrics for State-Based Imitation Learning","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Artificial intelligence; Perceptron; Artificial neural network; Machine learning; Imitation; Domain (mathematical analysis); State (computer science); Multilayer perceptron; Long short term memory; Layer (electronics); Recurrent neural network; Algorithm","score_opus":0.15105616236172434,"score_gpt":0.3704275475126838,"score_spread":0.21937138515095947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160648873","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":[0.11160116,0.000025157824,0.8782082,0.0065593147,0.0003490965,0.0004549766,0.000010340224,0.00011734067,0.0026744045],"genre_scores_gemma":[0.9489668,0.00015979473,0.049623016,0.00008221294,0.00010271306,0.00020512076,0.000004228782,0.000010040109,0.0008460616],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99783427,0.000020466725,0.0004154211,0.0004325763,0.0009734915,0.00032376268],"domain_scores_gemma":[0.99317884,0.0004336369,0.00020686854,0.00018755553,0.0059249164,0.00006815349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015695317,0.0001186304,0.0001335608,0.00012378689,0.000543406,0.00043814097,0.0017042033,0.00007552828,0.000027674516],"category_scores_gemma":[0.0010792667,0.00010427069,0.00021893976,0.0015306644,0.00021038823,0.00051083363,0.00045677653,0.00044412582,0.000011266995],"study_design_candidate":"theoretical_or_conceptual","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":[0.00003789375,0.00016788296,0.0017512296,0.00013176966,0.00006191135,1.9925058e-7,0.0013616116,0.001721919,0.17616467,0.6794975,0.0016390071,0.1374644],"study_design_scores_gemma":[0.000020526037,0.000056910365,0.00012350453,0.000038254704,0.000002562124,0.000001149064,0.0005783896,0.4304607,0.5330801,0.033378385,0.0021843864,0.00007512879],"about_ca_topic_score_codex":0.000021171281,"about_ca_topic_score_gemma":0.0000038830726,"teacher_disagreement_score":0.8373656,"about_ca_system_score_codex":0.00018237821,"about_ca_system_score_gemma":0.00040980466,"threshold_uncertainty_score":0.42520356},"labels":[],"label_agreement":null},{"id":"W3160767671","doi":"10.5334/jors.300","title":"VIFECO: An Open-Source Software for Counting Features on a Video","year":2021,"lang":"en","type":"article","venue":"Journal of Open Research Software","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Institut National de la Recherche Scientifique","funders":"","keywords":"Computer science; Java; Open source; Blank; Software; Open source software; Programming language; Feature (linguistics); Operating system; World Wide Web; Database; Linguistics","score_opus":0.10970226939776556,"score_gpt":0.4350559003223611,"score_spread":0.3253536309245955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160767671","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005174047,0.00020552812,0.9888277,0.0039328383,0.00012213615,0.0009323621,0.000019063049,0.00009659002,0.000689753],"genre_scores_gemma":[0.07167057,0.00010080282,0.91880053,0.0014582793,0.00038870794,0.00027474228,0.00001093284,0.00005435706,0.0072410507],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99760926,0.00028647535,0.00044076864,0.00044668606,0.0007915051,0.0004253311],"domain_scores_gemma":[0.99475765,0.0009530847,0.00030552488,0.00084159063,0.0028643245,0.00027785226],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003557843,0.00013643823,0.00031130284,0.0002293736,0.00092090375,0.0037776765,0.0053180302,0.00010199143,0.00007015993],"category_scores_gemma":[0.0023160898,0.00011650998,0.000103716644,0.0010410453,0.000062187464,0.002035167,0.0021737586,0.00072641583,0.000014129041],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030258668,0.0010898351,0.001117746,0.00009994626,0.00011606459,0.00019619837,0.0007403081,0.0003518961,0.0030283036,0.03248706,0.28954622,0.6709238],"study_design_scores_gemma":[0.0019863106,0.0034669894,0.005339395,0.0005708322,0.00001792982,0.0008002178,0.00061644573,0.0010925329,0.037782248,0.047232885,0.900561,0.00053323095],"about_ca_topic_score_codex":0.00008121502,"about_ca_topic_score_gemma":0.000040898503,"teacher_disagreement_score":0.6703906,"about_ca_system_score_codex":0.00015299242,"about_ca_system_score_gemma":0.00092736894,"threshold_uncertainty_score":0.9972565},"labels":[],"label_agreement":null},{"id":"W3162801118","doi":"10.21203/rs.3.rs-505984/v1","title":"Dear Watch, Should I get a COVID Test? Designing deployable machine learning for wearables","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Hospital for Sick Children; University of Toronto; SickKids Foundation","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Wearable computer; Test (biology); Computer science; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Artificial intelligence; Embedded system; Virology; Medicine; Biology; Infectious disease (medical specialty)","score_opus":0.11804884026597397,"score_gpt":0.4014868811061946,"score_spread":0.2834380408402207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162801118","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":[0.0017006333,0.001425477,0.9907386,0.002851671,0.00006893482,0.0016460647,0.000030640436,0.00097035104,0.0005676553],"genre_scores_gemma":[0.6548538,0.0013567753,0.33523244,0.00014984688,0.00024905917,0.003908727,0.000139416,0.00010191961,0.004007981],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961316,0.0004360909,0.0004029261,0.0012335407,0.00091252325,0.0008833212],"domain_scores_gemma":[0.995855,0.00147389,0.00015199621,0.0012560748,0.00094912184,0.00031395635],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0026245844,0.00030143638,0.00039709636,0.00031875708,0.0012011118,0.0012789607,0.0017104992,0.00040854388,0.00008084043],"category_scores_gemma":[0.0013025859,0.00030306083,0.00027000383,0.0008160573,0.00010805568,0.00025287294,0.00277948,0.0022252162,0.00003796409],"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":[0.00032383882,0.0045757317,0.067778446,0.020088675,0.0010834461,0.0007277543,0.012252388,0.09746284,0.09553079,0.110572904,0.09043352,0.49916965],"study_design_scores_gemma":[0.0007116118,0.0010276918,0.00044343484,0.0013872866,0.000033514247,0.000054765103,0.00063089625,0.6788209,0.07955539,0.035595153,0.20044643,0.0012929438],"about_ca_topic_score_codex":0.0008022635,"about_ca_topic_score_gemma":0.00007823854,"teacher_disagreement_score":0.65550613,"about_ca_system_score_codex":0.000323426,"about_ca_system_score_gemma":0.0008468172,"threshold_uncertainty_score":0.9999421},"labels":[],"label_agreement":null},{"id":"W3163723672","doi":"10.1016/j.ins.2021.05.021","title":"A three-way clustering approach for novelty detection","year":2021,"lang":"en","type":"article","venue":"Information Sciences","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Novelty detection; Computer science; Novelty; Set (abstract data type); Artificial intelligence; Data mining; Core (optical fiber); Reduction (mathematics); Key (lock); Machine learning; Pattern recognition (psychology); Mathematics","score_opus":0.03917483380950819,"score_gpt":0.2780907780157492,"score_spread":0.23891594420624102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163723672","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":[0.0007695651,0.000010277675,0.9900396,0.00032935414,0.00011206504,0.00020415752,0.0000029949967,0.00024115106,0.008290867],"genre_scores_gemma":[0.5187711,0.000003168421,0.48064333,0.00035444568,0.000029200246,0.00016174352,0.000003840801,0.000001346709,0.000031798554],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926543,0.000006707504,0.00021997416,0.00016112548,0.00020086767,0.00014591106],"domain_scores_gemma":[0.9994249,0.000034385685,0.00011183393,0.00021059233,0.00018086829,0.000037427526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038382868,0.00006258558,0.00006367359,0.00010671367,0.00049672637,0.00045725759,0.00038581976,0.00003940262,0.0000052047108],"category_scores_gemma":[0.000043267028,0.000056279972,0.000049552906,0.0008118302,0.000052885913,0.0022911362,0.00011360065,0.00004616625,0.000015994676],"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":[0.000002573626,0.000030236397,0.000062658546,0.000030306916,0.0000050724557,9.612785e-8,0.0005004477,0.0022585902,0.002865457,0.065343134,0.00042670744,0.9284747],"study_design_scores_gemma":[0.000098164724,0.000042337295,0.00055732596,0.0000030195376,0.0000014492854,0.000026494055,0.00014008001,0.94763863,0.029360063,0.0033513373,0.018679626,0.000101457794],"about_ca_topic_score_codex":0.00002123226,"about_ca_topic_score_gemma":0.000026678845,"teacher_disagreement_score":0.94538003,"about_ca_system_score_codex":0.000027783768,"about_ca_system_score_gemma":0.00006598355,"threshold_uncertainty_score":0.44093454},"labels":[],"label_agreement":null},{"id":"W3164152617","doi":"10.24963/ijcai.2021/494","title":"A Rule Mining-based Advanced Persistent Threats Detection System","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Advanced Research Projects Agency; Defense Advanced Research Projects Agency","keywords":"Exploit; Computer science; Leverage (statistics); Event (particle physics); Data mining; Causality (physics); Process (computing); TRACE (psycholinguistics); Computer security; Machine learning","score_opus":0.019917908491080893,"score_gpt":0.2537039936605537,"score_spread":0.2337860851694728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164152617","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":[0.01878307,0.00027345924,0.97046953,0.00029721376,0.00043556516,0.00046629237,0.0000037375569,0.001925694,0.0073454096],"genre_scores_gemma":[0.79368156,0.0000125442175,0.20467986,0.00010464049,0.0000466452,0.00060412317,0.000010630204,0.000015219137,0.000844757],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982803,0.000055697783,0.0003183958,0.0008650717,0.0002576148,0.00022292214],"domain_scores_gemma":[0.9981263,0.000033965436,0.00021365586,0.0013015349,0.00022075271,0.00010375778],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014941627,0.0002473545,0.0002750663,0.0001393144,0.00021539717,0.00035132025,0.0005947971,0.0002522472,0.000023772362],"category_scores_gemma":[0.0000072347657,0.00024536104,0.00039019386,0.00028395106,0.00002487744,0.00009491969,0.000652072,0.0002856508,0.000022361575],"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":[0.000040849893,0.00072171463,0.00017250312,0.0019012825,0.0003641713,0.00012072206,0.0011743759,0.048326608,0.025520043,0.018259667,0.0006799491,0.9027181],"study_design_scores_gemma":[0.00034292077,0.0001801252,0.00024792276,0.0004331446,0.00006624635,0.000089776506,0.0008656725,0.8119121,0.18268944,0.00038833218,0.0019510827,0.00083321973],"about_ca_topic_score_codex":0.00006708778,"about_ca_topic_score_gemma":0.00004233506,"teacher_disagreement_score":0.9018849,"about_ca_system_score_codex":0.0003098838,"about_ca_system_score_gemma":0.00017285644,"threshold_uncertainty_score":0.9999999},"labels":[],"label_agreement":null},{"id":"W3164687504","doi":"10.1016/j.petrol.2021.108988","title":"A visual analytics approach to anomaly detection in hydrocarbon reservoir time series data","year":2021,"lang":"en","type":"article","venue":"Journal of Petroleum Science and Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Agência Nacional do Petróleo, Gás Natural e Biocombustíveis; Computer Modelling Group; Shell Brasil; Schlumberger Foundation","keywords":"Anomaly detection; Analytics; Visual analytics; Series (stratigraphy); Time series; Anomaly (physics); Geology; Petroleum engineering; Data analysis; Computer science; Data mining; Visualization; Machine learning","score_opus":0.014437146335562576,"score_gpt":0.24519738895470655,"score_spread":0.23076024261914396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164687504","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":[0.33605158,0.00006231266,0.6631463,0.00032086615,0.00005622083,0.000025544206,5.874817e-7,0.00002798205,0.00030859158],"genre_scores_gemma":[0.9422525,0.00002535953,0.057576127,0.000033647768,0.000055955206,0.0000023246625,2.581179e-7,0.0000039775086,0.000049859787],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989752,0.000010254188,0.00024679097,0.00024543493,0.0003392964,0.00018301007],"domain_scores_gemma":[0.9993048,0.000017945778,0.00007344784,0.00032615647,0.0001525543,0.00012510846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092940097,0.00007434266,0.00013559026,0.00042104843,0.00008407448,0.00021364458,0.00069409894,0.000028566323,6.239613e-7],"category_scores_gemma":[0.00014440612,0.000070380775,0.000020583117,0.0016204899,0.000032110103,0.0012889126,0.00037751652,0.00015848079,0.0000012701234],"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":[0.00002280173,0.00019708005,0.0007231152,0.000046308647,0.000022123782,0.0000656675,0.00042454028,0.08062269,0.895083,0.0028005508,0.0001409678,0.019851167],"study_design_scores_gemma":[0.00008635812,0.00013329591,0.004085036,0.00002221553,0.000004294315,0.00034302598,0.000054791773,0.96752816,0.025777804,0.00006426823,0.0018000099,0.00010075804],"about_ca_topic_score_codex":0.0000073783344,"about_ca_topic_score_gemma":0.000004851194,"teacher_disagreement_score":0.88690543,"about_ca_system_score_codex":0.00008290849,"about_ca_system_score_gemma":0.00014591104,"threshold_uncertainty_score":0.28700447},"labels":[],"label_agreement":null},{"id":"W3165040079","doi":"10.1145/3433210.3453095","title":"DySan: Dynamically Sanitizing Motion Sensor Data Against Sensitive Inferences through Adversarial Networks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":27,"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 Montréal","funders":"Agence Nationale de la Recherche","keywords":"Adversarial system; Computer science; Motion (physics); Artificial intelligence; Computer vision; Wireless sensor network; Computer network","score_opus":0.0454410682938591,"score_gpt":0.2890852790982923,"score_spread":0.2436442108044332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165040079","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":[0.0021391362,0.00004824966,0.9847443,0.0009918334,0.000752792,0.0005027641,0.000056189514,0.0010411948,0.009723536],"genre_scores_gemma":[0.63475806,0.00027857043,0.36288607,0.0006195126,0.00039738644,0.00004404037,0.0008608763,0.000018697332,0.0001368018],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972271,0.00017417791,0.00050021254,0.001436638,0.00032719158,0.00033472796],"domain_scores_gemma":[0.99669015,0.00014423697,0.00032200897,0.0024621144,0.00029111785,0.000090387766],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002980901,0.00034728542,0.00038764588,0.00007927217,0.00026661923,0.0008225723,0.0018284821,0.00046463456,0.000026837108],"category_scores_gemma":[0.0000545195,0.00034712712,0.00014041066,0.00037418198,0.000094855255,0.0007549569,0.0063252365,0.00086721574,0.000013443484],"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":[0.000035112756,0.0007298714,0.00017972827,0.00022980472,0.00080266997,0.00045399703,0.0027125294,0.09283161,0.0014607861,0.15449184,0.006543077,0.73952895],"study_design_scores_gemma":[0.00010693567,0.000022135855,0.0001635052,0.00010537002,0.00002974491,0.000028356726,0.00026494803,0.99357975,0.0008045329,0.003568408,0.0008603845,0.00046594345],"about_ca_topic_score_codex":0.00054237165,"about_ca_topic_score_gemma":0.00015524685,"teacher_disagreement_score":0.90074813,"about_ca_system_score_codex":0.0001167664,"about_ca_system_score_gemma":0.00021980486,"threshold_uncertainty_score":0.9998981},"labels":[],"label_agreement":null},{"id":"W3166578842","doi":"10.1109/indin45582.2020.9442233","title":"Fast Anomaly Detection in Micro Data Centers Using Machine Learning Techniques","year":2020,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"McMaster University","funders":"","keywords":"Anomaly detection; Computer science; Anomaly (physics); Artificial intelligence; Machine learning","score_opus":0.04507305453968935,"score_gpt":0.2779293460117254,"score_spread":0.23285629147203607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3166578842","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":[0.01870397,0.00002304258,0.9787245,0.00089988316,0.000019950416,0.00018145324,0.0000037859545,0.00086722744,0.00057618483],"genre_scores_gemma":[0.7333289,0.000018801771,0.2661054,0.0004519237,0.000027188438,0.000010204301,0.0000067248548,0.000008767793,0.000042114913],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990452,0.00004076208,0.00020864274,0.00044984484,0.000093787465,0.00016178314],"domain_scores_gemma":[0.9993745,0.000014271094,0.00007470004,0.00044657633,0.000024222503,0.00006574909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014174417,0.00010255942,0.00010558584,0.000102042686,0.00011824837,0.00010314587,0.00083688385,0.00005157575,0.000015599224],"category_scores_gemma":[0.000017279699,0.000102715574,0.000027509754,0.000619106,0.00002033938,0.0005344141,0.00061485614,0.0002117528,0.000011500362],"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":[0.000010394047,0.000060961585,0.0077288616,0.000017589495,0.000008954268,0.000008165015,0.00021880788,0.0000910827,0.8004708,0.00106962,0.00009647377,0.19021828],"study_design_scores_gemma":[0.00007297581,0.0000624472,0.00038436102,0.000007384719,0.0000023865152,0.0000157584,0.000026807258,0.5784588,0.4084501,0.00006411273,0.012316702,0.0001381904],"about_ca_topic_score_codex":0.00077426294,"about_ca_topic_score_gemma":0.00014332877,"teacher_disagreement_score":0.7146249,"about_ca_system_score_codex":0.000043336855,"about_ca_system_score_gemma":0.00001809787,"threshold_uncertainty_score":0.41886196},"labels":[],"label_agreement":null},{"id":"W3167049326","doi":"10.1109/tkde.2023.3328882","title":"DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Anomaly detection; Support vector machine; Artificial intelligence; Pattern recognition (psychology); Anomaly (physics); Data mining; Data modeling","score_opus":0.058018093014621526,"score_gpt":0.298694577054953,"score_spread":0.24067648404033148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3167049326","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":[0.00091282884,0.000060047187,0.99620473,0.000098366676,0.00068725157,0.0003208499,0.00032819744,0.0013285967,0.000059155336],"genre_scores_gemma":[0.9505385,0.00014443627,0.048428647,0.000025674119,0.00014799401,0.00022126795,0.00014863473,0.00003493699,0.00030988464],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986665,0.000011615354,0.0002344904,0.000705694,0.00009711589,0.00028453427],"domain_scores_gemma":[0.99801856,0.0001386841,0.000038874197,0.0016426116,0.000047307014,0.000113988084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033608603,0.00017182714,0.00015092015,0.00029652193,0.00033152624,0.00014987525,0.0011836272,0.00008446008,0.000009373998],"category_scores_gemma":[0.000013974426,0.00018508438,0.000039053666,0.0007114841,0.000018027838,0.0010702515,0.00005725256,0.00016311894,0.00005968888],"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":[0.000026893022,0.00024747042,0.000010910898,0.0002973043,0.00014953507,0.000007874267,0.00058861304,0.0059007034,0.061463155,0.0022874982,0.006098426,0.9229216],"study_design_scores_gemma":[0.00018066049,0.000092700175,0.000108335335,0.00001765719,0.000025988149,0.00001735241,0.000016445869,0.9287847,0.023276621,0.000037578,0.047231443,0.00021049078],"about_ca_topic_score_codex":0.000013382231,"about_ca_topic_score_gemma":0.000045698747,"teacher_disagreement_score":0.9496257,"about_ca_system_score_codex":0.000040795214,"about_ca_system_score_gemma":0.00003843066,"threshold_uncertainty_score":0.7547522},"labels":[],"label_agreement":null},{"id":"W3167855819","doi":"10.3390/ijgi10060412","title":"A Trajectory Scoring Tool for Local Anomaly Detection in Maritime Traffic Using Visual Analytics","year":2021,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Fisheries and Oceans Canada; Ocean Frontier Institute","keywords":"Computer science; Visual analytics; Anomaly detection; TRIPS architecture; Domain (mathematical analysis); Analytics; Visualization; Bridge (graph theory); Interpolation (computer graphics); Trajectory; Task (project management); Data mining; Anomaly (physics); Artificial intelligence; Data science; Engineering","score_opus":0.011791660923250399,"score_gpt":0.2819742111157838,"score_spread":0.2701825501925334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3167855819","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":[0.190472,0.000019076197,0.80860907,0.00020330868,0.0004899634,0.000106974105,0.000006336253,0.000032384713,0.000060873936],"genre_scores_gemma":[0.95148176,0.00002198523,0.048082553,0.00022972815,0.00014519853,0.000011049373,0.000008774376,0.0000047742737,0.000014157556],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985447,0.000030110254,0.0007658612,0.00010148111,0.00040537785,0.00015245666],"domain_scores_gemma":[0.99830556,0.00006834758,0.00047522064,0.00011752864,0.0009838763,0.000049458085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042570045,0.000105453066,0.00015309398,0.000559898,0.00008798614,0.0002895218,0.00038943958,0.000084635314,0.000012377346],"category_scores_gemma":[0.00009440133,0.00011409533,0.00015982792,0.00038792082,0.000023935805,0.0033788309,0.000066713736,0.00018925809,0.000005827661],"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":[0.00013456115,0.00017003919,0.0004136722,0.00003732567,0.00010938383,0.000023400034,0.00065082853,0.104831316,0.004131543,0.004509578,0.000113238275,0.8848751],"study_design_scores_gemma":[0.0008637289,0.00013867448,0.0032233677,0.00007538459,0.00001703127,0.0005036413,0.00023136841,0.9673709,0.022571564,0.00073197304,0.004101254,0.00017107344],"about_ca_topic_score_codex":0.000021482741,"about_ca_topic_score_gemma":0.000024595438,"teacher_disagreement_score":0.88470405,"about_ca_system_score_codex":0.00040845692,"about_ca_system_score_gemma":0.00023778193,"threshold_uncertainty_score":0.46526727},"labels":[],"label_agreement":null},{"id":"W3169682556","doi":"10.71781/10875","title":"Estimating the probability of a fleet vehicle accident : a deep learning approach using conditional variational auto-encoders","year":2020,"lang":"en","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Autoencoder; Computer science; Aeronautics; Artificial intelligence; Vehicle accident; Engineering; Deep learning; Medicine","score_opus":0.010153482909265344,"score_gpt":0.1982634271694358,"score_spread":0.18810994426017044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169682556","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":[0.0741895,0.0005769403,0.9182665,0.00021403709,0.00017949822,0.00053784455,0.000011891818,0.00024460486,0.005779211],"genre_scores_gemma":[0.87715995,0.0000081476755,0.121472746,0.000046798297,0.000094156785,0.000080655656,0.00024193896,0.000015184189,0.00088043325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979646,0.00017115776,0.00042760198,0.00056408206,0.0006511101,0.00022144854],"domain_scores_gemma":[0.998409,0.00012517594,0.0006528224,0.00035694757,0.0003310032,0.00012504838],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00030069164,0.0002499996,0.00026630046,0.0001370898,0.0045435815,0.00008724563,0.00077793153,0.00021590837,0.000011169129],"category_scores_gemma":[0.00008643473,0.00024590414,0.0002337181,0.0005107858,0.00013601196,0.00039356353,0.00022050901,0.00048872124,0.000005076463],"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":[0.00016818281,0.00032724085,0.0035794831,0.00024941162,0.00037084526,0.00006298655,0.028464824,0.6606496,0.014318314,0.28515926,0.00010643966,0.006543446],"study_design_scores_gemma":[0.00022906633,0.000046104455,0.011093711,0.00005005773,0.00009419015,0.00014533469,0.0018969451,0.97736543,0.0026755002,0.005218941,0.00092313095,0.00026155898],"about_ca_topic_score_codex":0.002276773,"about_ca_topic_score_gemma":0.0001347822,"teacher_disagreement_score":0.80297047,"about_ca_system_score_codex":0.0016243982,"about_ca_system_score_gemma":0.0011726938,"threshold_uncertainty_score":0.99999934},"labels":[],"label_agreement":null},{"id":"W3171270113","doi":"10.1109/ispa-bdcloud-socialcom-sustaincom51426.2020.00146","title":"Temporal Data Analytics on COVID-19 Data with Ubiquitous Computing","year":2020,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Big data; Data science; Computer science; Analytics; Data analysis; Data mining","score_opus":0.19669565079477633,"score_gpt":0.35578384940426094,"score_spread":0.15908819860948462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171270113","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":[0.0002837926,0.0000064800583,0.97854865,0.01780373,0.000017462075,0.00016021026,0.000047810874,0.0008148749,0.0023169913],"genre_scores_gemma":[0.6824006,0.0000041812427,0.30395088,0.013269636,0.00010706948,0.0000021203628,0.00015499962,0.000008270464,0.000102255864],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987183,0.0000232511,0.00017144892,0.0007278535,0.00020773422,0.00015137301],"domain_scores_gemma":[0.9969156,0.00007278008,0.00008717214,0.0026518912,0.000028031182,0.00024456586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020177897,0.00010362789,0.00011471985,0.000036467813,0.00018807921,0.00016804687,0.0037708343,0.000032257773,0.000023924085],"category_scores_gemma":[0.00005543496,0.000080774604,0.000011799385,0.0005589469,0.000039367664,0.0003584682,0.002203391,0.00013189504,0.000051035586],"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":[0.00005332416,0.0003802729,0.0071084043,0.00009645541,0.00011975106,0.00011359322,0.00064825924,0.004003005,0.00017704433,0.25054508,0.63218534,0.10456946],"study_design_scores_gemma":[0.000118982025,0.00017389862,0.000079675854,0.0000031439727,0.000006250485,0.000012321467,0.000035714475,0.83741534,0.00016884411,0.00019000133,0.16166376,0.00013208861],"about_ca_topic_score_codex":0.00013097431,"about_ca_topic_score_gemma":0.000037500944,"teacher_disagreement_score":0.83341235,"about_ca_system_score_codex":0.00002205608,"about_ca_system_score_gemma":0.00015257066,"threshold_uncertainty_score":0.7007211},"labels":[],"label_agreement":null},{"id":"W3172128134","doi":"10.23919/mva51890.2021.9511378","title":"Predicting Next Local Appearance for Video Anomaly Detection","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données","keywords":"Artificial intelligence; Anomaly detection; Computer science; Object (grammar); Inference; Computer vision; Frame (networking); Anomaly (physics); Pattern recognition (psychology); Adversarial system; Image (mathematics)","score_opus":0.021584218831167696,"score_gpt":0.26116649438281553,"score_spread":0.23958227555164782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172128134","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":[0.009694341,0.0002819103,0.98588425,0.0003563732,0.00047314444,0.0007878248,0.0000045073352,0.0013138929,0.0012037405],"genre_scores_gemma":[0.8380716,0.000026401616,0.16009939,0.00029724182,0.00018673488,0.0010185044,0.000006100931,0.000019265197,0.00027476106],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981839,0.000035631467,0.00038665585,0.0009237257,0.00019123158,0.0002788224],"domain_scores_gemma":[0.9983749,0.000040715488,0.000231425,0.0010146279,0.0002483416,0.000090014415],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002521016,0.00023435916,0.00025565125,0.00011084863,0.00027124427,0.00056617596,0.0008449821,0.00025583242,0.000012027439],"category_scores_gemma":[0.000030237867,0.00024763684,0.00025577188,0.00030833523,0.000046167752,0.00035038384,0.00093903416,0.0004102716,0.000010534342],"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":[0.000017838292,0.0001793772,0.0003362897,0.00046245533,0.00010691146,0.000006904962,0.00030848407,0.004200798,0.010986425,0.020631185,0.00056554755,0.9621978],"study_design_scores_gemma":[0.00015090614,0.00008960561,0.00053789886,0.00011641639,0.000019282044,0.000034346882,0.00009953322,0.8879176,0.096602574,0.0056036646,0.008413948,0.00041426226],"about_ca_topic_score_codex":0.00027962116,"about_ca_topic_score_gemma":0.00015760372,"teacher_disagreement_score":0.9617835,"about_ca_system_score_codex":0.00011614376,"about_ca_system_score_gemma":0.00014240442,"threshold_uncertainty_score":0.9999976},"labels":[],"label_agreement":null},{"id":"W3178013428","doi":"10.18280/ria.350309","title":"A Framework for Anomaly Classification Using Deep Transfer Learning Approach","year":2021,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Transfer of learning; Computer science; Artificial intelligence; Preprocessor; Machine learning; Field (mathematics); Anomaly detection; Computer vision; Computer security","score_opus":0.09282876624586345,"score_gpt":0.3164997598376683,"score_spread":0.22367099359180487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3178013428","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":[0.006620546,0.00017466446,0.99085516,0.00042769444,0.00009297191,0.00030489985,0.0000014628525,0.0002648711,0.0012577254],"genre_scores_gemma":[0.57711434,0.000031751024,0.42221648,0.00007243539,0.00005996667,0.00011232707,0.0000057555367,0.000011612926,0.00037537157],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868095,0.0000529848,0.00034128432,0.00055012683,0.00011048476,0.00026419552],"domain_scores_gemma":[0.9989294,0.00014359709,0.000066183704,0.0005723667,0.00021409043,0.000074363044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025079044,0.00012835668,0.00015458392,0.00007667027,0.0003982205,0.00017315123,0.0004272285,0.00012646505,0.00003546602],"category_scores_gemma":[0.00008755037,0.00014222883,0.00014569168,0.0008074081,0.000044586428,0.00021784678,0.00006131349,0.0002225875,0.00003464539],"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":[0.000004392214,0.0001603005,0.00008626358,0.000047311656,0.0000125283605,0.0000017327809,0.00089247385,0.04904547,0.03114433,0.8370701,0.00001784936,0.081517264],"study_design_scores_gemma":[0.000014453121,0.000035659134,0.000022494514,0.000020669335,0.000008589354,0.000031359017,0.00046410362,0.8404408,0.13589697,0.0176395,0.0052725193,0.00015289642],"about_ca_topic_score_codex":0.000005983833,"about_ca_topic_score_gemma":0.0000012340236,"teacher_disagreement_score":0.8194306,"about_ca_system_score_codex":0.00005354884,"about_ca_system_score_gemma":0.0000596508,"threshold_uncertainty_score":0.57999235},"labels":[],"label_agreement":null},{"id":"W3180930078","doi":"10.48550/arxiv.2107.05053","title":"Anomaly Detection in Smart Manufacturing with an Application Focus on Robotic Finishing Systems: A Review","year":2021,"lang":"en","type":"review","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Software deployment; Anomaly detection; Computer science; Focus (optics); Anomaly (physics); Production (economics); Smart manufacturing; Systems engineering; Risk analysis (engineering); Manufacturing engineering; Engineering; Artificial intelligence; Business; Software engineering","score_opus":0.06673813312802132,"score_gpt":0.223613304076687,"score_spread":0.15687517094866568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3180930078","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000026049735,0.39462063,0.6037774,0.000013280824,0.00003932931,0.00093652384,0.0000019783392,0.00023493868,0.00034990706],"genre_scores_gemma":[0.039290015,0.95961237,0.00071330275,0.00003845111,0.00003714975,0.00007595073,0.000017544171,0.00003137098,0.0001838483],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99785626,0.00027531176,0.00037362886,0.0011302158,0.00009888732,0.00026567682],"domain_scores_gemma":[0.9979764,0.00009652014,0.00044644423,0.0012975248,0.00006714866,0.000115952935],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029876796,0.00035045645,0.0008600561,0.00034116372,0.00017527431,0.0001359182,0.00090688746,0.0002103945,0.0000040268274],"category_scores_gemma":[0.000008870303,0.00033854248,0.00019903369,0.0014722288,0.000029485402,0.00050971314,0.0001552659,0.0004568055,0.000044276047],"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":[0.000003987421,0.00013587104,0.0000065304807,0.0065453653,0.00005270269,0.00012973278,0.00001215491,0.00339383,5.8965713e-7,0.03916864,0.00001714124,0.95053345],"study_design_scores_gemma":[0.00060524436,0.0009565032,0.000060535764,0.08588504,0.0011438961,0.00047941823,0.000067325294,0.11335761,0.00015249076,0.0015429258,0.7930134,0.002735598],"about_ca_topic_score_codex":0.00023508038,"about_ca_topic_score_gemma":0.00016798778,"teacher_disagreement_score":0.94779783,"about_ca_system_score_codex":0.0004165995,"about_ca_system_score_gemma":0.00012870178,"threshold_uncertainty_score":0.99990666},"labels":[],"label_agreement":null},{"id":"W3184033781","doi":"10.3390/s21144805","title":"OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection","year":2021,"lang":"en","type":"article","venue":"MDPI (MDPI AG)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Autoencoder; Deep learning; Computer science; Anomaly detection; Latency (audio); Software deployment; Field-programmable gate array; Artificial intelligence; Field (mathematics); Embedded system; Computer architecture; Real-time computing; Telecommunications; Operating system","score_opus":0.01512265854475249,"score_gpt":0.26002079830483954,"score_spread":0.24489813976008706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184033781","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":[0.017669005,0.00021812742,0.97625554,0.00126487,0.00039910624,0.00051957864,0.000012711023,0.0009051683,0.0027558866],"genre_scores_gemma":[0.9385828,0.000012251049,0.057890628,0.0014463824,0.0004926394,0.00021039571,0.000011587768,0.000036400692,0.0013169567],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99797857,0.00008927819,0.00037449718,0.000747747,0.00027997606,0.0005299078],"domain_scores_gemma":[0.9982413,0.0002999536,0.0001784552,0.0009136964,0.00019817798,0.0001684089],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023602656,0.00028354482,0.00029162306,0.00015463862,0.0005404941,0.0002643865,0.00065046636,0.00015207073,0.000044573007],"category_scores_gemma":[0.00005951021,0.0002710825,0.00021913605,0.00077983504,0.000057189533,0.00010191798,0.00013259023,0.00029203904,0.000094105744],"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":[0.00017421992,0.0009417444,0.0010842559,0.0002236786,0.00035951522,0.0000810634,0.0012658089,0.54447365,0.018861638,0.06280105,0.021415355,0.34831804],"study_design_scores_gemma":[0.00079103763,0.0006975551,0.009143996,0.0000740552,0.00008113115,0.00013165026,0.000051437844,0.77635014,0.041239288,0.03413886,0.13638127,0.0009195571],"about_ca_topic_score_codex":0.000047435104,"about_ca_topic_score_gemma":0.00024113905,"teacher_disagreement_score":0.92091376,"about_ca_system_score_codex":0.00012164863,"about_ca_system_score_gemma":0.00009799461,"threshold_uncertainty_score":0.99997413},"labels":[],"label_agreement":null},{"id":"W3184074903","doi":"10.22215/etd/2021-14478","title":"Investigation of Few-Shot Learning for Fall Detection","year":2021,"lang":"en","type":"dissertation","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère de la Défense Nationale","keywords":"Generalizability theory; Deep learning; Artificial intelligence; Computer science; Convolutional neural network; Wearable computer; Shot (pellet); Modalities; Machine learning; Embedded system; Psychology","score_opus":0.028169743524470246,"score_gpt":0.2790733379371244,"score_spread":0.25090359441265414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184074903","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":[0.03004355,0.000068125744,0.96375465,0.00005893017,0.00022630002,0.00039972807,0.0000013713789,0.0003166197,0.0051307324],"genre_scores_gemma":[0.90618944,0.00006296007,0.06869934,0.000056019333,0.00008934105,0.0005686075,0.00037679757,0.000027753586,0.023929765],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99902827,0.00003004477,0.00031617362,0.0003574672,0.00015287122,0.00011518762],"domain_scores_gemma":[0.99891204,0.00006354838,0.0003224615,0.00028643225,0.000373785,0.000041726238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014674795,0.00013455564,0.00018438006,0.00015760868,0.00015386016,0.00007017301,0.0002819604,0.00021865343,0.000013355912],"category_scores_gemma":[0.000045309127,0.00014360102,0.00014014322,0.0004039783,0.00001223175,0.00016350616,0.000026892902,0.0001790091,0.0000041660865],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029409068,0.000050903272,0.0002542495,0.0007199453,0.00009777794,7.499281e-7,0.0018645383,0.00016346111,0.49987987,0.096466966,0.0010171047,0.399455],"study_design_scores_gemma":[0.000106476895,0.00014450328,0.0011450342,0.00005959546,0.000023245762,0.0000027130272,0.00033270527,0.016525678,0.96894413,0.0063527934,0.006133636,0.00022949387],"about_ca_topic_score_codex":0.000103708,"about_ca_topic_score_gemma":0.00035131976,"teacher_disagreement_score":0.8950553,"about_ca_system_score_codex":0.000036375593,"about_ca_system_score_gemma":0.00009970053,"threshold_uncertainty_score":0.5855879},"labels":[],"label_agreement":null},{"id":"W3184338320","doi":"10.1109/tnsm.2021.3098784","title":"TSAGen: Synthetic Time Series Generation for KPI Anomaly Detection","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Anomaly detection; Computer science; Data mining; Performance indicator; Time series; Anomaly (physics); Series (stratigraphy); Overhead (engineering); Machine learning","score_opus":0.011575568544009708,"score_gpt":0.211022926873319,"score_spread":0.1994473583293093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184338320","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":[0.0018910377,0.00005216284,0.993576,0.0021392913,0.000284923,0.00044778432,0.000005108464,0.0002970765,0.0013065998],"genre_scores_gemma":[0.8612567,0.0007475961,0.12908454,0.002549716,0.00025274395,0.0011095252,0.000011283621,0.0000320965,0.0049557616],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989943,0.0000411753,0.00019619429,0.0004433414,0.00011711806,0.00020787785],"domain_scores_gemma":[0.99933285,0.00002882634,0.00005339685,0.00042615552,0.000097630706,0.000061149796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001399714,0.00014273875,0.00012188993,0.0000699951,0.00059359905,0.00017660913,0.00016523154,0.000063484455,0.000030779407],"category_scores_gemma":[3.8854418e-7,0.00015353705,0.0000712473,0.0005419856,0.000013762504,0.00024089633,0.00000795907,0.00007761056,0.000031194617],"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":[0.00004739063,0.00026775186,0.0000014908283,0.00016021283,0.00020392495,0.000010839724,0.00022485659,0.058824692,0.010513801,0.01428999,0.0012830404,0.914172],"study_design_scores_gemma":[0.0005740271,0.00033188876,0.00012198921,0.000050954834,0.0001647883,0.00006350829,0.00010107267,0.7952775,0.09896606,0.0031210277,0.10068023,0.0005469791],"about_ca_topic_score_codex":0.00000898138,"about_ca_topic_score_gemma":0.00013893034,"teacher_disagreement_score":0.913625,"about_ca_system_score_codex":0.000036818128,"about_ca_system_score_gemma":0.0000138311125,"threshold_uncertainty_score":0.6261059},"labels":[],"label_agreement":null},{"id":"W3184976066","doi":"10.1155/2021/2020882","title":"Research on Abnormal Behavior Recognition of Buses Based on Improved Support Vector Machine","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Support vector machine; Computer science; Public security; Public transport; Computer security; Artificial intelligence; Engineering; Transport engineering; Psychology","score_opus":0.034078178617401714,"score_gpt":0.335099113256369,"score_spread":0.3010209346389673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184976066","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":[0.61948854,0.000026407144,0.37909934,0.00060386874,0.0002150267,0.00024366725,0.000047386588,0.000048030146,0.00022775392],"genre_scores_gemma":[0.9425915,0.000046381505,0.057132654,0.000087866996,0.000041441705,0.000028268452,0.000032870124,0.000008787359,0.000030242238],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998732,0.00005718302,0.0004773621,0.00017155588,0.00043007088,0.00013185783],"domain_scores_gemma":[0.99838346,0.000113711685,0.00033582884,0.00022196288,0.00087615906,0.00006890395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003692367,0.000083820334,0.00015442898,0.00026475816,0.000084710024,0.000022258204,0.00019825585,0.00005522115,0.00005264541],"category_scores_gemma":[0.000024142824,0.000078149315,0.00011663137,0.0005691256,0.000028600174,0.00035611127,0.0000030886256,0.0002981831,0.0000036560473],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009775647,0.0029198162,0.0014597466,0.00013489292,0.000046132664,0.00025517715,0.00077389664,0.020423485,0.5131896,0.0068274694,0.00018503309,0.4528072],"study_design_scores_gemma":[0.0012442024,0.0028831805,0.15215948,0.00013714802,0.000033233773,0.000025295378,0.00015204816,0.001993979,0.8390871,0.0010924011,0.0010258409,0.00016605607],"about_ca_topic_score_codex":0.0000063675902,"about_ca_topic_score_gemma":0.000020663305,"teacher_disagreement_score":0.45264113,"about_ca_system_score_codex":0.000049559392,"about_ca_system_score_gemma":0.0001588528,"threshold_uncertainty_score":0.31868365},"labels":[],"label_agreement":null},{"id":"W3190758134","doi":"10.1088/1742-6596/1994/1/012015","title":"Comparison of classifiers for different data in application of classification","year":2021,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial intelligence; Computer science; Support vector machine; Classifier (UML); Random subspace method; Pattern recognition (psychology); Machine learning; Random forest; Contextual image classification; Linear classifier; Task (project management); Image (mathematics)","score_opus":0.12383355656640335,"score_gpt":0.3691606563649291,"score_spread":0.24532709979852577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3190758134","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":[0.025366537,0.000041327068,0.9737303,0.0005869078,0.000047055597,0.00011094344,0.000019082803,0.000007587673,0.000090202964],"genre_scores_gemma":[0.9605798,0.00005338993,0.039278224,0.000007747681,0.00003123137,0.000012594619,0.000018141416,0.0000033704143,0.000015515301],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99903995,0.000031418127,0.00054202497,0.0001453467,0.0001690501,0.00007219524],"domain_scores_gemma":[0.99798423,0.00007138536,0.00079739245,0.00053026865,0.00058959133,0.000027154505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015683885,0.00006730141,0.00027120498,0.000053459826,0.000029012977,0.000026663361,0.00064537517,0.000040372455,0.0000019666966],"category_scores_gemma":[0.00003443098,0.000061468316,0.000056086883,0.00027006015,0.00007983847,0.00048651194,0.00011117122,0.00010003134,1.9676406e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018321027,0.00028243745,0.0042537297,0.00006047275,0.000016427006,1.3223986e-7,0.00035606878,0.000035570712,0.17666712,0.59162855,0.00009888543,0.22658227],"study_design_scores_gemma":[0.0002241454,0.00017340736,0.013509125,0.000053235155,0.000016841512,0.0000037773625,0.00069441367,0.04751225,0.88293815,0.05356315,0.0012275325,0.00008394693],"about_ca_topic_score_codex":0.0000043250725,"about_ca_topic_score_gemma":0.00001621778,"teacher_disagreement_score":0.93521327,"about_ca_system_score_codex":0.000022145132,"about_ca_system_score_gemma":0.0001614275,"threshold_uncertainty_score":0.2506605},"labels":[],"label_agreement":null},{"id":"W3193227769","doi":"10.2196/27283","title":"Chloe for COVID-19: Evolution of an Intelligent Conversational Agent to Address Infodemic Management Needs During the COVID-19 Pandemic","year":2021,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Mitacs","keywords":"Coronavirus disease 2019 (COVID-19); Pandemic; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Computer science; Data science; Virology; Medicine; Disease; Infectious disease (medical specialty)","score_opus":0.12596007534977163,"score_gpt":0.44563526753372334,"score_spread":0.3196751921839517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193227769","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":[0.06960713,0.0001809366,0.907556,0.021980334,0.00014474281,0.000392397,0.000005705382,0.000030065376,0.00010268709],"genre_scores_gemma":[0.9908501,0.00040339265,0.0050606155,0.0021347858,0.00021522466,0.00012202514,0.000003333709,0.000008128604,0.0012023783],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9966988,0.00031166046,0.0006502778,0.00021867653,0.0018502793,0.00027028078],"domain_scores_gemma":[0.99726874,0.0006185305,0.00021375877,0.00038463002,0.0006267257,0.0008876087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005520263,0.00008498562,0.00017477458,0.00057512015,0.0001831477,0.00012713937,0.0017472948,0.00010985743,0.00041746287],"category_scores_gemma":[0.001750924,0.00006426477,0.00013031089,0.0008385535,0.00019619832,0.00016150932,0.00069991575,0.000556822,0.000009078857],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012639867,0.0017396689,0.0070765316,0.0016282637,0.00085595646,0.0004923517,0.015797783,0.0061344537,0.0070338203,0.6503643,0.17786436,0.12974852],"study_design_scores_gemma":[0.0020634898,0.0011786039,0.002755383,0.00026326688,0.000036463585,0.0016426933,0.005034368,0.079563774,0.011343066,0.021891573,0.8739336,0.00029370954],"about_ca_topic_score_codex":0.00035454438,"about_ca_topic_score_gemma":0.00014932147,"teacher_disagreement_score":0.92124295,"about_ca_system_score_codex":0.0013363898,"about_ca_system_score_gemma":0.0013493276,"threshold_uncertainty_score":0.45709255},"labels":[],"label_agreement":null},{"id":"W3193246853","doi":"10.3390/jcp1030023","title":"RSSI-Based MAC-Layer Spoofing Detection: Deep Learning Approach","year":2021,"lang":"en","type":"article","venue":"Journal of Cybersecurity and Privacy","topic":"Anomaly Detection Techniques and Applications","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":"York University","funders":"","keywords":"Profiling (computer programming); Computer science; Spoofing attack; Autoencoder; Subnetwork; Wireless; Wireless network; Deep learning; Real-time computing; MAC address; Artificial intelligence; Computer network; Machine learning; Telecommunications","score_opus":0.016376076653542834,"score_gpt":0.24888985368334088,"score_spread":0.23251377702979803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193246853","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":[0.06873325,0.00053978746,0.92814744,0.0008830437,0.00009366249,0.00004638493,2.3984742e-7,0.00006007021,0.001496138],"genre_scores_gemma":[0.90027136,0.000115620925,0.09919149,0.00022924479,0.00011910487,0.0000033916542,4.1380488e-7,0.000005682799,0.000063705105],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990756,0.00009796698,0.00029841377,0.0001785702,0.00020829587,0.00014117279],"domain_scores_gemma":[0.9991278,0.00006855869,0.00026477792,0.00021681114,0.00020854239,0.00011352146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038596496,0.00009633678,0.00016846812,0.00010486774,0.0002745108,0.00018161489,0.00029422587,0.00007767753,0.000024533141],"category_scores_gemma":[0.00007086555,0.00008993824,0.000119236916,0.00037011053,0.00003325096,0.00039514567,0.00012124603,0.00045280153,0.0000029404457],"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":[0.00007742872,0.000672007,0.0025436692,0.00016743578,0.00015820329,0.00024382085,0.0056624953,0.0019956683,0.029943118,0.050842598,0.00048787915,0.9072057],"study_design_scores_gemma":[0.0023088674,0.0007037505,0.008560216,0.00015314372,0.00011735536,0.0041378243,0.0008643036,0.25776458,0.2949096,0.031356882,0.39820617,0.0009172944],"about_ca_topic_score_codex":0.0000055007413,"about_ca_topic_score_gemma":0.0000021187122,"teacher_disagreement_score":0.9062884,"about_ca_system_score_codex":0.000031612264,"about_ca_system_score_gemma":0.00007436631,"threshold_uncertainty_score":0.3667575},"labels":[],"label_agreement":null},{"id":"W3197309071","doi":"10.1109/isc253183.2021.9562915","title":"DeepFlow: Abnormal Traffic Flow Detection Using Siamese Networks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dynamic time warping; Computer science; Anomaly detection; Limiting; Traffic flow (computer networking); Process (computing); Trajectory; Artificial neural network; Real-time computing; Data mining; Artificial intelligence; Machine learning; Computer network; Engineering","score_opus":0.017660946214038558,"score_gpt":0.2513909948002839,"score_spread":0.23373004858624533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197309071","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":[0.05080904,0.00020273511,0.945919,0.00011070712,0.00069525454,0.00035457037,0.0000014226108,0.0012689652,0.0006383462],"genre_scores_gemma":[0.7035166,0.00007243969,0.29572946,0.00015115141,0.0002398407,0.000102155755,0.00000969532,0.000020044585,0.00015860825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981025,0.00007754613,0.00040278715,0.00083973847,0.00023549321,0.00034193328],"domain_scores_gemma":[0.99828976,0.000031694806,0.00019162432,0.0011843017,0.00017368054,0.00012895065],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022505967,0.0002972561,0.00028824652,0.00016259067,0.00032227449,0.00062938785,0.0008884419,0.00046289066,0.00006961302],"category_scores_gemma":[0.000008847931,0.00031079835,0.00027325295,0.00054507336,0.000037861293,0.0003018398,0.0012353664,0.0007666577,0.000010218527],"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":[0.0000022377983,0.000069738104,0.000013629633,0.00002513952,0.00003484854,0.000012297524,0.0001168768,0.6729056,0.00034480347,0.0005081799,0.00008602835,0.32588062],"study_design_scores_gemma":[0.000062104045,0.000022178108,0.00012606232,0.000032996617,0.000017726252,0.000068461726,0.000027313909,0.9949974,0.0034178705,0.00024042788,0.0006402978,0.00034720538],"about_ca_topic_score_codex":0.000119263044,"about_ca_topic_score_gemma":0.00011336359,"teacher_disagreement_score":0.6527076,"about_ca_system_score_codex":0.00015930372,"about_ca_system_score_gemma":0.00014605769,"threshold_uncertainty_score":0.99993443},"labels":[],"label_agreement":null},{"id":"W3197795840","doi":"10.1155/2021/8103333","title":"Grid-Based Anomaly Detection of Freight Vehicle Trajectory considering Local Temporal Window","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Anomaly detection; Trajectory; Computer science; Grid; Anomaly (physics); Tracing; Focus (optics); Sliding window protocol; Beijing; Key (lock); Temporal resolution; Real-time computing; Data mining; Window (computing); Computer security; Geography; Geodesy","score_opus":0.009412701335570607,"score_gpt":0.23044525348953326,"score_spread":0.22103255215396267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197795840","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":[0.38108996,0.00012187651,0.61843234,0.00010994089,0.00014160966,0.00005122887,0.000003211039,0.000031864525,0.000017964254],"genre_scores_gemma":[0.8925878,0.000021306143,0.10726215,0.000054593864,0.000049580405,0.000005579072,0.0000040721684,0.00000774821,0.0000072050734],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988814,0.000033967364,0.0005682434,0.00015852379,0.00024768882,0.00011019476],"domain_scores_gemma":[0.9987847,0.00005448164,0.0005029693,0.00018340538,0.00040674032,0.00006766438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001395928,0.000095837626,0.0001993846,0.00013635974,0.000072455805,0.000018453264,0.00015699983,0.000060539467,0.000010508364],"category_scores_gemma":[0.00001097351,0.00009724451,0.00015807318,0.00043740068,0.00004560443,0.00054519234,0.0000029427858,0.0001688195,7.6505205e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000077664685,0.00018324138,0.0026085468,0.00006074579,0.000033921857,0.00007422393,0.00036066433,0.08480603,0.8507594,0.0014158343,0.00000980168,0.05960996],"study_design_scores_gemma":[0.0007101756,0.00024082279,0.07860009,0.000054419994,0.000022884464,0.000034027493,0.00015454937,0.00301511,0.91493136,0.0010936217,0.00103128,0.000111661546],"about_ca_topic_score_codex":0.000012241462,"about_ca_topic_score_gemma":0.00009802958,"teacher_disagreement_score":0.5114978,"about_ca_system_score_codex":0.000051013125,"about_ca_system_score_gemma":0.00017470567,"threshold_uncertainty_score":0.3965516},"labels":[],"label_agreement":null},{"id":"W3197833682","doi":"10.1109/dslw51110.2021.9523400","title":"Multimodal Generative Neural Network for Anomaly Events Detection and Localization in Videos","year":2021,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Anomaly detection; Computer science; Artificial intelligence; Frame (networking); Generative grammar; Object detection; Generative model; Pattern recognition (psychology); Artificial neural network; Anomaly (physics); Computer vision; Generative adversarial network; Object (grammar); Deep learning","score_opus":0.013285477592851303,"score_gpt":0.25756600397501933,"score_spread":0.24428052638216802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197833682","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":[0.043114845,0.000041895593,0.95602185,0.00030903355,0.00006588939,0.00024075987,0.0000010488769,0.000108658074,0.00009603736],"genre_scores_gemma":[0.8708903,0.000011630228,0.1283249,0.00038865732,0.000054092587,0.00015246113,0.0000035057906,0.000004395198,0.00017001618],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993838,0.000033540837,0.00014298658,0.0002626912,0.000053630352,0.00012333703],"domain_scores_gemma":[0.9996708,0.00003467124,0.000038362734,0.00014587797,0.00007732008,0.00003293624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008523358,0.0000657351,0.000073792326,0.000037105605,0.00013409922,0.000049687624,0.000081424725,0.000047046364,0.000004719574],"category_scores_gemma":[0.00001295625,0.000065757464,0.000026060214,0.00035030892,0.00001019572,0.00017443934,0.000062583975,0.00004305806,0.0000012064856],"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":[0.00004915644,0.00027805776,0.037063062,0.000052393752,0.000041107967,0.000010255865,0.00060751004,0.08132485,0.05325015,0.13223566,0.0015933961,0.6934944],"study_design_scores_gemma":[0.00019521463,0.000040679777,0.00791157,0.0000035107387,0.0000018685123,0.0000091811335,0.00001091171,0.94025564,0.043221906,0.0073915944,0.0008755891,0.000082355094],"about_ca_topic_score_codex":0.000040517483,"about_ca_topic_score_gemma":0.00017494531,"teacher_disagreement_score":0.85893077,"about_ca_system_score_codex":0.000026821574,"about_ca_system_score_gemma":0.000017671762,"threshold_uncertainty_score":0.26815116},"labels":[],"label_agreement":null},{"id":"W3199819356","doi":"10.1007/s44163-021-00004-2","title":"Exploring Convolutional Recurrent architectures for anomaly detection in videos: a comparative study","year":2021,"lang":"en","type":"article","venue":"Discover Artificial Intelligence","topic":"Anomaly Detection Techniques and Applications","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 Waterloo","funders":"","keywords":"Computer science; Anomaly detection; Task (project management); Artificial intelligence; Deep learning; Focus (optics); Machine learning; Convolutional neural network; Anomaly (physics); Variety (cybernetics); Engineering","score_opus":0.2409233846886999,"score_gpt":0.368324819121254,"score_spread":0.12740143443255408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199819356","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":[0.40571603,0.000034488858,0.593423,0.00012078281,0.0001624695,0.00039985767,0.000006497159,0.000070602386,0.00006624086],"genre_scores_gemma":[0.99227494,0.000006812937,0.0065348945,0.000036343397,0.00008050073,0.0010346828,0.000003521826,0.000006578598,0.000021723794],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985565,0.00008504341,0.00040592163,0.0005325315,0.00018778293,0.00023221386],"domain_scores_gemma":[0.99921066,0.00016354817,0.0000873573,0.0003371745,0.00014817459,0.000053082345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021812989,0.00014125403,0.00017470388,0.00014662449,0.00020468023,0.00015335697,0.00032241075,0.00002997722,0.000011251619],"category_scores_gemma":[0.000056984965,0.00014460746,0.00009226824,0.0007292238,0.000051274725,0.0002520179,0.00013233225,0.00015870623,0.000019081079],"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":[0.00023965425,0.0031681673,0.0016939795,0.000038989474,0.00008226813,0.000026794707,0.018530235,0.012610106,0.03796736,0.29687202,0.00003432857,0.6287361],"study_design_scores_gemma":[0.00008981715,0.00054136675,0.010403602,0.00003576717,0.0000119694205,0.000013263008,0.0041158902,0.14054415,0.7598434,0.083342746,0.00066208286,0.00039596358],"about_ca_topic_score_codex":0.00012041726,"about_ca_topic_score_gemma":0.0013149163,"teacher_disagreement_score":0.721876,"about_ca_system_score_codex":0.00009255244,"about_ca_system_score_gemma":0.00009026992,"threshold_uncertainty_score":0.5896921},"labels":[],"label_agreement":null},{"id":"W3202369023","doi":"10.1109/icas49788.2021.9551129","title":"General Frameworks for Anomaly Detection Explainability: Comparative Study","year":2021,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Autoencoder; Anomaly detection; Computer science; Artificial intelligence; Transparency (behavior); Residual; Machine learning; Convolutional neural network; Trustworthiness; Realm; Deep learning; Anomaly (physics); Pattern recognition (psychology)","score_opus":0.03139639491234781,"score_gpt":0.3215248638131856,"score_spread":0.2901284689008378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3202369023","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":[0.17613125,0.000013453008,0.82136613,0.0003705722,0.00008654497,0.000540333,0.0000011252621,0.00033513395,0.0011554281],"genre_scores_gemma":[0.8589687,0.0000011725209,0.13892084,0.00021047377,0.00005910971,0.0006263302,0.0000013936269,0.000004239067,0.0012077445],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990891,0.000039001083,0.00018976933,0.0004240165,0.000104343395,0.00015374988],"domain_scores_gemma":[0.9990247,0.0000811554,0.000050636892,0.000527583,0.00026229338,0.000053661377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015259445,0.0000960917,0.00013856626,0.000045252687,0.00026401924,0.00014158849,0.00025570503,0.00009087318,0.000030940868],"category_scores_gemma":[0.000019651761,0.00009153943,0.000077680575,0.00042942754,0.000019579384,0.00022484566,0.00012242144,0.00015217003,0.000009893674],"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":[0.00005411255,0.003944139,0.00509623,0.000036760706,0.00020796944,0.000017697368,0.0058573764,0.0004462026,0.032850627,0.53856295,0.0047112023,0.40821475],"study_design_scores_gemma":[0.0009950062,0.0014762342,0.02147419,0.000006951588,0.000034604775,0.000048279315,0.0035585288,0.2529913,0.60738635,0.06969896,0.041615568,0.00071406295],"about_ca_topic_score_codex":0.000029734882,"about_ca_topic_score_gemma":0.00009248695,"teacher_disagreement_score":0.6828374,"about_ca_system_score_codex":0.00004954061,"about_ca_system_score_gemma":0.00004210256,"threshold_uncertainty_score":0.37328696},"labels":[],"label_agreement":null},{"id":"W3202911169","doi":"10.48550/arxiv.2110.01794","title":"Multi-axis Attentive Prediction for Sparse EventData: An Application to Crime Prediction","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Overfitting; Computer science; Generalization; Artificial intelligence; Event (particle physics); Machine learning; Semantics (computer science); Task (project management); Deep learning; Pattern recognition (psychology); Artificial neural network; Mathematics","score_opus":0.08081462571544762,"score_gpt":0.22660146109265925,"score_spread":0.14578683537721163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3202911169","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":[0.05423838,0.000013152943,0.9424201,0.0000828888,0.00032351055,0.0016795851,0.00032632647,0.000802294,0.00011375567],"genre_scores_gemma":[0.9379582,0.0000582204,0.060242534,0.000100639605,0.00015407152,0.00015475498,0.00052881474,0.000024797706,0.00077798666],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99764264,0.00008339283,0.00028988917,0.0016098105,0.000099771605,0.0002744712],"domain_scores_gemma":[0.9974157,0.00003516631,0.00025309462,0.0016050081,0.0004625516,0.00022846066],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025239744,0.00027413186,0.0002471751,0.00027271642,0.00033098247,0.0001813599,0.0011108716,0.00031480636,0.000009283341],"category_scores_gemma":[0.00002170401,0.00035432345,0.00020533182,0.0006798566,0.000044550874,0.000755075,0.00091559516,0.00030777446,0.00002855447],"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":[0.00034257272,0.005018406,0.01200482,0.00069472403,0.00087384303,0.00006559303,0.0033688173,0.56482804,0.037609294,0.322411,0.007110614,0.045672253],"study_design_scores_gemma":[0.0003859355,0.00019392751,0.012058645,0.0000543392,0.00010228502,0.0000048900592,0.00017068845,0.9754025,0.003021799,0.004075747,0.00415359,0.000375657],"about_ca_topic_score_codex":0.00015975516,"about_ca_topic_score_gemma":0.000052255356,"teacher_disagreement_score":0.8837198,"about_ca_system_score_codex":0.00033507333,"about_ca_system_score_gemma":0.00013194646,"threshold_uncertainty_score":0.99989086},"labels":[],"label_agreement":null},{"id":"W3202919320","doi":"10.18409/ispiv.v1i1.130","title":"A network-based perspective on coherent structure detection from very-sparse Lagrangian data","year":2021,"lang":"en","type":"article","venue":"14th International Symposium on Particle Image Velocimetry","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Lagrangian coherent structures; Lagrangian; Eulerian path; Representation (politics); Perspective (graphical); Flow (mathematics); Trajectory; Fluid dynamics; Fluid mechanics; Motion (physics); Computer science; Mathematics; Classical mechanics; Statistical physics; Physics; Applied mathematics; Mechanics; Artificial intelligence; Turbulence","score_opus":0.019277877340559375,"score_gpt":0.28411211553811533,"score_spread":0.26483423819755597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3202919320","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":[0.09728411,0.00008283257,0.8850189,0.012656762,0.0011084649,0.0003090436,0.00035355167,0.00054519007,0.0026411663],"genre_scores_gemma":[0.97353166,0.000010386505,0.023716567,0.0019980967,0.00041869577,0.000038380265,0.000117033036,0.000020787784,0.00014841872],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979638,0.000107561515,0.00028049067,0.0008619258,0.0005073746,0.00027883583],"domain_scores_gemma":[0.9980266,0.00016887799,0.00014912219,0.0011995242,0.0003322352,0.00012361778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001486351,0.0002052545,0.0001593223,0.000063144595,0.00022674174,0.00035471452,0.0011198558,0.000098930526,0.00025128873],"category_scores_gemma":[0.00008147974,0.0002054282,0.00008298202,0.0006010903,0.00005709241,0.0004433274,0.00032752362,0.00028933235,0.00016836514],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005072094,0.002047187,0.002257917,0.0000192814,0.0005864174,0.00022752184,0.0005986333,0.01431431,0.76484436,0.12586108,0.016150506,0.07258559],"study_design_scores_gemma":[0.0007764123,0.00017732092,0.0052570035,0.000044940643,0.000026895106,0.000012765086,0.000071259776,0.37663126,0.60175866,0.004433454,0.010460488,0.0003495424],"about_ca_topic_score_codex":0.00018833193,"about_ca_topic_score_gemma":0.00007825506,"teacher_disagreement_score":0.8762475,"about_ca_system_score_codex":0.0003254028,"about_ca_system_score_gemma":0.000082092745,"threshold_uncertainty_score":0.8377119},"labels":[],"label_agreement":null},{"id":"W3203192700","doi":"10.1109/tnnls.2021.3116212","title":"SmithNet: Strictness on Motion-Texture Coherence for Anomaly Detection","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Anomaly detection; Coherence (philosophical gambling strategy); Artificial intelligence; Convolutional neural network; Encoder; Benchmark (surveying); Computer vision; Pattern recognition (psychology); Frame (networking); Encoding (memory); Motion (physics); Mathematics","score_opus":0.013307374085172706,"score_gpt":0.23129411387177248,"score_spread":0.21798673978659977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203192700","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":[0.013642154,0.0001756374,0.9843191,0.00019707631,0.0007865675,0.00040313829,0.0000049610744,0.00038540902,0.00008596027],"genre_scores_gemma":[0.99736303,0.000051858828,0.0005916958,0.00011217707,0.00015806721,0.00031056485,0.0000026914101,0.000018559473,0.0013913574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868304,0.00016364994,0.00025175646,0.0005055533,0.00015225499,0.00024377483],"domain_scores_gemma":[0.9990973,0.00024438003,0.00012395125,0.00029945548,0.00013823625,0.00009669978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017507115,0.00018271648,0.00019207768,0.00009576363,0.00088072754,0.00029046566,0.00016982763,0.00015475096,0.0000058961023],"category_scores_gemma":[0.0000060386096,0.00017270913,0.00010708708,0.00050155795,0.000028618379,0.00018900611,0.0000028230536,0.00052321935,0.0000037428147],"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":[0.000016110807,0.00005712767,0.000022823555,0.000021453443,0.000017446464,0.0000031489865,0.000042577045,0.7837531,0.00059098745,0.0005260384,0.000054455388,0.21489473],"study_design_scores_gemma":[0.0002454045,0.00037790314,0.00030056984,0.000042653493,0.000015215521,0.0000748683,0.00006819545,0.99235845,0.0031088202,0.000031798732,0.003177294,0.00019883766],"about_ca_topic_score_codex":0.00004481753,"about_ca_topic_score_gemma":0.000016949178,"teacher_disagreement_score":0.9837274,"about_ca_system_score_codex":0.000039097562,"about_ca_system_score_gemma":0.000016011332,"threshold_uncertainty_score":0.7042874},"labels":[],"label_agreement":null},{"id":"W3203230109","doi":"10.1016/j.inffus.2021.09.001","title":"Data fusion and transfer learning empowered granular trust evaluation for Internet of Things","year":2021,"lang":"en","type":"article","venue":"Information Fusion","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Deanship of Scientific Research, King Saud University; King Saud University","keywords":"Computer science; Transfer of learning; The Internet; Transfer (computing); Internet of Things; Artificial intelligence; World Wide Web","score_opus":0.03055713461203815,"score_gpt":0.28583010567494177,"score_spread":0.25527297106290364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203230109","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":[0.0573516,0.000047956877,0.9408852,0.00035433087,0.0000664679,0.00033799096,0.000013524967,0.00009571482,0.0008472109],"genre_scores_gemma":[0.9608491,0.00008871491,0.03816605,0.00019307181,0.000012634679,0.00004750733,0.000553586,0.0000038278945,0.00008550689],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991722,0.000036317382,0.0003223132,0.00015550188,0.00023474524,0.00007893425],"domain_scores_gemma":[0.99913764,0.000039718354,0.00010198055,0.00035150046,0.00033824565,0.00003089137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006259876,0.000068550115,0.00009089833,0.000091526956,0.00012738854,0.000098022065,0.00028228434,0.00006448838,0.000040946263],"category_scores_gemma":[0.00009076748,0.00006625346,0.000029879788,0.00021749247,0.000017104358,0.0020905556,0.00021779235,0.00007674056,0.0000050800027],"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":[0.00002480134,0.000041031697,0.0001970298,0.000096566386,0.0000090187805,1.6743223e-7,0.004238196,0.00011752052,0.014991347,0.03179126,0.0012465237,0.94724655],"study_design_scores_gemma":[0.0004688849,0.00007634384,0.0005916192,0.000035693774,0.000014319675,0.000012125778,0.00019370286,0.88442194,0.02902494,0.0013897723,0.083673365,0.000097288015],"about_ca_topic_score_codex":0.000018582217,"about_ca_topic_score_gemma":0.000002442381,"teacher_disagreement_score":0.9471493,"about_ca_system_score_codex":0.000017841145,"about_ca_system_score_gemma":0.000049885555,"threshold_uncertainty_score":0.27017376},"labels":[],"label_agreement":null},{"id":"W3203254405","doi":"10.1109/ijcnn52387.2021.9534282","title":"Multi-Scale Deep Nearest Neighbors","year":2021,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Embedding; Cluster analysis; Computer science; k-nearest neighbors algorithm; Differentiable function; Artificial intelligence; Space (punctuation); Pattern recognition (psychology); Scale (ratio); Function (biology); Data mining; Mathematics","score_opus":0.014621289814669413,"score_gpt":0.2541925168532202,"score_spread":0.23957122703855077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203254405","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011671267,0.000034946086,0.98406583,0.0012184901,0.000050015304,0.000045499728,4.2054265e-7,0.00043977756,0.012977905],"genre_scores_gemma":[0.26247352,0.000014387459,0.73152316,0.0007696232,0.000023194314,0.000026993126,0.000001210446,0.0000039787355,0.0051639257],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995125,0.000011738809,0.00008819096,0.00021285344,0.00006909094,0.000105661435],"domain_scores_gemma":[0.99943715,0.000014218902,0.000018953857,0.00041039888,0.00006345265,0.000055831766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000034782814,0.000048892387,0.00005031401,0.000020970556,0.00010139127,0.000090287045,0.00026716455,0.000031501135,0.00015349974],"category_scores_gemma":[0.0000056228146,0.000045266916,0.000040576393,0.00032588592,0.000014841236,0.00013777806,0.00014675423,0.000054033964,0.00016978498],"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":[0.0000015417728,0.00059270713,0.0045832,0.000013921437,0.000023173603,0.000045179455,0.0005600596,0.00014534945,0.07710344,0.45545536,0.009646784,0.45182928],"study_design_scores_gemma":[0.00026347348,0.000039686063,0.020085694,0.000005172543,0.00000440008,0.000075971584,0.00010928072,0.5058649,0.2658984,0.004277584,0.20303981,0.00033563757],"about_ca_topic_score_codex":0.000023666757,"about_ca_topic_score_gemma":0.000076928336,"teacher_disagreement_score":0.50571954,"about_ca_system_score_codex":0.00001213143,"about_ca_system_score_gemma":0.000026395512,"threshold_uncertainty_score":0.2182299},"labels":[],"label_agreement":null},{"id":"W3203983689","doi":"10.18653/v1/2021.inlg-1.26","title":"Shared Task in Evaluating Accuracy: Leveraging Pre-Annotations in the Validation Process","year":2021,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Université Laval","funders":"","keywords":"Computer science; Task (project management); Interface (matter); Process (computing); Protocol (science); Web application; Annotation; Information retrieval; User interface; Artificial intelligence; World Wide Web; Natural language processing; Programming language","score_opus":0.05627887284597749,"score_gpt":0.3708462692766979,"score_spread":0.3145673964307204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203983689","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":[0.22204222,0.000019858451,0.77043164,0.0040960764,0.000022145314,0.00031580598,0.0000010733795,0.00012664602,0.002944526],"genre_scores_gemma":[0.9670456,0.000003331033,0.031959817,0.0004914391,0.000013564425,0.00035079248,0.000009523171,0.000003392945,0.00012251819],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990812,0.00010849452,0.00023181619,0.00026308073,0.00019508823,0.0001203247],"domain_scores_gemma":[0.99928933,0.00017893896,0.0000662035,0.00033607037,0.000114695795,0.000014770306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051576534,0.0000619699,0.000062520645,0.00008598794,0.00012132581,0.0002207282,0.00041446733,0.000027923918,0.000028528277],"category_scores_gemma":[0.00017700659,0.000051003724,0.000023264362,0.0012604402,0.00000851011,0.0005347757,0.00007031875,0.00012687908,0.000009337061],"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":[0.000011989656,0.0011539957,0.031003509,0.00013391321,0.00002523805,0.000043630942,0.06669315,0.038853988,0.04170628,0.33313045,0.0019862151,0.48525763],"study_design_scores_gemma":[0.0004403824,0.00004326035,0.10241947,0.0000671035,0.0000060640045,0.000038196966,0.0020121175,0.78716505,0.040637445,0.06607667,0.00079482386,0.0002993914],"about_ca_topic_score_codex":0.000059475722,"about_ca_topic_score_gemma":0.00006728365,"teacher_disagreement_score":0.7483111,"about_ca_system_score_codex":0.000037780515,"about_ca_system_score_gemma":0.00011536256,"threshold_uncertainty_score":0.21284871},"labels":[],"label_agreement":null},{"id":"W3204466917","doi":"10.31224/osf.io/d4e6a","title":"Log Message Anomaly Detection with Oversampling","year":2020,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Oversampling; Autoencoder; Anomaly detection; Computer science; Data mining; Anomaly (physics); Feature (linguistics); Artificial intelligence; Feature extraction; Pattern recognition (psychology); Deep learning; Computer network","score_opus":0.017657805879513495,"score_gpt":0.22312961553735572,"score_spread":0.20547180965784223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204466917","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":[0.008292563,0.0000068745685,0.9811888,0.0014117706,0.000013482946,0.00010925317,3.8377277e-7,0.0008473427,0.0081295315],"genre_scores_gemma":[0.89839107,0.0000027324572,0.10036975,0.0010335693,0.000033826716,0.000023726096,3.0232752e-7,0.0000050011026,0.00014003906],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99944794,0.000008269313,0.00008804514,0.00025190346,0.000096834825,0.000107004686],"domain_scores_gemma":[0.99962366,0.000014662583,0.00003963552,0.000210394,0.000033263346,0.00007841368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000037932823,0.0000688291,0.000063539985,0.000027235896,0.00011167074,0.000081576516,0.00026011647,0.00002981137,0.00003410332],"category_scores_gemma":[0.0000045513943,0.000055323973,0.000027092708,0.00040606692,0.000016100761,0.00026007055,0.00007343054,0.000075861004,0.000052134426],"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":[0.00007016525,0.00016520491,0.0047606044,0.00005382732,0.00008920697,0.000030728792,0.0013370382,0.0010944434,0.18900743,0.38129726,0.0027246203,0.4193695],"study_design_scores_gemma":[0.00060273864,0.0010045869,0.0056277723,0.000011540492,0.000018425668,0.00006698912,0.00014243665,0.33520713,0.55920666,0.0034309244,0.093995705,0.00068506185],"about_ca_topic_score_codex":0.00003987489,"about_ca_topic_score_gemma":0.000013461821,"teacher_disagreement_score":0.8900985,"about_ca_system_score_codex":0.000014546828,"about_ca_system_score_gemma":0.0000142704175,"threshold_uncertainty_score":0.22560462},"labels":[],"label_agreement":null},{"id":"W3204508375","doi":"10.1007/978-3-030-77939-9_1","title":"Deep Learning for Unmanned Autonomous Vehicles: A Comprehensive Review","year":2021,"lang":"en","type":"review","venue":"Studies in computational intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University; General Motors (Canada)","funders":"","keywords":"Deep learning; Computer science; Artificial intelligence; Adaptation (eye); Cognition; Focus (optics); Human–computer interaction; Psychology","score_opus":0.1805055120112475,"score_gpt":0.45335736332205046,"score_spread":0.2728518513108029,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204508375","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":[1.554874e-8,0.51881415,0.48001304,0.00011718824,0.0001322123,0.0007672262,0.0000028986333,0.0001117612,0.000041530264],"genre_scores_gemma":[0.000008188644,0.8935015,0.10414265,0.00033902793,0.00007070708,0.0017345496,0.00006189851,0.000024604875,0.00011682138],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972347,0.00022670822,0.0011029975,0.0008677212,0.00025352297,0.0003143706],"domain_scores_gemma":[0.996013,0.0022327276,0.00052924315,0.00040656835,0.0007610197,0.000057446086],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00035676698,0.00040725514,0.0015822881,0.00022690326,0.0002968446,0.000072999734,0.0009640087,0.00013345333,0.00000764103],"category_scores_gemma":[0.00027869572,0.00038451696,0.0005007206,0.0012872113,0.00016574856,0.00013565386,0.0005968682,0.00047026746,0.00004673919],"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":[3.3324713e-7,0.000032575696,1.7931214e-7,0.013852583,0.00011060159,0.000008190457,0.00011135741,0.005663375,7.1181336e-9,0.030489378,0.000520235,0.9492112],"study_design_scores_gemma":[0.000027811795,0.00007174079,4.397504e-7,0.026792211,0.00009123399,0.00007039325,0.000101535144,0.033909235,9.350241e-7,0.010154655,0.9283846,0.00039519972],"about_ca_topic_score_codex":0.0000035916191,"about_ca_topic_score_gemma":0.0000025026925,"teacher_disagreement_score":0.948816,"about_ca_system_score_codex":0.00030819554,"about_ca_system_score_gemma":0.00025577165,"threshold_uncertainty_score":0.9998607},"labels":[],"label_agreement":null},{"id":"W3204586135","doi":"10.1109/tits.2021.3114064","title":"Hybrid Group Anomaly Detection for Sequence Data: Application to Trajectory Data Analytics","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Anomaly detection; Computer science; Cluster analysis; Outlier; Data mining; Pruning; Sequence (biology); Algorithm; Artificial intelligence","score_opus":0.09761214058296823,"score_gpt":0.31808232040328827,"score_spread":0.22047017982032002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204586135","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":[0.0017007018,0.000082175146,0.9929283,0.00025324296,0.0009362544,0.0014932413,0.0019676243,0.0006046697,0.00003378603],"genre_scores_gemma":[0.95536464,0.00012228082,0.041970912,0.00019103318,0.000119504286,0.0010029683,0.00090516923,0.000038956834,0.00028451238],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968734,0.0000790864,0.0008482591,0.0014399068,0.00043673743,0.00032266084],"domain_scores_gemma":[0.99586487,0.00017250175,0.00021816949,0.003124496,0.00041098634,0.00020899402],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00053274736,0.00028132246,0.00030458055,0.0002938468,0.0004205722,0.00024491997,0.001703848,0.00012312959,0.000011730798],"category_scores_gemma":[0.0000089365285,0.00031756787,0.00012521801,0.000992429,0.000037957623,0.0009897359,0.0000059725494,0.00022280875,0.00005272323],"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":[0.00019622094,0.0016448178,0.00007009642,0.0005769589,0.0005459379,0.000031942676,0.00092692615,0.25192267,0.14520651,0.016701344,0.002144932,0.58003163],"study_design_scores_gemma":[0.0002806181,0.00023291464,0.00013723635,0.00006595447,0.00013940752,0.000047700552,0.00021052078,0.7070456,0.21686265,0.00019955747,0.07422335,0.00055450085],"about_ca_topic_score_codex":0.0002714005,"about_ca_topic_score_gemma":0.0008417461,"teacher_disagreement_score":0.95366395,"about_ca_system_score_codex":0.00016163253,"about_ca_system_score_gemma":0.00014164105,"threshold_uncertainty_score":0.99992764},"labels":[],"label_agreement":null},{"id":"W3205111034","doi":"10.1109/icas49788.2021.9551183","title":"Attentive Autoencoders For Improving Visual Anomaly Detection","year":2021,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Autoencoder; Computer science; Anomaly detection; Artificial intelligence; Hyperparameter; Modular design; Machine learning; Visualization; Deep learning; Pattern recognition (psychology)","score_opus":0.010448082675330256,"score_gpt":0.26279042962257565,"score_spread":0.2523423469472454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205111034","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":[0.007819841,0.000014212471,0.9894328,0.00046531644,0.00011109102,0.00018156621,0.0000010964233,0.00051073363,0.0014633595],"genre_scores_gemma":[0.7835909,0.0000014250559,0.21449505,0.0002511942,0.00004094403,0.00014389928,0.0000015927617,0.000005663803,0.0014693062],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993045,0.0000134716365,0.00013067285,0.00032080454,0.00007824184,0.00015232865],"domain_scores_gemma":[0.99948364,0.000035496538,0.00005123481,0.00022736214,0.00015797417,0.000044304885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000086624146,0.0000711363,0.000070313596,0.000049016184,0.00021373776,0.00012066109,0.00017185282,0.00004630282,0.000017877765],"category_scores_gemma":[0.000019228257,0.00007123838,0.00008340574,0.0003096437,0.000015093849,0.00027411152,0.000098324126,0.00005186287,0.000015860101],"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":[0.000005872395,0.00012984154,0.00017215185,0.000026847832,0.00002298168,0.0000036968604,0.00014236303,0.000049857455,0.23380019,0.06824733,0.00030260117,0.6970963],"study_design_scores_gemma":[0.00015367019,0.000107641274,0.0010199368,0.0000018591982,0.0000067068054,0.000021654338,0.0000882509,0.4059511,0.58399504,0.00298548,0.005519381,0.00014923252],"about_ca_topic_score_codex":0.000027603113,"about_ca_topic_score_gemma":0.00004879929,"teacher_disagreement_score":0.7757711,"about_ca_system_score_codex":0.000040413484,"about_ca_system_score_gemma":0.00004540744,"threshold_uncertainty_score":0.29050168},"labels":[],"label_agreement":null},{"id":"W3205429506","doi":"10.1016/j.eswa.2021.116060","title":"Anomaly detection for data accountability of Mars telemetry data","year":2021,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Mitacs; Polytechnique Montréal","keywords":"Telemetry; Mars Exploration Program; Anomaly detection; Computer science; Anomaly (physics); Remote sensing; South Atlantic Anomaly; Data mining; Geology; Telecommunications; Astrobiology","score_opus":0.06414976024322895,"score_gpt":0.3322822724052223,"score_spread":0.26813251216199335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205429506","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":[0.00051561405,0.00068435987,0.9959956,0.000306099,0.00007267139,0.0013541726,0.00038476108,0.00027116688,0.00041558896],"genre_scores_gemma":[0.7364277,0.00005039587,0.25970784,0.00007295183,0.00016730868,0.0028722002,0.0005049665,0.000020458256,0.00017614964],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981402,0.00004471636,0.00044197455,0.0009560845,0.00022881331,0.00018816636],"domain_scores_gemma":[0.9928542,0.00015728961,0.0002586756,0.006254075,0.000399497,0.000076302495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043225117,0.00013950533,0.00022457467,0.00007451328,0.00026709042,0.00012299184,0.0024013298,0.0000830134,0.000006533336],"category_scores_gemma":[0.000035084217,0.00012707755,0.000033748824,0.0008899887,0.00006553605,0.00068356417,0.00077385374,0.000083531355,0.000008240319],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000960623,0.0025686184,0.004767788,0.0010325714,0.0005682595,0.0000038265616,0.0008519337,0.00019849661,0.1599427,0.21532671,0.03813055,0.5765125],"study_design_scores_gemma":[0.00037484607,0.00008256357,0.0006996825,0.000035769146,0.000030456928,0.000098974495,0.00046673385,0.14604928,0.0400948,0.00045339327,0.81124943,0.00036407664],"about_ca_topic_score_codex":0.0004094703,"about_ca_topic_score_gemma":0.00015392505,"teacher_disagreement_score":0.77311885,"about_ca_system_score_codex":0.000049261005,"about_ca_system_score_gemma":0.00018216256,"threshold_uncertainty_score":0.5182072},"labels":[],"label_agreement":null},{"id":"W3205988755","doi":"10.3389/fpubh.2021.741030","title":"A Novel Matrix Profile-Guided Attention LSTM Model for Forecasting COVID-19 Cases in USA","year":2021,"lang":"en","type":"article","venue":"Frontiers in Public Health","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba; Manitoba Medical Service Foundation; Carnegie Mellon University","keywords":"Coronavirus disease 2019 (COVID-19); Mean squared error; Computer science; Recurrent neural network; Artificial intelligence; Machine learning; Statistics; Artificial neural network; Time series; Data mining; Medicine; Mathematics; Disease; Internal medicine","score_opus":0.17825352940962833,"score_gpt":0.3759375261358575,"score_spread":0.1976839967262292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205988755","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031845998,0.00009372551,0.9769885,0.018419364,0.00020413795,0.00077272375,0.000036238183,0.000117801814,0.0001829045],"genre_scores_gemma":[0.21822165,0.00004020887,0.7776682,0.0022022026,0.000034811885,0.00066692464,0.000031762967,0.000011864907,0.0011223687],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982186,0.00007206817,0.0005263675,0.00051301613,0.00016987762,0.00050009665],"domain_scores_gemma":[0.99895614,0.0000706176,0.00019534913,0.00039587283,0.00011815774,0.00026383426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011488175,0.00012146028,0.00023607125,0.00036659476,0.00022573615,0.00013731606,0.00037192297,0.00009184854,0.0000065633167],"category_scores_gemma":[0.00041354552,0.00013579972,0.00007106755,0.0011431056,0.000029133165,0.00044672846,0.00013158016,0.00016260287,0.0000019092497],"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":[0.000032076354,0.0015790268,0.050955936,0.0011706353,0.000044564727,0.000056203884,0.0038206184,0.010358072,0.00051165547,0.33139735,0.25949094,0.3405829],"study_design_scores_gemma":[0.0004885348,0.000040896957,0.0006476552,0.000018596616,9.254524e-7,0.000075402975,0.00016469053,0.9789609,0.000030420297,0.0048850817,0.01455262,0.00013431233],"about_ca_topic_score_codex":0.0003295179,"about_ca_topic_score_gemma":0.00032486042,"teacher_disagreement_score":0.9686028,"about_ca_system_score_codex":0.0007801452,"about_ca_system_score_gemma":0.0020799441,"threshold_uncertainty_score":0.5537752},"labels":[],"label_agreement":null},{"id":"W3206598928","doi":"10.36227/techrxiv.16828363.v2","title":"SELF-SUPERVISED ACOUSTIC ANOMALY DETECTION VIA CONTRASTIVE LEARNING","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Anomaly detection; Computer science; Anomaly (physics); Artificial intelligence; Pattern recognition (psychology); Representation (politics); Feature learning","score_opus":0.0090707888940164,"score_gpt":0.22911395960564698,"score_spread":0.22004317071163057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206598928","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":[0.015692517,0.00010676694,0.9764503,0.00016626254,0.0003229418,0.00051247835,0.0000014170037,0.0028002157,0.00394706],"genre_scores_gemma":[0.8113492,0.000072237824,0.18753214,0.0001258506,0.000106139174,0.00029954553,0.0000103534485,0.000020607424,0.0004839089],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803364,0.00012378994,0.0003657767,0.0009278569,0.00025374777,0.0002951959],"domain_scores_gemma":[0.9984809,0.000089025496,0.00021502459,0.00076551316,0.00032615988,0.00012337169],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021291223,0.0003027457,0.00032342967,0.00016484912,0.00031468563,0.00049666624,0.00076186674,0.00036438962,0.00007862187],"category_scores_gemma":[0.000031154952,0.00031597356,0.00021683273,0.00042849933,0.000030019728,0.00024768777,0.0011536835,0.0009500502,0.000046859008],"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":[0.000026406278,0.000973583,0.0012433993,0.0006965259,0.000764731,0.00011804941,0.0031870112,0.015875058,0.14494045,0.008704317,0.00023557627,0.8232349],"study_design_scores_gemma":[0.0001546834,0.000103858256,0.0013566487,0.00003468986,0.000048964466,0.000049118793,0.00010733125,0.9534873,0.0417019,0.0011349759,0.0013270173,0.0004935449],"about_ca_topic_score_codex":0.00018937136,"about_ca_topic_score_gemma":0.00009373311,"teacher_disagreement_score":0.93761224,"about_ca_system_score_codex":0.00017299548,"about_ca_system_score_gemma":0.00016210802,"threshold_uncertainty_score":0.99992925},"labels":[],"label_agreement":null},{"id":"W3207430416","doi":"10.23919/apnoms52696.2021.9562616","title":"Automating Web-based Infrastructure Management via Contextual Imitation Learning","year":2021,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Automation; Imitation; Learning Management; Human–computer interaction; Software engineering; Knowledge management; World Wide Web; Engineering","score_opus":0.0063309306601987934,"score_gpt":0.2316948225599426,"score_spread":0.2253638918997438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207430416","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":[0.0077054393,0.000010396468,0.9751981,0.00057810073,0.00004117982,0.00009112394,2.3304374e-7,0.00086329476,0.015512146],"genre_scores_gemma":[0.66340595,0.0000020660086,0.3353273,0.00038198492,0.000011618977,0.00003081987,0.00000408242,0.0000036814924,0.0008325051],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993552,0.00003519245,0.00014317599,0.00022650074,0.0001266851,0.00011325607],"domain_scores_gemma":[0.9995642,0.000032910135,0.000060878163,0.00022662157,0.00008006301,0.000035357938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008408291,0.00006966779,0.00006275762,0.000055135853,0.00018363574,0.00014329882,0.00018576642,0.000035190318,0.00010764454],"category_scores_gemma":[0.0000098159035,0.000068480105,0.000040404495,0.0003646251,0.000012069094,0.00015123165,0.00010026674,0.00010001548,0.000035477606],"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":[9.527775e-7,0.000030021214,0.0006468993,0.000022392347,0.000015685642,0.000011448006,0.00010011749,0.0030768595,0.010670073,0.25674614,0.0013319928,0.72734743],"study_design_scores_gemma":[0.00027474796,0.00003382852,0.0077968035,0.000013747393,0.0000057805037,0.000013293154,0.00012985668,0.9426044,0.023187326,0.003563193,0.022216506,0.00016048524],"about_ca_topic_score_codex":0.00000315731,"about_ca_topic_score_gemma":0.0000025348545,"teacher_disagreement_score":0.9395276,"about_ca_system_score_codex":0.000032645756,"about_ca_system_score_gemma":0.000030893403,"threshold_uncertainty_score":0.27925375},"labels":[],"label_agreement":null},{"id":"W3209570656","doi":"10.1109/access.2021.3131949","title":"Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review","year":2021,"lang":"en","type":"preprint","venue":"IEEE Access","topic":"Anomaly Detection Techniques and Applications","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":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; Government of Alberta; Case Western Reserve University; Radiological Society of North America","keywords":"Anomaly detection; Computer science; Anomaly (physics); Adversarial system; Generative grammar; Data mining; Data science; Artificial intelligence","score_opus":0.016406543434669536,"score_gpt":0.3015739593821122,"score_spread":0.28516741594744266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209570656","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":[0.00020459796,0.047027092,0.94904846,0.00021433979,0.00040520466,0.0026717861,0.000011009195,0.00016028086,0.0002572195],"genre_scores_gemma":[0.9211323,0.041737877,0.023493685,0.0007816179,0.0005377072,0.012091322,0.00005877977,0.000035271103,0.00013140535],"study_design_codex":"systematic_review","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976677,0.00024464307,0.0008949167,0.0007438961,0.00025518428,0.00019368749],"domain_scores_gemma":[0.99707687,0.00008831037,0.0007552551,0.0015572136,0.00045564395,0.000066696855],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040607966,0.00027758133,0.00075392314,0.00024971715,0.000094620846,0.0004408998,0.0019370494,0.000340099,0.000009002561],"category_scores_gemma":[0.000027466705,0.00026795795,0.00025143486,0.0019460123,0.00003917238,0.0004506932,0.000840755,0.00067932537,0.0000022309716],"study_design_candidate":"systematic_review","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":[0.00006043472,0.0020417098,0.0004842164,0.79798543,0.0011577317,0.00022887504,0.0032139844,0.051907796,0.0025877873,0.022397164,0.004970867,0.11296399],"study_design_scores_gemma":[0.0012235307,0.00022677735,0.0011518266,0.4144458,0.00080816314,0.00039700666,0.000105323845,0.51681846,0.047330853,0.01114271,0.002801859,0.0035476997],"about_ca_topic_score_codex":0.00006880903,"about_ca_topic_score_gemma":0.00012148304,"teacher_disagreement_score":0.9255548,"about_ca_system_score_codex":0.000114365095,"about_ca_system_score_gemma":0.00015592884,"threshold_uncertainty_score":0.9999773},"labels":[],"label_agreement":null},{"id":"W3210229763","doi":"10.1007/s11042-023-16445-z","title":"A critical study on the recent deep learning based semi-supervised video anomaly detection methods","year":2023,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Anomaly detection; Overfitting; Artificial intelligence; Deep learning; Machine learning; Context (archaeology); Task (project management); Pattern recognition (psychology); Artificial neural network","score_opus":0.05454966050259968,"score_gpt":0.3522480891863871,"score_spread":0.29769842868378743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210229763","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":[0.0059677325,0.000031661108,0.98690814,0.0042151627,0.000047293153,0.0013277339,0.0000062335666,0.0009510825,0.0005449707],"genre_scores_gemma":[0.9315928,0.00007124816,0.06049424,0.0005393571,0.00011969286,0.0070434185,0.000012106209,0.000022959006,0.00010417096],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844044,0.00025747193,0.00027801283,0.0005477501,0.0002106573,0.00026569024],"domain_scores_gemma":[0.99725074,0.0017507074,0.00006918852,0.0006628041,0.0001408786,0.0001256901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080650044,0.00016748761,0.00015204377,0.00013938909,0.00094991963,0.0003082344,0.00045782008,0.00007903416,0.00003351655],"category_scores_gemma":[0.00031974877,0.000130285,0.00006395919,0.0013702783,0.000090261994,0.00018115829,0.0001567312,0.00031868648,0.00013811473],"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":[0.000004864011,0.0002193166,0.0002575213,0.000006414706,0.000012986739,9.633375e-7,0.00036624697,0.00019477041,0.00804642,0.0057880017,0.00013124276,0.9849712],"study_design_scores_gemma":[0.00033984784,0.00027651858,0.021442609,0.000009210547,0.000028392247,0.0000042934957,0.0007501397,0.86808956,0.017026287,0.0019859963,0.08974795,0.00029920848],"about_ca_topic_score_codex":0.000018257671,"about_ca_topic_score_gemma":0.0000088885245,"teacher_disagreement_score":0.98467207,"about_ca_system_score_codex":0.000036843183,"about_ca_system_score_gemma":0.00002527064,"threshold_uncertainty_score":0.73061097},"labels":[],"label_agreement":null},{"id":"W3210728649","doi":"10.1049/cps2.12019","title":"A comparative analysis of CGAN‐based oversampling for anomaly detection","year":2021,"lang":"en","type":"article","venue":"IET Cyber-Physical Systems Theory & Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Oversampling; Artificial intelligence; Computer science; Anomaly detection; Random forest; Naive Bayes classifier; Machine learning; Context (archaeology); Pattern recognition (psychology); Anomaly (physics); Geology; Bandwidth (computing); Physics; Support vector machine","score_opus":0.021487873296498865,"score_gpt":0.3016077231402255,"score_spread":0.2801198498437266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210728649","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":[0.023859197,0.00007752207,0.97319627,0.00008183428,0.00004664787,0.0010253631,0.00010394288,0.00029697447,0.0013122289],"genre_scores_gemma":[0.9826065,0.0000022211332,0.0136459,0.000048302903,0.000114569964,0.0032944437,0.0000607917,0.000012148146,0.00021514835],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983502,0.00010567655,0.00045651602,0.0006218773,0.00023018605,0.00023556358],"domain_scores_gemma":[0.9973288,0.00062083587,0.00036031008,0.0010500831,0.00053308404,0.00010689475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002931287,0.00019199254,0.00054786936,0.00023350173,0.00033333065,0.00011665367,0.00050770125,0.000089598834,0.0000067191904],"category_scores_gemma":[0.000015712874,0.00019445045,0.00047036898,0.0029112769,0.00009850886,0.00017987502,0.00008499939,0.00012275006,0.000016414097],"study_design_candidate":"theoretical_or_conceptual","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":[0.00001338031,0.00042747008,0.00007254345,0.00003756872,0.0004827678,2.462519e-7,0.00024547527,0.0067384974,0.078728184,0.90891194,0.00005068394,0.0042912494],"study_design_scores_gemma":[0.00044377657,0.00014835646,0.0021029566,0.000028301449,0.0009869707,0.0000054026405,0.0004346174,0.6296056,0.30903184,0.030989187,0.025704587,0.0005184026],"about_ca_topic_score_codex":0.000061431274,"about_ca_topic_score_gemma":0.000021887132,"teacher_disagreement_score":0.9595504,"about_ca_system_score_codex":0.00009063266,"about_ca_system_score_gemma":0.000091373535,"threshold_uncertainty_score":0.79294586},"labels":[],"label_agreement":null},{"id":"W3211088471","doi":"10.48550/arxiv.2110.13223","title":"Identifying and Benchmarking Natural Out-of-Context Prediction Problems","year":2021,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Benchmarking; Suite; Computer science; Context (archaeology); Artificial intelligence; Machine learning; Data mining; Data science","score_opus":0.053246150004155245,"score_gpt":0.18821564886723213,"score_spread":0.13496949886307688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211088471","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":[0.18370715,0.00010525351,0.81512463,0.00004064261,0.00015037837,0.00006768576,0.000001516825,0.00011394474,0.00068881275],"genre_scores_gemma":[0.99543554,0.000086516324,0.003812428,0.000027901038,0.000020509418,6.461602e-7,0.000001960233,0.0000029703106,0.0006115327],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994528,0.000024026453,0.00009178568,0.00030130928,0.000034868404,0.0000951545],"domain_scores_gemma":[0.99953294,0.00002357439,0.00007193113,0.0002440168,0.0000889415,0.00003859302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000070910115,0.000060252427,0.000074471,0.00006332715,0.00012679445,0.000041215237,0.00018395502,0.00003802749,0.000009054573],"category_scores_gemma":[0.0000055967234,0.000070258764,0.00004253505,0.0003777115,0.000039370432,0.00038330202,0.0001786895,0.00008818754,0.0000037918887],"study_design_candidate":"theoretical_or_conceptual","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":[0.00000932597,0.00013460641,0.023205923,0.00010212796,0.00008379978,0.0000738261,0.001517522,0.0016860699,0.020280112,0.86392397,0.00037132762,0.088611364],"study_design_scores_gemma":[0.0006855453,0.00012132826,0.024747627,0.0001414605,0.00005576509,0.00004474366,0.00068255974,0.89166903,0.030215418,0.04508112,0.0061255726,0.00042984672],"about_ca_topic_score_codex":0.000019235371,"about_ca_topic_score_gemma":0.000027062038,"teacher_disagreement_score":0.88998294,"about_ca_system_score_codex":0.000027146469,"about_ca_system_score_gemma":0.000023830557,"threshold_uncertainty_score":0.28650692},"labels":[],"label_agreement":null},{"id":"W3211672407","doi":"10.1177/1071181321651206","title":"Human Factors in Interactive Machine Learning: A Cybersecurity Case Study","year":2021,"lang":"en","type":"article","venue":"Proceedings of the Human Factors and Ergonomics Society Annual Meeting","topic":"Anomaly Detection Techniques and Applications","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 Toronto","funders":"","keywords":"Computer science; Variety (cybernetics); Domain (mathematical analysis); Computer security; Domain knowledge; Data science; Visualization; Artificial intelligence; Knowledge management","score_opus":0.018208312881718945,"score_gpt":0.2661038363258339,"score_spread":0.24789552344411495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211672407","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":[0.9991949,0.000026556527,0.000107012456,0.00004898554,0.000038900987,0.00023135183,0.000007597707,0.000086140324,0.0002585274],"genre_scores_gemma":[0.998897,0.000009307811,0.0009197385,0.000022404967,0.000023014834,0.000015697968,0.0000012718233,0.000012803981,0.00009874356],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.99887925,0.000022039038,0.00035833544,0.00041817987,0.000113887814,0.00020830559],"domain_scores_gemma":[0.9992303,0.000059183116,0.00033447635,0.00013589593,0.00017846587,0.000061670624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033604397,0.00018889483,0.00025195975,0.000044254586,0.00078290637,0.00016011413,0.00038684445,0.00008189305,0.0000033094916],"category_scores_gemma":[0.000050720504,0.00015278005,0.00017607429,0.00030595905,0.00008438145,0.00044632758,0.00076867134,0.0004857671,1.5452233e-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":[0.000004366558,0.00051704433,0.8300354,0.000060637674,0.000093047005,0.00000595454,0.14891313,0.000029589888,0.0138161685,0.006158956,0.00012392363,0.00024180369],"study_design_scores_gemma":[0.0014076885,0.000664429,0.26433894,0.00021781129,0.00010135171,0.00020073276,0.61136043,0.006763725,0.10878427,0.004046377,0.0009176453,0.0011965982],"about_ca_topic_score_codex":0.0015716663,"about_ca_topic_score_gemma":0.00023401662,"teacher_disagreement_score":0.5656965,"about_ca_system_score_codex":0.000097562246,"about_ca_system_score_gemma":0.000020468553,"threshold_uncertainty_score":0.6230189},"labels":[],"label_agreement":null},{"id":"W3212297237","doi":"10.1109/wf-iot51360.2021.9595618","title":"Edge-Cloud Intelligence in Self-Diagnostic of Land Mobile Radio Systems","year":2021,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Scalability; Cloud computing; Interoperability; Modularity (biology); Enhanced Data Rates for GSM Evolution; Variety (cybernetics); Distributed computing; Resilience (materials science); Data stream mining; Process (computing); Telecommunications; Database; Artificial intelligence; Data mining; World Wide Web; Operating system","score_opus":0.00873592786672681,"score_gpt":0.24197050630516398,"score_spread":0.23323457843843717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212297237","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":[0.016213685,0.00051020755,0.97908056,0.00007885896,0.00014182119,0.0002086632,0.0000012358444,0.00022596282,0.0035389818],"genre_scores_gemma":[0.9663243,0.00019554581,0.03272685,0.000029856887,0.000027736043,0.00013425882,8.112162e-7,0.000003564346,0.0005570611],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999349,0.00003210316,0.00022079272,0.00020479133,0.000089926136,0.00010337028],"domain_scores_gemma":[0.9992683,0.00019158117,0.0000491461,0.0003762621,0.00007882255,0.000035871028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012221985,0.000057244135,0.00011287945,0.000055488454,0.000026888043,0.000039172424,0.0002990517,0.000039297465,0.000031518455],"category_scores_gemma":[0.000028637536,0.000053263517,0.000030495345,0.00056227465,0.00001508933,0.000090904905,0.000104269595,0.000063361826,0.000028731085],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000031347877,0.0011532315,0.025642445,0.0002666543,0.000045090295,0.00008788823,0.001625042,0.007100642,0.0030471764,0.8816376,0.0062526907,0.073138416],"study_design_scores_gemma":[0.00039279452,0.00041796835,0.00938506,0.00024121112,0.00001982472,0.0003303307,0.00072724087,0.5865241,0.31840682,0.012502609,0.0702313,0.0008206903],"about_ca_topic_score_codex":0.00012127363,"about_ca_topic_score_gemma":0.00002265013,"teacher_disagreement_score":0.9501106,"about_ca_system_score_codex":0.000026370943,"about_ca_system_score_gemma":0.000055521,"threshold_uncertainty_score":0.21720232},"labels":[],"label_agreement":null},{"id":"W3212441542","doi":"10.1109/tifs.2021.3125608","title":"UN-AVOIDS: Unsupervised and Nonparametric Approach for Visualizing Outliers and Invariant Detection Scoring","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Information Forensics and Security","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Anomaly detection; Visualization; Nonparametric statistics; Novelty detection; Outlier; Margin (machine learning); Pattern recognition (psychology); Data visualization; Invariant (physics)","score_opus":0.012292075696533896,"score_gpt":0.2286216271770909,"score_spread":0.216329551480557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212441542","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":[0.033880834,0.00004975074,0.96514964,0.00013442738,0.00009593546,0.0003358155,0.000021790423,0.00014161096,0.00019017662],"genre_scores_gemma":[0.9344653,0.00026194524,0.06490409,0.00022359341,0.0000124818425,0.00011184694,0.0000074268246,0.0000059096888,0.000007405616],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991878,0.000024424538,0.0002762833,0.00023194542,0.00012703222,0.00015249012],"domain_scores_gemma":[0.99937874,0.00007788856,0.00008731015,0.0001976813,0.00015587323,0.000102482096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022312002,0.00013168361,0.00014122471,0.0002169713,0.0005920815,0.00032773285,0.00007419014,0.00010294675,0.0000014058373],"category_scores_gemma":[0.000011337121,0.0001362564,0.00004749279,0.0005001665,0.000057291538,0.0010463236,0.000008320048,0.00016605578,7.6481507e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040713196,0.000113358816,0.000037116268,0.00025616374,0.000073536714,7.2690887e-7,0.0043118745,0.000798994,0.001093753,0.072282,0.000031739226,0.92096],"study_design_scores_gemma":[0.00096934114,0.00022273521,0.00030117392,0.000021856955,0.000043779728,0.000104741775,0.0008729619,0.9021982,0.0806893,0.01217325,0.0020487914,0.00035388878],"about_ca_topic_score_codex":0.000027514767,"about_ca_topic_score_gemma":0.000007989754,"teacher_disagreement_score":0.92060614,"about_ca_system_score_codex":0.000030265615,"about_ca_system_score_gemma":0.000027567687,"threshold_uncertainty_score":0.5556375},"labels":[],"label_agreement":null},{"id":"W3212534166","doi":"10.1016/j.forsciint.2021.111098","title":"Inter observer errors of cast-off stains using FARO zone 3D","year":2021,"lang":"en","type":"article","venue":"Forensic Science International","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Veterans Affairs Canada","funders":"","keywords":"Standard deviation; Envelope (radar); Volume (thermodynamics); Position (finance); Path (computing); Dowel; Geodesy; Mathematics; Computer science; Geology; Physics; Statistics; Engineering; Structural engineering","score_opus":0.03540017156326664,"score_gpt":0.30713931325919863,"score_spread":0.271739141695932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212534166","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":[0.2868292,0.000016718323,0.7097635,0.0006510469,0.00059639936,0.00006153847,0.000008478681,0.00006535485,0.002007735],"genre_scores_gemma":[0.80019724,0.0000049264645,0.1989394,0.00021186216,0.00005129258,0.0000075158905,0.0000028326663,0.000004224284,0.0005806753],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985842,0.000014770653,0.00027020226,0.00039909748,0.0005305691,0.00020116978],"domain_scores_gemma":[0.99856824,0.000030747502,0.00014511251,0.00043757356,0.0007470698,0.00007124072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027337336,0.000090926624,0.000105980296,0.00019969334,0.00014855343,0.00012884327,0.001040547,0.000034191708,0.00012485601],"category_scores_gemma":[0.000087527194,0.000087925466,0.00007430647,0.00096635905,0.00036820403,0.00072268065,0.0005522553,0.0000952269,0.000014720808],"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":[0.000015377418,0.00035793922,0.0083337575,0.000016120879,0.00006603738,0.000060884417,0.0017223492,0.0020729504,0.1549708,0.46326736,0.0015057044,0.36761072],"study_design_scores_gemma":[0.00028499964,0.00009484246,0.008865063,0.00006670078,0.000008999988,0.0002545223,0.00029749778,0.6067293,0.35225603,0.0073397774,0.023477186,0.00032507177],"about_ca_topic_score_codex":0.00007460855,"about_ca_topic_score_gemma":0.000044411277,"teacher_disagreement_score":0.60465634,"about_ca_system_score_codex":0.00014803046,"about_ca_system_score_gemma":0.00029197958,"threshold_uncertainty_score":0.35854965},"labels":[],"label_agreement":null},{"id":"W3212940494","doi":"10.48550/arxiv.2111.06549","title":"Bi-Discriminator Class-Conditional Tabular GAN","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Discriminator; Discriminative model; Computer science; Generator (circuit theory); Preprocessor; Benchmarking; Binary number; Metric (unit); Class (philosophy); Term (time); Artificial intelligence; Data mining; Machine learning; Algorithm; Mathematics; Engineering; Detector","score_opus":0.05563183401997562,"score_gpt":0.18926667058183608,"score_spread":0.13363483656186045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212940494","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":[0.07942202,0.000041073654,0.91478074,0.00028242264,0.00021753731,0.00020944458,0.000026547115,0.00052014017,0.004500087],"genre_scores_gemma":[0.9919705,0.000094882605,0.005724295,0.00022280481,0.00007783365,0.000006873245,0.00008028616,0.000015530502,0.0018070079],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983556,0.000064518754,0.00018051111,0.0010461418,0.00009977011,0.00025342527],"domain_scores_gemma":[0.99816215,0.000037956637,0.00019082689,0.0012409935,0.00021150762,0.00015658262],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010960858,0.00024436408,0.00023321836,0.00021243571,0.00023785589,0.00020121071,0.0013759402,0.00028424698,0.00014444147],"category_scores_gemma":[0.000011766521,0.00030367752,0.00025906766,0.00058481656,0.00010501344,0.00034629792,0.0011825785,0.00051619706,0.0000863846],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000039361375,0.00015878791,0.00040678904,0.000048318874,0.00007211315,0.00024290319,0.000084066145,0.013899643,0.00034627152,0.9819736,0.0019772477,0.00078630686],"study_design_scores_gemma":[0.00054572616,0.00010556364,0.0051284833,0.00014014001,0.00016389636,0.00004205907,0.00033392975,0.6053147,0.013857992,0.34571227,0.027085602,0.0015696913],"about_ca_topic_score_codex":0.00006986941,"about_ca_topic_score_gemma":0.000023189008,"teacher_disagreement_score":0.9125485,"about_ca_system_score_codex":0.00018061347,"about_ca_system_score_gemma":0.00024030007,"threshold_uncertainty_score":0.9999415},"labels":[],"label_agreement":null},{"id":"W3213179908","doi":"10.1109/bigdata52589.2021.9671411","title":"Detecting Fake Points of Interest from Location Data","year":2021,"lang":"en","type":"preprint","venue":"2021 IEEE International Conference on Big Data (Big Data)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Point of interest; Data mining; Ground truth; Reliability (semiconductor); Global Positioning System; Artificial intelligence; Perceptron; Machine learning; Artificial neural network","score_opus":0.5344346287500138,"score_gpt":0.3894898064020511,"score_spread":0.1449448223479627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213179908","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":[0.0047184373,0.000110903835,0.9523831,0.0024530704,0.005858099,0.00040223612,0.032738276,0.00017513205,0.0011607476],"genre_scores_gemma":[0.828269,0.00078498694,0.060160678,0.0002747112,0.0016047325,0.000058264486,0.10867168,0.000037260314,0.00013866002],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99431926,0.00020915784,0.0011044691,0.0031988863,0.0008545022,0.00031373333],"domain_scores_gemma":[0.980711,0.0002783469,0.001118677,0.016766734,0.00097086246,0.00015440138],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.00097036286,0.00048297318,0.000550603,0.00035436847,0.00014816495,0.0011767951,0.026674075,0.0003662082,0.00024452351],"category_scores_gemma":[0.00076667644,0.0005278684,0.000072276576,0.00047373754,0.00014498808,0.0015966075,0.033068746,0.0011370624,0.00011759101],"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":[0.00003302481,0.0004021103,0.0001299343,0.00008203667,0.00040290673,0.000031335658,0.00012376189,0.00004805826,0.0048663463,0.008139913,0.012363079,0.97337747],"study_design_scores_gemma":[0.0007299421,0.000112515496,0.0012178002,0.002643872,0.00016979255,0.000040460458,0.00042574678,0.8967146,0.026017757,0.011269782,0.059099417,0.0015582832],"about_ca_topic_score_codex":0.0023674876,"about_ca_topic_score_gemma":0.003321524,"teacher_disagreement_score":0.9718192,"about_ca_system_score_codex":0.00013312939,"about_ca_system_score_gemma":0.0010046858,"threshold_uncertainty_score":0.9998601},"labels":[],"label_agreement":null},{"id":"W3213505202","doi":"10.1109/wf-iot51360.2021.9595383","title":"SocialNet: Detecting Social Distancing Violations in Crowd Scene on IoT devices","year":2021,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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 British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Autoencoder; Computer science; Artificial intelligence; Social distance; Bounding overwatch; Convolutional neural network; Deep learning; Encoder; Computer security; Computer vision; Machine learning; Pattern recognition (psychology); Coronavirus disease 2019 (COVID-19)","score_opus":0.01865839561555153,"score_gpt":0.2803628705693692,"score_spread":0.26170447495381766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213505202","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":[0.09714325,0.000023735407,0.8857487,0.002019922,0.00006170013,0.000097039774,0.000001342926,0.00035220233,0.014552106],"genre_scores_gemma":[0.9608818,0.000002289297,0.038189217,0.00048647728,0.00008523261,0.000035225068,0.0000011045223,0.0000062215904,0.00031241038],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991961,0.000038883103,0.00019122324,0.0002783371,0.00012273063,0.0001726856],"domain_scores_gemma":[0.999587,0.000069050875,0.00006112503,0.00018791182,0.00006403581,0.000030860363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013773364,0.00007820544,0.0000951934,0.00007174229,0.0004242241,0.00013182274,0.0002135027,0.000059238122,0.00004115182],"category_scores_gemma":[0.00002412226,0.000082341176,0.000054913864,0.00071900705,0.000015858086,0.00011666142,0.00009451967,0.00013833982,0.000022571192],"study_design_candidate":"theoretical_or_conceptual","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":[0.000004186746,0.00022660823,0.0056310394,0.000019729503,0.000016253069,0.000016768361,0.0016734493,0.00015154136,0.019765781,0.5685329,0.0005405096,0.40342125],"study_design_scores_gemma":[0.0011429881,0.00016968354,0.32095534,0.00015162077,0.000021579217,0.00003690945,0.0022819822,0.1636845,0.39881772,0.07693209,0.03420901,0.0015965783],"about_ca_topic_score_codex":0.0000658848,"about_ca_topic_score_gemma":0.00080979243,"teacher_disagreement_score":0.8637386,"about_ca_system_score_codex":0.00009227183,"about_ca_system_score_gemma":0.000068088215,"threshold_uncertainty_score":0.33577758},"labels":[],"label_agreement":null},{"id":"W3215505471","doi":"10.1088/1742-6596/2113/1/012062","title":"Anomaly detection in multi-class time series","year":2021,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Anomaly detection; Cluster analysis; Series (stratigraphy); Data mining; Feature (linguistics); Key (lock); Artificial intelligence; Time series; Domain (mathematical analysis); Pattern recognition (psychology); Class (philosophy); Machine learning; Time domain; Wavelet transform; Wavelet; Signal processing; Computer vision; Mathematics; Digital signal processing","score_opus":0.022064770005146138,"score_gpt":0.2528002988909912,"score_spread":0.23073552888584506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215505471","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":[0.03034372,0.000046989237,0.9677338,0.0010505483,0.00012648308,0.000055373886,0.0000024816093,0.000051801617,0.00058881316],"genre_scores_gemma":[0.9554236,0.00009185008,0.043428637,0.000058678666,0.00007725101,0.0000074807153,8.4563385e-7,0.000006636092,0.00090505497],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991461,0.00005659677,0.0003315775,0.00015679523,0.00016831249,0.00014062085],"domain_scores_gemma":[0.99890596,0.000022548365,0.00026933372,0.00024554867,0.00050218025,0.000054450582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013608203,0.00010665506,0.0002062484,0.000069893824,0.00008749335,0.00017652939,0.00033394535,0.00005349125,0.000026778545],"category_scores_gemma":[0.00002710453,0.0001030076,0.00008878447,0.00049057306,0.000058682195,0.0015349162,0.00010081094,0.00021819602,0.000016585946],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003718874,0.00033238775,0.0004206031,0.000029047109,0.0000447726,0.00009087527,0.0010832242,0.00017307453,0.48028576,0.14887112,0.00014397068,0.36848795],"study_design_scores_gemma":[0.00020259537,0.00017672626,0.0021727202,0.000035458186,0.000006193897,0.00021848662,0.00016173226,0.0029238753,0.96932316,0.02061139,0.00402616,0.00014152417],"about_ca_topic_score_codex":0.000009124208,"about_ca_topic_score_gemma":0.000057860903,"teacher_disagreement_score":0.9250798,"about_ca_system_score_codex":0.000046451154,"about_ca_system_score_gemma":0.00022324744,"threshold_uncertainty_score":0.42005283},"labels":[],"label_agreement":null},{"id":"W3215711275","doi":"10.1109/iri51335.2021.00052","title":"Important Features Identification for Prostate Cancer Patients Stratification Using Isolation Forest and Interactive Clustering Method","year":2021,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Prostate cancer; Medicine; False positive paradox; Prostate-specific antigen; Rectal examination; Demographics; Prostate; Oncology; Cancer; Cluster analysis; Population; Internal medicine; Gynecology; Artificial intelligence; Computer science; Demography","score_opus":0.018614759272461315,"score_gpt":0.33286191765576795,"score_spread":0.31424715838330664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215711275","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":[0.03949885,0.000045734283,0.95937806,0.000357518,0.00008869483,0.0004861731,0.000011935698,0.0000950871,0.000037957267],"genre_scores_gemma":[0.6984628,0.000029227607,0.30092126,0.000059325477,0.000020424288,0.00021345066,0.000027996433,0.000006452689,0.00025908693],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918765,0.000027481918,0.00026039197,0.00033978146,0.00008381518,0.00010087603],"domain_scores_gemma":[0.9991704,0.00004654748,0.00020716863,0.00020551754,0.00033645023,0.000033914905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013914109,0.00008077909,0.00007838524,0.000055513286,0.00019619391,0.00023717812,0.000097877004,0.00004168438,0.0000035944172],"category_scores_gemma":[0.000025866051,0.00007834593,0.000030516487,0.00020600496,0.000010504114,0.0005593758,0.000063377985,0.00005176805,3.3869253e-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":[0.00005061933,0.00028860455,0.021799926,0.00013365119,0.0000790912,7.667701e-7,0.0029252653,0.002860883,0.35273883,0.06808819,0.00046462566,0.55056953],"study_design_scores_gemma":[0.0001727679,0.00003469442,0.036955077,0.000017088902,0.0000145097565,0.000005746654,0.000114052214,0.8618938,0.094713576,0.0055844034,0.00035867654,0.00013561387],"about_ca_topic_score_codex":0.00010542532,"about_ca_topic_score_gemma":0.00024984803,"teacher_disagreement_score":0.8590329,"about_ca_system_score_codex":0.000060743776,"about_ca_system_score_gemma":0.000045751975,"threshold_uncertainty_score":0.31948543},"labels":[],"label_agreement":null},{"id":"W3216567835","doi":"10.1049/ipr2.12379","title":"Crowd understanding and analysis","year":2021,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"","keywords":"Computer science; Field (mathematics); Section (typography); Action (physics); Crowd psychology; Object (grammar); Data science; Key (lock); Artificial intelligence; Human–computer interaction; Computer security","score_opus":0.02772116875082107,"score_gpt":0.28313765406393426,"score_spread":0.2554164853131132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216567835","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":[0.0020682672,0.0002744615,0.9934636,0.0008563707,0.000009414306,0.000023734445,6.8897094e-7,0.00020225729,0.0031012178],"genre_scores_gemma":[0.7497883,0.000018097136,0.24983984,0.00014374993,0.000011505867,0.000006518173,0.0000011489564,0.0000030979018,0.00018773675],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994672,0.000011787506,0.00009558507,0.00023965098,0.000081303384,0.00010445116],"domain_scores_gemma":[0.9996542,0.000014947289,0.000044822846,0.00018049213,0.00006452558,0.000041066694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008266566,0.00005468556,0.00008262231,0.000083791834,0.00024047229,0.00047349327,0.0001235241,0.000024489385,0.000011755153],"category_scores_gemma":[0.000010171733,0.000055356213,0.000037348207,0.00110704,0.000034103523,0.00042409732,0.00010445718,0.00005941734,0.0000027396397],"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":[0.0000051087354,0.00021477562,0.009819822,0.00025302955,0.00033672954,0.00013746487,0.0029311993,0.00007157156,0.18651837,0.20372453,0.0013386095,0.5946488],"study_design_scores_gemma":[0.00037696748,0.000041156753,0.007760065,0.000072681316,0.00032474616,0.00019253207,0.0011885146,0.60747784,0.22561428,0.1512485,0.0048971116,0.0008055824],"about_ca_topic_score_codex":0.0000029106218,"about_ca_topic_score_gemma":0.0000029307826,"teacher_disagreement_score":0.74772,"about_ca_system_score_codex":0.000029001898,"about_ca_system_score_gemma":0.000039570303,"threshold_uncertainty_score":0.45659065},"labels":[],"label_agreement":null},{"id":"W3217081785","doi":"10.16984/saufenbilder.903915","title":"Anomaly Detection and Performance Analysis by Using Big Data Filtering Techniques For Healthcare on IoT Edges","year":2021,"lang":"en","type":"article","venue":"Sakarya University Journal of Science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Big data; Computer science; Naive Bayes classifier; Data mining; Anomaly detection; Data set; Data pre-processing; Preprocessor; Internet of Things; Set (abstract data type); Random forest; Classifier (UML); Artificial intelligence; Machine learning; Support vector machine; Embedded system","score_opus":0.057423096387130716,"score_gpt":0.27543445350766593,"score_spread":0.2180113571205352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217081785","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":[0.30538028,0.000053219115,0.6939431,0.00043505093,0.000058497077,0.00004938216,0.000011665986,0.000026761158,0.000042032552],"genre_scores_gemma":[0.92090255,0.00015368656,0.078786984,0.00007685798,0.000034271856,1.761272e-7,8.318578e-7,0.000002171739,0.000042488202],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99914956,0.000025032567,0.0001478652,0.00030749687,0.0002188128,0.00015120549],"domain_scores_gemma":[0.9988548,0.00003789591,0.00021589467,0.00040209858,0.00036496302,0.00012435735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054049585,0.00007033277,0.00013339985,0.00045529008,0.00068661576,0.00010704713,0.0009199425,0.000032923537,9.4537694e-7],"category_scores_gemma":[0.000035979017,0.00007203827,0.000048818627,0.0018864354,0.00019625037,0.0008206213,0.0003651159,0.00011404684,1.4553738e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039762006,0.00007696671,0.0028802068,0.000035846406,0.00006056994,0.00001473438,0.00021211285,0.00012788901,0.18836649,0.0007039563,0.00013676278,0.8073447],"study_design_scores_gemma":[0.00031108662,0.0008583768,0.008947254,0.000105219275,0.00015353275,0.0002879395,0.0005742449,0.19996578,0.7713035,0.00023770731,0.016912436,0.00034298297],"about_ca_topic_score_codex":0.000032506636,"about_ca_topic_score_gemma":0.000013856638,"teacher_disagreement_score":0.8070017,"about_ca_system_score_codex":0.00012594397,"about_ca_system_score_gemma":0.0002472391,"threshold_uncertainty_score":0.52809626},"labels":[],"label_agreement":null},{"id":"W3217174898","doi":"10.1109/epec52095.2021.9621752","title":"Time Series Anomaly Detection for Smart Grids: A Survey","year":2021,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Anomaly detection; Computer science; Smart grid; Grid; Anomaly (physics); Time series; Power grid; Power (physics); Data mining; Real-time computing; Engineering; Electrical engineering; Machine learning; Geology","score_opus":0.0158386141066102,"score_gpt":0.24278015112348036,"score_spread":0.22694153701687017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217174898","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":[0.007945792,0.000018636598,0.9882784,0.00055800815,0.00008092578,0.00016466597,0.000008774873,0.0005029416,0.0024418586],"genre_scores_gemma":[0.7276486,0.000018928651,0.23846518,0.00051739666,0.00008680889,0.00036748295,0.00002560796,0.000016374057,0.032853633],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993358,0.000037910715,0.0001374507,0.00028127892,0.000072388386,0.00013513793],"domain_scores_gemma":[0.9992669,0.00006905358,0.000038851136,0.00037098842,0.00020989061,0.000044267454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021479813,0.000073247655,0.000088173976,0.000038714174,0.00017758203,0.00011529712,0.00020875073,0.000048841302,0.00006214214],"category_scores_gemma":[0.00004192016,0.00007143966,0.00006193038,0.00041093855,0.000022094839,0.0003057598,0.00008928349,0.00004167539,0.0000788896],"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":[0.00008912692,0.0006045024,0.003836219,0.000082936196,0.00015549235,0.000014597557,0.00036380035,0.000057432702,0.27772406,0.21677387,0.04880229,0.45149568],"study_design_scores_gemma":[0.000218398,0.00023306566,0.021459017,0.0000049833566,0.0000068526106,0.00008497319,0.000022264305,0.02843258,0.8023229,0.008121503,0.13876496,0.00032849854],"about_ca_topic_score_codex":0.000062535364,"about_ca_topic_score_gemma":0.0003415166,"teacher_disagreement_score":0.7498132,"about_ca_system_score_codex":0.000022081507,"about_ca_system_score_gemma":0.000049784634,"threshold_uncertainty_score":0.29132247},"labels":[],"label_agreement":null},{"id":"W4200208826","doi":"10.18280/ijsse.110611","title":"Detection and Localization of Anamoly in Videos Using Fruit Fly Optimization-Based Self Organized Maps","year":2021,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Anomaly detection; Artificial intelligence; Histogram; Heuristic; Pattern recognition (psychology); Field (mathematics); On the fly; Feature (linguistics); Process (computing); Feature extraction; Histogram of oriented gradients; Computer vision; Image (mathematics); Mathematics","score_opus":0.004434965693943664,"score_gpt":0.21059903516479136,"score_spread":0.2061640694708477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200208826","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":[0.026076535,0.00015491799,0.97336835,0.00020883189,0.00011122888,0.00003945291,0.0000030271162,0.000022601089,0.000015064423],"genre_scores_gemma":[0.9315525,0.00024967527,0.06811872,0.000037308197,0.000033481105,7.494145e-7,0.0000017626614,0.00000477033,9.737454e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932134,0.000019681513,0.00033840642,0.00009883399,0.00016411704,0.000057609202],"domain_scores_gemma":[0.99933696,0.00005301869,0.00015810315,0.00006349029,0.00035124636,0.00003717541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017940154,0.000062945735,0.00011780832,0.00020337789,0.000031467815,0.000047638954,0.00012591906,0.000048237056,0.0000053223516],"category_scores_gemma":[0.000051753188,0.000068999354,0.000029570536,0.0002837636,0.000010874803,0.00029971215,0.00004784638,0.00010287401,6.002931e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028942388,0.000071259194,0.0008683168,0.000034577857,0.000043683303,0.00002286261,0.000511759,0.98212576,0.008952946,0.0038575584,0.0000017909363,0.0034805355],"study_design_scores_gemma":[0.0004200157,0.000023629615,0.00053752546,0.000071073424,0.000006449487,0.00012738576,0.000024570425,0.9780368,0.019977385,0.00036533974,0.0003461864,0.00006360025],"about_ca_topic_score_codex":0.00001543594,"about_ca_topic_score_gemma":0.0000068053637,"teacher_disagreement_score":0.90547603,"about_ca_system_score_codex":0.00006720017,"about_ca_system_score_gemma":0.000056019388,"threshold_uncertainty_score":0.2813712},"labels":[],"label_agreement":null},{"id":"W4205241007","doi":"10.1109/smc52423.2021.9658992","title":"AutoEncoder regularization using Support Vector Data Description for Anomaly Detection","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Autoencoder; Anomaly detection; Discriminative model; Computer science; Artificial intelligence; Pattern recognition (psychology); Regularization (linguistics); Anomaly (physics); Deep learning; Machine learning","score_opus":0.1416915003365427,"score_gpt":0.3287248549798891,"score_spread":0.18703335464334642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205241007","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":[0.008306743,0.000057914956,0.9864964,0.0005387759,0.0013457077,0.00038114082,0.00010981938,0.00011698494,0.0026465445],"genre_scores_gemma":[0.972806,0.0001514883,0.020307517,0.00015589515,0.00038453174,0.000102561164,0.00022161823,0.000021147875,0.0058492464],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981634,0.00007710247,0.00044292235,0.00074047747,0.0003725747,0.000203525],"domain_scores_gemma":[0.99814045,0.00004929403,0.0002567738,0.0007380032,0.0007281698,0.00008727836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031259368,0.00018999228,0.0002005418,0.00014771373,0.00021823158,0.00075075624,0.0006537997,0.00014269837,0.000056288227],"category_scores_gemma":[0.000046145688,0.00020584183,0.000053877327,0.00023004453,0.000045806602,0.00057559815,0.00019120483,0.0001306076,0.000019498493],"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":[0.000053140633,0.000285482,0.00041588233,0.00012989622,0.00019402026,0.000023459266,0.0003273761,0.0011020638,0.14183982,0.81979734,0.0026880966,0.033143427],"study_design_scores_gemma":[0.00032181453,0.00011889666,0.00056144194,0.00008306835,0.000032398282,0.000115069626,0.00013281658,0.95267236,0.016322715,0.0034483913,0.025903953,0.00028708603],"about_ca_topic_score_codex":0.000116524214,"about_ca_topic_score_gemma":0.00007795722,"teacher_disagreement_score":0.96618885,"about_ca_system_score_codex":0.00011972474,"about_ca_system_score_gemma":0.00017877565,"threshold_uncertainty_score":0.8393986},"labels":[],"label_agreement":null},{"id":"W4206008239","doi":"10.18280/ts.380606","title":"A Deep Learning Based System for the Detection of Human Violence in Video Data","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Computer science; Key (lock); Artificial intelligence; ALARM; Deep learning; Artificial neural network; Point (geometry); Ground truth; Pattern recognition (psychology); Punching; Machine learning; Data mining; Computer security; Engineering","score_opus":0.03173463555759053,"score_gpt":0.2740572881920119,"score_spread":0.24232265263442138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206008239","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":[0.0084174685,0.00008477113,0.99092937,0.00012827035,0.000027057957,0.00027601604,0.0000034657185,0.0000926375,0.000040961182],"genre_scores_gemma":[0.991881,0.000005951429,0.007840818,0.00003911589,0.000030627612,0.00018079742,0.000008489749,0.0000045153315,0.0000086815935],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992094,0.000055967947,0.00023309496,0.0002603926,0.00013219527,0.00010893256],"domain_scores_gemma":[0.9992394,0.00013403184,0.00010394398,0.0004306265,0.00007256641,0.00001945152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044724546,0.0000622223,0.00008646846,0.000048387596,0.00018252306,0.000045836117,0.0005625227,0.000026638909,0.000010665998],"category_scores_gemma":[0.000013145255,0.000052240684,0.000038396636,0.00029976116,0.000020543666,0.00013171638,0.00011666903,0.000079730984,0.0000012582368],"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":[0.00002736471,0.00027059187,0.00093833765,0.0002484279,0.000040263218,0.0000073138003,0.00034412046,0.013175233,0.41314307,0.031276714,0.00003974014,0.54048884],"study_design_scores_gemma":[0.00026024572,0.00009055962,0.0021523996,0.00006730951,0.000009429695,0.000004440322,0.00012638113,0.87470186,0.12141461,0.00020728305,0.00089784246,0.00006763693],"about_ca_topic_score_codex":0.000057803754,"about_ca_topic_score_gemma":0.000094608964,"teacher_disagreement_score":0.9834635,"about_ca_system_score_codex":0.000038161823,"about_ca_system_score_gemma":0.000029251914,"threshold_uncertainty_score":0.21303132},"labels":[],"label_agreement":null},{"id":"W4206231027","doi":"10.3390/jrfm14120612","title":"Super RaSE: Super Random Subspace Ensemble Classification","year":2021,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Subspace topology; Random subspace method; Classifier (UML); Computer science; Algorithm; Ensemble learning; Artificial intelligence; Pattern recognition (psychology); Iterative method","score_opus":0.009062730634919516,"score_gpt":0.2234480259977533,"score_spread":0.21438529536283377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206231027","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":[0.04708684,0.0005561461,0.9503166,0.0007659355,0.00015357793,0.00008285891,0.0000012455207,0.000023301516,0.0010135022],"genre_scores_gemma":[0.94038033,0.004872322,0.05386813,0.00017878409,0.00012191154,0.000010508019,7.2785053e-7,0.0000048072366,0.00056249223],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992684,0.000045474404,0.00025681712,0.00015255454,0.0001675854,0.000109152184],"domain_scores_gemma":[0.99939424,0.000040946827,0.00014427023,0.00021470987,0.00014685275,0.000059003294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003261315,0.00007397583,0.00013994746,0.00009687307,0.00015639554,0.000099128374,0.00018844794,0.00004032382,0.000010552583],"category_scores_gemma":[0.00003643381,0.00006455855,0.0000828213,0.00029875038,0.000021154492,0.00023396376,0.00009349254,0.00013156857,0.0000066083526],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045415352,0.00016092599,0.001819733,0.000022854036,0.000020637033,0.00006806596,0.0004743924,0.00002148286,0.0022760073,0.22004926,0.0065844106,0.7684568],"study_design_scores_gemma":[0.0020507749,0.00014211186,0.0764909,0.0000501688,0.00008758316,0.00015306608,0.00041465927,0.0016746585,0.007132347,0.025389338,0.8861549,0.00025950646],"about_ca_topic_score_codex":0.0000049050786,"about_ca_topic_score_gemma":0.0000073979254,"teacher_disagreement_score":0.8964485,"about_ca_system_score_codex":0.000024680585,"about_ca_system_score_gemma":0.00003256346,"threshold_uncertainty_score":0.26326215},"labels":[],"label_agreement":null},{"id":"W4210261517","doi":"10.1109/icmla52953.2021.00114","title":"Modeling and Predicting Online Learning Activities of Students: An HMM-LSTM based Hybrid Solution","year":2021,"lang":"en","type":"article","venue":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","topic":"Anomaly Detection Techniques and Applications","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":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hidden Markov model; Computer science; Artificial intelligence; Machine learning; Anomaly detection; Outlier; Recurrent neural network; Popularity; Streaming data; Deep learning; Classifier (UML); Novelty; Artificial neural network; Data mining","score_opus":0.03401389725149059,"score_gpt":0.33095277693970343,"score_spread":0.29693887968821286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210261517","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":[0.24510762,0.00007808846,0.7528303,0.0008006789,0.000046614372,0.00015162335,0.000029974584,0.00015700633,0.00079806044],"genre_scores_gemma":[0.979801,0.0004726966,0.018619452,0.000085453525,0.00011400234,0.000133804,0.00014549168,0.000015139221,0.00061294064],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845123,0.00012500427,0.00033574575,0.0005549697,0.0003660074,0.00016705344],"domain_scores_gemma":[0.99903023,0.00009602891,0.00020297669,0.00026137554,0.0003106245,0.0000987881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002784957,0.00017498153,0.00019194634,0.00018162833,0.00041619965,0.00027064246,0.00038703912,0.00006272271,0.000039389797],"category_scores_gemma":[0.00005254406,0.0001895953,0.00005111532,0.00023099208,0.00006848495,0.00038913274,0.00017109109,0.00049145456,0.0000026340322],"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":[0.0000771046,0.0016858676,0.045225125,0.00013242762,0.00022736928,0.0000123677155,0.0009032123,0.12116953,0.09622477,0.20790154,0.000035978373,0.5264047],"study_design_scores_gemma":[0.0002742687,0.00010994982,0.0006371401,0.00006960853,0.000014007703,0.000019037247,0.00030821405,0.9909226,0.0047315457,0.0010477809,0.0016889457,0.00017691847],"about_ca_topic_score_codex":0.0001212033,"about_ca_topic_score_gemma":0.000038382488,"teacher_disagreement_score":0.86975306,"about_ca_system_score_codex":0.000040458817,"about_ca_system_score_gemma":0.00007956577,"threshold_uncertainty_score":0.7731472},"labels":[],"label_agreement":null},{"id":"W4210340044","doi":"10.1109/ithings-greencom-cpscom-smartdata-cybermatics53846.2021.00066","title":"Smart Data Analytics on COVID-19 Data","year":2021,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":25,"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 Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Big data; Data science; Cyberspace; Computer science; Analytics; Data analysis; World Wide Web; Data mining","score_opus":0.22539094052470038,"score_gpt":0.3819436789645371,"score_spread":0.15655273843983675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210340044","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004408245,0.000018635117,0.9727807,0.011897108,0.000049358794,0.000055971333,0.000071939125,0.00036500525,0.014717184],"genre_scores_gemma":[0.1786982,0.00019022744,0.7699386,0.032619577,0.00017127921,0.000019386778,0.001150897,0.000016850508,0.017194977],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990087,0.00002331218,0.00012530218,0.0005897591,0.00014469087,0.00010820742],"domain_scores_gemma":[0.9941844,0.000076913784,0.000034114855,0.0055448893,0.000036314374,0.00012339016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022797279,0.000060788647,0.00006849476,0.000036311467,0.00013215371,0.00014337488,0.0028711217,0.000031877113,0.00017615788],"category_scores_gemma":[0.00012642394,0.000055197554,0.000013931404,0.00046391514,0.000020195224,0.00033137633,0.0026026156,0.00007793789,0.00014641069],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010910796,0.00014052987,0.00026748815,0.000007569186,0.000017758544,0.000024618379,0.000017657247,0.000035145527,0.00017227491,0.45500877,0.50980765,0.034499437],"study_design_scores_gemma":[0.00005158042,0.000016760912,0.00011117819,0.0000014076687,0.0000043874766,0.000018407973,0.000012034107,0.20685604,0.0013186901,0.0031111215,0.78841424,0.000084150444],"about_ca_topic_score_codex":0.000066654124,"about_ca_topic_score_gemma":0.00009348037,"teacher_disagreement_score":0.45189765,"about_ca_system_score_codex":0.000024092737,"about_ca_system_score_gemma":0.00021342325,"threshold_uncertainty_score":0.5335306},"labels":[],"label_agreement":null},{"id":"W4210568765","doi":"10.1145/3508467.3508469","title":"Data analytics for cybersecurity enhancement of transformer protection","year":2021,"lang":"en","type":"article","venue":"ACM SIGEnergy Energy Informatics Review","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro-Québec; University of Toronto","funders":"","keywords":"Autoencoder; Computer science; IEC 61850; Anomaly detection; Smart grid; Software deployment; Deep learning; Transformer; Convolutional neural network; Context (archaeology); Artificial intelligence; Computer security; Real-time computing; Machine learning; Data mining; Engineering","score_opus":0.08245398385557982,"score_gpt":0.3156642491496253,"score_spread":0.23321026529404548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210568765","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000030152825,0.0063114944,0.99090606,0.00088649406,0.00006375703,0.0002686229,0.000045307846,0.00007170564,0.0014163763],"genre_scores_gemma":[0.02183309,0.20592318,0.76586044,0.0039035713,0.000089040834,0.0009488573,0.0006973153,0.000024697834,0.00071979384],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984592,0.000029636538,0.00087112363,0.00020483561,0.00024393959,0.0001912709],"domain_scores_gemma":[0.9970592,0.000052298037,0.0003775781,0.002121228,0.00032265676,0.000067031615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038740112,0.0001458824,0.00031912865,0.00006037694,0.00011246104,0.00003798627,0.001420051,0.00007003081,0.00003214518],"category_scores_gemma":[0.00010345451,0.00013381796,0.00013524001,0.00068473566,0.000029353712,0.0005534183,0.0003166354,0.0000659666,0.0000033562771],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000022646439,0.00014385984,9.061698e-7,0.0020516738,0.000107022286,5.1277726e-7,0.00009889515,0.00009861894,0.0017823947,0.1858964,0.0050160615,0.8048014],"study_design_scores_gemma":[0.00010923797,0.000063623054,0.0000012216973,0.00033956533,0.00005437666,0.000012876704,0.000016005073,0.030448519,0.15118,0.0032203137,0.814384,0.00017028043],"about_ca_topic_score_codex":0.00003293503,"about_ca_topic_score_gemma":0.000045660934,"teacher_disagreement_score":0.8093679,"about_ca_system_score_codex":0.00003697404,"about_ca_system_score_gemma":0.00018038167,"threshold_uncertainty_score":0.5456938},"labels":[],"label_agreement":null},{"id":"W4210715418","doi":"10.1109/icbase53849.2021.00044","title":"The Choice of Kernel Function for One-Class Support Vector Machine","year":2021,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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 Toronto","funders":"","keywords":"Radial basis function kernel; Support vector machine; Polynomial kernel; Artificial intelligence; Pattern recognition (psychology); Computer science; Anomaly detection; Kernel (algebra); Kernel method; Boosting (machine learning); Outlier; Tree kernel; Least squares support vector machine; Machine learning; Mathematics","score_opus":0.02418171736209011,"score_gpt":0.2719825193764077,"score_spread":0.24780080201431762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210715418","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":[0.0006392887,0.000033087897,0.9899483,0.002871649,0.00009864555,0.00014412917,0.0000040582004,0.00013337994,0.00612747],"genre_scores_gemma":[0.9343651,0.000031006308,0.04715496,0.00054292113,0.00007682632,0.00018083378,0.0000074498507,0.000007436317,0.017633492],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99951077,0.000011206279,0.00013993327,0.00015727765,0.00008823267,0.00009260902],"domain_scores_gemma":[0.9992766,0.00012256048,0.00005878418,0.00037470966,0.00014140947,0.000025962701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011386517,0.00004510355,0.00006019397,0.0000147638275,0.0001515387,0.00004584908,0.00024218699,0.000027440316,0.000051957348],"category_scores_gemma":[0.000025909825,0.00003311243,0.00006185451,0.00020079524,0.00001922458,0.000087294895,0.00007689872,0.00004126714,0.000010794222],"study_design_candidate":"theoretical_or_conceptual","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":[0.000006605534,0.00009405797,0.00024026667,0.000013030772,0.00002576595,1.7028823e-7,0.00002332066,0.0000057491748,0.017884813,0.88483673,0.010707738,0.08616173],"study_design_scores_gemma":[0.00023402653,0.00021164912,0.008335138,0.000003835046,0.000014952278,0.000005778834,0.000019858684,0.030952722,0.22374442,0.015021655,0.7213312,0.00012478951],"about_ca_topic_score_codex":0.000028826931,"about_ca_topic_score_gemma":0.00006371812,"teacher_disagreement_score":0.9427933,"about_ca_system_score_codex":0.000012610578,"about_ca_system_score_gemma":0.00004557155,"threshold_uncertainty_score":0.13502856},"labels":[],"label_agreement":null},{"id":"W4210935151","doi":"10.1007/978-3-030-91390-8_8","title":"Embedding Time-Series Features into Generative Adversarial Networks for Intrusion Detection in Internet of Things Networks","year":2022,"lang":"en","type":"book-chapter","venue":"Intelligent systems reference library","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Embedding; Computer science; Series (stratigraphy); Intrusion detection system; Adversarial system; Internet of Things; Generative grammar; Artificial intelligence; Theoretical computer science; Machine learning; Computer security; Geology","score_opus":0.01548838225510648,"score_gpt":0.23688132279605773,"score_spread":0.22139294054095124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210935151","genre_codex":"methods","genre_gemma":"other","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":[0.00003054079,0.0013642632,0.97296363,0.000084607855,0.0010782671,0.0014282956,0.000012548461,0.00038388395,0.02265394],"genre_scores_gemma":[0.34961691,0.005967873,0.095860995,0.00058319967,0.0024063662,0.0034609921,0.0013064684,0.00046976347,0.5403274],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975704,0.00010227185,0.00095878384,0.00078646204,0.00029467826,0.00028737937],"domain_scores_gemma":[0.9981794,0.00019680604,0.0008141268,0.00063277007,0.00008973413,0.000087158274],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027252207,0.00043698974,0.0006300515,0.0004603851,0.00018686453,0.00023145937,0.001158131,0.00057351653,0.00020365763],"category_scores_gemma":[0.00001366505,0.0004241954,0.00021459762,0.00021384117,0.000084646636,0.0013518076,0.00084288215,0.0008339149,0.000009307435],"study_design_candidate":"theoretical_or_conceptual","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":[0.00030920075,0.000057495272,0.000009576032,0.00022206851,0.00015701207,0.000011360774,0.0013496607,0.024308069,0.00030234558,0.8188699,0.0064240876,0.14797924],"study_design_scores_gemma":[0.00015303839,0.0006036085,0.000003612882,0.0006082674,0.00002771854,0.000032185602,0.00006291483,0.7401635,0.0061624534,0.0173883,0.23413876,0.00065563485],"about_ca_topic_score_codex":0.00020060429,"about_ca_topic_score_gemma":0.0000093520885,"teacher_disagreement_score":0.8771027,"about_ca_system_score_codex":0.00018768385,"about_ca_system_score_gemma":0.00008934048,"threshold_uncertainty_score":0.999821},"labels":[],"label_agreement":null},{"id":"W4211005114","doi":"10.1109/icecet52533.2021.9698794","title":"Anomalous energy detection for resource-constrained embedded systems using tracing data analysis","year":2021,"lang":"en","type":"article","venue":"2021 International Conference on Electrical, Computer and Energy Technologies (ICECET)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Concordia University; Polytechnique Montréal","funders":"","keywords":"Computer science; Anomaly detection; Energy consumption; Real-time computing; Tracing; Embedded system; Wireless sensor network; Distributed computing; Data mining; Computer network; Operating system; Engineering","score_opus":0.045628330174095115,"score_gpt":0.28415243810983976,"score_spread":0.23852410793574463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211005114","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":[0.00089239946,0.00023601876,0.99571216,0.00091675244,0.00019431605,0.00007647486,0.000034277127,0.0006895146,0.001248114],"genre_scores_gemma":[0.93409365,0.00036867455,0.06460399,0.00017489097,0.00014307084,0.000105272025,0.00013695493,0.000015051973,0.00035843847],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99765897,0.00008855411,0.00046328947,0.0011196854,0.00030857322,0.00036093194],"domain_scores_gemma":[0.9979835,0.00021064981,0.00023521918,0.0011084969,0.00039284024,0.00006925947],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022937077,0.00026931625,0.00038220506,0.00069445884,0.0003397605,0.0006537088,0.0014812852,0.00025450898,0.000012526554],"category_scores_gemma":[0.00007324882,0.00027013116,0.00013932437,0.0016029797,0.00009420732,0.0003335274,0.00069659104,0.00021783721,0.000001035664],"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":[0.000015177966,0.00009745958,0.000020036432,0.0000059379104,0.00035922628,0.00001746596,0.000017092245,0.0013626533,0.00261014,0.40848237,0.0002959948,0.5867165],"study_design_scores_gemma":[0.00021822451,0.0001475952,0.000020176498,0.00002279276,0.00007807961,0.00010837996,0.000090616195,0.9747062,0.007581192,0.005513253,0.011213502,0.00030000389],"about_ca_topic_score_codex":0.00015114187,"about_ca_topic_score_gemma":0.000051172858,"teacher_disagreement_score":0.97334355,"about_ca_system_score_codex":0.00013008899,"about_ca_system_score_gemma":0.00012641719,"threshold_uncertainty_score":0.9999751},"labels":[],"label_agreement":null},{"id":"W4212898032","doi":"10.1109/wacv51458.2022.00291","title":"One-Class Learned Encoder-Decoder Network with Adversarial Context Masking for Novelty Detection","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Adversarial system; Novelty; Encoder; Masking (illustration); Class (philosophy); Context (archaeology); Novelty detection; Artificial intelligence; Decoding methods; Speech recognition; Algorithm; Psychology","score_opus":0.028321615469852617,"score_gpt":0.2784198623457384,"score_spread":0.2500982468758858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212898032","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":[0.0016739243,0.00002076837,0.9922608,0.0020602222,0.0003843858,0.0019951635,0.000054357024,0.00043786914,0.0011124832],"genre_scores_gemma":[0.90904605,0.00001387121,0.08605878,0.00084163557,0.00032070527,0.0031971151,0.00003973378,0.00004181751,0.000440302],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99698806,0.0001472701,0.0007079299,0.0010861541,0.00061772956,0.00045287667],"domain_scores_gemma":[0.99719757,0.00024886394,0.00060840877,0.0013719586,0.00042478196,0.0001484412],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00051849027,0.00033806608,0.00045029784,0.00032229372,0.0009702022,0.00021558185,0.001669746,0.000117036136,0.00015812555],"category_scores_gemma":[0.0000060087564,0.00035680935,0.00023611117,0.0010140828,0.00013194943,0.0003450251,0.0005629986,0.00051314407,0.00003304301],"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":[0.00051842944,0.001053861,0.000039179846,0.00005514821,0.00016783955,0.0000017164498,0.0006180536,0.017815046,0.0091473535,0.19241938,0.0076510357,0.77051294],"study_design_scores_gemma":[0.0022735836,0.003632669,0.0003041386,0.000102928934,0.00006780196,0.000043644915,0.00024160754,0.8036603,0.017383473,0.02098144,0.15039495,0.00091349566],"about_ca_topic_score_codex":0.000036699446,"about_ca_topic_score_gemma":0.000048539852,"teacher_disagreement_score":0.9073721,"about_ca_system_score_codex":0.00018767008,"about_ca_system_score_gemma":0.00016446953,"threshold_uncertainty_score":0.99988836},"labels":[],"label_agreement":null},{"id":"W4212988722","doi":"10.5220/0010867900003120","title":"Evaluating Deep Learning-based NIDS in Adversarial Settings","year":2022,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Adversarial system; Computer science; Deep learning; Artificial intelligence; Machine learning","score_opus":0.01920505554538891,"score_gpt":0.30256914951615854,"score_spread":0.2833640939707696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212988722","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":[0.01992606,0.0000086197715,0.9746214,0.0012911872,0.00006128494,0.00016415188,3.0834002e-7,0.00044990226,0.0034770449],"genre_scores_gemma":[0.91200215,3.0231183e-7,0.08669369,0.0006214141,0.000017785009,0.00012695494,0.000002133262,0.0000046693913,0.0005309123],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915516,0.00010060862,0.00014749654,0.00024220641,0.00021654532,0.00013797134],"domain_scores_gemma":[0.9996194,0.00006203005,0.00005997553,0.00020874514,0.00002337792,0.000026430183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005894896,0.000055279615,0.00006116184,0.000104377184,0.00031792562,0.000038944803,0.00040196563,0.000017825349,0.0003190807],"category_scores_gemma":[0.000030536106,0.00006002435,0.0000366606,0.00056187017,0.000010713876,0.000091841815,0.0002505679,0.00022786089,0.0000148350155],"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":[0.000028391338,0.00031117056,0.0064158333,0.000014134704,0.000009793804,0.000012616357,0.0013575567,0.4457387,0.0073328014,0.092167884,0.0019324459,0.44467866],"study_design_scores_gemma":[0.00023020613,0.00017051585,0.00063400256,0.0000011420394,0.0000010878566,0.000002949509,0.00008892795,0.980018,0.0016447471,0.0012968535,0.015820237,0.00009130286],"about_ca_topic_score_codex":0.000079745405,"about_ca_topic_score_gemma":0.000008816355,"teacher_disagreement_score":0.8920761,"about_ca_system_score_codex":0.00008816082,"about_ca_system_score_gemma":0.00004944028,"threshold_uncertainty_score":0.349371},"labels":[],"label_agreement":null},{"id":"W4213358236","doi":"10.1016/j.compeleceng.2022.107784","title":"Artificial intelligence for intrusion detection systems in Unmanned Aerial Vehicles","year":2022,"lang":"en","type":"article","venue":"Computers & Electrical Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":102,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Spoofing attack; Computer science; Jamming; Intrusion detection system; Computer security; Software deployment; Task (project management); Real-time computing; Engineering; Systems engineering","score_opus":0.013806798991043008,"score_gpt":0.22572769747869798,"score_spread":0.21192089848765497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213358236","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":[0.018465308,0.00008929397,0.97976404,0.00015043389,0.00064840127,0.00043396626,0.000001356144,0.00044131465,0.0000059084487],"genre_scores_gemma":[0.9754209,0.000004901655,0.0238586,0.00003884299,0.0001609431,0.00049541093,0.0000025060156,0.0000126913465,0.000005226301],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988793,0.00003204608,0.00031585924,0.00033106963,0.00015962344,0.00028211382],"domain_scores_gemma":[0.99949986,0.00014335338,0.000058825954,0.00021264814,0.000029212642,0.000056099037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025961723,0.00011524605,0.00015291056,0.00030335822,0.00021079576,0.00009727569,0.00048610606,0.000045907902,0.0000014131845],"category_scores_gemma":[0.000025105806,0.00013545333,0.00006300798,0.0011039504,0.0000073691263,0.00012300035,0.00020499632,0.00023901784,0.0000021506064],"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":[0.000025618929,0.000081622326,0.0000037063307,0.00001472315,0.000006922537,0.0000037747961,0.000094278534,0.41708812,0.031375077,0.103861965,0.000080398706,0.4473638],"study_design_scores_gemma":[0.000049438328,0.00022674513,0.000044754273,0.0000051836164,0.0000018903629,0.000018958328,0.000005627792,0.97700226,0.016912207,0.0019095081,0.0036726624,0.00015076267],"about_ca_topic_score_codex":0.00003159391,"about_ca_topic_score_gemma":0.0000012355389,"teacher_disagreement_score":0.95695555,"about_ca_system_score_codex":0.0002611995,"about_ca_system_score_gemma":0.00002478779,"threshold_uncertainty_score":0.5523626},"labels":[],"label_agreement":null},{"id":"W4214832602","doi":"10.1155/2022/5631281","title":"Intelligent Transport Surveillance Memory Enhanced Method for Detection of Abnormal Behavior in Video","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":true,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Zhejiang Shuren University; Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Computer science; Autoencoder; Artificial intelligence; Convolutional neural network; Coding (social sciences); Process (computing); Pattern recognition (psychology); Computer vision; Artificial neural network","score_opus":0.011533587467529896,"score_gpt":0.2908274838851406,"score_spread":0.2792938964176107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214832602","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":[0.31856623,0.00005811296,0.6808039,0.000050628227,0.00017649001,0.00030185157,0.0000136210365,0.000020723677,0.000008451427],"genre_scores_gemma":[0.84650785,0.000045744815,0.15315431,0.00002286967,0.000020412464,0.00021838471,0.0000070401957,0.000008736686,0.000014667764],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985329,0.000051029863,0.00082158344,0.0001811842,0.00028056133,0.00013274902],"domain_scores_gemma":[0.9988054,0.00008645687,0.0006757254,0.00016323783,0.00022614551,0.000043007443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006579463,0.00009858303,0.0002458166,0.00028086043,0.0000926411,0.000005443682,0.00031508162,0.0000356949,0.000010956935],"category_scores_gemma":[0.00000860955,0.00010577477,0.00018575056,0.0005485881,0.00001536678,0.00040821618,0.0000043809164,0.00020588948,1.1768716e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003987076,0.00031852047,0.0007413703,0.000047876456,0.000018019686,0.000008809919,0.0018925341,0.16778539,0.5319715,0.0011828387,0.0000027221356,0.29563168],"study_design_scores_gemma":[0.0011619823,0.001270824,0.1425271,0.00001883202,0.000030530864,0.000034252782,0.0006743744,0.002625355,0.84831244,0.0018230611,0.0013141978,0.00020704689],"about_ca_topic_score_codex":0.000023487646,"about_ca_topic_score_gemma":0.00013110421,"teacher_disagreement_score":0.5279416,"about_ca_system_score_codex":0.000097542,"about_ca_system_score_gemma":0.00006688045,"threshold_uncertainty_score":0.431337},"labels":[],"label_agreement":null},{"id":"W4220900860","doi":"10.1109/tcsii.2022.3161049","title":"Appearance-Motion United Auto-Encoder Framework for Video Anomaly Detection","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Encoder; Artificial intelligence; Motion (physics); Normality; Computer vision; Anomaly detection; Exploit; Pattern recognition (psychology); Autoencoder; Anomaly (physics); Mathematics; Deep learning; Statistics","score_opus":0.022493948949380813,"score_gpt":0.25265176759037133,"score_spread":0.23015781864099052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220900860","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":[0.0041360147,0.00012344976,0.98960096,0.000282773,0.0021574358,0.0017730442,0.00013730241,0.0015911004,0.00019792933],"genre_scores_gemma":[0.98803216,0.000011774214,0.0044712094,0.0004083332,0.0001677566,0.006029392,0.000007196268,0.000059235572,0.00081293227],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99704534,0.0002631553,0.000642981,0.00094772386,0.00059746095,0.0005033204],"domain_scores_gemma":[0.997902,0.000182654,0.00034406874,0.00118032,0.00022496723,0.00016599234],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.000521955,0.00033372422,0.00034947987,0.00061822025,0.002994941,0.00025470383,0.00096144766,0.00017809997,0.0000384851],"category_scores_gemma":[0.000011531496,0.0003904913,0.00028778514,0.0017248438,0.00006197346,0.0005788069,0.000018062028,0.00066172535,0.000023451474],"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":[0.0001695843,0.0024628541,0.00002402193,0.00047628372,0.00040714533,0.000019909996,0.0048105996,0.4566109,0.10197025,0.11770247,0.0023747096,0.31297126],"study_design_scores_gemma":[0.0017793876,0.0018674665,0.0001569062,0.00029248188,0.00013257268,0.00046380115,0.0007660336,0.5401502,0.11387788,0.0066956296,0.33205295,0.0017646633],"about_ca_topic_score_codex":0.00033851629,"about_ca_topic_score_gemma":0.000011427602,"teacher_disagreement_score":0.9851297,"about_ca_system_score_codex":0.00037388798,"about_ca_system_score_gemma":0.0000973105,"threshold_uncertainty_score":0.9998547},"labels":[],"label_agreement":null},{"id":"W4220922038","doi":"10.1109/icais53314.2022.9743099","title":"Review on Various Methodologies of Predicting Crime","year":2022,"lang":"en","type":"article","venue":"2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Computer science; Identification (biology); Machine learning; Unsupervised learning; Artificial intelligence; Crime analysis; Supervised learning; Data science; Artificial neural network; Criminology; Psychology","score_opus":0.1430260245411246,"score_gpt":0.35244298735158147,"score_spread":0.20941696281045688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220922038","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":[0.0036512574,0.0012565847,0.90469146,0.006430735,0.0011266812,0.0003382687,0.000097996286,0.00030766297,0.082099386],"genre_scores_gemma":[0.9857352,0.0031968034,0.0047798413,0.004606731,0.00007212645,0.00029523592,0.000026072923,0.00001139541,0.001276608],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982663,0.00018640627,0.000516329,0.0004858505,0.00036992517,0.00017519764],"domain_scores_gemma":[0.99886376,0.00023263287,0.00026360853,0.00039406313,0.00018216131,0.00006375383],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006101503,0.00016034904,0.00023774378,0.00020754187,0.000297309,0.00007816587,0.00093851204,0.000044400927,0.0022722841],"category_scores_gemma":[0.00011347294,0.00015611824,0.00009990528,0.00032783634,0.00009479389,0.00017808755,0.00044655264,0.00029018088,0.000014567545],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018651599,0.00008162665,0.000009767338,0.000014809712,0.000021980055,0.000002472092,0.00008657906,0.00012373555,0.0018152238,0.88090533,0.0012282222,0.11569161],"study_design_scores_gemma":[0.00008691749,0.0021308172,0.00025713455,0.0003601372,0.00003145212,0.00007397866,0.00083055993,0.1710343,0.157999,0.4598619,0.20658675,0.0007470468],"about_ca_topic_score_codex":0.00010013825,"about_ca_topic_score_gemma":0.000024841247,"teacher_disagreement_score":0.9820839,"about_ca_system_score_codex":0.000054756405,"about_ca_system_score_gemma":0.000076526216,"threshold_uncertainty_score":0.99863976},"labels":[],"label_agreement":null},{"id":"W4221149902","doi":"10.1109/icc45855.2022.9839003","title":"Generative Adversarial Network-Driven Detection of Adversarial Tasks in Mobile Crowdsensing","year":2022,"lang":"en","type":"article","venue":"ICC 2022 - IEEE International Conference on Communications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Adversarial system; Discriminator; Adversarial machine learning; Classifier (UML); Artificial intelligence; Leverage (statistics); Machine learning; Binary number; Binary classification; Generative grammar; Detector; Support vector machine","score_opus":0.05784811603133822,"score_gpt":0.32571509824542305,"score_spread":0.26786698221408484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221149902","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":[0.039933626,0.00014552653,0.92031455,0.005758362,0.0030580459,0.0015086479,0.0002273525,0.00042188828,0.028632022],"genre_scores_gemma":[0.9836378,0.0001599967,0.014940268,0.00020213613,0.00012135543,0.0006731935,0.000057248686,0.000012090487,0.00019589449],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981705,0.00033454664,0.00049718,0.0003669961,0.00043668319,0.0001940761],"domain_scores_gemma":[0.99795264,0.00019098636,0.00035351102,0.0012194625,0.00023338338,0.00004998764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037960574,0.00014961902,0.00018972972,0.00031035035,0.00055221195,0.00007961972,0.0023212286,0.00006282083,0.00020501642],"category_scores_gemma":[0.000028112841,0.00018141085,0.00011117598,0.00070832873,0.00012283794,0.0002951093,0.0009297158,0.0005848518,0.000013589156],"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":[0.00017200099,0.0010216662,0.00037055675,0.000005781056,0.00014396688,0.0000072856205,0.002335878,0.0992495,0.115585744,0.71109563,0.0017656714,0.06824634],"study_design_scores_gemma":[0.0009382042,0.00046575587,0.00070170045,0.000032664757,0.000015245162,0.000023994859,0.0006531799,0.9316287,0.011326843,0.01753557,0.03628308,0.00039503057],"about_ca_topic_score_codex":0.00025292338,"about_ca_topic_score_gemma":0.0004047602,"teacher_disagreement_score":0.9437042,"about_ca_system_score_codex":0.000338461,"about_ca_system_score_gemma":0.00016888425,"threshold_uncertainty_score":0.739772},"labels":[],"label_agreement":null},{"id":"W4224063960","doi":"10.1007/s40840-022-01287-z","title":"Regressive Class Modelling for Predicting Trajectories of COVID-19 Fatalities Using Statistical and Machine Learning Models","year":2022,"lang":"en","type":"article","venue":"Bulletin of the Malaysian Mathematical Sciences Society","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mount Saint Vincent University; University of New Brunswick; University of Prince Edward Island","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Coronavirus disease 2019 (COVID-19); Class (philosophy); Statistical learning; Artificial intelligence; Applied mathematics; Statistics; Computer science","score_opus":0.08194529866307697,"score_gpt":0.2895259819851543,"score_spread":0.2075806833220773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224063960","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":[0.0140938675,0.00006953323,0.983055,0.0020702577,0.000022089851,0.00031860123,0.000053731583,0.00005578803,0.00026112853],"genre_scores_gemma":[0.6048528,0.0000066501734,0.394896,0.000085627216,0.000009384609,0.000053949436,7.0446225e-7,0.000004862553,0.000090041365],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987098,0.00010791437,0.00033240096,0.00027640347,0.00037939844,0.00019408933],"domain_scores_gemma":[0.9987208,0.0006681353,0.0002958583,0.00019903124,0.000046354762,0.00006979164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011555013,0.00009872671,0.00020211554,0.000023526543,0.0011465509,0.000052164745,0.00066172983,0.000033323537,0.00004353766],"category_scores_gemma":[0.00014252654,0.000072996314,0.00013218257,0.00027464758,0.00066391664,0.000059634935,0.0005864089,0.00016107483,1.10298124e-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":[0.0000044136405,0.00004258761,0.00013774657,0.00019509903,0.000013259554,1.0910771e-7,0.0030770858,0.29478964,0.00016572743,0.7012252,0.00016908436,0.00018002698],"study_design_scores_gemma":[0.00007514197,0.00007227482,0.0000020973762,0.00001649576,0.000012477686,0.000013216258,0.0018571229,0.8036008,0.0002977424,0.1935046,0.00048194817,0.00006608674],"about_ca_topic_score_codex":0.000096472206,"about_ca_topic_score_gemma":3.7981198e-7,"teacher_disagreement_score":0.5907589,"about_ca_system_score_codex":0.00005729356,"about_ca_system_score_gemma":0.000101231635,"threshold_uncertainty_score":0.8818458},"labels":[],"label_agreement":null},{"id":"W4224911291","doi":"10.1109/icde53745.2022.00269","title":"Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 38th International Conference on Data Engineering (ICDE)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Social Science Fund of China; National Natural Science Foundation of China","keywords":"Hypergraph; Computer science; Artificial intelligence; Supervised learning; Machine learning; Pattern recognition (psychology); Artificial neural network; Mathematics","score_opus":0.04609042430957209,"score_gpt":0.2723752101690917,"score_spread":0.22628478585951964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224911291","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":[0.0031660108,0.000015454081,0.99188524,0.00082497584,0.0013378024,0.00037766882,0.0004896943,0.0009334058,0.000969758],"genre_scores_gemma":[0.9640684,0.00003551507,0.03326011,0.00013076872,0.00024386296,0.0004927525,0.0011247905,0.00002754544,0.0006162215],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99835247,0.000036913363,0.00030482857,0.0006130544,0.00047128068,0.00022147545],"domain_scores_gemma":[0.9988704,0.000065478715,0.000118345575,0.00074518716,0.00012201844,0.000078625904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037896578,0.00017639097,0.00014278914,0.0002680897,0.0003093119,0.0001770498,0.0021078165,0.000045854744,0.00030655414],"category_scores_gemma":[0.000043913435,0.00020536807,0.00007164669,0.00028885916,0.000012228669,0.00044849108,0.00059240835,0.00040226046,0.00001854325],"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":[0.00018469867,0.0012060788,0.002475955,0.00014292606,0.00066388317,0.000032808635,0.0011026941,0.10810233,0.051407386,0.6764985,0.05939435,0.09878839],"study_design_scores_gemma":[0.00025222672,0.00019871525,0.00036894908,0.000009622435,0.000008598069,0.000014661376,0.000030098518,0.8970831,0.00091529434,0.00039050195,0.10054334,0.00018486222],"about_ca_topic_score_codex":0.000082959996,"about_ca_topic_score_gemma":0.0000050962476,"teacher_disagreement_score":0.96090245,"about_ca_system_score_codex":0.00014288178,"about_ca_system_score_gemma":0.00008284152,"threshold_uncertainty_score":0.83746666},"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":"W4226109456","doi":"10.1109/tim.2022.3165256","title":"Online Monitoring for Non-Stationary Operation via a Collaborative Neural Network","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial neural network; Computer science; Convergence (economics); Minification; Artificial intelligence; Machine learning; Data mining","score_opus":0.03415459713321814,"score_gpt":0.2779132302227505,"score_spread":0.24375863308953236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226109456","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":[0.016350493,0.000021801918,0.980963,0.0009458715,0.000611653,0.00089729717,0.000042953376,0.0001276325,0.00003931611],"genre_scores_gemma":[0.9407751,0.000028274084,0.056859087,0.00033335944,0.000055484245,0.0018549851,0.000012777729,0.0000097699485,0.00007118802],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890316,0.000059217655,0.00023834418,0.00028587665,0.0003679887,0.00014543146],"domain_scores_gemma":[0.99950296,0.000025548163,0.00009260083,0.00015478228,0.00016335094,0.000060782262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024876438,0.00011378677,0.00009235421,0.00010397029,0.001165875,0.00007693483,0.00013562517,0.000023131714,0.000020098923],"category_scores_gemma":[0.0000010397214,0.00012650108,0.000044979224,0.00036264097,0.000017828652,0.0002930365,0.0000041579137,0.00012382046,0.0000014261469],"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":[0.00015320731,0.00085850403,0.00009079201,0.000027668644,0.000100774414,0.000001031196,0.0018744522,0.21147558,0.026341448,0.0028967427,0.00075682125,0.75542295],"study_design_scores_gemma":[0.0035720482,0.0026060487,0.0030647714,0.000037340113,0.00008390256,0.000037849368,0.002356887,0.83276093,0.14131263,0.0026045348,0.010818421,0.0007446599],"about_ca_topic_score_codex":0.000016461523,"about_ca_topic_score_gemma":0.000015272368,"teacher_disagreement_score":0.9244246,"about_ca_system_score_codex":0.00023481347,"about_ca_system_score_gemma":0.000072535535,"threshold_uncertainty_score":0.89670855},"labels":[],"label_agreement":null},{"id":"W4226236539","doi":"","title":"Automated Threat Detection In X-Ray Imagery For Advanced Security Applications","year":2021,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Computer graphics (images)","score_opus":0.00946718601395534,"score_gpt":0.24597266534111792,"score_spread":0.2365054793271626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226236539","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":[0.011808987,0.0006118856,0.97735786,0.0032103898,0.00012798396,0.001705669,0.000044342345,0.0019437661,0.0031891319],"genre_scores_gemma":[0.70702136,0.00033854423,0.28849334,0.00007641715,0.00001959041,0.003184977,0.0002456155,0.00003582131,0.0005843584],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99602497,0.001347283,0.00068121904,0.0012630312,0.00029186765,0.00039165327],"domain_scores_gemma":[0.9936743,0.000863089,0.0005314544,0.0028263854,0.0019566258,0.00014816613],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002538889,0.00035445933,0.00041341892,0.00032516822,0.00044473514,0.00063860684,0.0017059101,0.00038218673,0.000016954991],"category_scores_gemma":[0.0003627435,0.00042278093,0.00028543768,0.0010387067,0.00012368674,0.0003766288,0.001342765,0.000629677,0.0000119684855],"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":[0.000024800824,0.00251939,0.0004613447,0.0007792355,0.00013868688,0.000008515635,0.008344611,0.0013059267,0.05194834,0.3316142,0.0007953519,0.6020596],"study_design_scores_gemma":[0.00078959356,0.0000013646652,0.0020910194,0.00085867353,0.00003799817,0.000017186992,0.0001590972,0.5591115,0.38335076,0.03755683,0.015093007,0.00093297166],"about_ca_topic_score_codex":0.00042425797,"about_ca_topic_score_gemma":0.0013635878,"teacher_disagreement_score":0.69521236,"about_ca_system_score_codex":0.00025347382,"about_ca_system_score_gemma":0.00030813407,"threshold_uncertainty_score":0.9998224},"labels":[],"label_agreement":null},{"id":"W4226248369","doi":"10.1109/tnse.2022.3157730","title":"ASTREAM: Data-Stream-Driven Scalable Anomaly Detection With Accuracy Guarantee in IIoT Environment","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":99,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Anomaly detection; Computer science; Scalability; Data mining; Data modeling; Database","score_opus":0.011404615246309943,"score_gpt":0.20474007062846655,"score_spread":0.1933354553821566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226248369","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":[0.07418999,0.000037014546,0.92510676,0.00009694437,0.00012403952,0.00020838132,0.000007747351,0.00017208187,0.00005702496],"genre_scores_gemma":[0.97923064,0.00007369689,0.02038501,0.000049328144,0.000024110692,0.00019082076,0.0000010514402,0.000009666387,0.00003566375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985472,0.00001615413,0.00016906681,0.0005592791,0.000365404,0.00034292237],"domain_scores_gemma":[0.9991663,0.000051064988,0.0000468289,0.00064781244,0.000017956205,0.000070056776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040212693,0.00013472154,0.000111799396,0.00024116726,0.000718433,0.00011817238,0.0007620281,0.0000250061,0.000015064397],"category_scores_gemma":[0.00000214383,0.00013368191,0.000018326367,0.0015092718,0.00007894394,0.00077738287,0.000037556212,0.00028951038,0.0000039383726],"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":[0.000008879922,0.00005411087,0.00004294804,0.0000028158413,0.000004448285,0.0000036371475,0.00005993985,0.93985355,0.0036206567,0.00015426362,0.000018128128,0.056176636],"study_design_scores_gemma":[0.00019321602,0.0002266517,0.0011223063,0.000014090479,0.0000069524312,0.00006556151,0.000040767136,0.9878819,0.0060378197,0.000029262808,0.0041797645,0.00020167978],"about_ca_topic_score_codex":0.00008249953,"about_ca_topic_score_gemma":0.00003804262,"teacher_disagreement_score":0.9050407,"about_ca_system_score_codex":0.00017184335,"about_ca_system_score_gemma":0.00007807903,"threshold_uncertainty_score":0.55256784},"labels":[],"label_agreement":null},{"id":"W4226378137","doi":"10.1016/j.media.2022.102526","title":"Constrained unsupervised anomaly segmentation","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"European Regional Development Fund; Generalitat Valenciana; European Commission","keywords":"Computer science; Segmentation; Anomaly detection; Constraint (computer-aided design); Artificial intelligence; Hyperparameter; Regularization (linguistics); Pattern recognition (psychology); Machine learning; Mathematics","score_opus":0.006985585779932297,"score_gpt":0.2591613300058138,"score_spread":0.2521757442258815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226378137","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":[0.012338574,0.00002390511,0.9813775,0.003921125,0.000028147411,0.00009544043,0.000009416964,0.00028847274,0.0019174265],"genre_scores_gemma":[0.94056404,0.000010971551,0.05696102,0.0016571463,0.000027920267,0.00021403984,0.000054609758,0.0000050398457,0.00050519436],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985488,0.00011110722,0.00024386955,0.0003131477,0.00062726287,0.00015577768],"domain_scores_gemma":[0.9992716,0.000053233674,0.000073515206,0.00040756384,0.000051896674,0.0001422184],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00044247176,0.00007906165,0.00015708928,0.00027772124,0.00036280515,0.00007365262,0.00069306954,0.00002804986,0.0046492023],"category_scores_gemma":[0.00003549769,0.000077895784,0.00018783347,0.0025704512,0.00006961974,0.00017391569,0.00029749557,0.00016804978,0.000028636427],"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":[0.000026108433,0.0017331444,0.01387519,0.000032253058,0.0029847855,0.0004973289,0.0023535152,0.0009972366,0.036570493,0.08613047,0.026842164,0.82795733],"study_design_scores_gemma":[0.00063154113,0.00016690232,0.004286754,0.0000019867393,0.00045589753,0.000053061503,0.00048435648,0.96226037,0.007831229,0.0024602101,0.020955898,0.00041177476],"about_ca_topic_score_codex":0.0001281947,"about_ca_topic_score_gemma":0.00001466732,"teacher_disagreement_score":0.9612632,"about_ca_system_score_codex":0.000054410004,"about_ca_system_score_gemma":0.00007358159,"threshold_uncertainty_score":0.9962607},"labels":[],"label_agreement":null},{"id":"W4226515701","doi":"10.36227/techrxiv.19210197","title":"Intrusion Detection in the IoT under Data and Concept Drifts: Online Deep Learning Approach","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Concept drift; Intrusion detection system; Computer science; Anomaly detection; Artificial intelligence; Weighting; Artificial neural network; Data mining; Outlier; Data stream mining; Machine learning; Deep learning","score_opus":0.040883178497945254,"score_gpt":0.29799635936903013,"score_spread":0.25711318087108487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226515701","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":[0.009825469,0.00019907435,0.98688716,0.00090068707,0.00007489534,0.00048319413,0.000009335937,0.00030812778,0.0013120815],"genre_scores_gemma":[0.917664,0.00018238944,0.080925606,0.00050753314,0.00007877944,0.00019504917,0.00019754558,0.000012454225,0.00023666953],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983373,0.00022939403,0.00026264545,0.0007820292,0.00023891371,0.00014970687],"domain_scores_gemma":[0.9983166,0.000087186214,0.00015232479,0.0013865993,0.000026210693,0.000031100222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059744244,0.00015983489,0.00015443926,0.00013313051,0.00032822698,0.00021958961,0.0019926187,0.0001382712,0.00003540333],"category_scores_gemma":[0.000022649236,0.0001234843,0.000032418702,0.00040527116,0.00005537385,0.000115637915,0.005560716,0.0012265365,0.0000013221833],"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":[0.000007754654,0.00039912187,0.00023173528,0.000053011354,0.000029976001,0.000003520779,0.003020843,0.019471575,0.00045409068,0.040623482,0.0005804496,0.93512446],"study_design_scores_gemma":[0.00009849295,0.000050971168,0.0019124311,0.0000071596774,0.000008200802,0.000025193622,0.0014583608,0.9711032,0.0000994172,0.004836456,0.020197595,0.0002025555],"about_ca_topic_score_codex":0.00034740157,"about_ca_topic_score_gemma":0.00014527161,"teacher_disagreement_score":0.9516316,"about_ca_system_score_codex":0.000063056206,"about_ca_system_score_gemma":0.00003561101,"threshold_uncertainty_score":0.6931035},"labels":[],"label_agreement":null},{"id":"W4229001174","doi":"10.3390/app12031021","title":"A CNN-RNN Combined Structure for Real-World Violence Detection in Surveillance Cameras","year":2022,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":121,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Anomaly detection; Convolutional neural network; Artificial intelligence; Computer vision; Computer security","score_opus":0.012839334489911942,"score_gpt":0.2614424929308597,"score_spread":0.24860315844094774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229001174","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":[0.40854076,0.00003893845,0.58328664,0.0009913642,0.00037568226,0.0014725457,0.000027461494,0.0005763886,0.0046902373],"genre_scores_gemma":[0.98499024,0.000011326743,0.013795042,0.00026523956,0.000028212948,0.0008186204,0.000002523893,0.0000056100857,0.000083179315],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985702,0.000040746556,0.00023202845,0.0005658466,0.00030106114,0.00029008993],"domain_scores_gemma":[0.99933356,0.0001237688,0.00013724457,0.00032902876,0.000030348192,0.00004603284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006034482,0.00011450654,0.00014312506,0.0002539876,0.000939676,0.00010544454,0.001044146,0.000026294649,0.000025274741],"category_scores_gemma":[0.000008930959,0.00011294573,0.000039549683,0.002262009,0.00013256099,0.00015113637,0.00024859793,0.00012849728,0.0000028222748],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048043592,0.00012883478,0.004248623,0.000018148983,0.000007164895,0.0000017014924,0.00050545327,0.0045973123,0.07169013,0.7128896,0.00047478644,0.2053902],"study_design_scores_gemma":[0.0016140398,0.0012186313,0.058906186,0.000022788661,0.0000079068495,0.000025901281,0.00092999265,0.24189116,0.1855768,0.480208,0.028130922,0.0014676529],"about_ca_topic_score_codex":0.00036135913,"about_ca_topic_score_gemma":0.0009836623,"teacher_disagreement_score":0.57644945,"about_ca_system_score_codex":0.00010655711,"about_ca_system_score_gemma":0.00008414233,"threshold_uncertainty_score":0.7227323},"labels":[],"label_agreement":null},{"id":"W4229446378","doi":"10.18280/ts.390231","title":"Recognition of Cheating Behaviors Based on Finetuning of Model Parameters","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Hefei Normal University; Anhui University","keywords":"Cheating; Computer science; Artificial intelligence; Classifier (UML); Machine learning; Pattern recognition (psychology); Psychology; Social psychology","score_opus":0.042655962599120946,"score_gpt":0.2567624379679129,"score_spread":0.214106475368792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229446378","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":[0.2909104,0.0000015426067,0.70845914,0.00008185536,0.000010079803,0.0001368854,0.000016613509,0.00005040401,0.00033311587],"genre_scores_gemma":[0.90018266,3.6219922e-7,0.099538684,0.00010751596,0.0000035217051,0.00014281692,0.000009976712,0.0000040876125,0.000010364494],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927306,0.000034299606,0.00022995358,0.00015032201,0.00022958204,0.00008278091],"domain_scores_gemma":[0.9995942,0.000041254756,0.00015511629,0.00015191674,0.00003516441,0.000022313749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022772042,0.000058133464,0.00008445571,0.00010958892,0.00010713488,0.000007703585,0.00022449387,0.000013319189,0.00009415554],"category_scores_gemma":[0.0000029695814,0.00006368942,0.00005963576,0.00023616724,0.00001810535,0.000059064463,0.000051591964,0.00007743087,5.664309e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004618534,0.00081013935,0.0005387141,0.000035697245,0.000014749345,0.0000014416321,0.0007233046,0.68124485,0.12476088,0.0064944946,0.0003004261,0.18502912],"study_design_scores_gemma":[0.00017214258,0.00036105784,0.00023170491,0.000011074444,0.0000070850656,6.835523e-7,0.000037247453,0.919668,0.078701265,0.00071874255,0.000025658514,0.00006531698],"about_ca_topic_score_codex":0.000021087251,"about_ca_topic_score_gemma":3.7761822e-7,"teacher_disagreement_score":0.6092723,"about_ca_system_score_codex":0.00003307601,"about_ca_system_score_gemma":0.000027890075,"threshold_uncertainty_score":0.2597179},"labels":[],"label_agreement":null},{"id":"W4230732791","doi":"10.5194/egusphere-egu2020-16801","title":"Real-world rogue wave probabilities","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Probabilistic logic; Rogue wave; Computer science; Econometrics; Economics; Physics; Artificial intelligence","score_opus":0.053877019591289495,"score_gpt":0.2832769172968798,"score_spread":0.22939989770559033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230732791","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002401212,0.000011453544,0.84963983,0.0076619918,0.0001460823,0.0005232234,0.000007824104,0.0021513824,0.13961807],"genre_scores_gemma":[0.31512883,0.000103180406,0.66596794,0.0007360366,0.00025881478,0.00085137,0.000020309823,0.000023109847,0.016910393],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99866843,0.00003029584,0.00028204932,0.00067458674,0.00017405416,0.00017059137],"domain_scores_gemma":[0.9985773,0.00003556313,0.000121751866,0.0010771367,0.00008655541,0.00010172052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010079359,0.00018881398,0.00021416077,0.000096751544,0.00009149391,0.00023872944,0.0009275572,0.00012062697,0.00008447658],"category_scores_gemma":[0.0000096967,0.00017298796,0.00014477829,0.00029279434,0.000045434736,0.00010119222,0.0020122605,0.0003988253,0.00008954621],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.0808534e-7,0.000023427014,0.000026071777,0.000051442126,0.000011922517,0.0000022362701,0.00016131114,0.000026047092,0.00007130684,0.97665995,0.011094724,0.011870857],"study_design_scores_gemma":[0.000056926783,0.000056810546,0.0008561735,0.000040287858,0.000011271949,0.000005213085,0.00002726108,0.07426485,0.0121461665,0.82793164,0.084068246,0.0005351235],"about_ca_topic_score_codex":0.0005708975,"about_ca_topic_score_gemma":0.000119701326,"teacher_disagreement_score":0.31488872,"about_ca_system_score_codex":0.00008614141,"about_ca_system_score_gemma":0.00014260544,"threshold_uncertainty_score":0.7054244},"labels":[],"label_agreement":null},{"id":"W4231109964","doi":"10.1561/2200000006","title":"Learning Deep Architectures for AI","year":2009,"lang":"en","type":"article","venue":"Foundations and Trends® in Machine Learning","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":6910,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Deep learning; Artificial intelligence; Computer science","score_opus":0.010033128702470627,"score_gpt":0.29528704334347866,"score_spread":0.285253914641008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231109964","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":[0.020858914,0.00010948731,0.97218555,0.0037808588,0.000025684942,0.00010895354,9.702073e-7,0.0003282179,0.0026013881],"genre_scores_gemma":[0.94576454,0.000012415866,0.05282867,0.00022450594,0.000039313996,0.000052773732,0.00002525473,0.0000067308047,0.0010458116],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999238,0.000046387777,0.00017600757,0.00028225782,0.00007365207,0.0001836633],"domain_scores_gemma":[0.999626,0.00008028842,0.00007119846,0.00014473802,0.000031725885,0.00004608299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018531017,0.0001054973,0.00011090198,0.00035833596,0.0005068338,0.0001772047,0.00017537577,0.000043099953,0.000029119296],"category_scores_gemma":[0.0000451285,0.000102133476,0.00005054301,0.00052568933,0.000022764716,0.00010359496,0.00004359459,0.00034105923,0.0000038087087],"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":[0.0000034120044,0.00002943102,0.0025658563,0.0000023674397,0.0000036911988,5.996582e-7,0.00033729992,0.00911263,0.00011316453,0.04795246,0.000019439656,0.9398596],"study_design_scores_gemma":[0.00039689982,0.0003315996,0.043332886,0.000010470969,0.0000069068738,0.00002137693,0.000029510013,0.86607236,0.00010613286,0.024741385,0.064730726,0.00021975883],"about_ca_topic_score_codex":0.000047040143,"about_ca_topic_score_gemma":0.000074023585,"teacher_disagreement_score":0.93963987,"about_ca_system_score_codex":0.000019459047,"about_ca_system_score_gemma":0.000008151549,"threshold_uncertainty_score":0.41648823},"labels":[],"label_agreement":null},{"id":"W4234030105","doi":"10.1016/j.asams.2009.08.014","title":"Inheritable exercise-induced collapse in labrador retrievers","year":2009,"lang":"en","type":"article","venue":"Advances in Small Animal Medicine and Surgery","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Medicine","score_opus":0.027512424496298614,"score_gpt":0.2783159015101134,"score_spread":0.2508034770138148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234030105","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":[0.9671731,0.0075336997,0.0147780385,0.0066304575,0.00016865527,0.00036494367,7.8898495e-7,0.00022850877,0.0031218294],"genre_scores_gemma":[0.99002457,0.004690519,0.0042438516,0.00088615273,0.000046387086,0.00003159422,9.785476e-7,0.000004734766,0.000071229435],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9989576,0.000027375347,0.0003336914,0.00033102397,0.00010661006,0.00024375106],"domain_scores_gemma":[0.9993974,0.000197701,0.00007640527,0.00021665033,0.00003389256,0.00007795923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040324134,0.00012120088,0.00030193836,0.00026321708,0.000056526504,0.000018932311,0.00018928401,0.0000655421,0.000009045538],"category_scores_gemma":[0.00008468227,0.00010719572,0.000026522224,0.0011163226,0.00004938272,0.00047386106,0.00003809863,0.0001505802,0.0000022140673],"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":[0.00013939384,0.00019481835,0.02624749,0.000051506995,0.000003047318,0.000153239,0.0008819624,0.000010882368,0.006950605,0.022550998,0.0015680372,0.941248],"study_design_scores_gemma":[0.004453004,0.004185051,0.5514625,0.003541612,0.000038028178,0.00026350023,0.0034740565,0.015550385,0.03122071,0.11374335,0.26908123,0.0029866004],"about_ca_topic_score_codex":0.00014177062,"about_ca_topic_score_gemma":0.00012427516,"teacher_disagreement_score":0.9382614,"about_ca_system_score_codex":0.000030576204,"about_ca_system_score_gemma":0.00003629716,"threshold_uncertainty_score":0.43713146},"labels":[],"label_agreement":null},{"id":"W4234646916","doi":"10.1109/icpr.2004.1334030","title":"Two-stage classification system combining model-based and discriminative approaches","year":2004,"lang":"en","type":"article","venue":"Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.","topic":"Anomaly Detection Techniques and Applications","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":"École de Technologie Supérieure","funders":"","keywords":"Discriminative model; Computer science; Artificial intelligence; Outlier; Pattern recognition (psychology); Anomaly detection; Machine learning; Data mining","score_opus":0.11431222462946068,"score_gpt":0.2868273199059716,"score_spread":0.17251509527651093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234646916","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":[0.09559685,0.000019597152,0.8598448,0.0052442783,0.00020550688,0.00074092514,0.00011923775,0.00032243037,0.037906375],"genre_scores_gemma":[0.9874767,0.000008632882,0.011735164,0.00026965994,0.000043703945,0.00022792566,0.000018927747,0.000017210845,0.00020204802],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984147,0.000014792309,0.00041656796,0.00050573435,0.00045196278,0.00019625142],"domain_scores_gemma":[0.9984965,0.000028820701,0.0005134012,0.00020454891,0.00067780813,0.000078949815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027595647,0.00022186238,0.0001864152,0.00020783226,0.00023460548,0.00022197158,0.00090598897,0.00008552151,0.000013608086],"category_scores_gemma":[0.000036788493,0.00018479233,0.00009172524,0.00026521832,0.00015963671,0.00050137803,0.00013367098,0.00023405308,0.000016709862],"study_design_candidate":"theoretical_or_conceptual","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":[0.000039407892,0.00039090446,0.0016780111,0.0001664589,0.000065375214,5.668898e-7,0.0008395838,0.0010488754,0.0046115154,0.9528529,0.0002114062,0.03809497],"study_design_scores_gemma":[0.002007422,0.00024960816,0.0034410234,0.0011063818,0.000053696418,0.000033838816,0.0023387277,0.7745915,0.13879648,0.076655865,0.00006806527,0.00065738003],"about_ca_topic_score_codex":0.00005462283,"about_ca_topic_score_gemma":0.000007002471,"teacher_disagreement_score":0.89187986,"about_ca_system_score_codex":0.00021979779,"about_ca_system_score_gemma":0.000103072736,"threshold_uncertainty_score":0.7535612},"labels":[],"label_agreement":null},{"id":"W4235312136","doi":"10.32920/ryerson.14649759.v1","title":"Pattern classification of time-series signals using Fisher kernels and support vector machines","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Support vector machine; Feature vector; Computer science; Recurrence plot; Artificial intelligence; Classifier (UML); Machine learning; Kernel method; Time domain; Data mining; Pattern recognition (psychology); Time series; Nonlinear system","score_opus":0.03360149319186357,"score_gpt":0.28107690337121305,"score_spread":0.2474754101793495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235312136","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":[0.05264423,0.00004837575,0.94492686,0.00053252873,0.00007284631,0.00023480564,0.000014920367,0.00019358133,0.0013318493],"genre_scores_gemma":[0.8975144,0.00007020657,0.10017841,0.000121334364,0.00005694909,0.00006840686,0.000031765205,0.000016205397,0.0019423112],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889725,0.00004332058,0.00033129557,0.00046139304,0.00015699997,0.000109709086],"domain_scores_gemma":[0.9988936,0.000029561455,0.0002552725,0.0006169696,0.00015637983,0.000048194917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014655273,0.00015852839,0.00024393696,0.00008122918,0.00006461562,0.00020023176,0.00038988306,0.00015621584,0.00031583838],"category_scores_gemma":[0.000008893979,0.00014994663,0.00008201774,0.00013957288,0.000055189023,0.00022380926,0.00072592584,0.00015390084,0.000005246764],"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":[0.000014282434,0.0005166913,0.022131624,0.0010109999,0.00031916625,0.000018372826,0.0019444483,0.0006062608,0.52574307,0.013060032,0.004465407,0.43016964],"study_design_scores_gemma":[0.00023331952,0.00018146727,0.07147661,0.00025267882,0.00009698787,0.00010268301,0.000106254316,0.6557491,0.25921226,0.008674305,0.0027006972,0.0012136354],"about_ca_topic_score_codex":0.00017800301,"about_ca_topic_score_gemma":0.000011743376,"teacher_disagreement_score":0.8448702,"about_ca_system_score_codex":0.000024411793,"about_ca_system_score_gemma":0.000093281036,"threshold_uncertainty_score":0.6114646},"labels":[],"label_agreement":null},{"id":"W4235609279","doi":"10.32920/ryerson.14649759","title":"Pattern classification of time-series signals using Fisher kernels and support vector machines","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Support vector machine; Feature vector; Artificial intelligence; Recurrence plot; Machine learning; Classifier (UML); Kernel method; Data mining; Pattern recognition (psychology); Nonlinear system","score_opus":0.03360149319186357,"score_gpt":0.28107690337121305,"score_spread":0.2474754101793495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235609279","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":[0.05264423,0.00004837575,0.94492686,0.00053252873,0.00007284631,0.00023480564,0.000014920367,0.00019358133,0.0013318493],"genre_scores_gemma":[0.8975144,0.00007020657,0.10017841,0.000121334364,0.00005694909,0.00006840686,0.000031765205,0.000016205397,0.0019423112],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889725,0.00004332058,0.00033129557,0.00046139304,0.00015699997,0.000109709086],"domain_scores_gemma":[0.9988936,0.000029561455,0.0002552725,0.0006169696,0.00015637983,0.000048194917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014655273,0.00015852839,0.00024393696,0.00008122918,0.00006461562,0.00020023176,0.00038988306,0.00015621584,0.00031583838],"category_scores_gemma":[0.000008893979,0.00014994663,0.00008201774,0.00013957288,0.000055189023,0.00022380926,0.00072592584,0.00015390084,0.000005246764],"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":[0.000014282434,0.0005166913,0.022131624,0.0010109999,0.00031916625,0.000018372826,0.0019444483,0.0006062608,0.52574307,0.013060032,0.004465407,0.43016964],"study_design_scores_gemma":[0.00023331952,0.00018146727,0.07147661,0.00025267882,0.00009698787,0.00010268301,0.000106254316,0.6557491,0.25921226,0.008674305,0.0027006972,0.0012136354],"about_ca_topic_score_codex":0.00017800301,"about_ca_topic_score_gemma":0.000011743376,"teacher_disagreement_score":0.8448702,"about_ca_system_score_codex":0.000024411793,"about_ca_system_score_gemma":0.000093281036,"threshold_uncertainty_score":0.6114646},"labels":[],"label_agreement":null},{"id":"W4236318019","doi":"10.36227/techrxiv.16828363.v1","title":"SELF-SUPERVISED ACOUSTIC ANOMALY DETECTION VIA CONTRASTIVE LEARNING","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Anomaly detection; Computer science; Anomaly (physics); Artificial intelligence; Pattern recognition (psychology); Representation (politics); Feature learning; Speech recognition","score_opus":0.0090707888940164,"score_gpt":0.22911395960564698,"score_spread":0.22004317071163057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236318019","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":[0.015692517,0.00010676694,0.9764503,0.00016626254,0.0003229418,0.00051247835,0.0000014170037,0.0028002157,0.00394706],"genre_scores_gemma":[0.8113492,0.000072237824,0.18753214,0.0001258506,0.000106139174,0.00029954553,0.0000103534485,0.000020607424,0.0004839089],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803364,0.00012378994,0.0003657767,0.0009278569,0.00025374777,0.0002951959],"domain_scores_gemma":[0.9984809,0.000089025496,0.00021502459,0.00076551316,0.00032615988,0.00012337169],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021291223,0.0003027457,0.00032342967,0.00016484912,0.00031468563,0.00049666624,0.00076186674,0.00036438962,0.00007862187],"category_scores_gemma":[0.000031154952,0.00031597356,0.00021683273,0.00042849933,0.000030019728,0.00024768777,0.0011536835,0.0009500502,0.000046859008],"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":[0.000026406278,0.000973583,0.0012433993,0.0006965259,0.000764731,0.00011804941,0.0031870112,0.015875058,0.14494045,0.008704317,0.00023557627,0.8232349],"study_design_scores_gemma":[0.0001546834,0.000103858256,0.0013566487,0.00003468986,0.000048964466,0.000049118793,0.00010733125,0.9534873,0.0417019,0.0011349759,0.0013270173,0.0004935449],"about_ca_topic_score_codex":0.00018937136,"about_ca_topic_score_gemma":0.00009373311,"teacher_disagreement_score":0.93761224,"about_ca_system_score_codex":0.00017299548,"about_ca_system_score_gemma":0.00016210802,"threshold_uncertainty_score":0.99992925},"labels":[],"label_agreement":null},{"id":"W4237606134","doi":"10.14393/ufu.s586a","title":"Aplicação de redes neurais recorrentes e self-organizing maps em dados reais de operador de telecomunicações para predição de tráfego de interface de bng e análise de relatório de falha de rede GPON","year":2020,"lang":"pt","type":"dissertation","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bibliographical Society of Canada","funders":"","keywords":"Computer science; Artificial neural network; Broadband; Computer network; Recurrent neural network; Data mining; Artificial intelligence; Telecommunications","score_opus":0.02191729724099194,"score_gpt":0.29180088770369333,"score_spread":0.2698835904627014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237606134","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":[0.33665162,0.00079881406,0.6556594,0.0020790165,0.00015294418,0.0013315639,0.000067965506,0.0022635225,0.0009951598],"genre_scores_gemma":[0.6170621,0.003129004,0.37370706,0.0015766945,0.00026961177,0.0010395331,0.00015135558,0.0002916178,0.0027730498],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9899675,0.0015449395,0.0019661733,0.0024936472,0.00062443566,0.0034032932],"domain_scores_gemma":[0.99298894,0.0010738955,0.0012172051,0.0023295337,0.000490551,0.0018998721],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.0027017642,0.0015742282,0.0013559985,0.0006775687,0.0017650499,0.0017795009,0.00430246,0.0018625673,0.00022105359],"category_scores_gemma":[0.0013294356,0.001810926,0.000769455,0.002160671,0.00021568686,0.0010396427,0.0009591627,0.0032517153,0.00008859377],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012773424,0.0026115547,0.049313936,0.0020555446,0.001330397,0.0005899887,0.17805292,0.006674876,0.590203,0.0051956447,0.033392385,0.12930241],"study_design_scores_gemma":[0.0010401452,0.0008950028,0.008791971,0.0013207496,0.00074056245,0.0027771832,0.012468725,0.45807454,0.49872813,0.0038548918,0.008826273,0.0024818266],"about_ca_topic_score_codex":0.0039114365,"about_ca_topic_score_gemma":0.0017908533,"teacher_disagreement_score":0.45139965,"about_ca_system_score_codex":0.005167398,"about_ca_system_score_gemma":0.004663387,"threshold_uncertainty_score":0.9997006},"labels":[],"label_agreement":null},{"id":"W4240428965","doi":"10.1007/3-540-36175-8_40","title":"HOT: Hypergraph-Based Outlier Test for Categorical Data","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Categorical variable; Computer science; Outlier; Data mining; Robustness (evolution); Linear subspace; Anomaly detection; Curse of dimensionality; Missing data; Computation; Hypergraph; Cluster analysis; Artificial intelligence; Algorithm; Machine learning; Mathematics","score_opus":0.033845504952530595,"score_gpt":0.2738144117525889,"score_spread":0.23996890680005833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240428965","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000021803828,0.00017142284,0.9948171,0.0019517435,0.00058960833,0.000739115,0.00004735832,0.00031854998,0.0013629217],"genre_scores_gemma":[0.03250258,0.000027679233,0.9622292,0.0041280403,0.0003272754,0.000092762304,0.000042869036,0.00004982277,0.0005997713],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99642575,0.000015674688,0.00047332863,0.0019529543,0.00057248614,0.00055979163],"domain_scores_gemma":[0.9953761,0.00068600534,0.00024623552,0.0032584954,0.00025590215,0.00017727836],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.00069351745,0.0004439936,0.00041524245,0.00060315675,0.000385371,0.00044316894,0.00545389,0.00033360464,0.000013045132],"category_scores_gemma":[0.00013859067,0.00040760543,0.00013585284,0.00075744407,0.00050330564,0.00041817152,0.00094149116,0.00053268584,0.000022888482],"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":[0.000007686996,0.00018324336,0.000075395896,0.000061910585,0.000015213519,0.000026046717,0.00007703852,0.004279451,0.00033201216,0.111487344,0.0022911131,0.88116354],"study_design_scores_gemma":[0.00027594378,0.00026799043,0.000035505378,0.000058991805,0.000015034126,0.000046239093,5.2893725e-8,0.7124498,0.002367777,0.18095627,0.102759905,0.0007664934],"about_ca_topic_score_codex":0.000013110508,"about_ca_topic_score_gemma":0.000028598404,"teacher_disagreement_score":0.880397,"about_ca_system_score_codex":0.00017201857,"about_ca_system_score_gemma":0.00047591628,"threshold_uncertainty_score":0.9999271},"labels":[],"label_agreement":null},{"id":"W4241615718","doi":"10.1016/s1701-2163(16)34420-6","title":"Accessory Ovary","year":2010,"lang":"en","type":"article","venue":"Journal of Obstetrics and Gynaecology Canada","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Feature (linguistics); Anomaly detection; Disjoint sets; Computer science; Linear subspace; Computational complexity theory; Subspace topology; Feature vector; Data mining; Exploit; Curse of dimensionality; False alarm; Pattern recognition (psychology); Artificial intelligence; Machine learning; Algorithm; Mathematics","score_opus":0.00921683868172175,"score_gpt":0.22628497537488237,"score_spread":0.21706813669316063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241615718","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":[0.7876021,0.0016217406,0.17001975,0.0035838205,0.02687939,0.00016685702,0.0000057655006,0.000049944236,0.0100706],"genre_scores_gemma":[0.9846004,0.000031608404,0.0148286875,0.0004176877,0.000019971749,0.0000015820319,9.600844e-8,0.0000025012237,0.00009744783],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9994801,0.000011378227,0.00020511245,0.000080246704,0.00012104385,0.000102116566],"domain_scores_gemma":[0.9989151,0.00047303265,0.00020130927,0.00014163145,0.00016340295,0.00010557152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013831013,0.000050110943,0.00010355589,0.00011693634,0.00009345707,0.00003777467,0.00038553393,0.000060461498,0.0000615266],"category_scores_gemma":[0.0003758613,0.00004245279,0.000024834384,0.0002800927,0.0000263376,0.00014690132,0.00007299836,0.00030220544,3.5286865e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.1454516e-7,0.000058656737,0.019859377,0.000007713697,0.000033052176,0.0004349405,0.000035935787,0.000009685183,0.0006574589,0.05950656,0.012547563,0.90684867],"study_design_scores_gemma":[0.0006347776,0.00023940798,0.50695306,0.0000011616919,0.000017884191,0.00032748922,0.000033815526,0.0015714082,0.0024282176,0.011996503,0.47555763,0.0002386385],"about_ca_topic_score_codex":0.0020819383,"about_ca_topic_score_gemma":0.005876281,"teacher_disagreement_score":0.90661,"about_ca_system_score_codex":0.000076638054,"about_ca_system_score_gemma":0.00059651065,"threshold_uncertainty_score":0.32791027},"labels":[],"label_agreement":null},{"id":"W4242515339","doi":"10.1109/wi-iatw.2007.11","title":"Brain Activation Detection by Neighborhood One-Class SVM","year":2007,"lang":"en","type":"article","venue":"2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops","topic":"Anomaly Detection Techniques and Applications","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 Regina","funders":"","keywords":"Support vector machine; Cluster analysis; Pattern recognition (psychology); Computer science; Artificial intelligence; Hyperplane; Voxel; Kernel (algebra); Fuzzy logic; Consistency (knowledge bases); Mathematics","score_opus":0.03205057601487849,"score_gpt":0.2937015207184988,"score_spread":0.2616509447036203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242515339","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":[0.01883797,0.00021410162,0.9579888,0.010450265,0.0012055344,0.00064006244,0.000015461594,0.0010303202,0.009617469],"genre_scores_gemma":[0.98948836,0.0014202133,0.005132411,0.0019687891,0.00023766115,0.00016779997,0.000033929213,0.000032951586,0.0015178621],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99619704,0.0000660218,0.001020306,0.0012289578,0.00078156893,0.00070611644],"domain_scores_gemma":[0.99714106,0.0005132439,0.00050106685,0.0010729996,0.0005429749,0.00022866622],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00090817374,0.0005241502,0.00038524528,0.0012464202,0.00043901516,0.00038625332,0.0028006334,0.0006720762,0.00038325685],"category_scores_gemma":[0.00041158198,0.0005272179,0.00016405879,0.0013689868,0.0004200578,0.00065057003,0.00060910726,0.0009835622,0.00035537206],"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":[0.000068825706,0.00038247067,0.0005585296,0.000008643021,0.00012033227,0.0000067733395,0.0001186265,0.00005920752,0.013618099,0.20328905,0.004258801,0.77751064],"study_design_scores_gemma":[0.00020745603,0.0007536269,0.00030917855,0.00017641885,0.000023727094,0.000049221042,0.00077724044,0.031637434,0.76050025,0.06586871,0.1388318,0.0008649235],"about_ca_topic_score_codex":0.00005941273,"about_ca_topic_score_gemma":0.000081513026,"teacher_disagreement_score":0.97065043,"about_ca_system_score_codex":0.00032287292,"about_ca_system_score_gemma":0.000111166875,"threshold_uncertainty_score":0.99971795},"labels":[],"label_agreement":null},{"id":"W4245331072","doi":"10.1007/978-1-4939-7131-2_100261","title":"Deep Learning Aspect Extraction","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Extraction (chemistry); Artificial intelligence; Computer science; Chromatography; Chemistry","score_opus":0.017662660266621107,"score_gpt":0.2573084025808946,"score_spread":0.2396457423142735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245331072","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[5.623264e-7,0.00002956619,0.5089998,0.000058493984,0.000060577848,0.00006843011,1.1663106e-7,0.00059079845,0.49019164],"genre_scores_gemma":[0.001549659,0.00009165804,0.06424351,0.00015962943,0.0002902898,0.000020366268,0.0000032760101,0.000022965753,0.93361866],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99914324,0.0000061557284,0.0001807154,0.0003915477,0.00015878446,0.00011955558],"domain_scores_gemma":[0.9992064,0.000026548087,0.00016010512,0.00045855367,0.00009402151,0.00005435556],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00009438077,0.00015779615,0.00012517748,0.00012088789,0.00018649233,0.00011167367,0.00040765858,0.00020596819,0.0025122468],"category_scores_gemma":[0.000004403899,0.0001519143,0.000104315935,0.000041761185,0.000038410315,0.00017508758,0.00013556992,0.00032102992,0.0020743147],"study_design_candidate":"theoretical_or_conceptual","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":[5.2442186e-7,0.000004270072,7.41045e-7,0.0000025966197,0.000009521575,0.0000019355152,0.000021612172,0.0000030829515,0.000048521633,0.89682883,0.0037180337,0.0993603],"study_design_scores_gemma":[0.000022231201,0.00007919512,0.0000089292025,0.000010812942,0.000006264312,0.000032030246,0.000002287986,0.007185332,0.0005431059,0.09202059,0.89989424,0.0001950042],"about_ca_topic_score_codex":0.0000074278314,"about_ca_topic_score_gemma":0.000009869013,"teacher_disagreement_score":0.89617616,"about_ca_system_score_codex":0.000058792506,"about_ca_system_score_gemma":0.00002025064,"threshold_uncertainty_score":0.9987027},"labels":[],"label_agreement":null},{"id":"W4247204284","doi":"10.1109/ijcnn.2006.1716669","title":"Clustering Vehicle Trajectories with Hidden Markov Models Application to Automated Traffic Safety Analysis","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"University of British Columbia; Clemson University","keywords":"Cluster analysis; Computer science; Hidden Markov model; Heuristic; Markov chain; Data mining; Traffic analysis; Machine learning; Artificial intelligence; Computer security","score_opus":0.01987366171158034,"score_gpt":0.24700673331215373,"score_spread":0.2271330716005734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247204284","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":[0.16518204,0.000007024016,0.8197406,0.0067069298,0.00013956193,0.00064324593,0.000015510026,0.0014392775,0.0061258087],"genre_scores_gemma":[0.9832015,0.000008648681,0.0152138565,0.000502719,0.00030445022,0.00027196333,0.00001612326,0.000017187185,0.00046357082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982271,0.000017897351,0.0004134858,0.0005435901,0.0004720716,0.00032580775],"domain_scores_gemma":[0.99895,0.000040080162,0.00024035067,0.00028651557,0.00040275746,0.00008031542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023739808,0.00023860896,0.00023132315,0.00020101435,0.00032237975,0.00040713488,0.001001097,0.000066525135,0.0000151856275],"category_scores_gemma":[0.0000037703853,0.00017576572,0.00010931347,0.0013674325,0.000063651976,0.00044930849,0.000111997564,0.0002084966,0.000020354635],"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":[0.0001435513,0.00009556849,0.00028513052,0.0000071892655,0.00014185924,0.0000018930747,0.0002283257,0.87344116,0.0027192277,0.08992083,0.0050528967,0.027962385],"study_design_scores_gemma":[0.00014543216,0.00009941203,0.0041301656,0.000024366973,0.00003881826,0.0000137251145,0.000022876682,0.99189144,0.0007904983,0.0021849107,0.0004309436,0.0002274275],"about_ca_topic_score_codex":0.00015632044,"about_ca_topic_score_gemma":0.0001277334,"teacher_disagreement_score":0.81801945,"about_ca_system_score_codex":0.00012359828,"about_ca_system_score_gemma":0.000029567394,"threshold_uncertainty_score":0.7167518},"labels":[],"label_agreement":null},{"id":"W4250032248","doi":"10.1007/978-1-4939-7131-2_100350","title":"Event Detection","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Event (particle physics); Computer science; Physics","score_opus":0.013882078871581879,"score_gpt":0.2368913694932448,"score_spread":0.22300929062166291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250032248","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[6.3542285e-7,0.000013166839,0.5400584,0.00007761195,0.00008189408,0.000090766516,6.6139637e-7,0.00042019857,0.45925668],"genre_scores_gemma":[0.0057234494,0.000041738756,0.020352587,0.00021987727,0.00021732847,0.00003241642,0.000001541161,0.000016723303,0.97339433],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.999264,0.0000028782981,0.00016810624,0.00033297,0.00013668578,0.00009533728],"domain_scores_gemma":[0.9991369,0.000009555348,0.00010503078,0.000614731,0.0000837469,0.00005001495],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000066558765,0.0001361744,0.00010280799,0.00009919984,0.00010508205,0.000059041526,0.00043273435,0.00017852968,0.0010536389],"category_scores_gemma":[0.0000014659289,0.00012574962,0.00010520755,0.000033537992,0.000033557313,0.000090804206,0.00016148499,0.00012930475,0.0017488141],"study_design_candidate":"theoretical_or_conceptual","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":[4.2354202e-7,0.000004151674,5.5216564e-8,0.000002666923,0.000008345479,6.761742e-7,0.000009974566,1.774114e-7,0.00010708286,0.7902051,0.007906808,0.2017545],"study_design_scores_gemma":[0.000018552302,0.00007002821,0.0000024612202,0.000008637299,0.0000042157753,0.00001401522,3.9612112e-7,0.0011656065,0.0054577743,0.1797634,0.8133448,0.0001501349],"about_ca_topic_score_codex":0.000005784043,"about_ca_topic_score_gemma":0.000014481656,"teacher_disagreement_score":0.805438,"about_ca_system_score_codex":0.00005453909,"about_ca_system_score_gemma":0.000022344588,"threshold_uncertainty_score":0.9998595},"labels":[],"label_agreement":null},{"id":"W4251215844","doi":"10.1007/978-1-4939-7131-2_100028","title":"Anomaly Detection","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Anomaly detection; Anomaly (physics); Computer science; Artificial intelligence; Physics","score_opus":0.014465222694496035,"score_gpt":0.2228933049751477,"score_spread":0.20842808228065166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251215844","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000020042794,0.000015681551,0.50517356,0.00006939616,0.00008034497,0.00009556775,0.0000010008895,0.00058246276,0.49397996],"genre_scores_gemma":[0.0052983984,0.000037823324,0.032498404,0.00026978238,0.0002614149,0.00003020807,0.0000020586147,0.000022901646,0.961579],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991055,0.0000035288936,0.00019434579,0.00042544148,0.0001480863,0.00012306239],"domain_scores_gemma":[0.998923,0.000013384268,0.00012621768,0.00076447206,0.00011059662,0.000062315245],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00007143331,0.00017591717,0.0001351287,0.00014195713,0.00013424452,0.00009584975,0.00056504493,0.00024170472,0.0012128917],"category_scores_gemma":[0.0000019962845,0.00016652406,0.00011681628,0.000049878672,0.000053362834,0.00014494073,0.0001892748,0.00015798115,0.0021188778],"study_design_candidate":"theoretical_or_conceptual","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":[7.1558276e-7,0.000004732554,2.6726707e-7,0.0000035687988,0.000011970373,0.0000014026115,0.000011769349,8.7397325e-8,0.000194765,0.882364,0.008830939,0.10857583],"study_design_scores_gemma":[0.000026004496,0.00009461091,0.00000708531,0.000008555665,0.0000061108517,0.000023499137,5.030877e-7,0.00088879163,0.0060315127,0.143377,0.84932345,0.00021287338],"about_ca_topic_score_codex":0.0000103748935,"about_ca_topic_score_gemma":0.000026498457,"teacher_disagreement_score":0.8404925,"about_ca_system_score_codex":0.000052752643,"about_ca_system_score_gemma":0.000028880919,"threshold_uncertainty_score":0.9997001},"labels":[],"label_agreement":null},{"id":"W4251599687","doi":"10.1515/iupac.68.0044","title":"Collision Frequency","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Glossary; Terminology; Field (mathematics); Computer science; Linguistics; Philosophy; Mathematics","score_opus":0.01211736831412548,"score_gpt":0.3838773159612388,"score_spread":0.37175994764711334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251599687","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000031530224,0.00014455314,0.29119882,0.00089809624,0.0002619832,0.00021511906,0.7069107,0.0002786736,0.000088892855],"genre_scores_gemma":[0.00001410019,0.00084054336,0.006948209,0.00035070663,0.00041408918,0.00007196544,0.9909012,0.000017730592,0.00044149673],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979132,0.000046792742,0.00039372727,0.00061680016,0.0007299222,0.00029957693],"domain_scores_gemma":[0.99749655,0.000056453788,0.00026379473,0.0016179145,0.00042213156,0.00014314945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003245968,0.0002911227,0.00032765543,0.00021463573,0.00020358892,0.00014030286,0.0015121093,0.00031742122,0.00046777967],"category_scores_gemma":[0.00009265807,0.0002201908,0.00014637878,0.0004193482,0.00007551814,0.00021655466,0.00039037832,0.00031541317,0.000008592442],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002700518,0.00006831222,6.124145e-7,0.000014503324,0.000011181385,0.000010034921,0.0000021093026,2.1044552e-7,0.000020499227,0.0013481531,0.9894754,0.00904623],"study_design_scores_gemma":[0.0001659829,0.00014304779,0.000007247431,0.000103978564,0.000013211147,0.000017455857,0.0000012555122,0.00003270653,0.00019810487,0.004901578,0.9941205,0.00029494887],"about_ca_topic_score_codex":0.000090228896,"about_ca_topic_score_gemma":0.00010660446,"teacher_disagreement_score":0.28425062,"about_ca_system_score_codex":0.00031392946,"about_ca_system_score_gemma":0.00051154406,"threshold_uncertainty_score":0.897912},"labels":[],"label_agreement":null},{"id":"W4251923949","doi":"10.22215/etd/2018-12718","title":"Configurable FPGA-Based Outlier Detection for Time Series Data","year":2018,"lang":"en","type":"dissertation","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Field-programmable gate array; Outlier; Computer science; MATLAB; Anomaly detection; Floating point; Series (stratigraphy); Embedded system; Real-time computing; Computer hardware; Algorithm; Data mining; Artificial intelligence","score_opus":0.021630441979727907,"score_gpt":0.2884225539059227,"score_spread":0.2667921119261948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251923949","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001766932,0.000022940341,0.9777227,0.00020897956,0.00038067155,0.0008353771,0.000087857836,0.00093925523,0.019625494],"genre_scores_gemma":[0.026893597,0.00003008157,0.5188549,0.0006392257,0.00069208053,0.0019702602,0.007199958,0.00012812002,0.4435918],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99857897,0.000017051705,0.00028808197,0.00072644284,0.00017418404,0.00021529132],"domain_scores_gemma":[0.99772644,0.000045004985,0.0002196285,0.00163455,0.000312747,0.0000616461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022236827,0.00021761164,0.00020982203,0.00014268131,0.00034468883,0.00025138148,0.0014110423,0.00025954234,0.00022938919],"category_scores_gemma":[0.000033429875,0.0002084312,0.00008470303,0.00027264163,0.000028418139,0.00053411414,0.0000677488,0.00011709337,0.00029502859],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027319076,0.00033439643,0.0000031545342,0.00050928915,0.00017730544,0.0000017601939,0.0002761113,0.000015038653,0.048223805,0.0308626,0.26004514,0.6592782],"study_design_scores_gemma":[0.00015577642,0.00022679952,0.00002664699,0.000027243392,0.000030588268,0.0000027368762,0.000030139332,0.05750526,0.45099717,0.0031018043,0.48754415,0.00035168047],"about_ca_topic_score_codex":0.000045913323,"about_ca_topic_score_gemma":0.00022456747,"teacher_disagreement_score":0.65892655,"about_ca_system_score_codex":0.000038208545,"about_ca_system_score_gemma":0.00015672446,"threshold_uncertainty_score":0.84995776},"labels":[],"label_agreement":null},{"id":"W4253082290","doi":"10.1037/e527382013-003","title":"High Quality Analytics with Poor Quality Data","year":2012,"lang":"en","type":"dataset","venue":"PsycEXTRA Dataset","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Quality (philosophy); Analytics; Data quality; Computer science; Data science; Data analysis; Data mining; Business; Marketing","score_opus":0.12325035049458621,"score_gpt":0.3961428459929356,"score_spread":0.2728924954983494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253082290","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000026142832,0.00008446114,0.20113619,0.00064954127,0.00018724025,0.0003603136,0.79734373,0.00021729924,0.000018627894],"genre_scores_gemma":[0.00007424586,0.00016947738,0.042447593,0.001065178,0.00035668258,0.00008851095,0.9557356,0.00002079646,0.000041883683],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99584424,0.0002778773,0.0009133389,0.00154035,0.00081397395,0.0006102435],"domain_scores_gemma":[0.98641133,0.00021499288,0.0008323333,0.012086704,0.00010744424,0.00034721487],"candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0017319667,0.00053668156,0.0006597838,0.00018996383,0.0002980647,0.0003950677,0.007867639,0.0003567478,0.00045281925],"category_scores_gemma":[0.00007459123,0.00044935656,0.00007392999,0.0007725681,0.0001835017,0.0010555112,0.0023011758,0.0007508027,0.00094842823],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013295656,0.00027408576,0.000010566725,0.000090189766,0.00007573416,0.000005136354,0.000002195197,0.0000012474194,0.0000059286726,0.0026250274,0.9946877,0.002208911],"study_design_scores_gemma":[0.000221913,0.00006112854,0.0002782195,0.00002297026,0.000115622504,0.000031464802,0.0000052595014,0.000079150144,0.000047584468,0.0003574844,0.99809545,0.00068377174],"about_ca_topic_score_codex":0.0041909385,"about_ca_topic_score_gemma":0.0010689079,"teacher_disagreement_score":0.15868859,"about_ca_system_score_codex":0.000073996765,"about_ca_system_score_gemma":0.00018313355,"threshold_uncertainty_score":0.9998295},"labels":[],"label_agreement":null},{"id":"W4253895314","doi":"10.22215/timreview1068","title":"Combining Exploratory Analysis and Automated Analysis for Anomaly Detection in Real-Time Data Streams","year":2017,"lang":"en","type":"article","venue":"Technology Innovation Management Review","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Anomaly detection; Computer science; STREAMS; Exploratory analysis; Exploratory data analysis; Data mining; Real-time computing; Data science; Operating system","score_opus":0.04841648876250258,"score_gpt":0.343317703687173,"score_spread":0.2949012149246704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253895314","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":[0.019145759,0.00054695306,0.974354,0.0024775416,0.000021932707,0.0011029183,0.00001203284,0.00169455,0.0006442751],"genre_scores_gemma":[0.90681404,0.008832085,0.082904175,0.00024155107,0.000007949484,0.00085057877,0.00018212595,0.000011745171,0.00015574253],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840134,0.00003110881,0.00058715075,0.00067480316,0.00012070367,0.00018486247],"domain_scores_gemma":[0.9964166,0.000022522872,0.0006542278,0.0027244787,0.00016437903,0.000017774628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011001902,0.00015204855,0.00043875055,0.0026034622,0.0003752733,0.00014865992,0.0015039281,0.00010446932,0.0000072234693],"category_scores_gemma":[0.00007357097,0.00015684885,0.000058941157,0.009327645,0.000086217646,0.00066689414,0.00087938237,0.000100181234,0.0000075222406],"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":[0.000003653761,0.000105676605,0.0176106,0.00047909233,0.0018120852,0.0000055658143,0.000012478104,0.000020079784,0.0011204001,0.20045127,0.0006077119,0.7777714],"study_design_scores_gemma":[0.00095951225,0.00018444637,0.115962245,0.00054087944,0.0050122254,0.0000054319676,0.0000725975,0.8390765,0.003331069,0.012938591,0.020972995,0.0009434902],"about_ca_topic_score_codex":0.000050587187,"about_ca_topic_score_gemma":0.0000905505,"teacher_disagreement_score":0.89144987,"about_ca_system_score_codex":0.0000563295,"about_ca_system_score_gemma":0.000010674604,"threshold_uncertainty_score":0.63961107},"labels":[],"label_agreement":null},{"id":"W4254182148","doi":"10.1145/342009.335388","title":"LOF","year":2000,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3862,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Outlier; Local outlier factor; Computer science; Anomaly detection; Object (grammar); Degree (music); Property (philosophy); Data mining; Binary number; Artificial intelligence; Mathematics","score_opus":0.005035874547235932,"score_gpt":0.21238199965864082,"score_spread":0.2073461251114049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254182148","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":[0.001504243,0.0000033317006,0.72943044,0.0007694231,0.0000049142054,0.000021873362,5.2534183e-8,0.0003527127,0.26791298],"genre_scores_gemma":[0.80056643,0.000013201953,0.15656976,0.00084448623,0.000016905697,0.000018026285,1.3401454e-7,0.0000015404602,0.041969478],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998142,0.0000022535978,0.00003524627,0.00007410763,0.00003040746,0.00004379972],"domain_scores_gemma":[0.9997831,0.0000030893673,0.0000040159807,0.00018569149,0.0000060516295,0.000018035007],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000018587878,0.000018338278,0.000016820959,0.000010058323,0.000036626006,0.00002374806,0.0001872002,0.000009861515,0.001260966],"category_scores_gemma":[2.827973e-7,0.000015298418,0.000012950251,0.00011234281,0.0000054535526,0.0000832296,0.000012960813,0.000016345075,0.0007187752],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.568692e-8,0.000008454404,0.000011031092,1.5173464e-7,4.33173e-7,1.9047826e-7,0.000008891323,0.0000026678754,0.000140365,0.29091328,0.0053544324,0.70356],"study_design_scores_gemma":[0.000033704822,0.000021371863,0.0010026115,5.8792114e-7,3.6841217e-7,0.000009709499,0.0000014584796,0.01441171,0.014947052,0.022595188,0.9469086,0.00006761793],"about_ca_topic_score_codex":0.000009841275,"about_ca_topic_score_gemma":6.263487e-7,"teacher_disagreement_score":0.9415542,"about_ca_system_score_codex":0.0000030925887,"about_ca_system_score_gemma":0.0000034213642,"threshold_uncertainty_score":0.999652},"labels":[],"label_agreement":null},{"id":"W4254210645","doi":"10.1515/iupac.68.0179","title":"Strong Collision","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Glossary; Terminology; Field (mathematics); Collision; Computer science; Epistemology; Linguistics; Philosophy; Mathematics; Programming language","score_opus":0.013899304454089458,"score_gpt":0.39413795581564653,"score_spread":0.3802386513615571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254210645","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000025266447,0.00008507086,0.35770133,0.0005963338,0.0001967236,0.00018000724,0.640969,0.0002161426,0.00005286464],"genre_scores_gemma":[0.000022438177,0.0005468978,0.0035727753,0.0002020928,0.00040048742,0.00005654073,0.99461085,0.00001636377,0.0005715507],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99805874,0.000041372215,0.0003458033,0.0005646581,0.00069954357,0.00028991135],"domain_scores_gemma":[0.9978096,0.000052999316,0.00023821753,0.001428587,0.00033855758,0.00013206646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030011262,0.0002674618,0.00030508323,0.00018900019,0.00019191268,0.00014572647,0.0013211995,0.000266055,0.00034563956],"category_scores_gemma":[0.00006247272,0.00020384626,0.00013302594,0.00033018534,0.000065409746,0.00019459608,0.00046369023,0.00029457695,0.000005661174],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000044303533,0.000059674814,3.435689e-7,0.000011924934,0.00001120095,0.0000060507696,0.0000016696158,8.755265e-7,0.000006964531,0.0009451524,0.9833682,0.015583496],"study_design_scores_gemma":[0.00017693012,0.00013925089,0.0000054424468,0.00009282781,0.000012844395,0.000011494789,0.000002077528,0.00011905215,0.00017762283,0.00078204833,0.9982192,0.00026123418],"about_ca_topic_score_codex":0.000047708752,"about_ca_topic_score_gemma":0.00008033959,"teacher_disagreement_score":0.35412857,"about_ca_system_score_codex":0.00028515607,"about_ca_system_score_gemma":0.00041029003,"threshold_uncertainty_score":0.8312609},"labels":[],"label_agreement":null},{"id":"W4254483371","doi":"10.1007/978-1-4614-6170-8_100085","title":"Event Detection","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Event (particle physics); Computer science; Physics","score_opus":0.010547868711491912,"score_gpt":0.2223032211309959,"score_spread":0.211755352419504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254483371","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.5862629e-7,0.000012089716,0.5433013,0.00012074056,0.000064259235,0.000083487954,3.6061408e-7,0.0004195288,0.4559981],"genre_scores_gemma":[0.019415239,0.00003825755,0.015503637,0.00031155607,0.00013739527,0.000036358048,0.0000016287596,0.000017240867,0.9645387],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99927694,0.000003959749,0.00017423397,0.00031714595,0.00013587417,0.000091831236],"domain_scores_gemma":[0.9991543,0.000015159707,0.00011164907,0.0006132873,0.00005382298,0.00005174722],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000079626254,0.00013806538,0.000119742384,0.00009696889,0.000091274516,0.00005488937,0.0004160756,0.00017476594,0.00020713669],"category_scores_gemma":[0.000001713654,0.00012797631,0.00011560322,0.00002661304,0.000018205894,0.00004842454,0.00013099486,0.00015822498,0.00078675844],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.394157e-7,0.0000012251369,3.463015e-8,0.0000018800241,0.0000032232374,2.0988801e-7,0.0000016009722,7.4403187e-7,0.00004495718,0.7112358,0.0012776635,0.2874325],"study_design_scores_gemma":[0.000021285816,0.000047179576,0.000003082595,0.000008360662,0.0000042944357,0.000012231668,1.3559068e-7,0.0030764337,0.002670398,0.11448587,0.87951344,0.00015725843],"about_ca_topic_score_codex":0.000007118897,"about_ca_topic_score_gemma":0.000010703892,"teacher_disagreement_score":0.8782358,"about_ca_system_score_codex":0.000046349793,"about_ca_system_score_gemma":0.000015728025,"threshold_uncertainty_score":0.99999124},"labels":[],"label_agreement":null},{"id":"W4255694901","doi":"10.2495/safe-v3-n4-318-332","title":"Cluster analysis of fatal accidents series in the INFOR.MO database: analysis, evidence and research perspectives","year":2013,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cluster (spacecraft); Database; Computer science; Data mining; Operating system","score_opus":0.019066597357251885,"score_gpt":0.31803785591882483,"score_spread":0.2989712585615729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255694901","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":[0.50228566,0.0007505808,0.4935482,0.0032366437,0.0000307482,0.00009890348,0.000009201509,0.000008836289,0.000031229207],"genre_scores_gemma":[0.99404633,0.001500579,0.004386366,0.00002708416,0.000026754256,0.0000046246473,0.0000015766632,0.0000015180373,0.00000513895],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99902165,0.000052604093,0.00032352423,0.00010908033,0.00040941092,0.000083743056],"domain_scores_gemma":[0.99902743,0.0002945308,0.00011820941,0.0001263817,0.00039835213,0.000035084686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010571496,0.00005742269,0.00015237676,0.00085984974,0.000044096138,0.00014600452,0.0005624244,0.000023810573,0.000009973648],"category_scores_gemma":[0.00014101015,0.000043534485,0.00007575501,0.0011047968,0.000049384667,0.0011974942,0.0001986337,0.00022510246,3.0185137e-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":[0.0004914349,0.0008355551,0.12186565,0.00020432689,0.01583243,0.000110466906,0.13546328,0.08719684,0.012079794,0.57565266,0.0007479798,0.049519558],"study_design_scores_gemma":[0.0002902201,0.00012671699,0.52196455,0.00012567436,0.00022267883,0.00010633062,0.0027542345,0.4706572,0.00091029954,0.0016979675,0.0009849739,0.00015915562],"about_ca_topic_score_codex":0.00016064678,"about_ca_topic_score_gemma":0.000048478803,"teacher_disagreement_score":0.5739547,"about_ca_system_score_codex":0.000034442288,"about_ca_system_score_gemma":0.000014796168,"threshold_uncertainty_score":0.17752849},"labels":[],"label_agreement":null},{"id":"W4256135761","doi":"10.36227/techrxiv.16828363","title":"SELF-SUPERVISED ACOUSTIC ANOMALY DETECTION VIA CONTRASTIVE LEARNING","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Anomaly detection; Computer science; Anomaly (physics); Artificial intelligence; Pattern recognition (psychology); Representation (politics); Feature learning; Speech recognition","score_opus":0.0090707888940164,"score_gpt":0.22911395960564698,"score_spread":0.22004317071163057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256135761","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":[0.015692517,0.00010676694,0.9764503,0.00016626254,0.0003229418,0.00051247835,0.0000014170037,0.0028002157,0.00394706],"genre_scores_gemma":[0.8113492,0.000072237824,0.18753214,0.0001258506,0.000106139174,0.00029954553,0.0000103534485,0.000020607424,0.0004839089],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803364,0.00012378994,0.0003657767,0.0009278569,0.00025374777,0.0002951959],"domain_scores_gemma":[0.9984809,0.000089025496,0.00021502459,0.00076551316,0.00032615988,0.00012337169],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021291223,0.0003027457,0.00032342967,0.00016484912,0.00031468563,0.00049666624,0.00076186674,0.00036438962,0.00007862187],"category_scores_gemma":[0.000031154952,0.00031597356,0.00021683273,0.00042849933,0.000030019728,0.00024768777,0.0011536835,0.0009500502,0.000046859008],"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":[0.000026406278,0.000973583,0.0012433993,0.0006965259,0.000764731,0.00011804941,0.0031870112,0.015875058,0.14494045,0.008704317,0.00023557627,0.8232349],"study_design_scores_gemma":[0.0001546834,0.000103858256,0.0013566487,0.00003468986,0.000048964466,0.000049118793,0.00010733125,0.9534873,0.0417019,0.0011349759,0.0013270173,0.0004935449],"about_ca_topic_score_codex":0.00018937136,"about_ca_topic_score_gemma":0.00009373311,"teacher_disagreement_score":0.93761224,"about_ca_system_score_codex":0.00017299548,"about_ca_system_score_gemma":0.00016210802,"threshold_uncertainty_score":0.99992925},"labels":[],"label_agreement":null},{"id":"W4256482217","doi":"10.31224/osf.io/w9v2b","title":"Hidden Markov Model: Tutorial","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Hidden Markov model; Viterbi algorithm; Computer science; Forward algorithm; Graphical model; Markov chain; Variable-order Markov model; Maximum-entropy Markov model; Markov property; Markov model; Variable-order Bayesian network; Expectation–maximization algorithm; Belief propagation; Artificial intelligence; Markov random field; Hidden semi-Markov model; Algorithm; Bayesian probability; Machine learning; Mathematics; Maximum likelihood; Bayesian inference; Statistics","score_opus":0.0177542283244411,"score_gpt":0.26573128441348026,"score_spread":0.24797705608903917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256482217","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030066504,0.000018703742,0.93370867,0.00085368025,0.00092098606,0.00038312562,0.000006295305,0.0011108452,0.06269701],"genre_scores_gemma":[0.2610932,0.000038195874,0.71893924,0.00033084615,0.00040104717,0.00021008126,0.000008969833,0.00001710273,0.018961318],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882644,0.000016903743,0.00021238229,0.0005756761,0.00020263593,0.00016596465],"domain_scores_gemma":[0.9983092,0.000019620706,0.00011101925,0.0014197148,0.00008055339,0.00005992108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012890871,0.00016641196,0.00017878553,0.00008514515,0.00005945366,0.00022244235,0.0014617045,0.00026328713,0.00004948757],"category_scores_gemma":[0.0000048185366,0.00015460001,0.00013852968,0.00010848826,0.000016048793,0.00011515746,0.002081341,0.00036026278,0.000210906],"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":[0.000005133734,0.00008899592,0.000038860435,0.000062951854,0.00003948908,0.0000017935891,0.00013407276,0.0035556147,0.00033912336,0.709723,0.114191994,0.17181894],"study_design_scores_gemma":[0.00006203646,0.000016075965,0.000029985236,0.00001084658,0.000005513202,0.0000024442722,0.00000147315,0.90606606,0.001443798,0.07849641,0.013599538,0.00026584274],"about_ca_topic_score_codex":0.00005779273,"about_ca_topic_score_gemma":0.000002186073,"teacher_disagreement_score":0.9025104,"about_ca_system_score_codex":0.00006425237,"about_ca_system_score_gemma":0.00017653046,"threshold_uncertainty_score":0.63044053},"labels":[],"label_agreement":null},{"id":"W4280555209","doi":"10.18280/isi.270207","title":"Detection and Localization of Abnormal Events for Smart Surveillance","year":2022,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Anomaly detection; Anomaly (physics); Convolutional neural network; Computer science; Artificial intelligence; Artificial neural network; Pattern recognition (psychology); Data mining; Physics","score_opus":0.00967722663198644,"score_gpt":0.222751850712842,"score_spread":0.21307462408085556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280555209","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":[0.04067559,0.000024443922,0.9583008,0.000025936219,0.000109593566,0.00037505673,0.000019767538,0.0001391093,0.00032968103],"genre_scores_gemma":[0.9926579,0.000010629046,0.0068401163,0.000068031055,0.000010595972,0.0003662877,0.000023898192,0.0000037846046,0.0000187443],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930453,0.00003498439,0.00031694304,0.00009313125,0.00014331058,0.000107084554],"domain_scores_gemma":[0.9993707,0.000033728888,0.0002563476,0.0001660742,0.00014674418,0.000026398375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037351146,0.00006718489,0.00009065813,0.00015030241,0.00045283604,0.000043051503,0.00017220034,0.000029418341,0.0000054023226],"category_scores_gemma":[0.00003409286,0.00007486495,0.00003383841,0.00039054453,0.000029899069,0.0011457397,0.00011713829,0.000048955506,0.0000012998449],"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":[0.0000861288,0.00007581888,0.011319147,0.00043748683,0.000036653833,1.798255e-7,0.004156743,0.008127808,0.0016745962,0.07243422,0.00036307782,0.90128815],"study_design_scores_gemma":[0.0007432334,0.0006753842,0.02730838,0.00002207924,0.000009172793,0.0000840508,0.00054078724,0.8761629,0.019359523,0.03000307,0.044733778,0.0003576801],"about_ca_topic_score_codex":0.000043318418,"about_ca_topic_score_gemma":0.0000060328903,"teacher_disagreement_score":0.9519823,"about_ca_system_score_codex":0.00009474244,"about_ca_system_score_gemma":0.000026269878,"threshold_uncertainty_score":0.34828943},"labels":[],"label_agreement":null},{"id":"W4281388959","doi":"10.1016/j.patrec.2022.05.023","title":"Bi-discriminator GAN for tabular data synthesis","year":2022,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; University of Windsor; Université du Québec à Montréal","funders":"","keywords":"Discriminator; Discriminative model; Computer science; Generator (circuit theory); Binary number; Preprocessor; Benchmarking; Metric (unit); Term (time); Data mining; Artificial intelligence; Scheme (mathematics); Algorithm; Pattern recognition (psychology); Machine learning; Mathematics; Detector; Engineering; Power (physics)","score_opus":0.06906643072439422,"score_gpt":0.2715178054476694,"score_spread":0.20245137472327518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281388959","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":[0.012752777,0.000012086933,0.9732724,0.012332428,0.00019338794,0.00043442586,0.00051951525,0.00034771633,0.00013530663],"genre_scores_gemma":[0.94335216,0.00000923624,0.040230732,0.012900674,0.00015365066,0.0028704978,0.00040484403,0.000029551038,0.000048681],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99881554,0.000067910434,0.00019943097,0.0005143122,0.00019451758,0.00020831378],"domain_scores_gemma":[0.99881953,0.00012587453,0.00011563624,0.0008500192,0.000033333527,0.00005559028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030516158,0.00010978925,0.000106595995,0.00013074234,0.00046537592,0.00009657948,0.0012023033,0.000021215092,0.00024258376],"category_scores_gemma":[0.00002577877,0.00012348697,0.00006663564,0.00023151284,0.000023591418,0.00032622216,0.00042087198,0.00011815279,0.000056742192],"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":[0.0000066019147,0.00012329058,0.00021442605,0.000033419816,0.000031625357,0.0000070639403,0.00011939302,0.000017996857,0.008695138,0.00022573816,0.06042813,0.93009716],"study_design_scores_gemma":[0.001067729,0.00032358873,0.0015360957,0.00005955321,0.00018100505,0.00015371223,0.00049850193,0.16476232,0.10905042,0.0074168313,0.71313965,0.001810556],"about_ca_topic_score_codex":0.00003702476,"about_ca_topic_score_gemma":0.000003337814,"teacher_disagreement_score":0.93304163,"about_ca_system_score_codex":0.000053057538,"about_ca_system_score_gemma":0.000016178139,"threshold_uncertainty_score":0.50356525},"labels":[],"label_agreement":null},{"id":"W4281763319","doi":"10.1038/s41598-022-13220-2","title":"Automated soccer head impact exposure tracking using video and deep learning","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Anomaly Detection Techniques and Applications","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":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Michael Smith Health Research BC","keywords":"Computer science; Head (geology); Artificial intelligence; Deep learning; Tracking (education); Computer vision; Biology; Psychology","score_opus":0.022493839697164893,"score_gpt":0.3040805301742825,"score_spread":0.2815866904771176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281763319","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":[0.7928682,0.00023563618,0.20418473,0.000095850344,0.0008931938,0.00021484152,6.3422476e-7,0.0012781285,0.00022877543],"genre_scores_gemma":[0.98885006,0.0000015124104,0.010623829,0.000025691503,0.00001968584,0.00003947728,0.000005123559,0.00000989644,0.00042470932],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983601,0.00007460567,0.00030634215,0.0006483845,0.00036310198,0.00024744932],"domain_scores_gemma":[0.9989811,0.000020521471,0.00026149373,0.0005647108,0.000081455,0.00009070131],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0011393756,0.0001038273,0.00011686803,0.00018891093,0.0018994219,0.00063862366,0.00022262169,0.000029915918,0.000084658706],"category_scores_gemma":[0.000029548428,0.000101503334,0.000076154276,0.0009397984,0.00007732251,0.00040477148,0.00036900476,0.00020102487,0.0000022378538],"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":[0.0000123461505,0.00037469718,0.07662799,0.000051618485,0.00009344915,0.000969882,0.007020869,0.076057285,0.46443793,0.0027913284,0.0077724215,0.36379015],"study_design_scores_gemma":[0.0001281311,0.00014500297,0.013024446,0.000014894392,0.000014633587,0.0035453364,0.00024496243,0.9197891,0.011258857,0.007972106,0.04344431,0.00041821503],"about_ca_topic_score_codex":0.00006690964,"about_ca_topic_score_gemma":0.000004759045,"teacher_disagreement_score":0.8437318,"about_ca_system_score_codex":0.00011351684,"about_ca_system_score_gemma":0.00008902924,"threshold_uncertainty_score":0.99939996},"labels":[],"label_agreement":null},{"id":"W4282832289","doi":"10.1007/s11042-022-13375-0","title":"Correction to: Crowd abnormality detection in video sequences using supervised convolutional neural network","year":2022,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Convolutional neural network; Abnormality; Artificial intelligence; Pattern recognition (psychology); Machine learning; Medicine","score_opus":0.02938536420424996,"score_gpt":0.27038240171262923,"score_spread":0.24099703750837925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282832289","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":[0.10244199,0.00008919994,0.8950569,0.0004939792,0.00035318104,0.0010064745,0.000037196183,0.00027504182,0.00024603438],"genre_scores_gemma":[0.96600914,0.000011945856,0.030775445,0.00034794366,0.00016834904,0.0025738901,0.000022518736,0.000008830646,0.00008191712],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987067,0.000085350905,0.0003039893,0.00045000977,0.00019796396,0.00025597156],"domain_scores_gemma":[0.9992564,0.00016111368,0.0000933404,0.00031623774,0.000061895844,0.00011105471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003273294,0.00012492125,0.00013259918,0.00012275313,0.0009203787,0.0001281565,0.0003421782,0.00003923582,0.000041612522],"category_scores_gemma":[0.000017431026,0.00014245968,0.000047843598,0.0011425724,0.00005443258,0.0003526837,0.00027676637,0.00025463945,0.000007787074],"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":[0.00004253319,0.0003901565,0.015935894,0.000021129144,0.000023577713,0.00000482792,0.0006988229,0.1708491,0.028165352,0.016346544,0.0019258728,0.7655962],"study_design_scores_gemma":[0.00019511202,0.00006059688,0.038988978,0.000004692142,0.0000058362148,0.00006165258,0.00008870006,0.9426179,0.0011797554,0.0011188682,0.015459762,0.00021812781],"about_ca_topic_score_codex":0.00045367345,"about_ca_topic_score_gemma":0.00013702594,"teacher_disagreement_score":0.8642815,"about_ca_system_score_codex":0.00016923789,"about_ca_system_score_gemma":0.00005903463,"threshold_uncertainty_score":0.7078902},"labels":[],"label_agreement":null},{"id":"W4283717003","doi":"10.1016/j.eswa.2022.117955","title":"Subspace-based outlier detection using linear programming and heuristic techniques","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Ottawa","funders":"","keywords":"Subspace topology; Linear subspace; Computer science; Best bin first; Outlier; Random subspace method; k-nearest neighbors algorithm; Linear programming; Curse of dimensionality; Anomaly detection; Heuristic; Pattern recognition (psychology); Algorithm; Mathematics; Data mining; Artificial intelligence","score_opus":0.01597098468121314,"score_gpt":0.2613519894864549,"score_spread":0.24538100480524175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283717003","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":[0.0020012227,0.00032461056,0.9944082,0.0002840059,0.0000364375,0.0017100364,0.0000061192827,0.001080624,0.00014877979],"genre_scores_gemma":[0.8029281,0.000005571442,0.18435498,0.00011572902,0.00008849998,0.01237278,0.0000055893975,0.000024456831,0.00010432736],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987234,0.000071634735,0.00024793748,0.00048455448,0.00025810214,0.00021440219],"domain_scores_gemma":[0.99892116,0.000042740212,0.00018445172,0.0006497034,0.00010527094,0.000096702315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022220143,0.00015706301,0.0001542954,0.0001833837,0.0011684439,0.00014169102,0.00036655748,0.000046320703,0.000003972303],"category_scores_gemma":[0.0000030211763,0.00014914964,0.000035967125,0.0008200594,0.00006343777,0.00014955043,0.00014259663,0.00017942897,0.0000032305827],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013948613,0.0023802167,0.0042978795,0.00048817002,0.00025040552,0.00003426897,0.0045898827,0.013133058,0.15898246,0.21797392,0.0016921747,0.5960381],"study_design_scores_gemma":[0.00027619433,0.0003410184,0.00009371564,0.000027678248,0.0000190794,0.00029822357,0.0005663961,0.36902067,0.02139336,0.00017976707,0.60723966,0.0005441933],"about_ca_topic_score_codex":0.0003029835,"about_ca_topic_score_gemma":0.000007575586,"teacher_disagreement_score":0.81005317,"about_ca_system_score_codex":0.00015818498,"about_ca_system_score_gemma":0.00007249479,"threshold_uncertainty_score":0.8986844},"labels":[],"label_agreement":null},{"id":"W4284664380","doi":"10.1155/2022/9463559","title":"A Three-Stage Anomaly Detection Framework for Traffic Videos","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Guangzhou Municipal Science and Technology Project; Shenzhen Fundamental Research Program; Guangdong Science and Technology Department; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Anomaly detection; Anomaly (physics); Computer science; Stage (stratigraphy); Real-time computing; Data mining; Geology","score_opus":0.012584580097091427,"score_gpt":0.26783159270289364,"score_spread":0.25524701260580224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4284664380","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":[0.18938978,0.00007010651,0.8096037,0.00031210616,0.00027368276,0.00025518655,0.000012368968,0.000076999,0.0000060882985],"genre_scores_gemma":[0.71004,0.000012479935,0.28966835,0.00007639462,0.00005746977,0.00011538852,0.0000030778235,0.0000088363195,0.000018007686],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99899536,0.000015801708,0.00043643275,0.00016781609,0.0002554105,0.00012915782],"domain_scores_gemma":[0.9989974,0.00008312833,0.00052859104,0.00017771675,0.0001580568,0.00005509184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000245715,0.00008912322,0.00014316067,0.0001702472,0.0003121493,0.000029424751,0.0003370184,0.00003720482,0.000019520197],"category_scores_gemma":[0.0000137475245,0.00009274463,0.000176321,0.00046300047,0.000013280729,0.0005041921,0.00000546791,0.00024965464,5.8779915e-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":[0.0002956628,0.00025543114,0.00012623704,0.00004092709,0.00004814561,0.000015373658,0.0016434687,0.46419892,0.02185916,0.07203247,0.000061031667,0.43942317],"study_design_scores_gemma":[0.006363633,0.011617466,0.100619905,0.0001465562,0.0002803085,0.00032410785,0.0033326494,0.1346789,0.116937235,0.38371265,0.24022548,0.0017611012],"about_ca_topic_score_codex":0.0000029489522,"about_ca_topic_score_gemma":0.000037079713,"teacher_disagreement_score":0.5206502,"about_ca_system_score_codex":0.00008528126,"about_ca_system_score_gemma":0.000053065316,"threshold_uncertainty_score":0.37820163},"labels":[],"label_agreement":null},{"id":"W4285121866","doi":"10.1109/icjece.2022.3154294","title":"A Vision-Based System Design and Implementation for Accident Detection and Analysis via Traffic Surveillance Video","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Collision; Coding (social sciences); Artificial intelligence; Cluster analysis; Computer vision; Real-time computing; Trajectory; Perspective (graphical); Simulation; Computer security; Mathematics","score_opus":0.005050773971737251,"score_gpt":0.20788156403622896,"score_spread":0.2028307900644917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285121866","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":[0.0768511,0.000285401,0.9225298,0.00012192786,0.000046137688,0.00014001683,0.0000014922683,0.000023916604,2.2163974e-7],"genre_scores_gemma":[0.9614028,0.0000047462822,0.038487002,0.000038493792,0.00003095639,0.000030231937,7.186801e-7,0.0000043410046,7.487799e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994229,0.000032563163,0.00019382828,0.0001385675,0.00007444674,0.00013767605],"domain_scores_gemma":[0.9994906,0.00012622168,0.00008325551,0.00006313758,0.00005229051,0.00018448278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002886394,0.00006980224,0.00013645238,0.00042540263,0.00022802399,0.00011223168,0.00010886597,0.000018252735,0.0000010892421],"category_scores_gemma":[0.0000038528265,0.00007077781,0.000045645447,0.0005521965,0.0000062272147,0.00008929619,0.000016222304,0.00009231851,2.4060864e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017745135,0.000014417254,0.0015914779,0.000035529978,0.00017570259,0.000018664465,0.0001730896,0.3642137,0.0011101026,0.0018320164,0.00010711177,0.6307104],"study_design_scores_gemma":[0.00018279327,0.00039722573,0.011192317,0.0000029684159,0.000025809764,0.00011930992,0.000005124585,0.98718864,0.00025685137,0.000029517863,0.00052200304,0.00007746223],"about_ca_topic_score_codex":0.00010567609,"about_ca_topic_score_gemma":0.00013396954,"teacher_disagreement_score":0.88455164,"about_ca_system_score_codex":0.00011493254,"about_ca_system_score_gemma":0.00006787246,"threshold_uncertainty_score":0.2886235},"labels":[],"label_agreement":null},{"id":"W4285132338","doi":"10.1007/978-3-031-06427-2_27","title":"AD-CGAN: Contrastive Generative Adversarial Network for Anomaly Detection","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Discriminative model; Computer science; Generative grammar; Anomaly detection; Anomaly (physics); Artificial intelligence; Pattern recognition (psychology); Normality; Sample (material); Machine learning; Mathematics; Statistics","score_opus":0.013507685870105863,"score_gpt":0.24085072677112368,"score_spread":0.2273430409010178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285132338","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000019940107,0.00021966307,0.99454737,0.0005808461,0.0019383272,0.0012573326,0.00003036156,0.0003314225,0.0010747119],"genre_scores_gemma":[0.1494015,0.000060223218,0.84601986,0.0016909182,0.0016135671,0.00047193916,0.000021718497,0.000058011152,0.000662244],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99681187,0.000040317496,0.00048179794,0.0015174599,0.0005557914,0.00059276656],"domain_scores_gemma":[0.9978526,0.00046354433,0.00039281798,0.0008812593,0.00027841958,0.00013135336],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00067748985,0.00044484518,0.0004517356,0.0004188095,0.0009759158,0.00032035113,0.0018776477,0.00026629324,0.000050216582],"category_scores_gemma":[0.00004788207,0.00045540516,0.00021474257,0.0007095845,0.00037565376,0.0004933213,0.00082927966,0.000692533,0.000009121651],"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":[0.000037442416,0.000034213794,0.000005633706,0.000014176738,0.000030712814,0.000013068682,0.00036398362,0.058894765,0.00076957885,0.071426935,0.00013037979,0.8682791],"study_design_scores_gemma":[0.0004815642,0.00082658615,0.00005784329,0.000054638786,0.00002290915,0.000056508445,4.031472e-7,0.7052785,0.008133233,0.23268491,0.051563445,0.000839447],"about_ca_topic_score_codex":0.000030718697,"about_ca_topic_score_gemma":0.00012360734,"teacher_disagreement_score":0.8674397,"about_ca_system_score_codex":0.00054752367,"about_ca_system_score_gemma":0.0004252106,"threshold_uncertainty_score":0.9997898},"labels":[],"label_agreement":null},{"id":"W4285223007","doi":"10.18653/v1/2022.acl-short.59","title":"Region-dependent temperature scaling for certainty calibration and application to class-imbalanced token classification","year":2022,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; National Research Council Canada; Vector Institute","funders":"","keywords":"Interpretability; Certainty; Calibration; Context (archaeology); Computer science; Metric (unit); Scaling; Class (philosophy); Range (aeronautics); Algorithm; Artificial intelligence; Mathematics; Statistics; Engineering","score_opus":0.014349034872373845,"score_gpt":0.2557438075222285,"score_spread":0.24139477264985462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285223007","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":[0.008155714,0.0000134483535,0.98049074,0.009352334,0.000057307785,0.0011218486,0.00000870049,0.00039716848,0.00040271934],"genre_scores_gemma":[0.9513372,0.000006421098,0.043452024,0.001357142,0.000055554963,0.0029615366,0.00003232521,0.000010110367,0.0007876936],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989716,0.000038319802,0.00021060128,0.00046331703,0.00017010656,0.00014606354],"domain_scores_gemma":[0.9992712,0.00004642786,0.00009255733,0.00042848013,0.00008014616,0.000081155966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002117957,0.00009671787,0.00009398869,0.00010210844,0.0006357264,0.0001401959,0.00034953115,0.00004891288,0.00000619269],"category_scores_gemma":[0.000011729395,0.00009787591,0.000036410733,0.00040300452,0.00001315265,0.00022607172,0.00017999626,0.00010999319,0.000001960251],"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":[0.000036903388,0.00010810434,0.0003050647,0.000019785803,0.000011495777,4.1665282e-7,0.0004923181,0.004848679,0.100662574,0.8047331,0.010409951,0.07837164],"study_design_scores_gemma":[0.0003119706,0.0001716829,0.001648944,0.0000034032685,0.000007396835,0.000028152323,0.00038377216,0.9174145,0.015532162,0.01129489,0.052911844,0.00029131435],"about_ca_topic_score_codex":0.000030492361,"about_ca_topic_score_gemma":0.000010357006,"teacher_disagreement_score":0.94318146,"about_ca_system_score_codex":0.000114877505,"about_ca_system_score_gemma":0.00003533637,"threshold_uncertainty_score":0.48895577},"labels":[],"label_agreement":null},{"id":"W4285813780","doi":"10.1109/iwcmc55113.2022.9824492","title":"Efficient Prediction of Blood Alcohol Level Using ML and Accelerometer Data","year":2022,"lang":"en","type":"article","venue":"2022 International Wireless Communications and Mobile Computing (IWCMC)","topic":"Anomaly Detection Techniques and Applications","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":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada; New Brunswick Innovation Foundation","keywords":"Decision tree; Computer science; Accelerometer; Random forest; Artificial intelligence; Machine learning; Linear discriminant analysis; Alcohol; Variety (cybernetics); Tree (set theory); Blood alcohol; Medicine; Poison control; Mathematics; Chemistry","score_opus":0.11043005046669424,"score_gpt":0.32931167112593257,"score_spread":0.21888162065923833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285813780","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":[0.65225583,0.0006231015,0.34533754,0.00040630807,0.00018434713,0.0003824405,0.00028776305,0.00014324253,0.0003794664],"genre_scores_gemma":[0.9649804,0.0003047772,0.03432444,0.00006658681,0.00002847839,0.00009337731,0.00013728105,0.00001181492,0.000052837735],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986126,0.000116817304,0.00041671522,0.0004222534,0.0002965228,0.00013509011],"domain_scores_gemma":[0.99771994,0.00015869594,0.00028775344,0.001639582,0.00014032834,0.000053682612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055117684,0.00011943074,0.00015558835,0.00021912048,0.0007854728,0.00009969862,0.0024072358,0.000034087447,0.000023780602],"category_scores_gemma":[0.0000134385,0.00013449432,0.000038923507,0.00038886495,0.00013241114,0.00016993623,0.0072674844,0.00026598104,5.519704e-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":[0.00003876712,0.0038482668,0.018033868,0.00012024516,0.00090993143,0.000007490444,0.0049379733,0.026564667,0.12809244,0.2747396,0.0012954171,0.54141134],"study_design_scores_gemma":[0.0002890972,0.00007955227,0.0030119035,0.000019100606,0.00002753017,0.00009554131,0.00017793779,0.9883647,0.00047722863,0.00021762727,0.0071201064,0.000119669814],"about_ca_topic_score_codex":0.00016262647,"about_ca_topic_score_gemma":0.000003859826,"teacher_disagreement_score":0.96180004,"about_ca_system_score_codex":0.000048266986,"about_ca_system_score_gemma":0.00005197424,"threshold_uncertainty_score":0.9058399},"labels":[],"label_agreement":null},{"id":"W4287280698","doi":"10.48550/arxiv.2103.05173","title":"PCOR: Private Contextual Outlier Release via Differentially Private\\n Search","year":2021,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Outlier; Anomaly detection; Context (archaeology); Computer science; Metric (unit); Population; Differential privacy; Data mining; Artificial intelligence; Geography; Engineering; Medicine","score_opus":0.06299439755621525,"score_gpt":0.20319782423955443,"score_spread":0.14020342668333918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287280698","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":[0.4090423,0.000059612124,0.58805394,0.00016156503,0.00040875535,0.000835188,0.000026241154,0.00045513187,0.0009572428],"genre_scores_gemma":[0.9846134,0.00109029,0.005672396,0.0001975047,0.00021334097,0.0000144642045,0.00006109414,0.000081426,0.008056056],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9933265,0.00060679246,0.0008754022,0.0036176518,0.00039360672,0.0011800433],"domain_scores_gemma":[0.99350613,0.00018115401,0.00069578283,0.003979359,0.00079777034,0.00083977764],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00049871404,0.0009979509,0.0009981372,0.0005445857,0.0010639761,0.0008448874,0.004248446,0.0009188578,0.00087591406],"category_scores_gemma":[0.000047229678,0.0012384224,0.0009303656,0.0020430456,0.00063917774,0.00086485164,0.007059876,0.0021284893,0.00056384207],"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":[0.000461348,0.0031745494,0.007885424,0.0006029701,0.0013174656,0.0021834692,0.0020374975,0.048083313,0.012248603,0.8561457,0.000499212,0.06536047],"study_design_scores_gemma":[0.0020864229,0.0005573361,0.010621132,0.00046171978,0.00048106423,0.00008968329,0.00042194885,0.92676514,0.021908602,0.020098206,0.013461,0.0030477236],"about_ca_topic_score_codex":0.00044247456,"about_ca_topic_score_gemma":0.000079698024,"teacher_disagreement_score":0.87868184,"about_ca_system_score_codex":0.0006446932,"about_ca_system_score_gemma":0.0006190005,"threshold_uncertainty_score":0.99900657},"labels":[],"label_agreement":null},{"id":"W4287728232","doi":"10.1103/physrevd.107.016002","title":"Variational autoencoders for anomalous jet tagging","year":2023,"lang":"en","type":"article","venue":"Physical review. D/Physical review. D.","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données","keywords":"Anomaly detection; Outlier; Jet (fluid); Computer science; Anomaly (physics); Regularization (linguistics); Artificial intelligence; Pattern recognition (psychology); Physics; Particle physics","score_opus":0.023781640817595467,"score_gpt":0.4251199105942585,"score_spread":0.40133826977666304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287728232","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":[0.009581454,0.015058999,0.90808713,0.048536133,0.0005013482,0.006872343,0.00013141245,0.003964464,0.00726669],"genre_scores_gemma":[0.6034517,0.20825016,0.10849525,0.054079294,0.0053241425,0.018244466,0.0005285533,0.00032884782,0.0012975703],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972224,0.00013300101,0.0005777502,0.0008926966,0.0005881501,0.0005859731],"domain_scores_gemma":[0.9975771,0.0006122287,0.000331528,0.0009805148,0.00024171594,0.00025692667],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005764572,0.00038670778,0.0009019303,0.00008566164,0.00031895767,0.00008840183,0.0011971329,0.000039494957,0.000012970006],"category_scores_gemma":[0.00034175307,0.00032267385,0.0008266756,0.0020457043,0.000075328426,0.00044185584,0.00033777257,0.0002898343,0.0009644443],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000034594123,0.00046694264,0.00011720189,0.003860422,0.0000682029,0.0000044816366,0.00006171428,0.000049381873,0.0041855574,0.76013446,0.13976377,0.091284394],"study_design_scores_gemma":[0.00023662865,0.00022990085,0.001013661,0.0028069937,0.00021450984,0.000009031848,0.000002683669,0.23682043,0.0024981638,0.24646418,0.5089353,0.0007685794],"about_ca_topic_score_codex":0.000007668536,"about_ca_topic_score_gemma":5.0691784e-7,"teacher_disagreement_score":0.7995919,"about_ca_system_score_codex":0.00008538306,"about_ca_system_score_gemma":0.00011826861,"threshold_uncertainty_score":0.9999225},"labels":[],"label_agreement":null},{"id":"W4287865431","doi":"","title":"DYSAN: Dynamically sanitizing motion sensor data against sensitive inferences through adversarial networks","year":2020,"lang":"en","type":"report","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Adversarial system; Computer science; Motion (physics); Artificial intelligence; Wireless sensor network; Computer vision; Computer network","score_opus":0.03548558274130817,"score_gpt":0.26290224131484224,"score_spread":0.22741665857353408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287865431","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":[0.00019314441,0.00020982738,0.87690794,0.0065027107,0.0003836117,0.0005662693,0.0001781577,0.0009446957,0.11411365],"genre_scores_gemma":[0.54661006,0.0044660247,0.43850672,0.00052286265,0.00040421681,0.000118651355,0.0048194164,0.000117378884,0.004434699],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9935298,0.0025861275,0.0008580356,0.001626178,0.0009286368,0.00047123694],"domain_scores_gemma":[0.9907459,0.0011697047,0.001015679,0.0038543285,0.0029918468,0.00022256485],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0036791416,0.0004878976,0.0005729266,0.00015503977,0.0006713831,0.0008209274,0.0032936302,0.0005055847,0.000029974932],"category_scores_gemma":[0.0015299151,0.00052636815,0.0002226751,0.0009886662,0.00026451473,0.00079960964,0.0028446277,0.0010062914,0.00004561673],"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":[0.000024318253,0.00087059784,0.00029711338,0.00029164614,0.0005060943,0.000103227314,0.006444738,0.00054831745,0.0014040336,0.1603125,0.02752332,0.80167407],"study_design_scores_gemma":[0.0004910395,0.0000026023588,0.0005968676,0.0014326216,0.0001304711,0.00012995554,0.00018804497,0.81652534,0.0063152853,0.0034351908,0.16945083,0.0013017438],"about_ca_topic_score_codex":0.001468393,"about_ca_topic_score_gemma":0.0008369226,"teacher_disagreement_score":0.81597704,"about_ca_system_score_codex":0.00028496582,"about_ca_system_score_gemma":0.00082683394,"threshold_uncertainty_score":0.9997188},"labels":[],"label_agreement":null},{"id":"W4288088872","doi":"10.48550/arxiv.1910.12084","title":"Detection of Adversarial Attacks and Characterization of Adversarial\\n Subspace","year":2019,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Adversarial system; Subspace topology; Linear subspace; Computer science; Detector; Spectrogram; Artificial intelligence; Relation (database); Pattern recognition (psychology); Algorithm; Mathematics; Data mining","score_opus":0.035460691609158906,"score_gpt":0.18128814764992535,"score_spread":0.14582745604076644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288088872","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":[0.45409408,0.00001226895,0.544259,0.000029718609,0.00048679276,0.0006035737,0.00004567801,0.00007109744,0.0003977703],"genre_scores_gemma":[0.99713314,0.0009734931,0.0006592549,0.000015292773,0.00010721718,0.0000028931058,0.000026822865,0.000025854544,0.00105604],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973731,0.00020527173,0.00061742926,0.0013205718,0.00016677387,0.00031687113],"domain_scores_gemma":[0.99619955,0.0001106339,0.0016139433,0.0013612168,0.00055780774,0.0001568482],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037521837,0.0004242447,0.0006909988,0.00050061883,0.00020472855,0.00005498366,0.00097220385,0.0006419443,0.00004943637],"category_scores_gemma":[0.000027213237,0.00054946996,0.00032042892,0.0011764588,0.00036170692,0.00064911536,0.001249068,0.0004953476,0.000022397648],"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":[0.0018443465,0.0010098065,0.010423753,0.0016450478,0.00078729517,0.00002639844,0.0025015362,0.038907148,0.75790167,0.16276124,0.000025170413,0.022166615],"study_design_scores_gemma":[0.0020285575,0.00077998143,0.025202574,0.00034546235,0.000503432,0.000013678995,0.00024693902,0.7573084,0.20549253,0.0054029836,0.0016094367,0.0010660128],"about_ca_topic_score_codex":0.00039843825,"about_ca_topic_score_gemma":0.000029840949,"teacher_disagreement_score":0.71840125,"about_ca_system_score_codex":0.00018140239,"about_ca_system_score_gemma":0.0002455267,"threshold_uncertainty_score":0.99969566},"labels":[],"label_agreement":null},{"id":"W4288102866","doi":"10.48550/arxiv.1909.11832","title":"Adversarial Deep Embedded Clustering: on a better trade-off between\\n Feature Randomness and Feature Drift","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; University of Windsor; Université du Québec à Montréal","funders":"","keywords":"Autoencoder; Cluster analysis; Computer science; Artificial intelligence; Discriminative model; Feature (linguistics); Randomness; Benchmark (surveying); Feature vector; Pattern recognition (psychology); Machine learning; Deep learning; Data mining; Mathematics; Geography","score_opus":0.0289823917868551,"score_gpt":0.19279713969344364,"score_spread":0.16381474790658854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288102866","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":[0.060872637,0.000060944625,0.9339336,0.0015064108,0.0004640729,0.0008417179,0.000032767246,0.00048719518,0.0018006291],"genre_scores_gemma":[0.9916493,0.00013271885,0.005557362,0.0003914771,0.00028321962,0.0000062717268,0.000034001452,0.00002942995,0.0019161616],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790424,0.00012151281,0.00016730854,0.0013329303,0.0001267624,0.0003472211],"domain_scores_gemma":[0.9980427,0.00013777085,0.00026324342,0.0013097706,0.00006207454,0.0001844417],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016566055,0.0004320712,0.00049747695,0.0002744025,0.00025229852,0.0002066888,0.0012985372,0.00077209115,0.000010434629],"category_scores_gemma":[0.000010997431,0.00045761073,0.00026820815,0.0004381845,0.00010157979,0.00026392087,0.0012564061,0.0012196859,0.000028661123],"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":[0.0031821476,0.0014650532,0.02745931,0.0020786987,0.0031840736,0.0014430167,0.007279615,0.28051206,0.0011441198,0.39212772,0.06552145,0.21460272],"study_design_scores_gemma":[0.0069039874,0.00046842254,0.011047744,0.0003757848,0.0004764432,0.000039687413,0.00016288148,0.9014216,0.001384185,0.024718266,0.050461825,0.0025391849],"about_ca_topic_score_codex":0.000015690828,"about_ca_topic_score_gemma":0.00001428225,"teacher_disagreement_score":0.9307767,"about_ca_system_score_codex":0.000121542515,"about_ca_system_score_gemma":0.0000754808,"threshold_uncertainty_score":0.99978757},"labels":[],"label_agreement":null},{"id":"W4288261457","doi":"10.48550/arxiv.1908.06351","title":"Anomaly Detection in Video Sequence with Appearance-Motion\\n Correspondence","year":2019,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Benchmark (surveying); Anomaly detection; Convolutional neural network; Frame (networking); Sequence (biology); Computer vision; Motion (physics); Encoder; Pattern recognition (psychology); Translation (biology); Object (grammar); Tree (set theory); Mathematics","score_opus":0.05873375725310867,"score_gpt":0.19640150488142694,"score_spread":0.13766774762831827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288261457","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":[0.31257743,0.00005312242,0.6842339,0.00008343031,0.00035344096,0.0010715822,0.000013443607,0.00031099524,0.001302672],"genre_scores_gemma":[0.9934388,0.0005299852,0.0018726728,0.00011718099,0.000075121025,0.000021448397,0.0000061745914,0.00004494773,0.0038936965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950593,0.00034252243,0.0005964227,0.0029457526,0.00026370582,0.0007922896],"domain_scores_gemma":[0.9957126,0.000129494,0.00082368834,0.0025857082,0.00047262694,0.00027592466],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006266832,0.000722684,0.00066280185,0.00087835325,0.00045111225,0.00030660868,0.0025614146,0.00065774453,0.00008412938],"category_scores_gemma":[0.000033467855,0.0008520588,0.00027546435,0.0038222678,0.00045716338,0.0013759326,0.001219104,0.0015552529,0.0006578733],"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":[0.0007502712,0.0005720377,0.028264564,0.00035374516,0.000114688635,0.0005461019,0.0006281051,0.86202276,0.0029178378,0.072957814,0.00003663918,0.03083542],"study_design_scores_gemma":[0.0009395219,0.00049074355,0.018844407,0.00062295905,0.00007183433,0.00009390332,0.00017570291,0.96356547,0.0048282356,0.007937981,0.0011990459,0.0012301931],"about_ca_topic_score_codex":0.0011151982,"about_ca_topic_score_gemma":0.0007825065,"teacher_disagreement_score":0.6823612,"about_ca_system_score_codex":0.0010292708,"about_ca_system_score_gemma":0.00058253773,"threshold_uncertainty_score":0.99939305},"labels":[],"label_agreement":null},{"id":"W4288636591","doi":"10.5281/zenodo.2538412","title":"The sufficiency of the evidence, the relevancy of the evidence, and quantifying both with a single number","year":2019,"lang":"en","type":"report","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Anomaly Detection Techniques and Applications","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 Ottawa","funders":"","keywords":"Psychology","score_opus":0.10723948303606466,"score_gpt":0.30007138374231834,"score_spread":0.19283190070625367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288636591","genre_codex":"other","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":[0.069600716,0.012883281,0.2362534,0.02900484,0.002214713,0.015140425,0.00030940637,0.0024663066,0.6321269],"genre_scores_gemma":[0.9914391,0.0024778538,0.00039385076,0.000060977487,0.0000708692,5.558432e-7,0.0000047857106,0.00035202326,0.005200008],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973845,0.0004672511,0.00041930834,0.0004378534,0.0010395835,0.00025150177],"domain_scores_gemma":[0.9958809,0.00030283333,0.000720119,0.0018811922,0.001171365,0.000043570402],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0019824544,0.00016695856,0.00018919358,0.00006285358,0.0027693475,0.0006153908,0.0038743953,0.000082913095,0.00014771154],"category_scores_gemma":[0.001310488,0.00007595585,0.000109701105,0.0013543232,0.00065779156,0.00026296207,0.002833101,0.00047951113,0.00008759508],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013942248,0.0007067006,0.0016863659,0.0016546679,0.00036620052,0.0000053429476,0.0068081673,0.00020628836,0.011742403,0.17540279,0.43232024,0.36896142],"study_design_scores_gemma":[0.00007778905,0.00021643435,0.0052416413,0.0010112374,0.0000420763,0.00022034442,0.00018550413,0.0005892726,0.0012680271,0.00030366442,0.9906803,0.00016372862],"about_ca_topic_score_codex":0.0000906404,"about_ca_topic_score_gemma":0.000004267051,"teacher_disagreement_score":0.92183834,"about_ca_system_score_codex":0.00011515512,"about_ca_system_score_gemma":0.00007989382,"threshold_uncertainty_score":0.9985289},"labels":[],"label_agreement":null},{"id":"W4291219640","doi":"10.1145/3552437.3555705","title":"Towards Automated Key-Point Detection in Images with Partial Pool View","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Simon Fraser University","funders":"","keywords":"Analytics; Computer science; Key (lock); Data science; Data collection; Work (physics); Field (mathematics); Point (geometry); Tracking (education); Athletes; Computer security; Engineering; Mathematics; Psychology; Statistics","score_opus":0.013542568712167436,"score_gpt":0.27332058416266297,"score_spread":0.25977801545049556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4291219640","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":[0.004310947,0.000050455226,0.98399687,0.001171395,0.0001731346,0.00077206513,0.000008427746,0.0039417273,0.0055749863],"genre_scores_gemma":[0.9175186,0.00009043848,0.07990129,0.000236346,0.00004577589,0.0017826284,0.000011774147,0.000022203149,0.00039093042],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982201,0.00010392049,0.00037310756,0.00075698725,0.0002954204,0.00025048706],"domain_scores_gemma":[0.99870324,0.000018788332,0.00019189277,0.00094479154,0.00007036394,0.00007092952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032697743,0.0002445051,0.00027296905,0.00024627856,0.00015589787,0.00021764968,0.0008904821,0.00014035885,0.00027599846],"category_scores_gemma":[0.000008911288,0.00021313113,0.00009607172,0.00065032166,0.000038330592,0.00019372783,0.0014673864,0.0006183085,0.000024312065],"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":[0.000059194714,0.00068807445,0.0002783591,0.00031097306,0.00012164252,0.00009974298,0.0009124009,0.011074176,0.0028987625,0.03362316,0.003812318,0.9461212],"study_design_scores_gemma":[0.00053425314,0.00054008205,0.009642554,0.00012366264,0.000037111786,0.00011866037,0.00010812925,0.778876,0.15106514,0.010238753,0.047406554,0.0013090817],"about_ca_topic_score_codex":0.0010181142,"about_ca_topic_score_gemma":0.00011017304,"teacher_disagreement_score":0.9448121,"about_ca_system_score_codex":0.0002446684,"about_ca_system_score_gemma":0.00018158593,"threshold_uncertainty_score":0.8691236},"labels":[],"label_agreement":null},{"id":"W4291821249","doi":"10.32470/ccn.2022.1237-0","title":"Deriving Loss Functions for Regression and Classification from Humans","year":2022,"lang":"en","type":"article","venue":"2022 Conference on Cognitive Computational Neuroscience","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Regression; Artificial intelligence; Machine learning; Pattern recognition (psychology); Statistics; Mathematics","score_opus":0.0771750903985761,"score_gpt":0.3185853822572361,"score_spread":0.24141029185865998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4291821249","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":[0.0511093,0.000017133325,0.9452706,0.0019095761,0.00024890446,0.00043182087,0.00021777708,0.00018699348,0.00060786796],"genre_scores_gemma":[0.99103767,0.000014535636,0.006397914,0.0013826304,0.00003081889,0.0007246056,0.00007261724,0.00000872812,0.00033045674],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983402,0.00010667515,0.00020347309,0.00075988553,0.00040733197,0.00018246408],"domain_scores_gemma":[0.9988628,0.00044864463,0.00017025352,0.00019299907,0.00024260995,0.00008269975],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00019450052,0.00013600357,0.000110685825,0.00016253178,0.0017060583,0.00020366984,0.00045545804,0.000024097451,0.000059454476],"category_scores_gemma":[0.0001027588,0.00014257553,0.00004733261,0.00052617106,0.00016310863,0.00031285832,0.00033672503,0.00020865837,0.000008849262],"study_design_candidate":"theoretical_or_conceptual","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":[0.000072907285,0.0004650397,0.0018449097,0.000012804059,0.000010108854,0.000008540219,0.0006531161,0.0026381216,0.029250748,0.8231805,0.0017705201,0.14009267],"study_design_scores_gemma":[0.00041377664,0.0004917237,0.05427832,0.000026139038,0.000009643049,0.000019082141,0.00034761196,0.8636531,0.00058460626,0.07596535,0.0039165565,0.00029405704],"about_ca_topic_score_codex":0.00000719092,"about_ca_topic_score_gemma":0.0000022375164,"teacher_disagreement_score":0.9399284,"about_ca_system_score_codex":0.000049172664,"about_ca_system_score_gemma":0.00013666606,"threshold_uncertainty_score":0.99959356},"labels":[],"label_agreement":null},{"id":"W4291910009","doi":"10.1109/isncc55209.2022.9851718","title":"Real-Time Data Generation and Anomaly Detection for Security User Profiles","year":2022,"lang":"en","type":"article","venue":"2022 International Symposium on Networks, Computers and Communications (ISNCC)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Taif University; Western University","keywords":"Computer science; Anomaly detection; Flagging; Python (programming language); Data modeling; Data mining; Database; Operating system","score_opus":0.022556803433896006,"score_gpt":0.26757716328003506,"score_spread":0.24502035984613904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4291910009","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":[0.031061029,0.00029417203,0.94925606,0.015583148,0.00072237756,0.001265306,0.00025364314,0.0005147115,0.0010495506],"genre_scores_gemma":[0.9539235,0.0015502436,0.041645065,0.0005703055,0.00029604012,0.00073177274,0.0008621872,0.000026526412,0.00039433464],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99835944,0.00018378336,0.0003596998,0.0006537048,0.0002542126,0.00018915068],"domain_scores_gemma":[0.9974813,0.00026968718,0.00023342839,0.001809228,0.0001251588,0.00008121531],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005525722,0.00018042263,0.00016506184,0.00014338075,0.0013784952,0.00033174639,0.002382208,0.00006036458,0.000016082675],"category_scores_gemma":[0.000010286633,0.00020309498,0.00005515378,0.00031581926,0.00011583492,0.00045890675,0.0031204305,0.00027370302,0.0000025366323],"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":[0.00015932365,0.0013305144,0.0008616481,0.00003221025,0.00044248794,0.0000038405983,0.001106882,0.036537055,0.015413325,0.71788764,0.0690522,0.15717286],"study_design_scores_gemma":[0.00027138513,0.00018843207,0.00039177897,0.000007659618,0.00001448325,0.000034915684,0.000037604314,0.92089134,0.0001583652,0.0010076596,0.076792635,0.00020373461],"about_ca_topic_score_codex":0.00015473136,"about_ca_topic_score_gemma":0.000052437164,"teacher_disagreement_score":0.92286247,"about_ca_system_score_codex":0.00014071209,"about_ca_system_score_gemma":0.000031096784,"threshold_uncertainty_score":0.99992156},"labels":[],"label_agreement":null},{"id":"W4292259733","doi":"10.1007/s10586-022-03659-3","title":"When explainable AI meets IoT applications for supervised learning","year":2022,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"Høgskulen på Vestlandet","keywords":"Computer science; Internet of Things; Field (mathematics); Artificial intelligence; Process (computing); Deep learning; Big data; Machine learning; Interpretation (philosophy); Computation; Data mining; Algorithm; Embedded system","score_opus":0.015019502098149678,"score_gpt":0.259273588772887,"score_spread":0.2442540866747373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292259733","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":[0.0018479316,0.00006341737,0.9914585,0.004072602,0.000087286,0.00087532174,0.0000024156957,0.0007394359,0.0008531246],"genre_scores_gemma":[0.7446465,0.0000010563253,0.2512808,0.0016035271,0.00013755863,0.0012428586,0.000013137517,0.000020637279,0.0010539254],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998753,0.00007527363,0.00025514135,0.00044703783,0.0001683741,0.00030118937],"domain_scores_gemma":[0.99913186,0.00015507305,0.00011571645,0.0004375323,0.000093646915,0.00006615207],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00043710668,0.0001173839,0.00013409078,0.000117092684,0.0018944007,0.00019215888,0.00085078686,0.000031656928,0.000030338724],"category_scores_gemma":[0.000012248095,0.00013676354,0.00009762574,0.00038308586,0.000020348607,0.00014711283,0.0009387435,0.00025766462,0.0000126028435],"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":[0.000018711973,0.0003173378,0.00047847256,0.00011714661,0.000051369865,0.0000030040078,0.004951387,0.09833608,0.003268702,0.22130218,0.02501378,0.6461418],"study_design_scores_gemma":[0.00019101323,0.00006748778,0.000022101289,0.0000028593718,0.0000037635866,0.000015452833,0.00019559907,0.6007734,0.0005360227,0.004366621,0.39369226,0.00013344573],"about_ca_topic_score_codex":0.000016396347,"about_ca_topic_score_gemma":9.369114e-7,"teacher_disagreement_score":0.74279857,"about_ca_system_score_codex":0.000103231716,"about_ca_system_score_gemma":0.000046344638,"threshold_uncertainty_score":0.999405},"labels":[],"label_agreement":null},{"id":"W4293057876","doi":"10.1109/vtc2022-spring54318.2022.9860972","title":"Support Vector-Based Unsupervised Learning Approaches for Radio Frequency Interference Detection","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Support vector machine; Computer science; Artificial intelligence; Novelty detection; Machine learning; Unsupervised learning; Pattern recognition (psychology); One-class classification; Binary classification; Novelty","score_opus":0.03059035772502548,"score_gpt":0.23782315188733588,"score_spread":0.2072327941623104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293057876","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":[0.10395233,0.00018046335,0.8893345,0.0017483319,0.0005686229,0.0012247171,0.000022218832,0.0026055227,0.0003633042],"genre_scores_gemma":[0.9725448,0.000024305113,0.021726975,0.00018942455,0.00006791158,0.0050495523,0.00002077557,0.00006237649,0.00031391176],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962931,0.00019609902,0.00069287454,0.0014738383,0.00052371563,0.0008203188],"domain_scores_gemma":[0.9976071,0.000108210166,0.00042735843,0.0014555117,0.0002505916,0.00015120678],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0008591888,0.0004926101,0.00054005894,0.0009784129,0.0016432855,0.0001983427,0.0025194203,0.00038974328,0.00022715332],"category_scores_gemma":[0.00009854706,0.00056527654,0.000313619,0.0020932234,0.00027082564,0.0003171117,0.0005842361,0.0016981476,0.000040403836],"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":[0.0001100253,0.0008957836,0.0059318687,0.00020718573,0.00031644097,0.000090728405,0.0006521724,0.008101878,0.28538907,0.5182353,0.00041260888,0.1796569],"study_design_scores_gemma":[0.0020348446,0.0032523894,0.0019537618,0.00004503229,0.00012602282,0.00016855997,0.0009929629,0.7074828,0.21348822,0.026643736,0.04195756,0.0018540701],"about_ca_topic_score_codex":0.00012945599,"about_ca_topic_score_gemma":0.00006053336,"teacher_disagreement_score":0.86859244,"about_ca_system_score_codex":0.00044324444,"about_ca_system_score_gemma":0.00042980805,"threshold_uncertainty_score":0.99967986},"labels":[],"label_agreement":null},{"id":"W4293771206","doi":"10.3390/su141710822","title":"An Improved Optimization Algorithm Based on Density Grid for Green Storage Monitoring System","year":2022,"lang":"en","type":"article","venue":"Sustainability","topic":"Anomaly Detection Techniques and Applications","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":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer data storage; Storage model; Computer science; Algorithm; Grid; Cluster analysis; Data processing; Data set; Real-time computing; Data mining; Process engineering; Database; Engineering; Machine learning; Mathematics; Artificial intelligence","score_opus":0.006871321427987642,"score_gpt":0.2536023545990015,"score_spread":0.24673103317101386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293771206","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":[0.008093077,0.0000031416057,0.9892825,0.00046946655,0.00021848147,0.0011677723,0.00003271196,0.00071017013,0.000022671813],"genre_scores_gemma":[0.8417974,1.043799e-7,0.15685086,0.00003650432,0.00010381839,0.0011333977,0.000015053134,0.00001033626,0.000052523694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987571,0.00014579741,0.00019123539,0.0004932631,0.00019069512,0.00022189433],"domain_scores_gemma":[0.9984225,0.00006233469,0.000104843755,0.0008645381,0.00046366334,0.00008211133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072418276,0.00011164194,0.00012159669,0.000091650676,0.0008534959,0.00006968012,0.0005191498,0.000040436662,0.0000039216534],"category_scores_gemma":[0.000037900605,0.0001223975,0.00007848782,0.00039476124,0.000023904391,0.00021756571,0.0001461977,0.00014022565,2.7057627e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000113715745,0.0009170388,0.0023464093,0.00036648294,0.000015522988,0.000011067542,0.0005240674,0.7332227,0.00032179352,0.017595205,0.0001572596,0.24440873],"study_design_scores_gemma":[0.00017401548,0.0004187188,0.0011154268,0.0000016184595,0.0000045923925,0.0000020640202,0.0003607403,0.9948345,0.0013201683,0.0009205269,0.0007187733,0.00012887943],"about_ca_topic_score_codex":0.00024131626,"about_ca_topic_score_gemma":0.0000012445087,"teacher_disagreement_score":0.8337043,"about_ca_system_score_codex":0.0016564354,"about_ca_system_score_gemma":0.00022033886,"threshold_uncertainty_score":0.65644866},"labels":[],"label_agreement":null},{"id":"W4293863351","doi":"10.1109/siu55565.2022.9864893","title":"Anomaly Detection in Surveillance Videos Using Regression With Recurrent Neural Networks","year":2022,"lang":"en","type":"article","venue":"2022 30th Signal Processing and Communications Applications Conference (SIU)","topic":"Anomaly Detection Techniques and Applications","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":"Stantec (Canada)","funders":"","keywords":"Anomaly detection; Computer science; Artificial intelligence; Artificial neural network; Receiver operating characteristic; Frame (networking); Feature (linguistics); Regression; Pattern recognition (psychology); Anomaly (physics); Feature extraction; Regression analysis; Machine learning; Statistics; Mathematics","score_opus":0.029524701021846116,"score_gpt":0.27871450527567,"score_spread":0.24918980425382387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293863351","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":[0.017168403,0.0015865576,0.9787362,0.0010572572,0.000017718496,0.0007576741,0.000011226725,0.00031219705,0.00035276715],"genre_scores_gemma":[0.975739,0.00024950848,0.02099008,0.000120388584,0.000024040046,0.0027657864,0.00003553984,0.000020522835,0.000055108365],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980249,0.00028384232,0.00045828582,0.0006292885,0.00029213232,0.00031156564],"domain_scores_gemma":[0.9978159,0.000114140974,0.00038769862,0.0013654635,0.00021190358,0.0001048603],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005053219,0.00023401914,0.00023898666,0.00029447346,0.0022420767,0.0002912385,0.0015639612,0.00006819132,0.000023802353],"category_scores_gemma":[0.0000049882474,0.0002303176,0.000043493976,0.0020725944,0.00021073465,0.00044731147,0.0009652704,0.00072617206,0.0000010032691],"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":[0.000033198816,0.0003750561,0.002810369,0.000032800042,0.000012770387,0.0000015051792,0.00044480988,0.006681998,0.0034277884,0.014211231,0.000026111227,0.97194237],"study_design_scores_gemma":[0.00023424748,0.000117305746,0.0012838657,0.000031169267,0.00001034581,0.00007016976,0.00039520804,0.9922423,0.00020131885,0.0013271307,0.003781041,0.00030591807],"about_ca_topic_score_codex":0.00015471321,"about_ca_topic_score_gemma":0.00018687166,"teacher_disagreement_score":0.9855603,"about_ca_system_score_codex":0.00017009536,"about_ca_system_score_gemma":0.00018363624,"threshold_uncertainty_score":0.9990569},"labels":[],"label_agreement":null},{"id":"W4293863405","doi":"10.1109/siu55565.2022.9864748","title":"Object-Centric Video Anomaly Detection with Covariance Features","year":2022,"lang":"en","type":"article","venue":"2022 30th Signal Processing and Communications Applications Conference (SIU)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Stantec (Canada)","funders":"","keywords":"Artificial intelligence; Computer science; Anomaly detection; Pattern recognition (psychology); Object detection; Probabilistic logic; Benchmark (surveying); Covariance; Autoregressive model; Computer vision; Mixture model; Curse of dimensionality; Probability distribution; Mathematics; Statistics","score_opus":0.015777374590116952,"score_gpt":0.24594334407549467,"score_spread":0.2301659694853777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293863405","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":[0.0010550626,0.0021498774,0.9891044,0.0021670985,0.00001479112,0.00086375256,0.00002739612,0.0006620884,0.0039555514],"genre_scores_gemma":[0.93592465,0.00032987943,0.056641474,0.00036926466,0.000030062012,0.005973992,0.000046659603,0.000023688395,0.00066030194],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979929,0.0001886412,0.00039026942,0.00070912676,0.00039054704,0.0003285596],"domain_scores_gemma":[0.9971686,0.00012961512,0.0003747646,0.001846425,0.00033949452,0.00014112594],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0003994629,0.0002554031,0.00023103795,0.0002952659,0.003929405,0.00049999624,0.0022846386,0.00007235739,0.00006563697],"category_scores_gemma":[0.000006749992,0.0002597981,0.000056149755,0.002357971,0.0002857499,0.000516378,0.0010516203,0.0007012212,0.000012806134],"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":[0.000027660088,0.00044396872,0.00028286292,0.000043412172,0.000037070055,0.0000013791912,0.00071467645,0.00029906488,0.0070840837,0.14864886,0.00037003928,0.8420469],"study_design_scores_gemma":[0.0017126321,0.0010447378,0.00730873,0.00010312324,0.00021352213,0.00092133135,0.0036660093,0.3911125,0.009887807,0.04692306,0.53480715,0.0022993688],"about_ca_topic_score_codex":0.000121560166,"about_ca_topic_score_gemma":0.000070146685,"teacher_disagreement_score":0.9348696,"about_ca_system_score_codex":0.00013838158,"about_ca_system_score_gemma":0.0003739722,"threshold_uncertainty_score":0.9999854},"labels":[],"label_agreement":null},{"id":"W4293863407","doi":"10.1109/siu55565.2022.9864669","title":"Active Learning for Online Nonlinear Neyman-Pearson Classification","year":2022,"lang":"en","type":"article","venue":"2022 30th Signal Processing and Communications Applications Conference (SIU)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Stantec (Canada)","funders":"","keywords":"Binary classification; Artificial intelligence; Computer science; Machine learning; Random forest; Context (archaeology); Ensemble learning; Constant false alarm rate; Decision tree; False alarm; Pattern recognition (psychology); Set (abstract data type); Data mining; Support vector machine","score_opus":0.054935450517146885,"score_gpt":0.3136604428057829,"score_spread":0.25872499228863605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293863407","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":[0.001324838,0.000706759,0.98826385,0.0061663506,0.000015369425,0.0011640844,0.00009564517,0.00060168875,0.0016614175],"genre_scores_gemma":[0.82524645,0.0004033167,0.16420002,0.00030650012,0.00005387938,0.008311986,0.0005223345,0.000028506745,0.00092702056],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99811983,0.00017601698,0.00046410444,0.00065752096,0.0002855076,0.00029703882],"domain_scores_gemma":[0.9974436,0.0002226904,0.0004416734,0.0013226602,0.00044540054,0.00012398456],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00043592503,0.00022107858,0.0002262701,0.00024920143,0.0039645005,0.0003153372,0.002103732,0.00007695256,0.000049365546],"category_scores_gemma":[0.000017976636,0.00025462918,0.00008477906,0.0011856493,0.00021558732,0.00043645262,0.0009733734,0.0007152921,0.000008095409],"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":[0.000012203394,0.00038866707,0.00004880569,0.0000274535,0.00001640714,9.745449e-8,0.0005245395,0.00015205912,0.004238018,0.12332814,0.00021130525,0.8710523],"study_design_scores_gemma":[0.00026797014,0.00014740723,0.00039259696,0.000013677888,0.00003031898,0.000016805143,0.0018566874,0.6651941,0.00043032804,0.010638721,0.32070348,0.00030790133],"about_ca_topic_score_codex":0.000033736014,"about_ca_topic_score_gemma":0.000015738273,"teacher_disagreement_score":0.8707444,"about_ca_system_score_codex":0.00013280116,"about_ca_system_score_gemma":0.00033421372,"threshold_uncertainty_score":0.9999906},"labels":[],"label_agreement":null},{"id":"W4293868308","doi":"10.1109/crv55824.2022.00031","title":"Anomaly Detection with Adversarially Learned Perturbations of Latent Space","year":2022,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Western University","funders":"","keywords":"Anomaly detection; Computer science; Space (punctuation); Anomaly (physics); Artificial intelligence; Physics","score_opus":0.01273389329985709,"score_gpt":0.21939119579673413,"score_spread":0.20665730249687705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293868308","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":[0.026071256,0.000008844593,0.966981,0.0011494205,0.000043178567,0.00018866855,0.0000019367455,0.00028914443,0.005266586],"genre_scores_gemma":[0.9564552,0.0000030894025,0.040557954,0.000090293506,0.000010389252,0.00012169923,0.0000013091023,0.0000052827327,0.0027548291],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993431,0.000034502624,0.00012703499,0.00020879347,0.00019048157,0.00009611804],"domain_scores_gemma":[0.9994425,0.00002383734,0.00009593898,0.00033717434,0.000067559,0.000032951495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001145159,0.00006216388,0.00007478886,0.00010087401,0.00029093836,0.00002339613,0.00031225203,0.000017292447,0.00013256197],"category_scores_gemma":[0.0000046372397,0.000054611497,0.00004168019,0.0005974514,0.00002370111,0.00014945821,0.00016202874,0.00009816755,0.000005437346],"study_design_candidate":"theoretical_or_conceptual","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":[0.00009952246,0.0005951377,0.0023848244,0.00001805486,0.00009382132,0.0000066647303,0.0012626408,0.014123313,0.1384025,0.7347403,0.0013844264,0.106888786],"study_design_scores_gemma":[0.0018871657,0.0048465105,0.034946963,0.000011973773,0.00006970195,0.00027748407,0.0008175855,0.3869126,0.39446038,0.015452755,0.15923265,0.0010842162],"about_ca_topic_score_codex":0.00017990333,"about_ca_topic_score_gemma":0.000041284635,"teacher_disagreement_score":0.9303839,"about_ca_system_score_codex":0.000052069096,"about_ca_system_score_gemma":0.000057316072,"threshold_uncertainty_score":0.22376922},"labels":[],"label_agreement":null},{"id":"W4294316556","doi":"10.21203/rs.3.rs-1999527/v1","title":"Detection of Abnormal Behaviour of Wireless Sensors in School Buildings Using Dynamic Time Warping","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Anomaly Detection Techniques and Applications","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 Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Research Manitoba","keywords":"Dynamic time warping; HVAC; Anomaly detection; Computer science; Real-time computing; Anomaly (physics); Wireless; Wireless sensor network; Point (geometry); Ventilation (architecture); Air conditioning; Simulation; Artificial intelligence; Engineering; Telecommunications; Meteorology; Mathematics; Geography","score_opus":0.03426897593350071,"score_gpt":0.366917529077332,"score_spread":0.3326485531438313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294316556","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":[0.84801334,0.00007789862,0.15084147,0.00005139869,0.000046856017,0.0007307891,0.000028769738,0.00010511976,0.00010436922],"genre_scores_gemma":[0.9892614,0.00007627421,0.010298662,0.0000021861765,0.000019293351,0.00020227743,0.00000858021,0.00002303406,0.000108276676],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997201,0.00036470743,0.00056027254,0.00060051656,0.0008867406,0.00038674477],"domain_scores_gemma":[0.9981429,0.00011965284,0.000310733,0.00092074607,0.00041090333,0.00009506089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016947481,0.00016533141,0.000336082,0.0011674071,0.00021173769,0.00006868892,0.0011365837,0.00021873797,0.00008387057],"category_scores_gemma":[0.000073592855,0.00019344866,0.0001582235,0.0013660988,0.000111674526,0.00018662633,0.0025711998,0.0015018733,0.000005679986],"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":[0.0001351636,0.0011289055,0.023192955,0.0031285544,0.000108918066,0.00007627607,0.0021508469,0.06323935,0.8278627,0.0043497523,0.0000647125,0.07456192],"study_design_scores_gemma":[0.00026664828,0.00030165305,0.028684204,0.0007336277,0.000011543571,0.00002519383,0.00040247868,0.8548481,0.112159945,0.0020514,0.00010618792,0.00040901112],"about_ca_topic_score_codex":0.0020486803,"about_ca_topic_score_gemma":0.000023731725,"teacher_disagreement_score":0.79160875,"about_ca_system_score_codex":0.0006685521,"about_ca_system_score_gemma":0.0003599083,"threshold_uncertainty_score":0.7888608},"labels":[],"label_agreement":null},{"id":"W4294975185","doi":"10.1109/embc48229.2022.9871621","title":"Self-supervised Anomaly Detection with Random-shape Pseudo-outliers","year":2022,"lang":"en","type":"article","venue":"2022 44th Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)","topic":"Anomaly Detection Techniques and Applications","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":"","keywords":"Anomaly detection; Outlier; Discriminative model; Artificial intelligence; Computer science; Pattern recognition (psychology); Pixel; Image (mathematics); Random forest; Anomaly (physics); Computer vision","score_opus":0.018032643561679794,"score_gpt":0.2560805643589475,"score_spread":0.2380479207972677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294975185","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":[0.290635,0.000089150795,0.7034297,0.0027549772,0.0015731149,0.0005351088,0.000047381487,0.0003334343,0.0006021117],"genre_scores_gemma":[0.97724795,0.00012111994,0.021425217,0.0003794732,0.00015805452,0.0002745877,0.00002075887,0.000015178387,0.00035767513],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855906,0.00009280441,0.0003857204,0.00039209178,0.0003257057,0.0002445941],"domain_scores_gemma":[0.998872,0.0001333431,0.00020253762,0.0004235614,0.00030828768,0.000060279075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059652736,0.00019517487,0.0002735046,0.00019890943,0.0001961578,0.000018283476,0.0013762941,0.000089613735,0.00017150378],"category_scores_gemma":[0.00007929502,0.00014989784,0.0001370938,0.00090037275,0.000146589,0.00015851842,0.0003025328,0.00057846156,0.0000026870216],"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":[0.0011394799,0.0020576902,0.0379521,0.00050397665,0.003172215,0.000016241513,0.04492037,0.10410537,0.6124428,0.09443414,0.059844784,0.039410815],"study_design_scores_gemma":[0.0030624524,0.00066932384,0.0031379303,0.00006695083,0.00004561169,0.000084801904,0.0010464344,0.9214503,0.0037406178,0.0009439836,0.06524476,0.0005068209],"about_ca_topic_score_codex":0.00018354076,"about_ca_topic_score_gemma":0.000044347027,"teacher_disagreement_score":0.81734496,"about_ca_system_score_codex":0.00016005432,"about_ca_system_score_gemma":0.00009133178,"threshold_uncertainty_score":0.61126566},"labels":[],"label_agreement":null},{"id":"W4295022510","doi":"","title":"Method for detecting at least one anomaly in an observed signal, computer program product and corresponding device","year":2013,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Anomaly (physics); SIGNAL (programming language); Anomaly detection; Product (mathematics); Computer science; Data mining; Mathematics; Physics; Programming language; Condensed matter physics","score_opus":0.06451169034650218,"score_gpt":0.29426159089801424,"score_spread":0.22974990055151207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295022510","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22870055,0.0005450117,0.7605314,0.0058898274,0.00013933143,0.002998878,0.000026847003,0.0005217858,0.00064637157],"genre_scores_gemma":[0.2922729,0.00011120162,0.70208716,0.00010352844,0.000067868496,0.0015053769,0.00011166458,0.0000596652,0.0036806224],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9899953,0.0056395456,0.0010858349,0.00212182,0.00039148485,0.00076599006],"domain_scores_gemma":[0.9910415,0.0022805575,0.0009961673,0.002446987,0.002855886,0.0003789141],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.008501389,0.00059427926,0.0007015727,0.0003774939,0.001055384,0.0015662068,0.0019740388,0.00040132547,0.00006790004],"category_scores_gemma":[0.00040365497,0.0007038483,0.00023259454,0.0010761243,0.00030243793,0.00077716686,0.0027608988,0.00081880967,0.000026156906],"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":[0.000027326925,0.0011103047,0.0035540035,0.0003312889,0.000052806794,0.0000026534026,0.005139409,0.000261103,0.014468008,0.07025956,0.000110114954,0.9046834],"study_design_scores_gemma":[0.0006465312,0.000013665833,0.021413956,0.0015944024,0.00006154417,0.00006168257,0.00007994563,0.8789859,0.07427035,0.003396452,0.018606652,0.00086896424],"about_ca_topic_score_codex":0.0031388518,"about_ca_topic_score_gemma":0.0054412666,"teacher_disagreement_score":0.90381444,"about_ca_system_score_codex":0.00035404557,"about_ca_system_score_gemma":0.00025616057,"threshold_uncertainty_score":0.9995413},"labels":[],"label_agreement":null},{"id":"W4295746342","doi":"10.1007/978-3-031-16525-2_1","title":"AugPaste: One-Shot Anomaly Detection for Medical Images","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Anomaly detection; Artificial intelligence; Computer science; Anomaly (physics); Pattern recognition (psychology); Image (mathematics); Scale (ratio); Cartography; Geography","score_opus":0.025876900182814046,"score_gpt":0.27773260976637254,"score_spread":0.2518557095835585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295746342","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000022558595,0.00016811736,0.9915831,0.001494622,0.000865106,0.00079853577,0.000015808842,0.0004525808,0.0045995396],"genre_scores_gemma":[0.27097377,0.00015324363,0.72267073,0.0029895594,0.0010953642,0.0005550434,0.000018050021,0.00008843548,0.0014557879],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962075,0.00002921963,0.0005418449,0.0015052283,0.0011827121,0.00053349853],"domain_scores_gemma":[0.99763846,0.00041520662,0.00029506194,0.0012227453,0.00021386913,0.00021464177],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010614851,0.00039117996,0.00041104032,0.00073684816,0.0006778228,0.00035571863,0.003183262,0.00032870157,0.00018339601],"category_scores_gemma":[0.000103003644,0.00040433405,0.00020168301,0.0007069323,0.00045870786,0.0005085351,0.0013615944,0.000842965,0.000016414007],"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":[0.000007901968,0.00004766528,0.000007639585,0.00003145555,0.000010358131,0.00001068074,0.00009725753,0.0010367794,0.0006161851,0.03222156,0.000083400395,0.96582913],"study_design_scores_gemma":[0.0005803351,0.0010319188,0.00014664713,0.00019018642,0.000025846977,0.0002648939,3.8903372e-7,0.6113327,0.031155229,0.2578892,0.09592932,0.0014533232],"about_ca_topic_score_codex":0.000038815793,"about_ca_topic_score_gemma":0.00008001909,"teacher_disagreement_score":0.9643758,"about_ca_system_score_codex":0.000378321,"about_ca_system_score_gemma":0.0005396156,"threshold_uncertainty_score":0.99984086},"labels":[],"label_agreement":null},{"id":"W4295767966","doi":"10.1109/fuzz-ieee55066.2022.9882557","title":"Enhanced Tree-Based Anomaly Detection","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"CHIST-ERA","keywords":"Computer science; Data mining; Cluster analysis; Anomaly detection; Centroid; Preprocessor; Tree (set theory); Hierarchical clustering; Fuzzy logic; Raw data; Data pre-processing; Tree traversal; Data cleansing; Artificial intelligence; Mathematics; Algorithm; Data quality; Engineering","score_opus":0.03865130500591533,"score_gpt":0.2841561644360786,"score_spread":0.24550485943016329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295767966","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":[0.07661302,0.00003838151,0.8722842,0.0013016755,0.0071279365,0.0009767055,0.00013525606,0.0010117847,0.040511098],"genre_scores_gemma":[0.99137974,0.00001793774,0.00072075543,0.0004863377,0.00039974504,0.0018577296,0.000030633862,0.00003558766,0.0050715413],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99636346,0.00029867416,0.0006858188,0.000999436,0.0012349794,0.00041764494],"domain_scores_gemma":[0.99786264,0.00012171453,0.00052336557,0.0009283556,0.0003934825,0.0001704682],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005948076,0.00035788986,0.00033874786,0.00059957674,0.0007022952,0.0004429874,0.0020532978,0.00012245966,0.0002825109],"category_scores_gemma":[0.000021800686,0.00039104276,0.00023276218,0.0008336123,0.00006995493,0.00040867855,0.00016102318,0.00061255594,0.0002477854],"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":[0.0003053796,0.0011058596,0.00025347693,0.00007836995,0.00027563266,0.00007479926,0.00065011316,0.018985972,0.6321461,0.26454762,0.009537409,0.072039254],"study_design_scores_gemma":[0.0015677745,0.0012348186,0.00064406864,0.00010430804,0.00003287504,0.00011229318,0.0005854714,0.67102695,0.28082016,0.0040598717,0.038446113,0.0013653213],"about_ca_topic_score_codex":0.00042435853,"about_ca_topic_score_gemma":0.000091998154,"teacher_disagreement_score":0.9147667,"about_ca_system_score_codex":0.0006835489,"about_ca_system_score_gemma":0.00024676803,"threshold_uncertainty_score":0.99985415},"labels":[],"label_agreement":null},{"id":"W4295929872","doi":"10.11606/d.55.2022.tde-15092022-141353","title":"Detecting outliers and annotating their types with indexing structures","year":2022,"lang":"en","type":"dissertation","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cascades (Canada)","funders":"","keywords":"Outlier; Computer science; Anomaly detection; Categorization; Data mining; Scalability; Scope (computer science); The Internet; Search engine indexing; Artificial intelligence; Information retrieval; Database; World Wide Web","score_opus":0.007325911939838359,"score_gpt":0.2434786647761562,"score_spread":0.23615275283631784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295929872","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":[0.3599219,0.00056750147,0.58502316,0.00016011564,0.00034375556,0.00085568626,0.000009640712,0.0018566332,0.051261585],"genre_scores_gemma":[0.93292946,0.000018225333,0.063994594,0.00010035847,0.00003652167,0.00013726379,0.000028568642,0.000027474158,0.0027275616],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991254,0.000022906166,0.00015816675,0.00039215654,0.00015213378,0.00014922756],"domain_scores_gemma":[0.9994036,0.000055563305,0.00018438262,0.00026372922,0.000051435203,0.000041304516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010234113,0.00017032395,0.00014561243,0.00014269401,0.00052915746,0.00018737334,0.0003333556,0.00007812771,0.000050654933],"category_scores_gemma":[0.00001094733,0.00013329348,0.00003061594,0.000324498,0.000015157132,0.0001522496,0.00008542556,0.00032970338,7.157029e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023899189,0.00001857957,0.00050319836,0.00011524999,0.00009778772,0.0000062535555,0.008249627,0.00019439009,0.0024033526,0.09306836,0.00014157902,0.8951777],"study_design_scores_gemma":[0.0022795442,0.0032523973,0.045662872,0.0006377391,0.00033578774,0.00073049776,0.13080494,0.15454501,0.3689608,0.19280398,0.09089539,0.009091055],"about_ca_topic_score_codex":0.00009265942,"about_ca_topic_score_gemma":0.00013878118,"teacher_disagreement_score":0.88608664,"about_ca_system_score_codex":0.00003102368,"about_ca_system_score_gemma":0.000053934917,"threshold_uncertainty_score":0.543555},"labels":[],"label_agreement":null},{"id":"W4296640192","doi":"10.1016/j.buildenv.2022.109620","title":"Unsupervised outlier detection using neural network-based mixtures of probabilistic principal component analyzers for building chiller fault diagnosis","year":2022,"lang":"en","type":"article","venue":"Building and Environment","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"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":"Outlier; Anomaly detection; Principal component analysis; Computer science; Probabilistic logic; Fault detection and isolation; Artificial neural network; Data mining; Chiller boiler system; Artificial intelligence; Fault (geology); Pattern recognition (psychology); Engineering; Water chiller","score_opus":0.020769749012538027,"score_gpt":0.24296347569328683,"score_spread":0.2221937266807488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296640192","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":[0.5282786,0.00008687931,0.47106037,0.00013297945,0.000049765156,0.00033445825,0.000006271714,0.000048092596,0.000002597173],"genre_scores_gemma":[0.902352,0.000015051819,0.0969455,0.00008352425,0.000038556067,0.0005447079,0.000003037668,0.000012348531,0.000005271529],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885845,0.00006120998,0.00025992666,0.00038940422,0.00020541766,0.00022559559],"domain_scores_gemma":[0.9994132,0.0000857506,0.00014676961,0.00027892913,0.000010090653,0.000065285094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027758224,0.00013635543,0.00016606975,0.00008570854,0.00059395237,0.000036469184,0.00023807169,0.000035010187,0.000015682914],"category_scores_gemma":[0.000007930009,0.00013857396,0.00009724093,0.00016442013,0.00005333476,0.00006339191,0.00023451193,0.000111324036,1.3463566e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015647041,0.00009979683,0.0016727825,0.000021541437,0.000018207907,6.4426894e-7,0.00006052909,0.9596949,0.017289681,0.0008778722,0.000017006412,0.02023138],"study_design_scores_gemma":[0.0003250465,0.00019636124,0.003744684,0.000011120359,0.000035395424,0.000006432934,0.00001538396,0.9765289,0.013792535,0.0007033132,0.004457343,0.0001835305],"about_ca_topic_score_codex":0.000050225906,"about_ca_topic_score_gemma":0.0000011399528,"teacher_disagreement_score":0.37411487,"about_ca_system_score_codex":0.00014561597,"about_ca_system_score_gemma":0.000010939076,"threshold_uncertainty_score":0.5650882},"labels":[],"label_agreement":null},{"id":"W4296912592","doi":"10.1109/adconip55568.2022.9894226","title":"A Modified Bag-of-Words Representation for Industrial Alarm Floods","year":2022,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; ALARM; Representation (politics); Natural language processing; Artificial intelligence; Engineering; Electrical engineering","score_opus":0.07701069623204877,"score_gpt":0.3144737951271119,"score_spread":0.23746309889506312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296912592","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":[0.010169541,0.0000046558134,0.98527217,0.00081411266,0.0001276328,0.0004098712,0.00000776366,0.00018588886,0.0030083489],"genre_scores_gemma":[0.91615844,0.0000013809048,0.08145137,0.000118332406,0.000055501892,0.00074272614,0.0000069545763,0.000004419316,0.0014608885],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938184,0.000028017343,0.0001696001,0.00019616123,0.00013762533,0.00008676918],"domain_scores_gemma":[0.9994857,0.00005138557,0.00008160191,0.0003180353,0.000037647063,0.000025680443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019720502,0.0000443369,0.00007273832,0.00007342855,0.00018038978,0.000022551903,0.0003698212,0.00002567725,0.0000612282],"category_scores_gemma":[0.000015588545,0.00004545131,0.00006289101,0.00043756602,0.0000119596725,0.000102888305,0.00019015378,0.00007269668,0.0000010709185],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000307609,0.00017872285,0.00015568377,0.000004556668,0.000019166067,4.1405357e-7,0.000318759,0.0029060082,0.005686617,0.78539574,0.013094942,0.19220863],"study_design_scores_gemma":[0.0017091961,0.000830217,0.00029220516,0.0000033421165,0.00002015339,0.000017281765,0.0003362677,0.61855704,0.18781696,0.06620224,0.12386486,0.00035026402],"about_ca_topic_score_codex":0.00006740057,"about_ca_topic_score_gemma":0.0000014331163,"teacher_disagreement_score":0.9059889,"about_ca_system_score_codex":0.000025976473,"about_ca_system_score_gemma":0.000042379794,"threshold_uncertainty_score":0.18534507},"labels":[],"label_agreement":null},{"id":"W4297990434","doi":"10.18280/ria.360406","title":"Faulty Node Detection in HDFS Using Machine Learning Techniques","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Operating system; Node (physics); Artificial intelligence; Embedded system; Machine learning; Engineering","score_opus":0.03775553364179932,"score_gpt":0.2866278195592028,"score_spread":0.24887228591740349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297990434","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":[0.027438728,0.00009380209,0.97061014,0.00026198363,0.00009325749,0.00026366397,0.0000022643874,0.00048907584,0.00074711203],"genre_scores_gemma":[0.9636328,0.000032822645,0.035474323,0.00010355516,0.000029492268,0.000189358,0.0000026434977,0.0000140561315,0.0005209175],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868083,0.00011258327,0.00036125118,0.00042597923,0.00016732675,0.0002520554],"domain_scores_gemma":[0.9992814,0.000052781525,0.00013534732,0.00044025193,0.00004433267,0.00004590063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005243924,0.00012302467,0.0001368277,0.00026947408,0.0005841601,0.00006975187,0.0005915699,0.00004554432,0.000113297785],"category_scores_gemma":[0.000030462546,0.0001432137,0.00007478337,0.0012739497,0.00003245345,0.00021521335,0.00040189372,0.0004763191,0.000028863406],"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":[0.000018082786,0.00035063297,0.0017183012,0.000029952715,0.0000088809,0.000028102839,0.0015667678,0.19651452,0.30038157,0.016018309,0.000030809762,0.4833341],"study_design_scores_gemma":[0.0000092243035,0.00006288059,0.000016722453,0.0000064984906,0.0000013389265,0.000047392907,0.00014441831,0.62695855,0.35673863,0.001292374,0.014608878,0.00011313116],"about_ca_topic_score_codex":0.00043629078,"about_ca_topic_score_gemma":0.000035985588,"teacher_disagreement_score":0.9361941,"about_ca_system_score_codex":0.00021124011,"about_ca_system_score_gemma":0.00002682082,"threshold_uncertainty_score":0.5840086},"labels":[],"label_agreement":null},{"id":"W4298872307","doi":"10.1007/978-3-030-99142-5_7","title":"Online Learning of Inverted Beta-Liouville HMMs for Anomaly Detection in Crowd Scenes","year":2012,"lang":"en","type":"book-chapter","venue":"Unsupervised and semi-supervised learning","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Hidden Markov model; Computer science; Inference; Expectation–maximization algorithm; Dirichlet distribution; Artificial intelligence; Anomaly detection; Flexibility (engineering); Maximization; Set (abstract data type); Pattern recognition (psychology); Algorithm; Data mining; Maximum likelihood; Mathematics; Mathematical optimization","score_opus":0.02190578144930409,"score_gpt":0.2387762029744592,"score_spread":0.21687042152515512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298872307","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":[0.1213075,0.014145942,0.82970303,0.0006339732,0.0005668257,0.0039314027,0.000102207276,0.002240582,0.027368534],"genre_scores_gemma":[0.9454787,0.0026891576,0.01482133,0.00017946388,0.00036945133,0.00018953628,0.00021549384,0.0001900852,0.03586678],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971003,0.00010806976,0.0009396951,0.0009226086,0.00035572244,0.0005736279],"domain_scores_gemma":[0.998181,0.00029162096,0.00043757414,0.00057075906,0.0002972711,0.0002217719],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005585664,0.00060405186,0.0008818026,0.0008067917,0.0004363571,0.00012066181,0.0006408176,0.000702717,0.000106966625],"category_scores_gemma":[0.000073028554,0.0006337656,0.00033286904,0.00033737073,0.00013985342,0.000494671,0.00032596188,0.0011813099,0.000012689886],"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":[0.00013874989,0.00035251077,0.005117872,0.0009794097,0.00025818823,0.000009876108,0.0032742904,0.0014315522,0.03910812,0.04096233,0.0001136266,0.9082535],"study_design_scores_gemma":[0.00442018,0.001958288,0.00444873,0.0014429663,0.00036242077,0.00009731486,0.00085867476,0.5756176,0.02432381,0.0061105997,0.37702152,0.0033378727],"about_ca_topic_score_codex":0.00013640613,"about_ca_topic_score_gemma":0.00006100448,"teacher_disagreement_score":0.90491563,"about_ca_system_score_codex":0.000103532846,"about_ca_system_score_gemma":0.00008535235,"threshold_uncertainty_score":0.9996114},"labels":[],"label_agreement":null},{"id":"W4302025110","doi":"10.36227/techrxiv.21215069.v1","title":"Intelligent Human Anomaly Identification and Classification in Crowded Scenes via Multi-fused Features and Restricted Boltzmann Machines","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Stochastic gradient descent; Word error rate; Cluster analysis; Classifier (UML); Anomaly detection; Boltzmann machine; Restricted Boltzmann machine; Gradient descent; Feature engineering; Machine learning; Data mining; Deep learning; Artificial neural network","score_opus":0.038724638882724614,"score_gpt":0.31502668642123405,"score_spread":0.27630204753850945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4302025110","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":[0.21240588,0.00059067097,0.7831298,0.0012847637,0.00016436628,0.0013049842,0.00001594091,0.0006790943,0.00042447192],"genre_scores_gemma":[0.9633642,0.000437753,0.033529975,0.000092851755,0.000034982673,0.00078420865,0.00011451786,0.000022317909,0.0016191814],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99775976,0.00015533478,0.0005963517,0.001057971,0.0002297888,0.00020079405],"domain_scores_gemma":[0.99843186,0.000057313653,0.0003735226,0.0009462317,0.00010339454,0.000087664506],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033765467,0.0002864566,0.0002734842,0.00058048655,0.0003862448,0.00049613224,0.0007521251,0.00023291564,0.00002527646],"category_scores_gemma":[0.000027910286,0.0002920205,0.00006167616,0.0005322959,0.00009431808,0.0002318507,0.0012920249,0.00056811125,0.0000031904685],"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":[0.000071044444,0.002023471,0.08100212,0.00078765466,0.00018114984,0.000029798313,0.004297728,0.00064201467,0.3273719,0.22184503,0.0041628047,0.3575853],"study_design_scores_gemma":[0.0002510422,0.000054940152,0.8537206,0.000030788236,0.000018692634,0.000022708837,0.00007382184,0.12620221,0.006527071,0.011562732,0.0010608751,0.00047451488],"about_ca_topic_score_codex":0.0011470283,"about_ca_topic_score_gemma":0.0005093045,"teacher_disagreement_score":0.7727185,"about_ca_system_score_codex":0.00012159889,"about_ca_system_score_gemma":0.000046176243,"threshold_uncertainty_score":0.9999532},"labels":[],"label_agreement":null},{"id":"W4302406157","doi":"","title":"Abormal Behaviors Modeling and Detection. Some of the Approaches Developed in the CANADA Project. - dans Vidéo-surveillance et détection automatique des comportements anormaux : Enjeux techniques et politiques","year":2011,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.06189275337535232,"score_gpt":0.2735224537660513,"score_spread":0.21162970039069895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4302406157","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":[0.33471793,0.00029007727,0.65461934,0.0039278055,0.00008827303,0.0013826513,0.000065497006,0.0002222447,0.0046862233],"genre_scores_gemma":[0.90225554,0.0007482371,0.095688544,0.00018530944,0.000012288822,0.00070997735,0.000044592758,0.000034659075,0.00032084037],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9914522,0.005687698,0.0010433069,0.00083269505,0.00053981016,0.00044427215],"domain_scores_gemma":[0.9952709,0.00047411304,0.00091699354,0.0018450611,0.0013973196,0.00009558096],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0072540785,0.0004586044,0.00042756286,0.00021031847,0.0007738351,0.0003595085,0.002058652,0.00031424165,0.000004793424],"category_scores_gemma":[0.00030418995,0.00038104024,0.0001527387,0.00092865154,0.0005113815,0.0006310312,0.0014169486,0.000989766,4.160876e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019152869,0.0012995421,0.014929088,0.0005079902,0.00010172516,0.000003325235,0.06706503,0.0006184751,0.009620138,0.8525536,0.0000806427,0.05320129],"study_design_scores_gemma":[0.0006101406,0.000009261986,0.23638089,0.003031834,0.00009286876,0.00028732745,0.003009895,0.36570764,0.3530873,0.0336086,0.0027576215,0.001416626],"about_ca_topic_score_codex":0.5779692,"about_ca_topic_score_gemma":0.7012965,"teacher_disagreement_score":0.818945,"about_ca_system_score_codex":0.00037236867,"about_ca_system_score_gemma":0.0015937564,"threshold_uncertainty_score":0.99986416},"labels":[],"label_agreement":null},{"id":"W4306156583","doi":"10.3389/frobt.2022.974397","title":"Novelty detection in rover-based planetary surface images using autoencoders","year":2022,"lang":"en","type":"article","venue":"Frontiers in Robotics and AI","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Novelty detection; Novelty; Artificial intelligence; Computer vision; Remote sensing; Geology","score_opus":0.010741410809393611,"score_gpt":0.22916770138106057,"score_spread":0.21842629057166696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306156583","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":[0.013710397,0.00012949655,0.9850021,0.0006317418,0.00025945567,0.00014019548,0.000006617883,0.00006803146,0.00005196124],"genre_scores_gemma":[0.6176481,0.000012178285,0.38204387,0.00024451563,0.000007146584,0.000009131551,0.000002748945,0.0000051979187,0.000027103526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926996,0.000042100415,0.00015803735,0.0002512974,0.00011790099,0.0001606962],"domain_scores_gemma":[0.999706,0.000015305093,0.00005355185,0.0001816017,0.000010872138,0.000032696335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016928252,0.00008310787,0.00011214167,0.00015395226,0.00017844215,0.00005072187,0.00020908151,0.000033841625,0.00000349902],"category_scores_gemma":[0.0000036040572,0.00009655041,0.000023046956,0.00044769648,0.000030270157,0.00014052118,0.00011313747,0.00022381758,2.6212837e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005485704,0.000045751756,0.008730562,0.0000058997944,0.0000023815858,0.0000058471305,0.00004664819,0.98521423,0.0004501693,0.00033484443,0.00046872816,0.004689444],"study_design_scores_gemma":[0.00020366827,0.000048093305,0.003297421,0.0000047765475,0.0000023376738,0.0000060086522,0.00005722133,0.9926262,0.0008859062,0.0019513109,0.00079826004,0.00011880724],"about_ca_topic_score_codex":0.0002601521,"about_ca_topic_score_gemma":0.000027168207,"teacher_disagreement_score":0.6039377,"about_ca_system_score_codex":0.00012111784,"about_ca_system_score_gemma":0.00004206101,"threshold_uncertainty_score":0.39372116},"labels":[],"label_agreement":null},{"id":"W4306726392","doi":"10.1155/2022/7223646","title":"Anomalous Trajectory Detection Using Masked Autoregressive Flow Considering Route Choice Probability","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Shenzhen Science and Technology Innovation Program","keywords":"Anomaly detection; Taxis; Trajectory; Computer science; Autoregressive model; Anomaly (physics); Traffic flow (computer networking); Data mining; Real-time computing; Computer network; Econometrics; Transport engineering; Engineering; Mathematics","score_opus":0.01788122188269472,"score_gpt":0.2567692235143592,"score_spread":0.2388880016316645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306726392","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":[0.50239205,0.000054491837,0.49702284,0.000057592908,0.00024068139,0.00014983467,0.0000056438175,0.000067474364,0.000009380414],"genre_scores_gemma":[0.8161043,0.000006773829,0.18374443,0.00003817882,0.00005432125,0.000030125551,0.0000025169027,0.000009445243,0.000009887124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986976,0.000069432506,0.0005306646,0.00021863771,0.00033319448,0.00015052127],"domain_scores_gemma":[0.9987925,0.000058332935,0.000665781,0.00020841257,0.00020527345,0.00006964713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026188826,0.00011692484,0.00018355236,0.00016973494,0.00037206488,0.00003397258,0.00027622315,0.0000355985,0.000017012695],"category_scores_gemma":[0.000018629073,0.00012311283,0.00014144117,0.00037886028,0.000029368734,0.0007896744,0.000011423978,0.0003160415,3.376206e-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":[0.00008595806,0.0001660257,0.0020921219,0.00003489692,0.000040640527,0.00004070169,0.0021070323,0.81994694,0.09489839,0.00065484375,0.000005629702,0.0799268],"study_design_scores_gemma":[0.0046403925,0.0026430052,0.49139193,0.00015327083,0.00025819967,0.0011444996,0.0016902961,0.28459603,0.16721807,0.026752334,0.01807848,0.0014334908],"about_ca_topic_score_codex":0.000021797388,"about_ca_topic_score_gemma":0.000041531108,"teacher_disagreement_score":0.5353509,"about_ca_system_score_codex":0.0002930923,"about_ca_system_score_gemma":0.00013464395,"threshold_uncertainty_score":0.5020395},"labels":[],"label_agreement":null},{"id":"W4307741923","doi":"10.1115/1.4056105","title":"A Model-Free Kullback–Leibler Divergence Filter for Anomaly Detection in Noisy Data Series","year":2022,"lang":"en","type":"article","venue":"Journal of Dynamic Systems Measurement and Control","topic":"Anomaly Detection Techniques and Applications","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":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kullback–Leibler divergence; Divergence (linguistics); Series (stratigraphy); Anomaly detection; Anomaly (physics); Filter (signal processing); Mathematics; Computer science; Pattern recognition (psychology); Artificial intelligence; Statistics; Geology; Physics; Computer vision","score_opus":0.04424346354661939,"score_gpt":0.2448868912196054,"score_spread":0.200643427672986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307741923","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":[0.012872713,0.0008566891,0.984577,0.0007272098,0.00034542909,0.00051999366,0.000042917884,0.000028332273,0.00002970316],"genre_scores_gemma":[0.99576646,0.00003392617,0.0038005917,0.000056687437,0.00004892749,0.0001780068,0.0000011005314,0.0000074451204,0.00010685151],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850816,0.00009556192,0.0005056494,0.00023549124,0.0004948332,0.00016029904],"domain_scores_gemma":[0.99873465,0.00002805113,0.00040811358,0.0005328842,0.00024252076,0.000053773834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018156888,0.000106737825,0.00023392675,0.0001830993,0.00023142424,0.00009901205,0.0009884858,0.000031374588,0.0000024873193],"category_scores_gemma":[0.00004167327,0.000096317934,0.0000617367,0.00020364451,0.000015780342,0.00056556147,0.00024721902,0.00015189084,2.895529e-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":[0.0028747234,0.0018395567,0.013929614,0.00086857536,0.001350634,0.00008179483,0.0027084188,0.09995073,0.5832167,0.030214831,0.011610404,0.25135398],"study_design_scores_gemma":[0.0013662714,0.00037351262,0.0009729343,0.000029870764,0.000031694355,0.00013276462,0.00012656747,0.99246234,0.00015276996,0.0013642433,0.002855981,0.00013107744],"about_ca_topic_score_codex":0.000041276813,"about_ca_topic_score_gemma":0.00009823116,"teacher_disagreement_score":0.98289376,"about_ca_system_score_codex":0.00018872159,"about_ca_system_score_gemma":0.00009138955,"threshold_uncertainty_score":0.39277312},"labels":[],"label_agreement":null},{"id":"W4308090777","doi":"10.1109/ccece49351.2022.9918216","title":"Time Series Anomaly Detection via Reinforcement Learning-Based Model Selection","year":2022,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Anomaly detection; Reinforcement learning; Anomaly (physics); Computer science; Series (stratigraphy); Artificial intelligence; Machine learning; Base (topology); Time series; Selection (genetic algorithm); Data modeling; Data mining; Mathematics","score_opus":0.006902678627198198,"score_gpt":0.2088445042760023,"score_spread":0.20194182564880409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308090777","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":[0.0030026694,0.0000031574104,0.9920419,0.0004182672,0.00003336034,0.00021377858,3.6187768e-7,0.001243079,0.003043432],"genre_scores_gemma":[0.9612921,9.813951e-7,0.024386892,0.0002804523,0.000016575268,0.00041282072,0.0000047905637,0.000009528791,0.013595875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909663,0.000046831334,0.0001757812,0.0002868925,0.00022750735,0.00016634483],"domain_scores_gemma":[0.99955696,0.000011116021,0.00009312068,0.00023343373,0.000059884085,0.00004550588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018845598,0.000098789744,0.00007846713,0.00014069429,0.00089166913,0.00006141359,0.0002902553,0.000030177847,0.00031935712],"category_scores_gemma":[0.0000038144753,0.00010720126,0.000062318686,0.0006008032,0.000020128346,0.00031301842,0.00016503622,0.00019567378,0.000055149918],"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":[0.000010455663,0.000030498788,0.000019559828,0.0000020836937,0.0000045036645,2.8038505e-7,0.000039037324,0.9708513,0.018768135,0.003324922,0.00026093895,0.0066882786],"study_design_scores_gemma":[0.00008466773,0.00037072747,0.00003138857,4.609967e-7,0.000003063829,0.000016614675,0.000009213592,0.92714417,0.061329782,0.00085809286,0.01002544,0.00012639201],"about_ca_topic_score_codex":0.000052558895,"about_ca_topic_score_gemma":0.000014743668,"teacher_disagreement_score":0.967655,"about_ca_system_score_codex":0.0001708047,"about_ca_system_score_gemma":0.000055123473,"threshold_uncertainty_score":0.68580884},"labels":[],"label_agreement":null},{"id":"W4308627439","doi":"10.1145/3549037.3570195","title":"Data quality and model under-specification issues (keynote)","year":2022,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Pipeline (software); Process (computing); Quality (philosophy); Data quality; Data modeling; Data science; Root cause; Software engineering; Artificial intelligence; Machine learning; Engineering; Reliability engineering; Programming language; Operations management","score_opus":0.21852674277196277,"score_gpt":0.3905440558722597,"score_spread":0.17201731310029694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308627439","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":[0.0021893673,0.000067894856,0.9835906,0.010787171,0.000016198293,0.000100752346,0.000022256123,0.00033418802,0.002891559],"genre_scores_gemma":[0.81182414,0.00004840554,0.18435681,0.00082407007,0.000016762282,0.00006396763,0.000025841735,0.000004141115,0.0028358432],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928874,0.000035571225,0.0001326193,0.00033107368,0.00013962507,0.00007238765],"domain_scores_gemma":[0.99883825,0.000028080667,0.000047749825,0.0010357569,0.00001951353,0.00003062938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031765027,0.000045564764,0.000052918316,0.000032953132,0.00030464327,0.00006751097,0.00081647845,0.000013189786,0.00004964139],"category_scores_gemma":[0.0000056088934,0.000045760335,0.000010156195,0.00018194078,0.000019333529,0.0002727486,0.0010898365,0.00007357315,0.0000061238684],"study_design_candidate":"theoretical_or_conceptual","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":[8.646909e-7,0.00003079773,0.000021824886,0.0000012338267,0.0000017053677,6.043278e-8,0.0000513993,0.0003922946,0.0011961502,0.9750319,0.006783227,0.016488548],"study_design_scores_gemma":[0.000051745046,0.000017685854,0.0014655318,3.190618e-7,0.0000013843978,0.000004548462,0.00009714983,0.8278422,0.00080632186,0.107380256,0.062232196,0.000100657424],"about_ca_topic_score_codex":0.000094563205,"about_ca_topic_score_gemma":0.000009111558,"teacher_disagreement_score":0.86765164,"about_ca_system_score_codex":0.000022106587,"about_ca_system_score_gemma":0.000019142239,"threshold_uncertainty_score":0.23431005},"labels":[],"label_agreement":null},{"id":"W4308775182","doi":"10.1089/big.2022.0217","title":"Big Multimodal Data Analysis: Applications","year":2022,"lang":"en","type":"editorial","venue":"Big Data","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Big data; Library science; Abu dhabi; Computer science; Geography; Archaeology","score_opus":0.09327113623168587,"score_gpt":0.3336765015199048,"score_spread":0.24040536528821893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308775182","genre_codex":"methods","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[9.523667e-8,0.0002862046,0.7819558,0.00022387048,0.19297667,0.00043103925,0.023027347,0.00057187205,0.0005271344],"genre_scores_gemma":[0.00014098201,0.0007509353,0.08597164,0.00010013526,0.74041545,0.0012233717,0.16925877,0.000050505063,0.0020882273],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99573904,0.00009321902,0.00050917926,0.002406831,0.0009348305,0.000316874],"domain_scores_gemma":[0.9775506,0.00033693062,0.00041098343,0.021434015,0.00013329413,0.00013414232],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.00083069253,0.00030142127,0.00042062646,0.0004862653,0.00055796275,0.00041676988,0.023617113,0.0003422011,0.000083810766],"category_scores_gemma":[0.00016730778,0.0003186662,0.00010853759,0.0028741679,0.00006596009,0.0004634007,0.020049123,0.0008319649,0.00012838302],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010852414,0.00007642009,0.000003556818,0.000010170242,0.00019278319,0.0000017172893,0.0000059357108,0.0000054732063,0.000003826921,0.00050363695,0.86382794,0.13536747],"study_design_scores_gemma":[0.00008791164,0.000016077372,0.000017517641,0.0000035132748,0.00038692862,0.000001233563,0.0000067350197,0.028410565,0.000005299694,0.0002772984,0.9704481,0.00033886926],"about_ca_topic_score_codex":0.0008927163,"about_ca_topic_score_gemma":0.0003500843,"teacher_disagreement_score":0.6959841,"about_ca_system_score_codex":0.000090376656,"about_ca_system_score_gemma":0.00059189904,"threshold_uncertainty_score":0.99992657},"labels":[],"label_agreement":null},{"id":"W4310228487","doi":"10.21203/rs.3.rs-2310302/v1","title":"Anomaly Detection in Three-Axis CNC Machines using LSTM Networks and Transfer Learning","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Anomaly detection; Anomaly (physics); Transfer (computing); Transfer of learning; Artificial intelligence; Computer science; Physics; Parallel computing","score_opus":0.057671112200209075,"score_gpt":0.36569473362352434,"score_spread":0.30802362142331524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310228487","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":[0.31201667,0.00067429105,0.68577874,0.00017435166,0.000077933844,0.0007168871,0.0000044601925,0.00024508522,0.00031159716],"genre_scores_gemma":[0.9947402,0.00040019388,0.0040207733,0.000013968275,0.00011298898,0.00054634345,0.000010049288,0.00003083737,0.00012468257],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99716216,0.000527539,0.0003301476,0.00088876643,0.00058187323,0.0005095187],"domain_scores_gemma":[0.99882543,0.00018946335,0.000054592812,0.000657526,0.00015522428,0.00011773993],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0016894027,0.00021837767,0.00026163354,0.0006966706,0.0007882747,0.00038044067,0.00077344506,0.0002628847,0.00008473467],"category_scores_gemma":[0.00004647852,0.00023397771,0.0001090822,0.0011594554,0.000100438985,0.00020484744,0.0018322706,0.0029755048,0.0000030094402],"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":[0.000088037224,0.0002890009,0.054316867,0.00071075646,0.00006919964,0.000088495384,0.0013590335,0.27905592,0.0031148188,0.011398641,0.000043144715,0.6494661],"study_design_scores_gemma":[0.00013587733,0.00017903879,0.020992836,0.00010148417,0.000004053841,0.000018519144,0.000079466874,0.9713124,0.0003643401,0.004812048,0.0017330113,0.00026694484],"about_ca_topic_score_codex":0.0028460368,"about_ca_topic_score_gemma":0.000803691,"teacher_disagreement_score":0.69225645,"about_ca_system_score_codex":0.0003176245,"about_ca_system_score_gemma":0.00012927892,"threshold_uncertainty_score":0.9993247},"labels":[],"label_agreement":null},{"id":"W4310432441","doi":"10.5281/zenodo.7378420","title":"Language Models for Novelty Detection in Kernel Traces","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Novelty; Novelty detection; Computer science; Kernel (algebra); Artificial intelligence; Natural language processing; Mathematics; Psychology","score_opus":0.03964542142132436,"score_gpt":0.2674047284066014,"score_spread":0.22775930698527705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310432441","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":[0.018581016,0.00001776024,0.9676534,0.0006210891,0.00004471411,0.00050418806,0.00004880521,0.0023964297,0.010132605],"genre_scores_gemma":[0.9954171,0.00003429568,0.0032178892,0.00006696585,0.000053547006,8.167054e-7,0.00013753402,0.00030011375,0.00077173236],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990299,0.000063764805,0.0001697663,0.00033691915,0.00015770715,0.00024195439],"domain_scores_gemma":[0.9993423,0.00002459212,0.000057485457,0.0003534724,0.00015755423,0.000064573724],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00050214486,0.00007936193,0.0000803123,0.00029173284,0.00085836434,0.00039558898,0.00083151687,0.000049957926,0.00014711996],"category_scores_gemma":[0.000094928844,0.00008654145,0.000041507858,0.0011245044,0.000035197405,0.00039469133,0.00046522846,0.00012136895,0.0009141121],"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":[0.000022542283,0.00011312173,9.107842e-7,0.00004523654,0.0000118449725,0.000005010755,0.0026172372,0.0013610667,0.030688716,0.0485348,0.01951429,0.89708525],"study_design_scores_gemma":[0.0005247099,0.0002185456,0.00057003245,0.000017642307,0.000004023796,0.000040222665,0.00046285233,0.41047743,0.017774904,0.008235746,0.5614221,0.00025175477],"about_ca_topic_score_codex":0.000023459108,"about_ca_topic_score_gemma":0.0000017622887,"teacher_disagreement_score":0.9768361,"about_ca_system_score_codex":0.00007724003,"about_ca_system_score_gemma":0.000002254813,"threshold_uncertainty_score":0.9998638},"labels":[],"label_agreement":null},{"id":"W4310509728","doi":"10.48550/arxiv.2211.15751","title":"Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council; McMaster University","keywords":"Computer science; Analytics; Data science; Cloud computing; Enhanced Data Rates for GSM Evolution; Variety (cybernetics); Big data; SPARK (programming language); Intersection (aeronautics); Edge device; Edge computing; Digital forensics; Law enforcement; Computer security; Artificial intelligence; Data mining; Engineering","score_opus":0.08017676908259376,"score_gpt":0.21899487281809688,"score_spread":0.13881810373550313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310509728","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":[0.005928757,0.00014859936,0.98965234,0.000054503715,0.00008919822,0.00092928903,0.000076837605,0.0008934691,0.0022270153],"genre_scores_gemma":[0.9945618,0.00072902424,0.0025213526,0.00008710509,0.00006125924,0.00008304302,0.00004878489,0.000020892234,0.0018867311],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813694,0.0001886126,0.00023258405,0.0011237111,0.000099810975,0.0002183609],"domain_scores_gemma":[0.9978157,0.00017660367,0.00029446452,0.0014450982,0.00014546425,0.00012263683],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00057968934,0.0002479658,0.00028111468,0.00042830792,0.00040602894,0.00021838454,0.001336394,0.00019496868,0.000016126263],"category_scores_gemma":[0.000016977561,0.0002978047,0.00010248574,0.0009127677,0.00007529396,0.00015243764,0.0018029563,0.00058348436,0.000015266163],"study_design_candidate":"theoretical_or_conceptual","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":[0.000026278749,0.00025121382,0.0045794277,0.0001726026,0.00012606397,0.000040697556,0.00008819336,0.026558248,0.00007706639,0.95705813,0.0026297795,0.008392289],"study_design_scores_gemma":[0.0004717238,0.0004260751,0.008142214,0.0002221026,0.00017336568,0.000024711719,0.0002735989,0.70842105,0.00150455,0.069921926,0.2081003,0.0023183567],"about_ca_topic_score_codex":0.0007620693,"about_ca_topic_score_gemma":0.000027362854,"teacher_disagreement_score":0.98863304,"about_ca_system_score_codex":0.00025799443,"about_ca_system_score_gemma":0.0001100458,"threshold_uncertainty_score":0.9999474},"labels":[],"label_agreement":null},{"id":"W4310800038","doi":"10.18280/ts.390528","title":"Identification and Analysis of Non-Stationary Time Series Signals Based on Data Preprocessing and Deep Learning","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities","keywords":"Preprocessor; Computer science; Time series; Series (stratigraphy); Artificial intelligence; Chaotic; Pattern recognition (psychology); SIGNAL (programming language); Data pre-processing; Stationary process; Gaussian; Signal processing; Algorithm; Machine learning; Mathematics; Statistics; Digital signal processing","score_opus":0.015721824630219827,"score_gpt":0.2619121647282115,"score_spread":0.24619034009799168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310800038","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":[0.057999805,0.000044457258,0.9413109,0.00028991417,0.000005316495,0.00015021134,0.000031489617,0.00006770146,0.000100194484],"genre_scores_gemma":[0.98766315,0.0000067135766,0.011949718,0.000075348195,0.0000065263444,0.000069319794,0.00012805115,0.0000041358708,0.00009706594],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907583,0.000063624335,0.00021911306,0.00034241957,0.00022687529,0.00007211002],"domain_scores_gemma":[0.99940884,0.00007543246,0.00017317249,0.0002720765,0.00004198904,0.000028499144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048946904,0.00006590896,0.00010862399,0.00023011645,0.00035417642,0.000077824494,0.00028903165,0.000012771321,0.00013091914],"category_scores_gemma":[0.0000071548297,0.00007079423,0.000021789607,0.0006225251,0.000036275407,0.0003287358,0.00020114181,0.00006751194,8.303853e-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":[0.00012885463,0.00065224426,0.011460029,0.00013628296,0.00050449435,0.0000055203336,0.0030556712,0.26113775,0.2530506,0.0053086863,0.00041811325,0.46414176],"study_design_scores_gemma":[0.00009103664,0.00012211238,0.018262113,0.0000039548795,0.00007173066,0.0000013576152,0.000075755146,0.9776264,0.0030480889,0.0001954166,0.0004281097,0.0000739511],"about_ca_topic_score_codex":0.0000057932125,"about_ca_topic_score_gemma":8.6860814e-7,"teacher_disagreement_score":0.9296633,"about_ca_system_score_codex":0.000016055215,"about_ca_system_score_gemma":0.000019795183,"threshold_uncertainty_score":0.28869048},"labels":[],"label_agreement":null},{"id":"W4311104044","doi":"10.3390/s22239383","title":"Deep Multi-Scale Features Fusion for Effective Violence Detection and Control Charts Visualization","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Process (computing); Artificial intelligence; Visualization; Scale (ratio); Deep learning; Control (management); Machine learning; Dimension (graph theory); Spatial analysis; Data mining; Data science","score_opus":0.005442953973494518,"score_gpt":0.249585506539645,"score_spread":0.2441425525661505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311104044","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":[0.113197714,0.000066933644,0.8853752,0.00011671005,0.00010467555,0.0008676333,0.000006350438,0.00024395011,0.000020811054],"genre_scores_gemma":[0.99076676,0.000019907364,0.008119372,0.00017303848,0.000033625325,0.0007934521,0.000002837147,0.000008839364,0.00008217845],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999282,0.00007764489,0.00010342353,0.00030131606,0.00010948575,0.00012615678],"domain_scores_gemma":[0.9995738,0.00007582971,0.00007889643,0.0001713629,0.000059208218,0.000040852727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016576391,0.000083885425,0.00008801571,0.000085697524,0.00061229937,0.000043241955,0.00012878444,0.000036013564,0.0000040055293],"category_scores_gemma":[0.000017156286,0.00008559963,0.000042157484,0.0002270752,0.000023447337,0.000090196794,0.00008195477,0.00008269613,0.0000016562224],"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":[0.00010996143,0.00020916271,0.0006097408,0.000040419825,0.000024747587,0.0000020535813,0.0016258649,0.0048620817,0.1836593,0.0084644165,0.000086442495,0.8003058],"study_design_scores_gemma":[0.0006191532,0.00033807516,0.010655822,0.00000688633,0.000010751746,0.000033409044,0.0001271327,0.9156163,0.068746716,0.0013729937,0.0023076423,0.00016515506],"about_ca_topic_score_codex":0.000030740175,"about_ca_topic_score_gemma":0.000019688021,"teacher_disagreement_score":0.9107542,"about_ca_system_score_codex":0.000049567912,"about_ca_system_score_gemma":0.0000059057356,"threshold_uncertainty_score":0.47093734},"labels":[],"label_agreement":null},{"id":"W4311305973","doi":"10.1080/10618600.2022.2157835","title":"Dependence Model Assessment and Selection with DecoupleNets","year":2022,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Selection (genetic algorithm); Model selection; Computer science; Econometrics; Mathematics; Statistics; Artificial intelligence","score_opus":0.009588893686394585,"score_gpt":0.2711973733594322,"score_spread":0.26160847967303763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311305973","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":[0.036101155,0.000038345093,0.96318585,0.0005663487,0.000012829529,0.000046441663,0.000015531064,0.000013535797,0.00001998072],"genre_scores_gemma":[0.5551342,0.000020259207,0.44471648,0.00010851287,0.0000072518087,0.0000046850364,9.968583e-7,0.0000017762728,0.000005852428],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927205,0.000030252158,0.00019170949,0.00010572777,0.00033258414,0.00006770471],"domain_scores_gemma":[0.9994265,0.00012569212,0.00016979752,0.000030039335,0.00017375794,0.00007420113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018483617,0.000056507444,0.00008883843,0.00009636638,0.0003023604,0.000059662212,0.000111802714,0.000013463573,0.0000060575135],"category_scores_gemma":[0.000004142633,0.00004673986,0.0000146040975,0.00021186861,0.000043503514,0.00012243596,0.000071167146,0.00019970212,5.7410116e-8],"study_design_candidate":"theoretical_or_conceptual","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":[0.000018829103,0.00008607285,0.0019571993,0.0000065517816,0.000024025938,0.000010094873,0.000051081537,0.24196297,0.000060894152,0.73001796,0.00050165824,0.025302662],"study_design_scores_gemma":[0.00014074465,0.00032796114,0.020569416,0.0000019618938,0.0000061859655,0.00042394176,0.000007007458,0.64451367,0.00000526868,0.33380008,0.0001568178,0.00004697105],"about_ca_topic_score_codex":0.0000031305535,"about_ca_topic_score_gemma":0.000002235195,"teacher_disagreement_score":0.519033,"about_ca_system_score_codex":0.000024644778,"about_ca_system_score_gemma":0.00010601556,"threshold_uncertainty_score":0.23255423},"labels":[],"label_agreement":null},{"id":"W4312423502","doi":"10.1007/978-3-031-17849-8_19","title":"Similarity-Based Unsupervised Evaluation of Outlier Detection","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Outlier; Cluster analysis; Anomaly detection; Similarity (geometry); Data mining; Index (typography); Artificial intelligence; Similarity measure; Unsupervised learning; Measure (data warehouse); Pattern recognition (psychology); Machine learning; Image (mathematics)","score_opus":0.03191990121459354,"score_gpt":0.27670299434741524,"score_spread":0.2447830931328217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312423502","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":[0.00019799422,0.00011628385,0.9950983,0.0003968391,0.00052525895,0.000713828,0.000008198309,0.00020777508,0.0027355172],"genre_scores_gemma":[0.7932705,0.00001393129,0.2057516,0.0006185364,0.000121162695,0.00011968943,0.00000911757,0.000028995006,0.0000664469],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996393,0.00008349655,0.00051003904,0.0010567065,0.0016545244,0.00030218257],"domain_scores_gemma":[0.99743086,0.00020328259,0.0003694664,0.0013555208,0.0005579399,0.000082926184],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0020668148,0.00030767333,0.000336392,0.00092324184,0.00034082335,0.0001372701,0.0019556654,0.00021463072,0.00016987209],"category_scores_gemma":[0.00006696337,0.00031918337,0.0001563697,0.0009598159,0.00031756065,0.00035627407,0.00061443704,0.000618776,0.000007921307],"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":[0.000004279644,0.000043988864,0.00001577156,0.000020342015,0.0000062513195,0.000002178025,0.00013005506,0.08052877,0.0009890158,0.008770628,0.000004754159,0.90948397],"study_design_scores_gemma":[0.00025716433,0.00018954377,0.00011294057,0.0000453966,0.000020829832,0.000008910514,1.3684372e-7,0.8869611,0.021712692,0.08763061,0.0027243947,0.00033630364],"about_ca_topic_score_codex":0.000039106115,"about_ca_topic_score_gemma":0.000056276993,"teacher_disagreement_score":0.9091477,"about_ca_system_score_codex":0.0005441698,"about_ca_system_score_gemma":0.0007480628,"threshold_uncertainty_score":0.99992603},"labels":[],"label_agreement":null},{"id":"W4312575430","doi":"10.1109/cloudsummit54781.2022.00019","title":"Context-Aware Feature Selection using Denoising Auto-Encoder for Fault Detection in Cloud Environments","year":2022,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Feature selection; Discriminative model; Feature (linguistics); Computer science; Context (archaeology); Artificial intelligence; Feature engineering; Encoder; Machine learning; Cloud computing; Fault detection and isolation; Data mining; Autoencoder; Feature extraction; Pattern recognition (psychology); Deep learning","score_opus":0.017406485140031645,"score_gpt":0.2585826171115599,"score_spread":0.24117613197152823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312575430","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":[0.036984928,0.000020460471,0.96180564,0.00028167732,0.00015156894,0.00046660827,0.0000033242184,0.00021770307,0.00006810746],"genre_scores_gemma":[0.9709955,0.0000024545106,0.027379269,0.0003030878,0.00004937446,0.0003120921,0.0000030170743,0.000011824163,0.00094337616],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907106,0.000052216677,0.00016388223,0.00035693907,0.00016161687,0.00019428387],"domain_scores_gemma":[0.9996372,0.000029113595,0.000089738154,0.00019179692,0.000017632176,0.000034532466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020139127,0.00010200635,0.000097406955,0.00014495481,0.00063788187,0.00006524259,0.00025294727,0.00006115636,0.000028047945],"category_scores_gemma":[0.000005474676,0.000112017326,0.000059426242,0.00048474292,0.000013303339,0.00026374817,0.00014814197,0.00020863015,0.000002621374],"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":[0.000082856204,0.0005297744,0.0021724936,0.00003334787,0.00005119998,0.000005003912,0.001256815,0.062184416,0.43162307,0.019291604,0.002481568,0.48028785],"study_design_scores_gemma":[0.00028939664,0.00013000028,0.00045504383,0.0000037765567,0.0000057709494,0.00004914388,0.00021979299,0.8897937,0.06887348,0.0015433502,0.038447104,0.00018942078],"about_ca_topic_score_codex":0.00014819068,"about_ca_topic_score_gemma":0.000090031506,"teacher_disagreement_score":0.93442637,"about_ca_system_score_codex":0.00041435665,"about_ca_system_score_gemma":0.00002894758,"threshold_uncertainty_score":0.4906136},"labels":[],"label_agreement":null},{"id":"W4312708109","doi":"10.1109/tfuzz.2022.3222025","title":"Choquet Integral-Based Aggregation for the Analysis of Anomalies Occurrence in Sustainable Transportation Systems","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Anomaly Detection Techniques and Applications","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":"University of Alberta","funders":"","keywords":"Choquet integral; Computer science; Quality (philosophy); Data quality; Data mining; Anomaly detection; Overexploitation; Data aggregator; Risk analysis (engineering); Artificial intelligence; Economics; Business; Wireless sensor network","score_opus":0.016125622504548433,"score_gpt":0.2518415483564244,"score_spread":0.23571592585187598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312708109","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":[0.009627836,0.00018069743,0.9876436,0.00019879219,0.00037704132,0.00140951,0.00036190066,0.00014390706,0.000056725505],"genre_scores_gemma":[0.9946119,0.000013150672,0.00047315843,0.000024802717,0.0000095337045,0.0044439877,0.000029941026,0.000007825476,0.00038565655],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984345,0.00014513433,0.0005337503,0.0003413116,0.0003271374,0.00021816877],"domain_scores_gemma":[0.9986182,0.00033279334,0.00027931528,0.000542475,0.00019155735,0.000035701585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068161555,0.00013820923,0.00028096116,0.0008382214,0.00050947955,0.000095362906,0.000550939,0.000050878105,0.000007388504],"category_scores_gemma":[0.0000036841232,0.00011993897,0.00022014286,0.0032834637,0.000043198263,0.00022600095,0.0000014771964,0.00018461245,8.487225e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029220186,0.00015312272,0.000117062824,0.00008875194,0.00011030302,0.0000010230691,0.00046884545,0.97498226,0.00016386178,0.020789873,0.00011258934,0.0029830674],"study_design_scores_gemma":[0.0003902461,0.00025351727,0.0007029014,0.000029690214,0.00022981536,0.00000240006,0.0031071908,0.98654896,0.002874808,0.00013350266,0.0055159065,0.00021105062],"about_ca_topic_score_codex":0.0020504429,"about_ca_topic_score_gemma":0.00018325899,"teacher_disagreement_score":0.98717046,"about_ca_system_score_codex":0.00022300651,"about_ca_system_score_gemma":0.000107055355,"threshold_uncertainty_score":0.48909694},"labels":[],"label_agreement":null},{"id":"W4312749883","doi":"10.1016/j.ifacol.2022.10.199","title":"Anomaly detection method applied to vehicle monitoring","year":2022,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Anomaly detection; Anomaly (physics); Truck; Series (stratigraphy); Computer science; Data mining; Time series; Pattern recognition (psychology); Artificial intelligence; Machine learning; Engineering; Geology; Automotive engineering; Physics","score_opus":0.01374942454405519,"score_gpt":0.27692352578894097,"score_spread":0.26317410124488577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312749883","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07073791,0.000027789158,0.92508626,0.0013942253,0.00023638924,0.00034381004,0.000008654097,0.0007506965,0.001414231],"genre_scores_gemma":[0.45909563,0.0000020867215,0.53966266,0.0003231442,0.00012551415,0.00036498514,0.0000016614052,0.000011986937,0.00041232246],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99866754,0.000053847758,0.00021341078,0.0004992887,0.00029826415,0.0002676472],"domain_scores_gemma":[0.9991688,0.000050608603,0.000080138045,0.00053302594,0.000041733474,0.0001256843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032019275,0.0001407934,0.00014554248,0.00016113157,0.0006507292,0.000077771416,0.0006765537,0.000040524697,0.0000585375],"category_scores_gemma":[0.000010177101,0.00015561958,0.00007633137,0.0010054666,0.000010848092,0.00013399367,0.00046392687,0.0002525552,0.000059770442],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017849447,0.00011098685,0.00018672907,0.0000050371355,0.000015533236,0.0000044805074,0.0005140395,0.0022074478,0.5079266,0.004624112,0.0000133624335,0.48437384],"study_design_scores_gemma":[0.00079096464,0.0008170515,0.010538189,0.000009495986,0.00003165084,0.0001134852,0.001030294,0.12514725,0.78136384,0.0022258419,0.07685783,0.0010740744],"about_ca_topic_score_codex":0.000093532435,"about_ca_topic_score_gemma":0.000008235833,"teacher_disagreement_score":0.48329976,"about_ca_system_score_codex":0.00016291233,"about_ca_system_score_gemma":0.000029881989,"threshold_uncertainty_score":0.63459826},"labels":[],"label_agreement":null},{"id":"W4312772600","doi":"10.1109/cvpr52688.2022.00951","title":"Anomaly Detection via Reverse Distillation from One-Class Embedding","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":732,"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":"","keywords":"Embedding; Computer science; Distillation; Anomaly detection; Class (philosophy); Bottleneck; Generalizability theory; Simple (philosophy); Anomaly (physics); Artificial intelligence; Representation (politics); Heuristics; Encoder; Benchmark (surveying); Machine learning; Mathematics","score_opus":0.03558917404709445,"score_gpt":0.267354595315034,"score_spread":0.2317654212679396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312772600","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":[0.14942788,0.000013687839,0.847443,0.00085259083,0.0007759631,0.0004115614,0.00010208606,0.00044297503,0.00053029787],"genre_scores_gemma":[0.98852277,0.00006531848,0.008713028,0.0019117895,0.000249353,0.00022836837,0.00015466906,0.000025410298,0.00012926637],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974336,0.00030334241,0.00048147058,0.0009592974,0.0005263946,0.00029590057],"domain_scores_gemma":[0.99863636,0.00014205351,0.00033179845,0.0005527925,0.0001638906,0.00017307897],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00031010207,0.00030174942,0.00030565055,0.00031973675,0.0009070916,0.00040784825,0.00047767677,0.00011185773,0.00095615286],"category_scores_gemma":[0.000005139078,0.00033051585,0.00012633615,0.00048642044,0.00005068892,0.0005231527,0.00039106765,0.00050446356,0.00017341999],"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":[0.00003611946,0.00017029738,0.00016501291,0.000012198975,0.000024070712,0.000010071244,0.00023605581,0.000096791075,0.008588663,0.00024082785,0.000791359,0.98962855],"study_design_scores_gemma":[0.0008242492,0.0011367884,0.005278611,0.00008993761,0.000030564006,0.00006605428,0.0000804283,0.9667654,0.0074676434,0.00881765,0.008729978,0.0007127212],"about_ca_topic_score_codex":0.00022035858,"about_ca_topic_score_gemma":0.000048130794,"teacher_disagreement_score":0.9889158,"about_ca_system_score_codex":0.00013078253,"about_ca_system_score_gemma":0.000034844958,"threshold_uncertainty_score":0.9999571},"labels":[],"label_agreement":null},{"id":"W4312821893","doi":"10.1007/978-3-031-21311-3_4","title":"Effective Segmentation of RSSI Timeseries Produced by Stationary IoT Nodes: Comparative Study","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Ontario Institute of Technology","funders":"","keywords":"Computer science; Time series; Profiling (computer programming); Independent and identically distributed random variables; Segmentation; Data mining; Node (physics); Real-time computing; Artificial intelligence; Random variable; Machine learning; Statistics","score_opus":0.015573308054621258,"score_gpt":0.2862315256465502,"score_spread":0.27065821759192893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312821893","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":[0.0037342191,0.00013781371,0.99263585,0.00022874554,0.00026243305,0.0020541467,0.000038958548,0.00017505462,0.0007328102],"genre_scores_gemma":[0.82761437,0.000014325236,0.17130657,0.00022234306,0.000075327065,0.00042952283,0.000028181434,0.00002486347,0.0002845292],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99711126,0.000093981915,0.00050525233,0.001183044,0.0008421163,0.00026434552],"domain_scores_gemma":[0.9979033,0.0003823477,0.0004817462,0.0008798407,0.00028436474,0.000068436064],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005929533,0.0003496095,0.00045549968,0.00051427697,0.00039439768,0.0001384313,0.0015568999,0.000090797235,0.00006268499],"category_scores_gemma":[0.00002189518,0.0003396446,0.00008051914,0.00083478534,0.00041868928,0.0004316539,0.0008324247,0.00047876948,0.000008247917],"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":[0.00009421449,0.0014300601,0.0007428742,0.00015206113,0.00018371537,0.00003307268,0.03158018,0.05463495,0.021511678,0.04243498,0.00068161945,0.8465206],"study_design_scores_gemma":[0.002309163,0.008945273,0.0029839354,0.00039436732,0.00011089088,0.000078109006,0.00010992278,0.46086574,0.3621949,0.15161717,0.006776057,0.0036144848],"about_ca_topic_score_codex":0.00006718243,"about_ca_topic_score_gemma":0.000014890606,"teacher_disagreement_score":0.8429061,"about_ca_system_score_codex":0.00032879206,"about_ca_system_score_gemma":0.0002648293,"threshold_uncertainty_score":0.9999056},"labels":[],"label_agreement":null},{"id":"W4312998458","doi":"10.1109/mass56207.2022.00070","title":"An Accurate and Energy-Efficient Anomaly Detection in Edge-Cloud Networks","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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 Tech University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Cloud computing; Computer science; Anomaly detection; Anomaly (physics); Enhanced Data Rates for GSM Evolution; Boundary (topology); Data mining; Edge computing; Artificial intelligence; Mathematics","score_opus":0.016857927010611302,"score_gpt":0.2559509576161777,"score_spread":0.2390930306055664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312998458","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":[0.22906543,0.0005864018,0.7645486,0.00019632943,0.0022956105,0.0004706233,0.00004284759,0.00024309028,0.002551093],"genre_scores_gemma":[0.99669266,0.0003158293,0.00026126223,0.00012921564,0.0001301113,0.0013830069,0.000018244276,0.000014016748,0.0010556452],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813664,0.00020127937,0.00039568578,0.0006526341,0.00037518,0.0002385894],"domain_scores_gemma":[0.9991295,0.000056541405,0.00020669942,0.00037529072,0.00010859333,0.00012338476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046952782,0.00019802069,0.00021051407,0.00028129903,0.0004296443,0.00030846603,0.0005654603,0.00007646744,0.000072809555],"category_scores_gemma":[0.0000052986966,0.00020581325,0.00004669597,0.0003637685,0.00005246045,0.0002372992,0.00014378084,0.0003101194,0.0000049596288],"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":[0.000315941,0.00093699724,0.0030799087,0.000057850273,0.00015735504,0.00008939497,0.00153441,0.2263205,0.04605038,0.4621202,0.0012091693,0.2581279],"study_design_scores_gemma":[0.00029125583,0.0005033294,0.0006228854,0.000018648001,0.0000043593072,0.000044108892,0.0004154751,0.97622514,0.00056527485,0.00048205027,0.02058204,0.00024546293],"about_ca_topic_score_codex":0.00015422639,"about_ca_topic_score_gemma":0.00007516941,"teacher_disagreement_score":0.76762724,"about_ca_system_score_codex":0.00016774518,"about_ca_system_score_gemma":0.000052861287,"threshold_uncertainty_score":0.8392821},"labels":[],"label_agreement":null},{"id":"W4313172514","doi":"10.1109/iros47612.2022.9982083","title":"Transmissibility-based DAgger For Fault Classification in Connected Autonomous Vehicles","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Anomaly Detection Techniques and Applications","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":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Memorial University of Newfoundland","keywords":"Transmissibility (structural dynamics); Platoon; Computer science; Robot; Fault (geology); Actuator; Artificial intelligence; Fault detection and isolation; Real-time computing; Set (abstract data type); Control theory (sociology); Control engineering; Engineering; Control (management); Vibration","score_opus":0.08627898525324403,"score_gpt":0.31946582235628157,"score_spread":0.23318683710303756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313172514","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":[0.038525496,0.0000796961,0.95364237,0.0035274925,0.0010270525,0.0012606039,0.0001316437,0.00022523107,0.0015804289],"genre_scores_gemma":[0.9949341,0.000045373272,0.0014626709,0.00034718378,0.00006199648,0.0018812526,0.00008197154,0.000015834761,0.0011696398],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99784285,0.00014914873,0.0006252805,0.0007004736,0.00044426395,0.00023800205],"domain_scores_gemma":[0.99885577,0.00016137866,0.00025432327,0.00041754532,0.0002129465,0.00009802094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062333373,0.00020528269,0.0002469623,0.00033420496,0.00033941068,0.00027077986,0.00081773225,0.000080115766,0.00015325581],"category_scores_gemma":[0.000026334294,0.00020527704,0.00010380649,0.0003103127,0.000058728732,0.0001658131,0.00007980886,0.00029419022,0.000010362543],"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":[0.00012910448,0.00060867105,0.0007120234,0.000070354654,0.000050207276,0.000006685359,0.0007500426,0.029249026,0.022071084,0.9180282,0.0019193307,0.026405282],"study_design_scores_gemma":[0.0004070392,0.00027817703,0.0006788903,0.00004190744,0.000005972456,0.00001144529,0.00044478747,0.97194797,0.0039869873,0.0033253008,0.018609101,0.00026240898],"about_ca_topic_score_codex":0.00018448802,"about_ca_topic_score_gemma":0.000026773598,"teacher_disagreement_score":0.95640856,"about_ca_system_score_codex":0.00030895387,"about_ca_system_score_gemma":0.00014690017,"threshold_uncertainty_score":0.8370955},"labels":[],"label_agreement":null},{"id":"W4313343335","doi":"10.1007/978-3-031-23028-8_12","title":"Self-supervised Out-of-Distribution Detection with Dynamic Latent Scale GAN","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Latent variable; Computer science; Hyperparameter; Probabilistic latent semantic analysis; Latent variable model; Algorithm; Pattern recognition (psychology); Artificial intelligence","score_opus":0.008967574715919288,"score_gpt":0.2227839771929546,"score_spread":0.21381640247703532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313343335","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":[0.00035118868,0.000071759634,0.99701345,0.00023307251,0.00048001585,0.00054390344,0.000017904966,0.00043209933,0.00085660437],"genre_scores_gemma":[0.6811066,0.000066027365,0.3183407,0.00016172783,0.00007555463,0.00006921656,0.000022683504,0.000029492994,0.00012798549],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997416,0.000028902452,0.00041423985,0.0010518583,0.00073144835,0.00035751626],"domain_scores_gemma":[0.99819267,0.000089098074,0.00032303116,0.001077357,0.00021713843,0.00010068572],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042258293,0.00033519784,0.00033892327,0.00039530415,0.00038480258,0.00015332818,0.0016306087,0.00017814056,0.000027666916],"category_scores_gemma":[0.0000073031633,0.00030649602,0.000112929694,0.0007711541,0.0002967701,0.00037678078,0.0005512618,0.0006036435,0.000009738785],"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":[0.000016458074,0.00011855638,0.00005529866,0.00007590318,0.000023245033,0.000018080853,0.00067600625,0.012623648,0.0020132475,0.0084816525,0.000006173656,0.9758917],"study_design_scores_gemma":[0.00025321209,0.0006845435,0.00021776985,0.00009726575,0.0000203933,0.00007380097,4.0050818e-7,0.9562637,0.015473219,0.021226007,0.005079515,0.00061014533],"about_ca_topic_score_codex":0.000026392681,"about_ca_topic_score_gemma":0.00013426713,"teacher_disagreement_score":0.9752816,"about_ca_system_score_codex":0.00054648024,"about_ca_system_score_gemma":0.0002705738,"threshold_uncertainty_score":0.9999387},"labels":[],"label_agreement":null},{"id":"W4313563643","doi":"10.1145/3551349.3556932","title":"Repairing Failure-inducing Inputs with Input Reflection","year":2022,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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 British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Foundation; Bộ Giáo dục và Ðào tạo; National Research Foundation Singapore; National University of Singapore; Cisco Systems","keywords":"Deep neural networks; Computer science; Reflection (computer programming); Artificial neural network; Training set; Training (meteorology); Artificial intelligence; Production (economics); Machine learning","score_opus":0.012027136739487857,"score_gpt":0.2361818366102402,"score_spread":0.22415469987075234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313563643","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":[0.023655241,0.0000040626187,0.95872957,0.0020004665,0.000044322176,0.00013284243,2.6839294e-7,0.001182493,0.014250727],"genre_scores_gemma":[0.86315405,7.9638625e-7,0.13447532,0.00050684146,0.000025005982,0.00018473866,6.1462333e-7,0.0000063544667,0.0016462523],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99924874,0.000029868906,0.000112764676,0.0002968819,0.00018124418,0.0001305035],"domain_scores_gemma":[0.9994494,0.000012484393,0.000055516848,0.0004136573,0.00003203527,0.00003693864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017424944,0.00006614001,0.000059731814,0.00011064286,0.0005633362,0.0000661401,0.0003337456,0.000016379561,0.00006628827],"category_scores_gemma":[0.000002809558,0.000059689668,0.00002750932,0.00074639794,0.000010596199,0.00023934443,0.000285177,0.00016964134,0.00001091181],"study_design_candidate":"theoretical_or_conceptual","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":[0.00003460309,0.00031602196,0.0057297666,0.000026262725,0.00007585489,0.00004611092,0.0026752918,0.0066580595,0.064008504,0.7347423,0.029294545,0.15639268],"study_design_scores_gemma":[0.0007301831,0.0016982714,0.0034059507,0.000021685373,0.000018148467,0.001186904,0.0005514463,0.10827499,0.20011756,0.0131844655,0.66965955,0.0011508533],"about_ca_topic_score_codex":0.0000697335,"about_ca_topic_score_gemma":0.000035516525,"teacher_disagreement_score":0.8394988,"about_ca_system_score_codex":0.00010169694,"about_ca_system_score_gemma":0.000036833328,"threshold_uncertainty_score":0.43327835},"labels":[],"label_agreement":null},{"id":"W4315643159","doi":"10.1007/s10489-022-04401-7","title":"Continuous image anomaly detection based on contrastive lifelong learning","year":2023,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Anomaly detection; Forgetting; Computer science; Artificial intelligence; Deep learning; Lifelong learning; Anomaly (physics); Artificial neural network; Transformer; Pattern recognition (psychology); Machine learning; Image (mathematics)","score_opus":0.010769562230794144,"score_gpt":0.2464419633080904,"score_spread":0.23567240107729626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315643159","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":[0.0074886885,0.000004853085,0.97719234,0.00026391342,0.000097386546,0.0003732575,0.00000158045,0.0019310865,0.012646902],"genre_scores_gemma":[0.9875343,0.000015933789,0.011328729,0.00037603866,0.000063607746,0.0003334683,0.0000044933176,0.000018628127,0.00032483772],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99858934,0.00003591943,0.00026518127,0.00053926185,0.00023873727,0.00033158716],"domain_scores_gemma":[0.99898845,0.00026054253,0.00012910478,0.0004336117,0.00009554415,0.00009276292],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00032511563,0.00017707302,0.00016490201,0.00023624394,0.00034886022,0.00015458446,0.0005473992,0.00008963434,0.000029981082],"category_scores_gemma":[0.00006111425,0.00017981711,0.00007494004,0.0012802087,0.00008084938,0.00014501312,0.000105124534,0.00031245046,0.0011950269],"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":[0.000046697005,0.00011043107,0.00021184114,0.000016685099,0.000019586794,0.000019487297,0.0003489923,0.0075456365,0.083123006,0.10092394,0.0006118091,0.8070219],"study_design_scores_gemma":[0.00008798126,0.00023937742,0.0018520389,0.000015256711,0.000005340098,0.000004549293,0.00014430843,0.4602232,0.52754223,0.0043905713,0.005198403,0.00029673337],"about_ca_topic_score_codex":0.000027440035,"about_ca_topic_score_gemma":0.0000044798967,"teacher_disagreement_score":0.98004556,"about_ca_system_score_codex":0.000057718476,"about_ca_system_score_gemma":0.00003333618,"threshold_uncertainty_score":0.99958265},"labels":[],"label_agreement":null},{"id":"W4315777889","doi":"10.1109/icnsc55942.2022.10004112","title":"Model of Gradient Boosting Random Forest Prediction","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":17,"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":"Xidian University; Science and Technology Development Fund; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Random forest; Gradient boosting; Interpretability; Boosting (machine learning); Decision tree; Computer science; Artificial intelligence; Random tree; Machine learning; Data mining; Statistical classification; Pattern recognition (psychology); Algorithm","score_opus":0.043844614354946576,"score_gpt":0.2552293678178009,"score_spread":0.21138475346285435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315777889","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":[0.028538093,0.000053861066,0.9630236,0.001042493,0.00091639865,0.00026269592,0.000036598907,0.0001838261,0.0059424504],"genre_scores_gemma":[0.9968742,0.000070965965,0.0018543765,0.00054812233,0.00019560734,0.000050246836,0.000011358383,0.000010631789,0.00038448037],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857503,0.00008642857,0.0003515992,0.0003812896,0.00041376572,0.00019190875],"domain_scores_gemma":[0.99914885,0.00010207286,0.00028777285,0.0002537978,0.00014632774,0.00006116855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040050127,0.00014131212,0.00019389568,0.00015328481,0.00041195314,0.00010119511,0.00036724613,0.000038679016,0.00001673358],"category_scores_gemma":[0.000013538717,0.00014862057,0.0000891582,0.00017312342,0.00005272209,0.000112127374,0.00012817592,0.00027908638,0.0000012015755],"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":[0.0005266901,0.00026629167,0.0014830695,0.000016129128,0.00024557975,0.000016988675,0.0010585653,0.36834493,0.02635599,0.35098183,0.003082441,0.24762148],"study_design_scores_gemma":[0.0008575147,0.00014776019,0.00012615098,0.000027805696,0.000013641539,0.000030094023,0.00005424824,0.9844559,0.00026857783,0.011871618,0.0020219483,0.00012472828],"about_ca_topic_score_codex":0.00008408758,"about_ca_topic_score_gemma":0.000026711356,"teacher_disagreement_score":0.9683361,"about_ca_system_score_codex":0.000079268066,"about_ca_system_score_gemma":0.00005404149,"threshold_uncertainty_score":0.6060571},"labels":[],"label_agreement":null},{"id":"W4318714592","doi":"10.2139/ssrn.4332146","title":"Bidirectional Memory Siamese Network for Video Anomaly Detection","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Anomaly detection; Computer science; Anomaly (physics); Artificial intelligence","score_opus":0.009675328060503396,"score_gpt":0.24857810834344712,"score_spread":0.23890278028294373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318714592","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":[0.015433063,0.00029724732,0.98137033,0.0010505894,0.00042951122,0.00025389856,0.0000016284868,0.00073609856,0.0004276507],"genre_scores_gemma":[0.9829119,0.00070749153,0.0096808765,0.00026081692,0.0012850857,0.00025308723,0.00000381154,0.00003163903,0.004865296],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99767506,0.000048030255,0.0002648739,0.0003012255,0.00020521808,0.0015056203],"domain_scores_gemma":[0.9992704,0.00009346542,0.000163149,0.00026120903,0.00012976913,0.00008202036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015767248,0.00013201359,0.00012917082,0.0002090878,0.0007403648,0.00012884694,0.00049282255,0.00007251188,0.000006393534],"category_scores_gemma":[0.00003800324,0.00012865146,0.00018252958,0.0011172025,0.000025975,0.00035072866,0.00006934641,0.0006866362,0.00007171849],"study_design_candidate":"theoretical_or_conceptual","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":[0.000047628895,0.00006516367,0.00028056855,0.000008759028,0.00015487793,0.0000031833406,0.000073229676,0.002153826,0.0045840163,0.457858,0.008512799,0.52625793],"study_design_scores_gemma":[0.0004973554,0.0005719468,0.0011815422,0.000011279766,0.000021334354,0.000818539,0.00015026794,0.048734315,0.004508623,0.86587,0.077293776,0.00034099497],"about_ca_topic_score_codex":0.00002913896,"about_ca_topic_score_gemma":0.00021098604,"teacher_disagreement_score":0.97168946,"about_ca_system_score_codex":0.00045119383,"about_ca_system_score_gemma":0.0006409197,"threshold_uncertainty_score":0.5694362},"labels":[],"label_agreement":null},{"id":"W4318827822","doi":"10.5705/ss.202022.0142","title":"Outlier Detection via a Minimum Ridge Covariance Determinant Estimator","year":2023,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Anhui Province; Natural Sciences and Engineering Research Council of Canada","keywords":"Covariance; Estimator; Ridge; Outlier; Minimum-variance unbiased estimator; Mathematics; Estimation of covariance matrices; Statistics; Anomaly detection; Pattern recognition (psychology); Computer science; Artificial intelligence; Geology","score_opus":0.01671460335240198,"score_gpt":0.28799321868621336,"score_spread":0.27127861533381137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318827822","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":[0.0013857547,0.000008197283,0.9950427,0.00071232853,0.00028039367,0.00027643092,0.000057647358,0.0012025384,0.0010340023],"genre_scores_gemma":[0.7714851,0.000016625649,0.22732441,0.00016709592,0.000059504306,0.00023456519,0.000010185393,0.000019042505,0.0006834699],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984992,0.000058029353,0.00037614253,0.00050011836,0.00022456325,0.00034190953],"domain_scores_gemma":[0.9986024,0.00031877204,0.00014702964,0.0007074856,0.00008767948,0.00013667042],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00028904236,0.00015415737,0.0001977764,0.00012875629,0.00033571132,0.00012913666,0.00053066126,0.00008155465,0.00002489159],"category_scores_gemma":[0.00016215262,0.00015191516,0.000067082554,0.00084817567,0.00009287482,0.000192715,0.00018212498,0.00015061586,0.0010248154],"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":[0.00002362727,0.00013861063,0.00008791952,0.00004819231,0.0000326803,0.000095693526,0.000284452,0.000036797697,0.020052088,0.23175468,0.011665211,0.73578006],"study_design_scores_gemma":[0.0005357436,0.00046716962,0.01847868,0.00003778904,0.000033559085,0.00012686841,0.000028366447,0.7881663,0.019869572,0.06783745,0.10370829,0.0007102239],"about_ca_topic_score_codex":0.000029619569,"about_ca_topic_score_gemma":0.000012739546,"teacher_disagreement_score":0.7881295,"about_ca_system_score_codex":0.000045414363,"about_ca_system_score_gemma":0.00006439694,"threshold_uncertainty_score":0.999753},"labels":[],"label_agreement":null},{"id":"W4318980282","doi":"10.1111/jori.12418","title":"Enhancing claim classification with feature extraction from anomaly‐detection‐derived routine and peculiarity profiles","year":2023,"lang":"en","type":"article","venue":"Journal of Risk & Insurance","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Anomaly (physics); Anomaly detection; Computer science; Feature (linguistics); TRIPS architecture; Data mining; Degree (music)","score_opus":0.011034947559507001,"score_gpt":0.2488976909789315,"score_spread":0.23786274341942448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318980282","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":[0.6046823,0.00011118815,0.39433855,0.0005330056,0.000096332245,0.00008678056,0.000009165393,0.0001194678,0.000023168655],"genre_scores_gemma":[0.9620449,0.0006321714,0.037042763,0.000027377922,0.00017905033,0.000018858207,0.000002229635,0.000009577671,0.00004308493],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9989737,0.00007703772,0.00030692064,0.0002479564,0.00025325525,0.0001410732],"domain_scores_gemma":[0.9985669,0.00011900273,0.0007034511,0.00026833333,0.0002648673,0.00007743564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036192624,0.00012354174,0.00018316148,0.00015705366,0.00030661075,0.00012711588,0.00025238766,0.00009610882,0.000003011853],"category_scores_gemma":[0.000060618393,0.00009886587,0.000060988437,0.00069925387,0.000048416787,0.00071658246,0.000039633895,0.00043645836,0.000007844489],"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":[0.00021141313,0.00014090881,0.09206657,0.000030289435,0.00009292149,0.000029899276,0.0013189332,0.00023222282,0.70365316,0.0014804337,0.00049487955,0.20024838],"study_design_scores_gemma":[0.000307256,0.00013020942,0.865393,0.000056021523,0.000016126642,0.00009917122,0.00016698509,0.005520822,0.12581483,0.0011460535,0.0012234839,0.00012605643],"about_ca_topic_score_codex":0.000100552235,"about_ca_topic_score_gemma":0.00007909966,"teacher_disagreement_score":0.7733264,"about_ca_system_score_codex":0.00006768897,"about_ca_system_score_gemma":0.0000410601,"threshold_uncertainty_score":0.4031633},"labels":[],"label_agreement":null},{"id":"W4319150660","doi":"10.1145/3526073.3527593","title":"Challenges in machine learning application development","year":2022,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Systems, Applications & Products in Data Processing (Canada); Concordia University; Polytechnique Montréal","funders":"","keywords":"Computer science; Workflow; Business process; Workbench; Database transaction; Process (computing); Suite; Server; Process management; Software engineering; Business process management; Software; Point (geometry); Engineering management; World Wide Web; Database; Artificial intelligence; Work in process; Business; Marketing; Engineering; Operating system","score_opus":0.03699836737462323,"score_gpt":0.25410867652027397,"score_spread":0.21711030914565074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319150660","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":[0.0014633033,0.0003399976,0.9744625,0.0019992392,0.000013400536,0.00014405361,8.811869e-8,0.0003813134,0.02119607],"genre_scores_gemma":[0.9503825,0.00008466994,0.048246533,0.00008354199,0.000004383852,0.0006316581,0.0000020810148,0.0000029999467,0.0005616651],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99950725,0.000025402935,0.00010079918,0.00019086585,0.00009744504,0.000078253244],"domain_scores_gemma":[0.9997674,0.000011657589,0.000030673826,0.00016533639,0.000008152391,0.000016783948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021226953,0.000038144037,0.000039331455,0.00007952339,0.00018024382,0.000011698856,0.00029513563,0.000009418386,0.000029604991],"category_scores_gemma":[0.0000016905151,0.000039871466,0.000010379935,0.00024503045,0.0000034364036,0.000059505972,0.00025918052,0.00011582796,0.000018554876],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.4557038e-7,0.000038759652,0.00054006843,0.0000013727561,8.326095e-7,3.5016134e-7,0.00027331803,0.00020471997,0.00026839183,0.17662354,0.000014818128,0.8220334],"study_design_scores_gemma":[0.00009179152,0.000041010877,0.009586682,8.3749126e-7,3.1507633e-7,0.000012483732,0.00015252427,0.07879498,0.0033135395,0.0029192376,0.9049593,0.00012730603],"about_ca_topic_score_codex":0.000021228288,"about_ca_topic_score_gemma":0.000049886807,"teacher_disagreement_score":0.9489192,"about_ca_system_score_codex":0.0000624493,"about_ca_system_score_gemma":0.000017424958,"threshold_uncertainty_score":0.16259113},"labels":[],"label_agreement":null},{"id":"W4319300944","doi":"10.1109/wacv56688.2023.00266","title":"Bi-directional Frame Interpolation for Unsupervised Video Anomaly Detection","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"University of Alberta","funders":"","keywords":"Anomaly detection; Computer science; Interpolation (computer graphics); Optical flow; Motion interpolation; Artificial intelligence; Computer vision; Frame (networking); Anomaly (physics); Inter frame; Pattern recognition (psychology); Reference frame; Motion (physics); Block-matching algorithm; Video tracking; Image (mathematics); Video processing; Telecommunications","score_opus":0.02643759241011934,"score_gpt":0.30185371270497097,"score_spread":0.27541612029485163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319300944","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":[0.0062321797,0.000011784079,0.987274,0.0020364982,0.0005174615,0.001584492,0.00006982201,0.001213574,0.0010602039],"genre_scores_gemma":[0.91547126,0.000042666543,0.08050458,0.00036586146,0.00035673613,0.0021902006,0.000084105704,0.00004191695,0.0009426438],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972279,0.00008642251,0.00082680286,0.0010121325,0.00044769314,0.00039905042],"domain_scores_gemma":[0.9971476,0.00033887834,0.0003983731,0.0012366916,0.00071079197,0.00016763536],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004498918,0.00035141507,0.00038889077,0.0009137866,0.0004095052,0.00027650577,0.0012878625,0.00023264255,0.000088754336],"category_scores_gemma":[0.000019143221,0.0003597443,0.00031898072,0.0019108626,0.00012570844,0.0005201415,0.0002693375,0.00028526684,0.00047727657],"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":[0.00016578016,0.00071945327,0.00014217709,0.0001442057,0.00015247143,0.0000017622814,0.0004777025,0.0009818602,0.13447869,0.11863726,0.029919665,0.714179],"study_design_scores_gemma":[0.0007379705,0.0010491958,0.00328302,0.00014745521,0.000026770873,0.000016494336,0.00005806978,0.87219507,0.048595823,0.02527465,0.048033245,0.0005822648],"about_ca_topic_score_codex":0.000038878658,"about_ca_topic_score_gemma":0.000023024599,"teacher_disagreement_score":0.9092391,"about_ca_system_score_codex":0.000093032744,"about_ca_system_score_gemma":0.00010371661,"threshold_uncertainty_score":0.99988544},"labels":[],"label_agreement":null},{"id":"W4320712900","doi":"10.1109/access.2023.3244741","title":"Generative Adversarial Networks for Anomaly Detection in Biomedical Imaging: A Study on Seven Medical Image Datasets","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Tehran University of Medical Sciences and Health Services","keywords":"Computer science; Anomaly detection; Medical imaging; Artificial intelligence; Modalities; Ground truth; Generative grammar; Field (mathematics); Anomaly (physics); Machine learning; Software deployment; Image (mathematics); Deep learning; Pattern recognition (psychology); Mathematics","score_opus":0.03027084576530132,"score_gpt":0.3587337700926335,"score_spread":0.3284629243273322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320712900","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":[0.05435858,0.000003863449,0.9425121,0.0012340889,0.0005598755,0.00082850206,0.000037891456,0.0004227277,0.000042347634],"genre_scores_gemma":[0.9967091,0.000005910996,0.0016318887,0.00037411088,0.00040746282,0.00078500167,0.000054936227,0.000013573719,0.000017993923],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839485,0.00009061078,0.00030243577,0.0005470765,0.00036730594,0.00029773588],"domain_scores_gemma":[0.9990944,0.00017426847,0.00008714972,0.00046845633,0.000047233792,0.00012843576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065255427,0.00013873418,0.0001637853,0.0003000406,0.00019169129,0.00020931642,0.0011378241,0.00009016378,0.000010780572],"category_scores_gemma":[0.000077462915,0.00012842882,0.00005355934,0.0012793994,0.000060343584,0.0005414455,0.00028029914,0.0002189278,0.000023816734],"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":[0.0004162765,0.003985577,0.006616776,0.00005259871,0.00017947669,0.0006586602,0.0023011537,0.002175064,0.0084700445,0.0031445401,0.16418922,0.8078106],"study_design_scores_gemma":[0.0015013989,0.00039308885,0.012305829,0.000019300984,0.000011189146,0.000014141166,0.00010162703,0.9708196,0.007008983,0.0014838326,0.0060421354,0.00029889715],"about_ca_topic_score_codex":0.00015603808,"about_ca_topic_score_gemma":0.00012859234,"teacher_disagreement_score":0.9686445,"about_ca_system_score_codex":0.00006618667,"about_ca_system_score_gemma":0.00007179697,"threshold_uncertainty_score":0.52371746},"labels":[],"label_agreement":null},{"id":"W4321165092","doi":"10.1371/journal.pone.0281778","title":"Face touch monitoring using an instrumented wristband using dynamic time warping and k-nearest neighbours","year":2023,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"University of Alberta","keywords":"Inertial measurement unit; Dynamic time warping; Computer science; Artificial intelligence; Accelerometer; Computer vision; Smartwatch; Medicine; Wearable computer; Embedded system","score_opus":0.08104910071738364,"score_gpt":0.28576006888536487,"score_spread":0.20471096816798123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321165092","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":[0.8287588,0.000019998724,0.1703403,0.00011671922,0.000022024582,0.00015557064,0.0000030539193,0.00054100744,0.000042575655],"genre_scores_gemma":[0.9291797,0.00002993528,0.070600905,0.000012757964,0.000042463125,0.000012548268,0.0000028339182,0.000015200783,0.00010364231],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909306,0.000024516257,0.00015299417,0.0003159781,0.00018639312,0.00022707153],"domain_scores_gemma":[0.99944896,0.000021752192,0.00007025031,0.00032415538,0.000045086737,0.000089790825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009699206,0.00010494259,0.00013101411,0.00013842846,0.0003424239,0.00019197843,0.00024930318,0.000053987995,0.000004154668],"category_scores_gemma":[0.000009826764,0.00011811832,0.00001999622,0.00058299355,0.000031894488,0.0004573311,0.00019950727,0.0001098452,0.000018110457],"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":[0.000004212508,0.00029669862,0.006487945,0.000054170512,0.0000662887,0.00000861814,0.0006059778,0.0010919286,0.9800855,0.00038412499,0.000004227187,0.010910329],"study_design_scores_gemma":[0.000090246256,0.00004099991,0.002941529,0.00010413013,0.000017103399,0.00000651099,0.00006472757,0.9351392,0.061049595,0.00039167097,0.0000072497687,0.00014703757],"about_ca_topic_score_codex":0.000078027064,"about_ca_topic_score_gemma":0.0000016392636,"teacher_disagreement_score":0.9340473,"about_ca_system_score_codex":0.000079778474,"about_ca_system_score_gemma":0.000024533656,"threshold_uncertainty_score":0.48167253},"labels":[],"label_agreement":null},{"id":"W4321615196","doi":"10.1049/cit2.12180","title":"Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey","year":2023,"lang":"en","type":"article","venue":"CAAI Transactions on Intelligence Technology","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":162,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada); University of Northern British Columbia","funders":"","keywords":"Leverage (statistics); Deep learning; Computer science; Artificial intelligence; Handwriting; Machine learning; Data science","score_opus":0.039066463099479726,"score_gpt":0.28699358220346705,"score_spread":0.24792711910398732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321615196","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":[0.0014545079,0.00031605124,0.9925642,0.0015920604,0.000061058694,0.000540131,0.000016764146,0.0032639545,0.0001912746],"genre_scores_gemma":[0.9632361,0.0014662164,0.03353238,0.00020764375,0.000014209884,0.0012328433,0.00001611262,0.000025142966,0.0002693569],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998371,0.000091527094,0.0003189952,0.0006864202,0.00016758422,0.00036448476],"domain_scores_gemma":[0.9983862,0.0004454818,0.00009490871,0.0008012485,0.00017440859,0.000097747754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021959466,0.00022305429,0.00022374604,0.00086288294,0.0005585317,0.00011041462,0.00084471866,0.0004881231,0.00001898928],"category_scores_gemma":[0.00003221659,0.00023144236,0.000069470094,0.0030545737,0.00029016836,0.0001478382,0.000051457675,0.0012378043,0.00016715632],"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":[0.0000067707774,0.0000617054,0.00005545134,0.000011429727,0.000023302102,0.0000028513496,0.00012664717,0.045991097,0.00011922733,0.075386986,0.00005510756,0.8781594],"study_design_scores_gemma":[0.00009845511,0.0003365689,0.0008794403,0.000032894008,0.000016077818,0.00011615707,0.0005560979,0.63226575,0.011649397,0.33768383,0.015788656,0.0005766771],"about_ca_topic_score_codex":0.0000343259,"about_ca_topic_score_gemma":0.000034258992,"teacher_disagreement_score":0.96178156,"about_ca_system_score_codex":0.00005016819,"about_ca_system_score_gemma":0.0000342616,"threshold_uncertainty_score":0.94379455},"labels":[],"label_agreement":null},{"id":"W4321995890","doi":"10.5194/egusphere-egu23-10590","title":"The impact of training dataset on a vision-based smart road sensor to measure the level of flood on the streets","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Flood myth; Deep learning; Machine learning; Internet of Things; Warning system; Real-time computing; Computer security; Telecommunications","score_opus":0.21355456233098666,"score_gpt":0.37100433092624424,"score_spread":0.15744976859525758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321995890","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":[0.029438019,0.000014891713,0.93176496,0.027145213,0.00023165412,0.002799523,0.0063516367,0.000399102,0.0018549844],"genre_scores_gemma":[0.9953706,0.000006199261,0.0036878237,0.00037822107,0.000038633385,0.00024050106,0.0000645134,0.000017129356,0.0001963291],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826574,0.00018505073,0.00038489403,0.00045098824,0.0005030762,0.00021027624],"domain_scores_gemma":[0.9960154,0.00092959974,0.000286115,0.0025641236,0.00014345319,0.000061273444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012888614,0.00022285742,0.00023058955,0.00009873147,0.00031744043,0.00013119979,0.0022500332,0.000108248736,0.000018584326],"category_scores_gemma":[0.00017822141,0.00009324211,0.0002588422,0.00043932605,0.000088438195,0.00002703739,0.00062498916,0.00044348164,0.000028205308],"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":[0.00023261855,0.0007661912,0.00012026162,0.000071280454,0.00068353384,0.000005339737,0.0029656927,0.086043976,0.005912149,0.054528017,0.3322944,0.51637655],"study_design_scores_gemma":[0.0010080154,0.0042179124,0.0875767,0.0015150103,0.00014087599,0.000011733571,0.0019866216,0.75367814,0.11337059,0.019645445,0.015145747,0.0017031789],"about_ca_topic_score_codex":0.0014097287,"about_ca_topic_score_gemma":0.00021213935,"teacher_disagreement_score":0.9659326,"about_ca_system_score_codex":0.00004887351,"about_ca_system_score_gemma":0.00026505868,"threshold_uncertainty_score":0.4181159},"labels":[],"label_agreement":null},{"id":"W4323240832","doi":"10.5220/0011749200003405","title":"XMeDNN: An Explainable Deep Neural Network System for Intrusion Detection in Internet of Medical Things","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Seneca Polytechnic; Toronto Metropolitan University","funders":"","keywords":"Computer science; Intrusion detection system; Internet of Things; Artificial neural network; The Internet; Artificial intelligence; Computer security; Computer network; World Wide Web","score_opus":0.015753738773657117,"score_gpt":0.2670063124241552,"score_spread":0.2512525736504981,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323240832","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":[0.07779192,0.000007900169,0.92043465,0.00040752464,0.00018151195,0.00028048805,1.6461318e-7,0.00065677735,0.00023905377],"genre_scores_gemma":[0.98423946,0.0000055150163,0.015259754,0.00009793843,0.00006837302,0.00021356759,0.0000028144254,0.0000071208965,0.00010544557],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990007,0.000040003742,0.0002791735,0.00025085837,0.00022899685,0.00020025378],"domain_scores_gemma":[0.99946314,0.000074849595,0.00007841327,0.00026293713,0.000049886024,0.00007078118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007622961,0.00007253302,0.00012365835,0.00013460276,0.00007432095,0.000043582924,0.0005546724,0.00010672296,0.000010929185],"category_scores_gemma":[0.000024817105,0.00006401956,0.0000433125,0.0007745545,0.000019715184,0.00036503215,0.00021602698,0.00009935311,0.0000071059308],"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":[0.000033941353,0.000080287005,0.00034168514,0.00015573924,0.000008953965,0.000008918945,0.0011019503,0.0014176246,0.0018402799,0.30247182,0.0012642056,0.6912746],"study_design_scores_gemma":[0.00013238435,0.00014180724,0.00033437635,0.000025123918,0.0000013171981,0.000012415484,0.00017824289,0.98318005,0.012906345,0.0019763838,0.0010419737,0.000069600384],"about_ca_topic_score_codex":0.00033779445,"about_ca_topic_score_gemma":0.00014076382,"teacher_disagreement_score":0.9817624,"about_ca_system_score_codex":0.00004901726,"about_ca_system_score_gemma":0.000015833264,"threshold_uncertainty_score":0.26106417},"labels":[],"label_agreement":null},{"id":"W4323244301","doi":"10.5220/0011697900003393","title":"Learning Spatio-Temporal Features via 3D CNNs to Forecast Time-to-Accident","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Accident (philosophy); Artificial intelligence","score_opus":0.010621758230461943,"score_gpt":0.2520816415764201,"score_spread":0.24145988334595817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323244301","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":[0.013248451,0.0000020767618,0.9700696,0.0039999136,0.000053807147,0.0003285514,7.6400397e-7,0.0018933635,0.010403472],"genre_scores_gemma":[0.7489837,0.0000020365385,0.15721953,0.0010944591,0.00010128775,0.00024449668,0.0000134442,0.000018700011,0.09232234],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99910766,0.000019338666,0.00014566323,0.00032764423,0.00017404558,0.00022567427],"domain_scores_gemma":[0.9993879,0.000027701764,0.000037327995,0.00034216905,0.000058811365,0.00014604474],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00016169487,0.00009874756,0.000095458585,0.00019777917,0.00021727556,0.00015198992,0.0004674428,0.000042689855,0.00023135872],"category_scores_gemma":[0.000017955796,0.000091332826,0.000048909103,0.0011277095,0.000007690193,0.00014432913,0.0003641861,0.00010293575,0.0058698724],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011881636,0.000054663196,0.0031523511,0.0000072589028,0.000019893772,0.000012150824,0.00091452105,0.00554085,0.005798398,0.012167605,0.32444394,0.6478765],"study_design_scores_gemma":[0.00013691488,0.00055678515,0.032670673,0.000019162713,0.000005295745,0.000027781107,0.000062706305,0.17351164,0.027411368,0.0014701507,0.7635719,0.0005556014],"about_ca_topic_score_codex":0.0001300431,"about_ca_topic_score_gemma":0.000054344437,"teacher_disagreement_score":0.81285006,"about_ca_system_score_codex":0.000035357338,"about_ca_system_score_gemma":0.000017644668,"threshold_uncertainty_score":0.99490416},"labels":[],"label_agreement":null},{"id":"W4327768027","doi":"10.1109/ccnc51644.2023.10060584","title":"A Deep Learning Approach for Real-Time Application-Level Anomaly Detection in IoT Data Streaming","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Concordia University","funders":"","keywords":"Anomaly detection; Computer science; Metric (unit); Inference; Internet of Things; Data mining; Deep learning; TRACE (psycholinguistics); Concept drift; Analytics; Time series; Artificial intelligence; Real-time computing; Machine learning; Data stream mining; Computer security","score_opus":0.04591300169840177,"score_gpt":0.29125889203790983,"score_spread":0.24534589033950807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327768027","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":[0.004764102,0.0000047956664,0.98995084,0.00012373169,0.000014486897,0.0006229135,0.0000058744754,0.0013243222,0.0031889149],"genre_scores_gemma":[0.6336471,0.000020507716,0.36376247,0.00002591625,0.00005157918,0.0008305896,0.00012374597,0.00001759538,0.0015204981],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864274,0.000031077627,0.00026023344,0.0006766573,0.0001304186,0.00025885418],"domain_scores_gemma":[0.9987461,0.00011445017,0.00009733249,0.00093484414,0.0000546424,0.00005266087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053177454,0.00011578979,0.0001297372,0.00025388665,0.00024503726,0.000098018594,0.0009629135,0.00008382609,0.0000048068428],"category_scores_gemma":[0.000040717907,0.00011951571,0.000037550188,0.0013667842,0.00001978787,0.00028011054,0.00039860356,0.00011951041,0.00006528951],"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":[0.0000099268955,0.00013425146,0.0008800447,0.000041539006,0.000017763297,8.5886165e-7,0.00024157624,0.0074041607,0.03980822,0.015252848,0.0005833683,0.93562543],"study_design_scores_gemma":[0.00013859768,0.000037858023,0.004109413,0.0000025990857,0.000003283159,0.0000042137594,0.00006461579,0.9889923,0.0034383829,0.00085928815,0.002204479,0.00014498005],"about_ca_topic_score_codex":0.00037668803,"about_ca_topic_score_gemma":0.000104653685,"teacher_disagreement_score":0.9815881,"about_ca_system_score_codex":0.00005299329,"about_ca_system_score_gemma":0.00002517473,"threshold_uncertainty_score":0.4873709},"labels":[],"label_agreement":null},{"id":"W4352981169","doi":"10.1109/iscmi56532.2022.10068441","title":"Anomaly Detection with Convolutional Autoencoder for Predictive Maintenance","year":2022,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Western University","funders":"","keywords":"Anomaly detection; Autoencoder; Computer science; Downtime; Sliding window protocol; Anomaly (physics); Benchmark (surveying); Artificial intelligence; Data set; Data modeling; Pattern recognition (psychology); Data mining; Deep learning; Window (computing)","score_opus":0.007550529174472557,"score_gpt":0.21082383681505748,"score_spread":0.20327330764058493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4352981169","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":[0.0012491014,0.000008367373,0.9940685,0.00061723497,0.000066306486,0.00043332166,0.000017898474,0.0005072057,0.0030320366],"genre_scores_gemma":[0.85422087,8.818307e-7,0.1405249,0.00035244494,0.000025460657,0.0018878664,0.0000033447407,0.0000062783674,0.0029779682],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999275,0.00001798902,0.0001058759,0.00029129928,0.00015786602,0.00015193691],"domain_scores_gemma":[0.9995351,0.00003529348,0.000063714564,0.0002294805,0.000099490884,0.000036960555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012964278,0.000069732516,0.00006307497,0.000063223946,0.00055355637,0.00003206351,0.0003058469,0.000016938238,0.00005728661],"category_scores_gemma":[0.0000043499504,0.00006038348,0.0000414316,0.00032461336,0.00003456544,0.00018753344,0.00011962738,0.000088491855,0.0000038063667],"study_design_candidate":"theoretical_or_conceptual","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":[0.00021217263,0.0003305415,0.0007130658,0.000014034082,0.000075475604,0.0000036065678,0.000316071,0.015080422,0.0053853416,0.9146262,0.014399504,0.048843555],"study_design_scores_gemma":[0.00037385296,0.00093102595,0.0031389343,0.0000016037885,0.0000059766967,0.00009491333,0.00009610932,0.86991197,0.005554734,0.012076135,0.107647166,0.00016761175],"about_ca_topic_score_codex":0.000028786959,"about_ca_topic_score_gemma":0.0000149497955,"teacher_disagreement_score":0.9025501,"about_ca_system_score_codex":0.000121657955,"about_ca_system_score_gemma":0.000066368026,"threshold_uncertainty_score":0.4257564},"labels":[],"label_agreement":null},{"id":"W4353100378","doi":"10.18280/ts.400110","title":"Hypotheses Generation and Verification Based Framework for Crowd Anomaly Detection in Single-Scene Surveillance Videos","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"King Abdulaziz University","keywords":"Anomaly detection; Computer science; Computer vision; Artificial intelligence","score_opus":0.05058257268945627,"score_gpt":0.2665275598632562,"score_spread":0.21594498717379995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353100378","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":[0.22015554,0.00001665527,0.7784775,0.00058506156,0.00005081911,0.0004110143,0.000006272093,0.00027730758,0.000019867073],"genre_scores_gemma":[0.9375872,0.000012055306,0.061604824,0.00015479905,0.00010419659,0.00049828785,0.000014788404,0.000010369143,0.0000135029095],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905664,0.000045897115,0.00024141077,0.00035024332,0.00013148953,0.00017434587],"domain_scores_gemma":[0.9994411,0.0001590116,0.000093067756,0.00020516416,0.00006105492,0.000040637755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003774427,0.00010426489,0.00010157357,0.0001824794,0.00016714736,0.00012974336,0.0001627731,0.000072143506,0.000010077682],"category_scores_gemma":[0.000037199025,0.000111311165,0.00003752794,0.00057310023,0.000025025734,0.00020487557,0.000023483066,0.00006161626,0.000006464347],"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":[0.00004234648,0.00019070352,0.0029404038,0.000045568013,0.000011827266,0.0000014999712,0.00022006688,0.001717816,0.63673586,0.019984525,0.00026770413,0.3378417],"study_design_scores_gemma":[0.0003452811,0.00024205209,0.04649912,0.000016757971,0.0000033501055,0.0000016796859,0.00001539681,0.6930214,0.25300115,0.0046969634,0.0019656352,0.00019116935],"about_ca_topic_score_codex":0.000019570236,"about_ca_topic_score_gemma":0.000055674624,"teacher_disagreement_score":0.7174316,"about_ca_system_score_codex":0.000059747344,"about_ca_system_score_gemma":0.000020451136,"threshold_uncertainty_score":0.45391378},"labels":[],"label_agreement":null},{"id":"W4360765736","doi":"10.1109/asonam55673.2022.10068694","title":"Classes versus Communities: Outlier Detection and Removal in Tabular Datasets via Social Network Analysis (ClaCO)","year":2022,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Anomaly detection; Computer science; Social network analysis; Outlier; Data mining; Artificial intelligence; Social media; World Wide Web","score_opus":0.02082656649920897,"score_gpt":0.2720180442162275,"score_spread":0.25119147771701855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360765736","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":[0.03731788,0.00004490221,0.9608495,0.00040594992,0.00009479368,0.00014516008,0.000024052684,0.00023161521,0.0008861133],"genre_scores_gemma":[0.98730075,0.000015102004,0.012168239,0.00022409158,0.000040851173,0.00010760719,0.000052595493,0.000005227683,0.00008554147],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991583,0.00013887566,0.00017599706,0.00020173751,0.00015789791,0.0001671691],"domain_scores_gemma":[0.9994391,0.000072261886,0.00006812156,0.00037155364,0.000017600853,0.000031347005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030667367,0.00008363174,0.00013441159,0.00018970789,0.0007708996,0.00008266825,0.00040446315,0.000035997822,0.00009324076],"category_scores_gemma":[0.0000024739013,0.000092296454,0.00005418433,0.0016160258,0.000039835755,0.00019019099,0.0005649674,0.00021643167,0.0000025422353],"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":[0.0006152942,0.0012448764,0.014709971,0.000049011916,0.0017580059,0.000109991684,0.0074594337,0.039925773,0.0026380336,0.1988792,0.039032158,0.69357824],"study_design_scores_gemma":[0.00085902057,0.0002948269,0.010268274,0.0000016494504,0.00014789299,0.000026483991,0.0019469621,0.7163708,0.0003810637,0.004658358,0.26455474,0.0004899041],"about_ca_topic_score_codex":0.0008665111,"about_ca_topic_score_gemma":0.0024893882,"teacher_disagreement_score":0.9499829,"about_ca_system_score_codex":0.00008433394,"about_ca_system_score_gemma":0.0000151179065,"threshold_uncertainty_score":0.59292144},"labels":[],"label_agreement":null},{"id":"W4360771751","doi":"10.1109/asonam55673.2022.10068680","title":"IEEE/ACM ASONAM 2022: Message from the General Chairs","year":2022,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Zoom; Computer science; Library science; Engineering","score_opus":0.016195621014269086,"score_gpt":0.2502630362685385,"score_spread":0.23406741525426944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360771751","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":[0.023309495,0.0000956855,0.95430183,0.012671998,0.00026270226,0.00020520101,0.000027717118,0.0005786742,0.0085466765],"genre_scores_gemma":[0.9237784,0.000024986615,0.056269865,0.004900127,0.00019457197,0.00054665,0.000008747629,0.0000107804935,0.014265831],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99924666,0.00005376441,0.000108055705,0.00025565666,0.00020735696,0.00012851994],"domain_scores_gemma":[0.9989188,0.00006635816,0.000043233253,0.00092126976,0.000015893907,0.000034487304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016224454,0.00006700652,0.000058803,0.000020858255,0.000582806,0.00007217607,0.0016210901,0.000016828155,0.0008922517],"category_scores_gemma":[0.0000053860913,0.000049027803,0.000058845566,0.00034844672,0.000021334108,0.00009195612,0.00073995796,0.00016143147,0.000037206686],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032309779,0.00011446682,0.0006288501,8.5407794e-7,0.00002814307,0.0000063853267,0.0005716787,0.000353667,0.008530428,0.5221495,0.36218515,0.1054276],"study_design_scores_gemma":[0.00015839565,0.000082528204,0.0040085902,0.0000010164889,0.0000060196894,0.000017042703,0.00019722791,0.08331615,0.0156276,0.045426507,0.8509008,0.0002581247],"about_ca_topic_score_codex":0.00038910267,"about_ca_topic_score_gemma":0.000019849234,"teacher_disagreement_score":0.90046895,"about_ca_system_score_codex":0.00003620507,"about_ca_system_score_gemma":0.000026659578,"threshold_uncertainty_score":0.976953},"labels":[],"label_agreement":null},{"id":"W4361272518","doi":"10.18280/ijsse.130106","title":"Normalized Attention Neural Network with Adaptive Feature Recalibration for Detecting the Unusual Activities Using Video Surveillance Camera","year":2023,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Feature (linguistics); Computer science; Artificial intelligence; Artificial neural network; Computer vision; Pattern recognition (psychology)","score_opus":0.010866245537306817,"score_gpt":0.23975482255629835,"score_spread":0.22888857701899154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361272518","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":[0.15244755,0.000074984775,0.845804,0.0012253525,0.0002706952,0.00010066221,0.0000058941237,0.00006251318,0.000008381753],"genre_scores_gemma":[0.98265517,0.0000891508,0.0168475,0.000043301057,0.00033821329,0.0000070972073,0.0000022620425,0.00000715492,0.000010175048],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993897,0.000024655426,0.00018790989,0.00009868917,0.00018519872,0.00011386362],"domain_scores_gemma":[0.9993444,0.00020398773,0.00019204814,0.0000662097,0.00016433028,0.000029037315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038270248,0.00007928323,0.00010307552,0.00008721797,0.00016365295,0.000120345,0.00022958206,0.0000369123,5.176933e-7],"category_scores_gemma":[0.000026384529,0.000059301783,0.00005767563,0.00024362002,0.000018420116,0.00052740343,0.000057653513,0.00017382413,9.19193e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007876553,0.000032489428,0.0013498373,0.000040001545,0.00039593436,0.000026521915,0.0020284061,0.8872531,0.0100840125,0.040156167,0.0002706924,0.05757515],"study_design_scores_gemma":[0.00028776663,0.000089219626,0.003148221,0.000054209937,0.0000067992155,0.00019408566,0.000111157264,0.9931244,0.00071563636,0.00050783233,0.0016737402,0.000086883716],"about_ca_topic_score_codex":0.000008511542,"about_ca_topic_score_gemma":0.0000085315105,"teacher_disagreement_score":0.8302076,"about_ca_system_score_codex":0.000042740638,"about_ca_system_score_gemma":0.000021518967,"threshold_uncertainty_score":0.24182566},"labels":[],"label_agreement":null},{"id":"W4361275775","doi":"10.3390/app13074337","title":"Considerations for Developing Robot-Assisted Crisis De-Escalation Practices","year":2023,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; York University","funders":"","keywords":"Robot; Leverage (statistics); Computer science; Computer security; Task (project management); Human–computer interaction; Order (exchange); Business; Engineering; Artificial intelligence; Systems engineering; Finance","score_opus":0.13200011279261803,"score_gpt":0.3664267835128175,"score_spread":0.23442667072019946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361275775","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":[0.0055061737,0.000007007316,0.97643524,0.0138443215,0.000066988425,0.00035443314,0.000001542921,0.0007646333,0.0030196758],"genre_scores_gemma":[0.6131669,0.0000073267674,0.3858876,0.00050116726,0.00002695127,0.00035248714,0.0000015396149,0.0000029091996,0.000053103504],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990099,0.000019651725,0.00018676887,0.00036711618,0.00017722539,0.00023930737],"domain_scores_gemma":[0.9989531,0.0005061534,0.00022026149,0.00020313935,0.00007534415,0.000042000054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079726457,0.00007771147,0.00008210008,0.00017257914,0.0010977128,0.00038794783,0.0003716083,0.00004785845,0.0000052414057],"category_scores_gemma":[0.00014411061,0.00007243723,0.00003455085,0.0013383756,0.000078550795,0.00039949294,0.000090598944,0.000051041745,0.000044341683],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.2389664e-7,0.000012446116,0.000083637184,0.000006010099,0.000004872901,3.195752e-7,0.00028787454,0.00039715006,0.012983653,0.9648691,0.008036553,0.013317564],"study_design_scores_gemma":[0.00027462855,0.0001017947,0.018901808,0.000013706849,0.00001875952,0.000030730895,0.0012667283,0.15584074,0.10190064,0.70138043,0.019741245,0.00052876765],"about_ca_topic_score_codex":0.000017773496,"about_ca_topic_score_gemma":0.000027418133,"teacher_disagreement_score":0.6076608,"about_ca_system_score_codex":0.00003743969,"about_ca_system_score_gemma":0.000204566,"threshold_uncertainty_score":0.84428304},"labels":[],"label_agreement":null},{"id":"W4362680523","doi":"10.1109/smartgencon56628.2022.10083546","title":"Law Enforcement Companion","year":2022,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Acknowledgement; Law enforcement; Upload; Cloud computing; Task (project management); Computer science; Enforcement; Computer security; Population; Workload; Face (sociological concept); Internet privacy; Artificial intelligence; Law; Political science; World Wide Web; Engineering; Sociology","score_opus":0.014617664448395573,"score_gpt":0.24266282456911764,"score_spread":0.22804516012072207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362680523","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":[0.00024720238,0.0000036670965,0.7909407,0.0004240982,0.000038253882,0.00006677913,4.6990897e-7,0.00031625933,0.20796254],"genre_scores_gemma":[0.97084594,7.187698e-7,0.025636246,0.0014223689,0.00000792812,0.00012310485,0.0000012054358,0.0000015208072,0.0019609567],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996587,0.000010957742,0.00006196664,0.000103951505,0.0001021104,0.000062337014],"domain_scores_gemma":[0.9997294,0.00000546797,0.000018166298,0.00021858359,0.000008565982,0.000019820927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006785883,0.000026708683,0.000026662558,0.00001886182,0.00031296784,0.000026651345,0.00031750955,0.000004394267,0.0006186649],"category_scores_gemma":[1.9619056e-7,0.000025986863,0.000020021098,0.00014526586,0.0000073861465,0.00006766562,0.0003052096,0.000044987744,0.00003088306],"study_design_candidate":"theoretical_or_conceptual","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":[1.1865746e-7,0.000013786326,0.0000052494406,1.9800109e-7,6.7221026e-7,1.5858168e-7,0.000019901196,0.00007717162,0.00023052696,0.9931889,0.0025753947,0.0038879237],"study_design_scores_gemma":[0.000041677853,0.000049991755,0.000032613847,1.4443629e-7,4.4119903e-7,0.0000080188165,0.000027776545,0.031500462,0.0056310706,0.017836142,0.94481933,0.000052311443],"about_ca_topic_score_codex":0.00015176398,"about_ca_topic_score_gemma":0.0000024636327,"teacher_disagreement_score":0.97535276,"about_ca_system_score_codex":0.000030764382,"about_ca_system_score_gemma":0.0000063342136,"threshold_uncertainty_score":0.6773947},"labels":[],"label_agreement":null},{"id":"W4362681904","doi":"10.1109/csde56538.2022.10089323","title":"Generating Robust Convolutional Networks by Injecting Partial Noise in the Training Data","year":2022,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"MNIST database; Convolutional neural network; Computer science; Noise (video); Artificial intelligence; Generalization; Training set; Deep learning; Data set; Set (abstract data type); Data modeling; Noise measurement; Pattern recognition (psychology); Training (meteorology); Test data; Machine learning; Image (mathematics); Noise reduction; Database; Mathematics","score_opus":0.07496413801348285,"score_gpt":0.2761934160739817,"score_spread":0.20122927806049884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362681904","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":[0.0053733364,0.000053658918,0.9920808,0.0012936449,0.00006952898,0.00013349563,0.000010372888,0.00013800226,0.00084715255],"genre_scores_gemma":[0.9497588,0.0000023291443,0.048428837,0.0013874422,0.000110527355,0.00016047715,0.00004974065,0.0000041109124,0.000097721895],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906284,0.00011165798,0.00018141425,0.00029526383,0.00017617916,0.00017265063],"domain_scores_gemma":[0.9992945,0.00009928086,0.000060869042,0.00051309075,0.0000115022585,0.000020761125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088936824,0.000059417154,0.000057354024,0.00003052367,0.00068360486,0.000111323054,0.0012801925,0.000017699196,0.000060265604],"category_scores_gemma":[0.000014671689,0.00005005433,0.000017919025,0.00046294025,0.000019047675,0.00022234034,0.0006903562,0.00025765566,0.0000013269909],"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":[0.000004297735,0.00018057552,0.0010528784,0.0000023382015,0.000013280645,0.000006945855,0.002458398,0.64108634,0.0019141515,0.14821552,0.08673697,0.11832828],"study_design_scores_gemma":[0.00006110527,0.00001950744,0.00007367289,7.528691e-7,0.0000010870904,0.000025115298,0.000526702,0.98383325,0.00003651166,0.00014496355,0.015207908,0.000069430476],"about_ca_topic_score_codex":0.00014945286,"about_ca_topic_score_gemma":0.000027897675,"teacher_disagreement_score":0.94438547,"about_ca_system_score_codex":0.00003200831,"about_ca_system_score_gemma":0.00004327969,"threshold_uncertainty_score":0.5257805},"labels":[],"label_agreement":null},{"id":"W4365796204","doi":"10.1007/978-3-031-30678-5_8","title":"Meta Pseudo Labels for Anomaly Detection via Partially Observed Anomalies","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Anomaly detection; Computer science; Anomaly (physics); Set (abstract data type); Data mining; Artificial intelligence; Pattern recognition (psychology); Machine learning","score_opus":0.06742611530413994,"score_gpt":0.2684141052730597,"score_spread":0.20098798996891976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4365796204","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008563001,0.0001668503,0.9954066,0.00086281786,0.0009765019,0.001051203,0.000019327174,0.0010166793,0.00041439215],"genre_scores_gemma":[0.23552571,0.00008689471,0.7585643,0.0012558268,0.00070430065,0.00065097114,0.000014336345,0.00011701429,0.0030806416],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99622184,0.000032710268,0.0007083179,0.0017251092,0.0006350306,0.0006769803],"domain_scores_gemma":[0.9969563,0.00051638123,0.00043257762,0.0015361287,0.00039351507,0.00016511249],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010387616,0.00056933163,0.0007115116,0.00078138505,0.00056259945,0.0005775528,0.0024295165,0.00040789315,0.000017271996],"category_scores_gemma":[0.000058064452,0.00052565493,0.0004221554,0.0010165378,0.00041117307,0.00061753776,0.0008888692,0.00048591598,0.000055852917],"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":[0.000016424921,0.00006034907,0.00002962899,0.000103265906,0.00024966468,0.000025276897,0.00028083017,0.008499466,0.0068822126,0.057943746,0.000051123232,0.925858],"study_design_scores_gemma":[0.0002702331,0.00050371804,0.00022864704,0.0000846419,0.00017274969,0.000057822523,1.8715438e-7,0.54446095,0.051943496,0.3966047,0.004631039,0.0010418538],"about_ca_topic_score_codex":0.00005383893,"about_ca_topic_score_gemma":0.000370402,"teacher_disagreement_score":0.92481613,"about_ca_system_score_codex":0.0001677914,"about_ca_system_score_gemma":0.00026604184,"threshold_uncertainty_score":0.9997195},"labels":[],"label_agreement":null},{"id":"W4366091323","doi":"10.1007/s10462-023-10466-8","title":"Deep learning modelling techniques: current progress, applications, advantages, and challenges","year":2023,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":960,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"University of Technology Sydney","keywords":"Computer science; Deep learning; Artificial intelligence; Machine learning; Field (mathematics); Convolutional neural network; Benchmark (surveying); Feature learning; Data science","score_opus":0.11413637978421688,"score_gpt":0.36394770500349455,"score_spread":0.24981132521927768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366091323","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000004377478,0.27009067,0.72591656,0.0021157959,0.000023144692,0.0006864924,4.3419323e-7,0.0009924603,0.00017003663],"genre_scores_gemma":[0.010716596,0.9462494,0.041159242,0.00008063165,0.000074826465,0.0016770351,0.00000766037,0.000014718309,0.000019878778],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99857545,0.00006646493,0.00042853385,0.00049629365,0.00017836304,0.00025488678],"domain_scores_gemma":[0.9991385,0.000070489965,0.00014869444,0.0004489784,0.00010326641,0.00009008481],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006228644,0.00015354769,0.00021440213,0.00011827633,0.00027964683,0.0000853495,0.0004783586,0.000049318718,0.000008188908],"category_scores_gemma":[0.000020694042,0.0001444347,0.00006943429,0.00090732466,0.00008482414,0.00025065165,0.00021535317,0.00022444212,0.00024540222],"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":[2.2871714e-7,0.000017977989,0.0000037317848,0.0004132155,0.0000020578238,5.420251e-7,0.000035755736,0.00004231491,0.0000067451438,0.18884255,0.000020105506,0.81061476],"study_design_scores_gemma":[0.000004073539,0.000051528164,0.0000054789716,0.00089746475,0.000015848516,0.000013684678,0.000063330626,0.17766248,0.0019627642,0.06740922,0.75163805,0.00027608263],"about_ca_topic_score_codex":0.000003087965,"about_ca_topic_score_gemma":0.0000017063118,"teacher_disagreement_score":0.8103387,"about_ca_system_score_codex":0.00002277638,"about_ca_system_score_gemma":0.000020352123,"threshold_uncertainty_score":0.5889876},"labels":[],"label_agreement":null},{"id":"W4366548200","doi":"10.21203/rs.3.rs-2795266/v1","title":"CS-YOLO: A new detection algorithm for alien intrusion on highway","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Anomaly Detection Techniques and Applications","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":"Dalhousie University","funders":"","keywords":"Pascal (unit); Computer science; Intrusion detection system; Intrusion; Object detection; Algorithm; Data mining; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.10865199263130042,"score_gpt":0.4122288490486225,"score_spread":0.30357685641732207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366548200","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00042646116,0.000052823263,0.9916623,0.0028807565,0.00050128956,0.0023379747,0.00006682503,0.0015780663,0.000493452],"genre_scores_gemma":[0.34655592,0.0013305804,0.5930181,0.00040155172,0.0049213897,0.015670674,0.00037122454,0.00035063724,0.037379924],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99676776,0.00018999333,0.00034189972,0.0011661481,0.0009130973,0.0006210993],"domain_scores_gemma":[0.9971297,0.00045082156,0.00013245732,0.0015034599,0.0005219632,0.00026160924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001335455,0.00025141667,0.0002639304,0.0007576895,0.00061964244,0.0004742549,0.0015366103,0.00045714722,0.00002301462],"category_scores_gemma":[0.00017580189,0.00024106202,0.00023869514,0.00095928024,0.000065723005,0.00013444632,0.0021446324,0.0012002497,0.0004225054],"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":[0.0000130764065,0.000071745715,0.0000033178278,0.000104642815,0.00001967483,0.0000047456165,0.00014170742,0.00009961123,0.0005475326,0.011200315,0.020657301,0.9671363],"study_design_scores_gemma":[0.0005324287,0.0015935068,0.00082197075,0.0005583046,0.000011486328,0.000009396218,0.000081970386,0.44564545,0.063021384,0.27260673,0.21440274,0.00071462145],"about_ca_topic_score_codex":0.00063930097,"about_ca_topic_score_gemma":0.00005372973,"teacher_disagreement_score":0.9664217,"about_ca_system_score_codex":0.00041299604,"about_ca_system_score_gemma":0.00037620636,"threshold_uncertainty_score":0.9830224},"labels":[],"label_agreement":null},{"id":"W4367043673","doi":"10.1016/j.eswa.2023.120276","title":"Remaining useful life prediction of bearings using multi-source adversarial online regression under online unknown conditions","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Data mining; Weighting; Machine learning; Multi-source; Domain adaptation; Classifier (UML)","score_opus":0.06376855239436252,"score_gpt":0.3184634452071968,"score_spread":0.2546948928128343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367043673","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":[0.035741333,0.000119408236,0.96104884,0.00042350352,0.00012282925,0.001062799,0.00015599314,0.0012533318,0.000071940834],"genre_scores_gemma":[0.9092236,0.000059025915,0.08824409,0.000117256975,0.00034238354,0.0009712249,0.00035962323,0.000045695324,0.00063711527],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99814683,0.00006620864,0.0005904508,0.0005658678,0.00035291028,0.00027770764],"domain_scores_gemma":[0.9980517,0.000109457644,0.00043294116,0.00093906565,0.00028706036,0.00017979919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020233898,0.00021287675,0.00028529964,0.0003369652,0.0005692851,0.000074585194,0.0005406246,0.0001506258,0.0000058320434],"category_scores_gemma":[0.000023702509,0.00018507565,0.000082974584,0.0016997423,0.000118954784,0.00033451075,0.0001857723,0.0001949257,0.000018009616],"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":[0.00014104239,0.005292679,0.011900066,0.00057643227,0.0008442783,0.000013060752,0.0104817385,0.21642609,0.4423558,0.28818494,0.014782701,0.009001138],"study_design_scores_gemma":[0.0011902184,0.0001606056,0.009467326,0.00042342336,0.0000458726,0.000075512035,0.0026708406,0.90808976,0.0029783428,0.00021310056,0.074210346,0.0004746729],"about_ca_topic_score_codex":0.00037310857,"about_ca_topic_score_gemma":0.000022366108,"teacher_disagreement_score":0.8734822,"about_ca_system_score_codex":0.00011111416,"about_ca_system_score_gemma":0.00016409595,"threshold_uncertainty_score":0.75471663},"labels":[],"label_agreement":null},{"id":"W4367841464","doi":"10.32920/22734269","title":"Human Action Recognition Using Convolutional Neural Network and Depth Sensor Data","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Convolutional neural network; Action recognition; Pooling; Data set; Artificial intelligence; Computer science; Pattern recognition (psychology); Set (abstract data type); Action (physics); Training set; Computer vision","score_opus":0.32616469220464245,"score_gpt":0.38190286941223556,"score_spread":0.05573817720759311,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367841464","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030551666,0.000030310603,0.9666538,0.00048724597,0.00041527132,0.00034564856,0.00005223307,0.0011340663,0.00032974756],"genre_scores_gemma":[0.49146843,0.000100481586,0.50529444,0.00028982616,0.0010859319,0.000098407225,0.00081421435,0.000037137324,0.0008111597],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998593,0.000056107427,0.0002534868,0.0007520625,0.00015769764,0.0001876602],"domain_scores_gemma":[0.99868536,0.000036593297,0.0001809934,0.0009476998,0.00008520915,0.00006415335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002661657,0.00015707528,0.00014673892,0.00009294345,0.00037541176,0.0002512929,0.000617029,0.00018308393,0.000016308142],"category_scores_gemma":[0.000009600905,0.00016594752,0.00004034993,0.00020944948,0.00004960794,0.00030880523,0.0024671324,0.00034864846,0.000022021906],"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":[0.000033581942,0.00042498263,0.0067600315,0.00072480703,0.00056518766,0.000048477115,0.00026873854,0.039162375,0.00913704,0.08472156,0.13162923,0.726524],"study_design_scores_gemma":[0.000070019516,0.000017853341,0.006705614,0.00004016702,0.00002408057,0.000040491937,0.000012671262,0.95186615,0.00016146149,0.0397445,0.0010494889,0.00026747838],"about_ca_topic_score_codex":0.00040024062,"about_ca_topic_score_gemma":0.00013908106,"teacher_disagreement_score":0.9127038,"about_ca_system_score_codex":0.00005293776,"about_ca_system_score_gemma":0.000048652997,"threshold_uncertainty_score":0.67671436},"labels":[],"label_agreement":null},{"id":"W4372271370","doi":"10.1109/icassp49357.2023.10094686","title":"Relapse Detection in Patients with Psychotic Disorders Using Unsupervised Learning on Smartwatch Signals","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Autoencoder; Artificial intelligence; Anomaly detection; Unsupervised learning; Computer science; Smartwatch; Actigraphy; Deep learning; Machine learning; Wearable computer; Pattern recognition (psychology); Medicine; Internal medicine","score_opus":0.01196343269803863,"score_gpt":0.23839321414647813,"score_spread":0.2264297814484395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4372271370","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":[0.5877854,7.492947e-7,0.41092357,0.000121270394,0.000030408983,0.00019302746,1.5458882e-7,0.0004683301,0.00047708495],"genre_scores_gemma":[0.99514943,0.00000550594,0.0044893464,0.00006949941,0.000007465031,0.000043283733,0.0000019457862,0.000011593572,0.00022193196],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991414,0.000044733013,0.00014771687,0.00030551,0.00017324062,0.00018740109],"domain_scores_gemma":[0.9995933,0.00004041607,0.000047867336,0.00024499805,0.000032955748,0.000040439427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013604596,0.00009532261,0.00008169994,0.00024709184,0.00016745717,0.000055867815,0.00019145315,0.00004652273,0.000013154038],"category_scores_gemma":[0.000009479027,0.000080935235,0.00002806926,0.0014580234,0.000015882937,0.00020547827,0.000041908483,0.0001555991,0.00006205902],"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":[0.00017819616,0.0008779486,0.29857972,0.000041009123,0.000042665815,0.0000045924753,0.0011951881,0.09682782,0.0059968648,0.007860962,0.00029377695,0.58810127],"study_design_scores_gemma":[0.0014045727,0.0010981818,0.3819275,0.00006180544,0.000006602357,0.000001050394,0.00010596321,0.6033469,0.006365481,0.0039911903,0.0012022115,0.00048854563],"about_ca_topic_score_codex":0.000096456366,"about_ca_topic_score_gemma":0.000068855676,"teacher_disagreement_score":0.5876127,"about_ca_system_score_codex":0.00004505207,"about_ca_system_score_gemma":0.000011460937,"threshold_uncertainty_score":0.33004433},"labels":[],"label_agreement":null},{"id":"W4376465056","doi":"10.2139/ssrn.4444957","title":"Deployment of Crowdsourced Occupant Data to Support Fault Detection and Diagnosis in Buildings","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Software deployment; Fault detection and isolation; Fault (geology); Computer science; Business; Artificial intelligence; Geology; Seismology","score_opus":0.04567107749418069,"score_gpt":0.3157288422301519,"score_spread":0.2700577647359712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376465056","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":[0.21645269,0.00022766797,0.7813772,0.0012034473,0.00014219407,0.000409196,0.000017392593,0.00015680626,0.000013399705],"genre_scores_gemma":[0.9871528,0.0049134474,0.0074414047,0.000068488116,0.000086075546,0.00017159518,0.0000071308864,0.000027853886,0.00013121069],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99737626,0.000063562664,0.000563472,0.0006774232,0.0003037667,0.0010154865],"domain_scores_gemma":[0.99842864,0.000060634196,0.0003545624,0.0009559611,0.000088426445,0.000111752975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020211656,0.00020919327,0.0003037424,0.0005038238,0.00012237106,0.00014436601,0.001742716,0.00016772878,0.0000029261298],"category_scores_gemma":[0.0000728254,0.00020986385,0.00007623062,0.0004994412,0.000028538494,0.00020370827,0.0023503741,0.0018105701,0.00000772558],"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":[0.00007055255,0.0003446776,0.010743879,0.00013188786,0.00033230436,0.000019390816,0.0009647327,0.0017525136,0.008528055,0.03436428,0.0011822339,0.9415655],"study_design_scores_gemma":[0.0024909782,0.004925165,0.04292413,0.0010703135,0.00032499034,0.0017340685,0.0016472234,0.069786794,0.07304122,0.76439613,0.03438262,0.0032763863],"about_ca_topic_score_codex":0.0007152796,"about_ca_topic_score_gemma":0.002345169,"teacher_disagreement_score":0.9382891,"about_ca_system_score_codex":0.00059252116,"about_ca_system_score_gemma":0.00083264033,"threshold_uncertainty_score":0.8558},"labels":[],"label_agreement":null},{"id":"W4376606508","doi":"10.1109/aero55745.2023.10115826","title":"Transfer Learning for Hypersonic Vehicle Trajectory Prediction","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"Lockheed Martin","keywords":"Trajectory; Computer science; Transfer of learning; Artificial intelligence; Hypersonic speed; Machine learning; Aerospace engineering; Engineering","score_opus":0.022115796875144776,"score_gpt":0.2469380071966197,"score_spread":0.22482221032147492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376606508","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":[0.053168014,0.000006392506,0.94226587,0.0006495656,0.000055963967,0.00018555687,0.0000010386635,0.0020903286,0.0015772472],"genre_scores_gemma":[0.9873091,0.000016667098,0.007844163,0.000115165036,0.000046763296,0.00025488614,0.0000037939938,0.000007211542,0.004402261],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995348,0.000009947263,0.00008316759,0.00017792414,0.00006652881,0.00012763256],"domain_scores_gemma":[0.99976504,0.000039218172,0.000007737027,0.00013165233,0.000025364569,0.000030999207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012342857,0.000045383178,0.000045504115,0.000069414236,0.00018315387,0.000034429355,0.00016343856,0.000035832218,0.000015027605],"category_scores_gemma":[0.0000048337515,0.00004307003,0.00005594463,0.00037731888,0.000010121996,0.00020128448,0.000015890135,0.000061168445,0.00006451412],"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":[0.000012385779,0.000087870096,0.00091879297,0.000043968812,0.000035153513,0.0000014511885,0.0010492617,0.005839268,0.18583733,0.3345361,0.015644478,0.45599392],"study_design_scores_gemma":[0.00026744598,0.00022055594,0.0043457905,0.0000043654677,0.0000054543384,0.0000037785424,0.00012407286,0.81083614,0.05844209,0.0022761095,0.12333709,0.00013712142],"about_ca_topic_score_codex":0.000007599869,"about_ca_topic_score_gemma":0.0000016870011,"teacher_disagreement_score":0.9344217,"about_ca_system_score_codex":0.000015974056,"about_ca_system_score_gemma":0.000014851892,"threshold_uncertainty_score":0.17563449},"labels":[],"label_agreement":null},{"id":"W4376956106","doi":"10.1007/s10618-023-00931-x","title":"On the evaluation of outlier detection and one-class classification: a comparative study of algorithms, model selection, and ensembles","year":2023,"lang":"en","type":"article","venue":"Data Mining and Knowledge Discovery","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Danmarks Frie Forskningsfond; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Anomaly detection; Outlier; Artificial intelligence; Machine learning; One-class classification; Computer science; Class (philosophy); Selection (genetic algorithm); Pattern recognition (psychology); Support vector machine; Focus (optics); Data mining","score_opus":0.27070642898415714,"score_gpt":0.3888650291681965,"score_spread":0.11815860018403934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376956106","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":[0.88581574,0.000089509755,0.113276705,0.00009201104,0.00002493556,0.00034951736,0.000037558864,0.000049076156,0.0002649139],"genre_scores_gemma":[0.99820554,0.000045523102,0.0015298021,0.0000058878923,0.000014772203,0.00008777652,0.00001629329,0.000004254017,0.000090163245],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990701,0.00012687524,0.00020292481,0.00034722633,0.00017967995,0.0000731687],"domain_scores_gemma":[0.99903196,0.00022881165,0.00013227752,0.0004188191,0.00016560793,0.000022515358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086294586,0.00008340736,0.0001336964,0.00012055347,0.00021958973,0.000078123936,0.00020266457,0.000034313944,5.378078e-7],"category_scores_gemma":[0.000058683472,0.00006561433,0.000009573524,0.00044612456,0.00006702246,0.00038907691,0.00024174947,0.00006057682,7.890523e-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":[0.00009432316,0.0016010215,0.0026836947,0.00013280942,0.00031052853,2.7900094e-7,0.045560196,0.0009770091,0.016568478,0.05696161,0.0033533983,0.8717567],"study_design_scores_gemma":[0.00023717545,0.00019170786,0.009416461,0.000023899895,0.00004443982,0.0000017252265,0.0033459677,0.98358506,0.0015853017,0.001462005,0.00003509375,0.00007114719],"about_ca_topic_score_codex":0.000025073796,"about_ca_topic_score_gemma":0.00019073917,"teacher_disagreement_score":0.9826081,"about_ca_system_score_codex":0.000014279511,"about_ca_system_score_gemma":0.0000648146,"threshold_uncertainty_score":0.26756746},"labels":[],"label_agreement":null},{"id":"W4378571875","doi":"10.1016/j.jobe.2023.106923","title":"Detecting thermal anomalies in buildings using frequency and temporal domains analysis","year":2023,"lang":"en","type":"article","venue":"Journal of Building Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Research Manitoba","keywords":"Anomaly detection; Anomaly (physics); Fourier transform; Series (stratigraphy); Time series; Thermal; Computer science; Support vector machine; Fourier series; Energy (signal processing); Data mining; Pattern recognition (psychology); Artificial intelligence; Geology; Mathematics; Geography; Machine learning; Statistics; Meteorology; Physics","score_opus":0.012749886603091183,"score_gpt":0.2533226989458934,"score_spread":0.2405728123428022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378571875","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":[0.5239214,0.00005950807,0.47583503,0.000043819524,0.000037876056,0.00002276325,2.2449244e-7,0.0000735872,0.0000058317464],"genre_scores_gemma":[0.7302024,0.000014646343,0.26971734,0.000006661335,0.00004712539,0.0000019598479,6.910358e-8,0.0000074920886,0.0000023113632],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991213,0.0000144695705,0.0003670711,0.00014670528,0.00015310074,0.00019734765],"domain_scores_gemma":[0.9994801,0.00007548773,0.00019231552,0.00014371808,0.000047356254,0.000061006656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005990821,0.00010004532,0.00021383645,0.0011614009,0.000077775214,0.00010666813,0.00027869022,0.000048579892,0.0000012270278],"category_scores_gemma":[0.00005136465,0.00009721427,0.00010130073,0.0022261802,0.000012015987,0.0004492012,0.000102396785,0.00020136092,2.4281633e-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":[0.0000034566058,0.00002151228,0.07703572,0.000041641582,0.000204336,0.000113824986,0.00066433736,0.295468,0.60576147,0.007771131,0.000007804955,0.01290673],"study_design_scores_gemma":[0.00019246114,0.000052008378,0.040564954,0.0000806406,0.000053968255,0.00013703418,0.000058638096,0.940227,0.017591823,0.0007096172,0.000120851175,0.00021105065],"about_ca_topic_score_codex":0.000044800832,"about_ca_topic_score_gemma":0.0000032389469,"teacher_disagreement_score":0.64475894,"about_ca_system_score_codex":0.0000728929,"about_ca_system_score_gemma":0.000020507734,"threshold_uncertainty_score":0.39642832},"labels":[],"label_agreement":null},{"id":"W4378640482","doi":"10.1007/s11548-023-02965-4","title":"Automated screening of computed tomography using weakly supervised anomaly detection","year":2023,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre; St. Michael's Hospital; Ontario Institute for Cancer Research; University of Toronto","funders":"Nippon Steel Corporation","keywords":"Anomaly detection; Computer science; Artificial intelligence; Workload; Ground truth; Annotation; Pattern recognition (psychology); Supervised learning; Convolutional neural network; Artificial neural network","score_opus":0.02973180157115531,"score_gpt":0.2784098397791177,"score_spread":0.2486780382079624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378640482","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":[0.4006142,0.000073055395,0.5981511,0.0002810314,0.0006368596,0.000037209436,0.0000022528338,0.00019545712,0.000008869511],"genre_scores_gemma":[0.93281186,0.00004549525,0.066784166,0.00013422477,0.00020591961,0.0000022532515,0.0000050691824,0.000007690285,0.0000033116585],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985094,0.00017029798,0.00070715585,0.00019679792,0.00025261616,0.000163745],"domain_scores_gemma":[0.99818355,0.00049871375,0.00055499503,0.0001571548,0.0005270035,0.00007858022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066951243,0.00012777501,0.00035317297,0.0012298061,0.00010484679,0.000067007735,0.00048549182,0.00011838196,0.000003478802],"category_scores_gemma":[0.000024771294,0.00011950177,0.000245756,0.000811971,0.000102834194,0.00035046277,0.00013831296,0.00017570588,0.000001071004],"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":[0.00033824722,0.00037995513,0.09102503,0.00006836235,0.0022740068,0.000717201,0.0006137874,0.013150338,0.08431923,0.003341085,0.0053895097,0.79838324],"study_design_scores_gemma":[0.0002769858,0.00009234283,0.2472417,0.000081743136,0.000017120481,0.002585865,0.000012352661,0.74476486,0.004034095,0.0003302113,0.00043085808,0.00013183862],"about_ca_topic_score_codex":0.000015637788,"about_ca_topic_score_gemma":0.0000012161652,"teacher_disagreement_score":0.7982514,"about_ca_system_score_codex":0.000026088253,"about_ca_system_score_gemma":0.00006419554,"threshold_uncertainty_score":0.48731405},"labels":[],"label_agreement":null},{"id":"W4378981313","doi":"10.18280/ijsse.130203","title":"A Review of Deep Learning Algorithms for Anomaly Detection in Videos","year":2023,"lang":"en","type":"review","venue":"International Journal of Safety and Security Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Anomaly detection; Computer science; Artificial intelligence; Anomaly (physics); Machine learning; Algorithm","score_opus":0.02207299434057938,"score_gpt":0.31360706206518396,"score_spread":0.2915340677246046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378981313","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":[8.9842246e-7,0.54646164,0.45313865,0.000041911913,0.00016253082,0.00015843702,0.000005052463,0.000023415067,0.000007469096],"genre_scores_gemma":[0.00015158253,0.99293584,0.006696704,0.000016193424,0.0001397118,0.000033280892,0.000004716095,0.00001479412,0.000007197936],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99857974,0.00003175912,0.0009373865,0.00015358436,0.00019474351,0.000102802944],"domain_scores_gemma":[0.9986964,0.0002567502,0.0006475221,0.00009726925,0.0002580199,0.000044077213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007375151,0.00014471052,0.00063699787,0.00045691952,0.000025561529,0.00003142272,0.0005066299,0.00010376776,0.0000018977885],"category_scores_gemma":[0.00020095163,0.00013532193,0.00030020726,0.000355657,0.000010957539,0.00021691367,0.00010716189,0.00037512192,7.527035e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030284555,0.000012289264,2.2468204e-7,0.009635292,0.00007657499,0.0000054762354,0.000049052505,0.000113769216,0.0000026717562,0.0010077126,0.0000040270893,0.9890899],"study_design_scores_gemma":[0.00020902081,0.00011419064,0.0000076215224,0.056505594,0.000078947065,0.00037319507,0.000008538711,0.04720276,0.000041579046,0.00036200357,0.89488924,0.00020729277],"about_ca_topic_score_codex":0.000008551783,"about_ca_topic_score_gemma":0.000003147621,"teacher_disagreement_score":0.9888826,"about_ca_system_score_codex":0.0001066181,"about_ca_system_score_gemma":0.000044986547,"threshold_uncertainty_score":0.55182683},"labels":[],"label_agreement":null},{"id":"W4379374897","doi":"10.1007/s10489-023-04661-x","title":"Explainable global error weighted on feature importance: The xGEWFI metric to evaluate the error of data imputation and data augmentation","year":2023,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Outlier; Metric (unit); Imputation (statistics); Data mining; Feature (linguistics); Weighting; Test data; Random forest; Pattern recognition (psychology); Context (archaeology); Algorithm; Artificial intelligence; Statistics; Missing data; Mathematics; Machine learning","score_opus":0.09666439399236094,"score_gpt":0.38951775371942904,"score_spread":0.2928533597270681,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379374897","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":[0.011623947,0.0001069725,0.97999245,0.005668401,0.000089869216,0.0011446718,0.00012287214,0.00024302882,0.0010077645],"genre_scores_gemma":[0.9707338,0.00011414023,0.02792423,0.00067475194,0.000042037474,0.0002076751,0.00017851806,0.000009174475,0.0001156247],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983922,0.000046732686,0.0002780027,0.0006708388,0.00040152392,0.0002106945],"domain_scores_gemma":[0.9971337,0.00021189933,0.00014538225,0.0023677268,0.00008649202,0.000054805496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010988415,0.00013884505,0.00012261499,0.0000950743,0.00033226347,0.00012040826,0.0029865939,0.000053764354,0.000013195613],"category_scores_gemma":[0.00006097819,0.000086378685,0.000018369117,0.003015041,0.00007833357,0.00034555612,0.0013622437,0.00013492798,0.00007644581],"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":[0.000050659375,0.000073059586,0.00020654227,0.000028974244,0.000054132488,0.0000021907645,0.00085982046,0.0010944494,0.0012832073,0.3516247,0.044892006,0.59983027],"study_design_scores_gemma":[0.0002119102,0.00027873437,0.0102351,0.00003637359,0.00008698296,0.00002117666,0.0026566484,0.8275506,0.03712872,0.09818346,0.0231221,0.0004882218],"about_ca_topic_score_codex":0.000060443293,"about_ca_topic_score_gemma":0.000038498307,"teacher_disagreement_score":0.9591099,"about_ca_system_score_codex":0.000046852,"about_ca_system_score_gemma":0.00005605981,"threshold_uncertainty_score":0.55498844},"labels":[],"label_agreement":null},{"id":"W4379383051","doi":"10.32920/23296241","title":"Anomaly Detection in Cloud Components","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cloud computing; Anomaly detection; Computer science; Robustness (evolution); Key (lock); Real-time computing; Autoencoder; Quality of service; Distributed computing; Data mining; Computer security; Computer network; Artificial intelligence; Operating system; Deep learning","score_opus":0.04768614865766477,"score_gpt":0.2821189447216154,"score_spread":0.23443279606395065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379383051","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":[0.03209873,0.000013544572,0.96190155,0.0007018442,0.000734757,0.00040194852,0.000003315902,0.0018363714,0.0023079286],"genre_scores_gemma":[0.97522545,0.000042878757,0.022477511,0.00010412963,0.00010355243,0.00034351757,0.000008431055,0.000017374923,0.0016771713],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986359,0.000043174077,0.00032545844,0.00061696896,0.00017355813,0.00020496931],"domain_scores_gemma":[0.99887604,0.000034786644,0.0001234574,0.00086448755,0.000045920995,0.00005529178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023399928,0.00016837651,0.0001882265,0.00033401372,0.00007367775,0.00014735272,0.000954169,0.00023507628,0.000011488565],"category_scores_gemma":[0.000009048317,0.00017573676,0.00009943216,0.0005119281,0.00002075653,0.00010232195,0.0014360363,0.00045646238,0.00027212454],"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":[0.000042614665,0.0013191996,0.011058171,0.00056138757,0.00020091231,0.00015261432,0.0013859292,0.009482957,0.03663601,0.3088071,0.015753979,0.6145991],"study_design_scores_gemma":[0.0004082364,0.00011315065,0.09656227,0.00015288465,0.000012349249,0.000024068917,0.000034160228,0.62582964,0.055424396,0.20069289,0.019521885,0.0012240711],"about_ca_topic_score_codex":0.0013090296,"about_ca_topic_score_gemma":0.00041039442,"teacher_disagreement_score":0.9431267,"about_ca_system_score_codex":0.00011995115,"about_ca_system_score_gemma":0.00003643755,"threshold_uncertainty_score":0.71663374},"labels":[],"label_agreement":null},{"id":"W4379383142","doi":"10.32920/23296241.v1","title":"Anomaly Detection in Cloud Components","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cloud computing; Anomaly detection; Computer science; Robustness (evolution); Key (lock); Distributed computing; Real-time computing; Quality of service; Autoencoder; Data mining; Computer security; Computer network; Operating system; Artificial intelligence","score_opus":0.04768614865766477,"score_gpt":0.2821189447216154,"score_spread":0.23443279606395065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379383142","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":[0.03209873,0.000013544572,0.96190155,0.0007018442,0.000734757,0.00040194852,0.000003315902,0.0018363714,0.0023079286],"genre_scores_gemma":[0.97522545,0.000042878757,0.022477511,0.00010412963,0.00010355243,0.00034351757,0.000008431055,0.000017374923,0.0016771713],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986359,0.000043174077,0.00032545844,0.00061696896,0.00017355813,0.00020496931],"domain_scores_gemma":[0.99887604,0.000034786644,0.0001234574,0.00086448755,0.000045920995,0.00005529178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023399928,0.00016837651,0.0001882265,0.00033401372,0.00007367775,0.00014735272,0.000954169,0.00023507628,0.000011488565],"category_scores_gemma":[0.000009048317,0.00017573676,0.00009943216,0.0005119281,0.00002075653,0.00010232195,0.0014360363,0.00045646238,0.00027212454],"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":[0.000042614665,0.0013191996,0.011058171,0.00056138757,0.00020091231,0.00015261432,0.0013859292,0.009482957,0.03663601,0.3088071,0.015753979,0.6145991],"study_design_scores_gemma":[0.0004082364,0.00011315065,0.09656227,0.00015288465,0.000012349249,0.000024068917,0.000034160228,0.62582964,0.055424396,0.20069289,0.019521885,0.0012240711],"about_ca_topic_score_codex":0.0013090296,"about_ca_topic_score_gemma":0.00041039442,"teacher_disagreement_score":0.9431267,"about_ca_system_score_codex":0.00011995115,"about_ca_system_score_gemma":0.00003643755,"threshold_uncertainty_score":0.71663374},"labels":[],"label_agreement":null},{"id":"W4379523218","doi":"10.21428/594757db.953eca4c","title":"Unsupervised Financial Fraud Detection Using Low-rank Recovery","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Queen's University; Verafin (Canada); Memorial University of Newfoundland","funders":"Mitacs","keywords":"Outlier; Anomaly detection; Computer science; Benchmark (surveying); Rank (graph theory); Data mining; Data set; Artificial intelligence; Mathematics","score_opus":0.019382085333825315,"score_gpt":0.24901083381506775,"score_spread":0.22962874848124243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379523218","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":[0.19885856,0.0000042885995,0.79826516,0.00017714319,0.00017489413,0.00011855265,0.0000010278803,0.0012413216,0.0011590518],"genre_scores_gemma":[0.9678558,0.00002372926,0.030722897,0.00033491224,0.000116395364,0.000048860322,0.0000015668134,0.00000956512,0.0008862682],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992536,0.00001837062,0.0001530989,0.0002695035,0.00012173396,0.00018374094],"domain_scores_gemma":[0.9994904,0.000028352211,0.000038196573,0.00034798367,0.00004776313,0.00004734066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014101889,0.00008105911,0.000078532,0.00016412331,0.00024394483,0.000085583015,0.00029778757,0.00007610504,0.00003673935],"category_scores_gemma":[0.000020515452,0.00008002388,0.00007049074,0.0012395018,0.00001585229,0.00031578587,0.0001204166,0.00008594513,0.00025734262],"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":[0.000014175058,0.00009139576,0.00028794742,0.000030383526,0.000014465245,0.000012646905,0.00019912246,0.0017171567,0.1603067,0.03438692,0.0038618376,0.7990772],"study_design_scores_gemma":[0.00024828437,0.000104653256,0.008304338,0.000018081355,0.0000063519774,0.000021473194,0.00002420283,0.7359644,0.2136961,0.024806656,0.016414816,0.00039064637],"about_ca_topic_score_codex":0.00008519302,"about_ca_topic_score_gemma":0.000017370827,"teacher_disagreement_score":0.7986866,"about_ca_system_score_codex":0.000041689575,"about_ca_system_score_gemma":0.00004323504,"threshold_uncertainty_score":0.33077046},"labels":[],"label_agreement":null},{"id":"W4379798269","doi":"10.21203/rs.3.rs-3024402/v1","title":"SSIVD-Net: A Novel Salient Super Image Classification &amp;amp; Detection Technique for Weaponized Violence","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Salient; Computer science; Scalability; Artificial intelligence; Benchmark (surveying); Classifier (UML); Inference; Machine learning; Computer security","score_opus":0.15689103267216678,"score_gpt":0.42581807236594704,"score_spread":0.2689270396937803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379798269","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020103813,0.00007418797,0.98729634,0.0021941254,0.00026476674,0.0060279225,0.00021568783,0.0016508228,0.00026576177],"genre_scores_gemma":[0.3899046,0.000926511,0.554566,0.00007811836,0.0005328258,0.050513253,0.0004133909,0.0001760554,0.0028892348],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.995362,0.00029106333,0.00066962425,0.0016911207,0.001123442,0.000862778],"domain_scores_gemma":[0.994532,0.00053166336,0.00026914818,0.0026871616,0.0017322708,0.0002477662],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0030778828,0.00039081636,0.00039152303,0.0009878763,0.00081247493,0.00071335636,0.0021578076,0.00067201426,0.000032611544],"category_scores_gemma":[0.0006096872,0.0003975354,0.0003596134,0.0015042489,0.00023355459,0.00031593937,0.0020079822,0.0015850022,0.00050949946],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009355275,0.00056338956,0.000053005722,0.0012022797,0.00006575563,0.0000027483163,0.00037735427,0.00014141826,0.90129447,0.027133383,0.008884878,0.060187764],"study_design_scores_gemma":[0.0013284031,0.000606383,0.005639187,0.0029538479,0.00005246559,0.000047723595,0.00026705785,0.108026855,0.3939309,0.2618687,0.22282341,0.002455051],"about_ca_topic_score_codex":0.0004932323,"about_ca_topic_score_gemma":0.00020409259,"teacher_disagreement_score":0.50736356,"about_ca_system_score_codex":0.0007038673,"about_ca_system_score_gemma":0.0004748959,"threshold_uncertainty_score":0.99984765},"labels":[],"label_agreement":null},{"id":"W4379983651","doi":"10.1109/lsens.2023.3284652","title":"One-Class Classification for Radar-Based Human Fall Event Detection","year":2023,"lang":"en","type":"article","venue":"IEEE Sensors Letters","topic":"Anomaly Detection Techniques and Applications","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":"National Research Council Canada; Carleton University","funders":"","keywords":"Radar; Computer science; Artificial intelligence; Event (particle physics); Feature (linguistics); Class (philosophy); Anomaly detection; Machine learning; Pattern recognition (psychology); Telecommunications","score_opus":0.04314910158031344,"score_gpt":0.28781886507876475,"score_spread":0.24466976349845132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379983651","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":[0.22123224,0.0000010855074,0.7673458,0.009684635,0.00021619331,0.0004667314,0.0000064462197,0.00096938945,0.000077515055],"genre_scores_gemma":[0.98689044,0.0000021331696,0.010677403,0.0014754845,0.00015908285,0.00037083658,0.00001714476,0.000022297416,0.00038515936],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987707,0.000047252444,0.00025752326,0.00043774332,0.00021163067,0.00027513367],"domain_scores_gemma":[0.99910307,0.00008475614,0.00013797416,0.00053909904,0.000065886954,0.000069208625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023719486,0.00012859919,0.00011945131,0.0002491228,0.0003758869,0.00009501699,0.00037845448,0.00007879965,0.0000021263897],"category_scores_gemma":[0.000011766191,0.00014861536,0.00013632058,0.00063490384,0.000043971937,0.00015208859,0.000022617298,0.00011505092,0.00010663609],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000048455954,0.000035796013,0.000020778023,0.000016050155,0.000010744626,9.247757e-7,0.0000521056,0.0007985607,0.9741378,0.0024745308,0.0063993516,0.016048523],"study_design_scores_gemma":[0.0005631965,0.00016448864,0.006500904,0.000021670447,0.000020633432,0.0000037404657,0.000024353936,0.29801923,0.645402,0.0019293164,0.046891324,0.00045912212],"about_ca_topic_score_codex":0.000027197513,"about_ca_topic_score_gemma":0.000018072496,"teacher_disagreement_score":0.7656582,"about_ca_system_score_codex":0.00011731667,"about_ca_system_score_gemma":0.000017262126,"threshold_uncertainty_score":0.6060359},"labels":[],"label_agreement":null},{"id":"W4380048172","doi":"10.1007/978-981-99-3581-9_10","title":"Several Misconceptions and Misuses of Deep Neural Networks and Deep Learning","year":2023,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Deep learning; Artificial intelligence; Deep neural networks; Computer science; Point (geometry); Artificial neural network; Cognitive science; Psychology; Mathematics","score_opus":0.028580471860879267,"score_gpt":0.2781438316203467,"score_spread":0.24956335975946747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380048172","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":[0.0002387031,0.0005652868,0.9834961,0.00049620355,0.000076889424,0.00028234496,0.000003389834,0.000169899,0.0146711795],"genre_scores_gemma":[0.7831729,0.01729926,0.19726059,0.00059926143,0.000055178305,0.000096185344,0.000048213504,0.0000229352,0.001445474],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988716,0.0000267441,0.00050225895,0.0002422683,0.00019436532,0.00016274108],"domain_scores_gemma":[0.9983493,0.0002192713,0.00029865513,0.0008289781,0.00021659321,0.00008719313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004704569,0.00015890333,0.00020320465,0.00055748643,0.0005638147,0.0003285061,0.0010623954,0.00010924968,0.00000281403],"category_scores_gemma":[0.000024574914,0.00016428086,0.000030454386,0.0004113448,0.001021236,0.00247464,0.0017516447,0.00038433008,0.0000036617778],"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":[0.0000011097891,0.0000067815677,0.00014466666,0.000022647468,0.0000050203953,1.5021982e-7,0.0012360086,0.003200334,0.000003287592,0.468292,0.000018221794,0.52706975],"study_design_scores_gemma":[0.00010879596,0.000048885624,0.002532154,0.000047788624,0.0000049468317,0.000025274932,0.000041877654,0.98514485,0.0000033357858,0.004115948,0.007756003,0.00017013526],"about_ca_topic_score_codex":0.000016025517,"about_ca_topic_score_gemma":0.000017344295,"teacher_disagreement_score":0.9819445,"about_ca_system_score_codex":0.000031725715,"about_ca_system_score_gemma":0.000033797078,"threshold_uncertainty_score":0.66991794},"labels":[],"label_agreement":null},{"id":"W4381199109","doi":"10.1109/tvt.2023.3285599","title":"Multivariate Variance-Based Genetic Ensemble Learning for Satellite Anomaly Detection","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Anomaly Detection Techniques and Applications","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":"National Research Council Canada; Actua; University of Waterloo","funders":"","keywords":"Anomaly detection; Computer science; Artificial intelligence; Bootstrapping (finance); Ensemble learning; Multivariate statistics; Random forest; Machine learning; Boosting (machine learning); Time series; Data mining; Mathematics","score_opus":0.014485417910878491,"score_gpt":0.24915101959235617,"score_spread":0.23466560168147768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381199109","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":[0.02806109,0.000031569132,0.96554196,0.0011959627,0.00024707403,0.00064203114,0.0000042848565,0.0042272224,0.000048815105],"genre_scores_gemma":[0.9231754,0.000046697904,0.07496645,0.000098074415,0.000023524224,0.0012743948,0.0000024972933,0.000032918084,0.0003800271],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99843174,0.000053374544,0.00030230105,0.0006367627,0.00015539982,0.00042043324],"domain_scores_gemma":[0.9988985,0.00011619317,0.000117839765,0.0006658745,0.0001366698,0.000064898966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019785627,0.00021106825,0.00020435659,0.0008978093,0.0006434345,0.000069024245,0.00050867366,0.00035226948,0.0000067181036],"category_scores_gemma":[0.000012985898,0.00022847916,0.00017792694,0.0022690727,0.00007788853,0.00012978306,0.000005733496,0.00040055547,0.00017574741],"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":[0.00002478321,0.0001274984,0.000034498702,0.00002494328,0.000046741643,0.000011681104,0.00003967288,0.07736891,0.23060499,0.0027021936,0.000016366384,0.68899775],"study_design_scores_gemma":[0.00043113946,0.0003880903,0.00043787484,0.000015023491,0.00002188342,0.000021773585,0.000017176635,0.41992563,0.56221956,0.0034222072,0.012859379,0.00024028713],"about_ca_topic_score_codex":0.000025984997,"about_ca_topic_score_gemma":0.000024787974,"teacher_disagreement_score":0.8951143,"about_ca_system_score_codex":0.00008950552,"about_ca_system_score_gemma":0.000052309024,"threshold_uncertainty_score":0.93171096},"labels":[],"label_agreement":null},{"id":"W4381232547","doi":"10.1007/978-3-031-34190-8_34","title":"Anomaly Detection in Ultrasonic Monitoring System Using Unsupervised Machine Learning","year":2023,"lang":"en","type":"book-chapter","venue":"Applied condition monitoring","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Anomaly detection; Computer science; Ultrasonic sensor; Outlier; Pipeline transport; Artificial intelligence; Data mining; Machine learning; Real-time computing; Engineering","score_opus":0.02888328942053726,"score_gpt":0.251505492532375,"score_spread":0.22262220311183772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381232547","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":[0.04128572,0.00061627215,0.83437896,0.000052181324,0.0044241925,0.002986376,0.0000500773,0.011246535,0.10495967],"genre_scores_gemma":[0.98378384,0.00018048586,0.005253592,0.000003851044,0.00086692534,0.0003270002,0.00002658296,0.00015101595,0.009406693],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972537,0.000034317436,0.000746036,0.0009842982,0.000498786,0.0004828201],"domain_scores_gemma":[0.9984425,0.00014289642,0.0004845117,0.00068657927,0.00010775132,0.00013577717],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037284143,0.0005249505,0.00050495344,0.00083435944,0.0005577265,0.00026456852,0.00065105414,0.00052000204,0.000010186402],"category_scores_gemma":[0.0000094456,0.00064923975,0.00017920155,0.0004610624,0.00004510786,0.0003403291,0.00023577311,0.0011186809,0.0001981594],"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":[0.000058422993,0.00009106408,0.0030416148,0.000927048,0.00037180362,0.00023160275,0.00086671003,0.01294694,0.26338184,0.49987245,0.000014470282,0.21819603],"study_design_scores_gemma":[0.0042008897,0.0005879723,0.010084142,0.006949523,0.0005333952,0.00046902572,0.0015024386,0.19509149,0.72230387,0.03328064,0.016720684,0.008275958],"about_ca_topic_score_codex":0.00013908163,"about_ca_topic_score_gemma":0.000008293205,"teacher_disagreement_score":0.94249815,"about_ca_system_score_codex":0.00094780175,"about_ca_system_score_gemma":0.000069806214,"threshold_uncertainty_score":0.9995959},"labels":[],"label_agreement":null},{"id":"W4381852594","doi":"10.1016/j.neucom.2023.126483","title":"MDGAD: Meta domain generalization for distribution drift in anomaly detection","year":2023,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Anomaly detection; Computer science; Generalization; Artificial intelligence; Concept drift; Merge (version control); Pattern recognition (psychology); Robustness (evolution); Data mining; Test set; Metric (unit); Machine learning; Mathematics; Data stream mining","score_opus":0.029267535230490128,"score_gpt":0.2756240665815084,"score_spread":0.24635653135101826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381852594","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":[0.101767056,0.000010740036,0.8961299,0.00058059156,0.00012929458,0.00044363228,0.0000049454293,0.0008722209,0.000061640196],"genre_scores_gemma":[0.97631955,0.000006831241,0.02299731,0.00018373033,0.00010175133,0.0002871597,0.000033083124,0.000013766783,0.000056823057],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988601,0.000059284997,0.00028089024,0.0004145464,0.00012271032,0.00026246987],"domain_scores_gemma":[0.9994192,0.00009386789,0.000114367256,0.0002717078,0.000060449114,0.000040370676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038243827,0.000116370444,0.00014462064,0.00017262046,0.00026575814,0.00010491001,0.00029955123,0.000063811,0.0000014008392],"category_scores_gemma":[0.00003021764,0.0001205675,0.00010903722,0.0014443164,0.000012771196,0.00028289043,0.00012818672,0.000092924965,0.000013691952],"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":[0.00003765609,0.00024383608,0.0019914208,0.00013586845,0.00013108618,0.000020713278,0.0006914305,0.04575785,0.15656413,0.31395787,0.0037736334,0.47669452],"study_design_scores_gemma":[0.00029531762,0.000091765505,0.012670132,0.0000067662004,0.00002084187,0.000012642963,0.000013077093,0.91110146,0.031435937,0.0118303895,0.032302316,0.00021933776],"about_ca_topic_score_codex":0.000030404417,"about_ca_topic_score_gemma":0.000019236246,"teacher_disagreement_score":0.8745525,"about_ca_system_score_codex":0.000046291087,"about_ca_system_score_gemma":0.000016005724,"threshold_uncertainty_score":0.49166},"labels":[],"label_agreement":null},{"id":"W4381955429","doi":"10.1016/b978-0-443-15299-3.00007-5","title":"Review of using machine learning in secure IoT healthcare","year":2023,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Government of Canada; Ontario Tech University; University of Victoria","funders":"","keywords":"Computer security; Computer science; Insider; Identity theft; Authentication (law); Confidentiality; Insider threat; Information security","score_opus":0.03611254319424456,"score_gpt":0.2948727157646439,"score_spread":0.25876017257039935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381955429","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000009081967,0.059674866,0.0071876617,0.0012029221,0.00022565319,0.0016566605,0.000032745626,0.0008191222,0.9291913],"genre_scores_gemma":[0.00024021139,0.021411944,0.015822884,0.0013498144,0.00010466659,0.00008367565,0.000018025676,0.00009153781,0.96087724],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985579,0.00004279264,0.0005755397,0.00041866663,0.00021850226,0.00018658728],"domain_scores_gemma":[0.9987633,0.000039931467,0.00042545825,0.0005926488,0.000113820905,0.00006483723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003947499,0.00022846283,0.0004911051,0.00022695932,0.0000829374,0.000017588982,0.0005329704,0.00019181942,0.000021520522],"category_scores_gemma":[0.000014522882,0.00022951102,0.00017792352,0.00009402473,0.000041557723,0.000027845055,0.00030293752,0.00064478844,0.000029769308],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.9486445e-7,0.0000027854092,0.0000074596637,0.0022905022,0.000009157554,0.00000668958,0.0000674489,0.0000025002626,0.000014558688,0.041504152,0.000031446863,0.9560627],"study_design_scores_gemma":[0.00005948428,0.00004858505,0.000005186507,0.016201548,0.00001788182,0.000021337035,0.0000017756939,0.001846903,0.00006981508,0.013311392,0.9681311,0.000284993],"about_ca_topic_score_codex":0.000014799247,"about_ca_topic_score_gemma":0.00006641505,"teacher_disagreement_score":0.96809965,"about_ca_system_score_codex":0.000086485685,"about_ca_system_score_gemma":0.00013770325,"threshold_uncertainty_score":0.93591875},"labels":[],"label_agreement":null},{"id":"W4382239711","doi":"10.1609/aaai.v37i6.25948","title":"Estimating Regression Predictive Distributions with Sample Networks","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Anomaly Detection Techniques and Applications","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":"Canada Research Chairs; University of Toronto; Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"Institut de Valorisation des Données","keywords":"Parametric statistics; Range (aeronautics); Computer science; Divergence (linguistics); Parametric model; Regression; Posterior predictive distribution; Sample (material); Statistics; Data mining; Econometrics; Artificial intelligence; Mathematics; Bayesian linear regression; Engineering","score_opus":0.05379158691958716,"score_gpt":0.3014396315167004,"score_spread":0.24764804459711323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382239711","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":[0.021278877,0.000002783071,0.9744536,0.0019584224,0.00011352532,0.00032250583,0.00001052328,0.00042907352,0.0014306639],"genre_scores_gemma":[0.97079426,0.000011824815,0.028887765,0.00003823964,0.00005323654,0.00011502486,0.00000231577,0.000007794573,0.00008955609],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882555,0.000008652953,0.00028828395,0.0003541025,0.00027742024,0.00024599404],"domain_scores_gemma":[0.9988685,0.00011679561,0.000271662,0.00026591722,0.0004144706,0.00006259988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025759297,0.00013975694,0.00013855328,0.0000857084,0.00041695926,0.00015284207,0.0010420553,0.00006209277,0.000014470826],"category_scores_gemma":[0.00019568558,0.00009072348,0.00006041248,0.0014617804,0.00019039623,0.0002598462,0.00030142057,0.0002276246,0.000025534793],"study_design_candidate":"theoretical_or_conceptual","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":[0.000022776348,0.000056615427,0.00027863638,0.000011255541,0.000008972696,2.0751284e-7,0.00035026728,0.0024531921,0.0023800174,0.9337297,0.00044319607,0.060265172],"study_design_scores_gemma":[0.000009745626,0.00015005124,0.00032952137,0.00016270952,0.0000061319934,0.000002642906,0.00015593493,0.7716027,0.0940927,0.13331306,0.00006861834,0.000106173415],"about_ca_topic_score_codex":0.00003391085,"about_ca_topic_score_gemma":0.0000032346484,"teacher_disagreement_score":0.94951534,"about_ca_system_score_codex":0.000033506178,"about_ca_system_score_gemma":0.00004690356,"threshold_uncertainty_score":0.3699596},"labels":[],"label_agreement":null},{"id":"W4382318225","doi":"10.1609/aaai.v37i13.26968","title":"Towards Safe Reinforcement Learning via OOD Dynamics Detection in Autonomous Driving System (Student Abstract)","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Anomaly Detection Techniques and Applications","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":"Thales (Canada)","funders":"","keywords":"Reinforcement learning; Software deployment; Variety (cybernetics); Relevance (law); Computer science; Dynamics (music); Artificial intelligence; Reinforcement; Function (biology); State (computer science); Human–computer interaction; Psychology; Social psychology; Algorithm","score_opus":0.03409419949122906,"score_gpt":0.28847391439392367,"score_spread":0.2543797149026946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382318225","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":[0.28841257,0.0000043266573,0.6913485,0.0012218005,0.00036726292,0.0007838463,9.1467604e-7,0.00078759494,0.017073186],"genre_scores_gemma":[0.9986983,0.000024208028,0.0008385824,0.000021112235,0.00003345901,0.00013262322,6.41945e-7,0.000011793465,0.00023928855],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982049,0.0000115562225,0.0006309825,0.0004240722,0.00041021843,0.00031828403],"domain_scores_gemma":[0.99897987,0.000041787676,0.00039945662,0.00024247919,0.00027427397,0.00006212096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006500174,0.00018044547,0.0002030866,0.0002707473,0.00028307398,0.00021051356,0.0011948749,0.00009935927,0.000013024977],"category_scores_gemma":[0.00007733578,0.00015687876,0.00009744713,0.0012372838,0.00007687488,0.00029265886,0.0004180219,0.0003852382,0.000104397426],"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":[0.000012840932,0.00006738969,0.00034341784,0.000067651,0.000010961562,9.570059e-7,0.0010619484,0.0050537516,0.035398617,0.6342457,0.0000085853435,0.3237282],"study_design_scores_gemma":[0.000016644237,0.00012245597,0.002384497,0.00016936574,0.000004423897,0.0000044587205,0.0010813904,0.68341917,0.30343756,0.009157559,0.00004514058,0.00015736386],"about_ca_topic_score_codex":0.00016861668,"about_ca_topic_score_gemma":0.00008256515,"teacher_disagreement_score":0.7102857,"about_ca_system_score_codex":0.00033444585,"about_ca_system_score_gemma":0.00006091517,"threshold_uncertainty_score":0.63973296},"labels":[],"label_agreement":null},{"id":"W4382394861","doi":"10.18280/ts.400341","title":"Leveraging and Refining Image Recognition Technology for Intelligent Logistics Sorting Systems","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Robustness (evolution); Computer science; Artificial intelligence; Sorting; sort; Anomaly detection; Machine learning; Pattern recognition (psychology); Data mining; Object detection; Algorithm","score_opus":0.06366102505898633,"score_gpt":0.28672543522097027,"score_spread":0.22306441016198394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382394861","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":[0.01500389,0.000032107982,0.98297626,0.00064185465,0.00006651647,0.0002487943,0.000005997842,0.00080890424,0.00021568811],"genre_scores_gemma":[0.9305421,0.000028303213,0.06880134,0.00006351115,0.00006707089,0.00039073054,0.000014138757,0.000009839707,0.000082951396],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917114,0.00001357966,0.00026003405,0.00026660596,0.00009042692,0.0001982195],"domain_scores_gemma":[0.99956304,0.000078742014,0.00011059338,0.0001323162,0.00008060342,0.000034677323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037959524,0.000085002335,0.00009911763,0.00020603956,0.00023139348,0.00012181469,0.00018229989,0.000049059017,0.0000051875263],"category_scores_gemma":[0.000026147676,0.00008644497,0.000028317449,0.00039042337,0.00003699357,0.00011454608,0.00010072368,0.00007370562,0.00001794333],"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":[0.000009708281,0.00006271608,0.0005396386,0.00015747297,0.000041376043,0.000009959636,0.0006279093,0.0008476379,0.04511958,0.11063762,0.0016227905,0.84032357],"study_design_scores_gemma":[0.00046206915,0.00037714868,0.00032175923,0.00014724028,0.000029891096,0.000051057043,0.0011523361,0.88941246,0.045033336,0.04174338,0.02080005,0.00046928434],"about_ca_topic_score_codex":0.000008795198,"about_ca_topic_score_gemma":7.037748e-7,"teacher_disagreement_score":0.9155382,"about_ca_system_score_codex":0.00003221967,"about_ca_system_score_gemma":0.000013414573,"threshold_uncertainty_score":0.35251236},"labels":[],"label_agreement":null},{"id":"W4382795691","doi":"10.1038/s41598-023-37686-w","title":"A new detection algorithm for alien intrusion on highway","year":2023,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Dalhousie University","funders":"","keywords":"Computer science; Intrusion detection system; Intrusion; Set (abstract data type); Feature (linguistics); Data mining; Data set; Object (grammar); Feature extraction; Pattern recognition (psychology); Artificial intelligence; Algorithm","score_opus":0.0163893462890505,"score_gpt":0.2656549660213553,"score_spread":0.2492656197323048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382795691","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":[0.0028923748,0.000005184364,0.99002427,0.00042383507,0.0041894345,0.0004682414,0.0000012878154,0.0013286651,0.00066670333],"genre_scores_gemma":[0.5478877,0.0000088414345,0.40103143,0.00022809335,0.00059854245,0.000781476,0.00006201274,0.000044148313,0.04935779],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984169,0.000013433771,0.00027319236,0.00074152596,0.0003135427,0.00024142985],"domain_scores_gemma":[0.9986716,0.000035827237,0.00016044483,0.0009129874,0.00010495702,0.000114200346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007482557,0.00009690015,0.00009254616,0.00030443183,0.00057214434,0.0003749987,0.00027781067,0.00006100187,0.000014121476],"category_scores_gemma":[0.000036177564,0.00008812732,0.00009236305,0.0015041527,0.000034032157,0.00022636991,0.00013171301,0.00006491052,0.00012866624],"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":[8.5019167e-7,0.000014417366,0.0000037583195,0.0000023608,0.0000026665696,0.000011639176,0.00006533403,0.000022932098,0.016256124,0.002595921,0.05233827,0.9286857],"study_design_scores_gemma":[0.00007680575,0.000097845426,0.00015762662,0.000010824356,0.0000035675084,0.00006436827,0.000009572155,0.07479504,0.33943427,0.11145869,0.4737339,0.00015748541],"about_ca_topic_score_codex":0.000022051334,"about_ca_topic_score_gemma":0.000005916091,"teacher_disagreement_score":0.92852825,"about_ca_system_score_codex":0.00005062228,"about_ca_system_score_gemma":0.0000798612,"threshold_uncertainty_score":0.44005296},"labels":[],"label_agreement":null},{"id":"W4383221436","doi":"10.1145/3579856.3595790","title":"Going Haywire: False Friends in Federated Learning and How to Find Them","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Backdoor; Computer science; Outlier; Anomaly detection; Reputation; Reliability (semiconductor); Deep learning; Curse of dimensionality; Set (abstract data type); Artificial intelligence; Computer security; Data mining; Power (physics)","score_opus":0.021982167058127864,"score_gpt":0.2637401798576258,"score_spread":0.24175801279949796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383221436","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":[0.30089882,0.0000053239646,0.68794006,0.005554852,0.000020943442,0.00011887114,2.612999e-7,0.00081611925,0.0046447753],"genre_scores_gemma":[0.97697794,0.000013556818,0.012156888,0.00014212429,0.00001030366,0.000047334142,6.7850027e-7,0.000004671142,0.010646528],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995185,0.000017775996,0.00006562956,0.00018913412,0.00006708008,0.00014188827],"domain_scores_gemma":[0.99976367,0.00004473876,0.000018415203,0.0001038308,0.000018977033,0.00005040025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015894283,0.000056140652,0.000061544175,0.00009679598,0.00015938783,0.00022218576,0.00013007004,0.000031670465,0.000012758169],"category_scores_gemma":[0.000020749567,0.000051400602,0.000012405202,0.00094991905,0.0000086666105,0.0001385579,0.00017881124,0.000086818916,0.00010130446],"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":[0.000006340727,0.00006872953,0.023262074,0.000018832097,0.0000138770565,0.000022162967,0.0020938984,0.00027937273,0.043768663,0.17130154,0.012277103,0.7468874],"study_design_scores_gemma":[0.00045693075,0.0002547495,0.059076194,0.0000521178,0.000003434949,0.000038033355,0.001058101,0.65813607,0.06350945,0.0070720194,0.209651,0.0006919278],"about_ca_topic_score_codex":0.000015542202,"about_ca_topic_score_gemma":0.000017359826,"teacher_disagreement_score":0.7461955,"about_ca_system_score_codex":0.000013449513,"about_ca_system_score_gemma":0.000010243195,"threshold_uncertainty_score":0.21425423},"labels":[],"label_agreement":null},{"id":"W4383876878","doi":"10.3390/computation11070139","title":"Incremental Learning-Based Algorithm for Anomaly Detection Using Computed Tomography Data","year":2023,"lang":"en","type":"article","venue":"Computation","topic":"Anomaly Detection Techniques and Applications","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":"Ontario Tech University","funders":"Mitacs","keywords":"Anomaly detection; Thresholding; Computer science; Artificial intelligence; Anomaly (physics); Data mining; Machine learning; Pattern recognition (psychology); Algorithm; Image (mathematics)","score_opus":0.05954382673452217,"score_gpt":0.32214944380375604,"score_spread":0.26260561706923385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383876878","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":[0.015328166,0.000018129787,0.98231083,0.000101918864,0.00019317094,0.0004634843,0.000018677492,0.0015498801,0.000015713542],"genre_scores_gemma":[0.6025182,0.0000012176168,0.39705676,0.000058103895,0.00007099168,0.000046112003,0.00022795514,0.0000122515885,0.000008433168],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886644,0.000055517427,0.00023023882,0.0004737985,0.00017447384,0.0001995577],"domain_scores_gemma":[0.99918276,0.00011009559,0.00014714549,0.00037225848,0.00013418442,0.00005357385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034382145,0.000117758944,0.00011151327,0.0003595394,0.0004554305,0.00016121496,0.00052498345,0.00006265764,0.000001445692],"category_scores_gemma":[0.000012438668,0.00013513777,0.00006307662,0.0014772674,0.000029433855,0.00039962583,0.00022726967,0.000096493895,0.000016534193],"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":[0.0000035858802,0.000042652144,0.00014504038,0.000014615742,0.00001615982,0.000001108097,0.000035088102,0.028669734,0.0036270525,0.00020413692,0.00029561037,0.96694523],"study_design_scores_gemma":[0.00029376952,0.00011896679,0.0021757712,0.000009369332,0.000009985635,0.0000047878602,0.000015529711,0.98629004,0.00835837,0.0008838813,0.0016885072,0.00015103591],"about_ca_topic_score_codex":0.000045150206,"about_ca_topic_score_gemma":0.0000047561657,"teacher_disagreement_score":0.9667942,"about_ca_system_score_codex":0.00005160481,"about_ca_system_score_gemma":0.000043267042,"threshold_uncertainty_score":0.5510759},"labels":[],"label_agreement":null},{"id":"W4383903547","doi":"10.1109/tnsm.2023.3293806","title":"ML Models for Detecting QoE Degradation in Low-Latency Applications: A Cloud-Gaming Case Study","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Agence Nationale de la Recherche","keywords":"Computer science; Anomaly detection; Robustness (evolution); Cloud computing; Data mining; Latency (audio); Machine learning; Metrics; Artificial intelligence; Real-time computing; Computer network","score_opus":0.02832418174741278,"score_gpt":0.2668916230136526,"score_spread":0.23856744126623983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383903547","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":[0.034671597,0.000016908341,0.9617305,0.0003460532,0.00012573405,0.0023629915,0.0000029251057,0.0005391664,0.00020411993],"genre_scores_gemma":[0.9738206,0.0001744036,0.020671882,0.00026350166,0.000054650118,0.004836847,0.0000024965802,0.000019270063,0.00015631528],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986727,0.000042704658,0.00033420915,0.000517452,0.00013846502,0.0002944174],"domain_scores_gemma":[0.9992437,0.00010023266,0.00008006458,0.0004585822,0.000057496694,0.000059945178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042986393,0.00016011513,0.00014261626,0.0002837015,0.00063402735,0.00012140847,0.00026539652,0.00005402178,0.0000016097634],"category_scores_gemma":[3.34981e-7,0.0001728903,0.000049092345,0.0019309935,0.0000090272015,0.0002730499,0.000015250005,0.00013446473,0.000009931647],"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":[0.000014836301,0.00029718212,0.000015393529,0.0001319057,0.00004922351,0.000046145637,0.0009526524,0.5025407,0.000011771143,0.0038096688,0.000041922787,0.4920886],"study_design_scores_gemma":[0.0005211525,0.000116049116,0.00006613121,0.000045750356,0.000038304373,0.000033102562,0.0022199033,0.99084026,0.0000802545,0.0052550384,0.0005667237,0.00021733175],"about_ca_topic_score_codex":0.00021840868,"about_ca_topic_score_gemma":0.0009270251,"teacher_disagreement_score":0.94105864,"about_ca_system_score_codex":0.000056192184,"about_ca_system_score_gemma":0.000010580301,"threshold_uncertainty_score":0.7050262},"labels":[],"label_agreement":null},{"id":"W4384158775","doi":"10.1109/i2mtc53148.2023.10176031","title":"Ambient-Aware Sound-Based Production Counter for Manufacturing Machines","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Production line; Task (project management); Production (economics); Tracking (education); Work (physics); Real-time computing; Productivity; Artificial intelligence; Engineering; Mechanical engineering","score_opus":0.023509713812972465,"score_gpt":0.281238990793179,"score_spread":0.25772927698020653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384158775","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":[0.025172418,0.0000015924356,0.97014505,0.0024317394,0.00019461827,0.0003333647,0.000003998307,0.0014768465,0.00024039143],"genre_scores_gemma":[0.96743083,0.0000016588572,0.025777513,0.00034902687,0.00009691184,0.00041339532,0.0000122548445,0.000009302379,0.005909104],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99937433,0.0000064185324,0.00010721856,0.00027565082,0.000092465154,0.00014389427],"domain_scores_gemma":[0.9995505,0.000023620574,0.000036125017,0.0003154228,0.000046130797,0.00002815798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012046631,0.00007053182,0.00005633112,0.00010149564,0.00020499087,0.00008519463,0.0002450662,0.000029450812,0.00001982319],"category_scores_gemma":[0.00000590682,0.000060650495,0.00005308669,0.00022178146,0.000014540828,0.00017086019,0.000050668714,0.000038873797,0.0000801336],"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":[0.000058374215,0.00043193495,0.0033341395,0.00040137063,0.00008484736,0.0000064078413,0.0006838534,0.008827228,0.035270885,0.15516904,0.42233938,0.37339255],"study_design_scores_gemma":[0.00020829232,0.000083290215,0.0025687593,0.000010804913,0.000005617117,0.0000062561676,0.00002696887,0.40820646,0.4808117,0.023981607,0.083825536,0.00026470536],"about_ca_topic_score_codex":0.00001939314,"about_ca_topic_score_gemma":0.000021638863,"teacher_disagreement_score":0.9443675,"about_ca_system_score_codex":0.000022933345,"about_ca_system_score_gemma":0.000012235141,"threshold_uncertainty_score":0.24732554},"labels":[],"label_agreement":null},{"id":"W4384159604","doi":"10.2139/ssrn.4502662","title":"Deep Unsupervised Anomaly Detection in High-Frequency Markets","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université de Montréal","funders":"","keywords":"Anomaly detection; Computer science; Artificial intelligence; Autoencoder; Transformer; Subsequence; Anomaly (physics); Pattern recognition (psychology); Data mining; Deep learning; Engineering; Mathematics","score_opus":0.00629425089580919,"score_gpt":0.22364775057408945,"score_spread":0.21735349967828027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384159604","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":[0.28066385,0.00019817213,0.717045,0.0009323921,0.000131709,0.00013844238,3.0714307e-7,0.00039384683,0.00049626874],"genre_scores_gemma":[0.99566835,0.0014357745,0.0022152078,0.00006907585,0.00010193629,0.000052637693,0.0000014070998,0.000015468366,0.00044011496],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976376,0.00009183751,0.0003093313,0.00029843624,0.00021767602,0.0014450753],"domain_scores_gemma":[0.99938065,0.000039725568,0.0001075268,0.0003339942,0.00006447155,0.00007363643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012910796,0.00013356772,0.00013405643,0.00045216794,0.0002582103,0.00010980911,0.0006937073,0.00009160773,0.000014136121],"category_scores_gemma":[0.00002788283,0.00013167017,0.00008650569,0.001705662,0.000022298382,0.00045730712,0.0000862757,0.0010429433,0.00012471004],"study_design_candidate":"theoretical_or_conceptual","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":[0.000017822054,0.00008353378,0.0029776103,0.0000062856775,0.000042168358,0.000022100277,0.00014640459,0.00013848938,0.010188752,0.4528506,0.000048551996,0.53347766],"study_design_scores_gemma":[0.00069260097,0.00028514068,0.046757124,0.000013999024,0.000008365145,0.000533715,0.00024052107,0.034165747,0.0036620526,0.9126177,0.00067323545,0.00034981428],"about_ca_topic_score_codex":0.00014549513,"about_ca_topic_score_gemma":0.0011765296,"teacher_disagreement_score":0.71500456,"about_ca_system_score_codex":0.0006690087,"about_ca_system_score_gemma":0.00045345273,"threshold_uncertainty_score":0.5369353},"labels":[],"label_agreement":null},{"id":"W4384209482","doi":"10.1145/3591569.3591605","title":"Unsupervised Anomaly Detection using Deep Autoencoding Mixture of Probabilistic Principal Component Analyzers","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Autoencoder; Principal component analysis; Computer science; Probabilistic logic; Dimensionality reduction; Artificial intelligence; Pattern recognition (psychology); Baseline (sea); Anomaly detection; Artificial neural network; Deep learning; Data mining; Machine learning","score_opus":0.027841211545470042,"score_gpt":0.2686463061320034,"score_spread":0.24080509458653337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384209482","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":[0.3459729,0.000005877565,0.6526784,0.00008561588,0.00005534556,0.00020241916,9.737503e-7,0.0006140054,0.00038448066],"genre_scores_gemma":[0.9303014,0.0000044919375,0.0695101,0.0000292584,0.000023399956,0.000035342884,0.000002224202,0.0000085729735,0.00008520567],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988971,0.00004009274,0.00030633894,0.00033466588,0.00020598747,0.00021585169],"domain_scores_gemma":[0.99921596,0.000049048223,0.000116527604,0.0004414678,0.000105824925,0.00007118989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002364858,0.00011759414,0.00015709527,0.00026696257,0.00018872318,0.000052720137,0.00041165831,0.00007080107,0.00002291245],"category_scores_gemma":[0.000021481148,0.000109212226,0.000097399476,0.0015769094,0.00004729049,0.00020188495,0.00017759984,0.000097361735,0.000016643224],"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":[0.00002283387,0.00028583987,0.0048179245,0.00022208544,0.000109758555,0.000016669223,0.0012475539,0.04859957,0.7792897,0.091069855,0.00010181121,0.074216366],"study_design_scores_gemma":[0.00009694867,0.000050053928,0.0066285864,0.000011235691,0.000010682515,0.0000112922135,0.00004535792,0.93771225,0.053652324,0.0013507176,0.00030036163,0.00013016914],"about_ca_topic_score_codex":0.000115192,"about_ca_topic_score_gemma":0.000041973577,"teacher_disagreement_score":0.8891127,"about_ca_system_score_codex":0.00008101487,"about_ca_system_score_gemma":0.000037285634,"threshold_uncertainty_score":0.44535452},"labels":[],"label_agreement":null},{"id":"W4384519193","doi":"10.1109/access.2023.3296311","title":"5G Aviation Networks Using Novel AI Approach for DDoS Detection","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":15,"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":"Cranfield University; Government of the United Kingdom; Department of Transport, UK Government","keywords":"Computer science; Denial-of-service attack; Deep learning; Convolutional neural network; Artificial intelligence; Robustness (evolution); Feature extraction; Data mining; Machine learning; Real-time computing; Pattern recognition (psychology)","score_opus":0.07753624705448252,"score_gpt":0.3513351605599842,"score_spread":0.2737989135055017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384519193","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":[0.0050685974,0.000005913141,0.99308866,0.000115014365,0.00030047394,0.00046705324,0.0000027944661,0.0007876504,0.0001638271],"genre_scores_gemma":[0.9555772,0.0000053794643,0.043465283,0.000227507,0.0002479314,0.00036510624,0.000007381434,0.00001325302,0.000090956266],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992012,0.000011259554,0.00016692434,0.00031221192,0.000110965455,0.00019748553],"domain_scores_gemma":[0.9994065,0.00003583114,0.00009868687,0.00031933637,0.00009918716,0.00004044731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019828047,0.0000882088,0.00008569434,0.00014258745,0.00030816562,0.0002863011,0.000551029,0.00008728519,0.0000013972783],"category_scores_gemma":[0.000008513712,0.00009080335,0.00006200307,0.0010846755,0.000014517923,0.000716524,0.00007083673,0.000092047216,0.000004897706],"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":[0.00003038427,0.0002953798,0.00076823664,0.000106822794,0.00006384978,9.596408e-7,0.0002567664,0.45754793,0.13543898,0.034231674,0.004240559,0.36701843],"study_design_scores_gemma":[0.00011324883,0.00001879111,0.000559303,0.000003359678,0.0000055425867,0.00000441301,0.0000039069855,0.9622369,0.033887245,0.001992108,0.0010633789,0.00011183409],"about_ca_topic_score_codex":0.00004972991,"about_ca_topic_score_gemma":0.0000049512983,"teacher_disagreement_score":0.9505086,"about_ca_system_score_codex":0.00005046087,"about_ca_system_score_gemma":0.000019204015,"threshold_uncertainty_score":0.3702853},"labels":[],"label_agreement":null},{"id":"W4384571004","doi":"10.23977/jaip.2023.060410","title":"Discussion on Key Technologies of Computer Artificial Intelligence Recognition","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Scope (computer science); Key (lock); Process (computing); Computer technology; Artificial intelligence; Marketing and artificial intelligence; Multimedia; Intelligent decision support system; Computer security","score_opus":0.09289606047032123,"score_gpt":0.3560808601860882,"score_spread":0.26318479971576697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384571004","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":[0.0046792496,0.000024729408,0.97813815,0.015654158,0.0006360053,0.00019656973,0.000003962841,0.0003223027,0.00034490216],"genre_scores_gemma":[0.80775964,0.00048218595,0.19120495,0.00019903349,0.00028689564,0.000017202614,0.000002035704,0.000016809046,0.000031269086],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975026,0.00015413856,0.001200316,0.00031016118,0.0005711649,0.0002615784],"domain_scores_gemma":[0.99662995,0.0008013975,0.0012484504,0.00046659613,0.0007788826,0.000074701165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016014476,0.00017472873,0.00028088366,0.0006676502,0.00020256489,0.00017089509,0.0009836073,0.00015242395,0.000023819348],"category_scores_gemma":[0.0011235229,0.00012607875,0.0001823415,0.001923859,0.00016009131,0.0012036593,0.00023175443,0.00055026804,0.00033778936],"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":[0.000091992515,0.0002740502,0.000002430208,0.00000954189,0.000020385673,0.000023425566,0.0004985706,0.0012290695,0.0044297706,0.08425509,0.0003462578,0.90881944],"study_design_scores_gemma":[0.000013320752,0.0013169757,0.00002125391,0.0001502601,0.00003205249,0.0001350791,0.0031954595,0.07101809,0.5637335,0.35492828,0.005217674,0.00023800782],"about_ca_topic_score_codex":0.0000123755135,"about_ca_topic_score_gemma":0.0000035575388,"teacher_disagreement_score":0.9085814,"about_ca_system_score_codex":0.000063599124,"about_ca_system_score_gemma":0.00009256512,"threshold_uncertainty_score":0.5141342},"labels":[],"label_agreement":null},{"id":"W4385060436","doi":"10.1007/978-3-031-38333-5_14","title":"A Novel System Architecture for Anomaly Detection for Loan Defaults","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Anomaly detection; Default; Computer science; Loan; Anomaly (physics); Receiver operating characteristic; Data mining; Artificial intelligence; Machine learning; Finance; Business","score_opus":0.020781098027374427,"score_gpt":0.23764001768241536,"score_spread":0.21685891965504092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385060436","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":[0.000010159156,0.0011715444,0.9928848,0.00013447984,0.0010158825,0.0029467875,0.00007973827,0.00064492406,0.0011116744],"genre_scores_gemma":[0.94146895,0.0002120069,0.036405835,0.00020709474,0.0033753214,0.004658108,0.00012963607,0.00030971837,0.013233333],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99791944,0.000019473739,0.00059519586,0.0008810714,0.00017381845,0.00041102333],"domain_scores_gemma":[0.9980849,0.0006773983,0.0003855839,0.00060496904,0.00014902074,0.000098119184],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043864961,0.0004392961,0.0006106408,0.00030590303,0.00029281146,0.0002804586,0.0004471951,0.00084394467,3.5250096e-7],"category_scores_gemma":[0.000031136555,0.0003934587,0.00023716985,0.00018544213,0.000044609285,0.000066259345,0.00011175899,0.00042586072,0.0000019737508],"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":[0.00013521669,0.000035954814,0.000017087325,0.0024155313,0.00023234094,0.000010329351,0.0002775575,0.17994267,0.0005692095,0.46722236,0.00055000035,0.34859174],"study_design_scores_gemma":[0.000543865,0.00031586422,0.000015368974,0.0008857478,0.000051376366,0.00016170324,0.000005798274,0.93744457,0.00011039464,0.013395237,0.046433307,0.000636783],"about_ca_topic_score_codex":0.0001035615,"about_ca_topic_score_gemma":0.0005493395,"teacher_disagreement_score":0.95647895,"about_ca_system_score_codex":0.0001385986,"about_ca_system_score_gemma":0.00003799774,"threshold_uncertainty_score":0.9998517},"labels":[],"label_agreement":null},{"id":"W4385078220","doi":"10.18280/isi.280328","title":"An Enhanced Outlier Detection Approach for Multidimensional Datasets Using a Synergistic Firefly and Grey Wolf Optimization-Based Method","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Firefly protocol; Anomaly detection; Outlier; Computer science; Firefly algorithm; Artificial intelligence; Data mining; Machine learning; Biology","score_opus":0.023843473640766496,"score_gpt":0.29139925413389806,"score_spread":0.26755578049313156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385078220","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009147926,0.00000521627,0.98923403,0.000014368486,0.00007753461,0.0006560571,0.0001291244,0.00067026354,0.0000654947],"genre_scores_gemma":[0.44610095,0.0000019527965,0.5528156,0.00007424671,0.000020897214,0.0002787441,0.0006941839,0.000008271945,0.0000051521424],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885494,0.00006213844,0.00040729903,0.00026407707,0.00018385862,0.00022769017],"domain_scores_gemma":[0.9989306,0.00010702469,0.0002501466,0.00037626058,0.00024418422,0.00009183036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005385015,0.00015753816,0.00015356531,0.0003451388,0.0006154667,0.00025039047,0.00020699462,0.00010996863,0.0000029317746],"category_scores_gemma":[0.00011085431,0.00015946123,0.000045046218,0.00076198357,0.00005480417,0.0024358544,0.000063341584,0.0000753535,0.000004986936],"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":[0.00003292847,0.00004646455,0.00001597213,0.00018894878,0.000020550324,3.1536916e-7,0.0009832805,0.82024133,0.02052383,0.0036404978,0.000059159967,0.15424673],"study_design_scores_gemma":[0.0002740355,0.00008228285,0.00016102058,0.000019180321,0.000013080583,0.000007591526,0.000107062304,0.97665906,0.021746965,0.00041461983,0.0003273106,0.00018779415],"about_ca_topic_score_codex":0.000042627387,"about_ca_topic_score_gemma":0.0000022258537,"teacher_disagreement_score":0.43695304,"about_ca_system_score_codex":0.00011982235,"about_ca_system_score_gemma":0.00006959279,"threshold_uncertainty_score":0.650264},"labels":[],"label_agreement":null},{"id":"W4385258600","doi":"10.23919/ist-africa60249.2023.10187760","title":"Anomaly Detection in IoT Data","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"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":"International Development Research Centre","keywords":"Anomaly detection; Computer science; Anomaly (physics); Internet of Things; Data mining; Computer security","score_opus":0.05435492911700477,"score_gpt":0.3057866826259493,"score_spread":0.25143175350894453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385258600","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":[0.027562037,0.000004508647,0.965158,0.00089776673,0.000055847337,0.00010158837,0.0000015502148,0.0011976168,0.005021128],"genre_scores_gemma":[0.98038465,0.000010513303,0.018283753,0.00011866861,0.000021825725,0.000033350672,0.0000039297065,0.0000034523644,0.0011398304],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942994,0.000011691468,0.00010413976,0.00026809165,0.00007186104,0.00011424942],"domain_scores_gemma":[0.99911296,0.000021531056,0.000019412499,0.0008096044,0.000012115005,0.000024391291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019818645,0.00004136179,0.00004332884,0.00015163286,0.000054733595,0.00004675871,0.000761151,0.000029787681,0.000014863904],"category_scores_gemma":[0.000010727622,0.00003931455,0.000011296095,0.0012397905,0.00000900162,0.00019987868,0.00041844693,0.00005570899,0.00031799427],"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":[0.000001667291,0.000048764927,0.0013984895,0.000005636637,0.000004034142,0.000008718424,0.00007684266,0.000065515254,0.013047255,0.060725693,0.0073516364,0.9172658],"study_design_scores_gemma":[0.00011904358,0.000040188028,0.043211013,0.0000037108273,0.000001078794,0.000010921322,0.000028570039,0.8396115,0.033673763,0.009007783,0.074127026,0.00016542165],"about_ca_topic_score_codex":0.00015484936,"about_ca_topic_score_gemma":0.00023660374,"teacher_disagreement_score":0.9528226,"about_ca_system_score_codex":0.000014969602,"about_ca_system_score_gemma":0.000012320931,"threshold_uncertainty_score":0.4087279},"labels":[],"label_agreement":null},{"id":"W4385287479","doi":"10.1109/icde55515.2023.00370","title":"Adversarial Deep Embedded Clustering: On a better trade-off between Feature Randomness and Feature Drift (Extended abstract)","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Discriminator; Feature (linguistics); Randomness; Cluster analysis; Computer science; Autoencoder; Artificial intelligence; Adversarial system; Pattern recognition (psychology); Data mining; Machine learning; Deep learning; Mathematics; Statistics","score_opus":0.012668216330795252,"score_gpt":0.2595749829643869,"score_spread":0.24690676663359165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385287479","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":[0.055820096,0.00006547652,0.88592917,0.042643867,0.0004773257,0.0013339636,0.000025431458,0.0036965737,0.010008096],"genre_scores_gemma":[0.9711233,0.000038157883,0.024420487,0.0012029463,0.00041917112,0.0001286264,0.000027278911,0.000026385494,0.002613662],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998678,0.00003219589,0.00017909761,0.0005451288,0.00025051986,0.00031501512],"domain_scores_gemma":[0.9990914,0.00014319332,0.00008221607,0.00051702565,0.000025956475,0.00014020324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020131157,0.00021634733,0.0002461368,0.00017098697,0.00027093603,0.00021062867,0.0004910028,0.00024308269,0.000022441669],"category_scores_gemma":[0.00001359746,0.00017953548,0.00010792846,0.0005744171,0.00004839612,0.00026418717,0.00019395578,0.0003717895,0.00005340409],"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":[0.000181557,0.00013126718,0.0004974387,0.00006912519,0.00015179203,0.00006282868,0.001668099,0.00012825907,0.0037706252,0.014734409,0.11396894,0.86463565],"study_design_scores_gemma":[0.013152195,0.0009908394,0.33766186,0.00016454859,0.00016666269,0.00016028082,0.0005262388,0.16756906,0.038162388,0.019187026,0.41935375,0.0029051383],"about_ca_topic_score_codex":0.000007416957,"about_ca_topic_score_gemma":0.000013654375,"teacher_disagreement_score":0.9153032,"about_ca_system_score_codex":0.000023740686,"about_ca_system_score_gemma":0.000019894536,"threshold_uncertainty_score":0.73212445},"labels":[],"label_agreement":null},{"id":"W4385342426","doi":"10.3390/s23156752","title":"SeniorSentry: Correlation and Mutual Information-Based Contextual Anomaly Detection for Aging in Place","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Anomaly Detection Techniques and Applications","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":"National Research Council Canada; Government of Canada; University of Victoria","funders":"","keywords":"Leverage (statistics); Anomaly detection; Anomaly (physics); Computer science; Internet of Things; Linear correlation; Sliding window protocol; False positive rate; Artificial intelligence; Smart city; Data mining; Machine learning; Window (computing); Computer security; Mathematics; Statistics","score_opus":0.008613484670486669,"score_gpt":0.23727425268007785,"score_spread":0.22866076800959118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385342426","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":[0.44730157,0.0000030121555,0.551247,0.00054193207,0.0000758743,0.00030745688,0.0000056954646,0.0003877861,0.00012968999],"genre_scores_gemma":[0.99493355,0.0000039015704,0.0046535395,0.00012351977,0.0000239178,0.00008676223,0.000014893753,0.000005221479,0.00015471273],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993699,0.00002376429,0.00019650011,0.00016057675,0.000093099916,0.0001561195],"domain_scores_gemma":[0.9995071,0.00017128386,0.000080004866,0.00015018728,0.000052642474,0.000038775917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002072982,0.00007860206,0.00007946391,0.00027941616,0.00014432365,0.000083322455,0.00009330205,0.00006213966,0.0000014896705],"category_scores_gemma":[0.00005551188,0.00008442413,0.00002881417,0.0006040272,0.000024852421,0.00036210753,0.000030683488,0.000075892895,0.000033186257],"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":[0.00018371035,0.00011719291,0.01746325,0.0002024671,0.00004078077,0.000010538492,0.008432178,0.11638798,0.00856779,0.06993221,0.0035520592,0.7751098],"study_design_scores_gemma":[0.0003942565,0.000051164712,0.0075975964,0.00000909904,0.0000023114208,0.0000047853478,0.00024631267,0.97759384,0.004924468,0.00037710156,0.008689553,0.00010951785],"about_ca_topic_score_codex":0.00006387331,"about_ca_topic_score_gemma":0.000066166096,"teacher_disagreement_score":0.8612059,"about_ca_system_score_codex":0.000042147454,"about_ca_system_score_gemma":0.000026272937,"threshold_uncertainty_score":0.34427163},"labels":[],"label_agreement":null},{"id":"W4385478116","doi":"10.1109/icphm57936.2023.10193957","title":"Application of Machine Learning for Anomaly Detection in Printed Circuit Boards Imbalance Date Set","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Anomaly detection; Computer science; Printed circuit board; Set (abstract data type); Electronics; Focus (optics); Machine learning; Training set; Artificial intelligence; Precision and recall; Anomaly (physics); Data set; Deep learning; Data mining; Engineering","score_opus":0.019454544914097323,"score_gpt":0.2756653213764184,"score_spread":0.2562107764623211,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385478116","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":[0.0378407,0.000006376365,0.9603623,0.00017997844,0.000024217596,0.00043450797,0.000004208578,0.0006201222,0.000527553],"genre_scores_gemma":[0.98975646,0.000016405313,0.0092536155,0.00002869825,0.000014014897,0.00045310403,0.000017903201,0.00000811091,0.000451708],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992115,0.00002121808,0.00023891321,0.00028590843,0.00009424101,0.0001482571],"domain_scores_gemma":[0.99942434,0.00005264338,0.00011735192,0.00030814618,0.000070705515,0.000026794782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031796037,0.00007412348,0.00011005825,0.00019728503,0.00007693715,0.00002319244,0.00030827968,0.000051518244,0.0000033201031],"category_scores_gemma":[0.00002605964,0.00007509669,0.00004527517,0.0010723381,0.000016941593,0.00015165786,0.00008850707,0.00009157925,0.000017952034],"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":[0.000020512392,0.00008864222,0.018414302,0.00009004872,0.000016710268,7.218832e-7,0.0003043239,0.0023211297,0.31539172,0.09559973,0.00013834263,0.5676138],"study_design_scores_gemma":[0.000165018,0.00007086332,0.022697609,0.0000048734273,0.0000018152551,0.0000024228138,0.000015566187,0.8202903,0.14398457,0.0030528978,0.009622291,0.000091732894],"about_ca_topic_score_codex":0.00019546649,"about_ca_topic_score_gemma":0.00008562619,"teacher_disagreement_score":0.95191574,"about_ca_system_score_codex":0.00002584683,"about_ca_system_score_gemma":0.000015038983,"threshold_uncertainty_score":0.3062354},"labels":[],"label_agreement":null},{"id":"W4385482630","doi":"10.1109/ijcnn54540.2023.10191806","title":"How Image Corruption and Perturbation Affect Out-of-Distribution Generalization and Calibration","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Generalization; Perturbation (astronomy); Language change; Computer science; Calibration; Econometrics; Degradation (telecommunications); Artificial neural network; Artificial intelligence; Distribution (mathematics); Affect (linguistics); Remote sensing; Environmental science; Mathematics; Statistics; Geology; Psychology","score_opus":0.018533691343819072,"score_gpt":0.25950317234972814,"score_spread":0.24096948100590906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385482630","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":[0.038342442,0.000014258195,0.9587555,0.0021435043,0.00006879373,0.00018396926,0.000006291561,0.0003817653,0.00010348462],"genre_scores_gemma":[0.9850106,0.00016697534,0.013744978,0.00004493422,0.00003718768,0.00003986265,0.000118758915,0.0000045265824,0.00083215715],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995163,0.000029390172,0.00010001818,0.00018891155,0.00009177576,0.000073576084],"domain_scores_gemma":[0.9996704,0.000026424215,0.00007058516,0.00013562078,0.00006586133,0.000031141233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013313582,0.00006145823,0.000063536616,0.00006993574,0.00010918754,0.00015658312,0.000060823837,0.00004978399,0.0000018810236],"category_scores_gemma":[0.000024868166,0.000056979254,0.000015795375,0.00033761843,0.00003202128,0.00063862174,0.000057597736,0.000028850282,0.0000018806999],"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":[0.000006211806,0.000035718313,0.0013565352,0.000068064466,0.000010234292,7.3881546e-7,0.0005129632,0.000049031943,0.1832164,0.7217494,0.008265991,0.08472871],"study_design_scores_gemma":[0.00015066988,0.00010113866,0.014347307,0.0000084494595,0.000006974863,0.000004779723,0.000051455885,0.9180591,0.054159842,0.010321686,0.0026582316,0.00013036915],"about_ca_topic_score_codex":0.000009930716,"about_ca_topic_score_gemma":0.000004229627,"teacher_disagreement_score":0.94666815,"about_ca_system_score_codex":0.000014465798,"about_ca_system_score_gemma":0.000008393296,"threshold_uncertainty_score":0.23235464},"labels":[],"label_agreement":null},{"id":"W4385485121","doi":"10.1109/cai54212.2023.00129","title":"Faulty Neural Networks","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence","score_opus":0.017707084679628962,"score_gpt":0.2645851256929582,"score_spread":0.24687804101332927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385485121","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":[0.0021271354,0.0000035406176,0.98625755,0.0014923117,0.00006397633,0.000051664996,1.4576288e-7,0.0020482435,0.0079554],"genre_scores_gemma":[0.98428804,0.000007719388,0.01148337,0.00045512235,0.00004040568,0.000034911707,0.0000011662994,0.0000026949754,0.0036865578],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965864,0.0000055711735,0.000058155543,0.00012316051,0.000049498605,0.000104965584],"domain_scores_gemma":[0.9996898,0.000014981377,0.00001241962,0.00023767262,0.000014496775,0.000030609193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000053683816,0.000032765267,0.000029970017,0.000035206296,0.000080702855,0.00004776125,0.0002787145,0.000021001462,0.000020458921],"category_scores_gemma":[0.0000019907832,0.000027543449,0.0000250796,0.0005896086,0.000008463562,0.00010116388,0.00011477008,0.00004262447,0.00016074452],"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":[4.511645e-7,0.000014205815,0.00041827114,0.0000012414533,0.0000034138811,0.0000041166554,0.000036898426,0.003937125,0.00023384954,0.532195,0.0795071,0.38364834],"study_design_scores_gemma":[0.000017532462,0.000009829081,0.0022538144,2.8062775e-7,2.7837723e-7,0.0000028618467,0.000003365155,0.9747485,0.0004171598,0.0024364896,0.020068021,0.000041867614],"about_ca_topic_score_codex":0.000010083393,"about_ca_topic_score_gemma":0.0000016858268,"teacher_disagreement_score":0.9821609,"about_ca_system_score_codex":0.0000045346987,"about_ca_system_score_gemma":0.0000027960734,"threshold_uncertainty_score":0.20660992},"labels":[],"label_agreement":null},{"id":"W4385486382","doi":"10.1007/978-3-031-33390-3_12","title":"Support Vector Machines","year":2023,"lang":"en","type":"book-chapter","venue":"Statisctics and computing/Statistics and computing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Hyperplane; Support vector machine; Mathematics; Real line; Separable space; Space (punctuation); Quadratic equation; Line (geometry); Kernel (algebra); Class (philosophy); Artificial intelligence; Combinatorics; Computer science","score_opus":0.017994683128614863,"score_gpt":0.26465094730332434,"score_spread":0.24665626417470948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385486382","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007011134,0.00019124131,0.9699002,0.0002895586,0.00045178173,0.00032062156,0.00026085923,0.0006299353,0.027885715],"genre_scores_gemma":[0.053296775,0.0014807018,0.8056377,0.0008314126,0.0007628397,0.000012905353,0.00026845429,0.00027944727,0.13742976],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997498,0.000027535534,0.00071754126,0.0009503675,0.0003483872,0.00045819336],"domain_scores_gemma":[0.99798024,0.0005504941,0.00054184726,0.0005231243,0.00016311594,0.00024118568],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003015983,0.00053057674,0.00057601585,0.00023714988,0.0007466553,0.00059675676,0.00048578638,0.00022846845,0.000022386039],"category_scores_gemma":[0.000027112508,0.0005547054,0.0000767111,0.00013171649,0.00018968855,0.00007777404,0.0011169981,0.0005798369,0.000038063754],"study_design_candidate":"theoretical_or_conceptual","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":[0.00000141473,0.0000086283535,0.000021809781,0.000078310775,0.00004345274,0.000035279645,0.0001839961,0.000017489348,0.000004648055,0.7302947,0.002788967,0.2665213],"study_design_scores_gemma":[0.00032227964,0.00036491643,0.00085429393,0.00020408332,0.000090670095,0.00018981291,0.000026814869,0.53066826,0.00001259514,0.42124096,0.04497031,0.0010550038],"about_ca_topic_score_codex":0.000027688815,"about_ca_topic_score_gemma":0.0000095147325,"teacher_disagreement_score":0.53065073,"about_ca_system_score_codex":0.000026818727,"about_ca_system_score_gemma":0.000093688104,"threshold_uncertainty_score":0.9996905},"labels":[],"label_agreement":null},{"id":"W4385487521","doi":"10.1109/actea58025.2023.10194147","title":"Deep Learning-based Anomaly Detection for 5G Core Mobility Management","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Autoencoder; Computer science; Anomaly detection; Deep learning; Core (optical fiber); Core network; Artificial intelligence; Learning network; Unsupervised learning; Network management; Distributed computing; Computer architecture; Machine learning; Computer network; Telecommunications","score_opus":0.022839886948864414,"score_gpt":0.27563478837974853,"score_spread":0.2527949014308841,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385487521","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":[0.006836385,0.000003792896,0.9872864,0.00028440487,0.0000667734,0.00056098826,4.876555e-7,0.0021665115,0.0027942464],"genre_scores_gemma":[0.9603793,0.0000052764935,0.036203634,0.00013682363,0.000021305601,0.0008506186,0.0000050398808,0.0000080477,0.0023899458],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920756,0.000013653522,0.0001462054,0.00034186686,0.000107736974,0.00018296756],"domain_scores_gemma":[0.9994107,0.000056371784,0.00005039401,0.0003746058,0.00006142093,0.00004651645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025096902,0.00008208857,0.00007221161,0.00012884104,0.00027193336,0.00006307481,0.00030862243,0.000045006433,0.000018700126],"category_scores_gemma":[0.000010514624,0.00007883703,0.00008175739,0.0007230562,0.000020319296,0.00010416114,0.00005068559,0.0000640652,0.00010038893],"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":[0.000026020156,0.00019933366,0.0011708997,0.00013761556,0.000035718458,0.000003903582,0.00008512393,0.024695689,0.0031895735,0.090615265,0.001773841,0.878067],"study_design_scores_gemma":[0.00016575409,0.00014736767,0.0078107542,0.0000025767324,0.0000045949846,9.943665e-7,0.000027034614,0.93771034,0.021252599,0.0057314057,0.027026007,0.00012057509],"about_ca_topic_score_codex":0.000021209262,"about_ca_topic_score_gemma":0.000027055614,"teacher_disagreement_score":0.95354295,"about_ca_system_score_codex":0.000043207852,"about_ca_system_score_gemma":0.000007640482,"threshold_uncertainty_score":0.32148808},"labels":[],"label_agreement":null},{"id":"W4385562814","doi":"10.32920/22734350","title":"Deep Clustering with a Dynamic Autoencoder: From Reconstruction towards Centroids Construction","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor; Toronto Metropolitan University","funders":"","keywords":"Autoencoder; Cluster analysis; Benchmark (surveying); Artificial intelligence; Centroid; Unsupervised learning; Computer science; Deep learning; Function (biology); Process (computing); Machine learning; Pattern recognition (psychology); Geography; Cartography","score_opus":0.014863722538617428,"score_gpt":0.24362376121723114,"score_spread":0.22876003867861372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385562814","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028614567,0.000032532527,0.98913944,0.0011339433,0.001029048,0.0005082669,0.000022928598,0.0036103022,0.0016620773],"genre_scores_gemma":[0.08408723,0.00018470004,0.91456795,0.00007197698,0.00011622446,0.00033477368,0.00006127261,0.000038530954,0.0005373368],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99793005,0.00004812538,0.00041723883,0.0010278645,0.0002859692,0.00029076557],"domain_scores_gemma":[0.99829245,0.00003243599,0.0003163339,0.0011047148,0.0001434337,0.00011062907],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001191991,0.00031791645,0.00031491322,0.00024947725,0.00022099,0.0003988737,0.00080991193,0.00031615703,0.000063732215],"category_scores_gemma":[0.000008372598,0.00029172943,0.0001306215,0.00040309574,0.00011236173,0.00028193562,0.0010519326,0.00055347005,0.000067365014],"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":[0.000020107382,0.000046610785,0.0005202934,0.00007139995,0.00017757028,0.000012827882,0.00032687964,0.024482934,0.00027377947,0.011805936,0.00020446446,0.9620572],"study_design_scores_gemma":[0.00015177585,0.000044545595,0.0017304394,0.00010115627,0.000024778594,0.00010513405,0.0000960147,0.96343154,0.0005144446,0.03308531,0.0003412704,0.00037359007],"about_ca_topic_score_codex":0.0012743613,"about_ca_topic_score_gemma":0.0008422856,"teacher_disagreement_score":0.96168363,"about_ca_system_score_codex":0.00028233396,"about_ca_system_score_gemma":0.0001871349,"threshold_uncertainty_score":0.9999535},"labels":[],"label_agreement":null},{"id":"W4385564986","doi":"10.21203/rs.3.rs-3024402/v2","title":"SSIVD-Net: A Novel Salient Super Image Classification &amp;amp; Detection Technique for Weaponized Violence","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Salient; Computer science; Scalability; Artificial intelligence; Benchmark (surveying); Inference; Machine learning; Classifier (UML); Computer security; Geography","score_opus":0.15689103267216678,"score_gpt":0.42581807236594704,"score_spread":0.2689270396937803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385564986","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020103813,0.00007418797,0.98729634,0.0021941254,0.00026476674,0.0060279225,0.00021568783,0.0016508228,0.00026576177],"genre_scores_gemma":[0.3899046,0.000926511,0.554566,0.00007811836,0.0005328258,0.050513253,0.0004133909,0.0001760554,0.0028892348],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.995362,0.00029106333,0.00066962425,0.0016911207,0.001123442,0.000862778],"domain_scores_gemma":[0.994532,0.00053166336,0.00026914818,0.0026871616,0.0017322708,0.0002477662],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0030778828,0.00039081636,0.00039152303,0.0009878763,0.00081247493,0.00071335636,0.0021578076,0.00067201426,0.000032611544],"category_scores_gemma":[0.0006096872,0.0003975354,0.0003596134,0.0015042489,0.00023355459,0.00031593937,0.0020079822,0.0015850022,0.00050949946],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009355275,0.00056338956,0.000053005722,0.0012022797,0.00006575563,0.0000027483163,0.00037735427,0.00014141826,0.90129447,0.027133383,0.008884878,0.060187764],"study_design_scores_gemma":[0.0013284031,0.000606383,0.005639187,0.0029538479,0.00005246559,0.000047723595,0.00026705785,0.108026855,0.3939309,0.2618687,0.22282341,0.002455051],"about_ca_topic_score_codex":0.0004932323,"about_ca_topic_score_gemma":0.00020409259,"teacher_disagreement_score":0.50736356,"about_ca_system_score_codex":0.0007038673,"about_ca_system_score_gemma":0.0004748959,"threshold_uncertainty_score":0.99984765},"labels":[],"label_agreement":null},{"id":"W4385805191","doi":"10.1109/cvprw59228.2023.00290","title":"Multi-Task Learning based Video Anomaly Detection with Attention","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Artificial intelligence; Anomaly detection; Leverage (statistics); Computer vision; Optical flow; Segmentation; Motion (physics); Context (archaeology); Object detection; Pattern recognition (psychology); Image (mathematics)","score_opus":0.014520899628122854,"score_gpt":0.24291908082548344,"score_spread":0.22839818119736058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385805191","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":[0.045355648,0.000002473898,0.9508977,0.00031264825,0.00003444587,0.00016394143,3.0694116e-7,0.002638598,0.00059423276],"genre_scores_gemma":[0.9284774,0.0000028504824,0.068880625,0.0000929672,0.000018475139,0.0001289977,0.000003623869,0.000009600278,0.0023854228],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921817,0.000032629472,0.00012300034,0.0003146436,0.00014105129,0.00017052483],"domain_scores_gemma":[0.999504,0.0000309163,0.00006355418,0.00028185002,0.00006885343,0.00005081405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017583388,0.0000878945,0.000069003836,0.00018257489,0.00028515497,0.00010528398,0.00021817542,0.000046482233,0.000015559992],"category_scores_gemma":[0.000009992192,0.0000741641,0.000045645906,0.001129619,0.000020788166,0.00026781904,0.000056766145,0.00011556823,0.00027272015],"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":[0.00003069637,0.00023690182,0.016392281,0.00004547523,0.000046052206,0.000022851906,0.00019271855,0.012187869,0.54134846,0.0054979487,0.0011064992,0.42289227],"study_design_scores_gemma":[0.0002569338,0.00017555912,0.050482083,0.000008053527,0.0000044168,0.000007988263,0.000032296917,0.90398216,0.037234694,0.00008672127,0.007571287,0.0001577789],"about_ca_topic_score_codex":0.00007322619,"about_ca_topic_score_gemma":0.000057736703,"teacher_disagreement_score":0.8917943,"about_ca_system_score_codex":0.000030569878,"about_ca_system_score_gemma":0.000019721794,"threshold_uncertainty_score":0.3505357},"labels":[],"label_agreement":null},{"id":"W4385872467","doi":"10.3850/978-981-18-5183-4_s33-01-310-cd","title":"Neuro-Symbolic AI for Sensor-based Human Performance Prediction; System Architectures and Applications","year":2022,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"European Commission; Canadian Institute of Steel Construction","keywords":"Computer science; Artificial intelligence; Artificial neural network; Domain (mathematical analysis); Deep learning; Focus (optics); Machine learning","score_opus":0.009847041038021788,"score_gpt":0.23478701734292465,"score_spread":0.22493997630490287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385872467","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":[0.020014344,0.000013984492,0.9759091,0.0008649011,0.000030642706,0.0009014196,0.00003355182,0.0009678117,0.0012642407],"genre_scores_gemma":[0.9760148,9.1511043e-7,0.01801553,0.00088156934,0.000061806626,0.0046174694,0.000009519733,0.000010217068,0.00038820124],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919564,0.000026490223,0.00017065485,0.00033003007,0.00013187584,0.00014528607],"domain_scores_gemma":[0.9993653,0.000050973056,0.00006064048,0.000420113,0.0000455374,0.00005738641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012533586,0.000089389054,0.00008560659,0.00012198567,0.0012513471,0.00007655175,0.00035096906,0.000020872094,0.0000105153385],"category_scores_gemma":[0.0000017202336,0.000088076915,0.000044174143,0.00030429763,0.000033082797,0.00005879397,0.00013672632,0.00012279977,0.0000021444064],"study_design_candidate":"theoretical_or_conceptual","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":[0.00001424701,0.00020610592,0.0046023587,0.00022817194,0.00002370272,7.71974e-7,0.00018151825,0.013988849,0.009077519,0.91607684,0.004754555,0.05084539],"study_design_scores_gemma":[0.0004430822,0.00045360034,0.0057464265,0.0000057189823,0.000015796564,0.00009534834,0.000100211306,0.72404253,0.0096965805,0.0017886984,0.2573365,0.0002755159],"about_ca_topic_score_codex":0.000013159903,"about_ca_topic_score_gemma":8.210169e-7,"teacher_disagreement_score":0.95789355,"about_ca_system_score_codex":0.000040607,"about_ca_system_score_gemma":0.000027040618,"threshold_uncertainty_score":0.96244764},"labels":[],"label_agreement":null},{"id":"W4385872630","doi":"10.3850/978-981-18-5183-4_s33-01-310","title":"Neuro-Symbolic AI for Sensor-based Human Performance Prediction; System Architectures and Applications","year":2022,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"European Commission; Canadian Institute of Steel Construction","keywords":"Computer science; Artificial intelligence; Domain (mathematical analysis); Artificial neural network; Deep learning; Focus (optics); Machine learning; Mathematics","score_opus":0.009847041038021788,"score_gpt":0.23478701734292465,"score_spread":0.22493997630490287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385872630","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":[0.020014344,0.000013984492,0.9759091,0.0008649011,0.000030642706,0.0009014196,0.00003355182,0.0009678117,0.0012642407],"genre_scores_gemma":[0.9760148,9.1511043e-7,0.01801553,0.00088156934,0.000061806626,0.0046174694,0.000009519733,0.000010217068,0.00038820124],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919564,0.000026490223,0.00017065485,0.00033003007,0.00013187584,0.00014528607],"domain_scores_gemma":[0.9993653,0.000050973056,0.00006064048,0.000420113,0.0000455374,0.00005738641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012533586,0.000089389054,0.00008560659,0.00012198567,0.0012513471,0.00007655175,0.00035096906,0.000020872094,0.0000105153385],"category_scores_gemma":[0.0000017202336,0.000088076915,0.000044174143,0.00030429763,0.000033082797,0.00005879397,0.00013672632,0.00012279977,0.0000021444064],"study_design_candidate":"theoretical_or_conceptual","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":[0.00001424701,0.00020610592,0.0046023587,0.00022817194,0.00002370272,7.71974e-7,0.00018151825,0.013988849,0.009077519,0.91607684,0.004754555,0.05084539],"study_design_scores_gemma":[0.0004430822,0.00045360034,0.0057464265,0.0000057189823,0.000015796564,0.00009534834,0.000100211306,0.72404253,0.0096965805,0.0017886984,0.2573365,0.0002755159],"about_ca_topic_score_codex":0.000013159903,"about_ca_topic_score_gemma":8.210169e-7,"teacher_disagreement_score":0.95789355,"about_ca_system_score_codex":0.000040607,"about_ca_system_score_gemma":0.000027040618,"threshold_uncertainty_score":0.96244764},"labels":[],"label_agreement":null},{"id":"W4385899432","doi":"10.32604/jcs.2023.042486","title":"Improving Intrusion Detection in UAV Communication Using an LSTM-SMOTE Classification Method","year":2022,"lang":"en","type":"article","venue":"Journal of Cyber Security","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":14,"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":"Jackson State University","keywords":"Computer science; Intrusion detection system; Oversampling; Artificial intelligence; Real-time computing; Data mining; Computer network; Bandwidth (computing)","score_opus":0.029768896732625495,"score_gpt":0.3158974166887242,"score_spread":0.28612851995609867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385899432","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":[0.30539376,0.000083029176,0.6939043,0.00029689007,0.00010106094,0.000109797125,7.560036e-7,0.000039922983,0.00007049951],"genre_scores_gemma":[0.8684248,0.000022751894,0.13138784,0.000087450695,0.000050980023,0.000013449939,0.0000010004666,0.0000072271177,0.0000045376573],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831945,0.00053593644,0.0005197109,0.00018388944,0.0003120598,0.0001289502],"domain_scores_gemma":[0.99847674,0.000061779516,0.0007326433,0.0004927599,0.00017266607,0.000063429485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019908955,0.000088270026,0.00015283423,0.00032965545,0.0004182717,0.00009152831,0.0007271065,0.00005666812,0.000014571418],"category_scores_gemma":[0.000034384168,0.000093220355,0.000076651064,0.00070854864,0.000019344487,0.00095931056,0.00029382925,0.0006114753,6.5755574e-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":[0.00007226913,0.00083061063,0.00067173,0.000024186547,0.000019641704,0.000010230483,0.0054903557,0.0032789363,0.2822712,0.022801442,0.000053845517,0.68447554],"study_design_scores_gemma":[0.00035882415,0.00028981108,0.0040012724,0.000016787939,0.000014651851,0.00037061807,0.0006179593,0.93366575,0.032824762,0.024632541,0.0030274244,0.0001795705],"about_ca_topic_score_codex":0.00027194122,"about_ca_topic_score_gemma":0.00007159345,"teacher_disagreement_score":0.93038684,"about_ca_system_score_codex":0.00038532764,"about_ca_system_score_gemma":0.00008479454,"threshold_uncertainty_score":0.3801416},"labels":[],"label_agreement":null},{"id":"W4385945537","doi":"10.2139/ssrn.4544162","title":"Sampling Balanced High Quality Data to Train an Automatic Mesh Generator for its Optimal Performance","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Generator (circuit theory); Computer science; Sampling (signal processing); Quality (philosophy); Data quality; Reliability engineering; Engineering; Operations management; Power (physics); Telecommunications; Physics","score_opus":0.12086356638906888,"score_gpt":0.37484143826175015,"score_spread":0.2539778718726813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385945537","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":[0.17796218,0.00012310344,0.8186699,0.0014752592,0.00035165515,0.0007051615,0.00011268758,0.00059380085,0.0000062505533],"genre_scores_gemma":[0.69508785,0.0007295019,0.30201852,0.0002142956,0.00081285357,0.0004212241,0.00015669901,0.000061053615,0.00049802527],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960112,0.00011892273,0.00072582584,0.0009869919,0.00038689555,0.0017701495],"domain_scores_gemma":[0.9972453,0.00008622618,0.00046025697,0.0017662874,0.00022731032,0.00021459746],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004458992,0.00031931838,0.00042218363,0.00021999226,0.0005479669,0.00047241783,0.0041221953,0.00021533716,0.000006333839],"category_scores_gemma":[0.00009935506,0.00031623672,0.00013618509,0.00036925363,0.000019945957,0.00055764895,0.001289475,0.0019282668,0.000026651664],"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":[0.000063415326,0.00030273903,0.00008603629,0.00033499996,0.00050349196,0.0000021702533,0.0007292061,0.02215122,0.010486203,0.49122262,0.0014534706,0.47266442],"study_design_scores_gemma":[0.0004961488,0.0007115234,0.0013602991,0.00012475076,0.000057361638,0.00011851113,0.00017327542,0.90246534,0.0027455762,0.08878717,0.0020188952,0.0009411594],"about_ca_topic_score_codex":0.00003958029,"about_ca_topic_score_gemma":0.00015291892,"teacher_disagreement_score":0.8803141,"about_ca_system_score_codex":0.0007611427,"about_ca_system_score_gemma":0.0026840274,"threshold_uncertainty_score":0.99992895},"labels":[],"label_agreement":null},{"id":"W4386020963","doi":"10.21203/rs.3.rs-3211233/v1","title":"RayBNN: A 3-D Biological Neural Network Transfer Learning Model","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Artificial neural network; Transfer of learning; Artificial intelligence; Machine learning; Train; Competitive learning; Base (topology); Deep learning; Transfer (computing)","score_opus":0.2073375779439189,"score_gpt":0.41428687253975927,"score_spread":0.20694929459584036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386020963","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":[0.014996526,0.00020507819,0.9782377,0.0025057727,0.00011836267,0.00090283074,0.00001270055,0.0019797252,0.0010413266],"genre_scores_gemma":[0.9744813,0.0006493623,0.02063355,0.00006361861,0.00037599323,0.001280268,0.00004251318,0.000039554154,0.0024338802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99668986,0.00047714918,0.0003164688,0.0010044691,0.0006921007,0.00081993936],"domain_scores_gemma":[0.9981553,0.00030595248,0.00003776418,0.0009827234,0.0003199636,0.0001983062],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0017121816,0.00022672895,0.00028346904,0.0002537729,0.0006532659,0.00040795398,0.0017949172,0.0004503077,0.00003043184],"category_scores_gemma":[0.00009336488,0.00019923552,0.00024875195,0.00094013324,0.00014486063,0.00010283938,0.0025278796,0.0029360687,0.0002174551],"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":[0.000020562173,0.00009810131,0.0008812899,0.00017196462,0.0000371386,0.0000399941,0.00046012324,0.85038316,0.00030442924,0.10060508,0.0100594275,0.03693872],"study_design_scores_gemma":[0.000066633125,0.00014638784,0.0008338245,0.00009089922,0.000001934714,0.0000037233367,0.000026122048,0.9314358,0.00011939432,0.062969014,0.0040744524,0.00023181507],"about_ca_topic_score_codex":0.00007209637,"about_ca_topic_score_gemma":0.000008343147,"teacher_disagreement_score":0.95948476,"about_ca_system_score_codex":0.00011211181,"about_ca_system_score_gemma":0.00020078065,"threshold_uncertainty_score":0.9993642},"labels":[],"label_agreement":null},{"id":"W4386041953","doi":"10.1016/j.engappai.2023.106955","title":"Meta pseudo labels for anomaly detection via partially observed anomalies","year":2023,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Anomaly detection; Robustness (evolution); Set (abstract data type); Data mining; Anomaly (physics); Outlier; Artificial intelligence; Pattern recognition (psychology); Machine learning","score_opus":0.08248570757483109,"score_gpt":0.28568448384069217,"score_spread":0.20319877626586108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386041953","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":[0.0060324627,0.00006750894,0.99146146,0.00036278696,0.00008616348,0.00080186693,0.000018290546,0.0011426286,0.000026848013],"genre_scores_gemma":[0.84590775,0.000028173998,0.15068115,0.0000182839,0.000079288795,0.003155318,0.000009450312,0.000021008977,0.000099601726],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99858004,0.000013852298,0.000552094,0.00041636158,0.00016386142,0.00027381736],"domain_scores_gemma":[0.99859506,0.00022228002,0.00016249987,0.0007215697,0.00022238442,0.00007617772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040300514,0.00017064178,0.00024729414,0.0002904311,0.00019948119,0.00007555868,0.0006810739,0.00008922943,0.000009687539],"category_scores_gemma":[0.00004579984,0.00018183042,0.00020385713,0.0017463441,0.00004899912,0.00023681857,0.00011662649,0.000096791715,0.00007207942],"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":[0.000008727249,0.00012954418,0.00001891697,0.00009188179,0.0002459299,4.761317e-7,0.00019695218,0.0417641,0.31627786,0.3904098,0.000056687924,0.25079912],"study_design_scores_gemma":[0.0000109000675,0.00005871548,0.00013661158,0.000003943277,0.000057424688,0.0000022213076,0.000016770062,0.43648916,0.54224133,0.017185288,0.003641176,0.00015641873],"about_ca_topic_score_codex":0.000044922523,"about_ca_topic_score_gemma":0.000018041932,"teacher_disagreement_score":0.8407803,"about_ca_system_score_codex":0.000029056266,"about_ca_system_score_gemma":0.000028074799,"threshold_uncertainty_score":0.7414829},"labels":[],"label_agreement":null},{"id":"W4386065385","doi":"10.1109/cvpr52729.2023.01878","title":"WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":363,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Shot (pellet); Anomaly (physics); Task (project management); Pattern recognition (psychology); Focus (optics); Computer vision; Field (mathematics); Image segmentation; Mathematics; Engineering","score_opus":0.04701788183755889,"score_gpt":0.3003302529879949,"score_spread":0.253312371150436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386065385","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":[0.062141646,0.000009401067,0.9265323,0.002192412,0.000049187114,0.0001943344,0.0000012810062,0.0012250349,0.007654363],"genre_scores_gemma":[0.96002764,0.000055111854,0.036985204,0.0002506262,0.000022395374,0.00010635039,0.0000092151395,0.00000555084,0.002537889],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9993595,0.000016731956,0.0001370632,0.00026190258,0.00010815148,0.000116656935],"domain_scores_gemma":[0.99955416,0.000032160584,0.000048860642,0.00027717222,0.000037474925,0.000050153823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014258565,0.00006564665,0.000055928966,0.000114991715,0.00016885503,0.00011026189,0.00019414116,0.00004007378,0.000018343833],"category_scores_gemma":[0.000005920694,0.00006166208,0.000021433254,0.0006224528,0.000026918535,0.00032058565,0.000100342404,0.000044708126,0.00017985226],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000025416953,0.000046250334,0.0058195996,0.0000139581125,0.000011606346,0.0000017465488,0.00033613408,0.000014367982,0.11235117,0.6388455,0.016648173,0.22590894],"study_design_scores_gemma":[0.00051541196,0.00022984293,0.45201033,0.000013981124,0.000015457705,0.00003860226,0.00038099167,0.3390429,0.090311676,0.06369972,0.053115953,0.0006251104],"about_ca_topic_score_codex":0.000022913295,"about_ca_topic_score_gemma":0.000004436203,"teacher_disagreement_score":0.89788604,"about_ca_system_score_codex":0.0000199761,"about_ca_system_score_gemma":0.000013306806,"threshold_uncertainty_score":0.25145066},"labels":[],"label_agreement":null},{"id":"W4386070858","doi":"10.11159/cist23.148","title":"Adaptable and Efficient Digit Recognition System for Challenging Datasets: A Case Study on Pump Flowmeter Digits","year":2023,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Numerical digit; Computer science; Flow measurement; Digit recognition; Speech recognition; Artificial intelligence; Arithmetic; Mathematics; Artificial neural network","score_opus":0.02074322061154248,"score_gpt":0.23732949859775251,"score_spread":0.21658627798621002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386070858","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":[0.9639607,0.00006472682,0.033933382,0.00013101411,0.0004729417,0.0010013537,0.000014952573,0.00036445714,0.00005649217],"genre_scores_gemma":[0.9992505,0.0000061744154,0.00047189885,0.000012051386,0.000051301733,0.00015693107,3.2951465e-7,0.0000073892484,0.000043423697],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879676,0.000004979451,0.00021821703,0.0004732924,0.00024000875,0.00026674385],"domain_scores_gemma":[0.9994115,0.0001322667,0.00009334569,0.00015521512,0.00010353457,0.00010409581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047451668,0.00013984967,0.00018853083,0.00035025636,0.00038990608,0.00046632285,0.0003348034,0.000024195951,2.2430696e-8],"category_scores_gemma":[0.000024436558,0.00010087498,0.000025456482,0.0011880092,0.000050201663,0.00019944139,0.00023343254,0.00010648037,3.753381e-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":[0.00014548593,0.0012366852,0.0017491999,0.0033965393,0.00030692667,0.00014587428,0.0034142453,0.03778961,0.011479418,0.40421858,0.0025113078,0.5336061],"study_design_scores_gemma":[0.00020580454,0.00030763543,0.00024013857,0.00023072588,0.0000081624985,0.00022378303,0.000091055255,0.99565876,0.0025396524,0.000020006917,0.00033719337,0.0001370815],"about_ca_topic_score_codex":0.000016219597,"about_ca_topic_score_gemma":4.3826915e-7,"teacher_disagreement_score":0.9578692,"about_ca_system_score_codex":0.000033646156,"about_ca_system_score_gemma":0.000011098411,"threshold_uncertainty_score":0.4496762},"labels":[],"label_agreement":null},{"id":"W4386213751","doi":"10.1109/icirca57980.2023.10220695","title":"Deep Learning for Uneven Data in Industrial IoT Using a Distributed Bias-Aware Adversarial Network","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Classifier (UML); Adversarial system; Deep learning; Convolutional neural network; Feature extraction; Data modeling; Data mining; Database","score_opus":0.17178881058614542,"score_gpt":0.3343084272399621,"score_spread":0.1625196166538167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386213751","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":[0.0059001925,0.000004709814,0.99243355,0.00040491,0.00019413028,0.00037909418,0.00001825518,0.0006141813,0.000050957624],"genre_scores_gemma":[0.92857623,0.000007194965,0.06987759,0.000074448406,0.0007151647,0.00009805285,0.00040713622,0.000017545612,0.00022665614],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892277,0.000048062422,0.00023346132,0.00039064538,0.00011593,0.00028914423],"domain_scores_gemma":[0.9991229,0.00017394386,0.00008553201,0.00053316203,0.00003480511,0.000049666793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052863447,0.000089937916,0.00012594521,0.0000921594,0.00022755194,0.00009968116,0.0008299024,0.00011042298,0.0000111224235],"category_scores_gemma":[0.000115266484,0.00008958121,0.000037020378,0.0015453675,0.00001711979,0.00019948592,0.00067693385,0.00017926481,0.000011554177],"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":[0.00011323206,0.00016151508,0.018774701,0.000029134853,0.00008179331,0.000021026175,0.0003385421,0.5066794,0.00051523745,0.049649913,0.04383715,0.37979832],"study_design_scores_gemma":[0.00036507958,0.000032600547,0.00048825398,0.0000110568935,0.0000043884147,0.0000018429987,0.00004351396,0.9664972,0.0000851435,0.0012745708,0.031081736,0.00011463556],"about_ca_topic_score_codex":0.0002176673,"about_ca_topic_score_gemma":0.00009338876,"teacher_disagreement_score":0.922676,"about_ca_system_score_codex":0.000053799147,"about_ca_system_score_gemma":0.00007006353,"threshold_uncertainty_score":0.36530158},"labels":[],"label_agreement":null},{"id":"W4386215194","doi":"10.1109/icmew59549.2023.00049","title":"Decomposed Key-Point Detector for Swimming Pool Localization","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Korea Institute for Advancement of Technology; Ministry of Trade, Industry and Energy","keywords":"Detector; Computer science; Key (lock); Latency (audio); Point (geometry); Artificial intelligence; Real-time computing; Computer vision; Machine learning; Pattern recognition (psychology); Telecommunications; Operating system; Mathematics","score_opus":0.018448744805277823,"score_gpt":0.2732470844292563,"score_spread":0.2547983396239785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386215194","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":[0.0017000491,0.000003417993,0.9945242,0.0008319052,0.00008385271,0.00034501526,0.0000015974972,0.0017227513,0.00078721996],"genre_scores_gemma":[0.8508058,0.000007637904,0.14692073,0.00046597063,0.000056399815,0.0003188666,0.000007987327,0.000012108842,0.0014044611],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934494,0.000009550885,0.00015671927,0.00023789362,0.00008528993,0.00016560797],"domain_scores_gemma":[0.9994754,0.00006696282,0.000044529308,0.00029535868,0.00006957878,0.000048149326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015177182,0.00006955534,0.00006989397,0.000115613635,0.00020216798,0.00008486169,0.0003142085,0.00004149879,0.000018436565],"category_scores_gemma":[0.000021188245,0.00006484422,0.00005863819,0.0006399215,0.000011180416,0.00018151753,0.00009359139,0.000031724045,0.00013779328],"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":[0.000010794396,0.0000922752,0.00024789825,0.000051141848,0.000026910802,0.000002593789,0.0004583485,0.001994875,0.0333429,0.52368164,0.025379525,0.4147111],"study_design_scores_gemma":[0.00013630665,0.000060029237,0.00036395635,0.0000046471587,0.0000025710262,0.0000029305863,0.000025064875,0.8101531,0.11751693,0.012861464,0.058743797,0.00012915887],"about_ca_topic_score_codex":0.00001404789,"about_ca_topic_score_gemma":0.000007876678,"teacher_disagreement_score":0.8491058,"about_ca_system_score_codex":0.000027766137,"about_ca_system_score_gemma":0.000018055167,"threshold_uncertainty_score":0.26442707},"labels":[],"label_agreement":null},{"id":"W4386243191","doi":"10.1109/crv60082.2023.00041","title":"Empirical Thresholding on Spatio-Temporal Autoencoders Trained on Surveillance Videos in a Dementia Care Unit","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"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; Toronto Metropolitan University; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada; Alzheimer Society","keywords":"Thresholding; Artificial intelligence; Computer science; Dementia; Outlier; False positive paradox; Proxy (statistics); Anomaly detection; Software deployment; Segmentation; Set (abstract data type); Pattern recognition (psychology); Machine learning; Image (mathematics); Medicine","score_opus":0.05431684462365509,"score_gpt":0.3278320541441256,"score_spread":0.2735152095204705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386243191","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":[0.36166042,0.00001464885,0.6151711,0.0061862255,0.00013408672,0.00058243657,0.000008105542,0.0022967057,0.013946318],"genre_scores_gemma":[0.9912702,0.0000062969734,0.0076003945,0.000707079,0.000021599437,0.00010502985,0.000015923644,0.0000094268025,0.00026404145],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989075,0.00004691257,0.00020870245,0.0003865385,0.00021312876,0.0002371908],"domain_scores_gemma":[0.9993713,0.00008415494,0.00004817547,0.00039851075,0.000038124992,0.000059736558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020744246,0.00011715151,0.00011736003,0.00026066054,0.0001320897,0.00007547376,0.0004083714,0.000059923757,0.000039785697],"category_scores_gemma":[0.000016180391,0.00010699482,0.000053764947,0.001222675,0.000022059186,0.00011002566,0.00008924006,0.00013787624,0.00012077833],"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":[0.00009104028,0.00043986758,0.60408515,0.0000755421,0.0000683915,0.000103837985,0.0058178054,0.02806927,0.00059815333,0.21988423,0.04315559,0.09761112],"study_design_scores_gemma":[0.0010832582,0.00077610445,0.3917871,0.000073443196,0.000004793014,0.000004950512,0.000889785,0.54666764,0.0062900465,0.007897856,0.043729052,0.0007959917],"about_ca_topic_score_codex":0.00011858706,"about_ca_topic_score_gemma":0.0004987218,"teacher_disagreement_score":0.62960976,"about_ca_system_score_codex":0.00005613174,"about_ca_system_score_gemma":0.000045061628,"threshold_uncertainty_score":0.4363122},"labels":[],"label_agreement":null},{"id":"W4386260304","doi":"10.1109/crv60082.2023.00025","title":"ProPanDL: A Modular Architecture for Uncertainty-Aware Panoptic Segmentation","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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 Toronto","funders":"","keywords":"Architecture; Modular design; Panopticon; Computer science; Segmentation; Artificial intelligence; Computer vision; Sociology; Programming language; Art; Visual arts","score_opus":0.018049823282269326,"score_gpt":0.27579062909059227,"score_spread":0.2577408058083229,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386260304","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":[0.007196683,0.000003880089,0.98828024,0.002178787,0.000042800544,0.00063684385,0.0000049846085,0.0013593282,0.00029647248],"genre_scores_gemma":[0.823039,0.000008840841,0.17224748,0.00038482508,0.00005671615,0.0013119468,0.000030440517,0.000012444412,0.0029082892],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993554,0.0000116047895,0.000115727875,0.00025148786,0.000102935184,0.00016287448],"domain_scores_gemma":[0.99954367,0.000038903672,0.00003652255,0.00028240384,0.000056492223,0.000041997944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010442369,0.00007438593,0.00006758946,0.00009948557,0.00015826372,0.00007170362,0.0002761167,0.000036035744,0.000008453298],"category_scores_gemma":[0.000007832842,0.000060885486,0.000055640874,0.00052944676,0.000014586564,0.00010081459,0.00006855798,0.00004594015,0.000048238566],"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":[0.000016726395,0.00010805537,0.00011213643,0.000117244585,0.00004702639,0.000004248403,0.0010399325,0.021292606,0.07141473,0.25617933,0.016812196,0.6328558],"study_design_scores_gemma":[0.00032830582,0.0002135845,0.00039336353,0.000010666076,0.000008135455,0.000010249417,0.0001300229,0.86028403,0.04604246,0.06632677,0.025999142,0.0002532847],"about_ca_topic_score_codex":0.000015959176,"about_ca_topic_score_gemma":0.000009752132,"teacher_disagreement_score":0.8389914,"about_ca_system_score_codex":0.000023746841,"about_ca_system_score_gemma":0.000024467092,"threshold_uncertainty_score":0.24828382},"labels":[],"label_agreement":null},{"id":"W4386320385","doi":"10.1109/access.2023.3310653","title":"An Interactive Threshold-Setting Procedure for Improved Multivariate Anomaly Detection in Time Series","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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 Tech University","funders":"Knowledge Foundation","keywords":"Computer science; Anomaly detection; Benchmark (surveying); Multivariate statistics; Artificial intelligence; Data mining; Context (archaeology); Series (stratigraphy); Machine learning; Schema (genetic algorithms); Ground truth; Pattern recognition (psychology)","score_opus":0.017599077595397843,"score_gpt":0.32434800039635264,"score_spread":0.3067489228009548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386320385","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":[0.33771175,0.0000034799527,0.659692,0.00045474354,0.00016065183,0.0008230705,0.000010117711,0.0010398848,0.00010432179],"genre_scores_gemma":[0.99056304,0.0000037390796,0.008141968,0.00011652715,0.000093167575,0.00085341383,0.0000069215534,0.000016666409,0.000204572],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902946,0.000022054865,0.00021149812,0.00042245217,0.00007846237,0.00023607329],"domain_scores_gemma":[0.9993447,0.00005568259,0.00012004402,0.00033733045,0.00009461431,0.000047642814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024495233,0.00011938485,0.00012098831,0.00021682227,0.0001872356,0.00037388128,0.000845365,0.000078544996,0.0000034749446],"category_scores_gemma":[0.000026535647,0.0001180272,0.000045836303,0.00085957895,0.000019071065,0.0021293033,0.00012464938,0.00012315474,0.000020659403],"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":[0.000143808,0.00020732895,0.0015805099,0.000065503315,0.00002857418,0.000004999849,0.0012133754,0.000790113,0.81986064,0.0010354467,0.0006010155,0.17446868],"study_design_scores_gemma":[0.00027012822,0.00016023759,0.011729988,0.000019800695,0.000003623984,0.000006762198,0.000031952874,0.5445856,0.43534514,0.007078674,0.0005626646,0.00020547095],"about_ca_topic_score_codex":0.00009058796,"about_ca_topic_score_gemma":0.00011233884,"teacher_disagreement_score":0.6528513,"about_ca_system_score_codex":0.000048924332,"about_ca_system_score_gemma":0.00003408963,"threshold_uncertainty_score":0.48130095},"labels":[],"label_agreement":null},{"id":"W4386397594","doi":"10.48550/arxiv.2308.16279","title":"Classification of Anomalies in Telecommunication Network KPI Time Series","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"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":"Mitacs","keywords":"Anomaly detection; Computer science; Anomaly (physics); Data mining; Series (stratigraphy); Classifier (UML); Time series; Modular design; Artificial intelligence; Categorization; Machine learning; Geology","score_opus":0.07814702858873795,"score_gpt":0.2027980979537972,"score_spread":0.12465106936505924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386397594","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":[0.07785012,0.00004760533,0.9174498,0.00058896956,0.00012006053,0.00046783857,0.0000127951025,0.0006972019,0.002765637],"genre_scores_gemma":[0.9827722,0.00043694535,0.014176687,0.000017585553,0.000028687047,0.000010224779,0.000033981345,0.000013823925,0.0025099018],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988543,0.00011691337,0.0002709189,0.00052540924,0.000053793126,0.00017868556],"domain_scores_gemma":[0.9980045,0.0000814534,0.0003530663,0.0014024719,0.000117745054,0.000040746683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028549347,0.00015388509,0.0002284125,0.00024769257,0.0001033042,0.000043299577,0.0014489567,0.00021831974,0.000011403013],"category_scores_gemma":[0.000014972112,0.0001937424,0.000099998804,0.0011296419,0.00009829365,0.00028663254,0.0011762484,0.00030760732,0.00007368526],"study_design_candidate":"theoretical_or_conceptual","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":[0.000022389311,0.00012640198,0.009604914,0.00007911797,0.000044375127,0.0000098208475,0.00021560187,0.18096252,0.00046407533,0.8043856,0.0015627983,0.0025223678],"study_design_scores_gemma":[0.00015218144,0.00006105754,0.075209826,0.00012381678,0.000022817301,0.0000021163712,0.00007049887,0.727401,0.0005080906,0.19418192,0.0018891187,0.00037750838],"about_ca_topic_score_codex":0.00014701585,"about_ca_topic_score_gemma":0.00012139527,"teacher_disagreement_score":0.90492207,"about_ca_system_score_codex":0.00012235566,"about_ca_system_score_gemma":0.00009308855,"threshold_uncertainty_score":0.79005855},"labels":[],"label_agreement":null},{"id":"W4386575409","doi":"10.1016/j.eswa.2023.121506","title":"An unsupervised spatiotemporal fusion network augmented with random mask and time-relative information modulation for anomaly detection of machines with multiple measuring points","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Anomaly detection; Artificial intelligence; Sensor fusion; Pattern recognition (psychology)","score_opus":0.010360710153165646,"score_gpt":0.22315314590424473,"score_spread":0.2127924357510791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386575409","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":[0.050518975,0.00003742924,0.9448684,0.00010067896,0.000023077266,0.003791196,0.000021074933,0.00057183026,0.00006734258],"genre_scores_gemma":[0.93706703,0.000009684056,0.05576583,0.00001912663,0.00007955813,0.0068551907,0.00014377202,0.000022147091,0.000037671845],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986872,0.00006774326,0.00040040954,0.00036010065,0.00027685464,0.00020769992],"domain_scores_gemma":[0.9983791,0.00011992076,0.00039321,0.00051408116,0.000497493,0.000096210846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003256065,0.00020269795,0.0002493427,0.00022496152,0.0005083567,0.000121585916,0.00021641095,0.00008419317,0.0000010462064],"category_scores_gemma":[0.000010845229,0.00015007402,0.000031114723,0.0010599287,0.00006267431,0.0012825732,0.00003659164,0.00007524701,0.0000061163137],"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":[0.0067000017,0.0011703152,0.059186343,0.0014291643,0.0011380864,0.0000030937538,0.01637273,0.35065484,0.18683995,0.065192625,0.00096371496,0.31034917],"study_design_scores_gemma":[0.0020358544,0.00054654153,0.008417684,0.00010905726,0.000020110296,0.00001289507,0.00019535556,0.98097765,0.005452201,0.00022794007,0.0017412789,0.0002634209],"about_ca_topic_score_codex":0.00034231172,"about_ca_topic_score_gemma":0.000065217435,"teacher_disagreement_score":0.8891026,"about_ca_system_score_codex":0.000054760283,"about_ca_system_score_gemma":0.000045815577,"threshold_uncertainty_score":0.6119841},"labels":[],"label_agreement":null},{"id":"W4386699381","doi":"10.1109/tcsvt.2023.3314895","title":"Autofocusing for Synthetic Aperture Imaging Based on Pedestrian Trajectory Prediction","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Computer vision; Trajectory; Pedestrian; Autoencoder; Perspective (graphical); Deep learning; Geography","score_opus":0.02131216858121601,"score_gpt":0.2515728868873722,"score_spread":0.23026071830615621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386699381","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":[0.0016423408,0.00008287695,0.99140996,0.0021225607,0.0006081595,0.0014030116,0.00014829166,0.0024924192,0.00009036701],"genre_scores_gemma":[0.99592,0.000022389126,0.0012422878,0.00015945018,0.000045563247,0.0023984655,0.0000047342382,0.000031683987,0.00017545215],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998587,0.000029078074,0.00031959842,0.0005895208,0.00013975908,0.00033503314],"domain_scores_gemma":[0.9988037,0.0003994016,0.00010929893,0.00052162015,0.00009855585,0.00006743413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031985372,0.00019217402,0.0002344187,0.0008874794,0.0007070011,0.00011782235,0.00030065028,0.00020114848,0.0000015851507],"category_scores_gemma":[0.000023491131,0.00018572499,0.00013057017,0.0008158846,0.00007627899,0.00014549524,0.0000022242427,0.00019676362,0.000008193415],"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":[0.000065882785,0.0004746997,0.00009940957,0.0007681975,0.0001312349,0.000011368726,0.00030259904,0.036390945,0.043947827,0.049440373,0.0030080131,0.8653594],"study_design_scores_gemma":[0.0006648212,0.00048208228,0.000042826952,0.0001428199,0.000033165565,0.000053336957,0.000121746925,0.95794123,0.0092126485,0.0016702163,0.029380579,0.0002545185],"about_ca_topic_score_codex":0.000017227103,"about_ca_topic_score_gemma":0.000005589575,"teacher_disagreement_score":0.99427766,"about_ca_system_score_codex":0.00009175047,"about_ca_system_score_gemma":0.00006127192,"threshold_uncertainty_score":0.7573645},"labels":[],"label_agreement":null},{"id":"W4386736613","doi":"10.1007/978-3-031-43524-9_7","title":"Artificial Intelligence for Work Measurement: A Promising Approach to Improving Productivity and Reducing Waste","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Computer science; Productivity; Work (physics); Measure (data warehouse); Artificial intelligence; Perspective (graphical); Object (grammar); Automation; Industrial engineering; Data mining; Engineering","score_opus":0.06133932275894516,"score_gpt":0.24984437959660155,"score_spread":0.1885050568376564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386736613","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":[0.000067206965,0.0013581457,0.99482936,0.00017483796,0.00037363553,0.0021741434,0.000002692053,0.00019271104,0.0008272571],"genre_scores_gemma":[0.9684986,0.000056272227,0.028214537,0.0000353894,0.0013460199,0.0007042461,0.0000052088417,0.00007274446,0.001067004],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980357,0.000030659226,0.0004705758,0.00093178987,0.0002257403,0.00030550972],"domain_scores_gemma":[0.9989665,0.00014589199,0.00021689133,0.00045847334,0.00012392605,0.00008828385],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012053592,0.00028805822,0.00040265935,0.00018985214,0.00024354257,0.000392922,0.0002650259,0.00030214313,1.5298018e-7],"category_scores_gemma":[0.00009230789,0.00026399162,0.000058496385,0.00025982072,0.000043895387,0.00008702877,0.00020008156,0.00038366055,5.850733e-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":[0.000024936598,0.00001962633,0.000008880112,0.00042390096,0.000032212345,0.0000015120713,0.0005743853,0.094716884,0.00019684472,0.063753664,0.00005304781,0.8401941],"study_design_scores_gemma":[0.000047827965,0.00015248521,0.00001102588,0.001536147,0.000033212284,0.00003043887,0.000015677297,0.9507144,0.0004802997,0.044943262,0.0013131469,0.0007220414],"about_ca_topic_score_codex":0.000049033828,"about_ca_topic_score_gemma":0.000035248355,"teacher_disagreement_score":0.96843135,"about_ca_system_score_codex":0.000092188086,"about_ca_system_score_gemma":0.000038183716,"threshold_uncertainty_score":0.9999812},"labels":[],"label_agreement":null},{"id":"W4386802598","doi":"10.23977/jaip.2023.060508","title":"Abnormal Event Detection and Localization Based on Crowd Analysis in Video Surveillance","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Frame (networking); Event (particle physics); Energy (signal processing); Computer vision; Artificial intelligence; Block (permutation group theory); Point (geometry); Key frame; Tracking (education); Feature (linguistics); Identification (biology); Pattern recognition (psychology); Index (typography); Key (lock); Computer security; Mathematics; Statistics","score_opus":0.026097760587632735,"score_gpt":0.3279151023470592,"score_spread":0.3018173417594264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386802598","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":[0.014440562,0.000019405259,0.9831739,0.0019700935,0.00012638354,0.00008902019,6.55414e-7,0.000055246208,0.00012470683],"genre_scores_gemma":[0.9925899,0.00011570696,0.006949344,0.000265718,0.00005599539,0.000007830943,5.1347564e-7,0.000004800827,0.000010172644],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986071,0.00018187371,0.0005647386,0.0001923476,0.00031051986,0.00014341596],"domain_scores_gemma":[0.99836177,0.0005841423,0.00048338145,0.00020937831,0.00029583756,0.00006550486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019030178,0.00008588684,0.0001588007,0.00079755276,0.00012524324,0.00015170043,0.00023897187,0.0000588933,0.00000957581],"category_scores_gemma":[0.0006699049,0.000081184524,0.00008428104,0.003341056,0.000031573312,0.0007450108,0.000040022525,0.00022766668,0.000021306543],"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":[0.00019626186,0.0002694399,0.0020635037,0.000010592105,0.000063194166,0.000050477454,0.00045566392,0.5118427,0.0020037463,0.008261757,0.000057976293,0.47472465],"study_design_scores_gemma":[0.000024461095,0.00023869591,0.004717564,0.000011468975,0.000026581565,0.000021441705,0.00018118483,0.97208846,0.018212486,0.0028333887,0.0015524967,0.00009174608],"about_ca_topic_score_codex":0.00007094599,"about_ca_topic_score_gemma":0.00012250438,"teacher_disagreement_score":0.97814935,"about_ca_system_score_codex":0.00007134771,"about_ca_system_score_gemma":0.00004944909,"threshold_uncertainty_score":0.3310609},"labels":[],"label_agreement":null},{"id":"W4386806325","doi":"10.1007/978-3-031-43430-3_15","title":"Multivariate Time-Series Anomaly Detection with Temporal Self-supervision and Graphs: Application to Vehicle Failure Prediction","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Autoencoder; Anomaly detection; Artificial intelligence; Data mining; Key (lock); Graph; Adjacency list; Heuristic; Time series; Task (project management); Machine learning; Pattern recognition (psychology); Deep learning; Algorithm; Theoretical computer science","score_opus":0.006529374539151251,"score_gpt":0.21132515350623113,"score_spread":0.20479577896707987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386806325","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00132335,0.000021057442,0.9955331,0.00067418185,0.00015182319,0.00095247105,0.000010833241,0.0011643068,0.00016887394],"genre_scores_gemma":[0.44065323,0.000028275317,0.5582873,0.0002723249,0.00017962385,0.00018825202,0.000012418078,0.000054846347,0.00032370578],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973367,0.000024419649,0.0003674477,0.0013837318,0.00053472864,0.00035299477],"domain_scores_gemma":[0.9984036,0.00009611998,0.00019385506,0.0009023088,0.00023419944,0.00016988625],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043528894,0.00037789665,0.0002992115,0.00078194245,0.00047113423,0.0004002612,0.0009050155,0.00027423806,0.0000028420582],"category_scores_gemma":[0.000014712649,0.00033510663,0.000056154877,0.0012509753,0.00021033398,0.0007633317,0.0005822199,0.00040998514,0.000053953263],"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":[0.000039477854,0.00006292368,0.0005043042,0.000069177375,0.000030252088,0.000015285681,0.001080039,0.008230102,0.016459975,0.011214496,0.00003355856,0.9622604],"study_design_scores_gemma":[0.0003438026,0.0011433208,0.0035957806,0.00024267843,0.000022833641,0.00011383395,0.0000011770884,0.9150026,0.013981001,0.06106603,0.0036327383,0.0008542074],"about_ca_topic_score_codex":0.000093551454,"about_ca_topic_score_gemma":0.00025602168,"teacher_disagreement_score":0.9614062,"about_ca_system_score_codex":0.000158593,"about_ca_system_score_gemma":0.00011447852,"threshold_uncertainty_score":0.9999101},"labels":[],"label_agreement":null},{"id":"W4386833124","doi":"10.18280/ria.370416","title":"Smart Crowd Monitoring and Suspicious Behavior Detection Using Deep Learning","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Computer security","score_opus":0.0477979650531384,"score_gpt":0.3020853213690935,"score_spread":0.2542873563159551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386833124","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":[0.3975881,0.00006057245,0.6013754,0.000049173257,0.00014763874,0.00013307552,1.8577086e-7,0.00051105063,0.00013478269],"genre_scores_gemma":[0.9917409,0.00012234504,0.007383359,0.00000754529,0.00007632441,0.00008331055,6.6543396e-7,0.000014113592,0.00057145476],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989684,0.000032771295,0.00024789895,0.0003775598,0.00010973472,0.00026360806],"domain_scores_gemma":[0.99938184,0.000066086635,0.00008315526,0.00032231503,0.000069858834,0.00007672748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025850363,0.000113542344,0.00010860037,0.00018300023,0.000523421,0.00015804023,0.00026612432,0.0000726321,0.000010007699],"category_scores_gemma":[0.000036056303,0.00012702707,0.000051668827,0.00094179524,0.000046947487,0.00024237958,0.0001805791,0.00019882788,0.00013731558],"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":[0.0000030027452,0.00004996358,0.0069430475,0.000027807519,0.000007836092,0.000022020138,0.0011878536,0.019630538,0.17637965,0.0028411364,0.0000061884475,0.792901],"study_design_scores_gemma":[0.000011465467,0.000056202425,0.0015368118,0.000018350755,0.000006889594,0.00004717199,0.00035703165,0.68073356,0.3152947,0.0005837169,0.0012126034,0.00014152036],"about_ca_topic_score_codex":0.00007067981,"about_ca_topic_score_gemma":0.0000059667827,"teacher_disagreement_score":0.7927594,"about_ca_system_score_codex":0.000047669244,"about_ca_system_score_gemma":0.000009851131,"threshold_uncertainty_score":0.5180013},"labels":[],"label_agreement":null},{"id":"W4386838008","doi":"10.3390/s23187941","title":"Detecting Cyber Attacks In-Vehicle Diagnostics Using an Intelligent Multistage Framework","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Ajman University; Université Abdelmalek Essaadi","keywords":"Computer science; False positive rate; Intrusion detection system; Robustness (evolution); Anomaly detection; Abnormality; Computer security; Data mining; Artificial intelligence; Real-time computing","score_opus":0.051726099708203806,"score_gpt":0.33410757557144444,"score_spread":0.28238147586324064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386838008","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":[0.63374454,0.000009988204,0.36537483,0.00009295497,0.000088677094,0.00011990375,0.0000015034647,0.00047308052,0.000094514646],"genre_scores_gemma":[0.9272214,0.000042786352,0.07245682,0.0001006509,0.0000622484,0.000018097808,0.0000011018275,0.000015288888,0.00008156734],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998985,0.000049078844,0.00021716626,0.00033351945,0.00013836022,0.0002768569],"domain_scores_gemma":[0.99910295,0.0002342163,0.0000646851,0.00047457733,0.00004157483,0.00008200617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021628835,0.000104343875,0.0001029577,0.0001714834,0.00017425415,0.00009606612,0.0003344427,0.000097200704,0.000009738917],"category_scores_gemma":[0.00013527811,0.000111380185,0.00004213459,0.0010905378,0.00003090202,0.00014455462,0.00016290096,0.0002335364,0.000100801895],"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":[0.000016329166,0.0006616769,0.052812546,0.000080823396,0.000048278584,0.00046167485,0.015650354,0.17705959,0.0259142,0.09711254,0.000530423,0.62965155],"study_design_scores_gemma":[0.000063215215,0.000045441247,0.008662819,0.0000429696,0.0000031406064,0.0000103445345,0.0003774354,0.9473045,0.03454082,0.0063515524,0.0023502877,0.00024749196],"about_ca_topic_score_codex":0.00017218968,"about_ca_topic_score_gemma":0.000048295602,"teacher_disagreement_score":0.7702449,"about_ca_system_score_codex":0.000062959785,"about_ca_system_score_gemma":0.00001643441,"threshold_uncertainty_score":0.4541952},"labels":[],"label_agreement":null},{"id":"W4386854143","doi":"10.1007/s00445-023-01674-9","title":"Cleaning volcano-seismic event catalogues: a machine learning application for robust systems and potential crises in volcano observatories","year":2023,"lang":"en","type":"article","venue":"Bulletin of Volcanology","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Volcano; Observatory; Seismology; Event (particle physics); Noise (video); Identification (biology); Geology; Consistency (knowledge bases); Computer science; Seismic noise; Machine learning; Artificial intelligence; Data mining","score_opus":0.02050577645346725,"score_gpt":0.24553466395821474,"score_spread":0.2250288875047475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386854143","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":[0.07865145,0.0005205818,0.9161638,0.003046644,0.00018991255,0.00081283937,0.000025518191,0.00051330024,0.00007594076],"genre_scores_gemma":[0.9877317,0.00025093256,0.010351835,0.00009146259,0.000062939835,0.0007454943,0.000075657816,0.000021996831,0.0006680071],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985547,0.00007711714,0.00044954437,0.00047935083,0.00012400177,0.00031526753],"domain_scores_gemma":[0.99909145,0.00014576032,0.00025461792,0.00034123176,0.00011191303,0.000055035463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004035615,0.00015722435,0.00029152157,0.00019814614,0.00018255957,0.000040155704,0.00042970522,0.00014328906,0.0000043941277],"category_scores_gemma":[0.00009432985,0.0001669432,0.00005896497,0.000452915,0.000106392545,0.00005472741,0.00025166108,0.00016871057,0.000025625977],"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":[0.0004639606,0.0009100919,0.0718464,0.0023011828,0.00035289756,0.00006716939,0.004607513,0.19039813,0.050737526,0.39305902,0.06306108,0.22219504],"study_design_scores_gemma":[0.00084070145,0.0004935636,0.009317935,0.000074846845,0.000024339639,0.000057713347,0.0005368756,0.63106644,0.0022377614,0.0021265906,0.3528059,0.00041734925],"about_ca_topic_score_codex":0.0018260704,"about_ca_topic_score_gemma":0.000077658595,"teacher_disagreement_score":0.9090802,"about_ca_system_score_codex":0.000052557487,"about_ca_system_score_gemma":0.000052348132,"threshold_uncertainty_score":0.6807746},"labels":[],"label_agreement":null},{"id":"W4386919580","doi":"10.1109/sas58821.2023.10254104","title":"Automated Stride Detection from OpenPose Keypoints Using Handheld Smartphone Video","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Children's Hospital of Eastern Ontario; Ottawa Hospital; University of Ottawa","funders":"","keywords":"Computer science; Mobile device; STRIDE; Computer graphics (images); Computer vision; Artificial intelligence; World Wide Web; Computer security","score_opus":0.029431180844352905,"score_gpt":0.28493919871587636,"score_spread":0.25550801787152344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386919580","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":[0.2658536,0.000006577392,0.72742194,0.00023352458,0.0001622061,0.00017078625,0.0000049442697,0.005551642,0.0005948077],"genre_scores_gemma":[0.94622165,0.00001314577,0.052721303,0.00018886206,0.000059722388,0.000049346352,0.000007135571,0.000012715383,0.00072610436],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989577,0.000034040837,0.00023054976,0.0003897824,0.00016904133,0.00021892102],"domain_scores_gemma":[0.99919164,0.00006342464,0.0000775139,0.00052131363,0.00006233623,0.00008378527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016795595,0.00011526256,0.000120848075,0.00017236786,0.00028012253,0.00019928518,0.00043125692,0.00008074502,0.00007698656],"category_scores_gemma":[0.000021508977,0.00011025837,0.00005984902,0.0012968674,0.000022484255,0.00041637433,0.0002709151,0.00009766825,0.0006105437],"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":[0.00001559713,0.00009984486,0.00037270234,0.0000093104045,0.000056575533,0.000023129198,0.0002692387,0.0011475238,0.7986613,0.00334105,0.010902356,0.1851014],"study_design_scores_gemma":[0.00013919763,0.000025332434,0.0031733948,0.000007703887,0.0000042157726,0.0000065296595,0.000018226825,0.64095956,0.35102525,0.0022977956,0.0022152932,0.00012748025],"about_ca_topic_score_codex":0.001323548,"about_ca_topic_score_gemma":0.00009570859,"teacher_disagreement_score":0.68036807,"about_ca_system_score_codex":0.00006124702,"about_ca_system_score_gemma":0.00003411665,"threshold_uncertainty_score":0.78475076},"labels":[],"label_agreement":null},{"id":"W4387225206","doi":"10.1007/978-981-99-4554-2_10","title":"Design and Application of Data Management System for the Coronavirus Pandemic","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in electrical engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; SQL; Database; Oracle; Python (programming language); Data management; Database administrator; Login; Data definition language; Database design; Stored procedure; World Wide Web; Operating system; Query by Example; Software engineering","score_opus":0.03828237559304909,"score_gpt":0.2658231145535625,"score_spread":0.2275407389605134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387225206","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[3.9298573e-7,0.0009260317,0.9974988,0.00006417067,0.00003747409,0.00099763,0.0000070644164,0.0003217571,0.00014665748],"genre_scores_gemma":[0.20680118,0.0023360967,0.78734404,0.0000928793,0.00024097697,0.0015070485,0.000064411324,0.00016051196,0.0014528447],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990903,0.0000052033865,0.00024056516,0.00038883628,0.00012394975,0.0001511563],"domain_scores_gemma":[0.9983873,0.00076820486,0.00009439436,0.0007028664,0.00002403204,0.000023226732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027177294,0.00015407866,0.0001885494,0.00014402483,0.000047462716,0.000024849174,0.00079925207,0.00015837238,2.69331e-7],"category_scores_gemma":[0.000017443144,0.00012460616,0.00003242289,0.00019613236,0.00001497861,0.00004802805,0.00022256366,0.0002377115,0.0000017406242],"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":[0.0000045833244,0.000003545835,0.0000011526622,0.00012425895,0.000037722042,9.789075e-7,0.000010605745,0.03335762,0.0002555044,0.21739323,0.000010795702,0.7488],"study_design_scores_gemma":[0.00007770839,0.00003270093,0.000015649248,0.000052604686,0.000030128345,0.000008813169,1.6028798e-7,0.9833298,0.00040753413,0.00806722,0.007844893,0.00013280893],"about_ca_topic_score_codex":0.000008041817,"about_ca_topic_score_gemma":0.0000020244584,"teacher_disagreement_score":0.94997215,"about_ca_system_score_codex":0.00007944059,"about_ca_system_score_gemma":0.000013319007,"threshold_uncertainty_score":0.5081292},"labels":[],"label_agreement":null},{"id":"W4387385765","doi":"10.1109/icjece.2023.3275975","title":"Unsupervised Anomaly Detection for Rural Fixed Wireless LTE Networks","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Mitacs","keywords":"Anomaly detection; Computer science; DBSCAN; Cluster analysis; Wireless; Benchmark (surveying); Wireless network; Data mining; Real-time computing; Computer network; Artificial intelligence; Telecommunications; Geography; Cartography","score_opus":0.0059407281058089425,"score_gpt":0.1812103187298789,"score_spread":0.17526959062406994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387385765","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":[0.08688559,0.0001633876,0.9123601,0.00020249175,0.00020977447,0.00008692498,7.742219e-7,0.00008621162,0.0000047655913],"genre_scores_gemma":[0.99048823,0.000035159483,0.009121218,0.00007510093,0.00023820653,0.0000126913465,8.26717e-7,0.000009472883,0.000019079065],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993329,0.000008946948,0.00020688033,0.00010989806,0.00006047397,0.00028091457],"domain_scores_gemma":[0.9993698,0.0000869726,0.000054356376,0.00009736119,0.00008959264,0.00030193618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013095669,0.00009185208,0.00013808346,0.00032900137,0.00013268273,0.00012558843,0.0002584539,0.00006040857,8.972745e-7],"category_scores_gemma":[0.000010053728,0.0000886685,0.0000769702,0.00072433474,0.000010014426,0.00015939282,0.000018556646,0.00015672513,9.347216e-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":[0.0000098810015,0.000013271576,0.00055086135,0.000026712594,0.000071347924,0.000046479425,0.0001390279,0.042206217,0.0033206684,0.01706045,0.0016077666,0.9349473],"study_design_scores_gemma":[0.00015788423,0.00019262455,0.0051408648,0.0000142312365,0.000005428972,0.000106618565,0.0000014529836,0.9889154,0.00073074526,0.00024370528,0.0043824897,0.00010856624],"about_ca_topic_score_codex":0.00009326481,"about_ca_topic_score_gemma":0.00006837605,"teacher_disagreement_score":0.94670916,"about_ca_system_score_codex":0.00004753737,"about_ca_system_score_gemma":0.00007666471,"threshold_uncertainty_score":0.36157963},"labels":[],"label_agreement":null},{"id":"W4387404244","doi":"10.1007/978-3-031-44689-4_9","title":"Unsupervised Liver Tumor Segmentation with Pseudo Anomaly Synthesis","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Segmentation; Computer science; Generalizability theory; Artificial intelligence; Benchmark (surveying); Anomaly (physics); Anomaly detection; Pattern recognition (psychology); Ground truth; Image segmentation; Machine learning; Mathematics","score_opus":0.01683446196139263,"score_gpt":0.22993120982515758,"score_spread":0.21309674786376495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387404244","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030684748,0.000046619134,0.9959049,0.0004982602,0.00024945757,0.0005515969,0.000008171494,0.0007120297,0.0017221149],"genre_scores_gemma":[0.12861028,0.000083149534,0.8682179,0.0011347062,0.00028060333,0.00018966277,0.000005883651,0.00007851722,0.0013992762],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970034,0.000025266902,0.00038797618,0.0013673045,0.0007529478,0.00046310254],"domain_scores_gemma":[0.9978084,0.00038101876,0.00025206103,0.0012027491,0.00021543345,0.00014031948],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043876274,0.0004289359,0.00036964653,0.0007753263,0.0003461812,0.00046417,0.002120537,0.00016986129,0.000034188404],"category_scores_gemma":[0.000028952487,0.0003678689,0.000102541344,0.0010592155,0.00043027065,0.00053982995,0.00062331744,0.00044313254,0.00014679943],"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":[0.000011455112,0.000050237522,0.00012522569,0.00006826896,0.000026366632,0.00019164951,0.00052395207,0.0063242326,0.0008849146,0.03687162,0.00006911346,0.95485294],"study_design_scores_gemma":[0.00039062937,0.0006047901,0.0012769308,0.0007806132,0.000048340666,0.00034214536,0.0000014444004,0.87670743,0.040417444,0.07615155,0.001398831,0.0018798361],"about_ca_topic_score_codex":0.00006765342,"about_ca_topic_score_gemma":0.00010665792,"teacher_disagreement_score":0.9529731,"about_ca_system_score_codex":0.00023675904,"about_ca_system_score_gemma":0.00032988156,"threshold_uncertainty_score":0.99987733},"labels":[],"label_agreement":null},{"id":"W4387408057","doi":"10.21203/rs.3.rs-3388986/v1","title":"Incremental Learning of LSTM-AutoEncoder Anomaly Detection in Three-Axis CNC Machines","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Autoencoder; Anomaly detection; Artificial intelligence; Anomaly (physics); Computer science; Pattern recognition (psychology); Machine learning; Deep learning; Computer vision; Physics","score_opus":0.07453823176844568,"score_gpt":0.3863911129610723,"score_spread":0.31185288119262666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387408057","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":[0.2269349,0.0001984397,0.7690271,0.0005690137,0.00018456708,0.0012486111,0.000020680694,0.0007441029,0.0010725688],"genre_scores_gemma":[0.9893083,0.00016860108,0.009173428,0.0000064416763,0.00008717097,0.00068196724,0.000016423846,0.0000339174,0.00052375],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99690765,0.0003703009,0.000504192,0.0008145945,0.0009174921,0.00048574724],"domain_scores_gemma":[0.99822193,0.00021400987,0.00018627108,0.00091427047,0.0003658787,0.00009765082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020176752,0.00021321137,0.0003106722,0.0011215243,0.00027742868,0.00017699943,0.0012581365,0.0003008179,0.00004395022],"category_scores_gemma":[0.00019401762,0.0002186365,0.0001638863,0.0015336612,0.00012683921,0.00019533823,0.0028647936,0.0018470598,0.00008505693],"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":[0.00013981242,0.001038721,0.19023788,0.0032110794,0.00020494578,0.00011978935,0.0030335595,0.01862364,0.03642177,0.03352014,0.0011422349,0.71230644],"study_design_scores_gemma":[0.00028472464,0.00047450067,0.1853544,0.0005309022,0.0000055109927,0.000009015021,0.00020348014,0.75468284,0.020605871,0.03616363,0.0012429609,0.00044216402],"about_ca_topic_score_codex":0.006317673,"about_ca_topic_score_gemma":0.0018278338,"teacher_disagreement_score":0.7623734,"about_ca_system_score_codex":0.0003007639,"about_ca_system_score_gemma":0.00019504609,"threshold_uncertainty_score":0.95504737},"labels":[],"label_agreement":null},{"id":"W4387519748","doi":"10.1007/s12652-023-04708-4","title":"Design of syntactic adaptive interactive system based on human perception state estimation within scenario context","year":2023,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"Higher Education Discipline Innovation Project","keywords":"Computer science; Computational intelligence; Perception; Context (archaeology); State (computer science); Artificial intelligence; Estimation; Context model; Human–computer interaction; Natural language processing; Algorithm","score_opus":0.0688916253740052,"score_gpt":0.31707286854570066,"score_spread":0.24818124317169546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387519748","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":[0.14425707,0.000007801584,0.8552214,0.000045891804,0.000121619516,0.00021160398,6.2060155e-7,0.00009326781,0.00004071821],"genre_scores_gemma":[0.95544606,0.000006577904,0.04444458,0.00004901212,0.000025279802,0.000004041067,7.739366e-7,0.00000873896,0.0000149523],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848086,0.00014426309,0.0007106085,0.00021105549,0.00030666564,0.000146579],"domain_scores_gemma":[0.9980461,0.000304988,0.0010181542,0.00018437527,0.00038316363,0.00006323704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009858457,0.00013414236,0.00026410673,0.00044133997,0.0002949709,0.00012323982,0.00033460872,0.00004089702,0.000004279003],"category_scores_gemma":[0.000036612593,0.00011866981,0.00008022767,0.00040515064,0.000055757406,0.00032895422,0.00007709328,0.00025589677,0.000011243079],"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":[0.0003598116,0.00034040437,0.00012699123,0.0001653569,0.00011840852,0.00004482727,0.020023277,0.72030216,0.02101654,0.053138774,0.000121849385,0.18424161],"study_design_scores_gemma":[0.00017719873,0.0011318009,0.00042706094,0.0005946222,0.000015768022,0.000036507918,0.0021278288,0.9731383,0.020488624,0.0017484737,0.0000041234457,0.00010973456],"about_ca_topic_score_codex":0.000033678396,"about_ca_topic_score_gemma":0.0000017141812,"teacher_disagreement_score":0.811189,"about_ca_system_score_codex":0.00016230984,"about_ca_system_score_gemma":0.000058581147,"threshold_uncertainty_score":0.48392144},"labels":[],"label_agreement":null},{"id":"W4387846642","doi":"10.1145/3583780.3615306","title":"Anomaly and Novelty detection for Satellite and Drone systems (ANSD '23)","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"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":"Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México; Institute for Information and Communications Technology Promotion; University of California, San Diego; Ministry of Science and ICT, South Korea; National Research Foundation of Korea; Korea Aerospace Research Institute; National University of Computer and Emerging Sciences; National Research Foundation; Sungkyunkwan University; Commonwealth Scientific and Industrial Research Organisation; Sangmyung University; University of Southern California; Kyungpook National University; Stony Brook University; State University of New York; Chungnam National University; Institute for Catastrophic Loss Reduction; University of Washington; National Aeronautics and Space Administration","keywords":"Drone; Anomaly detection; Computer science; Novelty; Satellite; Novelty detection; Scale (ratio); Data science; Anomaly (physics); Remote sensing; Real-time computing; Data mining; Geography; Cartography; Engineering; Aerospace engineering","score_opus":0.01691208634864753,"score_gpt":0.2467765818839601,"score_spread":0.22986449553531257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387846642","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":[0.060026046,0.00013045035,0.93768525,0.0003418981,0.00007472696,0.00037148272,0.000003473685,0.00071895286,0.0006477322],"genre_scores_gemma":[0.98486364,0.00018377042,0.012226275,0.000053885953,0.00003918023,0.00021249754,0.000002035859,0.0000069236958,0.0024117823],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993951,0.000009323795,0.00012774898,0.00027189893,0.000061526756,0.00013444481],"domain_scores_gemma":[0.9996124,0.000056492794,0.000038694754,0.00019677235,0.000040410443,0.000055264663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017907165,0.00007198692,0.00008378819,0.00009225109,0.00017902498,0.0001477877,0.00010636899,0.000050550712,8.218042e-7],"category_scores_gemma":[0.0000064413666,0.000065085456,0.000020033869,0.00032720433,0.000026076774,0.00018972026,0.00007986953,0.00003538323,0.0000135445225],"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":[0.000014517187,0.000045668574,0.0018034275,0.00017631378,0.000035679124,0.0000018462218,0.00035816288,0.000038661354,0.07950746,0.29938972,0.0013478296,0.6172807],"study_design_scores_gemma":[0.0007155356,0.00055150065,0.056642443,0.000029574498,0.00002197151,0.000118823824,0.00024046237,0.6698929,0.06596087,0.011536892,0.19368804,0.0006009722],"about_ca_topic_score_codex":0.00006656,"about_ca_topic_score_gemma":0.000024758323,"teacher_disagreement_score":0.92545897,"about_ca_system_score_codex":0.000012233012,"about_ca_system_score_gemma":0.0000068156637,"threshold_uncertainty_score":0.26541078},"labels":[],"label_agreement":null},{"id":"W4387883789","doi":"10.1109/icc45041.2023.10279480","title":"On Augmented Intelligence and Performance Anomaly Detection in Unlabeled OpenWiFi Data","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Anomaly detection; Computer science; Artificial intelligence; Cluster analysis; Machine learning; Outlier; Unsupervised learning; Feature extraction; Data mining; Feature (linguistics); Oversampling; Domain (mathematical analysis); Supervised learning; Pattern recognition (psychology); Artificial neural network; Mathematics","score_opus":0.05628677607066424,"score_gpt":0.296613012157754,"score_spread":0.24032623608708975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387883789","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":[0.36703342,0.0000064289984,0.6302978,0.00034479826,0.000042815598,0.00020478832,0.0000017429455,0.00047768262,0.0015905129],"genre_scores_gemma":[0.99429464,0.00011391499,0.0047541508,0.00012751415,0.000008286717,0.000051436928,0.000005297307,0.000004693654,0.0006400328],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991733,0.000018725814,0.00015903305,0.000399112,0.00010111139,0.00014872797],"domain_scores_gemma":[0.99915564,0.000050042134,0.000033510514,0.0007028438,0.000019708965,0.000038242157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028564763,0.000076175325,0.00007174126,0.00019451756,0.000109238994,0.00007877734,0.00067706633,0.00003714623,0.00001351418],"category_scores_gemma":[0.000017813752,0.000069501366,0.000008144399,0.001134081,0.00002374509,0.00045893376,0.00049026334,0.00009738738,0.00013356362],"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":[0.000012441665,0.0000543748,0.0010858352,0.000012053171,0.0000045385373,0.000003258415,0.000073472074,0.00014629604,0.0021462485,0.030680895,0.0004647704,0.9653158],"study_design_scores_gemma":[0.00008705506,0.00018515003,0.022182833,0.000015200859,0.0000012016973,0.000007422003,0.000026035796,0.93064725,0.04279638,0.0021157267,0.0018028419,0.00013288716],"about_ca_topic_score_codex":0.00009485908,"about_ca_topic_score_gemma":0.00009970817,"teacher_disagreement_score":0.9651829,"about_ca_system_score_codex":0.000023886598,"about_ca_system_score_gemma":0.000013623926,"threshold_uncertainty_score":0.28341833},"labels":[],"label_agreement":null},{"id":"W4387903956","doi":"10.3390/app132111612","title":"Integrated Artificial Intelligence in Data Science","year":2023,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"Eesti Teadusagentuur","keywords":"Computer science; Everyday life; Artificial intelligence; Data science; Epistemology; Philosophy","score_opus":0.12167423046355076,"score_gpt":0.35167014930921464,"score_spread":0.2299959188456639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387903956","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":[0.02705271,0.000011659163,0.9502663,0.0021633392,0.00018606888,0.00032316588,0.000005291978,0.001004715,0.01898677],"genre_scores_gemma":[0.94241136,0.00001837979,0.05731912,0.0001285669,0.000022377088,0.000055839268,0.0000033937965,0.0000029717044,0.00003796621],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980045,0.000013408814,0.00025937887,0.00086149585,0.0004577687,0.00040343066],"domain_scores_gemma":[0.99880695,0.000086395565,0.00006215438,0.00093121413,0.00004130542,0.000071982395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022823229,0.000095057505,0.00009715901,0.0006163106,0.0005341953,0.00038364483,0.0050998055,0.000033446435,0.000012760172],"category_scores_gemma":[0.00006981983,0.00008119911,0.000012874827,0.012326341,0.00093937904,0.00097560685,0.0012571092,0.00012985498,0.00035532322],"study_design_candidate":"theoretical_or_conceptual","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":[5.733696e-7,0.000017443685,0.000033117256,9.390754e-7,3.605612e-7,0.000001256865,0.0001157634,0.000158317,0.010018942,0.8230651,0.00019213335,0.16639605],"study_design_scores_gemma":[0.000022369084,0.00003430183,0.0010713595,0.000009777767,0.0000010520972,0.0000050979306,0.00073201285,0.7247577,0.05517764,0.21345256,0.0044767824,0.00025935943],"about_ca_topic_score_codex":0.0000559628,"about_ca_topic_score_gemma":0.000058680336,"teacher_disagreement_score":0.91535866,"about_ca_system_score_codex":0.000036719066,"about_ca_system_score_gemma":0.00030415665,"threshold_uncertainty_score":0.9476793},"labels":[],"label_agreement":null},{"id":"W4387951146","doi":"10.1109/ccece58730.2023.10288888","title":"Analyzing the Benefits of Data Augmentation for Smart Grid Anomaly Detection and Forecasting","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Anomaly detection; Computer science; Smart grid; Machine learning; Time series; Grid; Anomaly (physics); Data modeling; Data mining; Artificial intelligence; Generative grammar; Electricity; Engineering; Database","score_opus":0.10014184838130616,"score_gpt":0.30742184209445184,"score_spread":0.20727999371314568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387951146","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":[0.07544433,0.000024154022,0.92344534,0.00042991206,0.000044661112,0.00023737091,0.000012862186,0.00018275267,0.00017861382],"genre_scores_gemma":[0.95210373,0.00003229825,0.047588326,0.000028251525,0.000043794815,0.00007353033,0.000014945158,0.00000434315,0.00011076051],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999472,0.000011273912,0.00016014851,0.0002091781,0.000059903323,0.00008745449],"domain_scores_gemma":[0.9993154,0.00014523433,0.00009187688,0.000379171,0.000051548886,0.00001676608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040462054,0.000045771514,0.000056769062,0.00007611969,0.00021776905,0.000054682216,0.0003432712,0.000020232628,0.0000015870735],"category_scores_gemma":[0.000026593945,0.000034292905,0.000019481926,0.000501034,0.0000183473,0.0003646713,0.00023368708,0.000028013777,0.0000013964419],"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":[0.0000036562367,0.0000074077043,0.0010345806,0.000017139628,0.0000118627095,4.1234994e-8,0.000074268515,0.00011655699,0.0064349254,0.015297026,0.00038481518,0.9766177],"study_design_scores_gemma":[0.00015436493,0.00008075047,0.027174713,0.000009282857,0.0000130752105,0.000005995448,0.000092057686,0.92640495,0.041066535,0.0029516425,0.0019521918,0.00009446793],"about_ca_topic_score_codex":0.00007652978,"about_ca_topic_score_gemma":0.000105468425,"teacher_disagreement_score":0.9765233,"about_ca_system_score_codex":0.0000068243953,"about_ca_system_score_gemma":0.000007627613,"threshold_uncertainty_score":0.16749254},"labels":[],"label_agreement":null},{"id":"W4388115866","doi":"10.36001/phmconf.2023.v15i1.3509","title":"Promoting Explainability in Data-Driven Models for Anomaly Detection: A Step Toward Diagnosis","year":2023,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Anomaly Detection Techniques and Applications","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":true,"ca_institutions":"Hydro-Québec","funders":"","keywords":"Interpretability; Anomaly detection; Computer science; False positive paradox; Anomaly (physics); Transparency (behavior); Data mining; Artificial intelligence; Machine learning; Data science; Computer security","score_opus":0.0977526734080074,"score_gpt":0.31089493756523984,"score_spread":0.21314226415723242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388115866","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":[0.16473877,0.000013485407,0.8288388,0.0049353805,0.00008174588,0.0008921981,0.00014577605,0.0002852483,0.00006854316],"genre_scores_gemma":[0.98302275,0.000038407667,0.016140835,0.00007660615,0.00002702596,0.0006129562,0.000007297745,0.0000072541193,0.00006688784],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988068,0.000055977394,0.00028096946,0.0004452362,0.0001764382,0.00023461677],"domain_scores_gemma":[0.9984169,0.00015494403,0.00015163768,0.0009672231,0.00026972257,0.000039584225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006018035,0.00011025895,0.00016704854,0.00003056088,0.00020307797,0.00006724776,0.0016360459,0.0000817582,0.0000028740803],"category_scores_gemma":[0.00008385728,0.000090549045,0.00015591066,0.0007544663,0.00009284769,0.00072034233,0.0009999397,0.00014210945,0.000002088369],"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":[0.000055197546,0.0013202453,0.014867816,0.0011322077,0.00029189856,0.0000033239353,0.17920102,0.0043607205,0.0068651224,0.10225521,0.02551898,0.66412824],"study_design_scores_gemma":[0.00016886837,0.00006774606,0.002818987,0.00003162982,0.000008681284,0.0000016355149,0.0016398295,0.96405506,0.008191067,0.021271417,0.0016057417,0.00013933078],"about_ca_topic_score_codex":0.00016963191,"about_ca_topic_score_gemma":0.00006888766,"teacher_disagreement_score":0.9596943,"about_ca_system_score_codex":0.000051141556,"about_ca_system_score_gemma":0.00010847076,"threshold_uncertainty_score":0.3692483},"labels":[],"label_agreement":null},{"id":"W4388134157","doi":"10.1007/s10922-023-09781-w","title":"BoostSec: Adaptive Attack Detection for Vehicular Networks","year":2023,"lang":"en","type":"article","venue":"Journal of Network and Systems Management","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer security; Automotive industry; Authentication (law); Wireless; Attack surface; Cloud computing; Telecommunications","score_opus":0.027842869187820283,"score_gpt":0.2609326754857781,"score_spread":0.2330898062979578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388134157","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":[0.0026230763,0.0005873799,0.99502647,0.00021658726,0.00064480054,0.00042237912,3.73819e-7,0.00009518846,0.00038376005],"genre_scores_gemma":[0.9906938,0.0006228854,0.006977367,0.00007782421,0.00070709724,0.000096515534,6.315586e-7,0.000009929361,0.00081395684],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990984,0.000043216187,0.00035469307,0.00014997444,0.00015938542,0.00019430491],"domain_scores_gemma":[0.99928343,0.000056449117,0.00030901397,0.00017687882,0.00010746183,0.000066755914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008119078,0.00008780708,0.00016921856,0.000120094825,0.00020582289,0.00013236712,0.00023602897,0.000047801845,4.2432444e-7],"category_scores_gemma":[0.0000026231655,0.00007403117,0.0000921932,0.0004563512,0.000012710211,0.00016534956,0.000100735546,0.00008842715,0.0000031373033],"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":[0.000059006576,0.000044364202,0.0001505863,0.00013113707,0.000351825,0.00005284196,0.00011993191,0.5915686,0.00004397452,0.09375744,0.07857691,0.2351434],"study_design_scores_gemma":[0.00023845142,0.00027020418,0.0011911044,0.000078173965,0.000027701344,0.000046650148,0.000098775046,0.8645829,0.000011060369,0.0013380918,0.1320198,0.00009711284],"about_ca_topic_score_codex":0.000002405179,"about_ca_topic_score_gemma":0.0000012332426,"teacher_disagreement_score":0.9880707,"about_ca_system_score_codex":0.000036894828,"about_ca_system_score_gemma":0.000005230925,"threshold_uncertainty_score":0.30189034},"labels":[],"label_agreement":null},{"id":"W4388145470","doi":"10.1109/tpwrd.2023.3322380","title":"Condition Monitoring of Underground Power Cables Via Power-Line Modems and Anomaly Detection","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Power Delivery","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Overheating (electricity); Reliability engineering; Anomaly detection; Downtime; Engineering; Electric power system; Electric power transmission; Computer science; Electrical engineering; Power (physics); Artificial intelligence","score_opus":0.01607303871543309,"score_gpt":0.2494647460746936,"score_spread":0.23339170735926051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388145470","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":[0.36614043,0.00003873012,0.6325459,0.0000926817,0.00038195343,0.00016419045,0.000016487511,0.00044703088,0.000172632],"genre_scores_gemma":[0.9965323,0.00020189612,0.0027773108,0.00004287173,0.000015300453,0.000087469074,0.000001717367,0.000023143915,0.00031797233],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99862444,0.00004620562,0.00034766464,0.0004524569,0.00027051286,0.00025871082],"domain_scores_gemma":[0.9990404,0.00011306782,0.00013063234,0.00046706366,0.00014662764,0.00010219655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017741031,0.00019941272,0.00019874533,0.00037885507,0.0003544227,0.000081018785,0.000254646,0.00015105827,0.00002860544],"category_scores_gemma":[0.000002351554,0.00021482109,0.00012119595,0.00092127704,0.000089205736,0.0006397486,0.0000085515985,0.00022543142,0.000046843834],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022069157,0.0011116534,0.0005156354,0.00011172836,0.0004339198,0.000043014876,0.002114168,0.033864632,0.8088234,0.0039085103,0.0004846821,0.14836797],"study_design_scores_gemma":[0.0008168931,0.0015203919,0.007377667,0.00009936038,0.00006373484,0.000105168314,0.0006427956,0.06729998,0.9150822,0.0054429066,0.00081991206,0.00072898413],"about_ca_topic_score_codex":0.00015500437,"about_ca_topic_score_gemma":0.000023840572,"teacher_disagreement_score":0.6303919,"about_ca_system_score_codex":0.000087349035,"about_ca_system_score_gemma":0.000029816534,"threshold_uncertainty_score":0.87601495},"labels":[],"label_agreement":null},{"id":"W4388320619","doi":"10.1145/3600100.3626640","title":"Zone-Level Anomaly Detection in VAV Terminal Units Using an Unsupervised Learning Approach","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Carleton University","funders":"","keywords":"Variable air volume; HVAC; Anomaly detection; Principal component analysis; Adaptability; Fault detection and isolation; Computer science; Anomaly (physics); Fault (geology); Artificial intelligence; Real-time computing; Engineering; Air conditioning; Geology; Seismology","score_opus":0.10142304420613193,"score_gpt":0.29238732311008003,"score_spread":0.1909642789039481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388320619","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":[0.37907228,0.000001958719,0.6191123,0.00002233998,0.000023959246,0.00011928229,4.841198e-7,0.00070478464,0.00094256364],"genre_scores_gemma":[0.93749523,0.0000038890716,0.06151013,0.000041784187,0.000036022135,0.00005439596,0.000005530324,0.000012531723,0.0008404642],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888813,0.00008147867,0.00021365346,0.0004045309,0.0001527809,0.00025942712],"domain_scores_gemma":[0.9994342,0.000023130891,0.000054816945,0.00034009296,0.00007242877,0.00007536636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033106332,0.0001176976,0.000112517664,0.0003925372,0.00026064503,0.00013010754,0.00041253725,0.00008662922,0.000008242317],"category_scores_gemma":[0.000013514015,0.000119471166,0.000030434105,0.0027570094,0.000024474202,0.00057575025,0.00015257463,0.00020816409,0.000027765875],"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":[0.000020870988,0.00038062758,0.005130693,0.000054134343,0.00001883157,0.0000406373,0.0021574069,0.023601506,0.14549749,0.019011477,0.000097904245,0.80398846],"study_design_scores_gemma":[0.00014314463,0.00009823267,0.010912993,0.0000057792336,0.0000024134683,0.000044106746,0.00027014466,0.972937,0.014479132,0.00022426958,0.0007058373,0.00017693531],"about_ca_topic_score_codex":0.000543895,"about_ca_topic_score_gemma":0.00009037503,"teacher_disagreement_score":0.9493355,"about_ca_system_score_codex":0.000059546353,"about_ca_system_score_gemma":0.000049352046,"threshold_uncertainty_score":0.48718926},"labels":[],"label_agreement":null},{"id":"W4388334131","doi":"10.1007/978-3-031-47634-1_1","title":"Towards Explainable Computer Vision Methods via Uncertainty Activation Map","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Focus (optics); Artificial intelligence; Class (philosophy); Noise (video); Uncertainty quantification; Image (mathematics); Measurement uncertainty; Machine learning; Data mining; Mathematics","score_opus":0.024719767348718013,"score_gpt":0.3171863637576421,"score_spread":0.2924665964089241,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388334131","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000096149015,0.00004385937,0.9934762,0.0019216996,0.0014542865,0.0006620311,0.0000038961066,0.0010168408,0.0014115452],"genre_scores_gemma":[0.0094842715,0.000034688077,0.9872373,0.0012239694,0.0005974554,0.00007358318,0.000015423735,0.000058918165,0.0012744091],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957921,0.00008527322,0.0006556257,0.0018793121,0.0008894772,0.00069821783],"domain_scores_gemma":[0.9967689,0.0005027944,0.00040389103,0.0017416079,0.00038494985,0.00019789278],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016312106,0.00058222155,0.0005599596,0.0010988786,0.0005572669,0.0006594087,0.0030280028,0.00047803583,0.000030067604],"category_scores_gemma":[0.00003740353,0.00054296927,0.00020818306,0.0012995757,0.000445243,0.00084399513,0.0019950422,0.00088966975,0.00013238024],"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":[0.0000035987407,0.00002328554,0.0000011463753,0.000031460208,0.000008443644,0.000013508807,0.00017628098,0.02207128,0.00065038505,0.03376808,0.00017535107,0.9430772],"study_design_scores_gemma":[0.000115388284,0.00024135111,0.000043355252,0.000191424,0.0000052667992,0.000026686586,1.6773691e-7,0.6915249,0.008040019,0.28892514,0.010345853,0.0005404451],"about_ca_topic_score_codex":0.00010504781,"about_ca_topic_score_gemma":0.00002221284,"teacher_disagreement_score":0.9425367,"about_ca_system_score_codex":0.00055484957,"about_ca_system_score_gemma":0.00038152598,"threshold_uncertainty_score":0.99970216},"labels":[],"label_agreement":null},{"id":"W4388411620","doi":"10.21203/rs.3.rs-3024402/v3","title":"SSIVD-Net: A Novel Salient Super Image Classification &amp;amp; Detection Technique for Weaponized Violence","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Salient; Computer science; Scalability; Artificial intelligence; Benchmark (surveying); Inference; Classifier (UML); Machine learning; Computer security","score_opus":0.15689103267216678,"score_gpt":0.42581807236594704,"score_spread":0.2689270396937803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388411620","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020103813,0.00007418797,0.98729634,0.0021941254,0.00026476674,0.0060279225,0.00021568783,0.0016508228,0.00026576177],"genre_scores_gemma":[0.3899046,0.000926511,0.554566,0.00007811836,0.0005328258,0.050513253,0.0004133909,0.0001760554,0.0028892348],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.995362,0.00029106333,0.00066962425,0.0016911207,0.001123442,0.000862778],"domain_scores_gemma":[0.994532,0.00053166336,0.00026914818,0.0026871616,0.0017322708,0.0002477662],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0030778828,0.00039081636,0.00039152303,0.0009878763,0.00081247493,0.00071335636,0.0021578076,0.00067201426,0.000032611544],"category_scores_gemma":[0.0006096872,0.0003975354,0.0003596134,0.0015042489,0.00023355459,0.00031593937,0.0020079822,0.0015850022,0.00050949946],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009355275,0.00056338956,0.000053005722,0.0012022797,0.00006575563,0.0000027483163,0.00037735427,0.00014141826,0.90129447,0.027133383,0.008884878,0.060187764],"study_design_scores_gemma":[0.0013284031,0.000606383,0.005639187,0.0029538479,0.00005246559,0.000047723595,0.00026705785,0.108026855,0.3939309,0.2618687,0.22282341,0.002455051],"about_ca_topic_score_codex":0.0004932323,"about_ca_topic_score_gemma":0.00020409259,"teacher_disagreement_score":0.50736356,"about_ca_system_score_codex":0.0007038673,"about_ca_system_score_gemma":0.0004748959,"threshold_uncertainty_score":0.99984765},"labels":[],"label_agreement":null},{"id":"W4388444930","doi":"10.48550/arxiv.2311.01479","title":"Detecting Out-of-Distribution Through the Lens of Neural Collapse","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Cancer Institute; Canadian Institute for Advanced Research","keywords":"Generalizability theory; Leverage (statistics); Computer science; Detector; Artificial intelligence; Artificial neural network; Feature vector; Range (aeronautics); Feature (linguistics); Exploit; Machine learning; Engineering; Computer security; Mathematics; Telecommunications","score_opus":0.133265300991219,"score_gpt":0.22666390930232364,"score_spread":0.09339860831110464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388444930","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":[0.110936664,0.000014163168,0.8874465,0.00021156357,0.00029805748,0.0002844493,0.000047864964,0.0002948271,0.00046591336],"genre_scores_gemma":[0.99832565,0.000078637604,0.001123593,0.000018326971,0.000031090298,0.000003061464,0.000010195643,0.000009026105,0.0004003957],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989925,0.000076399796,0.00022894029,0.00046406974,0.00007457184,0.00016348776],"domain_scores_gemma":[0.99825406,0.00013374677,0.00042884855,0.0009624807,0.00019386255,0.000027005903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018472358,0.00014378082,0.0002023916,0.000062177445,0.0001771021,0.000028036018,0.0011979063,0.00015162407,0.0000040592636],"category_scores_gemma":[0.00003479595,0.00013613382,0.00019812405,0.0007222848,0.00013875718,0.00016674382,0.0012114854,0.00031618754,0.000012002171],"study_design_candidate":"theoretical_or_conceptual","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":[0.000030562645,0.00014564405,0.001122042,0.00018941452,0.00015672196,0.000024628047,0.0012100661,0.14803578,0.0013918732,0.8436721,0.001254824,0.0027663459],"study_design_scores_gemma":[0.00035046795,0.00018872817,0.003034202,0.00014043343,0.00014162397,0.0000044615426,0.000548711,0.8132175,0.020986924,0.15737449,0.0034403051,0.0005721506],"about_ca_topic_score_codex":0.00031220756,"about_ca_topic_score_gemma":0.000053588217,"teacher_disagreement_score":0.887389,"about_ca_system_score_codex":0.00007698728,"about_ca_system_score_gemma":0.00007654789,"threshold_uncertainty_score":0.55513763},"labels":[],"label_agreement":null},{"id":"W4388624337","doi":"10.1109/tii.2023.3312409","title":"Temporal Attention Source-Free Adaptation for Chemical Processes Fault Diagnosis","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Anomaly Detection Techniques and Applications","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":"Western University","funders":"Shanghai Rising-Star Program; National Natural Science Foundation of China","keywords":"Leverage (statistics); Computer science; Domain adaptation; Data mining; Process (computing); Artificial intelligence; Fault (geology); Domain (mathematical analysis); Adaptation (eye); Source code; Data source; Machine learning","score_opus":0.07127951843375917,"score_gpt":0.2792867481351804,"score_spread":0.20800722970142121,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388624337","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":[0.014674982,0.0000015338903,0.98210245,0.0008307402,0.0003545001,0.00077976706,0.00007463603,0.0010670739,0.000114291186],"genre_scores_gemma":[0.9523481,0.000049256054,0.043859575,0.00019945003,0.0001937066,0.002644559,0.000048026806,0.00002427694,0.0006330543],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876964,0.000015398988,0.0005316225,0.00016377546,0.00027425075,0.0002452841],"domain_scores_gemma":[0.9988596,0.00023327948,0.00019886217,0.00042918543,0.00018977758,0.000089300935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002298908,0.0001545522,0.0001517458,0.00027536394,0.0003457764,0.00018054298,0.0005381427,0.00022625175,0.000010124223],"category_scores_gemma":[0.00005579532,0.00015416334,0.00012201019,0.0013242118,0.000041336756,0.0007358106,0.000007132697,0.00024509177,0.00007915519],"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":[0.00010842897,0.0004921029,0.000048958496,0.0002410765,0.000120277524,7.9742756e-7,0.004023155,0.056648098,0.0004596123,0.002695373,0.049445737,0.8857164],"study_design_scores_gemma":[0.0023964406,0.00056852034,0.000010270083,0.00012633236,0.00007346605,0.000012542136,0.0015822331,0.7986254,0.12846038,0.0030680625,0.06442588,0.0006504497],"about_ca_topic_score_codex":0.000026783333,"about_ca_topic_score_gemma":0.000008285789,"teacher_disagreement_score":0.9382429,"about_ca_system_score_codex":0.00007385452,"about_ca_system_score_gemma":0.00012387565,"threshold_uncertainty_score":0.6286599},"labels":[],"label_agreement":null},{"id":"W4388691832","doi":"10.1109/tase.2023.3331347","title":"Unsupervised Fault Detection for Building Air Handling Unit Systems Using Deep Variational Mixture of Principal Component Analyzers","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":12,"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":"Autoencoder; Principal component analysis; Component (thermodynamics); Fault detection and isolation; Missing data; Computer science; Robust principal component analysis; Artificial intelligence; Deep learning; Artificial neural network; Function (biology); Data mining; Pattern recognition (psychology); Machine learning","score_opus":0.02259878845908984,"score_gpt":0.26548661270626733,"score_spread":0.2428878242471775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388691832","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":[0.21414062,0.0000062729196,0.7848225,0.000051108866,0.00026928072,0.0002601998,0.0000062578747,0.00043918696,0.0000045239312],"genre_scores_gemma":[0.9455542,0.0000070607352,0.054294415,0.000011319161,0.000021312333,0.00009520288,0.0000011446035,0.000008672664,0.000006692799],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988223,0.000012604292,0.00028772536,0.0002985115,0.00036888718,0.00020997915],"domain_scores_gemma":[0.9992479,0.00010669127,0.00009646907,0.0002026831,0.00026763626,0.00007861861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000623757,0.00011467288,0.00013202048,0.0007773323,0.0005118907,0.00011514655,0.00024733288,0.000061950646,0.000001045185],"category_scores_gemma":[0.000018342002,0.00011980322,0.000052204345,0.0021544378,0.000048927388,0.0006206603,0.000005531102,0.00009340258,0.0000013903322],"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":[0.0000018017051,0.000010676443,0.0000023524194,0.000032271735,0.0000075934026,1.269601e-7,0.0001461499,0.7543249,0.23858531,0.00176073,4.4648812e-7,0.005127635],"study_design_scores_gemma":[0.00014011978,0.000033009186,0.0005678642,0.00003675538,0.000010417188,0.000009557947,0.000051085954,0.88122606,0.11768617,0.000045491455,0.00008509042,0.000108384374],"about_ca_topic_score_codex":0.00002559781,"about_ca_topic_score_gemma":0.0000020733105,"teacher_disagreement_score":0.73141354,"about_ca_system_score_codex":0.000108379434,"about_ca_system_score_gemma":0.00006884395,"threshold_uncertainty_score":0.48854336},"labels":[],"label_agreement":null},{"id":"W4388699823","doi":"10.1515/jisys-2022-0270","title":"Anomaly detection for maritime navigation based on probability density function of error of reconstruction","year":2023,"lang":"en","type":"article","venue":"Journal of Intelligent Systems","topic":"Anomaly Detection Techniques and Applications","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":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Anomaly detection; Computer science; Anomaly (physics); Probability density function; Trajectory; Artificial intelligence; Feature (linguistics); Function (biology); Pattern recognition (psychology); SIGNAL (programming language); Unsupervised learning; Series (stratigraphy); Machine learning; Data mining; Mathematics; Statistics","score_opus":0.04047905756997482,"score_gpt":0.27869712481787334,"score_spread":0.2382180672478985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388699823","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":[0.2906368,0.000010924371,0.70826507,0.000059470127,0.0005586316,0.00037925667,0.000004213464,0.000047285022,0.000038364877],"genre_scores_gemma":[0.9930991,0.000005734553,0.0067212475,0.000006757356,0.000085103675,0.000036443027,0.0000023097398,0.0000064806504,0.000036820908],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853283,0.00009148913,0.00083989557,0.00016150816,0.00027385683,0.00010042473],"domain_scores_gemma":[0.99760526,0.00014816536,0.0010931168,0.00026361604,0.0008444604,0.00004536266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012048101,0.000089611414,0.00024294994,0.00032733483,0.000077600445,0.000025488844,0.00019762022,0.00009280138,0.0000028439317],"category_scores_gemma":[0.00008364088,0.0000798268,0.00020083252,0.0005861771,0.000037332822,0.00021935617,0.000017744918,0.000109176246,0.0000035201551],"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":[0.0015051587,0.0010297068,0.012444812,0.0023783424,0.00028932956,0.000004418572,0.000486032,0.08055903,0.27523133,0.028496524,0.0010561991,0.5965191],"study_design_scores_gemma":[0.00023689543,0.0019346286,0.006499872,0.0003879435,0.00003995435,0.000079406476,0.0001498599,0.4936836,0.49030724,0.005973772,0.00057444884,0.00013237014],"about_ca_topic_score_codex":0.000037410944,"about_ca_topic_score_gemma":0.0000063714365,"teacher_disagreement_score":0.7024623,"about_ca_system_score_codex":0.00012745785,"about_ca_system_score_gemma":0.00006485259,"threshold_uncertainty_score":0.32552424},"labels":[],"label_agreement":null},{"id":"W4388919384","doi":"10.1109/tgcn.2023.3335342","title":"Deep Learning-Driven Anomaly Detection for Green IoT Edge Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Green Communications and Networking","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Mitacs","keywords":"Anomaly detection; Computer science; Troubleshooting; Energy consumption; Deep learning; Efficient energy use; Wireless sensor network; Real-time computing; Reliability (semiconductor); Enhanced Data Rates for GSM Evolution; Transmission (telecommunications); Data transmission; Artificial intelligence; Data mining; Distributed computing; Computer network; Telecommunications; Engineering","score_opus":0.03193565485445973,"score_gpt":0.26881317308882513,"score_spread":0.2368775182343654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388919384","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":[0.0009650315,0.00030390857,0.9951534,0.0013408264,0.00023279202,0.00055483397,0.000004261067,0.0011677007,0.0002772394],"genre_scores_gemma":[0.98169184,0.0020241186,0.014214181,0.00017345503,0.00016049723,0.00084187015,0.000010284164,0.00003633219,0.0008474047],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985569,0.0001314822,0.00035409388,0.00044540307,0.00013500017,0.00037713797],"domain_scores_gemma":[0.99791706,0.0004614841,0.00015793167,0.0012241717,0.00011759974,0.00012173614],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00035608187,0.00021250689,0.00020531031,0.0003118336,0.0020848254,0.00014311298,0.00095609855,0.00016840774,0.0000043594714],"category_scores_gemma":[8.254516e-7,0.00023247028,0.00015805688,0.0013983109,0.0001213591,0.00020202495,0.000040182542,0.0004889892,0.00002020763],"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":[0.000009949457,0.00005026914,0.000048996648,0.000008485745,0.00003704508,3.94581e-7,0.0001594836,0.0069090854,0.00025523457,0.0004221089,0.00004125724,0.9920577],"study_design_scores_gemma":[0.00023369088,0.0002132868,0.00017319365,0.000028890547,0.000031245174,0.000013042469,0.000035017434,0.9528981,0.00034968092,0.00085193815,0.044929296,0.00024260298],"about_ca_topic_score_codex":0.00028284054,"about_ca_topic_score_gemma":0.00085449277,"teacher_disagreement_score":0.9918151,"about_ca_system_score_codex":0.00006141506,"about_ca_system_score_gemma":0.00001945993,"threshold_uncertainty_score":0.99921435},"labels":[],"label_agreement":null},{"id":"W4389114219","doi":"10.5281/zenodo.2538409","title":"The sufficiency of the evidence, the relevancy of the evidence, and quantifying both with a single number","year":2019,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Psychology","score_opus":0.03179304864829139,"score_gpt":0.25468943704811864,"score_spread":0.22289638839982726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389114219","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":[0.27582544,0.0056958264,0.6518246,0.04731741,0.00044257758,0.0026703188,0.000018765577,0.00022202793,0.01598304],"genre_scores_gemma":[0.9850423,0.0012698122,0.010692145,0.000076325756,0.000009285249,0.00010018207,9.3886536e-7,0.00001712955,0.00279188],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99531794,0.0027036162,0.0005388893,0.0005697238,0.0006237425,0.00024609233],"domain_scores_gemma":[0.9889703,0.0038276163,0.0011446885,0.004705421,0.0013058729,0.00004614558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048266775,0.00022639304,0.00024768777,0.000045954683,0.0009630692,0.0003970349,0.004571099,0.00012980979,0.00000764935],"category_scores_gemma":[0.001243997,0.000107015665,0.00020331985,0.0009778914,0.00087620877,0.00021374838,0.0032692081,0.00060638937,0.0000037724024],"study_design_candidate":"theoretical_or_conceptual","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":[0.000023050598,0.0006039655,0.042663507,0.00042335124,0.0001120383,4.2516817e-7,0.01395937,0.00031930703,0.008890607,0.8809072,0.0015422743,0.05055491],"study_design_scores_gemma":[0.0009015344,0.0000184749,0.25495374,0.043021586,0.0004278216,0.00016088581,0.0015362143,0.15166657,0.4424048,0.047710046,0.05527456,0.0019237514],"about_ca_topic_score_codex":0.0007334575,"about_ca_topic_score_gemma":0.0008009008,"teacher_disagreement_score":0.8331972,"about_ca_system_score_codex":0.000057959245,"about_ca_system_score_gemma":0.00039116107,"threshold_uncertainty_score":0.8494315},"labels":[],"label_agreement":null},{"id":"W4389170530","doi":"10.1007/978-3-031-47969-4_37","title":"Future Video Prediction from a Single Frame for Video Anomaly Detection","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Anomaly detection; Artificial intelligence; Benchmark (surveying); Task (project management); Computer vision; Frame (networking); Segmentation; Focus (optics); Motion (physics); Proxy (statistics); Pattern recognition (psychology); Machine learning","score_opus":0.01737283556606868,"score_gpt":0.23732635515744022,"score_spread":0.21995351959137155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389170530","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013371067,0.00013404842,0.9933189,0.0008811944,0.0027459117,0.00096803997,0.00007701938,0.0012666073,0.00047452937],"genre_scores_gemma":[0.1719155,0.00007290524,0.82013524,0.001879272,0.0044632424,0.00043147756,0.00005107847,0.00013565375,0.0009156216],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963762,0.000022847224,0.0005895388,0.0018195927,0.0006514624,0.00054034666],"domain_scores_gemma":[0.9972762,0.00046184618,0.00038565218,0.0013999238,0.0003178096,0.0001585876],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004985509,0.00048632204,0.00043516114,0.00079690287,0.0005141549,0.0005963529,0.001965038,0.00053352077,0.00001235128],"category_scores_gemma":[0.00007066832,0.0004858809,0.00023536809,0.0009581068,0.00028704244,0.0006550515,0.00063350704,0.0006863372,0.00005548445],"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":[0.000013473509,0.000041374326,0.000019884976,0.000027704134,0.000020745287,0.000009019673,0.00026646408,0.0027380828,0.0049689007,0.008319195,0.00014420657,0.9834309],"study_design_scores_gemma":[0.0002958694,0.0005425933,0.00028538294,0.0002145249,0.000024009369,0.000033315322,6.348989e-7,0.5619169,0.023747837,0.38470864,0.027505318,0.0007249645],"about_ca_topic_score_codex":0.000077813944,"about_ca_topic_score_gemma":0.00026782908,"teacher_disagreement_score":0.982706,"about_ca_system_score_codex":0.00041517627,"about_ca_system_score_gemma":0.00021781136,"threshold_uncertainty_score":0.99975926},"labels":[],"label_agreement":null},{"id":"W4389317800","doi":"10.1109/access.2023.3339379","title":"KianNet: A Violence Detection Model Using an Attention-Based CNN-LSTM Structure","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Anomaly Detection Techniques and Applications","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":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Focus (optics); Feature extraction; Competitor analysis; Harm; Feature (linguistics); Layer (electronics); Machine learning; Object detection; Pattern recognition (psychology)","score_opus":0.053543262929009176,"score_gpt":0.3356476714380638,"score_spread":0.2821044085090546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389317800","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":[0.40380606,0.0000032161222,0.5952175,0.00007493855,0.00012042853,0.00012407257,0.0000062041267,0.0006302112,0.00001732874],"genre_scores_gemma":[0.9869157,0.000005159012,0.012636049,0.00025629476,0.000081686776,0.000054009393,0.0000050247354,0.000014252368,0.000031787447],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989416,0.000029669538,0.00017982836,0.00042526834,0.00019925683,0.00022438577],"domain_scores_gemma":[0.99911636,0.000017034627,0.00010421158,0.00057595794,0.000103433085,0.00008298593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010437284,0.00012316763,0.00009604511,0.00021238675,0.00033582954,0.00030589532,0.00093180314,0.00009133485,0.00000809605],"category_scores_gemma":[0.0000051646184,0.00012459396,0.00005660439,0.0011687208,0.000030993968,0.0009761915,0.000090023255,0.00012851876,0.000018753308],"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":[0.000009604466,0.000088747285,0.0008500393,0.000036848403,0.000012933391,0.0000069988905,0.0000954686,0.615495,0.25845763,0.001720784,0.00025241394,0.12297349],"study_design_scores_gemma":[0.00007658019,0.000023392045,0.0013727061,0.00001791842,0.0000046598493,0.0000037778573,0.00000459452,0.9023063,0.081270896,0.014718979,0.000055180386,0.0001449678],"about_ca_topic_score_codex":0.00013598251,"about_ca_topic_score_gemma":0.000042542797,"teacher_disagreement_score":0.5831097,"about_ca_system_score_codex":0.00005308304,"about_ca_system_score_gemma":0.000057957535,"threshold_uncertainty_score":0.50807947},"labels":[],"label_agreement":null},{"id":"W4389456137","doi":"10.1109/csitss60515.2023.10334217","title":"Navigating Complex Multiclass Classification in High-Dimensional Spaces: A Hybrid Approach","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Machine learning; Artificial intelligence; Classifier (UML); Curse of dimensionality; Task (project management); Data mining; Categorization; Artificial neural network; Decision tree","score_opus":0.05627147987672071,"score_gpt":0.30659180648224765,"score_spread":0.25032032660552694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389456137","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":[0.1704594,0.0000028447666,0.82428646,0.0023321023,0.000035527573,0.00025865328,0.0000026651796,0.0011029495,0.0015194097],"genre_scores_gemma":[0.8112596,0.0000023145387,0.1881772,0.00016815742,0.000018877497,0.00013870504,0.000027147204,0.0000064311544,0.00020154317],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998971,0.00003742872,0.0002237891,0.00037277682,0.00018996751,0.00020505264],"domain_scores_gemma":[0.99938715,0.000064922875,0.00006959381,0.00037632056,0.00004809105,0.000053904692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026106022,0.00009545231,0.00010714708,0.00007409005,0.00013555214,0.00009055316,0.00037114936,0.00003561149,0.000013711837],"category_scores_gemma":[0.000014506528,0.00008564774,0.000036511603,0.000991949,0.000035755133,0.00020701787,0.00016758931,0.0001668015,0.00016396538],"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":[0.0000068983386,0.00042672208,0.0026166542,0.000040638202,0.00001757256,0.000014460991,0.0006405204,0.0047075436,0.051685408,0.74045426,0.016922379,0.18246694],"study_design_scores_gemma":[0.00014765846,0.00001478895,0.020102935,0.000008294757,8.4953854e-7,0.000008448795,0.000073051175,0.97412115,0.0020493055,0.0022604433,0.0010981146,0.000114965325],"about_ca_topic_score_codex":0.00018356214,"about_ca_topic_score_gemma":0.000008109046,"teacher_disagreement_score":0.9694136,"about_ca_system_score_codex":0.000050647217,"about_ca_system_score_gemma":0.000021332095,"threshold_uncertainty_score":0.34926137},"labels":[],"label_agreement":null},{"id":"W4389485261","doi":"10.1007/s00170-023-12713-2","title":"Incremental learning of LSTM-autoencoder anomaly detection in three-axis CNC machines","year":2023,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Autoencoder; Anomaly detection; Artificial intelligence; Computer science; Machine learning; Process (computing); Artificial neural network; Transfer of learning; Anomaly (physics)","score_opus":0.00942708967161831,"score_gpt":0.26356812604379,"score_spread":0.25414103637217167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389485261","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":[0.7071917,0.000037651156,0.2900144,0.0021954626,0.00024034698,0.000074441705,7.5351056e-7,0.00016431094,0.00008090235],"genre_scores_gemma":[0.98420966,0.00012656495,0.015514084,0.000035629677,0.00004302626,0.000015011642,4.8003824e-7,0.000008277747,0.00004729069],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989431,0.000022843107,0.00044382407,0.00015120718,0.0002906093,0.00014840814],"domain_scores_gemma":[0.9990891,0.000076449716,0.00046572174,0.00021661886,0.00013283815,0.000019264093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003706368,0.00009932279,0.000155909,0.00086614426,0.00007696355,0.000027084274,0.0013930731,0.00007120484,0.00000922892],"category_scores_gemma":[0.0000712622,0.0000776136,0.00007824615,0.00045929494,0.000077200246,0.0003008768,0.00039040163,0.00040910474,0.0000069990137],"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":[0.00007839723,0.00007716793,0.0030957568,0.000009275001,0.00009071265,0.000046973557,0.00019053662,0.024415694,0.22192861,0.012464724,0.00003638934,0.73756576],"study_design_scores_gemma":[0.00039114052,0.0001931455,0.017857218,0.000043974727,0.000004729116,0.00023214937,0.00012777551,0.021934241,0.891879,0.06560848,0.001626654,0.00010149278],"about_ca_topic_score_codex":0.00004699446,"about_ca_topic_score_gemma":0.00007600699,"teacher_disagreement_score":0.73746425,"about_ca_system_score_codex":0.000106479914,"about_ca_system_score_gemma":0.000025343097,"threshold_uncertainty_score":0.31649908},"labels":[],"label_agreement":null},{"id":"W4389575940","doi":"10.1109/csnet59123.2023.10339765","title":"Exploring Semantic vs. Syntactic Features for Unsupervised Learning on Application Log Files","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Dalhousie University","funders":"","keywords":"Computer science; Unsupervised learning; Anomaly detection; Artificial intelligence; Natural language processing","score_opus":0.0673249099224138,"score_gpt":0.2822303584163148,"score_spread":0.21490544849390097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389575940","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":[0.02344862,0.0000058004593,0.9704634,0.0020586622,0.000060443203,0.00045653613,0.0000012323904,0.0021603284,0.0013449949],"genre_scores_gemma":[0.9805369,0.000056371522,0.015870601,0.00019673775,0.00006582641,0.0016386018,0.000012326148,0.000014280423,0.0016083907],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999199,0.000017065022,0.00013170844,0.00034069005,0.00011962121,0.00019194397],"domain_scores_gemma":[0.99931127,0.00019652392,0.000049712726,0.00035085648,0.0000459105,0.00004572958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015867474,0.00009633195,0.000094750656,0.00015726894,0.0003100277,0.00009953132,0.00034297348,0.000039016784,0.0000075574267],"category_scores_gemma":[0.000036203382,0.000087956876,0.00006518281,0.0005228161,0.0000118542175,0.0002695647,0.000078898316,0.00010944644,0.00018356934],"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":[0.000019654051,0.00008181759,0.0002720227,0.0000844427,0.00002476911,0.0000016948085,0.0004751181,0.0026017872,0.016090933,0.6186524,0.006801439,0.3548939],"study_design_scores_gemma":[0.0005367106,0.0006642443,0.027831402,0.00007484356,0.000023911847,0.00001649423,0.00041096986,0.6532641,0.18710035,0.024731945,0.104563855,0.0007811935],"about_ca_topic_score_codex":0.00003138821,"about_ca_topic_score_gemma":0.0000038883522,"teacher_disagreement_score":0.95708823,"about_ca_system_score_codex":0.00002681594,"about_ca_system_score_gemma":0.000012110966,"threshold_uncertainty_score":0.35867774},"labels":[],"label_agreement":null},{"id":"W4389952571","doi":"10.2139/ssrn.4668704","title":"Self-Supervised Dual-Layer 2d Normalized Flow Method for Industrial Anomaly Detection","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Download; Computer science; Anomaly detection; Dual (grammatical number); Dual layer; Anomaly (physics); Layer (electronics); Flow (mathematics); Data mining; World Wide Web; Mathematics; Physics","score_opus":0.0398110202005813,"score_gpt":0.3049174129147265,"score_spread":0.2651063927141452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389952571","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021504022,0.0002691451,0.9918671,0.0015443832,0.0013498479,0.0013093243,0.000021747412,0.0013609553,0.00012705209],"genre_scores_gemma":[0.16690417,0.0034345945,0.81971145,0.0002655137,0.0042093587,0.001930945,0.00005182014,0.00021810684,0.0032740394],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951954,0.0002998287,0.0008455736,0.0009148462,0.00047232257,0.002272053],"domain_scores_gemma":[0.9976953,0.00019645363,0.0006627244,0.0008863593,0.00037433518,0.00018478725],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.004166816,0.00047294176,0.0005479944,0.00053769595,0.0007217322,0.00053772435,0.0014608172,0.00077089976,0.000010927482],"category_scores_gemma":[0.00009792885,0.0004696674,0.00065728935,0.00069432793,0.000024160174,0.00036852006,0.00071226084,0.0047753286,0.000046498124],"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":[0.0002355207,0.0003449753,0.000072958515,0.00011566104,0.0014909618,0.000011642989,0.0005631454,0.0025979893,0.0047651683,0.10461536,0.0016620181,0.8835246],"study_design_scores_gemma":[0.0017704864,0.0007005848,0.000033298773,0.000046527566,0.00020611136,0.0005860494,0.00013179451,0.5640939,0.009181738,0.40360415,0.018823558,0.00082179304],"about_ca_topic_score_codex":0.00018093601,"about_ca_topic_score_gemma":0.0004231753,"teacher_disagreement_score":0.8827028,"about_ca_system_score_codex":0.0015948312,"about_ca_system_score_gemma":0.0034489727,"threshold_uncertainty_score":0.9997755},"labels":[],"label_agreement":null},{"id":"W4390062548","doi":"10.1038/s41598-023-49903-7","title":"An anomaly detection method for identifying locations with abnormal behavior of temperature in school buildings","year":2023,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Anomaly Detection Techniques and Applications","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":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Research Manitoba","keywords":"HVAC; Dynamic time warping; Anomaly detection; Computer science; Anomaly (physics); Data mining; Set (abstract data type); Air conditioning; Artificial intelligence; Engineering","score_opus":0.019704959249891168,"score_gpt":0.3217532817860204,"score_spread":0.30204832253612923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390062548","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":[0.4061819,0.0000066830753,0.59260243,0.000047705173,0.00037599937,0.00050108304,0.0000017377599,0.00025048715,0.000031982272],"genre_scores_gemma":[0.83526564,9.150167e-7,0.16364491,0.000009342523,0.000021728982,0.0006298063,0.00001517603,0.0000101559,0.000402332],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99830836,0.000033629483,0.00043477624,0.0007028329,0.00028603085,0.00023439675],"domain_scores_gemma":[0.99837834,0.000029101551,0.00027501347,0.0009308377,0.00029239868,0.000094340954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015784187,0.0001060269,0.00013935153,0.00060232444,0.00039022407,0.00036838246,0.00033226138,0.00007636806,0.0000051870265],"category_scores_gemma":[0.0000418481,0.00009776171,0.00006123535,0.0026906473,0.000070056434,0.00082462723,0.000063576874,0.00011428185,0.0000036078168],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005705438,0.00012273015,0.006862207,0.000033875956,0.0000063774855,0.000030807783,0.0003117232,0.00088627846,0.9688284,0.0021705434,0.0004165379,0.020324811],"study_design_scores_gemma":[0.00015632769,0.00015208754,0.037492406,0.000044236014,0.000020222304,0.0002691768,0.00020084341,0.023920966,0.92917883,0.0056845625,0.002625122,0.00025522168],"about_ca_topic_score_codex":0.00011312911,"about_ca_topic_score_gemma":0.0001807267,"teacher_disagreement_score":0.42908373,"about_ca_system_score_codex":0.000052678562,"about_ca_system_score_gemma":0.00014049256,"threshold_uncertainty_score":0.3986607},"labels":[],"label_agreement":null},{"id":"W4390196573","doi":"10.18280/ijsse.130618","title":"A Robust Multi Descriptor Fusion with One-Class CNN for Detecting Anomalies in Video Surveillance","year":2023,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer science; Class (philosophy); Computer vision; Fusion; Pattern recognition (psychology)","score_opus":0.021171743256212947,"score_gpt":0.23531505989219167,"score_spread":0.21414331663597874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390196573","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":[0.107710935,0.00007580244,0.89103216,0.00082194945,0.00016954748,0.000091028385,0.0000057035063,0.00007320512,0.000019668156],"genre_scores_gemma":[0.9395029,0.00021873876,0.06013344,0.000027575974,0.00008543482,0.0000099595845,0.0000014331429,0.0000070116434,0.00001354038],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992965,0.000010317731,0.0002777185,0.00012457809,0.00017172798,0.000119159595],"domain_scores_gemma":[0.9994424,0.00012973855,0.00012513911,0.00007242598,0.00018674514,0.000043539898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004091074,0.00007738115,0.00012618079,0.00025251077,0.00005321577,0.00007433839,0.00029116208,0.00003851565,0.0000012455764],"category_scores_gemma":[0.000072958115,0.00007222877,0.000042241438,0.00024821147,0.000012611848,0.00032533254,0.000076491124,0.00014168788,5.8064626e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014898158,0.0006511308,0.024709934,0.00046979697,0.0007123729,0.00031332838,0.012565027,0.48360887,0.064801514,0.10725194,0.00031330306,0.30311295],"study_design_scores_gemma":[0.000935819,0.00013594334,0.02030775,0.00021197263,0.0000033767658,0.00012389944,0.00010932915,0.96952355,0.003369322,0.0005008581,0.00460748,0.00017071403],"about_ca_topic_score_codex":0.00001447351,"about_ca_topic_score_gemma":0.000042619744,"teacher_disagreement_score":0.83179194,"about_ca_system_score_codex":0.00006265725,"about_ca_system_score_gemma":0.000022339747,"threshold_uncertainty_score":0.29454038},"labels":[],"label_agreement":null},{"id":"W4390413835","doi":"10.1016/j.imavis.2023.104897","title":"Exploiting classifier inter-level features for efficient out-of-distribution detection","year":2023,"lang":"en","type":"article","venue":"Image and Vision Computing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Mitacs","keywords":"Computer science; Classifier (UML); Inference; Artificial intelligence; Exploit; Deep learning; Software deployment; Training set; Machine learning; Network architecture; Architecture; Pattern recognition (psychology); Data mining","score_opus":0.038847516406728594,"score_gpt":0.34271412174305194,"score_spread":0.30386660533632337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390413835","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":[0.060896132,0.00001554865,0.93794835,0.00024949183,0.00020885198,0.00020219808,0.000010580155,0.0003811126,0.00008772605],"genre_scores_gemma":[0.9642998,0.0000058632545,0.035478417,0.000032199012,0.00007966735,0.00001834121,0.000011597846,0.000007069111,0.00006702436],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991829,0.000023541756,0.00023064484,0.00028637928,0.00010346848,0.00017309994],"domain_scores_gemma":[0.9993401,0.0001647177,0.00013197048,0.00018782854,0.00013415204,0.00004124196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003832409,0.0000889191,0.000111155,0.00009840856,0.00032472005,0.00011689774,0.00017212906,0.000051685754,7.783274e-7],"category_scores_gemma":[0.000065876855,0.00008371747,0.00006700886,0.0003564958,0.000033907887,0.00012447625,0.00022203785,0.00008720733,0.000006247261],"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":[0.0000062164318,0.000028784696,0.000021224007,0.000039942573,0.000005745301,7.9611027e-7,0.00033966184,0.000108212116,0.07295154,0.0036076745,0.0014021932,0.921488],"study_design_scores_gemma":[0.00022670923,0.0001433546,0.0038432952,0.00007579032,0.0000054234856,0.0000072268813,0.00017655295,0.85423744,0.13652848,0.0012171397,0.003394887,0.00014368504],"about_ca_topic_score_codex":0.0000074595205,"about_ca_topic_score_gemma":0.0000016386849,"teacher_disagreement_score":0.92134434,"about_ca_system_score_codex":0.000020834736,"about_ca_system_score_gemma":0.000010005543,"threshold_uncertainty_score":0.3413899},"labels":[],"label_agreement":null},{"id":"W4390603833","doi":"10.1109/tifs.2024.3350389","title":"Manipulating Pre-Trained Encoder for Targeted Poisoning Attacks in Contrastive Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Information Forensics and Security","topic":"Anomaly Detection Techniques and Applications","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":"Simon Fraser University","funders":"Wuhan Yellow Crane Talents Program; National Natural Science Foundation of China","keywords":"Computer science; Encoder; Classifier (UML); Artificial intelligence; Downstream (manufacturing); Feature learning; Machine learning; Pattern recognition (psychology); Autoencoder; Deep learning; Data mining","score_opus":0.008793565095512209,"score_gpt":0.2516754333139183,"score_spread":0.24288186821840607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390603833","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":[0.016451947,0.00002472224,0.9818936,0.0002676709,0.00018067923,0.00041294418,0.000023031022,0.0003583168,0.00038711817],"genre_scores_gemma":[0.984645,0.000026383972,0.014981819,0.000111894864,0.000016356582,0.00016311363,0.000011296088,0.0000064323112,0.000037722053],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992003,0.000018072313,0.00032012488,0.00017302642,0.000115253664,0.00017322155],"domain_scores_gemma":[0.9995605,0.00013482121,0.00006333213,0.00010747268,0.00008360968,0.00005027419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022438022,0.000119121585,0.00011652301,0.00022152014,0.0002836638,0.0002691436,0.00009601363,0.00008857284,0.0000063332764],"category_scores_gemma":[0.000008164997,0.000117952535,0.000067234345,0.00034685453,0.000030383779,0.0012317672,0.0000029133987,0.00028418883,0.0000046802056],"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":[0.00007541568,0.000076263,0.00007588498,0.00031224644,0.00008082439,0.0000024396336,0.022882892,0.05013758,0.00067189755,0.24695031,0.00033965224,0.6783946],"study_design_scores_gemma":[0.0002585901,0.00012464165,0.00022725566,0.000060254817,0.000007758886,0.000009967113,0.00024834348,0.98277926,0.0040749237,0.008416707,0.0036342395,0.00015807693],"about_ca_topic_score_codex":0.00003830997,"about_ca_topic_score_gemma":0.000026923457,"teacher_disagreement_score":0.96819305,"about_ca_system_score_codex":0.00005444595,"about_ca_system_score_gemma":0.000033904784,"threshold_uncertainty_score":0.48099646},"labels":[],"label_agreement":null},{"id":"W4390640588","doi":"10.1016/j.comcom.2024.01.004","title":"Memory-enhanced appearance-motion consistency framework for video anomaly detection","year":2024,"lang":"en","type":"article","venue":"Computer Communications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of British Columbia Hospital","funders":"","keywords":"Computer science; Anomaly detection; Benchmark (surveying); Consistency (knowledge bases); Motion (physics); Artificial intelligence; Computer vision; Representation (politics); Anomaly (physics); Focus (optics); Data consistency; Pattern recognition (psychology); Database","score_opus":0.029579345908221547,"score_gpt":0.30316696884916416,"score_spread":0.27358762294094263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390640588","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":[0.00027732222,0.0016253038,0.9893187,0.004104562,0.0006223555,0.00075748935,0.0000092215305,0.0020101187,0.001274925],"genre_scores_gemma":[0.57374114,0.00009389577,0.4250465,0.00029937082,0.00013584927,0.00059636537,0.0000074809595,0.000016210066,0.000063190004],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985658,0.00009783281,0.0004070034,0.0005308026,0.00014259889,0.0002559891],"domain_scores_gemma":[0.9965784,0.00038793872,0.00011191765,0.002621047,0.0002116723,0.00008902939],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030281153,0.0001885101,0.00018625878,0.00022382136,0.0007627058,0.00048500454,0.0018447324,0.00012202936,0.0000068333666],"category_scores_gemma":[0.000033098055,0.0001993766,0.00020880021,0.00092880224,0.000136511,0.00055701507,0.0005242312,0.00034523965,0.00012006012],"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":[0.0000023992623,0.00007478337,0.000003098059,0.000046375375,0.000032151824,4.4228372e-7,0.0003039822,0.00007003464,0.00144687,0.40807062,0.000718436,0.5892308],"study_design_scores_gemma":[0.00017924048,0.00016558716,0.0003911878,0.00023341819,0.000033618722,0.000042486172,0.000030925374,0.71688026,0.0174612,0.16794989,0.09621017,0.00042204524],"about_ca_topic_score_codex":0.000018484712,"about_ca_topic_score_gemma":0.00002022906,"teacher_disagreement_score":0.7168102,"about_ca_system_score_codex":0.000102133534,"about_ca_system_score_gemma":0.000071992494,"threshold_uncertainty_score":0.8130342},"labels":[],"label_agreement":null},{"id":"W4390693085","doi":"10.1109/icitr61062.2023.10382919","title":"Early Identification of Deforestation using Anomaly Detection","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"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":"International Development Research Centre","keywords":"Anomaly detection; Identification (biology); Deforestation (computer science); Computer science; Anomaly (physics); Remote sensing; Artificial intelligence; Geology","score_opus":0.023022922941677952,"score_gpt":0.27742490469887504,"score_spread":0.2544019817571971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390693085","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":[0.42884946,0.0000012860816,0.5705121,0.000030926447,0.000035124656,0.00007514267,5.797326e-7,0.00032702656,0.00016835825],"genre_scores_gemma":[0.98536605,0.0000043156597,0.014321932,0.0000091441625,0.000013170029,0.00002498959,0.0000015834815,0.0000044603785,0.00025438535],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999389,0.000016174306,0.00022530509,0.00016320786,0.00011898615,0.00008731534],"domain_scores_gemma":[0.9994693,0.00001814007,0.0001250141,0.00027148693,0.00009397506,0.000022126229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018514317,0.00004583702,0.000051660078,0.00020710376,0.00009813711,0.000045098528,0.00019512427,0.000035115772,0.0000036607255],"category_scores_gemma":[0.00001105551,0.000046065394,0.000036099304,0.0011382235,0.000018097246,0.00035882997,0.00004801629,0.000031262323,0.000050717423],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023527832,0.000026904292,0.001607152,0.000012990163,0.000007500678,5.0180273e-7,0.00023396713,0.00075710024,0.82580787,0.07362216,0.000046608893,0.09787487],"study_design_scores_gemma":[0.000047107445,0.00003320858,0.09915469,0.000002630031,0.000003289507,0.0000035394387,0.000023415658,0.41147083,0.4798173,0.009259683,0.00011690323,0.00006739466],"about_ca_topic_score_codex":0.00015098925,"about_ca_topic_score_gemma":0.000010357726,"teacher_disagreement_score":0.5565166,"about_ca_system_score_codex":0.00002339504,"about_ca_system_score_gemma":0.000014559271,"threshold_uncertainty_score":0.18784922},"labels":[],"label_agreement":null},{"id":"W4390874443","doi":"10.1109/iccv51070.2023.00578","title":"Inter-Realization Channels: Unsupervised Anomaly Detection Beyond One-Class Classification","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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 Victoria","funders":"","keywords":"Anomaly detection; Computer science; Realization (probability); Artificial intelligence; Class (philosophy); Pattern recognition (psychology); Code (set theory); Anomaly (physics); Channel (broadcasting); Training set; Test data; Unsupervised learning; Data mining; Mathematics; Set (abstract data type); Statistics","score_opus":0.04094575305629263,"score_gpt":0.2679572460933625,"score_spread":0.22701149303706988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390874443","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":[0.0122115,0.000005364925,0.97210413,0.0021270267,0.0002615585,0.00035112226,0.0000023301786,0.0029840257,0.009952915],"genre_scores_gemma":[0.9908123,0.000040969724,0.005602561,0.00031788813,0.00010527983,0.00030276607,0.000032494154,0.000017747838,0.0027679885],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986949,0.00005466759,0.00030395394,0.00048459574,0.00021718467,0.00024468318],"domain_scores_gemma":[0.99896264,0.000044058906,0.000108169545,0.00063567545,0.00016097788,0.00008848786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027951642,0.00013860359,0.00012119887,0.00037360995,0.0002848645,0.00021755295,0.00052193773,0.00011980358,0.000047308702],"category_scores_gemma":[0.000024249572,0.00014260896,0.0000747035,0.0020247248,0.0000338903,0.0005894059,0.00015076279,0.00010844062,0.00050014746],"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":[0.000009514151,0.00016541785,0.0004436346,0.00003159157,0.000031973545,0.0000019038912,0.0005057416,0.00015561447,0.10948447,0.4599362,0.0058695017,0.42336443],"study_design_scores_gemma":[0.00025377978,0.00015700949,0.009274126,0.000013594631,0.000010769268,0.000009270619,0.00014849716,0.8392382,0.09369505,0.02330647,0.03353876,0.00035442476],"about_ca_topic_score_codex":0.00008074588,"about_ca_topic_score_gemma":0.000052651892,"teacher_disagreement_score":0.9786008,"about_ca_system_score_codex":0.000085663894,"about_ca_system_score_gemma":0.000028284361,"threshold_uncertainty_score":0.64285505},"labels":[],"label_agreement":null},{"id":"W4390880818","doi":"10.1016/j.engappai.2024.107897","title":"Improvement in Monte Carlo localization using information theory and statistical approaches","year":2024,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Particle filter; Computer science; Monte Carlo localization; Monte Carlo method; Algorithm; Outlier; Probability density function; Artificial intelligence; Kalman filter; Mathematics; Statistics","score_opus":0.022964622950778713,"score_gpt":0.2606293004395632,"score_spread":0.2376646774887845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390880818","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":[0.0033842935,0.00013932587,0.99586606,0.000048642054,0.000029769892,0.00032276392,0.000008094189,0.00016580115,0.000035275974],"genre_scores_gemma":[0.9020502,0.000022391456,0.097702056,0.000008800086,0.000015768239,0.00018948401,0.0000029017835,0.0000051448574,0.0000032541218],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928623,0.000011270265,0.0003351404,0.00017031867,0.0000949058,0.00010215381],"domain_scores_gemma":[0.99957806,0.00010254801,0.00003878456,0.00021018351,0.000036855203,0.00003354614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003227715,0.000078739045,0.00007848929,0.00023176656,0.000044547214,0.000100987156,0.0001753862,0.000042907544,0.000002996392],"category_scores_gemma":[0.000024741805,0.000083073945,0.000017587221,0.0006100657,0.0000394343,0.00038154374,0.00006414021,0.0000895126,0.0000056169415],"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":[9.3321586e-7,0.00001162498,0.000004302406,0.000037910308,0.000002710909,9.5586785e-8,0.0002060739,0.05247136,0.0006216782,0.65391684,0.000001737435,0.29272473],"study_design_scores_gemma":[0.0000046408763,0.000018149074,0.000025070261,0.000021560872,0.000003794323,0.0000023431642,0.00010071197,0.95535433,0.016395584,0.02738654,0.0006093172,0.00007794224],"about_ca_topic_score_codex":0.000055766457,"about_ca_topic_score_gemma":0.0000028670286,"teacher_disagreement_score":0.902883,"about_ca_system_score_codex":0.000044425044,"about_ca_system_score_gemma":0.00002704029,"threshold_uncertainty_score":0.33876574},"labels":[],"label_agreement":null},{"id":"W4390886362","doi":"10.1016/j.neunet.2024.106106","title":"Self-supervised anomaly detection in computer vision and beyond: A survey and outlook","year":2024,"lang":"en","type":"article","venue":"Neural Networks","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":82,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec; Fonds de recherche du Québec – Nature et technologies; Department of Electricial and Computer Engineering, Boston University; AGE-WELL","keywords":"Anomaly detection; Computer science; Margin (machine learning); Artificial intelligence; Field (mathematics); Machine learning; Supervised learning; Anomaly (physics); Deep learning; State of art; Data science; Artificial neural network; Mathematics","score_opus":0.008824441086658565,"score_gpt":0.2391377088774286,"score_spread":0.23031326779077005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390886362","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":[0.2237496,0.0005857515,0.7744224,0.00030152904,0.0001921054,0.00020118163,0.0000010915796,0.00047933243,0.000067000576],"genre_scores_gemma":[0.9863417,0.00015349528,0.013086915,0.00026707503,0.000089879395,0.000027594713,0.0000023291225,0.000009814016,0.000021170175],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990823,0.00008400858,0.00016965748,0.00041478002,0.00007895196,0.00017027785],"domain_scores_gemma":[0.9995726,0.00012053641,0.000022845406,0.0001954538,0.00002231238,0.000066299486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025218242,0.000119846765,0.000116988595,0.00011542347,0.00009740696,0.00031840845,0.00014431406,0.00009311396,0.000001943953],"category_scores_gemma":[0.0000022317618,0.00010586778,0.000026226195,0.00047404921,0.0000278897,0.00035832266,0.00017664295,0.0002138413,0.0000025385723],"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":[0.000007991807,0.000026857058,0.00270288,0.00002087799,0.00000686135,0.00001540648,0.00013484045,0.000524938,0.00015946734,0.0010288146,0.00021523926,0.9951558],"study_design_scores_gemma":[0.000093524744,0.00014900826,0.038725343,0.000009646878,0.0000023914506,0.0000498131,0.0000015156552,0.959894,0.00006367806,0.00033415167,0.00056544953,0.0001114881],"about_ca_topic_score_codex":0.000076322074,"about_ca_topic_score_gemma":0.000179979,"teacher_disagreement_score":0.99504435,"about_ca_system_score_codex":0.00002118459,"about_ca_system_score_gemma":0.0000069660796,"threshold_uncertainty_score":0.4317163},"labels":[],"label_agreement":null},{"id":"W4390992230","doi":"10.1109/bibm58861.2023.10385253","title":"Infectious Disease Forecasting using Multivariate Incomplete Time-series: A Hybrid Architecture with Stacked Dilated Causal Convolutions","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Trent University; Queen's University","funders":"","keywords":"Computer science; Convolutional neural network; Multivariate statistics; Convolution (computer science); Artificial intelligence; Deep learning; Infectious disease (medical specialty); Recurrent neural network; Time series; Architecture; Artificial neural network; Machine learning; Disease; Pathology; Medicine","score_opus":0.025845309718832553,"score_gpt":0.2506403072603043,"score_spread":0.22479499754147172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390992230","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":[0.121017314,0.000003369354,0.87520146,0.0003537412,0.00003868335,0.00040112657,0.00003052278,0.002393669,0.00056009233],"genre_scores_gemma":[0.9227311,0.0000020200139,0.076070175,0.000102431106,0.000046143097,0.000113908296,0.000029077,0.000025232794,0.0008799315],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875814,0.000050223847,0.0002257158,0.00043261735,0.00018269548,0.0003506169],"domain_scores_gemma":[0.99901205,0.00007989099,0.00011139704,0.00048568344,0.000121715464,0.00018926898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012214051,0.00018533593,0.00015875837,0.00023058172,0.0005683686,0.0001505256,0.0003124488,0.000033660057,0.00003815998],"category_scores_gemma":[0.000036798196,0.0001540767,0.0000614738,0.0013130048,0.0000907274,0.0003349271,0.00026872533,0.00016583058,0.00007878871],"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":[0.0006563874,0.0012707317,0.012833936,0.0003649426,0.0010988164,0.001352666,0.0040131863,0.4367019,0.15320481,0.27226844,0.007924048,0.10831013],"study_design_scores_gemma":[0.00031076508,0.000114996205,0.0018924617,0.000033219916,0.000020079076,0.00011098365,0.000014420083,0.98673815,0.0012765684,0.0068351096,0.0023477213,0.0003055352],"about_ca_topic_score_codex":0.00023777982,"about_ca_topic_score_gemma":0.000029788733,"teacher_disagreement_score":0.80171376,"about_ca_system_score_codex":0.0000731013,"about_ca_system_score_gemma":0.00012360024,"threshold_uncertainty_score":0.62830657},"labels":[],"label_agreement":null},{"id":"W4391021384","doi":"10.1109/iisec59749.2023.10391014","title":"Improving Measurement Accuracy of Acoustic Intensity in Vibrating Machines Using a Doosan Robot","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Acoustics; Robot; Sound intensity; Intensity (physics); Computer science; Materials science; Artificial intelligence; Sound (geography); Optics; Physics","score_opus":0.057260965350352924,"score_gpt":0.2919685193273743,"score_spread":0.23470755397702137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391021384","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":[0.11820456,0.0000066110138,0.88102955,0.00013652787,0.00003917652,0.0001411946,3.178547e-7,0.0002987446,0.00014330517],"genre_scores_gemma":[0.8882258,0.0000018162143,0.111675166,0.00005051207,0.000013187631,0.0000137278275,2.636959e-7,0.000004191108,0.000015316193],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999221,0.000015351694,0.00024137259,0.00020222264,0.00017919124,0.0001408413],"domain_scores_gemma":[0.9994433,0.00003824382,0.0001090902,0.0002706685,0.000111001675,0.00002768895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040401262,0.00007004452,0.000111443755,0.00016105414,0.00007913449,0.000042970416,0.0002816793,0.000028533346,0.0000048756856],"category_scores_gemma":[0.000103105755,0.00006273167,0.00003621337,0.00085123227,0.000015378588,0.0002260596,0.00025853547,0.00007814069,0.000003994187],"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":[0.000002431794,0.000032637385,0.0015580842,0.000032149084,0.0000047031053,0.000002203337,0.00026821025,0.006307391,0.92211545,0.0019102598,0.000040896284,0.06772556],"study_design_scores_gemma":[0.000057700665,0.00001780303,0.004524015,0.000023028359,0.0000021142018,0.0000041675626,0.000072332405,0.92402315,0.07046503,0.00073264865,0.00000544871,0.00007255209],"about_ca_topic_score_codex":0.001154041,"about_ca_topic_score_gemma":0.00016870508,"teacher_disagreement_score":0.9177158,"about_ca_system_score_codex":0.000050128805,"about_ca_system_score_gemma":0.000046389596,"threshold_uncertainty_score":0.25581235},"labels":[],"label_agreement":null},{"id":"W4391219455","doi":"10.1007/978-981-99-5329-5_9","title":"Old and New Perspectives on Optimal Scaling","year":2023,"lang":"en","type":"book-chapter","venue":"Behaviormetrics","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Japan Society for the Promotion of Science; Natural Sciences and Engineering Research Council of Canada","keywords":"Scaling; Computer science; Mathematics; Geometry","score_opus":0.06870340810150205,"score_gpt":0.29066934307269676,"score_spread":0.2219659349711947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391219455","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00093295495,0.00092726474,0.82622755,0.0005688055,0.0004348652,0.0008577212,0.00004824792,0.0026203564,0.16738224],"genre_scores_gemma":[0.004447162,0.0020774084,0.070535116,0.000104746905,0.00025951816,0.000037572197,0.00000922632,0.00008706148,0.9224422],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987252,0.0000041847343,0.00020748965,0.00058161875,0.0003061111,0.00017543133],"domain_scores_gemma":[0.99901336,0.00007888562,0.00013497731,0.00054548995,0.00008007899,0.00014721336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000113496135,0.00022635271,0.00020284235,0.0007453607,0.00015328695,0.00018155703,0.00045347045,0.0002625685,0.000031729483],"category_scores_gemma":[0.00002249588,0.00023284205,0.00010837649,0.00035690135,0.000046440626,0.00010785015,0.0002674475,0.00037024784,0.00019557352],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000011778201,0.000017793272,0.000011475167,0.0000041269536,0.000006943628,0.000013731135,0.00014701429,0.000004821775,0.000008902316,0.8395343,0.0025666761,0.15768304],"study_design_scores_gemma":[0.0014892882,0.002632706,0.007298691,0.0006683502,0.00053589424,0.00032516907,0.00058245286,0.0056452365,0.0024885715,0.083090596,0.8896444,0.0055986615],"about_ca_topic_score_codex":0.000030284938,"about_ca_topic_score_gemma":8.690936e-7,"teacher_disagreement_score":0.8870777,"about_ca_system_score_codex":0.00009330105,"about_ca_system_score_gemma":0.000058734404,"threshold_uncertainty_score":0.94950235},"labels":[],"label_agreement":null},{"id":"W4391307793","doi":"10.1109/smc53992.2023.10394282","title":"Multivariate Beta Normality Scores Approach for Deep Anomaly Detection in Images Using Transformations","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multivariate statistics; Normality; Anomaly detection; Anomaly (physics); Artificial intelligence; BETA (programming language); Multivariate analysis; Computer science; Statistics; Beta distribution; Pattern recognition (psychology); Mathematics; Physics","score_opus":0.037060844364364263,"score_gpt":0.2963466886884532,"score_spread":0.25928584432408897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391307793","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":[0.025330242,0.0000063441307,0.97211367,0.00013661491,0.000036321304,0.0006103154,0.000008033706,0.00071727135,0.0010412056],"genre_scores_gemma":[0.7212867,0.0000053946683,0.27830246,0.000030323228,0.000017906834,0.00028125232,0.000008274409,0.0000062702256,0.000061416904],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990848,0.000030849653,0.0002741989,0.0002776203,0.00009940689,0.00023316781],"domain_scores_gemma":[0.9994988,0.000050503586,0.00005896672,0.00028358304,0.00006422737,0.000043925003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035049647,0.00010089381,0.00011171349,0.00027509697,0.00026845702,0.000106567146,0.00030825907,0.0000646234,0.0000035411763],"category_scores_gemma":[0.000009961365,0.00009646627,0.00007882301,0.0011307116,0.000026007825,0.00070785306,0.000059188318,0.000081362254,0.000007912645],"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":[0.000060939587,0.00076227385,0.0047857743,0.00035926682,0.00008591759,0.0000034176544,0.0035140046,0.03921196,0.1920522,0.25736347,0.0003594271,0.50144136],"study_design_scores_gemma":[0.00019317909,0.000025555626,0.01114621,0.000002805763,0.000004195194,0.000004615915,0.00005005661,0.91397744,0.07210999,0.0021892448,0.00017212398,0.00012458334],"about_ca_topic_score_codex":0.0004083398,"about_ca_topic_score_gemma":0.000100365876,"teacher_disagreement_score":0.87476546,"about_ca_system_score_codex":0.000050671235,"about_ca_system_score_gemma":0.000022489256,"threshold_uncertainty_score":0.39337805},"labels":[],"label_agreement":null},{"id":"W4391405915","doi":"10.1007/978-3-031-51497-5_5","title":"FLAM: Fault Localization and Mapping","year":2024,"lang":"en","type":"book-chapter","venue":"Springer proceedings in advanced robotics","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Fault (geology); Computer science; Geography; Geology; Seismology","score_opus":0.013711747761254478,"score_gpt":0.23760829467991423,"score_spread":0.22389654691865976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391405915","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000013669533,0.0008541233,0.81004965,0.0004241636,0.00020977535,0.00039584038,0.0000016354654,0.0006273313,0.18742383],"genre_scores_gemma":[0.014696108,0.0031842743,0.68292886,0.00035998394,0.00031463287,0.00014001157,0.0000071579548,0.0001789966,0.29819],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99848056,9.26307e-7,0.00038414207,0.0006963331,0.00020758898,0.0002304521],"domain_scores_gemma":[0.9993798,0.000016438953,0.00017455415,0.00021984098,0.00013615374,0.00007319979],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012131422,0.000292618,0.00026339493,0.0003762259,0.00010270842,0.00020828043,0.00039154122,0.00026672694,0.0000049938167],"category_scores_gemma":[0.000013265876,0.00031526666,0.000058434936,0.00021629888,0.00006769494,0.00032307365,0.0004467586,0.0005014497,0.000037903912],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.951719e-7,0.0000052469813,0.000015954012,0.000159994,0.000008371973,0.000004153552,0.00012855447,0.00085781416,0.00016313703,0.9735113,0.000099409284,0.025045214],"study_design_scores_gemma":[0.00015286928,0.000062033956,0.000026573221,0.0010854027,0.00002114693,0.000041938867,0.000031602976,0.094009995,0.0007549236,0.5646398,0.3384357,0.00073800323],"about_ca_topic_score_codex":0.0000014486844,"about_ca_topic_score_gemma":0.000001994029,"teacher_disagreement_score":0.40887147,"about_ca_system_score_codex":0.00014131238,"about_ca_system_score_gemma":0.000025713765,"threshold_uncertainty_score":0.99992996},"labels":[],"label_agreement":null},{"id":"W4391407085","doi":"10.1109/jiot.2024.3360882","title":"Neural Architecture Search for Anomaly Detection in Time-Series Data of Smart Buildings: A Reinforcement Learning Approach for Optimal Autoencoder Design","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Anomaly Detection Techniques and Applications","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":"Concordia University","funders":"","keywords":"Autoencoder; Anomaly detection; Computer science; Reinforcement learning; Artificial intelligence; Machine learning; Recurrent neural network; Time series; Architecture; Artificial neural network; Deep learning; Data mining; Anomaly (physics); Pattern recognition (psychology)","score_opus":0.040719106262796026,"score_gpt":0.2959187986770702,"score_spread":0.2551996924142742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391407085","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":[0.010422397,0.00007698991,0.98855513,0.00019536162,0.00013257528,0.00048442086,0.000003282823,0.00009154639,0.000038290942],"genre_scores_gemma":[0.5363436,0.000007854823,0.4630866,0.000018635974,0.000056811477,0.000050385315,0.0000029144273,0.000012731051,0.0004205172],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986386,0.00005751387,0.00049974455,0.00032756798,0.00024760244,0.00022897648],"domain_scores_gemma":[0.9991886,0.00014308964,0.0002051335,0.00027469074,0.00013608896,0.0000523914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014443607,0.00013162327,0.00020751175,0.00034963348,0.00008494781,0.0002515922,0.0012055078,0.00007833612,0.000005678961],"category_scores_gemma":[0.000049552233,0.000116114556,0.0001261661,0.00026351959,0.000048641374,0.00084907794,0.00021668285,0.0004298339,5.809198e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00080368354,0.00016022584,0.00007469557,0.00058759376,0.00022488012,0.000007058355,0.006452383,0.6726652,0.14816563,0.0025318083,0.001955326,0.16637154],"study_design_scores_gemma":[0.00016259472,0.0007848258,0.0000052159753,0.00009426171,0.000012611635,0.00017909984,0.000032923454,0.81094486,0.18616824,0.00036217162,0.0011639069,0.00008930688],"about_ca_topic_score_codex":0.00004307473,"about_ca_topic_score_gemma":8.148431e-7,"teacher_disagreement_score":0.52592117,"about_ca_system_score_codex":0.00006581596,"about_ca_system_score_gemma":0.000075206226,"threshold_uncertainty_score":0.4735014},"labels":[],"label_agreement":null},{"id":"W4391457419","doi":"10.1504/ijes.2023.136376","title":"&lt;i&gt;AR&lt;/i&gt;&lt;SUP align=\"right\"&gt;2&lt;/SUP&gt;&lt;i&gt;PNET&lt;/i&gt;: an adversarially robust re-weighting prototypical network for few-shot learning","year":2023,"lang":"en","type":"article","venue":"International Journal of Embedded Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Weighting; Computer science; Physics","score_opus":0.03182528465471989,"score_gpt":0.2903528276723231,"score_spread":0.2585275430176032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391457419","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":[0.04026294,0.0010262348,0.9361221,0.0025074782,0.0060281865,0.0037406846,0.00014331088,0.0014300365,0.008739001],"genre_scores_gemma":[0.9392458,0.00025111454,0.043862745,0.000302806,0.008585412,0.0010021708,0.00018900403,0.0002180566,0.0063428883],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9899883,0.0009134026,0.0033393148,0.0014929167,0.0028163728,0.0014496478],"domain_scores_gemma":[0.9911821,0.0011197951,0.002872267,0.0011956892,0.0028348898,0.0007952685],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.004973422,0.00093808403,0.0013369322,0.0011506219,0.0012441344,0.001549337,0.004341427,0.0007014996,0.00019470858],"category_scores_gemma":[0.00063575193,0.000886383,0.0010238823,0.0016315463,0.00024012478,0.0020452992,0.000722033,0.0010904218,0.00022226643],"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":[0.0010755433,0.00097468216,0.00043089525,0.00028682308,0.001945651,0.0006630397,0.0031607242,0.37517262,0.13439257,0.35714036,0.11147656,0.0132805165],"study_design_scores_gemma":[0.002093501,0.0010670499,0.00052227796,0.0009775942,0.00013899834,0.00093602715,0.00012935694,0.535293,0.0013551622,0.0036275347,0.45273426,0.001125284],"about_ca_topic_score_codex":0.00001874008,"about_ca_topic_score_gemma":0.00012104808,"teacher_disagreement_score":0.8989829,"about_ca_system_score_codex":0.0008368266,"about_ca_system_score_gemma":0.0007088563,"threshold_uncertainty_score":0.99948716},"labels":[],"label_agreement":null},{"id":"W4391614403","doi":"10.1145/3645101","title":"Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models","year":2024,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":133,"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; University of British Columbia; University of Toronto","funders":"China Scholarship Council; Fudan University; National Natural Science Foundation of China","keywords":"Computer science; Anomaly detection; Scope (computer science); Taxonomy (biology); Unsupervised learning; Event (particle physics); Artificial intelligence; Data science; Machine learning; Deep learning","score_opus":0.10204055724339232,"score_gpt":0.35221675890570386,"score_spread":0.2501762016623115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391614403","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00000977519,0.48226327,0.5162691,0.000007129061,0.00017823602,0.0009349764,0.0000039531524,0.00028272305,0.000050836676],"genre_scores_gemma":[0.08421407,0.80413175,0.11026394,0.00002108154,0.0002130378,0.00089583633,0.00002255489,0.000101098834,0.00013659902],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956412,0.0012501982,0.001641866,0.0008625175,0.00029818213,0.00030607765],"domain_scores_gemma":[0.9963301,0.0007105985,0.0010617129,0.0016327119,0.0001514734,0.00011340422],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0021751488,0.00049726665,0.0022433905,0.00039560028,0.00021700692,0.0002626478,0.0015862122,0.00027426082,0.0000023023872],"category_scores_gemma":[0.000092834576,0.00041430813,0.0004955018,0.0010889089,0.00006690678,0.00016831429,0.0013250717,0.00042736536,0.000028100185],"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":[2.5810172e-7,0.000040872103,0.0000043062155,0.06525126,0.00017460244,0.0000021669225,0.000078049976,0.00016187005,0.0000010007058,0.0035040178,0.00007431462,0.9307073],"study_design_scores_gemma":[0.00019546294,0.00020292951,0.000013435171,0.04247851,0.0013187211,0.00025236027,0.000030752206,0.8879513,0.00006973039,0.0035833903,0.06264233,0.0012610881],"about_ca_topic_score_codex":0.00010569389,"about_ca_topic_score_gemma":0.00002949548,"teacher_disagreement_score":0.92944616,"about_ca_system_score_codex":0.00012327648,"about_ca_system_score_gemma":0.00008259733,"threshold_uncertainty_score":0.9998309},"labels":[],"label_agreement":null},{"id":"W4391769794","doi":"10.1109/iceeat60471.2023.10425916","title":"Performance comparison of three deep learning techniques applied to the fault detection of a transmission network","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Computer science; Transmission (telecommunications); Deep learning; Fault detection and isolation; Fault (geology); Artificial intelligence; Transmission network; Telecommunications; Geology; Seismology","score_opus":0.018418734826222787,"score_gpt":0.26663874626196676,"score_spread":0.248220011435744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391769794","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":[0.04232424,0.0000151511085,0.9540279,0.0002133767,0.000020407988,0.00035991744,1.3636901e-7,0.0006720508,0.0023668096],"genre_scores_gemma":[0.9433436,0.000030116547,0.056337237,0.000026191221,0.000027140028,0.00014260878,7.0414603e-7,0.0000063501448,0.0000860677],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991952,0.000019110292,0.00027749722,0.00018162839,0.0001784155,0.00014815631],"domain_scores_gemma":[0.99942,0.00005165607,0.000119642966,0.0003107427,0.0000642677,0.000033696066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033243038,0.00007882991,0.00014142282,0.0000942506,0.00021402753,0.00001635954,0.0004544205,0.000055254357,0.0000059058493],"category_scores_gemma":[0.000004325095,0.00005585431,0.000049947877,0.001366873,0.000026984882,0.00007006423,0.00011127417,0.00013306357,0.000014350996],"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":[0.000008157216,0.000014742778,0.0004338737,0.000013407683,0.0000039649963,2.3093799e-8,0.00028553372,0.00837892,0.047180478,0.0030168775,0.000106058076,0.94055796],"study_design_scores_gemma":[0.00003408193,0.00017709674,0.0035444482,0.000015897644,0.000003736641,7.683002e-7,0.00005482255,0.46652177,0.5163715,0.0004991854,0.012705202,0.00007151609],"about_ca_topic_score_codex":0.000025146379,"about_ca_topic_score_gemma":0.000024921694,"teacher_disagreement_score":0.94048643,"about_ca_system_score_codex":0.000012063285,"about_ca_system_score_gemma":0.000009060155,"threshold_uncertainty_score":0.22776727},"labels":[],"label_agreement":null},{"id":"W4391877129","doi":"10.1109/icaic60265.2024.10433846","title":"Link-based Anomaly Detection with Sysmon and Graph Neural Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Systems, Applications & Products in Data Processing (Canada); Royal Military College of Canada; Department of National Defence","funders":"","keywords":"Computer science; Anomaly detection; Graph; Link (geometry); Artificial intelligence; Theoretical computer science; Computer network","score_opus":0.0061762438722124365,"score_gpt":0.21097886948722333,"score_spread":0.2048026256150109,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391877129","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":[0.014295969,0.00019130604,0.98268265,0.0007308444,0.000070448645,0.00014175229,3.2369772e-7,0.0012153795,0.00067136006],"genre_scores_gemma":[0.98138463,0.000011247702,0.018099217,0.00022655951,0.000053730004,0.000060709885,6.655531e-7,0.000007870917,0.00015536994],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939704,0.000013783818,0.00009350156,0.0002962148,0.00007712019,0.00012236614],"domain_scores_gemma":[0.999641,0.00003303593,0.000018589633,0.00022758183,0.000026568809,0.00005323315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000072097086,0.00008758002,0.00006183018,0.00010802809,0.000121500656,0.000270633,0.00013856818,0.000050025825,0.0000058807523],"category_scores_gemma":[9.786171e-7,0.000064995926,0.000030457213,0.0005752903,0.000035569818,0.00022457077,0.000035601806,0.00011978279,0.0000037518696],"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":[0.000012172107,0.000025990588,0.00059099926,0.00003848997,0.00002147033,0.00001967003,0.000041060735,0.0040375325,0.001676806,0.048952382,0.0002754439,0.944308],"study_design_scores_gemma":[0.00005103784,0.00017549675,0.0013068425,0.000010488365,0.0000061096334,0.000036031663,0.0000029908758,0.99052286,0.005082559,0.0007827094,0.0019178826,0.00010499198],"about_ca_topic_score_codex":0.000054611603,"about_ca_topic_score_gemma":0.000039209757,"teacher_disagreement_score":0.9864853,"about_ca_system_score_codex":0.000012965982,"about_ca_system_score_gemma":0.000011960787,"threshold_uncertainty_score":0.26504567},"labels":[],"label_agreement":null},{"id":"W4391878547","doi":"10.32920/25234624.v1","title":"Multimodal Sensor Fusion Frameworks With Application to Human Action Recognition","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Modality (human–computer interaction); Convolutional neural network; Sensor fusion; Computer vision; Inertial measurement unit; Fuse (electrical); Wearable computer; Modalities; Field (mathematics); Pattern recognition (psychology); Engineering; Embedded system","score_opus":0.02459633998613482,"score_gpt":0.30662044305898734,"score_spread":0.28202410307285253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391878547","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":[0.030678421,0.000010673234,0.9592586,0.0025279508,0.00018569584,0.0013576464,0.000013667893,0.0022657712,0.0037015658],"genre_scores_gemma":[0.64539933,0.000013779489,0.35142216,0.00042779994,0.00024574617,0.0015433293,0.000077903795,0.000031295625,0.00083862693],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99813855,0.000033068605,0.00030263222,0.0010394929,0.00027993106,0.00020630275],"domain_scores_gemma":[0.9984494,0.000026876822,0.0001493129,0.0010284197,0.00021522507,0.00013080562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016170883,0.00027279707,0.00019457377,0.00030729757,0.00022750274,0.00038937107,0.00054306834,0.0006415277,0.00004037042],"category_scores_gemma":[0.000005833958,0.00023748382,0.000096887365,0.000530138,0.000024296967,0.00011334815,0.001061293,0.0013016087,0.00053508225],"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":[0.000020180247,0.0001890772,0.000025026446,0.00019788345,0.00005042451,0.0000043263854,0.0003782524,0.0011134641,0.02329776,0.03325308,0.0034119587,0.93805856],"study_design_scores_gemma":[0.0003334464,0.0006372362,0.0016158818,0.0008308448,0.00013823668,0.00007831269,0.00017865529,0.3816499,0.14272742,0.44574,0.023873372,0.0021966766],"about_ca_topic_score_codex":0.0003282331,"about_ca_topic_score_gemma":0.00009042616,"teacher_disagreement_score":0.9358619,"about_ca_system_score_codex":0.00016599242,"about_ca_system_score_gemma":0.00005431976,"threshold_uncertainty_score":0.96843094},"labels":[],"label_agreement":null},{"id":"W4391885393","doi":"10.32920/25234624","title":"Multimodal Sensor Fusion Frameworks With Application to Human Action Recognition","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Modality (human–computer interaction); Sensor fusion; Convolutional neural network; Inertial measurement unit; Computer vision; Fuse (electrical); Wearable computer; Modalities; Field (mathematics); Pattern recognition (psychology); Engineering; Embedded system","score_opus":0.02459633998613482,"score_gpt":0.30662044305898734,"score_spread":0.28202410307285253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391885393","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":[0.030678421,0.000010673234,0.9592586,0.0025279508,0.00018569584,0.0013576464,0.000013667893,0.0022657712,0.0037015658],"genre_scores_gemma":[0.64539933,0.000013779489,0.35142216,0.00042779994,0.00024574617,0.0015433293,0.000077903795,0.000031295625,0.00083862693],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99813855,0.000033068605,0.00030263222,0.0010394929,0.00027993106,0.00020630275],"domain_scores_gemma":[0.9984494,0.000026876822,0.0001493129,0.0010284197,0.00021522507,0.00013080562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016170883,0.00027279707,0.00019457377,0.00030729757,0.00022750274,0.00038937107,0.00054306834,0.0006415277,0.00004037042],"category_scores_gemma":[0.000005833958,0.00023748382,0.000096887365,0.000530138,0.000024296967,0.00011334815,0.001061293,0.0013016087,0.00053508225],"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":[0.000020180247,0.0001890772,0.000025026446,0.00019788345,0.00005042451,0.0000043263854,0.0003782524,0.0011134641,0.02329776,0.03325308,0.0034119587,0.93805856],"study_design_scores_gemma":[0.0003334464,0.0006372362,0.0016158818,0.0008308448,0.00013823668,0.00007831269,0.00017865529,0.3816499,0.14272742,0.44574,0.023873372,0.0021966766],"about_ca_topic_score_codex":0.0003282331,"about_ca_topic_score_gemma":0.00009042616,"teacher_disagreement_score":0.9358619,"about_ca_system_score_codex":0.00016599242,"about_ca_system_score_gemma":0.00005431976,"threshold_uncertainty_score":0.96843094},"labels":[],"label_agreement":null},{"id":"W4391903813","doi":"10.24251/hicss.2023.691","title":"Introduction to the Minitrack on Digital Transformations of Business Operations","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Quest University Canada","funders":"","keywords":"Computer science; Architecture; Quality (philosophy); Computer architecture; Database; Embedded system","score_opus":0.036613000507678496,"score_gpt":0.286450521458942,"score_spread":0.24983752095126352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391903813","genre_codex":"other","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":[0.057827484,0.0000075660755,0.0023809134,0.058291662,0.0033196718,0.0017854491,0.0009863814,0.00038552142,0.8750153],"genre_scores_gemma":[0.9966614,0.000022863547,0.0006860651,0.00013479388,0.00046764524,0.00027076036,0.00000631174,0.00001944705,0.0017307047],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.99326587,0.000038772087,0.0015154252,0.0011629672,0.0034631263,0.00055386353],"domain_scores_gemma":[0.9909312,0.00016873746,0.0013453135,0.00043910337,0.0069576423,0.00015798555],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0025771933,0.00049712596,0.0005519241,0.0010536882,0.0014160348,0.0012627164,0.009914103,0.00016809048,0.000035105186],"category_scores_gemma":[0.00016271135,0.0002979695,0.0003635799,0.004733664,0.0012574728,0.003041337,0.00097188586,0.00042017727,0.00010805626],"study_design_candidate":"theoretical_or_conceptual","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":[0.00005064192,0.00012778635,0.00036601716,0.00008563247,0.000049021008,1.6351461e-7,0.0018782255,0.0013550379,0.0012372357,0.9891771,0.004588722,0.0010843955],"study_design_scores_gemma":[0.0009769275,0.0020810303,0.010089722,0.004687358,0.00007986472,0.00021978786,0.93074834,0.018779742,0.016399857,0.0071761194,0.0074316636,0.0013295828],"about_ca_topic_score_codex":0.00014079236,"about_ca_topic_score_gemma":0.000014155131,"teacher_disagreement_score":0.982001,"about_ca_system_score_codex":0.00028974903,"about_ca_system_score_gemma":0.00043321148,"threshold_uncertainty_score":0.99994725},"labels":[],"label_agreement":null},{"id":"W4392152516","doi":"10.1109/globecom54140.2023.10437840","title":"Anomalous Behaviour Detection via Event-Based Metric with Sequential Tracking in a V2X Environment","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Metric (unit); Computer science; Event (particle physics); Tracking (education); Artificial intelligence; Data mining; Real-time computing; Engineering","score_opus":0.014623130669824298,"score_gpt":0.25054033801643893,"score_spread":0.23591720734661464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392152516","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":[0.15449497,0.0000059448766,0.8444266,0.00016189578,0.00002719532,0.00023720416,8.476322e-7,0.000546806,0.00009854587],"genre_scores_gemma":[0.9830524,0.0000039589618,0.01644631,0.000056351815,0.00001544771,0.00021054021,0.0000025966467,0.000010525139,0.00020187823],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902123,0.00003286473,0.0001850171,0.00034339054,0.00020680696,0.00021070578],"domain_scores_gemma":[0.99951977,0.00002278367,0.0000651848,0.0003240342,0.000015822785,0.000052409727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019630502,0.00010456352,0.00009129504,0.00044711467,0.00011299652,0.00006816001,0.00025122133,0.000054981512,0.00003587224],"category_scores_gemma":[0.000002594168,0.000093717084,0.000045924135,0.0015960057,0.000020509618,0.0001948367,0.0000620398,0.00010876108,0.00008518934],"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":[0.000049101312,0.0008247997,0.0373995,0.000036396603,0.000045245994,0.00017195843,0.00033865124,0.034106877,0.08317479,0.0039622686,0.00011188157,0.83977854],"study_design_scores_gemma":[0.0007242876,0.0005489836,0.111744374,0.000013899915,0.000015230092,0.000045118748,0.000047717043,0.55144465,0.33281085,0.00072160753,0.0014293065,0.00045395514],"about_ca_topic_score_codex":0.00023075863,"about_ca_topic_score_gemma":0.00010593732,"teacher_disagreement_score":0.8393246,"about_ca_system_score_codex":0.000120098266,"about_ca_system_score_gemma":0.00002457827,"threshold_uncertainty_score":0.3821672},"labels":[],"label_agreement":null},{"id":"W4392191198","doi":"10.1002/9781119865667.ch17","title":"The Peculiar Case of Danger Modeling","year":2024,"lang":"en","type":"other","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science","score_opus":0.015784647238964517,"score_gpt":0.2752511871725928,"score_spread":0.2594665399336283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392191198","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.1618614e-7,0.0004581895,0.55568725,0.00016357638,0.00005454638,0.00006757658,0.0000018029781,0.00031393056,0.44325274],"genre_scores_gemma":[0.0027507106,0.0001446524,0.035834223,0.00003961373,0.000074214026,0.000050931456,3.091934e-7,0.00007094629,0.9610344],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99959266,0.000006554685,0.00010202191,0.00017189603,0.000059112514,0.000067775],"domain_scores_gemma":[0.99940145,0.000008540344,0.000042178683,0.0005124815,0.000016976863,0.000018393674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000064613065,0.000069756854,0.0000664373,0.000056739384,0.000038188537,0.00005301228,0.00034389866,0.0000678581,0.00006382651],"category_scores_gemma":[0.0000016976688,0.000042093066,0.00005706937,0.0001499343,0.000020677295,0.000015714299,0.00013671565,0.00008249769,0.00013830187],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.169469e-8,0.0000042598053,2.2969274e-8,0.000014056808,0.0000124414755,0.000018909353,0.000021630474,0.0000058243986,0.0000084076855,0.38893527,0.592698,0.018281134],"study_design_scores_gemma":[0.000008124615,0.0000064566875,5.1463416e-9,0.000019696956,0.0000056586773,0.00011028665,0.000015808086,0.16798513,0.00011810804,0.006234825,0.8254319,0.000064027656],"about_ca_topic_score_codex":0.00036665428,"about_ca_topic_score_gemma":0.000093994626,"teacher_disagreement_score":0.519853,"about_ca_system_score_codex":0.000006655556,"about_ca_system_score_gemma":0.00001443023,"threshold_uncertainty_score":0.17776369},"labels":[],"label_agreement":null},{"id":"W4392251861","doi":"10.1109/tetci.2024.3358103","title":"Skeletal Video Anomaly Detection Using Deep Learning: Survey, Challenges, and Future Directions","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Emerging Topics in Computational Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; AGE-WELL; Alzheimer's Association","keywords":"Anomaly detection; Deep learning; Computer science; Anomaly (physics); Artificial intelligence; Physics","score_opus":0.04009611501808458,"score_gpt":0.3082281182185302,"score_spread":0.2681320032004456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392251861","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":[0.006719828,0.0023285171,0.9882313,0.00096249307,0.00094213913,0.00017479382,0.000003836598,0.00046692087,0.00017016008],"genre_scores_gemma":[0.97256017,0.0032303974,0.02383843,0.000039682596,0.00015930952,0.00005876884,0.0000023206462,0.000018523262,0.0000923853],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984731,0.00014027431,0.00035336256,0.00058106333,0.00023692856,0.00021528907],"domain_scores_gemma":[0.9992491,0.0003012613,0.00005496269,0.0002096442,0.00011195497,0.00007308649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003657876,0.00019033419,0.00014813537,0.0005051874,0.00036117475,0.00016168317,0.0002449255,0.00012071933,0.000019073668],"category_scores_gemma":[0.00000797911,0.00021086774,0.000083896615,0.00092701236,0.000068912115,0.0004194666,0.00000855615,0.0005497547,0.000014912295],"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":[0.0000033968388,0.000057879217,0.000011203883,0.000024085884,0.00002014957,0.0000048727934,0.0005707564,0.1431479,0.000043845404,0.017485948,0.0000011353496,0.8386288],"study_design_scores_gemma":[0.00003780237,0.00009334638,0.0021389562,0.00003799671,0.000009091549,0.000066388704,0.00010036148,0.97235763,0.0013226555,0.012804283,0.0107896775,0.0002417849],"about_ca_topic_score_codex":0.00015733483,"about_ca_topic_score_gemma":0.00037823938,"teacher_disagreement_score":0.96584034,"about_ca_system_score_codex":0.00015001938,"about_ca_system_score_gemma":0.000049979026,"threshold_uncertainty_score":0.8598937},"labels":[],"label_agreement":null},{"id":"W4392356934","doi":"10.18280/ria.380130","title":"Harmonizing Dimensionality: Unveiling the Prowess of Variational Auto-Encoder in Spark for Big Data Processing","year":2024,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"SPARK (programming language); Curse of dimensionality; Autoencoder; Big data; Computer science; Artificial intelligence; Encoder; Data mining; Pattern recognition (psychology); Deep learning","score_opus":0.19745740306781576,"score_gpt":0.3360312385711755,"score_spread":0.13857383550335975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392356934","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":[0.0020481253,0.00067621155,0.9931532,0.003149989,0.00021360003,0.0004058272,0.000013258078,0.00012665993,0.00021314133],"genre_scores_gemma":[0.95629084,0.000029449915,0.04298128,0.00009739449,0.00013888496,0.0001313775,0.000012223461,0.0000102769,0.00030829132],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877596,0.000030163921,0.00041904926,0.0004562527,0.00014750223,0.00017107445],"domain_scores_gemma":[0.9987923,0.00030877918,0.00008978887,0.00067499955,0.00010824459,0.000025900023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009840473,0.00009102412,0.00011371987,0.00010374911,0.00017358984,0.00014674338,0.0009122797,0.000045841716,0.0000059077133],"category_scores_gemma":[0.0000828373,0.000071651084,0.00004537287,0.0008447779,0.000057453253,0.00032856505,0.0002825702,0.00014098818,0.0000118289245],"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":[0.00000945335,0.00018703713,0.00012956018,0.00039815757,0.000022049493,0.000003849225,0.0027049652,0.041576926,0.013898105,0.28573906,0.0008987715,0.65443206],"study_design_scores_gemma":[0.0000101171145,0.000016721304,0.00004018095,0.00015600167,0.000005635215,0.000008129435,0.00011708686,0.9365197,0.029722765,0.0213254,0.011994042,0.000084250816],"about_ca_topic_score_codex":0.0000285031,"about_ca_topic_score_gemma":0.000012064827,"teacher_disagreement_score":0.9542427,"about_ca_system_score_codex":0.000033214248,"about_ca_system_score_gemma":0.00015919805,"threshold_uncertainty_score":0.29218462},"labels":[],"label_agreement":null},{"id":"W4392360972","doi":"10.1016/j.asoc.2024.111442","title":"Anomaly detection in time-series data using evolutionary neural architecture search with non-differentiable functions","year":2024,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Colleges and Universities; Secretaría de Educación Superior, Ciencia, Tecnología e Innovación; Ministry of Training, Colleges and Universities","keywords":"Computer science; Benchmark (surveying); Artificial neural network; Anomaly detection; Artificial intelligence; Network architecture; Backpropagation; Differentiable function; Machine learning; Data mining; Mathematics","score_opus":0.017870058537364945,"score_gpt":0.24992440617974201,"score_spread":0.23205434764237706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392360972","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":[0.13690922,0.000070638634,0.86127627,0.00013657477,0.00007667215,0.00032332045,0.0000073088963,0.00074050284,0.00045952937],"genre_scores_gemma":[0.8953834,0.0000015285119,0.10420283,0.000040632938,0.00012552355,0.000024237603,0.000029155632,0.00002499746,0.00016769607],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982569,0.00003498498,0.00026779913,0.0007887766,0.00027945498,0.0003720456],"domain_scores_gemma":[0.99891764,0.00010800563,0.000051731466,0.0008099739,0.0000416522,0.0000710117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027480218,0.00020005883,0.0001747767,0.00032041507,0.00046344503,0.00038608286,0.0008759073,0.00008665521,0.000012661769],"category_scores_gemma":[0.0000043970117,0.00018539633,0.0000374836,0.0014055037,0.00006616991,0.00044863016,0.00080947456,0.0004620194,0.000044121487],"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":[0.00015165396,0.000441311,0.0034075982,0.000415205,0.00022379261,0.00008734911,0.0019163841,0.21771279,0.15417297,0.01537463,0.0012643544,0.60483193],"study_design_scores_gemma":[0.00012417107,0.0000570917,0.0019990872,0.000053825093,0.000013016095,0.00013899097,0.000046665853,0.9930088,0.0024708344,0.00069650204,0.0011567152,0.00023429136],"about_ca_topic_score_codex":0.00012315207,"about_ca_topic_score_gemma":0.000031533647,"teacher_disagreement_score":0.77529603,"about_ca_system_score_codex":0.00010615947,"about_ca_system_score_gemma":0.00010655799,"threshold_uncertainty_score":0.7560243},"labels":[],"label_agreement":null},{"id":"W4392374367","doi":"10.1038/s41598-024-52378-9","title":"A GAN-based anomaly detector using multi-feature fusion and selection","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":11,"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":"Hefei University of Technology; Hefei University; Tsinghua University","keywords":"MNIST database; Pattern recognition (psychology); Artificial intelligence; Computer science; Feature (linguistics); Anomaly detection; Feature selection; Convolution (computer science); Feature vector; Standard deviation; Set (abstract data type); Segmentation; Unsupervised learning; Detector; Deep learning; Artificial neural network; Mathematics; Statistics","score_opus":0.01768427810600802,"score_gpt":0.27164143460556867,"score_spread":0.25395715649956063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392374367","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":[0.18751656,0.00025677448,0.8097753,0.0001917277,0.00122641,0.00022661865,7.6200905e-7,0.00069870235,0.000107168154],"genre_scores_gemma":[0.9104185,0.0000016725554,0.08812998,0.000029772578,0.00004372094,0.000027190581,0.000002272469,0.000009648823,0.0013372037],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854124,0.000023896258,0.00020391833,0.00081101834,0.00023215378,0.00018777786],"domain_scores_gemma":[0.9993074,0.000019178486,0.000085881045,0.00039782884,0.000101680394,0.00008803165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052654056,0.00011290671,0.0000892509,0.000281019,0.0004952373,0.0010240648,0.0001336856,0.00007997814,0.000015361396],"category_scores_gemma":[0.000021283235,0.00010012561,0.00006291086,0.0012593098,0.000083640225,0.00034430623,0.000047276757,0.00013077933,0.000007386984],"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":[0.0000018145421,0.000054567026,0.0013239217,0.0000605253,0.000009290343,0.00014465998,0.0001909467,0.00033918692,0.95561534,0.0005880154,0.002736984,0.03893473],"study_design_scores_gemma":[0.000037249,0.000026986647,0.0010605397,0.000049441525,0.000009886518,0.0005330988,0.0000058730125,0.7330945,0.21872571,0.0014164179,0.044877313,0.0001629944],"about_ca_topic_score_codex":0.0000374186,"about_ca_topic_score_gemma":0.000022105864,"teacher_disagreement_score":0.73688966,"about_ca_system_score_codex":0.000066593624,"about_ca_system_score_gemma":0.00016168974,"threshold_uncertainty_score":0.98750806},"labels":[],"label_agreement":null},{"id":"W4392510002","doi":"10.3390/info15030146","title":"Algorithm-Based Data Generation (ADG) Engine for Dual-Mode User Behavioral Data Analytics","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Taif University","keywords":"Computer science; Analytics; Dual mode; Mode (computer interface); Dual (grammatical number); Data analysis; Data mining; Human–computer interaction; Engineering","score_opus":0.10994547212387706,"score_gpt":0.36282674131657877,"score_spread":0.2528812691927017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392510002","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018195598,0.000023864543,0.99680054,0.00077047007,0.0002418027,0.00033739101,0.001079703,0.00050112273,0.00006312364],"genre_scores_gemma":[0.09744455,0.00001837616,0.8894945,0.00033981988,0.00029099698,0.00011804188,0.0121577475,0.000010671394,0.00012529964],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918693,0.000007998988,0.0002867746,0.00022997329,0.00016848197,0.00011985727],"domain_scores_gemma":[0.9983217,0.000026754167,0.000066222194,0.001447812,0.00009851011,0.000039003513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030124187,0.00008731991,0.00006783556,0.00015413224,0.00012098455,0.0005446119,0.00085675693,0.000061247934,0.000009442501],"category_scores_gemma":[0.000014294772,0.00008440252,0.000024084611,0.00034818077,0.000012752264,0.004994945,0.00028019436,0.00007532169,0.000049501825],"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":[0.0000011616761,0.000027646274,0.0000037116754,0.000029044328,0.000009796617,4.5478282e-7,0.00009639858,0.0024451043,0.00023884856,0.009957284,0.039446846,0.9477437],"study_design_scores_gemma":[0.000066241926,0.000027313403,0.000013844603,0.0000061851565,0.000014085257,0.0000029819937,0.000005234843,0.7672927,0.001746998,0.00018137456,0.23056053,0.00008253943],"about_ca_topic_score_codex":0.00003846638,"about_ca_topic_score_gemma":0.000013133679,"teacher_disagreement_score":0.94766116,"about_ca_system_score_codex":0.000043718777,"about_ca_system_score_gemma":0.0001027513,"threshold_uncertainty_score":0.5251705},"labels":[],"label_agreement":null},{"id":"W4392659383","doi":"10.3390/a17030114","title":"Deep-Shallow Metaclassifier with Synthetic Minority Oversampling for Anomaly Detection in a Time Series","year":2024,"lang":"en","type":"article","venue":"Algorithms","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Enbridge; Natural Sciences and Engineering Research Council of Canada","keywords":"Anomaly detection; Computer science; Oversampling; Anomaly (physics); Artificial intelligence; Deep learning; Pipeline (software); Series (stratigraphy); Machine learning; Time series; Pattern recognition (psychology); Geology","score_opus":0.012388342570198135,"score_gpt":0.24312112211814535,"score_spread":0.23073277954794721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392659383","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":[0.0058905026,0.0001585096,0.9922421,0.00037261713,0.000095044335,0.00037033914,0.0000051523953,0.00050036056,0.00036537458],"genre_scores_gemma":[0.6296052,0.000020563515,0.36808932,0.00006030347,0.00011238647,0.00067248766,0.000004406213,0.000021692293,0.0014136732],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990129,0.000019015368,0.00016925661,0.00043844388,0.00014437846,0.00021600412],"domain_scores_gemma":[0.9994705,0.00009261277,0.00003545209,0.0003040428,0.000047851383,0.000049579765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021380957,0.0001309558,0.00014030693,0.00016318777,0.000119047545,0.0002565577,0.00029693497,0.000073095565,0.00001170016],"category_scores_gemma":[0.000010531784,0.000113193804,0.00007265058,0.00058016693,0.000038762224,0.000510928,0.00006283707,0.000121737095,0.000030174073],"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":[0.00006173733,0.00022311327,0.00039956713,0.00020146267,0.00016464539,0.00007295347,0.0007891829,0.00039652552,0.015890967,0.042410042,0.00016072529,0.9392291],"study_design_scores_gemma":[0.00019098917,0.0003255202,0.00097143935,0.00005001758,0.000033273445,0.00015224448,0.000033711498,0.94096017,0.03011703,0.0074652657,0.019383064,0.0003172852],"about_ca_topic_score_codex":0.00007682976,"about_ca_topic_score_gemma":0.000085245454,"teacher_disagreement_score":0.9405636,"about_ca_system_score_codex":0.00008014635,"about_ca_system_score_gemma":0.00003917599,"threshold_uncertainty_score":0.46159092},"labels":[],"label_agreement":null},{"id":"W4392662712","doi":"10.1007/s11042-024-18687-x","title":"A hybrid transformer with domain adaptation using interpretability techniques for the application to the detection of risk situations","year":2024,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Interpretability; Computer science; Initialization; Artificial intelligence; Transfer of learning; Machine learning; Domain adaptation; Modal; Artificial neural network; Transformer; Pattern recognition (psychology); Data mining; Classifier (UML)","score_opus":0.016669539202471714,"score_gpt":0.27027868804787314,"score_spread":0.2536091488454014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392662712","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":[0.0020375703,0.00022581013,0.99241245,0.0013804173,0.000023766534,0.0034305137,0.00017310701,0.00026681594,0.00004953859],"genre_scores_gemma":[0.78980756,0.00007603405,0.20188917,0.000052736785,0.00008314144,0.008058828,0.000012725366,0.0000118602375,0.000007970736],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990479,0.000033142685,0.00028070126,0.00036663626,0.00013884985,0.00013282312],"domain_scores_gemma":[0.9984947,0.0006679602,0.000098978846,0.0005343168,0.00015430343,0.00004975186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043983804,0.00012465524,0.00010955478,0.000085898544,0.0005451341,0.00017612889,0.00035160888,0.000041852963,0.000001658511],"category_scores_gemma":[0.00002417817,0.000074909796,0.00007297666,0.00057673204,0.00011215404,0.0002682229,0.000027458376,0.00013214821,0.0000035069384],"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":[0.000009051777,0.00003107762,0.000012573593,0.000019920413,0.000021154308,1.9591399e-8,0.0008810699,0.00040174197,0.01846896,0.016888937,0.000015236078,0.9632503],"study_design_scores_gemma":[0.00010538212,0.00009886028,0.0010225034,0.000028385322,0.00009932813,0.000014557686,0.00035050776,0.8283625,0.07011854,0.01457277,0.08504194,0.00018469576],"about_ca_topic_score_codex":0.00015934327,"about_ca_topic_score_gemma":0.0001683416,"teacher_disagreement_score":0.96306556,"about_ca_system_score_codex":0.000044460136,"about_ca_system_score_gemma":0.000053339783,"threshold_uncertainty_score":0.41927862},"labels":[],"label_agreement":null},{"id":"W4392720662","doi":"10.2139/ssrn.4757204","title":"A Comprehensive Study of Auto-Encoders for Anomaly Detection: Efficiency and Trade-Offs","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; Douglas College","funders":"","keywords":"Anomaly detection; Encoder; Computer science; Anomaly (physics); Business; Artificial intelligence; Physics","score_opus":0.014891632648598335,"score_gpt":0.2736570200704122,"score_spread":0.2587653874218139,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392720662","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":[0.16774462,0.003051414,0.8269464,0.00062552013,0.00032168967,0.0010400965,0.0000057055395,0.0001825257,0.00008207584],"genre_scores_gemma":[0.9961438,0.00081012934,0.0024626693,0.00003362652,0.00015070102,0.00020431867,8.8259947e-7,0.000024685412,0.00016914538],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974829,0.00008075891,0.0005545491,0.0006371013,0.00028234,0.0009623526],"domain_scores_gemma":[0.99881476,0.0000835567,0.00038558722,0.00048385607,0.00014873051,0.00008350543],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00060809887,0.00028337454,0.00038879388,0.0003520803,0.00032353314,0.0002139416,0.00082366826,0.00018684886,0.0000013593054],"category_scores_gemma":[0.000012246213,0.0002634037,0.00022532482,0.00038488972,0.00006767253,0.00009847149,0.00058302085,0.0024534604,0.0000018621381],"study_design_candidate":"theoretical_or_conceptual","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":[0.00015486006,0.0020065717,0.00023626458,0.0007830346,0.0019333806,0.000017486116,0.0074675926,0.0025817526,0.0075636264,0.3265348,0.00030785202,0.6504128],"study_design_scores_gemma":[0.0011620864,0.005454149,0.0006605156,0.00012499082,0.00029249053,0.0010503934,0.004666232,0.12925929,0.0019713843,0.8518305,0.0027867514,0.0007412639],"about_ca_topic_score_codex":0.000099697165,"about_ca_topic_score_gemma":0.00014861303,"teacher_disagreement_score":0.82839924,"about_ca_system_score_codex":0.0004172003,"about_ca_system_score_gemma":0.0012671354,"threshold_uncertainty_score":0.9999818},"labels":[],"label_agreement":null},{"id":"W4392729533","doi":"10.1080/15472450.2024.2315126","title":"Deep survival analysis model for incident clearance time prediction","year":2024,"lang":"en","type":"article","venue":"Journal of Intelligent Transportation Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"National Research Foundation of Korea","keywords":"Computer science; Survival analysis; Statistics; Mathematics","score_opus":0.02179941674874112,"score_gpt":0.2756031962151872,"score_spread":0.25380377946644606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392729533","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":[0.0045940513,0.00089514395,0.99343777,0.00014246082,0.0004584654,0.00025366392,0.000026482976,0.00013309575,0.00005885955],"genre_scores_gemma":[0.97337216,0.00021639175,0.025549987,0.000016614109,0.00014159901,0.00004846714,0.000013944518,0.000011056413,0.00062980223],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848676,0.00002758764,0.00083486584,0.00019063635,0.00034768513,0.00011248973],"domain_scores_gemma":[0.9989844,0.00006899618,0.00028707317,0.00017358598,0.0004091144,0.00007680616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006439268,0.00010092471,0.00024016439,0.0004181128,0.00007112986,0.00017765685,0.00029716972,0.00006580118,0.000006145825],"category_scores_gemma":[0.000006572161,0.00008682503,0.00037413958,0.00079110323,0.000013463404,0.00047289467,0.000002880671,0.00011434802,0.000011907173],"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":[0.000040504066,0.00008189701,0.0005015359,0.00014799743,0.00060034194,0.000008717944,0.0019994155,0.919604,0.0011705374,0.054351825,0.0008298584,0.020663342],"study_design_scores_gemma":[0.00005332765,0.0000859471,0.0007811395,0.000059563645,0.00018920493,0.000010210936,0.000066397704,0.9921599,0.00071268383,0.0006081871,0.0051957252,0.00007769973],"about_ca_topic_score_codex":0.000008385255,"about_ca_topic_score_gemma":0.000009193385,"teacher_disagreement_score":0.9687781,"about_ca_system_score_codex":0.00008765898,"about_ca_system_score_gemma":0.00006616604,"threshold_uncertainty_score":0.3540622},"labels":[],"label_agreement":null},{"id":"W4392748476","doi":"10.2139/ssrn.4757427","title":"Harnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Anomaly detection; Transformation (genetics); Anomaly (physics); Series (stratigraphy); Computer science; Artificial intelligence; Pattern recognition (psychology); Geology; Physics","score_opus":0.005769316081706288,"score_gpt":0.24281885068406653,"score_spread":0.23704953460236025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392748476","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":[0.028962087,0.0018252056,0.9666823,0.0013521481,0.00020925129,0.00043378366,0.0000042877705,0.0003614063,0.0001695428],"genre_scores_gemma":[0.9943494,0.0010759549,0.0033864912,0.000020312133,0.00024058108,0.0001305422,0.000006459766,0.000021565358,0.00076864427],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99826187,0.00006482682,0.00033041916,0.0003601275,0.00014128487,0.0008414908],"domain_scores_gemma":[0.999407,0.00004316183,0.00023734863,0.00011786632,0.00013645494,0.00005817443],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.000866672,0.00021677942,0.00021723866,0.00020873726,0.00053880166,0.00066130253,0.0002651785,0.00018412792,0.0000018759541],"category_scores_gemma":[0.00001985044,0.00020781161,0.00015594208,0.00016262218,0.00004208408,0.00044166783,0.0001624209,0.002739384,0.000004705368],"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":[0.000052252188,0.000019496152,0.000013133598,0.00010495376,0.00015633414,0.0000012620758,0.000685571,0.00044146995,0.005832897,0.12635954,0.0000119244605,0.86632115],"study_design_scores_gemma":[0.00025574345,0.00055727613,0.000046902438,0.000090113776,0.00008936663,0.0009830158,0.00034690124,0.30933595,0.00512451,0.6803001,0.0025104177,0.0003596994],"about_ca_topic_score_codex":0.000016813505,"about_ca_topic_score_gemma":0.000056596415,"teacher_disagreement_score":0.96538734,"about_ca_system_score_codex":0.00046058663,"about_ca_system_score_gemma":0.00063460745,"threshold_uncertainty_score":0.9995613},"labels":[],"label_agreement":null},{"id":"W4392754529","doi":"10.3389/frobt.2024.1214043","title":"General value functions for fault detection in multivariate time series data","year":2024,"lang":"en","type":"article","venue":"Frontiers in Robotics and AI","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Mitsubishi Electric Research Laboratories","keywords":"Computer science; Series (stratigraphy); Multivariate statistics; Time series; Data mining; Value (mathematics); Artificial intelligence; Pattern recognition (psychology); Machine learning","score_opus":0.015329858859109508,"score_gpt":0.2674050719231934,"score_spread":0.25207521306408387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392754529","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041390635,0.00024847387,0.9967206,0.0015938061,0.00055197434,0.0002288785,0.000026853835,0.00014017141,0.00007533686],"genre_scores_gemma":[0.124239385,0.00013694075,0.8723242,0.00017054017,0.00014841145,0.00013118733,0.00004406107,0.000017935386,0.0027872997],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992907,0.00001710831,0.0001570313,0.00034354848,0.00005342065,0.00013817215],"domain_scores_gemma":[0.99959946,0.000018668878,0.000019693316,0.0003133365,0.000018339882,0.000030478375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016925053,0.000079862846,0.00009919627,0.00016570199,0.00009209423,0.00016443818,0.00024870294,0.00006305192,0.000001196906],"category_scores_gemma":[0.000012175852,0.00007843618,0.000021624084,0.0003524942,0.000027151553,0.00046548466,0.00014543769,0.0001181548,0.0000032536268],"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":[0.000043957665,0.00021827553,0.0010103523,0.00017561189,0.000087792105,0.00001687334,0.00067163666,0.109231174,0.006211308,0.15378273,0.057492893,0.6710574],"study_design_scores_gemma":[0.00009958255,0.00003931299,0.0003588488,0.000017017128,0.0000056500185,0.000005318757,0.000015647409,0.9595834,0.0004363985,0.013445597,0.025896678,0.0000965607],"about_ca_topic_score_codex":0.00005306744,"about_ca_topic_score_gemma":0.000025175492,"teacher_disagreement_score":0.8503522,"about_ca_system_score_codex":0.000038437636,"about_ca_system_score_gemma":0.000027094171,"threshold_uncertainty_score":0.31985345},"labels":[],"label_agreement":null},{"id":"W4392772331","doi":"10.37934/araset.40.2.127139","title":"BiasTrap: Runtime Detection of Biased Prediction in Machine Learning Systems","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Research in Applied Sciences and Engineering Technology","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Universiti Teknologi Petronas","keywords":"Computer science; Artificial intelligence; Machine learning","score_opus":0.025939869731591455,"score_gpt":0.3032125516861288,"score_spread":0.27727268195453736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392772331","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":[0.41364253,0.002378696,0.58291626,0.00040381463,0.00016422996,0.00020113897,9.1229913e-7,0.00015290303,0.00013952565],"genre_scores_gemma":[0.9807165,0.00044536812,0.018780679,7.12572e-7,0.000017602579,0.000026791,7.876885e-8,0.0000045346233,0.00000770905],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998929,0.000021836513,0.00036290265,0.00019638252,0.0002696495,0.00022022023],"domain_scores_gemma":[0.99960196,0.00013305893,0.00007178206,0.00010304496,0.00005539441,0.00003473827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018388878,0.000067076675,0.0001552632,0.0025797645,0.00006878368,0.00006576805,0.0003694886,0.000089201116,6.277081e-7],"category_scores_gemma":[0.00005603198,0.00005727889,0.000020600775,0.00353956,0.00014272369,0.00021616214,0.00009728292,0.00070721394,5.9768155e-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":[0.000010440256,0.00003568467,0.00033277943,0.00011435925,0.0000069186244,0.000017816297,0.000100527795,0.14356256,0.6415791,0.06624837,0.0000031567083,0.14798832],"study_design_scores_gemma":[0.00021883535,0.0005616121,0.00056392193,0.00031137117,0.0000013773639,0.00014053221,0.00026700157,0.94278276,0.04740208,0.0053037717,0.0023634771,0.00008325301],"about_ca_topic_score_codex":0.000013624964,"about_ca_topic_score_gemma":0.0000034909738,"teacher_disagreement_score":0.7992202,"about_ca_system_score_codex":0.00007326376,"about_ca_system_score_gemma":0.000047816673,"threshold_uncertainty_score":0.30725318},"labels":[],"label_agreement":null},{"id":"W4392845602","doi":"10.2139/ssrn.4755948","title":"Wastewater-Based Surveillance of Sars-Cov-2: Short-Term Projection, Smoothing and Outlier Identification Using Bayesian Smoothing","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Public Health; Hamilton Health Sciences; University of Toronto; University of Waterloo; Ministry of the Environment, Conservation and Parks; University of Ottawa; University of Windsor; Public Health Agency of Canada; Ottawa Hospital","funders":"","keywords":"Smoothing; Bayesian probability; Identification (biology); Term (time); Outlier; Coronavirus disease 2019 (COVID-19); Computer science; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Projection (relational algebra); Artificial intelligence; Environmental science; Data mining; Computer vision; Medicine; Algorithm; Biology; Physics","score_opus":0.026437421229757656,"score_gpt":0.3031426035746759,"score_spread":0.2767051823449182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392845602","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":[0.2937984,0.0008899841,0.7039867,0.00040490195,0.0003363214,0.00032295674,0.0000055522787,0.00019480589,0.000060353377],"genre_scores_gemma":[0.98897886,0.00044004814,0.010179882,0.000025711948,0.00018886694,0.000040602055,0.000006094738,0.00004589905,0.00009400359],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9969055,0.00014872503,0.00083814503,0.000727021,0.00038488454,0.0009957448],"domain_scores_gemma":[0.9984493,0.000035833124,0.0005600043,0.00066073815,0.0002404556,0.00005362598],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0023511054,0.0003232598,0.00037064284,0.0005252149,0.0003483037,0.0006568682,0.00082011917,0.00027142212,0.0000011842591],"category_scores_gemma":[0.000024150966,0.00031560962,0.00021046566,0.00043134252,0.000079595484,0.00026809872,0.00054003176,0.0028037522,0.0000020469197],"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":[0.000064280706,0.0003736629,0.0051607797,0.0013039575,0.0008775826,0.000020618223,0.002376008,0.002861185,0.65291,0.16092634,0.00015241964,0.17297316],"study_design_scores_gemma":[0.00030244136,0.000232912,0.00074164884,0.0006911865,0.00014228998,0.0007993905,0.00029458365,0.3779088,0.19281301,0.42477858,0.00028051817,0.001014652],"about_ca_topic_score_codex":0.00020635116,"about_ca_topic_score_gemma":0.0002025715,"teacher_disagreement_score":0.6951805,"about_ca_system_score_codex":0.000886833,"about_ca_system_score_gemma":0.0018537535,"threshold_uncertainty_score":0.9999296},"labels":[],"label_agreement":null},{"id":"W4392845778","doi":"10.1145/3625007.3630112","title":"ROBUREC: Building a Robust Recommender using Autoencoders with Anomaly Detection","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Toronto Metropolitan University","funders":"","keywords":"Recommender system; Anomaly detection; Computer science; Anomaly (physics); Artificial intelligence; Machine learning","score_opus":0.044557122635001,"score_gpt":0.26914030652385634,"score_spread":0.22458318388885534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392845778","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":[0.017132418,0.0000049106416,0.97781825,0.0008192575,0.00007116876,0.00017468283,4.4494237e-7,0.0020617505,0.0019171328],"genre_scores_gemma":[0.5101316,0.000007809885,0.4890052,0.00015126797,0.000030634423,0.00004877836,6.362565e-7,0.000012737965,0.0006113297],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990492,0.000024555526,0.00015427232,0.00036898113,0.00014744439,0.00025554828],"domain_scores_gemma":[0.99939436,0.000031019666,0.00007033966,0.00037622623,0.00005906636,0.00006899071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018755763,0.000117744064,0.00009742997,0.0002376658,0.00033039716,0.00014843304,0.0003284516,0.000059424958,0.000026664806],"category_scores_gemma":[0.000005318121,0.000100397796,0.00004567223,0.0015822046,0.000024900255,0.00043444612,0.00012417411,0.0001100496,0.000044207714],"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":[0.000031945972,0.0001895967,0.0016354717,0.000041764462,0.00015395625,0.000043567215,0.00076473295,0.569632,0.0526938,0.08936249,0.008281762,0.27716893],"study_design_scores_gemma":[0.00010037428,0.00006808676,0.0004042038,0.0000083875,0.00000549683,0.00004855847,0.00006573022,0.9779392,0.014676492,0.0009290464,0.0055794306,0.000175016],"about_ca_topic_score_codex":0.00019584339,"about_ca_topic_score_gemma":0.000067722074,"teacher_disagreement_score":0.4929992,"about_ca_system_score_codex":0.00008353675,"about_ca_system_score_gemma":0.000039814568,"threshold_uncertainty_score":0.40941033},"labels":[],"label_agreement":null},{"id":"W4392889692","doi":"10.1007/978-3-031-53963-3_25","title":"Benchmarking Jetson Edge Devices with an End-to-End Video-Based Anomaly Detection System","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Windsor","keywords":"End-to-end principle; Benchmarking; Enhanced Data Rates for GSM Evolution; Anomaly detection; End user; Computer science; Telecommunications; Operating system; Artificial intelligence; Business","score_opus":0.011474976770956562,"score_gpt":0.22290024757139046,"score_spread":0.21142527080043388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392889692","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":[0.00025200687,0.003724627,0.985397,0.00009449451,0.00074789964,0.0010238526,0.000010943097,0.00062545313,0.008123763],"genre_scores_gemma":[0.99253505,0.00003410097,0.0049387347,0.00014242115,0.0010872334,0.00026366708,0.000021279482,0.00007781008,0.00089967897],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975314,0.000060774826,0.00055922836,0.0011306808,0.00033483736,0.00038309046],"domain_scores_gemma":[0.99840206,0.00021418376,0.00029592772,0.0007931702,0.00011026663,0.00018438573],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00046409192,0.0005271442,0.0005847431,0.0004612407,0.00028613352,0.00074894173,0.00049234275,0.00061087316,0.0000065935083],"category_scores_gemma":[0.000003677566,0.0004277449,0.000100755184,0.00037347153,0.000060209204,0.00018344341,0.00012302132,0.0006894539,0.000008141744],"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":[0.00013028337,0.000053782653,0.00047577836,0.00224237,0.00024679405,0.00025331054,0.0004959461,0.3262756,0.00022219279,0.16294983,0.00011858476,0.50653553],"study_design_scores_gemma":[0.00015620563,0.00066663057,0.00008228429,0.0026460392,0.0000698055,0.00019416884,0.0000103039265,0.96285063,0.00014913711,0.00091788155,0.03150926,0.0007476792],"about_ca_topic_score_codex":0.00038836824,"about_ca_topic_score_gemma":0.0020962984,"teacher_disagreement_score":0.99228305,"about_ca_system_score_codex":0.00024318686,"about_ca_system_score_gemma":0.00006284242,"threshold_uncertainty_score":0.99981743},"labels":[],"label_agreement":null},{"id":"W4393077169","doi":"10.34190/iccws.19.1.1974","title":"Anomaly Detection for the MIL-STD-1553B Multiplex Data Bus Using an LSTM Autoencoder","year":2024,"lang":"en","type":"article","venue":"International Conference on Cyber Warfare and Security","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Autoencoder; Multiplex; Anomaly detection; Computer science; Artificial intelligence; Deep learning; Bioinformatics","score_opus":0.14037728871586183,"score_gpt":0.3715145856432548,"score_spread":0.23113729692739296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393077169","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":[0.02454938,0.00016266399,0.9699344,0.002998802,0.0006144437,0.0003681316,0.00019840727,0.00032280287,0.00085095357],"genre_scores_gemma":[0.99138945,0.00009534438,0.0077325515,0.00027413334,0.00021872674,0.00008151719,0.000045384964,0.000011061045,0.00015181304],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876904,0.00003420992,0.00020798571,0.000602541,0.00022626636,0.0001599603],"domain_scores_gemma":[0.9989071,0.00013889684,0.000059028167,0.0006314941,0.00019788949,0.00006559016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028030828,0.0001474548,0.00009973399,0.00010220267,0.00031957703,0.0006555389,0.0010762099,0.00008222556,0.00004615035],"category_scores_gemma":[0.00003176975,0.00011309668,0.00005061234,0.00016154062,0.0000687658,0.00081048114,0.00029347744,0.00019905134,0.000009213547],"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":[0.00005275505,0.00024121077,0.00017105964,0.000044993183,0.00017065712,0.000012092192,0.0016472867,0.0002138883,0.006611533,0.6398127,0.0012111603,0.34981063],"study_design_scores_gemma":[0.000103738814,0.00007196893,0.00052430754,0.000027832313,0.00001380581,0.00003138386,0.0000794338,0.9442771,0.0016148137,0.01868776,0.034421235,0.00014662412],"about_ca_topic_score_codex":0.00047981506,"about_ca_topic_score_gemma":0.00043836594,"teacher_disagreement_score":0.9668401,"about_ca_system_score_codex":0.000051265302,"about_ca_system_score_gemma":0.00007827361,"threshold_uncertainty_score":0.6321377},"labels":[],"label_agreement":null},{"id":"W4393153098","doi":"10.1609/aaai.v38i12.29210","title":"When Model Meets New Normals: Test-Time Adaptation for Unsupervised Time-Series Anomaly Detection","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Korea Advanced Institute of Science and Technology; National Research Foundation of Korea; National Research Foundation","keywords":"Anomaly detection; Adaptation (eye); Series (stratigraphy); Anomaly (physics); Time series; Computer science; Artificial intelligence; Test (biology); Pattern recognition (psychology); Machine learning; Psychology; Geology; Neuroscience","score_opus":0.06147079474627257,"score_gpt":0.27815928186316863,"score_spread":0.21668848711689606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393153098","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":[0.0056802314,0.00002838979,0.9834609,0.007006197,0.00012439802,0.0007720842,0.000015622496,0.00052867644,0.002383477],"genre_scores_gemma":[0.93075365,0.000027275306,0.064839125,0.00012857527,0.00009215497,0.00016874324,0.000001072007,0.000020299622,0.0039691245],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984927,0.000007206848,0.00046016055,0.0005067099,0.00028640206,0.00024682318],"domain_scores_gemma":[0.9988966,0.00012207519,0.00017894717,0.00025246592,0.00046684087,0.00008307741],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033797667,0.00020360154,0.00018914747,0.00016169873,0.00024933528,0.00045710293,0.0010461884,0.00010785155,0.000049882376],"category_scores_gemma":[0.00019498642,0.00016642743,0.00015014784,0.000537146,0.0001038109,0.00092559104,0.0001576087,0.00016642903,0.00014680094],"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":[0.00004434238,0.000051840703,0.000001580905,0.00004925318,0.000010639307,8.1689016e-8,0.0010217387,0.00041992354,0.37249422,0.40826693,0.0006356102,0.21700384],"study_design_scores_gemma":[0.000008094829,0.00014137775,0.0000031022673,0.000054090444,0.000009712242,0.0000023323253,0.000037490354,0.46777362,0.40256482,0.12893845,0.0003706451,0.00009626705],"about_ca_topic_score_codex":0.000042548843,"about_ca_topic_score_gemma":0.000010041497,"teacher_disagreement_score":0.9250734,"about_ca_system_score_codex":0.000058135694,"about_ca_system_score_gemma":0.00016878973,"threshold_uncertainty_score":0.67867136},"labels":[],"label_agreement":null},{"id":"W4393160153","doi":"10.1609/aaai.v38i16.29800","title":"On Unsupervised Domain Adaptation: Pseudo Label Guided Mixup for Adversarial Prompt Tuning","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Fundamental Research Funds for the Central Universities; State Key Laboratory of Software Development Environment; National Natural Science Foundation of China","keywords":"Adversarial system; Adaptation (eye); Domain adaptation; Computer science; Artificial intelligence; Domain (mathematical analysis); Machine learning; Psychology; Neuroscience; Mathematics","score_opus":0.10762809480916702,"score_gpt":0.3235439369031405,"score_spread":0.21591584209397346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393160153","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":[0.028915778,0.000019596957,0.9543075,0.007913928,0.00057041104,0.0011901569,0.000014622802,0.00046159243,0.006606418],"genre_scores_gemma":[0.94792724,0.0000136838935,0.05094741,0.00023234313,0.00012890552,0.00028658065,0.0000012067655,0.000017291373,0.0004453302],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99832916,0.000012185282,0.00047922056,0.0005612616,0.00035113824,0.0002670275],"domain_scores_gemma":[0.998849,0.0001575548,0.00017188354,0.00028278597,0.0004685976,0.000070194095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004691428,0.00020241742,0.00018862079,0.00016011887,0.00029959736,0.00038995367,0.0013014423,0.00009890107,0.000038525035],"category_scores_gemma":[0.00016288752,0.00015746884,0.0001388774,0.00075233215,0.00013462851,0.00034942813,0.00016625125,0.00022928961,0.00006200454],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003649822,0.00006938283,0.0000019055515,0.00004107888,0.000012277794,2.060489e-7,0.00084926834,0.00004774951,0.03581491,0.91513026,0.0006224013,0.047374092],"study_design_scores_gemma":[0.000033215238,0.00027101976,0.0000063283474,0.00020716277,0.000009657013,0.0000033280294,0.00024813873,0.3618459,0.19962138,0.4367045,0.00089529104,0.00015409742],"about_ca_topic_score_codex":0.000020322477,"about_ca_topic_score_gemma":0.00000364266,"teacher_disagreement_score":0.9190115,"about_ca_system_score_codex":0.00006534523,"about_ca_system_score_gemma":0.00013446862,"threshold_uncertainty_score":0.64213926},"labels":[],"label_agreement":null},{"id":"W4393160778","doi":"10.1609/aaai.v38i16.29742","title":"Unsupervised Layer-Wise Score Aggregation for Textual OOD Detection","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Anomaly Detection Techniques and Applications","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":"Canadian Institute for Advanced Research; McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"Grand Équipement National De Calcul Intensif","keywords":"Anomaly detection; Mahalanobis distance; Computer science; Oracle; Robustness (evolution); Encoder; Leverage (statistics); Layer (electronics); Artificial intelligence; Discriminator; Embedding; Benchmark (surveying); Data mining; Machine learning; Detector; Pattern recognition (psychology)","score_opus":0.08825742206946548,"score_gpt":0.30917179030660924,"score_spread":0.22091436823714378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393160778","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":[0.056397665,0.000047684578,0.9357616,0.003028278,0.0003462969,0.00089465175,0.000010753743,0.00049363397,0.003019495],"genre_scores_gemma":[0.9942675,0.000036276757,0.004943215,0.00009307881,0.00010649629,0.0002450168,7.187667e-7,0.0000135300015,0.00029411685],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986635,0.000007536809,0.0003853318,0.00046712166,0.0002609241,0.00021563],"domain_scores_gemma":[0.9990152,0.00008371483,0.00014512439,0.00023559178,0.00046416727,0.00005617126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003192459,0.00016313203,0.00014262863,0.00015821331,0.0002531157,0.00038306933,0.00097364123,0.00009648203,0.00002292981],"category_scores_gemma":[0.0001230395,0.00012660246,0.0001491431,0.00082360586,0.0001166736,0.00044299153,0.00013918876,0.0001962761,0.000052020645],"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":[0.000012617073,0.00003359394,0.0000058109213,0.000040156952,0.0000062842546,5.6128275e-8,0.0002182668,0.000009499976,0.07603496,0.58509517,0.000087501736,0.3384561],"study_design_scores_gemma":[0.000010933895,0.00016556431,0.0000212367,0.00014443371,0.000009614311,0.000003618191,0.00012466843,0.16941197,0.68034947,0.14875297,0.0008918974,0.0001136069],"about_ca_topic_score_codex":0.00002201244,"about_ca_topic_score_gemma":0.0000125908455,"teacher_disagreement_score":0.9378699,"about_ca_system_score_codex":0.000055023356,"about_ca_system_score_gemma":0.00007421836,"threshold_uncertainty_score":0.51626986},"labels":[],"label_agreement":null},{"id":"W4393186008","doi":"10.1109/icpads60453.2023.00088","title":"ESIREOS: Efficient, Scalable, Internal, Relative Evaluation of Outliers Solutions","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Scalability; Outlier; Artificial intelligence; Database","score_opus":0.06647029475240107,"score_gpt":0.32129756952623195,"score_spread":0.2548272747738309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393186008","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":[0.019893952,0.000016816599,0.95199955,0.0006717934,0.00008637691,0.00025619016,0.0000027450267,0.0005641264,0.026508478],"genre_scores_gemma":[0.9817667,0.0000071039813,0.016286897,0.00003409511,0.000011518648,0.00008935046,0.0000032994137,0.0000045316497,0.0017965292],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903864,0.00005858009,0.00019969117,0.00021550665,0.00033972468,0.0001478434],"domain_scores_gemma":[0.9991925,0.00005009215,0.000091634,0.00038066518,0.00024482762,0.000040327915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008785723,0.0000616519,0.000076484306,0.00020048337,0.00014281622,0.000029630284,0.00036190008,0.00003931847,0.000067722765],"category_scores_gemma":[0.000053677155,0.00005623523,0.000060181734,0.0012148814,0.00005255097,0.00016904718,0.00017193529,0.00006564281,0.00019645474],"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":[0.0000023289288,0.00011284863,0.0002954474,0.0000058267624,0.00003568007,4.2688103e-7,0.0007264363,0.009782232,0.0027950606,0.8982665,0.013441624,0.07453561],"study_design_scores_gemma":[0.00013846184,0.000036282345,0.003056233,0.000009014559,0.0000138560445,0.0000017970023,0.00010098267,0.9682634,0.0067022755,0.020325273,0.0012727721,0.00007965897],"about_ca_topic_score_codex":0.000048253107,"about_ca_topic_score_gemma":0.0000052193705,"teacher_disagreement_score":0.9618727,"about_ca_system_score_codex":0.000060388116,"about_ca_system_score_gemma":0.00006091649,"threshold_uncertainty_score":0.2525094},"labels":[],"label_agreement":null},{"id":"W4393480402","doi":"10.5281/zenodo.6388029","title":"Data sets and models for the Deep API Learning Revisited paper","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Natural language processing","score_opus":0.06065387771372477,"score_gpt":0.2791026434401597,"score_spread":0.21844876572643496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393480402","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[9.1930485e-7,0.00037862745,0.42346227,0.0011345856,0.000046635898,0.00083410705,0.57246614,0.00068561547,0.0009910946],"genre_scores_gemma":[0.00018280723,0.0017499531,0.0020944474,0.00040196278,0.00009157181,0.0000011263727,0.99469566,0.00047658317,0.00030591138],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99812704,0.00026836892,0.00025232456,0.00075016776,0.0003307055,0.00027136793],"domain_scores_gemma":[0.99747705,0.00010604487,0.0001934363,0.0018796262,0.0002466601,0.00009719812],"candidate_categories":["sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010626882,0.00017073189,0.00015895137,0.0001585564,0.0048470935,0.0013728912,0.004676682,0.000083589446,0.009687555],"category_scores_gemma":[0.00030183487,0.00015233085,0.000044453,0.00054871594,0.000096858435,0.00051100354,0.008217734,0.0005799052,0.0002605201],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000055437404,0.000027606555,1.15915295e-8,0.000040797295,0.000025315274,0.000001359941,0.000055893077,0.00013037003,0.00001198462,0.0032201994,0.9263597,0.07012122],"study_design_scores_gemma":[0.000121869576,0.000111446614,0.0000029683465,0.000010059373,0.00002619364,0.00006721979,0.000046796882,0.0488129,0.0000027459494,0.0006245006,0.9500046,0.00016870143],"about_ca_topic_score_codex":0.00002371201,"about_ca_topic_score_gemma":6.6192575e-7,"teacher_disagreement_score":0.4222295,"about_ca_system_score_codex":0.000068322624,"about_ca_system_score_gemma":0.0000056425943,"threshold_uncertainty_score":0.9998036},"labels":[],"label_agreement":null},{"id":"W4393583881","doi":"10.5281/zenodo.3674790","title":"CoPhy: Counterfactual Learning of Physical Dynamics (Benchmark Dataset)","year":2019,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Counterfactual thinking; Benchmark (surveying); Dynamics (music); Computer science; Artificial intelligence; Machine learning; Econometrics; Mathematics; Psychology; Geography; Cartography; Social psychology","score_opus":0.02314614858594055,"score_gpt":0.26463808886885465,"score_spread":0.2414919402829141,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393583881","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00005285627,0.000018237675,0.09022732,0.000086121756,0.00007226756,0.00039927324,0.9059928,0.00039883627,0.0027522517],"genre_scores_gemma":[0.0056456747,0.0000793837,0.00039402948,0.000055518318,0.00009726568,1.0572479e-7,0.99318016,0.0003552377,0.00019262501],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998103,0.00022588218,0.00030669896,0.00060055015,0.00047146901,0.00029234853],"domain_scores_gemma":[0.9978597,0.000054988104,0.00033123972,0.0012644217,0.0003785428,0.00011106513],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00038135462,0.00021620926,0.00028147106,0.0002673244,0.00097154314,0.0005797632,0.003099859,0.0001331351,0.001967779],"category_scores_gemma":[0.00016946564,0.00023028658,0.00008375358,0.0005776839,0.00015123286,0.0002997319,0.0028860148,0.0006626298,0.0053055757],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009984484,0.00011412728,7.199625e-8,0.00008519964,0.000026509142,0.0000024793933,0.000056903682,0.00008004477,0.00011724214,0.0025472739,0.98382443,0.013135751],"study_design_scores_gemma":[0.00013625761,0.00038127782,0.000008104191,0.000034427358,0.00001742689,0.000035603985,0.000031925803,0.012403836,0.00014883735,0.000078080026,0.98650587,0.00021838397],"about_ca_topic_score_codex":0.000041004125,"about_ca_topic_score_gemma":3.5544417e-7,"teacher_disagreement_score":0.0898333,"about_ca_system_score_codex":0.00017428775,"about_ca_system_score_gemma":0.000010539499,"threshold_uncertainty_score":0.9989446},"labels":[],"label_agreement":null},{"id":"W4393878722","doi":"10.5281/zenodo.6388030","title":"Data sets and models for the Deep API Learning Revisited paper","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Data science","score_opus":0.06065387771372477,"score_gpt":0.2791026434401597,"score_spread":0.21844876572643496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393878722","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[9.1930485e-7,0.00037862745,0.42346227,0.0011345856,0.000046635898,0.00083410705,0.57246614,0.00068561547,0.0009910946],"genre_scores_gemma":[0.00018280723,0.0017499531,0.0020944474,0.00040196278,0.00009157181,0.0000011263727,0.99469566,0.00047658317,0.00030591138],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99812704,0.00026836892,0.00025232456,0.00075016776,0.0003307055,0.00027136793],"domain_scores_gemma":[0.99747705,0.00010604487,0.0001934363,0.0018796262,0.0002466601,0.00009719812],"candidate_categories":["sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010626882,0.00017073189,0.00015895137,0.0001585564,0.0048470935,0.0013728912,0.004676682,0.000083589446,0.009687555],"category_scores_gemma":[0.00030183487,0.00015233085,0.000044453,0.00054871594,0.000096858435,0.00051100354,0.008217734,0.0005799052,0.0002605201],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000055437404,0.000027606555,1.15915295e-8,0.000040797295,0.000025315274,0.000001359941,0.000055893077,0.00013037003,0.00001198462,0.0032201994,0.9263597,0.07012122],"study_design_scores_gemma":[0.000121869576,0.000111446614,0.0000029683465,0.000010059373,0.00002619364,0.00006721979,0.000046796882,0.0488129,0.0000027459494,0.0006245006,0.9500046,0.00016870143],"about_ca_topic_score_codex":0.00002371201,"about_ca_topic_score_gemma":6.6192575e-7,"teacher_disagreement_score":0.4222295,"about_ca_system_score_codex":0.000068322624,"about_ca_system_score_gemma":0.0000056425943,"threshold_uncertainty_score":0.9998036},"labels":[],"label_agreement":null},{"id":"W4393970287","doi":"10.1016/j.eswa.2024.123718","title":"Memory-enhanced spatial-temporal encoding framework for industrial anomaly detection system","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of British Columbia","funders":"China Scholarship Council","keywords":"Anomaly detection; Computer science; Encoding (memory); Reliability (semiconductor); Artificial intelligence; Consistency (knowledge bases); Workspace; Data mining; Process (computing); Spatial analysis; Temporal database","score_opus":0.024396139954589443,"score_gpt":0.27519592361198475,"score_spread":0.2507997836573953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393970287","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":[0.00026911753,0.00057906803,0.9908728,0.00031800984,0.0006531972,0.0032509803,0.000022393966,0.002716391,0.0013180295],"genre_scores_gemma":[0.9062773,0.000009728222,0.07062185,0.000033049604,0.0012616486,0.021405146,0.000014575657,0.00004299761,0.00033373304],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980265,0.000054162112,0.0004979424,0.0008118994,0.00027921976,0.00033029699],"domain_scores_gemma":[0.9983367,0.00022966463,0.00019347787,0.0009180835,0.0001732437,0.00014886307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000316316,0.00025704596,0.00027615915,0.00023587403,0.0006117697,0.0006108405,0.0006199308,0.0002528324,0.0000043758005],"category_scores_gemma":[0.0000138360465,0.00022224714,0.00011924693,0.0010599256,0.000050181254,0.00038251237,0.00007022038,0.00025695947,0.00006957542],"study_design_candidate":"theoretical_or_conceptual","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":[0.00004242097,0.00013024353,0.000030023268,0.00044029098,0.00016738239,0.000005206051,0.0013508487,0.00037257533,0.026994862,0.77912116,0.0019963798,0.18934861],"study_design_scores_gemma":[0.0008347851,0.0006483531,0.00003244001,0.001229771,0.00007527682,0.0002855828,0.0021490292,0.2833898,0.27807996,0.0030672553,0.42874116,0.0014665689],"about_ca_topic_score_codex":0.0005962969,"about_ca_topic_score_gemma":0.000037848058,"teacher_disagreement_score":0.92025095,"about_ca_system_score_codex":0.0002801356,"about_ca_system_score_gemma":0.00014679757,"threshold_uncertainty_score":0.90629756},"labels":[],"label_agreement":null},{"id":"W4394042508","doi":"10.5281/zenodo.4139415","title":"BIRD: Big Impulse Response Dataset","year":2020,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université de Sherbrooke","funders":"","keywords":"Impulse (physics); Geography; Environmental science; Computer science; Physics","score_opus":0.040248866612860215,"score_gpt":0.2661941935537605,"score_spread":0.2259453269409003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394042508","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000042018073,0.000026982096,0.069547154,0.0017561415,0.00012135391,0.00047106014,0.9244392,0.003258497,0.0003754028],"genre_scores_gemma":[0.00020549532,0.00019506515,0.0013987337,0.000829958,0.00024704784,2.6433264e-7,0.99588317,0.0011331042,0.00010719012],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972783,0.0005592777,0.00038451244,0.0009056838,0.00047757349,0.0003946859],"domain_scores_gemma":[0.99715614,0.000052178304,0.00024639783,0.0019172169,0.0003099013,0.00031814384],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00080366933,0.0002711488,0.0002438139,0.00040827022,0.0021301343,0.0016985219,0.00485647,0.00018228882,0.0021828662],"category_scores_gemma":[0.00054578175,0.00029242382,0.0000869833,0.0012376088,0.0001446332,0.00028341936,0.004821104,0.0006477477,0.053746037],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050112027,0.0000720859,1.5622135e-8,0.000036199453,0.000021314927,0.000026400669,0.000038993596,0.0000019550796,0.00030901018,0.00046250707,0.9662904,0.032690987],"study_design_scores_gemma":[0.00017994457,0.00030618103,0.000014587334,0.00001970761,0.000015176852,0.00014414082,0.000014353533,0.00018069035,0.00014308082,0.00013788635,0.9985361,0.00030811824],"about_ca_topic_score_codex":0.00006599369,"about_ca_topic_score_gemma":9.177516e-7,"teacher_disagreement_score":0.07144394,"about_ca_system_score_codex":0.00015419054,"about_ca_system_score_gemma":0.000015846821,"threshold_uncertainty_score":0.9999528},"labels":[],"label_agreement":null},{"id":"W4394341108","doi":"10.6084/m9.figshare.1011782","title":"Linear loss rates (% loss rates in parentheses) of DOM (<em>a</em><sub>320</sub> and four EEMs components) over the first four days of incubation as a function of time (bold means regression <em>p</em>-values","year":2013,"lang":"en","type":"dataset","venue":"Figshare","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Incubation; Statistics; Function (biology); Mathematics; Econometrics; Philosophy; Biology; Epistemology","score_opus":0.03218299277722717,"score_gpt":0.26537144846658156,"score_spread":0.2331884556893544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394341108","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044220977,0.0003521666,0.0005154717,0.00007727404,0.00008940641,0.0011926383,0.95344746,0.00008907398,0.0000155296],"genre_scores_gemma":[0.17703198,0.00036042804,0.00020506683,0.00009190092,0.00016821353,0.0006504406,0.8214086,0.000040140498,0.000043227235],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99742955,0.00018013213,0.00086962845,0.00064745895,0.0005869383,0.00028632098],"domain_scores_gemma":[0.9964999,0.0004525683,0.0013755031,0.0011514729,0.0004331458,0.00008739994],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00028888532,0.0004298636,0.0006359355,0.0003487561,0.00020032814,0.0001398229,0.0010995659,0.0004096153,0.0019518232],"category_scores_gemma":[0.00032124025,0.00032709612,0.00017927604,0.0006885527,0.000067028006,0.0004910244,0.00070161273,0.00041247337,0.00021681387],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005088025,0.00021313888,0.00014024564,0.00061440095,0.000064888525,0.0000047679073,0.00027795992,0.00013036489,0.000972579,0.000018896471,0.99402994,0.003481958],"study_design_scores_gemma":[0.004037802,0.002446739,0.05824136,0.030817032,0.00045107465,0.00014767662,0.0006237908,0.09279685,0.21672946,0.007521424,0.5828103,0.0033764946],"about_ca_topic_score_codex":0.00014646241,"about_ca_topic_score_gemma":0.00022621352,"teacher_disagreement_score":0.41121963,"about_ca_system_score_codex":0.00006336414,"about_ca_system_score_gemma":0.00008305037,"threshold_uncertainty_score":0.9999181},"labels":[],"label_agreement":null},{"id":"W4394581148","doi":"10.7554/elife.88173.2","title":"A Synergistic Workspace for Human Consciousness Revealed by Integrated Information Decomposition","year":2024,"lang":"en","type":"preprint","venue":"eLife","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"","keywords":"Workspace; Consciousness; Decomposition; Computer science; Human–computer interaction; Artificial intelligence; Cognitive science; Knowledge management; Business; Psychology; Neuroscience; Ecology; Biology","score_opus":0.009669692395418483,"score_gpt":0.29954177436239693,"score_spread":0.28987208196697845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394581148","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":[0.0043617077,0.00016314519,0.99041945,0.0014811577,0.00041689837,0.0009895158,0.0001520342,0.0012336181,0.0007824557],"genre_scores_gemma":[0.8772942,0.00004926059,0.11730433,0.0006556406,0.00015889021,0.002441387,0.0009148483,0.000028140372,0.0011533329],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876237,0.000034600962,0.00043093436,0.00038319392,0.00019890329,0.00019001572],"domain_scores_gemma":[0.99872094,0.000062134364,0.00025435814,0.0005884652,0.00029273002,0.00008134291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023923711,0.00022186997,0.00023044855,0.00019833281,0.00022162723,0.00059168926,0.00065704796,0.0002690278,0.000008172518],"category_scores_gemma":[0.000032094027,0.0002193709,0.00013449628,0.00031584833,0.000040771276,0.00019028639,0.000595163,0.00045018923,0.00007610877],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019104242,0.00012783728,0.000024846082,0.0007840241,0.00013382331,0.0000025221414,0.00076786487,0.0006626889,0.0063300077,0.38499507,0.5581242,0.04802799],"study_design_scores_gemma":[0.0007865155,0.00031474172,0.00015696847,0.001650137,0.00020450288,0.00002211401,0.00014654336,0.37635264,0.04394498,0.25290975,0.32153666,0.00197445],"about_ca_topic_score_codex":0.00010157169,"about_ca_topic_score_gemma":0.000014859514,"teacher_disagreement_score":0.8731151,"about_ca_system_score_codex":0.0001679301,"about_ca_system_score_gemma":0.000102419166,"threshold_uncertainty_score":0.8945685},"labels":[],"label_agreement":null},{"id":"W4394615962","doi":"10.1007/978-3-031-57853-3_30","title":"Anomaly Detection with Generalized Isolation Forest","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes on data engineering and communications technologies","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Anomaly detection; Anomaly (physics); Isolation (microbiology); Geography; Computer science; Artificial intelligence; Physics; Biology; Microbiology; Condensed matter physics","score_opus":0.023994928393761817,"score_gpt":0.2373686037372294,"score_spread":0.21337367534346757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394615962","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":[0.000039546136,0.0043503935,0.9827759,0.0030394283,0.000046093068,0.00035105762,0.0000833682,0.0050435346,0.0042706234],"genre_scores_gemma":[0.6284824,0.007130249,0.36051333,0.00009522511,0.000057977326,0.00029490973,0.0004814063,0.00011606348,0.0028284623],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988467,0.0000056977838,0.00023362755,0.00061029696,0.00014076839,0.00016287816],"domain_scores_gemma":[0.99424815,0.00017400009,0.00011904209,0.005370486,0.00006047013,0.00002784344],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009369573,0.00034011362,0.00023653031,0.00043369303,0.0002353632,0.00026004086,0.002594421,0.00040697263,0.0000016833825],"category_scores_gemma":[0.000046691286,0.00027893786,0.000042478525,0.00023927062,0.00012734227,0.00022358193,0.001635623,0.00082547305,0.00002036564],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000051480993,0.000016908027,0.000004169197,0.00006407137,0.000088858316,0.000002625853,0.000023334824,0.0014595329,0.0006376568,0.756822,0.00011051316,0.24076517],"study_design_scores_gemma":[0.00014990699,0.00034468647,0.000026243102,0.00040325522,0.00012398134,0.00010764345,0.0000060013376,0.48691905,0.0030869937,0.105143845,0.40284124,0.0008471627],"about_ca_topic_score_codex":0.00001841405,"about_ca_topic_score_gemma":0.00012951178,"teacher_disagreement_score":0.65167814,"about_ca_system_score_codex":0.000060606577,"about_ca_system_score_gemma":0.000024327226,"threshold_uncertainty_score":0.99996626},"labels":[],"label_agreement":null},{"id":"W4394750820","doi":"10.1002/cjce.25268","title":"A novel industrial process situation awareness model based on multi‐time scale dynamic feature fusion with applications to float glass manufacturing","year":2024,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Computer science; Autoencoder; Neighbourhood (mathematics); Process (computing); Data mining; Scale (ratio); Time series; Industrial engineering; Artificial intelligence; Machine learning; Artificial neural network; Engineering; Mathematics","score_opus":0.011064778967726873,"score_gpt":0.2239686899590272,"score_spread":0.21290391099130032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394750820","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":[0.057050947,0.000014297929,0.9403342,0.0022378562,0.000038346945,0.0002194062,0.000010037095,0.000077820885,0.00001707322],"genre_scores_gemma":[0.9596445,2.1196492e-7,0.04007493,0.00008798122,0.00008457751,0.000054051845,0.000002848797,0.000015939197,0.00003495547],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932927,0.0000041034423,0.00014900578,0.00016202687,0.00018185235,0.00017377026],"domain_scores_gemma":[0.99938065,0.000033554745,0.000046372243,0.00020627395,0.00007688778,0.00025626633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001351516,0.00011616764,0.000105416046,0.00020651688,0.000115616844,0.00017277084,0.0004669534,0.00008468991,0.0000021062688],"category_scores_gemma":[0.000012011096,0.000084414765,0.00004845501,0.00040331803,0.000016545315,0.00014102437,0.00001595468,0.00038309686,0.000003900714],"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":[0.0000068733243,0.00001309076,0.0000017609708,0.000019847439,0.000009893013,0.0000029928235,0.00017523837,0.88024944,0.1112011,0.00025687434,0.00007017953,0.00799268],"study_design_scores_gemma":[0.000112923444,0.000021113226,0.000008925964,0.00014599433,0.000009861236,0.000034060733,0.0000025237591,0.863046,0.1361843,0.000055860153,0.0002817755,0.00009666622],"about_ca_topic_score_codex":0.00007387992,"about_ca_topic_score_gemma":0.00007134641,"teacher_disagreement_score":0.90259355,"about_ca_system_score_codex":0.0002743299,"about_ca_system_score_gemma":0.00047262947,"threshold_uncertainty_score":0.3442334},"labels":[],"label_agreement":null},{"id":"W4394907649","doi":"10.21203/rs.3.rs-4254664/v1","title":"Field Robot Self-Exploration Based on Deep Reinforcement Learning and Safety Control Mechanism","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Geomechanica (Canada)","funders":"","keywords":"Reinforcement learning; Mechanism (biology); Field (mathematics); Robot; Reinforcement; Computer science; Artificial intelligence; Control (management); Engineering; Human–computer interaction; Physics; Structural engineering; Mathematics","score_opus":0.03066027142451939,"score_gpt":0.34673206129820017,"score_spread":0.3160717898736808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394907649","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":[0.00007329752,0.000109185086,0.98690426,0.006444015,0.00009663196,0.0012426537,0.0000025334186,0.0007184933,0.004408901],"genre_scores_gemma":[0.9843049,0.00032213837,0.013490425,0.0001889614,0.00011517903,0.0009868986,0.000020635607,0.000021349344,0.00054953387],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976718,0.00026624574,0.000280541,0.0007031586,0.00073834596,0.0003398979],"domain_scores_gemma":[0.998479,0.00039368018,0.00007952276,0.00063476997,0.0002784948,0.00013454724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012795666,0.00018968347,0.0001921284,0.00040169753,0.0004450326,0.00052800635,0.00048725246,0.00025857892,0.000054893397],"category_scores_gemma":[0.00010210855,0.00017845428,0.0000971653,0.0003536032,0.000025369221,0.000103852704,0.0010755976,0.0017406463,0.00009678027],"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":[0.00014120148,0.00018783902,0.00004345054,0.0013768595,0.00010375469,0.00004112744,0.0010433018,0.12836313,0.0005945518,0.76293725,0.0014173313,0.1037502],"study_design_scores_gemma":[0.0001812367,0.0007076739,0.000017949436,0.00025707038,0.000007590264,0.0000012956515,0.000047152844,0.946195,0.0024628423,0.044775315,0.005169443,0.0001774051],"about_ca_topic_score_codex":0.000060937036,"about_ca_topic_score_gemma":0.0000071609506,"teacher_disagreement_score":0.9842316,"about_ca_system_score_codex":0.00022208337,"about_ca_system_score_gemma":0.00019023672,"threshold_uncertainty_score":0.7562339},"labels":[],"label_agreement":null},{"id":"W4394964535","doi":"10.1016/j.jfds.2024.100129","title":"Deep unsupervised anomaly detection in high-frequency markets","year":2024,"lang":"en","type":"article","venue":"The Journal of Finance and Data Science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université de Montréal","funders":"Institut de Valorisation des Données; Mitacs","keywords":"Anomaly detection; Anomaly (physics); Computer science; Artificial intelligence; Physics","score_opus":0.01669828190244714,"score_gpt":0.2716569828885816,"score_spread":0.25495870098613443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394964535","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":[0.30499443,0.0015584818,0.69169515,0.0012580362,0.00018674284,0.00007013511,0.0000034191435,0.00002829228,0.00020527917],"genre_scores_gemma":[0.98157674,0.0016868605,0.016597426,0.00007896397,0.000039319573,0.0000018993328,1.8049306e-7,0.0000023090518,0.000016302534],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914867,0.000032828266,0.00023273959,0.00021531816,0.000224007,0.00014646108],"domain_scores_gemma":[0.99912745,0.000078865196,0.00008575895,0.0006077081,0.000065372435,0.00003485263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019998702,0.00006212051,0.000081964645,0.00019826974,0.0001922505,0.0002061931,0.0019242615,0.000021453903,0.0000032317382],"category_scores_gemma":[0.00004578128,0.000040257597,0.000014109823,0.0014281229,0.00020396881,0.0026193876,0.0003348953,0.00017971793,0.000004471405],"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":[0.00001437163,0.00003810359,0.000392663,0.000019131689,0.000004533421,0.000033134555,0.0004542738,0.000060543945,0.040668268,0.043616038,0.00018032055,0.9145186],"study_design_scores_gemma":[0.0004425397,0.00045259387,0.2773538,0.0003109787,0.000029987852,0.0018755997,0.00015833542,0.6223474,0.032265488,0.053116906,0.011217245,0.00042908159],"about_ca_topic_score_codex":0.00005650557,"about_ca_topic_score_gemma":0.000036896134,"teacher_disagreement_score":0.91408956,"about_ca_system_score_codex":0.000034132645,"about_ca_system_score_gemma":0.00014187116,"threshold_uncertainty_score":0.35757887},"labels":[],"label_agreement":null},{"id":"W4394994370","doi":"10.26599/bdma.2024.9020016","title":"Call for Papers: Special Issue on Data-Driven Spatial and Temporal Anomaly Detection","year":2024,"lang":"en","type":"paratext","venue":"Big Data Mining and Analytics","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Anomaly detection; Anomaly (physics); Computer science; Data science; Data mining; Physics","score_opus":0.1020988067490999,"score_gpt":0.32436322554375274,"score_spread":0.22226441879465286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394994370","genre_codex":"methods","genre_gemma":"other","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":[0.00040597227,0.00082745904,0.9661219,0.0012638286,0.004884462,0.00081049034,0.009935776,0.0003092932,0.0154408105],"genre_scores_gemma":[0.21565764,0.0088310875,0.23869331,0.0018294516,0.12131097,0.000337205,0.03710448,0.00039750617,0.37583834],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99791366,0.000027163103,0.00034427716,0.0012853298,0.00019142487,0.00023814454],"domain_scores_gemma":[0.997755,0.00011228423,0.00018708242,0.0017796448,0.000049247246,0.000116765965],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025905092,0.0002900077,0.00033861032,0.00022128744,0.0002640351,0.00062053185,0.0012981268,0.00025892214,0.000025167916],"category_scores_gemma":[0.000044625915,0.000269866,0.000042016392,0.00025215995,0.000085663305,0.00023183905,0.0014981442,0.00024336386,0.000096132084],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008878319,0.00002263342,0.0000077371305,0.00008816235,0.000054184162,0.000003918743,0.000046675294,0.0000031800814,0.00002667026,0.000061572595,0.41568255,0.58399385],"study_design_scores_gemma":[0.000118480944,0.00020147447,0.000021444685,0.00009662609,0.000097940705,0.000015788175,0.000023506742,0.18015563,0.00007814849,0.000030018877,0.8188911,0.00026979978],"about_ca_topic_score_codex":0.00021351042,"about_ca_topic_score_gemma":0.0005759403,"teacher_disagreement_score":0.7274286,"about_ca_system_score_codex":0.000030533218,"about_ca_system_score_gemma":0.000111810594,"threshold_uncertainty_score":0.9999753},"labels":[],"label_agreement":null},{"id":"W4395074611","doi":"10.1007/s11042-024-19204-w","title":"A comprehensive analysis of real-time video anomaly detection methods for human and vehicular movement","year":2024,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Anomaly detection; Movement (music); Anomaly (physics); Artificial intelligence; Computer vision; Real-time computing","score_opus":0.02661447333831778,"score_gpt":0.34469887295395385,"score_spread":0.31808439961563606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395074611","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018442675,0.00040321008,0.97958434,0.00019267308,0.000013707167,0.00090459525,0.000067758665,0.00024121431,0.00014982451],"genre_scores_gemma":[0.38192362,0.00033633423,0.61374927,0.000089473055,0.00006602992,0.003542351,0.00007188125,0.00001781265,0.00020322733],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990059,0.000034561195,0.00029750424,0.00045284317,0.00007840918,0.0001307392],"domain_scores_gemma":[0.9989797,0.00036156672,0.00008889977,0.00037293418,0.00011733366,0.00007960736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020929897,0.00012404926,0.00024134605,0.00027597812,0.00022947136,0.0001547436,0.00017225524,0.000071891656,0.000007915827],"category_scores_gemma":[0.000007751841,0.00011781847,0.000120834826,0.0009338564,0.00008329373,0.00017141284,0.000099391866,0.00006557751,0.0000025402303],"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":[0.0000015727384,0.00003805103,0.00003395727,0.000049156613,0.00021919713,1.29983e-7,0.00013726809,0.000041208794,0.40977025,0.028728398,0.000032983506,0.56094784],"study_design_scores_gemma":[0.00018295697,0.000113512964,0.010231127,0.000015044727,0.0004871619,0.0000025730885,0.000041462095,0.84029084,0.08966737,0.00931968,0.04940838,0.00023990148],"about_ca_topic_score_codex":0.000113597845,"about_ca_topic_score_gemma":0.000009437427,"teacher_disagreement_score":0.8402496,"about_ca_system_score_codex":0.000023686238,"about_ca_system_score_gemma":0.000014840009,"threshold_uncertainty_score":0.48044977},"labels":[],"label_agreement":null},{"id":"W4395463738","doi":"10.18280/isi.290229","title":"An Efficient Abnormal Event Detection System in Video Surveillance Using Deep Learning-Based Reconfigurable Autoencoder","year":2024,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Vision Group on Science and Technology; Visvesvaraya Technological University","keywords":"Autoencoder; Deep learning; Artificial intelligence; Computer science; Event (particle physics); Computer vision; Real-time computing","score_opus":0.00945721289563278,"score_gpt":0.23973076620673622,"score_spread":0.23027355331110344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395463738","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":[0.097628035,0.0001006283,0.8993728,0.000016790558,0.00033579807,0.0003512016,0.000003079229,0.0012917294,0.00089992594],"genre_scores_gemma":[0.9891647,0.0000046989985,0.010572575,0.000029938969,0.00003913393,0.00015117515,0.000015259282,0.000012411189,0.000010070198],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840766,0.00013047873,0.0006337925,0.00026501008,0.0002538124,0.00030925608],"domain_scores_gemma":[0.999152,0.000057214245,0.00019750804,0.00034835874,0.00016414693,0.00008079363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093236484,0.00017665452,0.0001717245,0.0005336219,0.00035452037,0.0007014599,0.00031879035,0.00012707619,0.0000097464435],"category_scores_gemma":[0.000034079345,0.00017932683,0.00007483416,0.0011490979,0.000043244396,0.0022522716,0.000030233496,0.00024074884,0.00006207867],"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":[0.0000099442095,0.000023637504,0.0002828539,0.00033928812,0.000007417891,0.0000037964435,0.0013126079,0.8381114,0.0010633089,0.0034380874,0.000004774958,0.15540285],"study_design_scores_gemma":[0.000117370306,0.000100331716,0.0022543045,0.00018988845,0.0000033822018,0.00006516972,0.00026845396,0.987995,0.007485734,0.00015390689,0.0011576881,0.00020871412],"about_ca_topic_score_codex":0.0003103157,"about_ca_topic_score_gemma":0.000057550093,"teacher_disagreement_score":0.8915367,"about_ca_system_score_codex":0.0009083251,"about_ca_system_score_gemma":0.00011796956,"threshold_uncertainty_score":0.7312736},"labels":[],"label_agreement":null},{"id":"W4395468639","doi":"10.1027/2151-2604/a000558","title":"Unsupervised Anomaly Detection in Sequential Process Data","year":2024,"lang":"en","type":"article","venue":"Zeitschrift für Psychologie","topic":"Anomaly Detection Techniques and Applications","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":"Centre for Advancing Health Outcomes; University of Alberta","funders":"","keywords":"Anomaly detection; Computer science; Process (computing); Data mining; Artificial intelligence; Anomaly (physics); Pattern recognition (psychology); Programming language","score_opus":0.07427815089437531,"score_gpt":0.3961518283792416,"score_spread":0.3218736774848663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395468639","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":[0.029924087,0.0005109243,0.9513965,0.001839518,0.00074334216,0.00040215405,0.000018746927,0.0020615675,0.013103109],"genre_scores_gemma":[0.99022675,0.000089870206,0.008945979,0.00026591728,0.00013734768,0.00013213279,0.000017686049,0.000017999582,0.00016631327],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981459,0.000056703746,0.00030914208,0.0009801156,0.00019227326,0.00031588957],"domain_scores_gemma":[0.998335,0.00004880459,0.00004105988,0.0014644901,0.000042564458,0.00006807138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045207594,0.0001667548,0.00013920733,0.00028506696,0.000101730184,0.0003059341,0.0018648616,0.00015297747,0.000048927937],"category_scores_gemma":[0.000032262695,0.00015385935,0.00005042308,0.0013963057,0.000051102208,0.0010117418,0.00033140022,0.00034170816,0.00023569858],"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":[0.000022891263,0.0002731246,0.00074109645,0.00010191875,0.000037626287,0.00012030014,0.00039403094,0.000065610686,0.023513917,0.05163099,0.0038933344,0.9192052],"study_design_scores_gemma":[0.0010406728,0.000572514,0.01883734,0.00020447251,0.000036672336,0.00035128347,0.00016147025,0.3504686,0.07902705,0.014531256,0.53333724,0.0014314515],"about_ca_topic_score_codex":0.00005797092,"about_ca_topic_score_gemma":0.000048762115,"teacher_disagreement_score":0.96030265,"about_ca_system_score_codex":0.000013092365,"about_ca_system_score_gemma":0.000080439204,"threshold_uncertainty_score":0.62742025},"labels":[],"label_agreement":null},{"id":"W4395673742","doi":"10.3390/app14093669","title":"Pruning Deep Neural Network Models via Minimax Concave Penalty Regression","year":2024,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Hubei Provincial Department of Education; National Social Science Fund of China; National Office for Philosophy and Social Sciences; National Natural Science Foundation of China; Canadian Institute for Advanced Research","keywords":"Minimax; Artificial neural network; Computer science; Pruning; Regression; Deep neural networks; Artificial intelligence; Mathematics; Mathematical optimization; Statistics; Biology","score_opus":0.0291427606070673,"score_gpt":0.2788633953610094,"score_spread":0.2497206347539421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395673742","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":[0.0057986416,0.0004927145,0.9658212,0.0008276161,0.00023958221,0.00022899496,5.054729e-7,0.00079525553,0.025795484],"genre_scores_gemma":[0.90934134,0.000017360131,0.08990769,0.00033658647,0.000136217,0.000099479235,8.940792e-7,0.000006646223,0.0001537912],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984709,0.000022716795,0.00021446917,0.00061084935,0.00033095735,0.000350097],"domain_scores_gemma":[0.9993851,0.000111552654,0.000066763016,0.00031687182,0.000027560814,0.00009216113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005026682,0.00013950714,0.00012427915,0.00009625989,0.00067904906,0.0004965915,0.000958467,0.00005958402,0.000019824585],"category_scores_gemma":[0.0000027728258,0.00010419244,0.000058354388,0.0012901265,0.00022618739,0.00055930635,0.0002801934,0.00016810549,0.000051952346],"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":[0.0000018806198,0.000013681972,0.00001745162,0.000010440056,0.0000052917553,0.0000052516743,0.00045874377,0.017794184,0.0036644635,0.7870127,0.002115408,0.18890052],"study_design_scores_gemma":[0.000028444738,0.000038762035,0.000037886246,0.00001710972,0.0000034272277,0.000018517288,0.000047175145,0.88915336,0.0026848011,0.10488974,0.002936482,0.00014427239],"about_ca_topic_score_codex":0.000013222716,"about_ca_topic_score_gemma":0.0000050904796,"teacher_disagreement_score":0.9035427,"about_ca_system_score_codex":0.000028912522,"about_ca_system_score_gemma":0.00005635428,"threshold_uncertainty_score":0.52227646},"labels":[],"label_agreement":null},{"id":"W4395683488","doi":"10.3390/jrfm17050181","title":"Machine Learning to Enhance the Detection of Terrorist Financing and Suspicious Transactions in Migrant Remittances","year":2024,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universiteit van die Vrystaat","keywords":"Remittance; False positive paradox; Terrorism; Anomaly detection; Outlier; Finance; Poverty; Financial transaction; Computer science; Business; Computer security; Database; Artificial intelligence; Economics; Database transaction; Economic growth; Political science; Law","score_opus":0.003952923672452689,"score_gpt":0.22764379527253573,"score_spread":0.22369087160008302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395683488","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":[0.10832901,0.0025088603,0.8885923,0.00032142768,0.00010076281,0.000097245305,9.476797e-7,0.000014886125,0.000034544926],"genre_scores_gemma":[0.98946244,0.0035049373,0.006903558,0.000022275766,0.00002501696,0.000010199047,3.0854586e-8,0.0000027802596,0.00006877185],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994446,0.000025808356,0.00024623374,0.000114235874,0.0000936492,0.00007548399],"domain_scores_gemma":[0.99973935,0.000042318814,0.000096902266,0.0000763414,0.000022527502,0.000022580609],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039507847,0.000057544938,0.000101623016,0.0001775766,0.00012523925,0.00004784619,0.00011867834,0.000020881078,7.2228266e-7],"category_scores_gemma":[0.000012846423,0.000041405234,0.00003801735,0.0004132776,0.000021940426,0.0001258503,0.000025983696,0.0002001682,3.2146846e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000966818,0.000012154354,0.00013034896,0.000025846111,0.0000035078212,0.000007817355,0.0013130265,0.00041295373,0.00034709068,0.0010895679,0.000005544603,0.9966425],"study_design_scores_gemma":[0.0007406513,0.002037688,0.1210123,0.0013300403,0.00020054232,0.00030345254,0.0018175787,0.15344794,0.025708364,0.02587331,0.666882,0.0006461346],"about_ca_topic_score_codex":0.00013257306,"about_ca_topic_score_gemma":0.00025664462,"teacher_disagreement_score":0.99599636,"about_ca_system_score_codex":0.000020997599,"about_ca_system_score_gemma":0.000009523034,"threshold_uncertainty_score":0.16884564},"labels":[],"label_agreement":null},{"id":"W4396216055","doi":"10.5753/jisa.2024.3809","title":"MEDAVET: Traffic Vehicle Anomaly Detection Mechanism based on spatial and temporal structures in vehicle traffic","year":2024,"lang":"en","type":"article","venue":"Journal of Internet Services and Applications","topic":"Anomaly Detection Techniques and Applications","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":"Brock University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Computer science; Anomaly detection; Vehicle Information and Communication System; Road traffic; Traffic optimization; Mechanism (biology); Floating car data; Real-time computing; Artificial intelligence; Transport engineering; Engineering; Traffic congestion","score_opus":0.00709289264655146,"score_gpt":0.23735042447859447,"score_spread":0.230257531832043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396216055","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":[0.67659116,0.00026685352,0.3219924,0.00074305135,0.00005951782,0.00020187542,0.0000041004064,0.00008213352,0.000058895268],"genre_scores_gemma":[0.9965832,0.000046691734,0.002978443,0.00018974036,0.00011370645,0.000053454165,0.000001482234,0.00001073564,0.000022518318],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990897,0.000029164905,0.0003500836,0.00025640865,0.00015399486,0.00012063989],"domain_scores_gemma":[0.99948305,0.000060469367,0.00014372409,0.00016938201,0.00005598069,0.000087387176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018622239,0.00012429676,0.00014824692,0.00027365342,0.00007558051,0.00025520747,0.00028807632,0.00007809989,0.000010255123],"category_scores_gemma":[0.0000014759245,0.00010437939,0.00005788165,0.0003332953,0.000028327986,0.00025607325,0.000046652247,0.00023437587,0.0000034548566],"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":[0.00008417995,0.00032272175,0.000505634,0.00035819833,0.00006382581,0.000035036668,0.001504925,0.004267135,0.016816,0.06507879,0.00007555257,0.910888],"study_design_scores_gemma":[0.000293212,0.00037440803,0.0039683986,0.00010121562,0.000015340494,0.00008983421,0.00010967374,0.9764967,0.009194623,0.003797375,0.00541103,0.00014817379],"about_ca_topic_score_codex":0.00010562167,"about_ca_topic_score_gemma":0.0002869168,"teacher_disagreement_score":0.9722296,"about_ca_system_score_codex":0.000039228442,"about_ca_system_score_gemma":0.000029734943,"threshold_uncertainty_score":0.42564678},"labels":[],"label_agreement":null},{"id":"W4396561785","doi":"10.1038/s41598-024-60709-z","title":"Novel applications of Convolutional Neural Networks in the age of Transformers","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"University of New South Wales; Australian Government","keywords":"Convolutional neural network; Computer science; Transformer; Artificial intelligence; Machine learning; Electrical engineering; Engineering","score_opus":0.01494201404331969,"score_gpt":0.25802904693245665,"score_spread":0.24308703288913697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396561785","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":[0.004576518,0.00018328405,0.9928972,0.0003216453,0.00043660466,0.0003244643,0.0000023446928,0.000058751233,0.0011991741],"genre_scores_gemma":[0.9949824,0.0000034336822,0.004634984,0.000020123198,0.000017161688,0.00013336008,0.000011367957,0.0000025951688,0.00019455879],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989832,0.000012646372,0.00036197307,0.00029590132,0.00024070057,0.00010560091],"domain_scores_gemma":[0.9993445,0.00004579098,0.000083554536,0.0004502443,0.000058638478,0.000017269365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080079545,0.000051541112,0.000075895114,0.00014419234,0.00008742352,0.00009243735,0.00030882488,0.00003057794,0.00000747641],"category_scores_gemma":[0.000004842143,0.000038003087,0.000077542165,0.0014281279,0.00022065135,0.00014911295,0.000031179996,0.00008853432,8.1225255e-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":[0.000004117567,0.00062819367,0.0013308029,0.00018855107,0.000038862006,0.00008562294,0.002890814,0.009936288,0.12229743,0.66073656,0.009538072,0.19232465],"study_design_scores_gemma":[0.000116947784,0.00006817602,0.0062107937,0.0000742308,0.000024168718,0.00069822674,0.00020970326,0.7214382,0.016433634,0.06889849,0.18553476,0.0002926542],"about_ca_topic_score_codex":0.000034278703,"about_ca_topic_score_gemma":0.000015436317,"teacher_disagreement_score":0.9904059,"about_ca_system_score_codex":0.000013654077,"about_ca_system_score_gemma":0.00006252074,"threshold_uncertainty_score":0.15497208},"labels":[],"label_agreement":null},{"id":"W4396627216","doi":"10.7554/elife.88173.2.sa7","title":"Author response: A Synergistic Workspace for Human Consciousness Revealed by Integrated Information Decomposition","year":2024,"lang":"en","type":"peer-review","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"","keywords":"Workspace; Consciousness; Decomposition; Computer science; Psychology; Human–computer interaction; Cognitive science; Artificial intelligence; Neuroscience; Biology; Ecology","score_opus":0.021976500775953912,"score_gpt":0.35626108467901446,"score_spread":0.33428458390306054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396627216","genre_codex":"methods","genre_gemma":"other","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":[0.0000062621552,0.0019182893,0.87280476,0.11959371,0.0007594113,0.0018811234,0.00054002885,0.001404028,0.0010923793],"genre_scores_gemma":[0.0015351379,0.00023953154,0.10721102,0.0057712933,0.00015915229,0.005114726,0.0045256033,0.000059573973,0.875384],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978984,0.00014174548,0.00078768446,0.0005469276,0.00032657705,0.0002986576],"domain_scores_gemma":[0.99768,0.00029229114,0.00040990565,0.000859295,0.00063249207,0.00012601094],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009753404,0.0003782454,0.0004986098,0.00036290206,0.00034094992,0.0005752002,0.0009622208,0.0003931363,0.00010087522],"category_scores_gemma":[0.00016482004,0.00033314436,0.00024570915,0.0009955233,0.00006191225,0.00043765391,0.00020537886,0.0004788694,0.00017444979],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013805561,0.000019603698,6.248065e-8,0.0008514619,0.00002482307,6.754409e-7,0.00002395661,0.0000015059521,0.0001903624,0.021259047,0.9667841,0.010830558],"study_design_scores_gemma":[0.00011083598,0.00013270226,0.0000018236447,0.0020842834,0.00009648398,0.0000154975,0.000009599857,0.004788685,0.0004032335,0.0041104606,0.9878439,0.00040254334],"about_ca_topic_score_codex":0.00012662496,"about_ca_topic_score_gemma":0.000045440425,"teacher_disagreement_score":0.8742916,"about_ca_system_score_codex":0.0002942424,"about_ca_system_score_gemma":0.00021491901,"threshold_uncertainty_score":0.9999121},"labels":[],"label_agreement":null},{"id":"W4396841042","doi":"10.22214/ijraset.2024.61792","title":"Unusual Human Activity Recognition in ATM’S","year":2024,"lang":"en","type":"article","venue":"International Journal for Research in Applied Science and Engineering Technology","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Impact","funders":"","keywords":"Communication; Computer science; Psychology","score_opus":0.08905979818883224,"score_gpt":0.4289201098243664,"score_spread":0.33986031163553415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396841042","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":[0.39615163,0.00009771658,0.5925801,0.008878499,0.00047494928,0.000381742,0.0000033767128,0.00035224383,0.001079781],"genre_scores_gemma":[0.9866501,0.00006767497,0.013017576,0.000011489404,0.00005774423,0.00017323368,3.8503563e-7,0.000005158233,0.00001659954],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988325,0.0000063595962,0.00015456343,0.00029208817,0.00041184545,0.0003026292],"domain_scores_gemma":[0.9995513,0.00008057374,0.000017658174,0.00011571545,0.00018306119,0.00005171286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022075968,0.00006080185,0.000068314024,0.0028246213,0.000152043,0.00038204197,0.00090343354,0.000071903996,0.0000016184862],"category_scores_gemma":[0.00009099437,0.00005939152,0.000015857266,0.0019706837,0.00017919864,0.00035052648,0.0002580639,0.00058452506,0.0000047302906],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000044110684,0.00003035053,0.000034082324,0.000009408291,0.0000039115116,0.000024336241,0.00005846421,0.00007530048,0.1658357,0.39069614,0.00008003211,0.44314787],"study_design_scores_gemma":[0.0004472337,0.0001999604,0.001344581,0.00021611576,0.0000011130704,0.00042200074,0.00010795545,0.36813095,0.09844844,0.50637126,0.024046563,0.00026379124],"about_ca_topic_score_codex":0.00001544507,"about_ca_topic_score_gemma":0.000009089051,"teacher_disagreement_score":0.5904985,"about_ca_system_score_codex":0.00035698747,"about_ca_system_score_gemma":0.00012772299,"threshold_uncertainty_score":0.36840394},"labels":[],"label_agreement":null},{"id":"W4396891816","doi":"10.1364/ofc.2024.th3i.4","title":"Detecting Anomalies in the Optical Layer Using Unsupervised Machine Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Unsupervised learning; Artificial intelligence; Layer (electronics); Pattern recognition (psychology); Materials science; Nanotechnology","score_opus":0.038358231426907204,"score_gpt":0.292608583238373,"score_spread":0.2542503518114658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396891816","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":[0.09647516,0.00016592574,0.8969279,0.0009793846,0.000037629765,0.00010035772,1.7787876e-7,0.00048158882,0.0048318747],"genre_scores_gemma":[0.9180446,0.0000078898065,0.08153633,0.00017520387,0.000034351662,0.000020621372,2.7916371e-7,0.000005786144,0.00017490669],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999343,0.00004685061,0.00013646884,0.00021609227,0.00011149143,0.00014611376],"domain_scores_gemma":[0.9996228,0.00013076245,0.000011854619,0.00020086364,0.000013567811,0.000020100459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003628488,0.00007164124,0.000060012197,0.00009735987,0.00015965145,0.0003204775,0.00035811772,0.000035373854,0.000025289524],"category_scores_gemma":[0.000016454766,0.00004738704,0.000042241052,0.0006443176,0.000020008529,0.00021741402,0.00010788671,0.00024566532,0.000022098773],"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":[0.000002636599,0.00006105905,0.002167076,0.000038910246,0.000015661333,0.00005609809,0.0023549185,0.0016658109,0.03834779,0.6666854,0.00007571939,0.2885289],"study_design_scores_gemma":[0.000031619136,0.000027759459,0.00036568244,0.000013890372,0.000002689585,0.00006692005,0.00013635345,0.9819655,0.008897855,0.0018263853,0.0065789586,0.00008642003],"about_ca_topic_score_codex":0.000112459435,"about_ca_topic_score_gemma":0.000026686648,"teacher_disagreement_score":0.98029965,"about_ca_system_score_codex":0.000024596024,"about_ca_system_score_gemma":0.000019383504,"threshold_uncertainty_score":0.3090372},"labels":[],"label_agreement":null},{"id":"W4396943949","doi":"10.48550/arxiv.2406.06968","title":"Beyond the Norms: Detecting Prediction Errors in Regression Models","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Grand Équipement National De Calcul Intensif","keywords":"Computer science; Regression; Regression analysis; Artificial intelligence; Statistics; Machine learning; Mathematics","score_opus":0.054291131548286534,"score_gpt":0.20139570760518755,"score_spread":0.14710457605690103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396943949","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":[0.16666536,0.00010544776,0.825619,0.00035527884,0.00039585517,0.0004117447,0.000010447112,0.00068378117,0.005753105],"genre_scores_gemma":[0.9969939,0.0001517241,0.0015926226,0.00006041427,0.00006218354,0.000011395565,0.000004004341,0.000015270423,0.0011084874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855435,0.00007652893,0.00021742375,0.00084713264,0.00008350108,0.00022108995],"domain_scores_gemma":[0.99865663,0.000057835063,0.00015963647,0.0009997578,0.00006445726,0.00006167067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031842187,0.00021101485,0.00016297479,0.00030258266,0.0002279065,0.00012692013,0.0011985307,0.00025741325,0.0000049642276],"category_scores_gemma":[0.000009147065,0.0001739424,0.00015001361,0.00097055343,0.000066022185,0.00027203877,0.002137371,0.0009941984,0.000030139974],"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":[0.000014872413,0.00007429203,0.0003747661,0.00009848923,0.000043368702,0.00008897549,0.0010436361,0.51780105,0.00016780934,0.4676899,0.00082426495,0.0117786],"study_design_scores_gemma":[0.00004859563,0.000014749686,0.000088119654,0.000078737634,0.000014351913,0.0000042098222,0.000086890876,0.6301713,0.0003467425,0.3688736,0.0001558028,0.00011691996],"about_ca_topic_score_codex":0.00021280206,"about_ca_topic_score_gemma":0.00008690012,"teacher_disagreement_score":0.8303285,"about_ca_system_score_codex":0.00024624684,"about_ca_system_score_gemma":0.00012243871,"threshold_uncertainty_score":0.70931655},"labels":[],"label_agreement":null},{"id":"W4396951633","doi":"10.1016/j.knosys.2024.111942","title":"Normality learning reinforcement for anomaly detection in surveillance videos","year":2024,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"China Scholarship Council; Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Normality; Anomaly detection; Anomaly (physics); Artificial intelligence; Computer science; Reinforcement learning; Psychology; Social psychology; Physics","score_opus":0.01658107596017541,"score_gpt":0.2767708898191404,"score_spread":0.260189813858965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396951633","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":[0.007383234,0.0007642085,0.98734117,0.000115081326,0.0006715912,0.00082937564,0.0000034816198,0.0009033442,0.0019885432],"genre_scores_gemma":[0.9960727,0.0000065211566,0.0012806666,0.000015329475,0.00015391041,0.0011525949,0.000006784963,0.000016431055,0.0012950739],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985838,0.00012407295,0.00043189,0.00045679547,0.00013274887,0.0002706991],"domain_scores_gemma":[0.999104,0.0002302953,0.00008503693,0.0003813606,0.00013465791,0.000064616295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010826413,0.00015156847,0.00018967959,0.00027225574,0.0001813687,0.00029005506,0.00032921342,0.00009750275,0.000003960936],"category_scores_gemma":[0.000039542665,0.00014839636,0.00011161146,0.0008304357,0.00002226481,0.00024338905,0.000051678482,0.00018117535,0.00007140292],"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":[0.00016995757,0.00064099807,0.014723841,0.0070593553,0.00018748663,0.000038558057,0.0029708552,0.119549714,0.086595334,0.23759608,0.005379706,0.52508813],"study_design_scores_gemma":[0.00020348848,0.00014777301,0.00066377624,0.00011292527,0.000002792154,0.000005895043,0.000023007078,0.88253754,0.01720924,0.00012110563,0.09878171,0.00019072462],"about_ca_topic_score_codex":0.00021111543,"about_ca_topic_score_gemma":0.00016860149,"teacher_disagreement_score":0.9886895,"about_ca_system_score_codex":0.0002620325,"about_ca_system_score_gemma":0.0001224634,"threshold_uncertainty_score":0.6051428},"labels":[],"label_agreement":null},{"id":"W4397026412","doi":"10.1109/jsen.2024.3397966","title":"Sensor and Decision Fusion-Based Intrusion Detection and Mitigation Approach for Connected Autonomous Vehicles","year":2024,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":12,"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":"Intrusion detection system; Sensor fusion; Computer science; Data mining; Redundancy (engineering); Real-time computing; Artificial intelligence; Engineering; Reliability engineering","score_opus":0.011871149483324354,"score_gpt":0.2501387676834033,"score_spread":0.23826761820007897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4397026412","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":[0.39502224,0.00012241397,0.6040373,0.0002530678,0.00016128234,0.0001886615,0.0000030138442,0.00018806521,0.000023961333],"genre_scores_gemma":[0.7877118,0.00007098056,0.21192089,0.00006986943,0.00015679232,0.000023272201,0.000001330259,0.00001232346,0.000032723958],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903643,0.00004401023,0.00026321167,0.00033714564,0.00015608467,0.00016312732],"domain_scores_gemma":[0.99924606,0.00025954843,0.00008945604,0.00015111695,0.0001312356,0.00012259978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036240843,0.00012901919,0.00012353486,0.00025872508,0.0005090513,0.0005056002,0.000113179085,0.00011022908,0.000003290011],"category_scores_gemma":[0.000037388996,0.000110744724,0.00006319015,0.00028327957,0.000053033105,0.0002777308,0.000028766117,0.00021717141,0.0000022083054],"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":[0.000042360723,0.000035440607,0.0000311116,0.000048022936,0.00001672662,0.000008228672,0.00021492278,0.0012890855,0.18458837,0.001279957,0.00037748797,0.8120683],"study_design_scores_gemma":[0.00029657202,0.00016047744,0.00047055096,0.000047521262,0.000015825293,0.0005630578,0.000040103565,0.92076457,0.06908758,0.0061046984,0.0023021773,0.0001468842],"about_ca_topic_score_codex":0.0000061802325,"about_ca_topic_score_gemma":0.0000017106739,"teacher_disagreement_score":0.9194755,"about_ca_system_score_codex":0.00006009418,"about_ca_system_score_gemma":0.00004630653,"threshold_uncertainty_score":0.4875514},"labels":[],"label_agreement":null},{"id":"W4398140629","doi":"10.1007/s11036-024-02319-7","title":"Towards Real-world Violence Recognition via Efficient Deep Features and Sequential Patterns Analysis","year":2024,"lang":"en","type":"article","venue":"Mobile Networks and Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology)","score_opus":0.00787240797893607,"score_gpt":0.258898843485157,"score_spread":0.25102643550622095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398140629","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":[0.008171383,0.0011370986,0.9888917,0.00014684371,0.000043969838,0.00056301977,0.000020658968,0.00046641412,0.00055887515],"genre_scores_gemma":[0.9904062,0.0022968247,0.004620592,0.00009416701,0.00018978666,0.0022335052,0.000056246245,0.000011197278,0.00009148709],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988823,0.00002685706,0.00021459919,0.0005689655,0.00011552684,0.00019176635],"domain_scores_gemma":[0.9993481,0.00005577216,0.000056043322,0.00037454494,0.000054237305,0.000111315516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015138253,0.00014330221,0.00014928047,0.00022500487,0.00030502028,0.0003508872,0.0002123282,0.000074240095,0.000022699476],"category_scores_gemma":[7.536556e-7,0.00013224901,0.00008687581,0.0014457197,0.0000651815,0.000108921,0.00015169272,0.000169242,0.0000074529894],"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":[0.0000013676134,0.000046450437,0.00028055033,0.000022171886,0.00007586044,0.0000014818877,0.000040745905,0.01149737,0.000096360134,0.023877967,0.000105533945,0.96395415],"study_design_scores_gemma":[0.000058283684,0.000040242325,0.008463467,0.000040708204,0.00019506854,0.000021693879,0.000023132394,0.9780899,0.0005215504,0.004921227,0.0073616253,0.0002630727],"about_ca_topic_score_codex":0.0002747719,"about_ca_topic_score_gemma":0.00017773312,"teacher_disagreement_score":0.98427117,"about_ca_system_score_codex":0.000033291297,"about_ca_system_score_gemma":0.0000145688555,"threshold_uncertainty_score":0.5392958},"labels":[],"label_agreement":null},{"id":"W4398184165","doi":"10.1201/9781003488682-34","title":"Crowd Counting for Risk Management Using Deep Learning","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.01879112699367539,"score_gpt":0.25853955580709187,"score_spread":0.2397484288134165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398184165","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[8.816714e-7,0.00015798217,0.574769,0.000027996817,0.00008046487,0.0002557964,0.0000018558675,0.00051734113,0.42418864],"genre_scores_gemma":[0.0005732209,0.00020303942,0.35898373,0.000059583526,0.000116143994,0.000059763875,0.0000036257927,0.000036672132,0.6399642],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99897045,0.0000030430956,0.00023285349,0.0004844236,0.0001436136,0.00016564228],"domain_scores_gemma":[0.99935454,0.00003674693,0.0001610338,0.00034759694,0.000064065614,0.00003602749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018998726,0.00018328687,0.00014680937,0.00016715877,0.00030136286,0.00027382493,0.00039320262,0.0001275764,0.000062415216],"category_scores_gemma":[0.000002208698,0.00017931448,0.00016368914,0.00005702089,0.000020730393,0.00009839357,0.00032065684,0.00027292778,0.000117996395],"study_design_candidate":"theoretical_or_conceptual","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":[3.5991332e-7,0.0000018226332,7.226021e-7,0.000044816086,0.000035684443,0.0000018581491,0.000013345691,0.00013981061,0.00000451906,0.9155886,0.0002949385,0.08387352],"study_design_scores_gemma":[0.000027453087,0.00001989429,6.151926e-7,0.000052451396,0.00005068059,0.000005301092,0.000006007911,0.18899271,0.000054038985,0.1717042,0.6389055,0.00018113528],"about_ca_topic_score_codex":0.000013827604,"about_ca_topic_score_gemma":0.0000042613983,"teacher_disagreement_score":0.7438844,"about_ca_system_score_codex":0.000082897415,"about_ca_system_score_gemma":0.000010819075,"threshold_uncertainty_score":0.7312232},"labels":[],"label_agreement":null},{"id":"W4398561900","doi":"10.7910/dvn/lxvzrc","title":"Replication Data for: Application of Bayesian Additive Regression Tree to quantify the uncertainty of machine-learning derived variables: a case study in human activity patterns learned from accelerometer data","year":2023,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Replication (statistics); Computer science; Bayesian probability; Regression; Machine learning; Artificial intelligence; Regression analysis; Data mining; Tree (set theory); Statistics; Mathematics","score_opus":0.10964439463450093,"score_gpt":0.36385142039707696,"score_spread":0.25420702576257603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398561900","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025525102,4.5061822e-7,0.3660514,0.00002923225,0.00003605758,0.0017496692,0.62950134,0.000077985176,0.0000013249389],"genre_scores_gemma":[0.087857254,0.0000641466,0.0050908052,0.00003140557,0.000058117945,0.00074939785,0.90610677,0.000029440276,0.000012690661],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9961471,0.00045902506,0.0007648776,0.0019946327,0.00037321364,0.00026115894],"domain_scores_gemma":[0.9831844,0.0007755417,0.0011310513,0.014664045,0.00015859756,0.00008635667],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.001756754,0.00033769105,0.00056031643,0.0003578383,0.00030893728,0.000120331155,0.0065224823,0.00021661834,0.000062432126],"category_scores_gemma":[0.00063349976,0.00028037402,0.00006596927,0.00092519127,0.000066620596,0.0007118739,0.0059431177,0.00052557274,0.00013120787],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019637104,0.0013125284,0.00053098606,0.0002077358,0.00029104482,0.00005589444,0.00060185837,0.00020335024,0.0046730596,0.00008966107,0.9035051,0.08833244],"study_design_scores_gemma":[0.0007497394,0.00032279562,0.0015827448,0.00019661292,0.00020972278,0.00001758712,0.00095422263,0.14564079,0.0007510224,0.00014026399,0.8489947,0.00043984439],"about_ca_topic_score_codex":0.07213653,"about_ca_topic_score_gemma":0.028387526,"teacher_disagreement_score":0.3609606,"about_ca_system_score_codex":0.00009206027,"about_ca_system_score_gemma":0.00011007042,"threshold_uncertainty_score":0.99996483},"labels":[],"label_agreement":null},{"id":"W4399046466","doi":"10.1007/978-3-031-59933-0_4","title":"A Deep Learning Based Automatic Outdoor Home Video Surveillance Approach","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Deep learning; Computer graphics (images); Multimedia; Real-time computing","score_opus":0.01161768496567485,"score_gpt":0.23478361515416657,"score_spread":0.2231659301884917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399046466","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000018688297,0.00053563254,0.99053943,0.0006252786,0.0005713377,0.00049106166,0.0000026048226,0.0013250124,0.005890964],"genre_scores_gemma":[0.28660515,0.000029827694,0.7107005,0.00072542316,0.00030523268,0.00009637965,0.000008970425,0.00006428977,0.0014641985],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962562,0.000038443457,0.00054629875,0.0017659782,0.00080283737,0.00059029664],"domain_scores_gemma":[0.9976783,0.00035735167,0.00026211506,0.0013588024,0.00017566617,0.00016776155],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00091746886,0.0005255913,0.00052174815,0.0009212636,0.0003526506,0.00088380324,0.0027703985,0.00032602626,0.0000314839],"category_scores_gemma":[0.00004745857,0.0004801018,0.00020506844,0.0011941706,0.00045512946,0.00033119245,0.00093719084,0.0011901066,0.00016404697],"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":[0.0000020885996,0.000044473527,0.000053814143,0.00019727618,0.000016794784,0.00004226827,0.00037481665,0.11197999,0.000071174945,0.04026518,0.000035551908,0.84691656],"study_design_scores_gemma":[0.00008866778,0.00010053487,0.0000834791,0.00016047433,0.000005564822,0.000049379192,1.56907e-7,0.92462206,0.00020179522,0.07101094,0.0031402025,0.0005367709],"about_ca_topic_score_codex":0.000015349404,"about_ca_topic_score_gemma":0.000017601235,"teacher_disagreement_score":0.8463798,"about_ca_system_score_codex":0.0003214243,"about_ca_system_score_gemma":0.0003042867,"threshold_uncertainty_score":0.99976504},"labels":[],"label_agreement":null},{"id":"W4399058545","doi":"10.1007/978-3-031-60597-0_19","title":"Don’t Explain Noise: Robust Counterfactuals for Randomized Ensembles","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Counterfactual conditional; Computer science; Noise (video); Artificial intelligence; Algorithm; Counterfactual thinking; Epistemology; Philosophy","score_opus":0.018528492080288474,"score_gpt":0.2576441696403247,"score_spread":0.23911567756003624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399058545","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000011818174,0.0008845807,0.9902274,0.0016388302,0.0011740399,0.0017497579,0.000023127122,0.000585731,0.003704729],"genre_scores_gemma":[0.049787454,0.00027353867,0.9408294,0.0018210693,0.00084887265,0.0006341285,0.000014552802,0.00009471001,0.0056962864],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99643993,0.000026806612,0.0007145051,0.0016396743,0.0006167469,0.0005623199],"domain_scores_gemma":[0.9968081,0.0012314041,0.00029085678,0.0012590658,0.0002669082,0.00014364887],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00149688,0.0005508018,0.0008511974,0.00078416464,0.00032758684,0.0008923035,0.002526073,0.00035481597,0.000033724333],"category_scores_gemma":[0.00008324541,0.00046173096,0.0003686769,0.00048579823,0.0007150473,0.00041657055,0.0008115586,0.0005641778,0.000060189894],"study_design_candidate":"theoretical_or_conceptual","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":[0.00032423896,0.000044416825,9.889355e-7,0.00018020615,0.000059605863,0.000026025895,0.00085807126,0.023540264,0.0005321904,0.5642259,0.00064882793,0.40955928],"study_design_scores_gemma":[0.0030549967,0.0000959511,5.3386236e-7,0.0003055718,0.000024564739,0.000047063622,2.5703216e-7,0.53034145,0.004912914,0.4449709,0.015665852,0.0005799285],"about_ca_topic_score_codex":0.000021323593,"about_ca_topic_score_gemma":0.000031139745,"teacher_disagreement_score":0.5068012,"about_ca_system_score_codex":0.00026956777,"about_ca_system_score_gemma":0.00032392514,"threshold_uncertainty_score":0.99978346},"labels":[],"label_agreement":null},{"id":"W4399159967","doi":"10.3997/2214-4609.202410486","title":"Gabor-Based Learnable Sparse Representation for Self-Supervised Ground Roll Removal","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Interpretability; Computer science; Artificial intelligence; Pattern recognition (psychology); Representation (politics); Noise reduction; Noise (video); Artificial neural network; Machine learning; Sparse approximation; Limit (mathematics); Deep learning; Image (mathematics); Mathematics","score_opus":0.02963590407213225,"score_gpt":0.29496813267406874,"score_spread":0.2653322286019365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399159967","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040051537,0.000068867936,0.98135823,0.00239359,0.00013998259,0.00045948502,0.0000025823276,0.002068202,0.009503905],"genre_scores_gemma":[0.33504415,0.000015729067,0.6567283,0.00047598023,0.00012223993,0.00034304845,0.000010071633,0.000017285014,0.007243197],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990954,0.000020590345,0.00017438165,0.0004043689,0.00013522587,0.00017003319],"domain_scores_gemma":[0.99931395,0.000096231975,0.000026315625,0.000429907,0.0000779561,0.000055641005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019583452,0.000090983114,0.00008569255,0.00010889237,0.00015527167,0.00035371567,0.000314113,0.000054473025,0.000054718104],"category_scores_gemma":[0.000010560937,0.000082679275,0.00010059688,0.0005727483,0.000013678538,0.00040242972,0.0000473419,0.00007107601,0.000081841055],"study_design_candidate":"theoretical_or_conceptual","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":[0.000016363812,0.00016062702,0.000068346904,0.00010404401,0.000041230498,0.000019058683,0.00025182884,0.0005797494,0.010232124,0.84833366,0.031686246,0.10850673],"study_design_scores_gemma":[0.00014831274,0.000070799615,0.00006101916,0.00000767079,0.000008548977,0.000014141224,0.000019286801,0.8224906,0.016545946,0.0130863115,0.14743297,0.00011440309],"about_ca_topic_score_codex":0.00009870509,"about_ca_topic_score_gemma":0.000008630087,"teacher_disagreement_score":0.83524734,"about_ca_system_score_codex":0.00005050739,"about_ca_system_score_gemma":0.00008179287,"threshold_uncertainty_score":0.34108883},"labels":[],"label_agreement":null},{"id":"W4399221686","doi":"10.1080/08839514.2024.2360283","title":"Generation of Vessel Track Characteristics Using a Conditional Generative Adversarial Network (CGAN)","year":2024,"lang":"en","type":"article","venue":"Applied Artificial Intelligence","topic":"Anomaly Detection Techniques and Applications","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":"Defence Research and Development Canada","funders":"","keywords":"Computer science; Generative adversarial network; Generative grammar; Artificial intelligence; Adversarial system; Track (disk drive); Machine learning; Deep learning","score_opus":0.10313875237258795,"score_gpt":0.3193184826681696,"score_spread":0.21617973029558163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399221686","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":[0.012740656,0.000049387687,0.98564273,0.00015322372,0.00047189806,0.00026327375,0.00002406647,0.00021529208,0.00043944168],"genre_scores_gemma":[0.90075713,0.000018513638,0.09799136,0.00010619316,0.0010021867,0.00005956108,0.0000330126,0.000011312823,0.000020701757],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871063,0.000025516052,0.00047086808,0.0003954268,0.00020254267,0.00019501314],"domain_scores_gemma":[0.9993792,0.000075696895,0.00012097748,0.00026587737,0.000103326376,0.000054874625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021394709,0.00013859685,0.00016057905,0.00008550141,0.00020997987,0.00018246207,0.00031388138,0.00009055948,0.000065491535],"category_scores_gemma":[0.000009947161,0.0001425621,0.00007181208,0.0005891961,0.00011074588,0.00021994405,0.000078112724,0.0001519327,0.000066534114],"study_design_candidate":"theoretical_or_conceptual","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":[0.000004652113,0.000032862983,0.0000014640121,0.000008071402,0.000014424717,0.0000018346099,0.00021671262,0.0039817807,0.09478595,0.85319376,0.00025933966,0.04749914],"study_design_scores_gemma":[0.000007780394,0.00003092641,0.000013301698,0.000011940897,0.000012297912,0.000006266922,0.000025874784,0.58729756,0.2815055,0.12940884,0.0015391909,0.00014055424],"about_ca_topic_score_codex":0.0000152225375,"about_ca_topic_score_gemma":0.000005197381,"teacher_disagreement_score":0.8880165,"about_ca_system_score_codex":0.000050387134,"about_ca_system_score_gemma":0.000118125325,"threshold_uncertainty_score":0.5813514},"labels":[],"label_agreement":null},{"id":"W4399257125","doi":"10.1038/s41467-024-48747-7","title":"A 3D ray traced biological neural network learning model","year":2024,"lang":"en","type":"article","venue":"Nature Communications","topic":"Anomaly Detection Techniques and Applications","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":"University of Victoria","funders":"Defense Threat Reduction Agency; Alliance de recherche numérique du Canada; Natural Sciences and Engineering Research Council of Canada; Government of Canada; U.S. Department of Defense","keywords":"Computer science; Artificial neural network; Computational biology; Artificial intelligence; Biology","score_opus":0.03604522261591197,"score_gpt":0.3163668149784933,"score_spread":0.2803215923625813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399257125","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":[0.0009777419,0.008891714,0.97140944,0.009981753,0.00012585505,0.00021246712,0.0000032917053,0.0020653806,0.0063323276],"genre_scores_gemma":[0.7396183,0.00048202707,0.25892243,0.00039893304,0.000053778105,0.00012216133,0.000014353721,0.000008100874,0.00037994576],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912596,0.000109061926,0.00018457515,0.00028461142,0.00010125671,0.0001945266],"domain_scores_gemma":[0.998224,0.00024476033,0.000042227413,0.0013632069,0.00006609307,0.000059678005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025703906,0.00011070092,0.00010285839,0.00007330976,0.000536229,0.00021280357,0.001857656,0.0002460923,0.000008247935],"category_scores_gemma":[0.000038431062,0.00009413207,0.000096126845,0.0008278101,0.00008059363,0.00022791061,0.0005976875,0.0014876052,0.00004246154],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000015272508,0.000052472984,0.00010700898,0.0000042298925,0.000018453395,0.0000013298807,0.00019848454,0.023342067,0.0009429442,0.89376915,0.009850501,0.071711846],"study_design_scores_gemma":[0.000024173152,0.000020252332,0.00021405687,0.000009753442,0.0000044835233,0.000009597836,0.0000054269694,0.8379773,0.00003646711,0.00586228,0.15573604,0.00010018811],"about_ca_topic_score_codex":0.000003278168,"about_ca_topic_score_gemma":0.0000096411595,"teacher_disagreement_score":0.88790685,"about_ca_system_score_codex":0.000036971083,"about_ca_system_score_gemma":0.000045118188,"threshold_uncertainty_score":0.6462987},"labels":[],"label_agreement":null},{"id":"W4399262340","doi":"10.53555/kuey.v30i5.5095","title":"Hybrid Approach For Anomaly Detection Using Clustering Mechanism","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centennial College; University of Toronto","funders":"","keywords":"Anomaly detection; Cluster analysis; Mechanism (biology); Computer science; Anomaly (physics); Data mining; Artificial intelligence; Pattern recognition (psychology); Physics","score_opus":0.029559661490800776,"score_gpt":0.26991317073972226,"score_spread":0.2403535092489215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399262340","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":[0.0032159626,0.000031237894,0.99349535,0.000067032284,0.00015946901,0.0003279193,0.0000018294267,0.001259367,0.0014418538],"genre_scores_gemma":[0.56981117,0.0000018227508,0.42964047,0.000044131313,0.000056223205,0.000101057616,7.4119623e-7,0.0000076268684,0.00033674113],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999307,0.000008196356,0.00013056955,0.00033448686,0.000073343814,0.00014642942],"domain_scores_gemma":[0.999648,0.00001959854,0.00002121923,0.00024209052,0.00003145241,0.00003764375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001486276,0.00008275325,0.000067620174,0.000115470895,0.00017147065,0.00025934348,0.00023411776,0.000034057317,0.000005137189],"category_scores_gemma":[0.0000027967917,0.00007669971,0.00007754615,0.00021963022,0.000008764651,0.0003220591,0.000098692435,0.000060472015,0.000006506551],"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":[0.0000035798264,0.00004137675,0.0000014941833,0.00011465218,0.000028234077,0.0000024432716,0.000082709186,0.0009234174,0.17316428,0.5882005,0.00016758309,0.23726971],"study_design_scores_gemma":[0.00002634813,0.00003196516,0.0000019911386,0.0000040235714,0.000004514844,0.0000614147,0.0000065036425,0.8048536,0.18024476,0.012297409,0.0023826535,0.00008481776],"about_ca_topic_score_codex":0.000030922154,"about_ca_topic_score_gemma":0.000001496715,"teacher_disagreement_score":0.80393016,"about_ca_system_score_codex":0.00005523536,"about_ca_system_score_gemma":0.000020574063,"threshold_uncertainty_score":0.31277236},"labels":[],"label_agreement":null},{"id":"W4399318710","doi":"10.1145/3655693.3655703","title":"Increasing Detection Rate for Imbalanced Malicious Traffic using Generative Adversarial Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"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":"Computer science; Discriminative model; Leverage (statistics); Anomaly detection; Generative grammar; Intrusion detection system; Machine learning; Artificial intelligence; Dimensionality reduction; Adversarial system; Class (philosophy); Data mining","score_opus":0.016657796808509012,"score_gpt":0.27274467138178465,"score_spread":0.2560868745732756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399318710","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":[0.043157153,0.000068210386,0.9546447,0.00015438916,0.00045944314,0.00037203083,0.0000019044169,0.0009752806,0.00016691233],"genre_scores_gemma":[0.8619123,0.0000098653545,0.13736221,0.00013069042,0.00034807945,0.0000919501,0.0000021721683,0.000011518679,0.00013117636],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991937,0.000049350172,0.00016897885,0.0003416467,0.00006024285,0.00018606371],"domain_scores_gemma":[0.9995707,0.000100217425,0.00003824304,0.00019073187,0.000054348435,0.000045748282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002893779,0.000106841726,0.00009721266,0.00008975599,0.00026477722,0.0002812411,0.0001793044,0.00007723117,0.0000065136683],"category_scores_gemma":[0.000010309748,0.00009784514,0.00008729478,0.00041780918,0.00002049398,0.0003005876,0.000047860678,0.00009415736,0.000004517694],"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":[0.00007030219,0.000056981524,0.000010861554,0.00003102524,0.0000911511,0.0000067611877,0.00039244688,0.13490352,0.24863851,0.07331661,0.000720464,0.54176134],"study_design_scores_gemma":[0.00010360239,0.00005866405,0.000019410714,0.000010602311,0.000010723459,0.000034213856,0.000011166782,0.9649873,0.03151597,0.00085910584,0.0022645027,0.00012476336],"about_ca_topic_score_codex":0.00005209825,"about_ca_topic_score_gemma":0.000021455664,"teacher_disagreement_score":0.8300837,"about_ca_system_score_codex":0.00009064534,"about_ca_system_score_gemma":0.000040923984,"threshold_uncertainty_score":0.3990009},"labels":[],"label_agreement":null},{"id":"W4399339292","doi":"10.1109/tdsc.2024.3408816","title":"Efficient and Privacy-Preserving Weighted Range Set Sampling in Cloud","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Dependable and Secure Computing","topic":"Anomaly Detection Techniques and Applications","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":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Range (aeronautics); Computer science; Cloud computing; Set (abstract data type); Privacy protection; Mathematics; Computer security; Engineering","score_opus":0.021307218232329363,"score_gpt":0.27278505826806154,"score_spread":0.2514778400357322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399339292","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":[0.22076625,0.00047610106,0.77745914,0.00038115418,0.00022589986,0.00018496318,0.000005290569,0.00037324004,0.00012795546],"genre_scores_gemma":[0.9759868,0.00007650481,0.023732133,0.000073320494,0.000048857797,0.00001743238,6.978387e-7,0.000014305217,0.00004998077],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879354,0.000044478144,0.0002529625,0.0005096445,0.00014145092,0.00025794993],"domain_scores_gemma":[0.99936634,0.00020572204,0.00003384542,0.00028160954,0.000027697122,0.0000848108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030262672,0.00015859524,0.00015917531,0.00027064022,0.00035258982,0.00031228102,0.00026338632,0.00008913895,0.000011134584],"category_scores_gemma":[0.0000027433884,0.00015269479,0.000049701594,0.0006245972,0.000029411549,0.00013511688,0.000023996,0.00037465358,0.000007052981],"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":[0.0000483563,0.00039541864,0.00026990758,0.00073909387,0.00014094308,0.00015572648,0.014026349,0.12728958,0.0066704517,0.030823627,0.00043092592,0.8190096],"study_design_scores_gemma":[0.00017424069,0.000046876452,0.00009555988,0.00018159061,0.000009251359,0.000080354206,0.000074009244,0.9929265,0.003140617,0.001228415,0.0018564808,0.0001861179],"about_ca_topic_score_codex":0.00008454319,"about_ca_topic_score_gemma":0.000027329039,"teacher_disagreement_score":0.8656369,"about_ca_system_score_codex":0.000042327334,"about_ca_system_score_gemma":0.000029274797,"threshold_uncertainty_score":0.6226713},"labels":[],"label_agreement":null},{"id":"W4399351381","doi":"10.2139/ssrn.4853350","title":"Pipeline Monitoring: Leveraging Attention-Based 1dcnn-Bilstm for Accurate Leak Detection with Minimal False Alarms","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Pipeline (software); Leak detection; Leak; Computer science; Real-time computing; Petroleum engineering; Reliability engineering; Environmental science; Engineering; Operating system; Environmental engineering","score_opus":0.018687792110652808,"score_gpt":0.27804229201026703,"score_spread":0.25935449989961423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399351381","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":[0.030332202,0.0012875382,0.96451044,0.0015196664,0.0009280142,0.00073215674,0.000009774878,0.00059408485,0.00008610783],"genre_scores_gemma":[0.9813057,0.0004291281,0.014720021,0.000050471594,0.0012849233,0.0004982423,0.000010718012,0.00007433861,0.0016264538],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99628943,0.00007062467,0.00061408215,0.0008883608,0.0004303376,0.0017071728],"domain_scores_gemma":[0.9981168,0.00008228408,0.0005384758,0.0006511464,0.0004707804,0.00014049881],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0014277025,0.00045097555,0.0003619797,0.00044225215,0.0005470296,0.0007602688,0.0010872073,0.00027822756,0.000003007197],"category_scores_gemma":[0.000034686745,0.0003927843,0.0004095433,0.0004736327,0.000048742524,0.00023636468,0.00035731992,0.0039442317,0.00001673324],"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":[0.0007151949,0.00092338823,0.0017066883,0.0010274666,0.0019277213,0.00006351472,0.00068010786,0.027635079,0.021292707,0.061792254,0.001982234,0.8802537],"study_design_scores_gemma":[0.0017313384,0.0017485333,0.0003486552,0.0008251951,0.00045746772,0.0010002138,0.0006376072,0.51072484,0.036687326,0.43368813,0.010307442,0.0018432499],"about_ca_topic_score_codex":0.000062373474,"about_ca_topic_score_gemma":0.00023016236,"teacher_disagreement_score":0.9509735,"about_ca_system_score_codex":0.001435145,"about_ca_system_score_gemma":0.0027508566,"threshold_uncertainty_score":0.9998524},"labels":[],"label_agreement":null},{"id":"W4399369033","doi":"10.21428/d82e957c.885a78c0","title":"Feature Density Estimation for Out-of-Distribution Detection via Normalizing Flows","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Feature (linguistics); Estimation; Pattern recognition (psychology); Density estimation; Distribution (mathematics); Mathematics; Computer science; Artificial intelligence; Statistics; Engineering; Estimator; Mathematical analysis","score_opus":0.012660130609578187,"score_gpt":0.2694363708563547,"score_spread":0.2567762402467765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399369033","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":[0.0029422715,0.000042128588,0.99488634,0.00065666373,0.0003140203,0.0002785409,0.000007502566,0.0007211926,0.0001513369],"genre_scores_gemma":[0.824541,0.0000039239194,0.17509317,0.000023836219,0.000045388006,0.00007562447,0.000019032621,0.0000043688983,0.00019366422],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947315,0.000010061311,0.00011851846,0.000210914,0.000088152694,0.00009919049],"domain_scores_gemma":[0.9995933,0.00004045654,0.00004011546,0.00020079585,0.00009716454,0.000028130364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016423759,0.00007020448,0.000070733404,0.000056029243,0.00013005655,0.00008866932,0.00014008876,0.000074335505,0.000002925555],"category_scores_gemma":[0.0000134882275,0.00006436996,0.00007805008,0.0002929609,0.000010768453,0.0003565195,0.000040850555,0.00007084183,0.000014413872],"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":[0.0000061173428,0.000027450133,0.000011664087,0.00009961544,0.000017029794,6.029628e-7,0.00011864369,0.0002507617,0.07877347,0.10526668,0.0028221875,0.8126058],"study_design_scores_gemma":[0.000028977196,0.00004476462,0.00012132229,0.000010325614,0.0000070526194,0.000007117324,0.0000032929393,0.72691244,0.25205618,0.006454211,0.014291847,0.00006246269],"about_ca_topic_score_codex":0.000020502232,"about_ca_topic_score_gemma":0.000031292413,"teacher_disagreement_score":0.8215987,"about_ca_system_score_codex":0.000057039066,"about_ca_system_score_gemma":0.000019197449,"threshold_uncertainty_score":0.26249307},"labels":[],"label_agreement":null},{"id":"W4399655979","doi":"10.48550/arxiv.2406.07467","title":"LLM meets ML: Data-efficient Anomaly Detection on Unstable Logs","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"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":"Alliance de recherche numérique du Canada; Fonds National de la Recherche Luxembourg; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Anomaly detection; Anomaly (physics); Computer science; Data mining; Physics","score_opus":0.09849332833791093,"score_gpt":0.20922295842170827,"score_spread":0.11072963008379734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399655979","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":[0.06235797,0.000067467154,0.9265176,0.00031405294,0.0007036654,0.0004756091,0.0000728973,0.001411955,0.008078769],"genre_scores_gemma":[0.9951218,0.000093877534,0.0019468308,0.00010126925,0.000100040954,0.0000067267692,0.00003094228,0.000027631908,0.0025709153],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973391,0.00007731518,0.00023328526,0.0018795306,0.00013181486,0.00033890008],"domain_scores_gemma":[0.9964439,0.00006114361,0.00019881492,0.0030329968,0.0001122482,0.00015086193],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031117612,0.00032914834,0.0002588819,0.00042655767,0.00027451746,0.0002583405,0.0026280365,0.0003138549,0.000029792154],"category_scores_gemma":[0.000014717873,0.00036627546,0.00016461761,0.0010512258,0.00008564023,0.00017747017,0.0048808414,0.00078981457,0.00049399625],"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":[0.00003806036,0.00053359976,0.0000479781,0.00017225498,0.00017004852,0.0002650495,0.00012504807,0.27407485,0.0006259608,0.7006367,0.0032359427,0.02007454],"study_design_scores_gemma":[0.0001259426,0.00012115655,0.000106607506,0.00007990969,0.0000742305,0.000010690245,0.000021950844,0.94412255,0.0043872213,0.031510856,0.018982742,0.00045613293],"about_ca_topic_score_codex":0.00033572174,"about_ca_topic_score_gemma":0.000076531345,"teacher_disagreement_score":0.9327638,"about_ca_system_score_codex":0.00031894277,"about_ca_system_score_gemma":0.0001651353,"threshold_uncertainty_score":0.99987894},"labels":[],"label_agreement":null},{"id":"W4399701234","doi":"10.1016/j.patcog.2024.110695","title":"Robust Self-expression Learning with Adaptive Noise Perception","year":2024,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"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":"Basic and Applied Basic Research Foundation of Guangdong Province; China Scholarship Council; University of Alberta; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China; Science, Technology and Innovation Commission of Shenzhen Municipality; Department of Electrical and Computer Engineering, Western Michigan University","keywords":"Perception; Noise (video); Expression (computer science); Computer science; Perceptual learning; Artificial intelligence; Speech recognition; Computer vision; Psychology; Pattern recognition (psychology); Neuroscience; Image (mathematics)","score_opus":0.02752581639713011,"score_gpt":0.23015027983611855,"score_spread":0.20262446343898843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399701234","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":[0.044606708,0.00002288134,0.9515181,0.00022129453,0.00006859968,0.00018417665,0.0000034183254,0.0015652246,0.0018095694],"genre_scores_gemma":[0.9369092,0.000045143064,0.06252276,0.000088655426,0.00010501012,0.00016307898,0.000024862857,0.000014839894,0.00012645649],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919355,0.000047740083,0.00011711148,0.00035971758,0.00014881193,0.00013308102],"domain_scores_gemma":[0.9996561,0.000031719548,0.000043352797,0.00015016628,0.00007110938,0.000047581947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011024041,0.00010486745,0.00006736645,0.000115256815,0.00016444329,0.00019525753,0.00013474777,0.0000579718,0.000093940595],"category_scores_gemma":[0.0000024696062,0.0000875026,0.00004154883,0.00027882756,0.000013593527,0.00051652093,0.000050536666,0.0002113343,0.00046731372],"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":[0.000005058278,0.000052356063,0.00027628613,0.00003339596,0.000012103132,0.000009295392,0.0006034891,0.00011352711,0.006708069,0.00010348954,0.00034828295,0.9917346],"study_design_scores_gemma":[0.00036908823,0.00093137077,0.010754135,0.00083907327,0.000059941198,0.00021577353,0.0004226677,0.93518716,0.03935515,0.0030168542,0.008047259,0.00080152723],"about_ca_topic_score_codex":0.000022995408,"about_ca_topic_score_gemma":0.000003792564,"teacher_disagreement_score":0.9909331,"about_ca_system_score_codex":0.00006122171,"about_ca_system_score_gemma":0.000018510205,"threshold_uncertainty_score":0.6006528},"labels":[],"label_agreement":null},{"id":"W4399802396","doi":"10.31219/osf.io/6p2yg","title":"A variable template matching algorithm for anomaly detection versus kernel density and gradient convolution algorithms and ResNet-50","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Research & Development Corporation","funders":"","keywords":"Algorithm; Convolution (computer science); Kernel (algebra); Anomaly detection; Anomaly (physics); Matching (statistics); Variable (mathematics); Computer science; Pattern recognition (psychology); Mathematics; Artificial intelligence; Statistics; Combinatorics; Physics","score_opus":0.020723534671126687,"score_gpt":0.26632101287843846,"score_spread":0.24559747820731176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399802396","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010056459,0.00045719522,0.986058,0.00025475092,0.0009050495,0.0009942424,0.000038497164,0.0008796428,0.0003561564],"genre_scores_gemma":[0.16514763,0.00018293652,0.83323455,0.000058660244,0.00018400738,0.0005601842,0.000017973152,0.000030375877,0.00058368716],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980612,0.000038694772,0.00035148955,0.0010840354,0.0001688936,0.00029572542],"domain_scores_gemma":[0.99892414,0.0001146283,0.00016567916,0.00052354165,0.0001353111,0.00013670327],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00046684736,0.0003034156,0.00029519136,0.0002158885,0.0003927448,0.00054700766,0.00028805944,0.00032641692,0.0000023704797],"category_scores_gemma":[0.0000111416375,0.00030286348,0.000092365386,0.00021997611,0.00007047203,0.00017495443,0.0014703611,0.00047188773,0.0000057489174],"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":[0.000056812118,0.00008314744,0.0000152139155,0.00043846952,0.00023606692,0.0000075543803,0.0004543916,0.00011890892,0.00301852,0.13949268,0.0006231225,0.8554551],"study_design_scores_gemma":[0.0004015064,0.00021480095,0.00027696934,0.00007612161,0.00008529237,0.00007916774,0.00004729203,0.7748114,0.0061019645,0.21384792,0.0036315569,0.00042602923],"about_ca_topic_score_codex":0.0014514643,"about_ca_topic_score_gemma":0.00007302825,"teacher_disagreement_score":0.8550291,"about_ca_system_score_codex":0.00016043354,"about_ca_system_score_gemma":0.00008788828,"threshold_uncertainty_score":0.99994236},"labels":[],"label_agreement":null},{"id":"W4399870203","doi":"10.23977/acss.2024.080204","title":"Academic Behavior Analysis and Early Warning System Based on K-Means Algorithm","year":2024,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Warning system; Algorithm; Computer science; Telecommunications","score_opus":0.012389260785594101,"score_gpt":0.2814894089047138,"score_spread":0.2691001481191197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399870203","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":[0.009674497,0.005850607,0.9833339,0.000055510565,0.0003027152,0.0003002876,0.0000066965363,0.00038782333,0.00008796207],"genre_scores_gemma":[0.9560221,0.00019003487,0.043369077,0.00003710693,0.00012678318,0.0002113156,0.0000015810543,0.000009773742,0.00003221351],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985606,0.00009456915,0.00037164014,0.0005742088,0.00020304334,0.00019592911],"domain_scores_gemma":[0.99932307,0.00020062031,0.0000832676,0.000275575,0.000033786942,0.0000836653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042391397,0.0001625019,0.00029323835,0.000475826,0.000121104276,0.0003563611,0.0002706107,0.00009075928,7.48145e-7],"category_scores_gemma":[0.0000014015292,0.0001393701,0.00006880154,0.000977523,0.000034813802,0.0004503944,0.00008429657,0.00023396898,0.000003727653],"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":[0.0000026081887,0.000034790344,0.00877342,0.00027811056,0.000061077204,0.00009703202,0.00041814175,0.028469106,0.00010855657,0.020837143,0.0000352674,0.94088477],"study_design_scores_gemma":[0.000076086835,0.00011695887,0.00237085,0.00029246553,0.000036500813,0.000028763963,0.000019346811,0.994586,0.000079183534,0.00009336956,0.0021352507,0.00016520878],"about_ca_topic_score_codex":0.000065749846,"about_ca_topic_score_gemma":0.0000026971513,"teacher_disagreement_score":0.9661169,"about_ca_system_score_codex":0.000043778262,"about_ca_system_score_gemma":0.000015143858,"threshold_uncertainty_score":0.5683347},"labels":[],"label_agreement":null},{"id":"W4399891569","doi":"10.1007/978-3-031-63219-8_24","title":"An Evaluation Framework for Synthetic Data Generation Models","year":2024,"lang":"en","type":"book-chapter","venue":"IFIP advances in information and communication technology","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Novelis (Canada)","funders":"","keywords":"Computer science","score_opus":0.05892436286099193,"score_gpt":0.35058331940201004,"score_spread":0.2916589565410181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399891569","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000043496516,0.01045976,0.94524276,0.001980504,0.00012482946,0.0010830354,0.00006574315,0.00060558337,0.040433455],"genre_scores_gemma":[0.11905087,0.05672946,0.81542546,0.000619121,0.00010248985,0.0026829857,0.0027995526,0.00006153647,0.0025285417],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985027,0.000024145384,0.000666735,0.00043699765,0.00022326427,0.00014619935],"domain_scores_gemma":[0.99587244,0.0000965503,0.00040831222,0.003254469,0.00033033255,0.000037888305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007751262,0.00021739551,0.00022230651,0.00079074677,0.00021795991,0.0002675292,0.0020110342,0.0005462096,0.000016319522],"category_scores_gemma":[0.00006709838,0.00023244039,0.000032803906,0.00023078792,0.00014766707,0.004810897,0.00057759904,0.00047268972,0.000026849815],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013397032,0.0000060768098,1.8185695e-7,0.000020257372,0.0000037254665,2.5030355e-8,0.00006950192,0.0002599403,0.000002224288,0.6446617,0.00006915773,0.35490587],"study_design_scores_gemma":[0.000053239084,0.000030284386,1.5796738e-7,0.000065254535,0.000010867538,0.0000049387427,0.000029222174,0.38337088,0.00004543697,0.46738955,0.14888412,0.00011604428],"about_ca_topic_score_codex":0.0000035430648,"about_ca_topic_score_gemma":0.00003642808,"teacher_disagreement_score":0.38311094,"about_ca_system_score_codex":0.00012076617,"about_ca_system_score_gemma":0.0000881493,"threshold_uncertainty_score":0.9478644},"labels":[],"label_agreement":null},{"id":"W4399899092","doi":"10.21203/rs.3.rs-4612382/v1","title":"A variable template matching algorithm for anomaly detection versus kernel density and gradient convolution algorithms and ResNet-50","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Research & Development Corporation","funders":"","keywords":"Algorithm; Convolution (computer science); Kernel (algebra); Anomaly detection; Anomaly (physics); Variable (mathematics); Matching (statistics); Computer science; Pattern recognition (psychology); Mathematics; Artificial intelligence; Statistics; Combinatorics; Physics","score_opus":0.050929103543332896,"score_gpt":0.3572764019998762,"score_spread":0.3063472984565433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399899092","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":[0.019852638,0.0012286216,0.9755811,0.0003930031,0.0004476911,0.0017187732,0.00009221009,0.00049734244,0.00018861995],"genre_scores_gemma":[0.5208879,0.0007239836,0.47547987,0.00002041752,0.0003967025,0.0018259764,0.00004707587,0.00005858523,0.00055950176],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972534,0.00017309944,0.00032559567,0.0012119231,0.0004932753,0.00054270757],"domain_scores_gemma":[0.9981853,0.00035131758,0.00010800911,0.0006887018,0.00044683588,0.00021987055],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017347673,0.00026556346,0.00028877487,0.00046534164,0.000781152,0.0008253923,0.00042768652,0.00036635937,0.0000028696643],"category_scores_gemma":[0.000060082417,0.00027130154,0.000096150645,0.00047307936,0.00015622732,0.00018619529,0.0024745064,0.0011568702,0.00001210407],"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":[0.00014013286,0.00014822125,0.000047775033,0.0018719104,0.00022372363,0.000028043825,0.0010310672,0.000112784575,0.0033576777,0.0876006,0.0010558481,0.9043822],"study_design_scores_gemma":[0.00058637356,0.0006562707,0.0010225056,0.00039513555,0.000041671516,0.00006840457,0.00019736393,0.72117573,0.004949221,0.26297715,0.007460517,0.00046964225],"about_ca_topic_score_codex":0.0017135123,"about_ca_topic_score_gemma":0.000069108384,"teacher_disagreement_score":0.90391254,"about_ca_system_score_codex":0.00034341228,"about_ca_system_score_gemma":0.00021701278,"threshold_uncertainty_score":0.9999739},"labels":[],"label_agreement":null},{"id":"W4399920617","doi":"10.34190/eccws.23.1.2101","title":"Feature Engineering for a MIL-STD-1553B LSTM Autoencoder Anomaly Detector","year":2024,"lang":"en","type":"article","venue":"European Conference on Cyber Warfare and Security","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Air Canada","funders":"","keywords":"Autoencoder; Feature (linguistics); Artificial intelligence; Computer science; Anomaly detection; Feature engineering; Detector; Pattern recognition (psychology); Deep learning; Telecommunications","score_opus":0.018903839739112777,"score_gpt":0.2486705398233183,"score_spread":0.22976670008420552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399920617","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":[0.02149942,0.0006496874,0.9594847,0.0049733715,0.00036567135,0.0005535849,0.00007293946,0.0016849911,0.010715642],"genre_scores_gemma":[0.9788149,0.00008036676,0.019311504,0.00030196088,0.00016529491,0.000065350876,0.000008135876,0.00003017499,0.0012223362],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987996,0.000046656867,0.0001565914,0.00059742306,0.00013320269,0.00026649408],"domain_scores_gemma":[0.99925673,0.00007654586,0.00003655626,0.00041506157,0.00008465042,0.0001304318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022679934,0.00021752762,0.00015866093,0.00011684749,0.00017963404,0.00048976706,0.0004291731,0.00006899001,0.000021402204],"category_scores_gemma":[0.000023389895,0.00019306378,0.00009710977,0.0002537642,0.000034025667,0.00024678963,0.00015308957,0.00028885412,0.000055342305],"study_design_candidate":"theoretical_or_conceptual","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":[0.000025187497,0.00012539412,0.00008210039,0.00033250652,0.00009793508,0.00012088141,0.003545467,0.00003869327,0.007971855,0.8039742,0.027425246,0.1562605],"study_design_scores_gemma":[0.00028499364,0.00039463432,0.001935615,0.00022914815,0.000025708396,0.000067777604,0.0000463106,0.35284534,0.0059022014,0.005398808,0.6321927,0.0006768168],"about_ca_topic_score_codex":0.000008899934,"about_ca_topic_score_gemma":0.00000898789,"teacher_disagreement_score":0.95731544,"about_ca_system_score_codex":0.000027661124,"about_ca_system_score_gemma":0.000045849425,"threshold_uncertainty_score":0.7872912},"labels":[],"label_agreement":null},{"id":"W4399976741","doi":"10.18280/ijsse.140326","title":"VidAnomalyNet: An Efficient Anomaly Detection in Public Surveillance Videos Through Deep Learning Architectures","year":2024,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Anomaly detection; Deep learning; Anomaly (physics); Computer science; Artificial intelligence; Computer security","score_opus":0.006071693849521583,"score_gpt":0.23383026188300593,"score_spread":0.22775856803348435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399976741","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":[0.25705773,0.00065148325,0.74115235,0.0005813076,0.0003112149,0.000044638622,0.0000013618978,0.00010159838,0.00009834165],"genre_scores_gemma":[0.9939105,0.00023826987,0.005630637,0.000029187206,0.00017337457,0.0000040252685,0.0000010699099,0.000008774023,0.000004179753],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903095,0.000038986233,0.00035637117,0.00018741719,0.00024848676,0.00013778982],"domain_scores_gemma":[0.9995332,0.00010792535,0.000088031855,0.00009056923,0.0001169154,0.00006336349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044113636,0.00010527754,0.00012801861,0.00033579892,0.00005480451,0.00025932884,0.00038531554,0.00005545076,0.0000059968133],"category_scores_gemma":[0.00005666353,0.00010049334,0.00006713298,0.0003010198,0.000019348194,0.00041710242,0.00008276815,0.0004035642,0.0000012237546],"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":[0.000097640026,0.00019196134,0.0018646385,0.00008971892,0.00020041525,0.0002594281,0.0072197616,0.473084,0.012646576,0.11825717,0.000004484085,0.3860842],"study_design_scores_gemma":[0.00017663653,0.00011077838,0.008838896,0.00006907273,0.0000025431125,0.000538492,0.000045852066,0.9752784,0.002159274,0.0016965471,0.010939185,0.00014432297],"about_ca_topic_score_codex":0.000026995904,"about_ca_topic_score_gemma":0.000043073018,"teacher_disagreement_score":0.73685277,"about_ca_system_score_codex":0.0001017773,"about_ca_system_score_gemma":0.00002609922,"threshold_uncertainty_score":0.40979993},"labels":[],"label_agreement":null},{"id":"W4399990335","doi":"10.1109/tmlcn.2024.3418756","title":"Incremental Adversarial Learning for Polymorphic Attack Detection","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Machine Learning in Communications and Networking","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":12,"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":"Adversarial system; Computer science; Artificial intelligence; Machine learning","score_opus":0.0322891384170731,"score_gpt":0.2947834248186806,"score_spread":0.2624942864016075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399990335","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":[0.002731033,0.0013951596,0.9931565,0.0009915528,0.00031958293,0.00033340626,0.0000025014635,0.0007284167,0.0003418645],"genre_scores_gemma":[0.98262894,0.0019145069,0.014685265,0.00006588326,0.00008762662,0.00031545805,0.000006473339,0.000025682417,0.00027016285],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882495,0.00017543802,0.00029454182,0.00036643737,0.00011043184,0.00022816923],"domain_scores_gemma":[0.99885774,0.0005191627,0.000068396264,0.0004705448,0.00003004279,0.00005409427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051012594,0.00015875055,0.00014284483,0.00033383627,0.0010521036,0.00025509414,0.00045261087,0.00010050425,0.0000075771727],"category_scores_gemma":[0.000004234431,0.00017119295,0.000098442586,0.0007214681,0.00006551626,0.000289481,0.00002269026,0.00094851037,0.000007922193],"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":[0.000018340801,0.00006342815,0.00011661585,0.000020404927,0.000029396084,8.425659e-7,0.00033020455,0.020306941,0.0016490496,0.0012899622,0.000010740749,0.9761641],"study_design_scores_gemma":[0.00023262776,0.00019852187,0.000058962723,0.00009245296,0.000017200247,0.00002008316,0.00003680551,0.9341157,0.0009080976,0.00032702868,0.063819036,0.00017348335],"about_ca_topic_score_codex":0.0001681182,"about_ca_topic_score_gemma":0.00028398944,"teacher_disagreement_score":0.9798979,"about_ca_system_score_codex":0.00010121892,"about_ca_system_score_gemma":0.00003102316,"threshold_uncertainty_score":0.8092036},"labels":[],"label_agreement":null},{"id":"W4400033057","doi":"10.1109/tkde.2024.3419215","title":"Ze-HFS: Zentropy-Based Uncertainty Measure for Heterogeneous Feature Selection and Knowledge Discovery","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Measure (data warehouse); Feature selection; Selection (genetic algorithm); Feature (linguistics); Knowledge extraction; Data mining; Artificial intelligence","score_opus":0.018792428517041014,"score_gpt":0.2692210126805711,"score_spread":0.2504285841635301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400033057","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":[0.00097602874,0.0023049328,0.99490196,0.00019807967,0.00039825996,0.00032318506,0.00019685057,0.0006625709,0.00003813776],"genre_scores_gemma":[0.98630637,0.00013629855,0.012864007,0.00001930343,0.00012206664,0.0001871325,0.00003091596,0.000028867638,0.00030503675],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989806,0.000016045911,0.00014353263,0.000586026,0.000070607784,0.00020318493],"domain_scores_gemma":[0.9992468,0.00014339463,0.000017565753,0.0004466204,0.000053212545,0.000092422175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015683149,0.00019433376,0.00014459461,0.00021105206,0.00023530447,0.00034255724,0.00029462064,0.00010713734,0.0000017322722],"category_scores_gemma":[0.000004303283,0.00018277913,0.0000615668,0.00041133558,0.000020328955,0.00058469747,0.000011671264,0.00022117552,0.000006103894],"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":[0.000086264896,0.00057398644,0.0000073142232,0.0016049924,0.00039542018,0.00000961273,0.00080188416,0.10413976,0.04746437,0.0148332855,0.00623766,0.82384545],"study_design_scores_gemma":[0.00018525937,0.0001155016,0.000007127246,0.00010153555,0.000046239104,0.000036393358,0.000004735117,0.9303893,0.021426426,0.000085672844,0.04738554,0.00021625712],"about_ca_topic_score_codex":0.000007699914,"about_ca_topic_score_gemma":0.00005128375,"teacher_disagreement_score":0.98533034,"about_ca_system_score_codex":0.00006047479,"about_ca_system_score_gemma":0.00007672987,"threshold_uncertainty_score":0.7453517},"labels":[],"label_agreement":null},{"id":"W4400066878","doi":"10.1145/3663976.3664232","title":"SW-CAM: Class Activation Map with Feature Map Selection and Weight Modification","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bishop's University","funders":"","keywords":"Class (philosophy); Selection (genetic algorithm); Computer science; Artificial intelligence; Feature (linguistics); Feature selection; Pattern recognition (psychology); Computer vision","score_opus":0.009050794978950972,"score_gpt":0.23673848487427207,"score_spread":0.2276876898953211,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400066878","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":[0.0036616717,0.00006979457,0.9811662,0.012017096,0.00006417006,0.00022422998,8.1166206e-7,0.0010298392,0.0017661741],"genre_scores_gemma":[0.9140522,0.000024769111,0.07858427,0.00018264532,0.00009541759,0.00013023443,0.0000075605517,0.000010972802,0.0069119325],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930865,0.000015308357,0.00008651182,0.0003552585,0.00012653027,0.00010777425],"domain_scores_gemma":[0.9996333,0.000023475235,0.000032423763,0.0002040974,0.00006357168,0.000043119315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000077493576,0.00009568432,0.00005981776,0.000117273106,0.00015305619,0.000277692,0.00013288153,0.00007986749,0.000016869799],"category_scores_gemma":[0.0000013238126,0.00007285432,0.000019409475,0.0004719551,0.000018746026,0.00059833884,0.000033947614,0.00014362413,0.000040583527],"study_design_candidate":"theoretical_or_conceptual","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":[0.000008711519,0.000044660504,0.00016813092,0.00007209155,0.0000282169,7.83562e-7,0.00018962217,0.00006466788,0.030587489,0.83877003,0.033748362,0.09631722],"study_design_scores_gemma":[0.00012482062,0.00015895593,0.0019124575,0.000054574186,0.000014028416,0.000044920445,0.000030258929,0.47670585,0.12752038,0.012484888,0.38066673,0.00028212892],"about_ca_topic_score_codex":0.000022155893,"about_ca_topic_score_gemma":0.000010867967,"teacher_disagreement_score":0.91039056,"about_ca_system_score_codex":0.00005738902,"about_ca_system_score_gemma":0.00003282627,"threshold_uncertainty_score":0.29709128},"labels":[],"label_agreement":null},{"id":"W4400114929","doi":"10.1109/i2mtc60896.2024.10560888","title":"Machine Status Tracking Using Vibration via Sparse Sampling and Without Reconstruction","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Tracking (education); Vibration; Artificial intelligence; Sampling (signal processing); Computer vision; Pattern recognition (psychology); Acoustics; Physics","score_opus":0.03972942241545683,"score_gpt":0.29787621685157734,"score_spread":0.2581467944361205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400114929","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":[0.021929896,0.00020465333,0.9760597,0.00016515196,0.0001350193,0.000085919186,0.0000014657211,0.0006147603,0.0008034735],"genre_scores_gemma":[0.6114763,0.000045905756,0.38832584,0.000044942648,0.000033419532,0.0000051688994,0.0000011281121,0.0000051417005,0.00006213518],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941534,0.000013885166,0.00013640035,0.0002592634,0.00006537898,0.00010976186],"domain_scores_gemma":[0.9997282,0.000018844996,0.000028011038,0.00014759452,0.00003298465,0.000044333272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000090192916,0.00006705466,0.000058547754,0.000085314445,0.00014501806,0.000319738,0.00005743519,0.0000368402,0.00002231564],"category_scores_gemma":[0.000004236494,0.00006264717,0.000021461508,0.00027148816,0.000019388894,0.00062470284,0.000047292142,0.000083245744,0.0000065175486],"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":[0.0000012292662,0.0000065033128,0.00087892264,0.000015152637,0.0000071646464,9.51668e-7,0.00008844145,0.00012818731,0.04172091,0.06204853,0.000004695798,0.8950993],"study_design_scores_gemma":[0.00003703791,0.0000137478555,0.0009015057,0.000022194345,0.000006633596,0.00026721906,0.000012770393,0.9540403,0.02463693,0.018045165,0.0019182422,0.00009827382],"about_ca_topic_score_codex":0.00009907231,"about_ca_topic_score_gemma":0.0000241588,"teacher_disagreement_score":0.9539121,"about_ca_system_score_codex":0.0000341455,"about_ca_system_score_gemma":0.00003080582,"threshold_uncertainty_score":0.30832407},"labels":[],"label_agreement":null},{"id":"W4400146946","doi":"10.1145/3675405","title":"Libby-Novick Beta-Liouville Distribution for Enhanced Anomaly Detection in Proportional Data","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Anomaly Detection Techniques and Applications","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; Anomaly (physics); Anomaly detection; Distribution (mathematics); BETA (programming language); Artificial intelligence; Mathematics; Physics; Condensed matter physics; Mathematical analysis","score_opus":0.029525830544751917,"score_gpt":0.2919575922318553,"score_spread":0.26243176168710336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400146946","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":[0.0052182516,0.00063186354,0.98953307,0.0022423011,0.00044775422,0.00086317083,0.00013838426,0.00087263365,0.000052595646],"genre_scores_gemma":[0.99235725,0.00033232573,0.005766921,0.000014435194,0.00003849351,0.0010999696,0.000051102306,0.000014621684,0.0003248486],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984878,0.000021419755,0.00042785704,0.0007075563,0.0001248339,0.0002305439],"domain_scores_gemma":[0.9986722,0.00010039458,0.000071766575,0.0010220242,0.000088712906,0.00004494045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027367828,0.0001615102,0.000187001,0.00051068474,0.00022307952,0.00014461021,0.0006880411,0.00023877477,0.000009969225],"category_scores_gemma":[0.000019367237,0.00015119379,0.000050876137,0.0011412243,0.00007863723,0.0003502461,0.000048427846,0.00026394977,0.000021619677],"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":[0.000021133494,0.00023743669,0.000034794164,0.00018375041,0.00008411087,0.000005143158,0.000073469455,0.00026750134,0.008492265,0.18067607,0.0003490532,0.80957526],"study_design_scores_gemma":[0.00039311955,0.000946383,0.00014062862,0.00030548006,0.00006363497,0.00026000576,0.00037000616,0.2658504,0.42298248,0.031236216,0.27678388,0.0006677685],"about_ca_topic_score_codex":0.000105548184,"about_ca_topic_score_gemma":0.000098620076,"teacher_disagreement_score":0.98713905,"about_ca_system_score_codex":0.00011646218,"about_ca_system_score_gemma":0.000056399647,"threshold_uncertainty_score":0.6165504},"labels":[],"label_agreement":null},{"id":"W4400163651","doi":"10.31224/3763","title":"Fast Probabilistic Seismic Hazard Analysis through Adaptive Importance Sampling","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"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":"York University","keywords":"Probabilistic logic; Hazard; Computer science; Sampling (signal processing); Seismic hazard; Seismology; Importance sampling; Statistics; Geology; Mathematics; Artificial intelligence; Monte Carlo method; Telecommunications","score_opus":0.0432014281823409,"score_gpt":0.303925497325348,"score_spread":0.2607240691430071,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400163651","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":[0.0017266636,0.0002752321,0.97848207,0.0012277022,0.0001972959,0.0005908902,0.00004094526,0.0018065202,0.015652707],"genre_scores_gemma":[0.6260938,0.000055771503,0.37144524,0.00030239508,0.00009671397,0.0005252705,0.000022351614,0.000020540096,0.0014379547],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975513,0.00003274508,0.0005141464,0.0013304676,0.0002824743,0.00028886183],"domain_scores_gemma":[0.99785894,0.000060829192,0.0002364329,0.0016010049,0.00016385406,0.000078944664],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020519864,0.0003341581,0.0004479705,0.00025904,0.00014718088,0.00044993518,0.0012715921,0.0002457244,0.000065316475],"category_scores_gemma":[0.000012871827,0.00029999544,0.0004992097,0.0015995267,0.000069752685,0.00012679318,0.0024738747,0.0007527455,0.000128408],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000032899009,0.00008866698,0.00019580404,0.00014922605,0.0012711512,0.0000122897145,0.00063958165,0.07274378,0.000038495036,0.9069573,0.001998393,0.015901979],"study_design_scores_gemma":[0.00001814478,0.000021430556,0.0001702058,0.00003384368,0.0003178082,0.00000460009,0.000028251292,0.5498484,0.0002496752,0.44715914,0.0018344503,0.00031407503],"about_ca_topic_score_codex":0.00020744024,"about_ca_topic_score_gemma":0.00013323186,"teacher_disagreement_score":0.6243671,"about_ca_system_score_codex":0.00020367769,"about_ca_system_score_gemma":0.00020316211,"threshold_uncertainty_score":0.9999452},"labels":[],"label_agreement":null},{"id":"W4400340432","doi":"10.1016/j.eswa.2024.124678","title":"Physics-informed machine learning: A comprehensive review on applications in anomaly detection and condition monitoring","year":2024,"lang":"en","type":"review","venue":"Expert Systems with Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":251,"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":"Natural Sciences and Engineering Research Council of Canada; McMaster University","keywords":"Anomaly detection; Computer science; Anomaly (physics); Artificial intelligence; Machine learning; Physics","score_opus":0.03957793814183662,"score_gpt":0.34584854767913187,"score_spread":0.30627060953729524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400340432","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":[3.858455e-7,0.7140672,0.27980754,0.000046796576,0.00005232744,0.0050787777,0.00002806543,0.00058697746,0.00033190835],"genre_scores_gemma":[0.00041706918,0.94056135,0.001981557,0.00003973616,0.00027481318,0.056249376,0.00014155271,0.000071527196,0.000263023],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973015,0.0001334862,0.0008757833,0.0010528315,0.00031759543,0.00031877658],"domain_scores_gemma":[0.99777806,0.00024972137,0.0005907072,0.0010754158,0.00015018121,0.00015591127],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015424368,0.00060178223,0.001153114,0.0004133182,0.0004147697,0.0002748719,0.0006126266,0.0002413152,0.0000019011467],"category_scores_gemma":[0.0000073286787,0.0004653723,0.000200522,0.0020619254,0.000082905644,0.0002682769,0.00015857894,0.00075394387,0.00014762336],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025896984,0.00013620363,0.000005674594,0.030370649,0.00012613779,0.0000032614887,0.00014600363,0.000020565984,0.000012043103,0.02037859,0.00028063596,0.9485176],"study_design_scores_gemma":[0.00008831551,0.00009329805,0.0000045647153,0.01611049,0.00014629915,0.0001009985,0.000043475186,0.00084121234,0.000018449946,0.000092591086,0.9819948,0.00046550695],"about_ca_topic_score_codex":0.00013796033,"about_ca_topic_score_gemma":0.000009522163,"teacher_disagreement_score":0.9817142,"about_ca_system_score_codex":0.00039931256,"about_ca_system_score_gemma":0.00017955586,"threshold_uncertainty_score":0.9997798},"labels":[],"label_agreement":null},{"id":"W4400381602","doi":"10.1016/j.asoc.2024.111928","title":"Self-supervised dual-layer 2D normalizing flow method for industrial anomaly detection","year":2024,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Anomaly detection; Dual (grammatical number); Computer science; Anomaly (physics); Flow (mathematics); Dual layer; Layer (electronics); Pattern recognition (psychology); Artificial intelligence; Mathematics; Materials science; Physics; Composite material; Geometry","score_opus":0.030824517408631957,"score_gpt":0.2875966023330008,"score_spread":0.2567720849243688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400381602","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031225395,0.00008127546,0.9902318,0.00025392542,0.00054590777,0.00085705327,0.0000041457965,0.0033666722,0.001536647],"genre_scores_gemma":[0.47083184,0.000002144777,0.5283606,0.00016273992,0.0004463774,0.00013840244,0.0000041718467,0.000025678579,0.000028021792],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980551,0.000048410013,0.00045829118,0.0007692642,0.0002252552,0.00044369337],"domain_scores_gemma":[0.998764,0.00048374996,0.00010692118,0.00045384603,0.000082539045,0.00010897376],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00090505765,0.00025083227,0.00024554672,0.00022930944,0.0005969741,0.0005405601,0.00046778767,0.0002151517,0.000006895193],"category_scores_gemma":[0.000021336282,0.00026036013,0.0001718531,0.0009521817,0.000019088522,0.000270358,0.0002912347,0.00036178256,0.000049693914],"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":[0.000008897557,0.000040737363,0.000009709021,0.00007005603,0.000060959595,0.0000030931826,0.0007048242,0.0013364843,0.017653093,0.03851287,0.0006088077,0.94099045],"study_design_scores_gemma":[0.00029309152,0.000061628016,0.000014539977,0.00002296486,0.000029194603,0.00003424218,0.00004337422,0.91452205,0.040404648,0.0027869279,0.04149273,0.00029461127],"about_ca_topic_score_codex":0.000028661194,"about_ca_topic_score_gemma":0.00000409666,"teacher_disagreement_score":0.9406959,"about_ca_system_score_codex":0.000119410404,"about_ca_system_score_gemma":0.000094550196,"threshold_uncertainty_score":0.99998486},"labels":[],"label_agreement":null},{"id":"W4400476161","doi":"10.1007/s11036-024-02358-0","title":"Real-Time Tracking of Basketball Trajectory Based on the Associative MCMC Model","year":2024,"lang":"en","type":"article","venue":"Mobile Networks and Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Basketball; Trajectory; Markov chain Monte Carlo; Tracking (education); Computer science; Associative property; Artificial intelligence; Mathematics; Bayesian probability; Geography; Psychology","score_opus":0.010479256711337957,"score_gpt":0.24431440818065656,"score_spread":0.2338351514693186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400476161","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":[0.0012277632,0.00013197739,0.9913093,0.0005059655,0.000014354683,0.00065638375,0.000016527594,0.00032201415,0.0058157514],"genre_scores_gemma":[0.9914485,0.00017733443,0.0053607896,0.00018501161,0.0000836319,0.0023335735,0.0000066486914,0.00001408884,0.0003904193],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991932,0.000034286262,0.00019449327,0.0003119612,0.00012391676,0.0001421411],"domain_scores_gemma":[0.99901944,0.00038472455,0.00007378598,0.00042236547,0.00005476772,0.000044892582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027604104,0.00010739795,0.00011437724,0.00005512479,0.0002370675,0.0001079378,0.00032966858,0.0000714824,0.00001667725],"category_scores_gemma":[0.0000030371402,0.00008026163,0.00008042302,0.00050126994,0.00006780397,0.00008873502,0.00004980959,0.0001640983,0.000008750097],"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":[0.0000030417252,0.00016582581,0.000031493782,0.000022711381,0.000030299752,4.2275127e-7,0.00014881227,0.39802554,0.0026154974,0.49826628,0.00572497,0.094965115],"study_design_scores_gemma":[0.00003121544,0.00003786749,0.00014331385,0.000022937946,0.0000104419905,5.9564223e-7,0.000013231269,0.9900314,0.00068883965,0.003424318,0.0055087125,0.000087108165],"about_ca_topic_score_codex":0.000010272896,"about_ca_topic_score_gemma":0.000001601966,"teacher_disagreement_score":0.9902207,"about_ca_system_score_codex":0.000034993398,"about_ca_system_score_gemma":0.000045663728,"threshold_uncertainty_score":0.32729742},"labels":[],"label_agreement":null},{"id":"W4400644438","doi":"10.1109/ojcoms.2024.3428531","title":"Fortifying the Connection: Cybersecurity Tactics for WSN-Driven Smart Manufacturing in the Era of Industry 5.0","year":2024,"lang":"en","type":"article","venue":"IEEE Open Journal of the Communications Society","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":9,"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":"Natural Sciences and Engineering Research Council of Canada; Prince Sultan University","keywords":"Computer science; Industry 4.0; Computer security; Intrusion detection system; Denial-of-service attack; Wireless sensor network; Anomaly detection; Big data; Context (archaeology); Cyber-physical system; Resilience (materials science); Artificial intelligence; Computer network; Embedded system; The Internet; Data mining","score_opus":0.0780809022285899,"score_gpt":0.3585822246630235,"score_spread":0.2805013224344336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400644438","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":[0.11289067,0.0014055418,0.67917114,0.20066848,0.0010010608,0.0023914103,0.00002744509,0.00007285784,0.002371383],"genre_scores_gemma":[0.9646196,0.00034482597,0.034289222,0.0005420262,0.000052387535,0.000083322775,4.5568183e-7,0.000006773575,0.000061388244],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989978,0.0001986085,0.00042637181,0.0000976612,0.00016963223,0.00010991664],"domain_scores_gemma":[0.99729526,0.00074309856,0.00036243297,0.0014430716,0.0001342824,0.000021861715],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.0016620697,0.0000789406,0.00012596023,0.000022688977,0.0007288993,0.00036143945,0.00597371,0.00009125883,0.0000038196604],"category_scores_gemma":[0.00003481198,0.000042867097,0.00028801954,0.0004523004,0.00015605794,0.00047754377,0.0006591823,0.0012590588,7.5287755e-7],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004984277,0.0014235016,0.0027268743,0.00029807832,0.0012817315,0.000003610989,0.09915759,0.0071506067,0.0058018467,0.55152035,0.15623094,0.17435506],"study_design_scores_gemma":[0.001252333,0.00034754816,0.009506196,0.0012399202,0.00034530179,0.00081951375,0.021125434,0.21178539,0.054840818,0.12892513,0.56918126,0.00063115207],"about_ca_topic_score_codex":0.000075745236,"about_ca_topic_score_gemma":0.00005908511,"teacher_disagreement_score":0.8517289,"about_ca_system_score_codex":0.00009086079,"about_ca_system_score_gemma":0.0001667899,"threshold_uncertainty_score":0.99940443},"labels":[],"label_agreement":null},{"id":"W4400647494","doi":"10.1109/jsyst.2024.3423752","title":"Multiagent Detection System Based on Spatial Adaptive Feature Aggregation","year":2024,"lang":"en","type":"article","venue":"IEEE Systems Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Zhejiang Province; Ministry of Natural Resources","keywords":"Computer science; Feature (linguistics); Artificial intelligence; Data mining; Pattern recognition (psychology)","score_opus":0.013734300087097516,"score_gpt":0.2397715769945311,"score_spread":0.22603727690743358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400647494","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":[0.0011009402,0.0002412287,0.99264055,0.00026420335,0.003937263,0.000351784,0.000005529836,0.0007159496,0.0007425585],"genre_scores_gemma":[0.9942733,0.0000067764713,0.004216204,0.000034197965,0.0010439564,0.0000952782,6.969631e-7,0.000017069524,0.0003124887],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986373,0.00015070048,0.00029105158,0.00031566713,0.00041333915,0.00019191975],"domain_scores_gemma":[0.9991907,0.00006080037,0.00016690415,0.00030722423,0.0001529444,0.00012145858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004667094,0.00015635337,0.00014309505,0.00030341587,0.00036830336,0.00066076795,0.00031304514,0.00012784443,0.0000029732307],"category_scores_gemma":[0.000007422785,0.00012762636,0.00013960153,0.00041760583,0.000015208359,0.00030545815,0.000015771402,0.00043676837,0.000104160456],"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":[0.00010797205,0.00021228747,0.000049531744,0.0005758517,0.00021844664,0.0005667781,0.0008149121,0.22128421,0.040791545,0.02310216,0.01571413,0.6965622],"study_design_scores_gemma":[0.00013557539,0.00023382467,0.00007048985,0.00047918348,0.000011806205,0.00069757045,0.000038176357,0.96965027,0.019403642,0.000053320648,0.009080503,0.0001456671],"about_ca_topic_score_codex":0.00007775835,"about_ca_topic_score_gemma":0.000008560866,"teacher_disagreement_score":0.9931724,"about_ca_system_score_codex":0.00046152007,"about_ca_system_score_gemma":0.000094089715,"threshold_uncertainty_score":0.63718003},"labels":[],"label_agreement":null},{"id":"W4400648408","doi":"10.1109/iv55156.2024.10588729","title":"Diving Deeper Into Pedestrian Behavior Understanding: Intention Estimation, Action Prediction, and Event Risk Assessment","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; Huawei Technologies (Canada)","funders":"","keywords":"Pedestrian; Event (particle physics); Action (physics); Computer science; Estimation; Engineering; Transport engineering","score_opus":0.03823060207067832,"score_gpt":0.3366267712814418,"score_spread":0.2983961692107635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400648408","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":[0.01687747,0.000048414673,0.9801933,0.0008703972,0.00026908822,0.0002930966,0.0000025165518,0.000963335,0.00048236965],"genre_scores_gemma":[0.91066986,0.00012769367,0.08870423,0.00002089352,0.000033186832,0.00018745994,0.0000066727744,0.000007715899,0.00024229329],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991153,0.000036167294,0.00022590614,0.00034413725,0.00016982821,0.00010870139],"domain_scores_gemma":[0.9995644,0.000042500986,0.000057657795,0.00021767887,0.000050636514,0.00006713448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029670255,0.00009810627,0.00006780815,0.00016850812,0.00038024233,0.0005011463,0.00012541765,0.000060108105,0.000034028777],"category_scores_gemma":[0.000013683023,0.000089035566,0.000047780366,0.00034299312,0.000031250267,0.0008464714,0.00009635562,0.0001527456,0.000010437957],"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":[0.000002496359,0.0001280004,0.014486386,0.00006875096,0.000043029522,0.0000029091802,0.00054619345,0.00020696448,0.0012841164,0.62048817,0.0014965108,0.36124647],"study_design_scores_gemma":[0.000121989404,0.00018938564,0.04688011,0.000055218432,0.000067861736,0.000046774,0.00037765293,0.82579064,0.0009962046,0.12240507,0.0028756447,0.00019342627],"about_ca_topic_score_codex":0.00015652514,"about_ca_topic_score_gemma":0.000054920063,"teacher_disagreement_score":0.8937924,"about_ca_system_score_codex":0.00032533225,"about_ca_system_score_gemma":0.000043906526,"threshold_uncertainty_score":0.48325658},"labels":[],"label_agreement":null},{"id":"W4400728619","doi":"10.1109/iwcmc61514.2024.10592418","title":"Domain adaptive deep semi-supervised transfer learning for anomaly detection in OpenWiFi","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Transfer of learning; Anomaly detection; Computer science; Artificial intelligence; Domain (mathematical analysis); Deep learning; Anomaly (physics); Mathematics; Physics","score_opus":0.016082606177888384,"score_gpt":0.2516227325935283,"score_spread":0.23554012641563993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400728619","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":[0.024862435,0.0000919139,0.97035116,0.00040397063,0.00007536571,0.0005305496,9.3814947e-7,0.00072858547,0.0029551026],"genre_scores_gemma":[0.9293254,0.000014069747,0.069140054,0.000087416236,0.00004018876,0.00053384225,0.000001492089,0.000013746608,0.00084378483],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904156,0.00003861319,0.0002043079,0.00041883593,0.00009426017,0.00020242488],"domain_scores_gemma":[0.9996235,0.000090000416,0.000009632144,0.0001904189,0.00003840631,0.00004809147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027250344,0.00011293069,0.00011037343,0.00018952996,0.00013862463,0.00018415954,0.00028624787,0.00008223018,0.000033948098],"category_scores_gemma":[0.000004150287,0.00010484763,0.000092883805,0.000670352,0.000019264942,0.00043278365,0.00004327265,0.00018068589,0.000031873547],"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":[0.000028865954,0.00006474717,0.00006337601,0.00004290993,0.000024808856,0.000008257763,0.0013964614,0.0010124263,0.05684085,0.37669867,0.00010336373,0.5637153],"study_design_scores_gemma":[0.00025623222,0.00029749126,0.00033934237,0.00002645517,0.000005636015,0.000018195511,0.00025041297,0.8646926,0.08312635,0.013040235,0.037706163,0.00024084459],"about_ca_topic_score_codex":0.000080235106,"about_ca_topic_score_gemma":0.00016823606,"teacher_disagreement_score":0.904463,"about_ca_system_score_codex":0.0000761739,"about_ca_system_score_gemma":0.00003185989,"threshold_uncertainty_score":0.42755625},"labels":[],"label_agreement":null},{"id":"W4400783223","doi":"10.7554/elife.88173.4.sa3","title":"Author response: A synergistic workspace for human consciousness revealed by Integrated Information Decomposition","year":2024,"lang":"en","type":"peer-review","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Workspace; Consciousness; Decomposition; Computer science; Human–computer interaction; Psychology; Cognitive science; Neuroscience; Artificial intelligence; Biology; Ecology","score_opus":0.021976500775953912,"score_gpt":0.35626108467901446,"score_spread":0.33428458390306054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400783223","genre_codex":"methods","genre_gemma":"other","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":[0.0000062621552,0.0019182893,0.87280476,0.11959371,0.0007594113,0.0018811234,0.00054002885,0.001404028,0.0010923793],"genre_scores_gemma":[0.0015351379,0.00023953154,0.10721102,0.0057712933,0.00015915229,0.005114726,0.0045256033,0.000059573973,0.875384],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978984,0.00014174548,0.00078768446,0.0005469276,0.00032657705,0.0002986576],"domain_scores_gemma":[0.99768,0.00029229114,0.00040990565,0.000859295,0.00063249207,0.00012601094],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009753404,0.0003782454,0.0004986098,0.00036290206,0.00034094992,0.0005752002,0.0009622208,0.0003931363,0.00010087522],"category_scores_gemma":[0.00016482004,0.00033314436,0.00024570915,0.0009955233,0.00006191225,0.00043765391,0.00020537886,0.0004788694,0.00017444979],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013805561,0.000019603698,6.248065e-8,0.0008514619,0.00002482307,6.754409e-7,0.00002395661,0.0000015059521,0.0001903624,0.021259047,0.9667841,0.010830558],"study_design_scores_gemma":[0.00011083598,0.00013270226,0.0000018236447,0.0020842834,0.00009648398,0.0000154975,0.000009599857,0.004788685,0.0004032335,0.0041104606,0.9878439,0.00040254334],"about_ca_topic_score_codex":0.00012662496,"about_ca_topic_score_gemma":0.000045440425,"teacher_disagreement_score":0.8742916,"about_ca_system_score_codex":0.0002942424,"about_ca_system_score_gemma":0.00021491901,"threshold_uncertainty_score":0.9999121},"labels":[],"label_agreement":null},{"id":"W4400915954","doi":"10.21203/rs.3.rs-4660420/v1","title":"Multilevel Learning for Enhanced Traffic Congestion Prediction Using Anomaly Detection and Ensemble Learning.","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Anomaly detection; Ensemble learning; Anomaly (physics); Computer science; Artificial intelligence; Machine learning; Physics","score_opus":0.07035856944648967,"score_gpt":0.38237268457263707,"score_spread":0.3120141151261474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400915954","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":[0.31291196,0.00037270805,0.6840648,0.00011492503,0.00019169375,0.0013203609,0.000011175335,0.0008637063,0.00014864055],"genre_scores_gemma":[0.98209715,0.00026750396,0.015212379,0.000004004986,0.00027141048,0.0011265004,0.000028363993,0.000048156402,0.00094453077],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972286,0.00032200804,0.00035598723,0.0011039667,0.0005087021,0.00048071492],"domain_scores_gemma":[0.9982908,0.00032819266,0.00014863405,0.00042474206,0.0006636774,0.00014400139],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014599712,0.00025133265,0.00025347935,0.00065276155,0.00092101505,0.0006784266,0.00035881545,0.00046287104,0.000006426503],"category_scores_gemma":[0.00029603098,0.0002697831,0.00014319763,0.0004912986,0.0001056197,0.00019457469,0.0010224578,0.0021891852,0.000021167607],"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":[0.000075993485,0.0001171656,0.00008385119,0.0019953393,0.00008877031,0.0000056057725,0.0016889096,0.13908337,0.15573761,0.0026026226,0.0001252458,0.69839555],"study_design_scores_gemma":[0.00018252923,0.00047088368,0.0009515526,0.000429539,0.000020673013,0.00001704063,0.00015262644,0.95918494,0.032615487,0.003435746,0.0022936608,0.00024531083],"about_ca_topic_score_codex":0.000101524834,"about_ca_topic_score_gemma":0.000024518175,"teacher_disagreement_score":0.82010156,"about_ca_system_score_codex":0.00035964066,"about_ca_system_score_gemma":0.0002219217,"threshold_uncertainty_score":0.99997544},"labels":[],"label_agreement":null},{"id":"W4400976359","doi":"10.1109/icca62789.2024.10591904","title":"Autonomous Lifeguard Unmanned Aerial Vehicle Prototype for Information-Weighted Optical Flow Analysis and Rip Current Detection with Depth Risk Models","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bishop's University","funders":"","keywords":"Optical flow; Computer science; Current (fluid); Flow (mathematics); Artificial intelligence; Computer vision; Remote sensing; Engineering; Geology; Physics; Electrical engineering","score_opus":0.0099236574402178,"score_gpt":0.2442038407661062,"score_spread":0.2342801833258884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400976359","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":[0.010792264,0.000041238007,0.98672146,0.00013618478,0.00007711308,0.0010391566,0.000016833234,0.00080587337,0.00036989892],"genre_scores_gemma":[0.8417256,0.00002500697,0.15717945,0.000025803998,0.00006505913,0.00093298644,0.000013515094,0.000006545881,0.000026061132],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910915,0.0000182247,0.00024861866,0.00029769167,0.00015274071,0.00017356456],"domain_scores_gemma":[0.99938834,0.000051807427,0.000060089464,0.00027277909,0.00013353852,0.00009342398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017347938,0.00012813254,0.00014401872,0.00028644595,0.00022408743,0.0004535721,0.00017614961,0.0000651142,0.0000049048504],"category_scores_gemma":[0.0000075576063,0.00009732213,0.00008831441,0.0010340952,0.00003576875,0.0010505762,0.000061495055,0.00012412074,0.000010601199],"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":[0.00007428039,0.000045496185,0.00010999519,0.000047901463,0.00018708601,3.447201e-7,0.00032882945,0.0024546676,0.000087211294,0.04796972,0.00012425195,0.9485702],"study_design_scores_gemma":[0.00018923207,0.00029551281,0.00040928466,0.0000072182906,0.00013564057,0.0000037929335,0.000008606034,0.97828466,0.010302206,0.0043004774,0.0059125144,0.00015088059],"about_ca_topic_score_codex":0.00005571399,"about_ca_topic_score_gemma":0.00008408519,"teacher_disagreement_score":0.97582996,"about_ca_system_score_codex":0.000049885042,"about_ca_system_score_gemma":0.0000670434,"threshold_uncertainty_score":0.4373806},"labels":[],"label_agreement":null},{"id":"W4401011390","doi":"10.1016/b978-0-443-24010-2.00009-3","title":"Deep learning","year":2024,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Psychology; Artificial intelligence; Computer science","score_opus":0.011409665239320977,"score_gpt":0.23618127926809285,"score_spread":0.22477161402877188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401011390","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.3265869e-7,0.0010164425,0.052542593,0.00013528271,0.00019106062,0.00021986091,0.0000011178022,0.0010586404,0.94483477],"genre_scores_gemma":[0.00037203298,0.00010785762,0.007946498,0.00016594076,0.00020746964,0.000073744195,0.0000031851685,0.000049989536,0.9910733],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99884665,0.00000678784,0.00025312562,0.00051817216,0.00019801129,0.00017723345],"domain_scores_gemma":[0.9991237,0.000025099522,0.00011309622,0.00060177036,0.000054811942,0.000081548336],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001126355,0.00024456898,0.0002151583,0.00015799473,0.00014934324,0.00018599037,0.00063467835,0.00022201368,0.00009651902],"category_scores_gemma":[0.0000025487445,0.00023465909,0.00020645288,0.000025067906,0.000050697043,0.00005061156,0.00037011414,0.00066506973,0.0016493768],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.4011817e-7,7.0924597e-7,6.514479e-8,0.000011750864,0.000011858234,0.0000071894697,0.00003515594,0.0000010003008,0.0000062053223,0.3695998,0.000057982423,0.63026816],"study_design_scores_gemma":[0.000016610626,0.000030112786,5.1706047e-7,0.00007694612,0.000017705894,0.000024502926,9.958442e-7,0.0013394258,0.0000630894,0.17180207,0.8264127,0.0002153542],"about_ca_topic_score_codex":1.8672658e-7,"about_ca_topic_score_gemma":0.0000025079926,"teacher_disagreement_score":0.8263547,"about_ca_system_score_codex":0.000060836435,"about_ca_system_score_gemma":0.00003578952,"threshold_uncertainty_score":0.999128},"labels":[],"label_agreement":null},{"id":"W4401024146","doi":"10.24963/ijcai.2024/77","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Science and Technology Commission of Shanghai Municipality","keywords":"Noise reduction; Computer science; Anomaly detection; Diffusion; Artificial intelligence; Computer vision; Physics","score_opus":0.031812328125185786,"score_gpt":0.3131941489824246,"score_spread":0.2813818208572388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401024146","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":[0.029024826,0.000100928926,0.96632594,0.0024643925,0.00026132062,0.0002470327,0.0000014599586,0.0011180069,0.00045609407],"genre_scores_gemma":[0.6892144,0.000011755947,0.31020406,0.00025407586,0.0001295793,0.00008035529,0.000002204717,0.0000123876525,0.000091192145],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892294,0.000028408287,0.00020332208,0.0005470642,0.00010479643,0.00019347097],"domain_scores_gemma":[0.9994712,0.000091471375,0.000029063323,0.00023707953,0.0000705623,0.00010065302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028606234,0.00011858094,0.000104423045,0.00017673956,0.00029552777,0.00042727703,0.00014059037,0.0000729409,0.000027250851],"category_scores_gemma":[0.000022402945,0.00011263634,0.00005350027,0.0003134973,0.00002400416,0.00070223666,0.00008631843,0.00023325124,0.000011879626],"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":[0.0000065421455,0.000010491926,0.00019654524,0.000015524758,0.000007665815,5.8237504e-7,0.00029338468,0.0016204261,0.0013861749,0.14187433,0.00009076514,0.85449755],"study_design_scores_gemma":[0.000036802565,0.00010404427,0.00007978271,0.00003792747,0.000009715021,0.000062187224,0.00020283082,0.918755,0.001261322,0.07580666,0.003496336,0.00014740186],"about_ca_topic_score_codex":0.000038678892,"about_ca_topic_score_gemma":0.000007675285,"teacher_disagreement_score":0.9171346,"about_ca_system_score_codex":0.000045594894,"about_ca_system_score_gemma":0.000025580974,"threshold_uncertainty_score":0.45931765},"labels":[],"label_agreement":null},{"id":"W4401024377","doi":"10.24963/ijcai.2024/271","title":"Efficient Visual Representation Learning with Heat Conduction Equation","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Ministry of Transportation of Ontario","funders":"National Office for Philosophy and Social Sciences; National Natural Science Foundation of China","keywords":"Computer science; Anomaly detection; Noise (video); Graph; Artificial intelligence; Machine learning; Pattern recognition (psychology); Theoretical computer science","score_opus":0.025444876021470463,"score_gpt":0.30791007963532174,"score_spread":0.2824652036138513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401024377","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":[0.05231276,0.000026719077,0.941705,0.0007151593,0.00007758317,0.00013954057,9.106098e-8,0.0012017274,0.003821414],"genre_scores_gemma":[0.97658706,0.0000041508565,0.02192035,0.000035474928,0.000045230918,0.000059315582,0.0000035262074,0.0000056279737,0.0013392805],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993744,0.000024841123,0.00010157207,0.00026721868,0.00014732295,0.00008463581],"domain_scores_gemma":[0.999745,0.000033846165,0.00001527544,0.00012916134,0.000048648748,0.000028084432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011610859,0.00005671471,0.000043439915,0.000100724435,0.00013323243,0.00020674535,0.00008226327,0.000026559943,0.00003419749],"category_scores_gemma":[0.0000055740556,0.00004427141,0.00002363236,0.0005742636,0.000016977685,0.00017879624,0.00003129071,0.00009211174,0.00008839431],"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":[0.000009194632,0.000092616785,0.00026579757,0.00002738343,0.000027360042,0.000007097084,0.0008458151,0.105393626,0.046673078,0.6321411,0.0007893359,0.21372758],"study_design_scores_gemma":[0.00003844054,0.0001066953,0.00020198147,0.000010616346,0.0000035165292,0.000022083059,0.000069066,0.9655551,0.030538427,0.00055026315,0.0028339485,0.000069872396],"about_ca_topic_score_codex":0.000053704345,"about_ca_topic_score_gemma":0.0000019652284,"teacher_disagreement_score":0.92427427,"about_ca_system_score_codex":0.000041366886,"about_ca_system_score_gemma":0.000024467243,"threshold_uncertainty_score":0.19936502},"labels":[],"label_agreement":null},{"id":"W4401163968","doi":"10.1109/bmsb62888.2024.10608270","title":"Optimizing Data Quality in Deep Learning through Advanced Analytics","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Quality (philosophy); Analytics; Data science; Data analysis; Data quality; Learning analytics; Artificial intelligence; Machine learning; Data mining; Engineering","score_opus":0.07458423191664845,"score_gpt":0.3779632087282159,"score_spread":0.30337897681156745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401163968","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005425673,0.0002730045,0.9851646,0.0010640796,0.000045467117,0.00007026802,8.7717024e-7,0.00072508096,0.01211407],"genre_scores_gemma":[0.45355174,0.00010491987,0.54530305,0.00015103244,0.000017173938,0.0000121728335,0.0000058585647,0.000004916435,0.0008491571],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916816,0.000029627703,0.0001917143,0.00038591994,0.000097330805,0.00012727776],"domain_scores_gemma":[0.99919695,0.00007898272,0.00002532241,0.0006589061,0.000017317388,0.000022508286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030815013,0.000062484665,0.00008015669,0.000055867164,0.000066714565,0.0001662784,0.0007061877,0.000032360887,0.000023665698],"category_scores_gemma":[0.000024992118,0.000057131165,0.000022895276,0.00066807,0.00001535677,0.0008199131,0.0004436808,0.00017812203,0.00003422129],"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":[0.0000012539689,0.000033155207,0.000120611556,0.000026206386,0.000010254264,0.000007972169,0.00070322654,0.0073833023,0.00085346156,0.6483384,0.00046169173,0.3420605],"study_design_scores_gemma":[0.000039686707,0.0000150496,0.00014431203,0.000012292154,0.0000018771086,0.0000032810676,0.000112748785,0.9164872,0.000973652,0.007045032,0.07505426,0.000110586734],"about_ca_topic_score_codex":0.00009769441,"about_ca_topic_score_gemma":0.000059351874,"teacher_disagreement_score":0.90910393,"about_ca_system_score_codex":0.000032442105,"about_ca_system_score_gemma":0.000020942296,"threshold_uncertainty_score":0.23297413},"labels":[],"label_agreement":null},{"id":"W4401211649","doi":"10.3390/app14156712","title":"Violence-YOLO: Enhanced GELAN Algorithm for Violence Detection","year":2024,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Computer security","score_opus":0.013162015696953508,"score_gpt":0.27204180777392645,"score_spread":0.2588797920769729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401211649","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":[0.0048030573,0.00023046951,0.99011964,0.00018924603,0.000352308,0.0004889058,0.0000051321217,0.001020832,0.002790427],"genre_scores_gemma":[0.8158584,0.00014152608,0.1829364,0.00015006032,0.0001221519,0.0007170247,9.750318e-7,0.0000069733755,0.000066434724],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984341,0.000012058249,0.00021828742,0.00074454787,0.00026196893,0.0003290099],"domain_scores_gemma":[0.9993767,0.00013484796,0.000058124704,0.00030853457,0.000047159905,0.000074596326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049439585,0.00014386303,0.00011841358,0.00017684879,0.0005765643,0.00042249254,0.0009258103,0.00007512057,0.000011557616],"category_scores_gemma":[0.000007713861,0.0001222329,0.00007771287,0.0011932892,0.00021474427,0.0003659992,0.00011898633,0.000116862364,0.0001019656],"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":[5.710293e-7,0.000009872316,5.5952586e-7,0.0000065405866,0.000002805945,2.2275908e-7,0.00004973611,0.000016249662,0.030462893,0.07105867,0.000048288934,0.8983436],"study_design_scores_gemma":[0.000052303054,0.00015755213,0.000035606638,0.00008662256,0.0000061083574,0.000011004059,0.00007704981,0.2577983,0.58784467,0.14748795,0.0061917594,0.00025107214],"about_ca_topic_score_codex":0.000023989594,"about_ca_topic_score_gemma":0.0000071113036,"teacher_disagreement_score":0.8980925,"about_ca_system_score_codex":0.000045013712,"about_ca_system_score_gemma":0.00008606308,"threshold_uncertainty_score":0.4984513},"labels":[],"label_agreement":null},{"id":"W4401349519","doi":"10.18280/rces.110201","title":"A Cross-Domain Abnormal Behavior Recognition Model and Application Based on Transfer Learning","year":2024,"lang":"en","type":"article","venue":"Review of Computer Engineering Studies","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Transfer of learning; Computer science; Domain (mathematical analysis); Artificial intelligence; Pattern recognition (psychology); Mathematics","score_opus":0.018001209677557056,"score_gpt":0.29369353507698176,"score_spread":0.2756923253994247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401349519","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":[0.004233351,0.015338327,0.97946423,0.0001988255,0.000046187084,0.0003054455,0.0000027235667,0.00038183125,0.000029095192],"genre_scores_gemma":[0.7020226,0.0212613,0.27549833,0.0002593503,0.000084956446,0.00082136504,0.0000079241045,0.000023240935,0.000020927933],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993062,0.000013656921,0.00023269313,0.0002396661,0.0001126075,0.00009517983],"domain_scores_gemma":[0.99964887,0.00006775516,0.000022505263,0.00016448648,0.000071109134,0.000025260799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002368446,0.000115663934,0.00019117654,0.00009190262,0.0000592314,0.000042372172,0.00012340983,0.000026302385,9.2511317e-7],"category_scores_gemma":[0.0000054585103,0.00010333848,0.00007225509,0.00024562806,0.000022766742,0.0001336126,0.000044471246,0.00011822645,0.000003868871],"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":[0.0000020652392,0.00006648121,0.00005304703,0.01163514,0.0000595754,0.000004038982,0.00026277147,0.10797939,0.0007434387,0.019906478,0.0001473175,0.8591403],"study_design_scores_gemma":[0.00004922244,0.00007418196,0.00031708786,0.0026860412,0.00002049742,0.000007421526,8.345302e-7,0.9930758,0.00041015766,0.00015006089,0.0030898077,0.00011889424],"about_ca_topic_score_codex":6.3489495e-7,"about_ca_topic_score_gemma":6.782125e-8,"teacher_disagreement_score":0.88509643,"about_ca_system_score_codex":0.000023525265,"about_ca_system_score_gemma":0.000011062302,"threshold_uncertainty_score":0.42140213},"labels":[],"label_agreement":null},{"id":"W4401373207","doi":"10.32388/rzi40z","title":"Review of: \"An Intelligent Analytics for People Detection Using Deep Learning\"","year":2024,"lang":"en","type":"peer-review","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Analytics; Computer science; Deep learning; Data science; Artificial intelligence; Learning analytics; Machine learning","score_opus":0.053056586805503486,"score_gpt":0.36118348851475984,"score_spread":0.3081269017092564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401373207","genre_codex":"methods","genre_gemma":"review","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":[0.0000012489642,0.140097,0.8540291,0.0032982149,0.00053175865,0.0010848949,0.000014864733,0.00036537836,0.000577514],"genre_scores_gemma":[0.0003117794,0.59428364,0.31776065,0.0061460664,0.00060171995,0.0010684304,0.0003272864,0.00012269328,0.07937775],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99817556,0.000060843675,0.00070548354,0.00060070225,0.0002588002,0.00019858702],"domain_scores_gemma":[0.9980976,0.00007265057,0.00041601658,0.0007884421,0.0005393067,0.00008597949],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007270625,0.00025591237,0.0006330864,0.00020055081,0.0001220289,0.00007706048,0.0007409132,0.00016927253,0.000116256255],"category_scores_gemma":[0.00011306197,0.00022558686,0.0004326328,0.0010423356,0.000017909493,0.00012401897,0.00023523535,0.00037856458,0.000029626073],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011564013,0.000067133136,2.2863435e-7,0.09224416,0.00008106941,6.55645e-7,0.000028297129,0.00010852992,0.0000502121,0.0048447163,0.119390056,0.7831838],"study_design_scores_gemma":[0.000012257746,0.00012432132,2.581758e-7,0.011820537,0.00022797911,0.000023004577,0.0000031651828,0.18451768,0.00092660286,0.00084375625,0.8012922,0.00020826086],"about_ca_topic_score_codex":0.00015686899,"about_ca_topic_score_gemma":0.00023170981,"teacher_disagreement_score":0.7829755,"about_ca_system_score_codex":0.00012964207,"about_ca_system_score_gemma":0.00011928737,"threshold_uncertainty_score":0.9199165},"labels":[],"label_agreement":null},{"id":"W4401389866","doi":"10.1145/3665026.3665052","title":"Detection of Abnormal Activities in a Crowd Video Surveillance using Contextual Information","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence","score_opus":0.012624548561148748,"score_gpt":0.2535494336297349,"score_spread":0.24092488506858617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401389866","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":[0.18630986,0.00003115321,0.8120182,0.00005127737,0.00006909767,0.00009013057,0.0000018149726,0.00021909305,0.0012093505],"genre_scores_gemma":[0.99322695,0.000009150498,0.0066504776,0.000032241096,0.000013367257,0.00002011329,6.1748375e-7,0.000002306034,0.00004481097],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949664,0.000017770091,0.000207465,0.00009672419,0.00009248973,0.000088911125],"domain_scores_gemma":[0.9997202,0.000047254776,0.000043761105,0.00014087511,0.000032053522,0.000015831163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017830134,0.000055045737,0.00007359726,0.00020781833,0.00003806068,0.00010156712,0.00013338294,0.000040067236,0.00000821023],"category_scores_gemma":[0.000008235347,0.00005182073,0.000032687763,0.00050132396,0.000023999659,0.0014622767,0.00005237913,0.00007349783,0.00000667477],"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":[0.000019535799,0.000050639454,0.001129019,0.00015024042,0.000019944438,0.0000025213008,0.0021300707,0.000976867,0.11311508,0.16549462,0.00014319396,0.71676826],"study_design_scores_gemma":[0.000113524395,0.000073633244,0.004785895,0.000035010507,0.000001469798,0.00003290667,0.00021647867,0.75340474,0.23446795,0.0013453633,0.0053723413,0.00015066296],"about_ca_topic_score_codex":0.00036906084,"about_ca_topic_score_gemma":0.0001165108,"teacher_disagreement_score":0.8069171,"about_ca_system_score_codex":0.00004933023,"about_ca_system_score_gemma":0.000038082413,"threshold_uncertainty_score":0.2113188},"labels":[],"label_agreement":null},{"id":"W4401437168","doi":"10.1016/j.asoc.2024.112070","title":"Consistency-guided semi-supervised outlier detection in heterogeneous data using fuzzy rough sets","year":2024,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Outlier; Consistency (knowledge bases); Anomaly detection; Computer science; Data mining; Artificial intelligence; Fuzzy logic; Pattern recognition (psychology); Similarity (geometry); Fuzzy set; Fuzzy classification; Construct (python library); Machine learning; Image (mathematics)","score_opus":0.058440039431155125,"score_gpt":0.3102903186382752,"score_spread":0.2518502792071201,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401437168","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":[0.10006083,0.00023259972,0.89617544,0.00012300376,0.00027508568,0.00041982692,0.0000065395443,0.0012915274,0.0014151545],"genre_scores_gemma":[0.90377104,0.0000076164238,0.095780015,0.0002573815,0.00009948706,0.000022700582,0.000014277393,0.000030883486,0.00001661367],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977432,0.00004873604,0.00053964957,0.0010111759,0.00024578598,0.00041146827],"domain_scores_gemma":[0.9983912,0.00016701745,0.00009666077,0.0012123566,0.00004639905,0.00008632802],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00054585695,0.00024013978,0.0002475374,0.00023428566,0.00032596572,0.00040999835,0.0011013435,0.0001393676,0.0000066935954],"category_scores_gemma":[0.000017001332,0.0002585852,0.00006851893,0.0009094104,0.000052736887,0.0003195833,0.0010012792,0.00033439277,0.000064799824],"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":[0.000008545378,0.0001286452,0.00018488526,0.00016016237,0.00008173442,0.00008757151,0.0014225533,0.026285013,0.046856087,0.018886851,0.00040822368,0.90548974],"study_design_scores_gemma":[0.0001549925,0.000015615808,0.00007818449,0.000054072483,0.0000132736,0.00015722861,0.00003728669,0.98426527,0.008391557,0.004466588,0.0020657906,0.000300131],"about_ca_topic_score_codex":0.0001300417,"about_ca_topic_score_gemma":0.00002874038,"teacher_disagreement_score":0.9579803,"about_ca_system_score_codex":0.00015078981,"about_ca_system_score_gemma":0.00010421524,"threshold_uncertainty_score":0.99998665},"labels":[],"label_agreement":null},{"id":"W4401540141","doi":"10.1109/icphm61352.2024.10627589","title":"Mitigating Data Scarcity for Satellite Reaction Wheel Fault Diagnosis with Wasserstein Generative Adversarial Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"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; University of Windsor","keywords":"Adversarial system; Scarcity; Computer science; Satellite; Generative grammar; Fault (geology); Generative adversarial network; Artificial intelligence; Aerospace engineering; Deep learning; Engineering; Geology","score_opus":0.03620116451762757,"score_gpt":0.28881073207515245,"score_spread":0.2526095675575249,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401540141","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":[0.0025415397,0.00014840439,0.9936322,0.0015535795,0.00017864311,0.00047908112,0.000019824947,0.00068602397,0.0007606986],"genre_scores_gemma":[0.5977892,0.00014619385,0.40062982,0.00020143914,0.0003479538,0.000436105,0.000077657074,0.000014073999,0.00035754626],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889755,0.000026539841,0.00017670997,0.0005878686,0.00012815453,0.0001831704],"domain_scores_gemma":[0.9989893,0.00016740274,0.000052872656,0.0006517149,0.00007780819,0.000060942715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002502993,0.00012023162,0.00010450305,0.00005744857,0.0002582996,0.00035103658,0.0005315318,0.00006993914,0.000006114672],"category_scores_gemma":[0.000013460707,0.00009498516,0.000044842545,0.00042056778,0.000036501413,0.00089578377,0.00019478185,0.00013068879,0.000006776714],"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":[0.000039549133,0.00013298093,0.001053446,0.00008364016,0.0002111694,0.000010042742,0.0006181794,0.0014637667,0.009218859,0.22609997,0.014263698,0.7468047],"study_design_scores_gemma":[0.00013697962,0.0001260396,0.0002508726,0.000047863814,0.000027541248,0.0000099409,0.000068172485,0.9188964,0.026705178,0.0016253596,0.051898494,0.00020719133],"about_ca_topic_score_codex":0.00013141523,"about_ca_topic_score_gemma":0.00013687916,"teacher_disagreement_score":0.9174326,"about_ca_system_score_codex":0.000055286957,"about_ca_system_score_gemma":0.0000505841,"threshold_uncertainty_score":0.38733822},"labels":[],"label_agreement":null},{"id":"W4401585697","doi":"10.1080/03155986.2024.2382545","title":"Index tracking via reparameterizable subset sampling in neural networks","year":2024,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Index (typography); Benchmark (surveying); Tracking (education); Computer science; Cardinality (data modeling); Constraint (computer-aided design); Artificial neural network; Component (thermodynamics); Portfolio; Tracking error; Sampling (signal processing); Differentiable function; Data mining; Mathematical optimization; Artificial intelligence; Mathematics; Filter (signal processing); Finance; Control (management)","score_opus":0.07421487349658153,"score_gpt":0.3711311126281178,"score_spread":0.2969162391315363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401585697","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":[0.012047039,0.00030164426,0.98464376,0.00042767942,0.00018210441,0.0004985878,0.000006863064,0.00016595826,0.001726351],"genre_scores_gemma":[0.99737895,0.000036127192,0.0019139043,0.000104554514,0.00007884195,0.00028727925,0.000032728847,0.0000045439924,0.00016308684],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855447,0.000054062813,0.0005049716,0.00016863848,0.00046260734,0.00025522232],"domain_scores_gemma":[0.99919146,0.0002003203,0.000038809394,0.00021071339,0.00028453005,0.00007414907],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0015988746,0.00008790052,0.00010404947,0.0005313745,0.00030551772,0.0024523505,0.00026031362,0.000096025855,0.000008067275],"category_scores_gemma":[0.000050670867,0.000077935074,0.00002762136,0.0009772654,0.000042815027,0.004053924,0.00013465318,0.00034495813,0.00003092856],"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":[0.000018923629,0.000021023065,0.0019014906,0.00021368191,0.000019276178,0.0000054740576,0.0018678772,0.15192918,0.00017439855,0.55708677,0.0017905956,0.2849713],"study_design_scores_gemma":[0.00007032646,0.000034051307,0.0015104504,0.000046853642,3.814576e-7,0.000048353795,0.000095785465,0.9414066,0.00003402505,0.00028827967,0.056379884,0.00008499887],"about_ca_topic_score_codex":0.0003403634,"about_ca_topic_score_gemma":0.000013382115,"teacher_disagreement_score":0.9853319,"about_ca_system_score_codex":0.00009315589,"about_ca_system_score_gemma":0.00009409733,"threshold_uncertainty_score":0.9985832},"labels":[],"label_agreement":null},{"id":"W4401629876","doi":"10.1016/j.engappai.2024.109088","title":"Advancing unsupervised anomaly detection with normalizing flow and multi-scale ensemble learning","year":2024,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Anomaly Detection Techniques and Applications","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":"Ballard Power Systems (Canada)","funders":"","keywords":"Computer science; Anomaly detection; Scale (ratio); Ensemble learning; Artificial intelligence; Machine learning; Unsupervised learning; Anomaly (physics); Pattern recognition (psychology)","score_opus":0.00976443678066586,"score_gpt":0.24031815247259639,"score_spread":0.23055371569193053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401629876","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":[0.010423654,0.00033444088,0.9880575,0.00007795597,0.000036607315,0.00027728794,0.0000015593755,0.00072949636,0.000061495244],"genre_scores_gemma":[0.63736767,0.00004429972,0.3623294,0.0000041684993,0.000028469589,0.00018971533,0.0000011391771,0.000012588613,0.000022530896],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990541,0.000010598161,0.00027498568,0.00035882927,0.00011614263,0.00018536612],"domain_scores_gemma":[0.9994125,0.00009222198,0.000046760008,0.00029359048,0.00008488868,0.000070051756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000190141,0.00012994844,0.00011822394,0.00021845543,0.00016568872,0.00013109899,0.00024280473,0.000053722502,0.0000037813318],"category_scores_gemma":[0.000013843241,0.00013053631,0.000038364768,0.00091470755,0.00004138202,0.00032188208,0.00007183234,0.00020211957,0.000015124863],"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":[0.0000028212849,0.000035578894,0.000040144503,0.00008742348,0.00001396885,0.0000010263591,0.0004681719,0.07914044,0.19096436,0.01754829,0.0000018779951,0.7116959],"study_design_scores_gemma":[0.000007235168,0.000047551493,0.0000472711,0.000037001206,0.0000065400877,0.000015821763,0.00007563512,0.72257817,0.27508742,0.00035344998,0.0016323255,0.00011159714],"about_ca_topic_score_codex":0.000056310437,"about_ca_topic_score_gemma":0.000032724558,"teacher_disagreement_score":0.7115843,"about_ca_system_score_codex":0.000034988203,"about_ca_system_score_gemma":0.000025477084,"threshold_uncertainty_score":0.5323117},"labels":[],"label_agreement":null},{"id":"W4401726558","doi":"10.1109/tnsm.2024.3447532","title":"Real-Time Adaptive Anomaly Detection in Industrial IoT Environments","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":32,"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; Anomaly detection; Internet of Things; Real-time computing; Embedded system; Data mining","score_opus":0.014855758813272366,"score_gpt":0.2152373455103004,"score_spread":0.20038158669702805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401726558","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":[0.010619025,0.000028312825,0.98418736,0.0005869712,0.00030199802,0.0004807698,0.000003086702,0.00033891137,0.0034535488],"genre_scores_gemma":[0.99055165,0.0006893038,0.0070577455,0.00026500158,0.00009634508,0.00030252003,0.0000010990153,0.00001743316,0.0010188876],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888515,0.000050457093,0.00021661594,0.00046147747,0.00015591197,0.00023038244],"domain_scores_gemma":[0.99956113,0.00003959023,0.000031436575,0.00029706227,0.000006868118,0.000063888845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018179893,0.0001549879,0.000118416545,0.00020071174,0.00019034282,0.00012690543,0.00021554879,0.00009336276,0.00003252604],"category_scores_gemma":[9.148087e-8,0.00015940196,0.00004853289,0.00091645407,0.000017108847,0.00016689781,0.000010757732,0.00021766416,0.00010225416],"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":[0.000048884634,0.00015389088,0.000004501554,0.00003203166,0.00009958378,0.000028574008,0.00022317779,0.040875908,0.00070840336,0.0039369515,0.00028695422,0.9536011],"study_design_scores_gemma":[0.0006541843,0.0004057311,0.00089014415,0.00018182334,0.00008177225,0.000016210963,0.00010249623,0.94971037,0.00410232,0.002461644,0.04088255,0.00051077135],"about_ca_topic_score_codex":0.0001517243,"about_ca_topic_score_gemma":0.00012408065,"teacher_disagreement_score":0.97993267,"about_ca_system_score_codex":0.00010720611,"about_ca_system_score_gemma":0.000010251829,"threshold_uncertainty_score":0.65002227},"labels":[],"label_agreement":null},{"id":"W4401732379","doi":"10.1016/j.jprocont.2024.103295","title":"Improved similarity analysis of industrial alarm flood sequences by considering alarm correlations","year":2024,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Science and Technology Department of Hubei Province; National Aerospace Science Foundation of China; National Natural Science Foundation of China; Wuhan Municipal Science and Technology Bureau; Hubei Provincial Collaborative Innovation Centre of Agricultural E-Commerce","keywords":"ALARM; Similarity (geometry); Flood myth; Computer science; Environmental science; Mathematics; Data mining; Engineering; Artificial intelligence; Geography; Electrical engineering","score_opus":0.018000119026307878,"score_gpt":0.27920069401796455,"score_spread":0.26120057499165666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401732379","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":[0.024628183,0.00076024444,0.9722344,0.001802869,0.00019008096,0.00015610982,0.000035719528,0.000079575504,0.00011277274],"genre_scores_gemma":[0.99654776,0.00003464294,0.0031707576,0.000095820156,0.00009593138,0.000015678637,0.0000014381156,0.0000056491704,0.000032300788],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987145,0.00004517428,0.00064574,0.00018528826,0.00027046914,0.00013881584],"domain_scores_gemma":[0.9986815,0.0002056184,0.00046186356,0.00017260296,0.0003895608,0.00008885128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048330287,0.00010877134,0.00035467962,0.00040918757,0.00009746829,0.00016932706,0.00044845667,0.00010762554,0.000027045642],"category_scores_gemma":[0.00011386449,0.00009060711,0.00025017877,0.0014107422,0.000056892804,0.0005822281,0.000030034233,0.00036410097,8.562545e-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":[0.0004057471,0.002360524,0.052872304,0.0005487486,0.027261855,0.0002232634,0.00754657,0.05054141,0.33300328,0.11287697,0.020445453,0.3919139],"study_design_scores_gemma":[0.0009415668,0.00033386474,0.00029758044,0.000093712784,0.0014676839,0.00006647087,0.00014773241,0.9593243,0.029005606,0.0047654402,0.0033180472,0.00023801455],"about_ca_topic_score_codex":0.000021080254,"about_ca_topic_score_gemma":0.000007620598,"teacher_disagreement_score":0.9719196,"about_ca_system_score_codex":0.0000425703,"about_ca_system_score_gemma":0.00028916408,"threshold_uncertainty_score":0.36948505},"labels":[],"label_agreement":null},{"id":"W4401768889","doi":"10.18280/isi.290414","title":"A Machine Learning Approach on Outlier Removal for Decision Tree Regression Method","year":2024,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universitas Gadjah Mada","keywords":"Decision tree; Machine learning; Computer science; Outlier; Artificial intelligence; Regression; Decision tree learning; Incremental decision tree; Statistics; Mathematics","score_opus":0.02154406024813839,"score_gpt":0.2909169692559945,"score_spread":0.2693729090078561,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401768889","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008232678,0.00016206941,0.9813774,0.000109883156,0.00020409004,0.00050524663,0.000009243526,0.0010554306,0.015753344],"genre_scores_gemma":[0.28778675,0.000037098518,0.7110093,0.00013663425,0.00007468661,0.00033286648,0.0000539279,0.000015405945,0.0005533115],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878526,0.000052932974,0.00043391052,0.0002566853,0.00025704998,0.0002141636],"domain_scores_gemma":[0.999078,0.00024372377,0.00014513875,0.00034213328,0.00012566635,0.000065372595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078085746,0.0001705776,0.00016174224,0.00038499408,0.00041766363,0.0005951024,0.0003677115,0.0001235394,0.000007178563],"category_scores_gemma":[0.00018679026,0.00013412941,0.00013037953,0.00063541334,0.000028190565,0.0018898705,0.00010415341,0.00021259283,0.00006455836],"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":[0.000020802494,0.000013743111,0.0000044912777,0.00009608132,0.000009641586,7.9551444e-7,0.0011993353,0.001176611,0.00013007372,0.1026336,0.0007513956,0.8939634],"study_design_scores_gemma":[0.00012895024,0.00016889429,0.000051842806,0.0001953558,0.00000780185,0.00008758197,0.00008028593,0.8480283,0.0018160293,0.019581309,0.12968859,0.00016506632],"about_ca_topic_score_codex":0.000014099999,"about_ca_topic_score_gemma":0.000001034006,"teacher_disagreement_score":0.89379835,"about_ca_system_score_codex":0.00018366303,"about_ca_system_score_gemma":0.000047469523,"threshold_uncertainty_score":0.5738586},"labels":[],"label_agreement":null},{"id":"W4401829019","doi":"10.18280/ria.380406","title":"Outlier Detection in Wireless Sensor Networks Using Machine Learning and Statistical Based Approaches","year":2024,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Anomaly detection; Computer science; Wireless sensor network; Outlier; Statistical learning; Machine learning; Artificial intelligence; Wireless; Data mining; Computer network; Telecommunications","score_opus":0.05181408790663502,"score_gpt":0.28182554603841564,"score_spread":0.23001145813178062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401829019","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":[0.016532745,0.0002724211,0.9823444,0.00016153799,0.000084527754,0.00017396928,0.0000016165739,0.0002537952,0.00017502312],"genre_scores_gemma":[0.9641411,0.000044286015,0.03555003,0.000021466118,0.000038163096,0.000031483738,0.0000024385229,0.000012670768,0.00015837088],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898714,0.0000656305,0.00025293772,0.0004151085,0.0000788041,0.00020035305],"domain_scores_gemma":[0.9995325,0.00017166455,0.00003584194,0.00018253484,0.000020258993,0.0000571729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032493423,0.00011147774,0.00011722581,0.00015076813,0.0001473791,0.00019732426,0.00014053196,0.00007344963,0.000020146856],"category_scores_gemma":[0.000019650475,0.000111124005,0.000033055523,0.0006008084,0.00006059282,0.00014304624,0.000071479495,0.00030449088,0.00001773392],"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":[0.000009813412,0.00008438113,0.0008556727,0.000102330094,0.00000930826,0.000033782708,0.00039779508,0.21263856,0.007879866,0.044893075,0.000006424076,0.73308897],"study_design_scores_gemma":[0.000011716323,0.00004089953,0.000031900592,0.000045788427,0.000004236404,0.000028671893,0.00007065719,0.9709801,0.026923085,0.0006488651,0.0010893138,0.00012474004],"about_ca_topic_score_codex":0.00008003479,"about_ca_topic_score_gemma":0.000029566247,"teacher_disagreement_score":0.94760835,"about_ca_system_score_codex":0.000052082818,"about_ca_system_score_gemma":0.000019283103,"threshold_uncertainty_score":0.45315054},"labels":[],"label_agreement":null},{"id":"W4401871638","doi":"10.1016/j.aej.2024.08.048","title":"Utilizing correlation in space and time: Anomaly detection for Industrial Internet of Things (IIoT) via spatiotemporal gated graph attention network","year":2024,"lang":"en","type":"article","venue":"Alexandria Engineering Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":12,"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","funders":"King Saud University; National Natural Science Foundation of China","keywords":"Anomaly detection; Computer science; Graph; Data mining; GRASP; Convolutional neural network; Artificial intelligence; Theoretical computer science","score_opus":0.010694564459500538,"score_gpt":0.21346842133581787,"score_spread":0.20277385687631733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401871638","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":[0.08967239,0.0001937791,0.90918887,0.000095239455,0.00047233514,0.00019782045,6.031793e-7,0.00014376466,0.000035179008],"genre_scores_gemma":[0.96264094,0.00002776452,0.03705835,0.000005640899,0.00017062401,0.000013429382,0.000002941684,0.000012925934,0.00006740091],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991967,0.000022534654,0.0003336178,0.00018537579,0.000104605766,0.00015717557],"domain_scores_gemma":[0.9996077,0.00007990247,0.000117409254,0.00009400571,0.0000511905,0.000049811948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048154627,0.00010626355,0.00013418986,0.00028465863,0.000060263428,0.0001576609,0.00012494883,0.000115514034,0.0000054972425],"category_scores_gemma":[0.000024864645,0.00010878343,0.00006872521,0.0005124867,0.0000133975145,0.00057960517,0.000038065496,0.0002926735,0.000001398864],"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":[0.00027609704,0.0001844327,0.01511257,0.00044215043,0.00040506903,0.00004597824,0.0024678719,0.109068945,0.22723393,0.09543562,0.0035497884,0.54577756],"study_design_scores_gemma":[0.00028045746,0.00013547039,0.002253562,0.00021316634,0.000014259151,0.00012025358,0.0000048110287,0.9905672,0.003901702,0.0014393517,0.00095763686,0.00011213469],"about_ca_topic_score_codex":0.00003373335,"about_ca_topic_score_gemma":0.0000021108126,"teacher_disagreement_score":0.8814983,"about_ca_system_score_codex":0.000059664242,"about_ca_system_score_gemma":0.000022796032,"threshold_uncertainty_score":0.44360596},"labels":[],"label_agreement":null},{"id":"W4401879061","doi":"10.1109/compsac61105.2024.00148","title":"Enhancing Valid Test Input Generation with Distribution Awareness for Deep Neural Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Artificial neural network; Test (biology); Artificial intelligence","score_opus":0.01841692417481953,"score_gpt":0.2711883901525361,"score_spread":0.25277146597771655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401879061","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":[0.0026836735,0.000058993297,0.9954568,0.00054855004,0.00013599228,0.0002653513,0.0000050914946,0.00074404443,0.000101459176],"genre_scores_gemma":[0.9585164,0.0000054601683,0.040589377,0.000102865175,0.00023050458,0.00028564487,0.00006254445,0.000007746743,0.00019941074],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993609,0.000008706863,0.00013111703,0.0002823606,0.00007630902,0.00014057518],"domain_scores_gemma":[0.99957746,0.00007489048,0.00002599223,0.00020762382,0.00007620497,0.000037817477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010960378,0.00008259441,0.00006256909,0.000029494226,0.00020849428,0.0003139978,0.00016694092,0.000043940727,0.0000050429676],"category_scores_gemma":[0.000008004151,0.000064027685,0.000036732596,0.0003564749,0.000013998379,0.00025507296,0.000039374016,0.00006022075,0.0000034806185],"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":[0.000008139609,0.000112137655,0.00070833525,0.00008997924,0.000036691898,0.0000082355655,0.00018498565,0.036953975,0.023361381,0.50344163,0.005731804,0.4293627],"study_design_scores_gemma":[0.00003875194,0.000094776595,0.00007346121,0.0000068353197,0.0000058829573,0.000020016128,0.0000036650615,0.9549458,0.042105213,0.0005269627,0.0020816273,0.00009702408],"about_ca_topic_score_codex":0.000014961048,"about_ca_topic_score_gemma":0.00004589324,"teacher_disagreement_score":0.9558328,"about_ca_system_score_codex":0.000044578617,"about_ca_system_score_gemma":0.000024774563,"threshold_uncertainty_score":0.3027888},"labels":[],"label_agreement":null},{"id":"W4401899025","doi":"10.1088/2632-2153/ad7457","title":"An exponential reduction in training data sizes for machine learning derived entanglement witnesses","year":2024,"lang":"en","type":"article","venue":"Machine Learning Science and Technology","topic":"Anomaly Detection Techniques and Applications","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 Toronto","funders":"","keywords":"Quantum entanglement; Reduction (mathematics); Data reduction; Training (meteorology); Exponential function; Computer science; Artificial intelligence; Mathematics; Statistics; Physics; Mathematical analysis; Geometry; Quantum mechanics","score_opus":0.029663286882694092,"score_gpt":0.3137040867244099,"score_spread":0.2840407998417158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401899025","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":[0.29221475,0.0007969821,0.6999511,0.0048333486,0.0001798754,0.00035062974,0.0000063098646,0.0015867555,0.0000802523],"genre_scores_gemma":[0.9616876,0.000114098664,0.037924737,0.00002308144,0.000041941494,0.00010755341,0.000024464598,0.000011517435,0.0000649823],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832064,0.000037829017,0.00022533168,0.00087176985,0.00020579193,0.00033866725],"domain_scores_gemma":[0.9992765,0.000051880088,0.00007070482,0.0004701952,0.00007557419,0.00005511074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011922762,0.00013470941,0.00015004881,0.0008087031,0.0006464866,0.00033952465,0.0011167544,0.000082015475,0.000008430061],"category_scores_gemma":[0.00019894057,0.00012608388,0.000016379767,0.0019744162,0.00036296961,0.0009989546,0.00048035607,0.00043046873,0.0000025317152],"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":[0.0000065434015,0.000050392584,0.0016006134,0.000026640464,0.0000075579524,0.000007127851,0.00092207536,0.0003170107,0.24618536,0.051912803,0.000015967478,0.6989479],"study_design_scores_gemma":[0.00019864997,0.00041723103,0.00024931945,0.00003867989,0.0000083425575,0.000120684635,0.0006116284,0.9498372,0.022579422,0.006185637,0.019542245,0.00021098091],"about_ca_topic_score_codex":0.000119085824,"about_ca_topic_score_gemma":0.000054934844,"teacher_disagreement_score":0.9495202,"about_ca_system_score_codex":0.00005536683,"about_ca_system_score_gemma":0.00012780554,"threshold_uncertainty_score":0.51415515},"labels":[],"label_agreement":null},{"id":"W4402118991","doi":"10.1007/s13042-024-02365-3","title":"Learning from high-dimensional cyber-physical data streams: a case of large-scale smart grid","year":2024,"lang":"en","type":"article","venue":"International Journal of Machine Learning and Cybernetics","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick; University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computational intelligence; Scale (ratio); Cyber-physical system; Smart grid; Grid; STREAMS; Data stream mining; Artificial intelligence; Data mining; Distributed computing; Data science; Computer network; Mathematics; Geography; Engineering; Cartography","score_opus":0.010757887907720259,"score_gpt":0.2905633147037719,"score_spread":0.27980542679605164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402118991","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":[0.7745285,0.00091565,0.22231232,0.0013486111,0.0004898761,0.00004276779,0.000084970634,0.00008729792,0.00019000188],"genre_scores_gemma":[0.9786852,0.0001791005,0.0203351,0.00004038833,0.00043458224,0.0000012642245,0.00004585182,0.00001209074,0.00026642167],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881935,0.00008692063,0.0003606143,0.00025129569,0.0003694487,0.000112366084],"domain_scores_gemma":[0.9989896,0.00027838233,0.00024491598,0.00018298089,0.00022213407,0.0000819859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035529933,0.00011603916,0.00018094137,0.00015171831,0.0000843749,0.00015809698,0.00059815793,0.000049599406,0.000037580252],"category_scores_gemma":[0.000078932164,0.00009899297,0.00007864466,0.00013791167,0.00004901,0.00027456967,0.0004970856,0.0006204508,0.0000069291987],"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":[0.00012448146,0.0008975039,0.015790032,0.00005022199,0.0010971628,0.0033008645,0.005148906,0.012547794,0.007173792,0.037011217,0.002749821,0.9141082],"study_design_scores_gemma":[0.00038015234,0.00032010055,0.0010630611,0.00014774405,0.000057008907,0.0023769848,0.00013141084,0.9582511,0.0012899662,0.0033070762,0.03252177,0.00015359586],"about_ca_topic_score_codex":0.00042413588,"about_ca_topic_score_gemma":0.000035825273,"teacher_disagreement_score":0.9457033,"about_ca_system_score_codex":0.000025997577,"about_ca_system_score_gemma":0.00005795042,"threshold_uncertainty_score":0.4036816},"labels":[],"label_agreement":null},{"id":"W4402129300","doi":"10.1007/s11063-024-11681-2","title":"Within-Class Constraint Based Multi-task Autoencoder for One-Class Classification","year":2024,"lang":"en","type":"article","venue":"Neural Processing Letters","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Fundamental Research Funds for the Provincial Universities of Zhejiang; National Key Research and Development Program of China; Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Autoencoder; Computational intelligence; Class (philosophy); Constraint (computer-aided design); Task (project management); Artificial intelligence; Computer science; Pattern recognition (psychology); Machine learning; Mathematics; Artificial neural network; Engineering","score_opus":0.05504942963749451,"score_gpt":0.29864014249172094,"score_spread":0.24359071285422643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402129300","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":[0.004132732,0.00005311741,0.9576182,0.03589042,0.0002661767,0.00043283612,0.000008525364,0.0014526321,0.00014536892],"genre_scores_gemma":[0.79199094,8.374256e-7,0.20218395,0.0052723843,0.00009099371,0.00031126942,0.000008894914,0.00002172478,0.00011902373],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856764,0.000029673658,0.00030775418,0.0006085299,0.00020731089,0.00027908647],"domain_scores_gemma":[0.9992952,0.00008439709,0.00011596807,0.0003335524,0.00008756858,0.00008334535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020569026,0.00017592541,0.00014032821,0.00015105015,0.0003125476,0.0006683606,0.00047426342,0.00008287887,0.0000046914693],"category_scores_gemma":[0.00002345679,0.00016510807,0.00010266035,0.0004536555,0.00013586991,0.00053422095,0.000040920342,0.00023606686,0.000019192772],"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":[0.000014144919,0.00014980491,0.000058045116,0.00053835654,0.00003112453,0.000011646331,0.0010066107,0.0030512665,0.5830789,0.03047434,0.007655053,0.37393072],"study_design_scores_gemma":[0.00015628364,0.000034901936,0.00018410462,0.00007413902,0.000014138195,0.000012987665,0.000022846856,0.97870463,0.010369819,0.0004996252,0.009718011,0.00020848369],"about_ca_topic_score_codex":0.0000055342693,"about_ca_topic_score_gemma":0.0000027089463,"teacher_disagreement_score":0.9756534,"about_ca_system_score_codex":0.00009721688,"about_ca_system_score_gemma":0.00015649285,"threshold_uncertainty_score":0.67329115},"labels":[],"label_agreement":null},{"id":"W4402156440","doi":"10.1109/icc51166.2024.10622518","title":"EADD: An Intelligent Edge-Based Anomaly Detection Platform for Car Driving","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Ministry of Science and Technology","keywords":"Anomaly detection; Enhanced Data Rates for GSM Evolution; Computer science; Anomaly (physics); Artificial intelligence; Physics","score_opus":0.02598526357854816,"score_gpt":0.28572991558692823,"score_spread":0.2597446520083801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402156440","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":[0.015426754,0.000038321672,0.98094034,0.00027069636,0.0002552873,0.00036563378,0.0000022219813,0.0016696358,0.0010311089],"genre_scores_gemma":[0.8909982,0.000004349563,0.10785146,0.00014822363,0.00011024657,0.00028991557,0.0000036582958,0.000013225396,0.00058071583],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913365,0.00000780295,0.00018385824,0.0003896471,0.000100850724,0.00018419394],"domain_scores_gemma":[0.9993669,0.00006853314,0.000028048395,0.00038984933,0.000062524436,0.00008415169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018542896,0.000108924556,0.00008128818,0.00016616275,0.00019102303,0.00031873386,0.0003533843,0.0000690643,0.00002668716],"category_scores_gemma":[0.0000076904025,0.00009710045,0.00009912874,0.0003910194,0.00001953325,0.0004871736,0.00004945977,0.00008865396,0.00004076926],"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":[0.0000036105494,0.000051973788,0.000036218717,0.000032618045,0.000012298353,0.0000015018088,0.00012520663,0.00012558704,0.0176858,0.18465601,0.0005336425,0.7967355],"study_design_scores_gemma":[0.000037610225,0.0002046219,0.00009170903,0.000011686211,0.0000056167282,0.000006822434,0.000019110119,0.6181349,0.30708092,0.007283024,0.066986986,0.00013694244],"about_ca_topic_score_codex":0.000049745544,"about_ca_topic_score_gemma":0.00011174578,"teacher_disagreement_score":0.8755714,"about_ca_system_score_codex":0.00008635281,"about_ca_system_score_gemma":0.000056136014,"threshold_uncertainty_score":0.39596415},"labels":[],"label_agreement":null},{"id":"W4402159510","doi":"10.1109/tdsc.2024.3446587","title":"PulseAnomaly: Unsupervised Anomaly Detection on Avionic Platforms With Seasonality and Trend Modeling in Transformer Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Dependable and Secure Computing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"McGill University; Queen's University","funders":"Innovation for Defence Excellence and Security","keywords":"Anomaly detection; Avionics; Computer science; Anomaly (physics); Transformer; Data mining; Electrical engineering; Engineering","score_opus":0.014893546637415612,"score_gpt":0.22876703060585732,"score_spread":0.21387348396844172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402159510","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":[0.36238286,0.00017247346,0.6366756,0.00011523016,0.00007816701,0.00019940718,0.0000041562807,0.00026993683,0.000102131504],"genre_scores_gemma":[0.9969522,0.00014241449,0.0026727507,0.0000894366,0.000038753828,0.000041048424,0.0000022885279,0.000023947267,0.000037198773],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984735,0.000030593746,0.00029479884,0.00066522835,0.0001938129,0.0003420571],"domain_scores_gemma":[0.99944186,0.00013510324,0.000033222692,0.00024103784,0.000027873206,0.00012090192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002798227,0.00025297576,0.00021668756,0.00024376121,0.00046226836,0.0003160953,0.00016626257,0.00014543592,0.000006976307],"category_scores_gemma":[9.0174103e-7,0.00021260705,0.000072136325,0.0007679871,0.000040301227,0.00049760053,0.0000038071198,0.00057728327,0.0000025566526],"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":[0.000105283434,0.00018402992,0.000073443865,0.00009963447,0.00006549277,0.00003894298,0.0010479734,0.30021277,0.00047657313,0.0032147933,0.0000031866548,0.69447786],"study_design_scores_gemma":[0.00040995236,0.00029956203,0.00012682016,0.00017205157,0.000022451073,0.00012731511,0.00009900116,0.99425215,0.0035437557,0.00060396286,0.00007820093,0.00026475807],"about_ca_topic_score_codex":0.00010140756,"about_ca_topic_score_gemma":0.0005379899,"teacher_disagreement_score":0.6942131,"about_ca_system_score_codex":0.000077877754,"about_ca_system_score_gemma":0.000040682036,"threshold_uncertainty_score":0.86698633},"labels":[],"label_agreement":null},{"id":"W4402159528","doi":"10.1109/icc51166.2024.10622882","title":"Generative Adversarial Networks for Robust Anomaly Detection in Noisy IoT Environments","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; Université Laval; Polytechnique Montréal","funders":"","keywords":"Adversarial system; Computer science; Anomaly detection; Generative grammar; Artificial intelligence; Internet of Things; Anomaly (physics); Machine learning; Pattern recognition (psychology); Computer security","score_opus":0.013918059596502547,"score_gpt":0.23607039608784736,"score_spread":0.2221523364913448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402159528","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":[0.001870821,0.00006310117,0.9960678,0.00033259662,0.00028784928,0.0003890625,0.0000018441506,0.00026791883,0.0007189857],"genre_scores_gemma":[0.860818,0.00001901188,0.13706237,0.00014107175,0.00020729683,0.00036152068,0.0000027812675,0.000009889073,0.0013780385],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922794,0.00001800714,0.00016330717,0.00035585073,0.00007356248,0.00016133378],"domain_scores_gemma":[0.99968356,0.0000445851,0.000023918365,0.00020304816,0.0000082526585,0.000036635793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013372941,0.00009194439,0.00007712293,0.00010244556,0.00009691686,0.00011822839,0.00020728166,0.00007972015,0.00001916689],"category_scores_gemma":[0.0000037186435,0.00008571002,0.00006097419,0.0003171559,0.000018545767,0.00018762563,0.00006962087,0.000100214675,0.000019207655],"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":[0.000032545355,0.00017788316,0.00016104012,0.000022102915,0.000056549514,0.0000099785675,0.00036221667,0.08577669,0.020859813,0.13509807,0.0030741731,0.75436896],"study_design_scores_gemma":[0.00011805693,0.000083517574,0.00032582792,0.000005070663,0.0000035600829,0.0000039732417,0.000008276391,0.96121126,0.015894385,0.0013525361,0.02088419,0.000109348635],"about_ca_topic_score_codex":0.00004909148,"about_ca_topic_score_gemma":0.00009188121,"teacher_disagreement_score":0.8754346,"about_ca_system_score_codex":0.00009998157,"about_ca_system_score_gemma":0.000016920827,"threshold_uncertainty_score":0.3495153},"labels":[],"label_agreement":null},{"id":"W4402282043","doi":"10.3390/a17090392","title":"Correction: Reshadi et al. Deep-Shallow Metaclassifier with Synthetic Minority Oversampling for Anomaly Detection in a Time Series. Algorithms 2024, 17, 114","year":2024,"lang":"en","type":"article","venue":"Algorithms","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Oversampling; Series (stratigraphy); Anomaly (physics); Computer science; Algorithm; Anomaly detection; Wish; Artificial intelligence; Geology; Telecommunications; Physics; Art; Literature","score_opus":0.014431596062621128,"score_gpt":0.2619960801712845,"score_spread":0.24756448410866339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402282043","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007859475,0.00034106502,0.9922133,0.0023527478,0.0009783724,0.00078533194,0.000024046454,0.0008634579,0.0016557465],"genre_scores_gemma":[0.13105285,0.0003654547,0.81668025,0.002965803,0.00067253,0.0042436225,0.00007412329,0.00018868939,0.043756694],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977068,0.000071119684,0.00040281127,0.0010039058,0.00036555302,0.00044983026],"domain_scores_gemma":[0.99872845,0.000265408,0.000105087376,0.0006249221,0.00014413473,0.0001319768],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006751516,0.000314067,0.00031489268,0.00036589647,0.00026812658,0.00053288415,0.00052969524,0.00015698669,0.000064689695],"category_scores_gemma":[0.000035658926,0.00029045125,0.00017050558,0.0013833188,0.00008515463,0.0009840666,0.00014484287,0.00043912116,0.000057179077],"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":[0.00018768874,0.00059430604,0.00031893852,0.00017559528,0.00043601048,0.00022686673,0.0011985425,0.0014820383,0.00807682,0.00872923,0.033506516,0.94506747],"study_design_scores_gemma":[0.00030395918,0.00046413802,0.00054808153,0.00009774094,0.00004733219,0.00039817987,0.000054998905,0.7776441,0.0077250795,0.0015998271,0.21065015,0.00046642235],"about_ca_topic_score_codex":0.00027162844,"about_ca_topic_score_gemma":0.00029085545,"teacher_disagreement_score":0.944601,"about_ca_system_score_codex":0.00023784721,"about_ca_system_score_gemma":0.00012742233,"threshold_uncertainty_score":0.99995476},"labels":[],"label_agreement":null},{"id":"W4402305497","doi":"10.1016/j.ifacol.2024.08.441","title":"Identification of Most Critical Alarms for Alarm Flood Reduction","year":2024,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"ALARM; Identification (biology); Flood myth; Reduction (mathematics); Computer science; Computer security; Geography; Engineering; Biology; Mathematics; Archaeology","score_opus":0.013186678937494688,"score_gpt":0.3106054045570662,"score_spread":0.2974187256195715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402305497","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":[0.00836728,0.00031296967,0.9828093,0.0070292666,0.00048298444,0.00031333268,0.000060083516,0.0004815893,0.00014320685],"genre_scores_gemma":[0.57190436,0.000053234333,0.42668474,0.000066125525,0.0003379188,0.00017361352,0.000024956913,0.000014884689,0.0007401507],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989815,0.000015735775,0.000321581,0.00037428434,0.00015476318,0.00015217534],"domain_scores_gemma":[0.9992967,0.00007842406,0.000051852756,0.00036311665,0.00015587937,0.00005401688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023414398,0.000099068195,0.00011360627,0.00011377208,0.00011225376,0.0001127161,0.00030348828,0.00007907309,0.000013759569],"category_scores_gemma":[0.00006373296,0.000094589486,0.00010081439,0.0004900278,0.00006513309,0.00033600925,0.000047962396,0.0000981409,0.00002449023],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000069305784,0.00018357392,0.000006662533,0.00014622773,0.000032586624,0.0000016191257,0.00046051177,0.000039240167,0.63477874,0.23444466,0.00028629284,0.12961297],"study_design_scores_gemma":[0.00020849351,0.00025060898,0.00022801374,0.000080018086,0.000063955325,0.000088840374,0.00018073457,0.3509286,0.614107,0.019600043,0.01394504,0.00031865324],"about_ca_topic_score_codex":0.000010865409,"about_ca_topic_score_gemma":0.0000016795485,"teacher_disagreement_score":0.5635371,"about_ca_system_score_codex":0.000034747623,"about_ca_system_score_gemma":0.00005128857,"threshold_uncertainty_score":0.38572472},"labels":[],"label_agreement":null},{"id":"W4402387438","doi":"10.48550/arxiv.2408.06283","title":"Proportion-Based Hypergraph Burning","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"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":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Hypergraph; Computer science; Mathematics; Combinatorics","score_opus":0.04925980566641569,"score_gpt":0.18473014575903104,"score_spread":0.13547034009261535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402387438","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":[0.048928738,0.00006394925,0.94399107,0.00035960224,0.00026531224,0.00028643952,0.000007836903,0.0014154454,0.004681596],"genre_scores_gemma":[0.9906268,0.000034638106,0.007082841,0.00009957933,0.00005628447,0.000007786291,0.000009327572,0.000016393866,0.0020663352],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986045,0.000036432917,0.00016527231,0.0009146877,0.00007485487,0.00020427894],"domain_scores_gemma":[0.9986231,0.000028610686,0.00015044348,0.00097527774,0.00011869725,0.0001039087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014020658,0.00020934855,0.00017094612,0.00032489235,0.00016867643,0.0001594631,0.0010530681,0.00021171314,0.000029468893],"category_scores_gemma":[0.0000072331736,0.00023162075,0.00023747924,0.0008498659,0.000070115944,0.00009820702,0.0010940665,0.0005955987,0.00017084517],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000053264152,0.00008726325,0.0006112575,0.00013556762,0.000057135436,0.00017027889,0.00005295937,0.078969955,0.00019767732,0.9146823,0.0012123872,0.0038178617],"study_design_scores_gemma":[0.00008223668,0.00003458036,0.00017594843,0.00008536523,0.000046230285,0.0000037836141,0.000013269917,0.77876896,0.0013147133,0.21494003,0.0041908845,0.00034402322],"about_ca_topic_score_codex":0.000082066814,"about_ca_topic_score_gemma":0.000008392061,"teacher_disagreement_score":0.9416981,"about_ca_system_score_codex":0.00012350755,"about_ca_system_score_gemma":0.00021989997,"threshold_uncertainty_score":0.944522},"labels":[],"label_agreement":null},{"id":"W4402390737","doi":"10.23889/ijpds.v9i5.2518","title":"Leveraging Machine Learning to Combat Missingness and Error in Data","year":2024,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Victoria Park","funders":"","keywords":"Missing data; Computer science; Artificial intelligence; Machine learning","score_opus":0.11322706319805584,"score_gpt":0.41989037256053413,"score_spread":0.3066633093624783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402390737","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":[0.02735101,0.00014994979,0.9639165,0.0072844485,0.000944985,0.00013688985,0.00006901108,0.0001004426,0.000046810223],"genre_scores_gemma":[0.90558577,0.00003143297,0.09381937,0.0001722111,0.00011777172,0.000005908751,0.000115445706,0.0000057452944,0.00014635127],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861634,0.000018131692,0.00026616588,0.0005299501,0.00041769297,0.00015174928],"domain_scores_gemma":[0.99912924,0.00007415366,0.00006987332,0.0005036864,0.00012514665,0.00009787617],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0014940385,0.000072570154,0.00006842954,0.0004941182,0.00032289873,0.0015467805,0.003404221,0.000018741235,0.0000066790767],"category_scores_gemma":[0.0002687286,0.00006696417,0.000012123284,0.00059152895,0.000041105162,0.003946093,0.0015568453,0.00017514318,0.000004640075],"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":[0.00001873643,0.00006321211,0.011772387,0.000021091562,0.000021403344,0.000032653155,0.0009086651,0.0032160778,0.0044852355,0.06700683,0.002832877,0.9096208],"study_design_scores_gemma":[0.0000786767,0.000014357688,0.008179114,0.00006392325,0.0000020811299,0.00015989997,0.000027189433,0.92405707,0.00008999627,0.0033231052,0.06391559,0.00008902024],"about_ca_topic_score_codex":0.00016445224,"about_ca_topic_score_gemma":0.00004878252,"teacher_disagreement_score":0.920841,"about_ca_system_score_codex":0.00009151742,"about_ca_system_score_gemma":0.00009586851,"threshold_uncertainty_score":0.9994897},"labels":[],"label_agreement":null},{"id":"W4402461027","doi":"10.1007/978-981-97-5116-7_2","title":"Artificial Intelligence","year":2024,"lang":"en","type":"book-chapter","venue":"City development","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Artificial intelligence; Computer science","score_opus":0.054638088091073796,"score_gpt":0.26789126003914415,"score_spread":0.21325317194807036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402461027","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000022553065,0.00010518457,0.62667453,0.00037908164,0.00020759506,0.00014152513,0.0000018653413,0.00046844874,0.3720195],"genre_scores_gemma":[0.0037155994,0.000075310236,0.14167194,0.00022715668,0.0001471118,0.00009125528,0.000010987904,0.000029249833,0.8540314],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99876994,0.0000019808958,0.00035733377,0.0004934268,0.0002270787,0.00015023317],"domain_scores_gemma":[0.99932355,0.000017100869,0.000081857885,0.0004398806,0.0000630635,0.00007457908],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00012656442,0.00020192316,0.00015398314,0.00015476797,0.00013199881,0.00015848185,0.00065280835,0.00017210514,0.00022863263],"category_scores_gemma":[0.0000025987845,0.00020079767,0.0000794267,0.00007580798,0.00004144622,0.00006251971,0.00043891152,0.00028821998,0.0026230693],"study_design_candidate":"theoretical_or_conceptual","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":[2.0376189e-7,0.0000040470163,1.0221481e-7,0.000008408542,0.000010686302,0.0000047699277,0.00006112649,5.147073e-7,0.0000048076026,0.7108826,0.00086999056,0.28815272],"study_design_scores_gemma":[0.0000014230671,0.000007315986,0.0000031883264,0.000043862787,0.0000032744651,0.0000067922547,0.000001044146,0.00022116657,0.0040154234,0.41764817,0.57787174,0.00017663182],"about_ca_topic_score_codex":0.0000014834125,"about_ca_topic_score_gemma":0.0000065147524,"teacher_disagreement_score":0.57700175,"about_ca_system_score_codex":0.00015619889,"about_ca_system_score_gemma":0.00018065103,"threshold_uncertainty_score":0.9981535},"labels":[],"label_agreement":null},{"id":"W4402474820","doi":"10.1109/ccece59415.2024.10667303","title":"Enhanced Abnormal Activity Detection: Utilizing YOLOv8 and Deep SORT with TSAI and LSTM Classifiers","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Lakehead University","funders":"","keywords":"sort; Computer science; Artificial intelligence; Pattern recognition (psychology); Machine learning; Information retrieval","score_opus":0.01038878854006033,"score_gpt":0.23791540539479442,"score_spread":0.22752661685473408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402474820","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":[0.1048041,0.000105175255,0.88716435,0.0004192563,0.000046637582,0.00012484554,4.378784e-7,0.000630229,0.0067049833],"genre_scores_gemma":[0.9782382,0.00006137203,0.020964675,0.00009652846,0.00002926202,0.000058865142,1.6739007e-7,0.000007002961,0.0005439037],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992788,0.000014207578,0.00008473585,0.00037762447,0.00009507551,0.00014955943],"domain_scores_gemma":[0.9996348,0.000036628986,0.000023064504,0.00020297791,0.00002428385,0.000078257035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009654754,0.00010379987,0.000085903666,0.00007973188,0.00020606458,0.0002587018,0.00010872473,0.000057383608,0.0000138477235],"category_scores_gemma":[0.0000024398707,0.00008176769,0.000020498639,0.00032019877,0.000079421574,0.0005239549,0.00007709954,0.00014447904,0.0000054552493],"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":[0.000010285701,0.00001692171,0.000091316695,0.000030468162,0.000021852655,0.0000058000765,0.00027121764,0.0000033188785,0.02837808,0.034003958,0.0000345576,0.93713224],"study_design_scores_gemma":[0.00028996164,0.0005343313,0.011295216,0.000050684986,0.000030987936,0.0004406123,0.00031856276,0.33846575,0.62557435,0.0033657255,0.019047502,0.00058629917],"about_ca_topic_score_codex":0.000032647753,"about_ca_topic_score_gemma":0.00008163521,"teacher_disagreement_score":0.9365459,"about_ca_system_score_codex":0.000023497356,"about_ca_system_score_gemma":0.000023809896,"threshold_uncertainty_score":0.33343896},"labels":[],"label_agreement":null},{"id":"W4402480071","doi":"10.1201/9781003483755-179","title":"Anomaly detection with switching Kalman filter and imitation learning","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Kalman filter; Imitation; Anomaly detection; Anomaly (physics); Computer science; Artificial intelligence; Computer vision; Psychology; Neuroscience; Physics","score_opus":0.012910294673242588,"score_gpt":0.2174605269132035,"score_spread":0.2045502322399609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402480071","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015412761,0.00010072565,0.6732576,0.00016108593,0.00004275321,0.00016523569,5.473546e-7,0.00072564447,0.3253923],"genre_scores_gemma":[0.30144882,0.00010819679,0.031348392,0.00012739433,0.00012536495,0.00006308265,0.000005350479,0.000056836674,0.6667166],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989958,0.000006203302,0.00018773785,0.00052604853,0.00015944926,0.00012480728],"domain_scores_gemma":[0.9994528,0.000035685283,0.00011449535,0.000276457,0.00006271874,0.000057798283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010072267,0.00021608616,0.00014896564,0.0002283137,0.00020664647,0.00029232772,0.00017405482,0.00015926124,0.000036927573],"category_scores_gemma":[0.0000022401614,0.00017847678,0.00005500296,0.000074110976,0.000026206553,0.00027196173,0.00013655568,0.00044109926,0.00009058882],"study_design_candidate":"theoretical_or_conceptual","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":[0.000004017551,0.0000040461337,0.000009366711,0.000044754866,0.000042263244,0.000009304259,0.00015687429,0.00002108635,0.0010998918,0.78569126,0.000116552575,0.2128006],"study_design_scores_gemma":[0.00026322802,0.0010512337,0.00030032545,0.00042215484,0.00015059194,0.0004793668,0.000059967213,0.08893457,0.0071127946,0.24249546,0.65725803,0.0014722779],"about_ca_topic_score_codex":0.000027035594,"about_ca_topic_score_gemma":0.00006154095,"teacher_disagreement_score":0.65714145,"about_ca_system_score_codex":0.000046617057,"about_ca_system_score_gemma":0.000019788335,"threshold_uncertainty_score":0.72780716},"labels":[],"label_agreement":null},{"id":"W4402509773","doi":"10.1109/tgrs.2024.3460649","title":"Indicating Ambiguous False Positives to Improve Wide-Area SAR Vessel Detection","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Anomaly Detection Techniques and Applications","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":"Defence Research and Development Canada","funders":"","keywords":"False positive paradox; Computer science; Synthetic aperture radar; Remote sensing; Artificial intelligence; Geology","score_opus":0.009790738475260454,"score_gpt":0.24533537208358422,"score_spread":0.23554463360832376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402509773","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":[0.056412544,0.000044263277,0.9408334,0.0011178377,0.0005166659,0.00025487712,0.000004777058,0.0006388634,0.0001767587],"genre_scores_gemma":[0.8608568,0.0000567072,0.13833062,0.00040341052,0.000025567902,0.0000012218923,1.7698835e-7,0.00001204711,0.00031350306],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985359,0.000034819608,0.00022091328,0.00068647135,0.00022698549,0.00029495652],"domain_scores_gemma":[0.9992962,0.00008511169,0.000045992747,0.0003520393,0.00006198967,0.00015866927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002505292,0.0001684477,0.00012645213,0.00039390923,0.00080878567,0.0004591942,0.00021684887,0.000085507934,0.0000015594558],"category_scores_gemma":[0.000009345081,0.0001553462,0.00007321473,0.0012379264,0.00011099534,0.0004744466,0.000008801118,0.00027732828,0.00003044985],"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":[0.0000024235935,0.000009811165,6.3164066e-8,0.000009040476,0.0000049052646,0.0000061426153,0.0003786838,0.000080407895,0.10007013,0.00005316149,0.000005710652,0.89937955],"study_design_scores_gemma":[0.00005500567,0.00021500092,0.00013968146,0.00013519925,0.000014199525,0.00015144126,0.000115131916,0.5499542,0.44647512,0.0011525225,0.0013432971,0.00024921147],"about_ca_topic_score_codex":0.0004342347,"about_ca_topic_score_gemma":0.00003318912,"teacher_disagreement_score":0.8991303,"about_ca_system_score_codex":0.00007413787,"about_ca_system_score_gemma":0.000055187098,"threshold_uncertainty_score":0.6334834},"labels":[],"label_agreement":null},{"id":"W4402594041","doi":"10.1109/sds60720.2024.00037","title":"Extracting Decision Paths via Surrogate Modeling for Explainability of Black Box Classifiers","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Dalhousie University","funders":"","keywords":"Black box; Computer science; Artificial intelligence; Machine learning; Surrogate model; Pattern recognition (psychology); Data mining","score_opus":0.03179234643922195,"score_gpt":0.30724399495959104,"score_spread":0.2754516485203691,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402594041","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":[0.035930566,0.000038134647,0.962038,0.00031863424,0.00009901827,0.00027595344,0.000002143293,0.0004212638,0.0008762893],"genre_scores_gemma":[0.73797184,0.000009178365,0.26179066,0.000020418713,0.000019156083,0.000053433272,6.595948e-7,0.0000057770008,0.00012887677],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990988,0.000012848003,0.00028462886,0.00033951696,0.00012074222,0.00014348404],"domain_scores_gemma":[0.99917555,0.0002567867,0.00004273794,0.0003671326,0.00011163435,0.000046139237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047000434,0.00007937268,0.000101156365,0.00008056817,0.000097349155,0.000085831,0.00028879812,0.00005845256,0.0000113677725],"category_scores_gemma":[0.000042590964,0.00006840323,0.00011418205,0.0002975043,0.000026205991,0.00035481132,0.000088951274,0.00008339247,0.0000074526984],"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":[0.000013003542,0.00007879601,0.000052701304,0.00012644548,0.000014736963,0.0000015762095,0.00052447454,0.005351876,0.012889847,0.17952125,0.00081805914,0.80060726],"study_design_scores_gemma":[0.00004020226,0.000032821466,0.000011943199,0.000021975346,0.0000034002683,0.0000025996662,0.00007966891,0.9198808,0.014347535,0.06360493,0.0019005227,0.00007359039],"about_ca_topic_score_codex":0.000027997034,"about_ca_topic_score_gemma":0.0000054598136,"teacher_disagreement_score":0.9145289,"about_ca_system_score_codex":0.00004550924,"about_ca_system_score_gemma":0.000041962816,"threshold_uncertainty_score":0.27894026},"labels":[],"label_agreement":null},{"id":"W4402681632","doi":"10.4050/f-0080-2024-1158","title":"Safety Data Analysis with Machine Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Computer science; Data science","score_opus":0.018752506438322585,"score_gpt":0.2723593466546199,"score_spread":0.2536068402162973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402681632","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":[0.000073340394,0.000101020516,0.9860847,0.0011276224,0.000009704884,0.00003817571,0.00000468313,0.0009944085,0.011566323],"genre_scores_gemma":[0.73060495,0.00004389977,0.2624588,0.00009252405,0.000018450719,0.000008222922,0.0000392519,0.0000044641242,0.006729456],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944866,0.000011242865,0.00007507007,0.00031035437,0.00008443097,0.00007024992],"domain_scores_gemma":[0.9992448,0.000026954194,0.000012191447,0.00067432533,0.00001345731,0.000028290435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014250157,0.000046028836,0.000058348392,0.00011212224,0.00009139459,0.00016711724,0.00057343586,0.00001431581,0.000118323194],"category_scores_gemma":[0.0000024106155,0.00003109628,0.000024614366,0.0014537888,0.000011323355,0.00027952026,0.00025555884,0.000091534814,0.000050972758],"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":[0.0000029579069,0.00002810357,0.001983423,0.000010501848,0.00044052265,0.000013839187,0.00009903281,0.0014036244,0.00017441585,0.6614333,0.002110112,0.33230016],"study_design_scores_gemma":[0.00001167786,0.00001704309,0.00035024533,0.0000017858642,0.000037150527,0.000005466728,0.000003248304,0.8019239,0.00019243125,0.00028419236,0.19712298,0.000049888138],"about_ca_topic_score_codex":0.00011284091,"about_ca_topic_score_gemma":0.00007700573,"teacher_disagreement_score":0.80052024,"about_ca_system_score_codex":0.000009612995,"about_ca_system_score_gemma":0.000016591312,"threshold_uncertainty_score":0.16115154},"labels":[],"label_agreement":null},{"id":"W4402806881","doi":"10.1145/3696110","title":"Automated anomaly detection for categorical data by repurposing a form filling recommender system","year":2024,"lang":"en","type":"article","venue":"Journal of Data and Information Quality","topic":"Anomaly Detection Techniques and Applications","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 Ottawa","funders":"Fonds National de la Recherche Luxembourg; Natural Sciences and Engineering Research Council of Canada; BNP Paribas Cardif; Canada Research Chairs; Science Foundation Ireland","keywords":"Repurposing; Computer science; Categorical variable; Recommender system; Anomaly detection; Data mining; Anomaly (physics); Artificial intelligence; Information retrieval; Machine learning","score_opus":0.08194400033841359,"score_gpt":0.3697688114118219,"score_spread":0.2878248110734083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402806881","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":[0.0012130277,0.00023338906,0.9966207,0.000775709,0.00021874401,0.0001719187,0.00032391513,0.00032582798,0.00011674934],"genre_scores_gemma":[0.9201317,0.00018204504,0.07889946,0.00021098444,0.00011143066,0.000011761376,0.00043603918,0.000005614565,0.000010987786],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869835,0.000042132186,0.00081735704,0.00015782466,0.00017829669,0.000106010826],"domain_scores_gemma":[0.9985504,0.00013718833,0.00042686006,0.0006579789,0.00016117361,0.00006639721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002117968,0.000079500125,0.00014528641,0.00013258107,0.0001938142,0.000597074,0.0007927285,0.000065961096,0.0000014382089],"category_scores_gemma":[0.00008071769,0.00006465517,0.000032611835,0.00027205906,0.00001384393,0.013151905,0.0003088981,0.00014054556,0.0000026571686],"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":[0.00003397198,0.00003673903,0.000021048028,0.00061780174,0.00008695166,0.0000010573366,0.00062501535,0.000009430137,0.0007996479,0.029941596,0.076522484,0.89130425],"study_design_scores_gemma":[0.00011914455,0.0000619399,0.000049821127,0.000035163095,0.000015588672,0.00015843163,0.00017915499,0.72506285,0.0005627057,0.00032365962,0.27335697,0.000074561365],"about_ca_topic_score_codex":0.000029945893,"about_ca_topic_score_gemma":0.000001940222,"teacher_disagreement_score":0.91891867,"about_ca_system_score_codex":0.00007920488,"about_ca_system_score_gemma":0.000066132874,"threshold_uncertainty_score":0.9534814},"labels":[],"label_agreement":null},{"id":"W4402821815","doi":"10.1007/978-3-031-53652-6_9","title":"Real-Time Anomaly Detection in Connected Autonomous Vehicles: A Data-Driven Approach","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Anomaly detection; Computer science; Anomaly (physics); Real-time computing; Artificial intelligence; Physics","score_opus":0.026006013471142096,"score_gpt":0.24870691929424096,"score_spread":0.22270090582309887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402821815","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000049494756,0.000058396367,0.30503458,0.00018643637,0.00008348966,0.00063478196,0.000051991883,0.0021761535,0.69172466],"genre_scores_gemma":[0.0261422,0.00027221325,0.10002343,0.00014172592,0.00024991404,0.00025449257,0.0002874037,0.00012957098,0.87249905],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976293,0.00001807619,0.00052712066,0.0013255337,0.00023443355,0.00026555007],"domain_scores_gemma":[0.99757147,0.00005796541,0.00017903847,0.0020211332,0.00007278572,0.000097621385],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022441459,0.00036414928,0.0003913114,0.00049506075,0.000100228215,0.0002560284,0.0016487637,0.00044067414,0.00011038053],"category_scores_gemma":[0.0000055620253,0.00035655187,0.000114693656,0.00026938313,0.00007018276,0.00035876987,0.0009900036,0.0005185154,0.00087240187],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000069420826,0.000069570015,0.0000013908287,0.00007820895,0.00008375383,0.000036264,0.00010884865,0.000053062446,0.001879981,0.9385271,0.006174231,0.05298063],"study_design_scores_gemma":[0.00016318067,0.00013232595,0.000033764525,0.00008184472,0.0000470028,0.00010003676,0.000007871663,0.7781889,0.0006682119,0.030872796,0.18896975,0.0007343104],"about_ca_topic_score_codex":0.0003023228,"about_ca_topic_score_gemma":0.000094292634,"teacher_disagreement_score":0.90765435,"about_ca_system_score_codex":0.00022726167,"about_ca_system_score_gemma":0.0001363846,"threshold_uncertainty_score":0.9999055},"labels":[],"label_agreement":null},{"id":"W4402905912","doi":"10.1167/jov.24.10.1259","title":"Evaluating the Alignment of Machine and Human Explanations in Visual Object Recognition through a Novel Behavioral Approach","year":2024,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Object (grammar); Artificial intelligence; Cognitive neuroscience of visual object recognition; Cognitive psychology; Human–computer interaction; Psychology; Cognitive science; Computer vision","score_opus":0.10133072663825991,"score_gpt":0.4334510801855166,"score_spread":0.33212035354725666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402905912","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":[0.42893568,0.00019056219,0.57018924,0.00032241206,0.000043440796,0.00012192866,0.0000024976844,0.00001611453,0.00017813184],"genre_scores_gemma":[0.9376204,0.000031080934,0.062276065,0.000018163773,0.000029725446,0.000009764902,0.0000015949033,0.000003393612,0.000009826254],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925756,0.000041425414,0.00031828505,0.00010118704,0.00022507647,0.000056453093],"domain_scores_gemma":[0.99963164,0.00005014969,0.00015101329,0.000086403685,0.00006427138,0.000016494128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006716294,0.0000507651,0.00008496071,0.00011869752,0.00009691746,0.00007084455,0.00014212428,0.000027437856,0.0000043240384],"category_scores_gemma":[0.000008224237,0.000033621087,0.000045848108,0.00027695796,0.000022100963,0.0003220258,0.000058332538,0.00014299054,4.5380182e-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":[0.00003161138,0.0015994394,0.0004140624,0.00008992133,0.00004261925,0.000008875581,0.0065541584,0.0005363781,0.37138137,0.033801097,0.00034046225,0.5852],"study_design_scores_gemma":[0.0016018575,0.007232433,0.03773633,0.0009963413,0.00013710142,0.0008306546,0.0012823121,0.88054115,0.034190092,0.033779945,0.0012577357,0.00041406698],"about_ca_topic_score_codex":0.000050905594,"about_ca_topic_score_gemma":0.000005470477,"teacher_disagreement_score":0.88000476,"about_ca_system_score_codex":0.000037095513,"about_ca_system_score_gemma":0.000022691112,"threshold_uncertainty_score":0.13710281},"labels":[],"label_agreement":null},{"id":"W4402916307","doi":"10.1109/cvprw63382.2024.00474","title":"Drone-HAT: Hybrid Attention Transformer for Complex Action Recognition in Drone Surveillance Videos","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Ottawa","funders":"","keywords":"Drone; Transformer; Computer science; Action recognition; Artificial intelligence; Action (physics); Computer vision; Engineering; Electrical engineering; Physics; Voltage","score_opus":0.06099052872930029,"score_gpt":0.31585758659271834,"score_spread":0.25486705786341807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402916307","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":[0.027169647,0.000037763,0.96756715,0.0021950882,0.00017192723,0.0005815427,0.000020240075,0.00070675946,0.0015498945],"genre_scores_gemma":[0.96607095,0.000084514424,0.03233592,0.00013309723,0.00006926419,0.00045467174,0.000068481844,0.00001194285,0.00077116094],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904835,0.0000246238,0.00025354468,0.00037784732,0.000112107766,0.00018353485],"domain_scores_gemma":[0.9996179,0.00006276396,0.00003372736,0.00019167225,0.000055975233,0.000037972462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027527157,0.000103575585,0.00010891155,0.00017465485,0.00010125593,0.0001513775,0.00017083772,0.000046215922,0.00006744536],"category_scores_gemma":[0.000005215892,0.000101750964,0.00009197191,0.00040572626,0.00001975551,0.0005893708,0.000014052594,0.00009357524,0.00009216403],"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":[0.000014679804,0.000092372895,0.000084900414,0.00009962431,0.000014487065,0.0000019284867,0.000060718154,0.000011842593,0.08233217,0.018910795,0.005087073,0.8932894],"study_design_scores_gemma":[0.0007991444,0.00036327215,0.01179651,0.000116253956,0.000015169601,0.00008873449,0.00007920044,0.6275345,0.15250087,0.08146897,0.1245178,0.0007195791],"about_ca_topic_score_codex":0.00008374586,"about_ca_topic_score_gemma":0.00015520614,"teacher_disagreement_score":0.9389013,"about_ca_system_score_codex":0.00008765639,"about_ca_system_score_gemma":0.000024499595,"threshold_uncertainty_score":0.41492838},"labels":[],"label_agreement":null},{"id":"W4402917235","doi":"10.1109/cvprw63382.2024.00408","title":"BMAD: Benchmarks for Medical Anomaly Detection","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":69,"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":"","keywords":"Anomaly detection; Computer science; Artificial intelligence","score_opus":0.006793689405046456,"score_gpt":0.2734587328745697,"score_spread":0.26666504346952324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402917235","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":[0.0007846026,0.0000804357,0.9839437,0.0025331092,0.0002512554,0.00019158494,0.0000011745126,0.00094754796,0.011266572],"genre_scores_gemma":[0.94026387,0.000017038396,0.057299603,0.00033655696,0.00015024224,0.00027703686,0.0000026051375,0.000006435186,0.0016465952],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929756,0.0000082770575,0.00013953615,0.0002752989,0.00015110384,0.00012824933],"domain_scores_gemma":[0.99956226,0.00008528926,0.0000130743365,0.00022804616,0.000033691245,0.00007763823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022746588,0.00006412516,0.000056966743,0.0000864082,0.0001050306,0.00015768397,0.00032709131,0.00008343114,0.0002505992],"category_scores_gemma":[0.000020503301,0.000053088166,0.00007660066,0.00032676582,0.000019066929,0.00021891728,0.000069313435,0.000085594176,0.000046604906],"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":[0.0000010010474,0.000015515787,0.000007452336,0.00001547305,0.000007796965,0.0000019770002,0.000025760308,0.0000011239235,0.001081705,0.42475972,0.008326851,0.5657556],"study_design_scores_gemma":[0.00006516619,0.00011838212,0.00012669138,0.0000139098265,0.000004787575,0.00005601115,0.00000820995,0.46828687,0.042645063,0.028693132,0.45984206,0.000139703],"about_ca_topic_score_codex":0.000025638921,"about_ca_topic_score_gemma":0.000030191115,"teacher_disagreement_score":0.9394793,"about_ca_system_score_codex":0.000026479476,"about_ca_system_score_gemma":0.000055084925,"threshold_uncertainty_score":0.27438855},"labels":[],"label_agreement":null},{"id":"W4402951111","doi":"10.18280/mmep.110924","title":"Deep Learning-Based STR Analysis for Missing Person Identification in Mass Casualty Incidents","year":2024,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Identification (biology); Mass Casualty; Psychology; Computer science; Medicine; Medical emergency","score_opus":0.02956141072450799,"score_gpt":0.25156543494278444,"score_spread":0.22200402421827645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402951111","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":[0.007022351,0.00021148198,0.9919656,0.00017824327,0.000021571486,0.00018999178,6.899757e-7,0.00039065705,0.000019413419],"genre_scores_gemma":[0.78942865,0.000008571311,0.21035342,0.0000040987265,0.000010071474,0.00012745093,0.0000034717316,0.000010576241,0.000053685468],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992081,0.000009346793,0.00022927583,0.0002858973,0.00010617389,0.00016121191],"domain_scores_gemma":[0.99961734,0.00012294787,0.000029459557,0.00015123379,0.000026356027,0.000052674644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003847505,0.00009995542,0.00014350243,0.00027746218,0.0000738475,0.00029123225,0.00012910084,0.000060384686,0.00000201477],"category_scores_gemma":[0.00001927219,0.00009691578,0.00007190994,0.00056358235,0.000009433872,0.00013748584,0.000014025659,0.00013456972,0.000003239261],"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":[4.116906e-7,0.0000121827225,0.000016146048,0.00028684502,0.00001911136,5.5101754e-7,0.0004231428,0.98448735,0.00048282067,0.011851271,8.362103e-7,0.00241931],"study_design_scores_gemma":[0.00004324268,0.000016145332,0.000024971054,0.00010005359,0.000033332282,0.0000015505598,0.00001380891,0.9874734,0.0004443635,0.011656441,0.00008323849,0.000109404915],"about_ca_topic_score_codex":0.000010068124,"about_ca_topic_score_gemma":8.4456974e-7,"teacher_disagreement_score":0.78240633,"about_ca_system_score_codex":0.000042632022,"about_ca_system_score_gemma":0.000008711603,"threshold_uncertainty_score":0.39521107},"labels":[],"label_agreement":null},{"id":"W4402959357","doi":"10.1007/978-3-031-72998-0_12","title":"Agglomerative Token Clustering","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Vector Institute","funders":"","keywords":"Computer science; Cluster analysis; Hierarchical clustering; Security token; Artificial intelligence; Computer network","score_opus":0.015540423641621136,"score_gpt":0.2594737992649406,"score_spread":0.24393337562331943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402959357","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000006393483,0.00039136602,0.97270536,0.0011566202,0.0009042795,0.00033420158,0.000004601871,0.0005436695,0.023953505],"genre_scores_gemma":[0.11243469,0.00014490714,0.8759328,0.001964539,0.00088081975,0.00007839993,0.000004879918,0.00006953538,0.008489391],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99742085,0.000009138451,0.00037452363,0.0012844743,0.0005062257,0.00040479325],"domain_scores_gemma":[0.99841917,0.00012868016,0.00013593168,0.0010650326,0.0001318932,0.00011931376],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003572874,0.00037246587,0.00031266775,0.00067883584,0.00023676174,0.0007045391,0.002165351,0.00022709183,0.00002878891],"category_scores_gemma":[0.000012508896,0.0003392576,0.00012629286,0.00067399413,0.00035818707,0.00041657154,0.0015931015,0.0007247782,0.00019025952],"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":[0.0000012656244,0.000011686411,0.0000023353807,0.00003489875,0.000010793977,0.00005410474,0.00050654553,0.006664249,0.0002292593,0.1790859,0.00007290355,0.81332606],"study_design_scores_gemma":[0.000054800217,0.00010299854,0.000009745439,0.00025655213,0.0000057923767,0.000083280065,1.1570778e-7,0.58667195,0.0028887286,0.3930789,0.01635819,0.00048895687],"about_ca_topic_score_codex":0.00001650751,"about_ca_topic_score_gemma":0.00004737596,"teacher_disagreement_score":0.8128371,"about_ca_system_score_codex":0.000284883,"about_ca_system_score_gemma":0.00022145039,"threshold_uncertainty_score":0.99990594},"labels":[],"label_agreement":null},{"id":"W4402979770","doi":"10.1109/otcon60325.2024.10687910","title":"Efficient Data Sampling and Reduction Methods in Large-Scale Forensic Analysis","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Computer science; Scale (ratio); Reduction (mathematics); Sampling (signal processing); Data reduction; Data mining; Data science; Mathematics; Computer vision","score_opus":0.05488089126165763,"score_gpt":0.3940329991446129,"score_spread":0.3391521078829553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402979770","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007688877,0.00021758325,0.9905904,0.0005457702,0.00005883586,0.00008289372,0.000004069303,0.00030118632,0.00051037804],"genre_scores_gemma":[0.2892395,0.000016340327,0.71054864,0.00002109874,0.0000160442,0.000012538423,0.000005885515,0.000002447859,0.0001375451],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924433,0.000029887204,0.00013915294,0.00041873837,0.00006315466,0.00010475991],"domain_scores_gemma":[0.9993152,0.000046126963,0.000016039503,0.0005781293,0.000014632213,0.000029841061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006417409,0.00005212852,0.00008489039,0.00028056762,0.00006233341,0.00013922728,0.00026842993,0.00002988983,0.00001330606],"category_scores_gemma":[0.000006859676,0.00004481582,0.00002937674,0.0017000942,0.000015752474,0.00011581728,0.00032314175,0.00007525939,0.000003845687],"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":[0.0000015372491,0.000079340054,0.0004478151,0.000025840207,0.000098738674,0.0000018397578,0.0007719586,0.004336284,0.0059603364,0.15116915,0.00042756024,0.8366796],"study_design_scores_gemma":[0.00001857208,0.0000063461857,0.0014421513,0.0000044845588,0.00002699671,0.0000067131436,0.000060402836,0.9902594,0.0017689738,0.0022979644,0.004049656,0.000058318423],"about_ca_topic_score_codex":0.00006759899,"about_ca_topic_score_gemma":0.000049752394,"teacher_disagreement_score":0.9859231,"about_ca_system_score_codex":0.000019765057,"about_ca_system_score_gemma":0.000012392524,"threshold_uncertainty_score":0.18275361},"labels":[],"label_agreement":null},{"id":"W4403016954","doi":"10.1016/j.ijar.2024.109301","title":"Uncertainty-based knowledge distillation for Bayesian deep neural network compression","year":2024,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Artificial neural network; Computer science; Bayesian network; Artificial intelligence; Bayesian probability; Distillation; Machine learning; Compression (physics); Chemistry; Chromatography; Materials science","score_opus":0.013642516649959103,"score_gpt":0.30462567631032733,"score_spread":0.29098315966036825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403016954","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":[0.0023762689,0.0005887508,0.9940157,0.0010956152,0.0010221662,0.0001428478,0.0000046524515,0.00015524676,0.00059873314],"genre_scores_gemma":[0.8050662,0.0000168444,0.19416124,0.000055099204,0.0006266303,0.000021951251,0.00000900427,0.000012279784,0.000030702835],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897164,0.000035051165,0.00038874696,0.0001886666,0.00025790033,0.0001579826],"domain_scores_gemma":[0.99893063,0.00022372195,0.00023623886,0.00013140052,0.00040551298,0.00007247938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041222633,0.00011195339,0.000137274,0.00017611921,0.00011894845,0.000379034,0.00062804826,0.00005305178,0.0000101997275],"category_scores_gemma":[0.000042150154,0.00009339276,0.0001799419,0.0002379743,0.000025484898,0.00039984545,0.00007401277,0.00016221832,0.0000026601494],"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":[0.000095626696,0.0001057476,0.00028310312,0.00006620984,0.00013714153,0.000030308096,0.0003443581,0.17763378,0.0011426978,0.28811184,0.0022331735,0.52981603],"study_design_scores_gemma":[0.00019372335,0.00006848226,0.00012436809,0.00025441774,0.000011771344,0.00009439522,0.000011073357,0.96801496,0.0008428522,0.009895317,0.020391835,0.0000968076],"about_ca_topic_score_codex":0.000003520562,"about_ca_topic_score_gemma":0.0000018232353,"teacher_disagreement_score":0.80268997,"about_ca_system_score_codex":0.0001305823,"about_ca_system_score_gemma":0.00007841751,"threshold_uncertainty_score":0.3808446},"labels":[],"label_agreement":null},{"id":"W4403023916","doi":"10.1109/iccims61672.2024.10690403","title":"Patch-based Learning for Radar-based Fall Event Detection using Gramian Angular Field","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Radar; Computer science; Gramian matrix; Event (particle physics); Field (mathematics); Artificial intelligence; Remote sensing; Mathematics; Geology; Physics; Telecommunications","score_opus":0.016565993600522712,"score_gpt":0.2846613281731617,"score_spread":0.268095334572639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403023916","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":[0.010967991,0.000046029167,0.9862933,0.000981592,0.0001495824,0.00032366446,8.961753e-7,0.0011100201,0.0001269319],"genre_scores_gemma":[0.8197954,0.0000010856686,0.17947163,0.00035239296,0.0000540324,0.00009993703,0.0000025101415,0.000011344349,0.00021163454],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991906,0.000024521914,0.00016491007,0.0003276733,0.00011776683,0.0001744969],"domain_scores_gemma":[0.99950904,0.00010296059,0.000037365397,0.00024650406,0.0000520786,0.000052062547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018620065,0.00010261886,0.00008227563,0.00013410741,0.00024202235,0.00019982074,0.00020705284,0.000077259225,0.000015719505],"category_scores_gemma":[0.000017629212,0.000096128766,0.0001371147,0.0003896392,0.0000123538175,0.00014454249,0.000029304181,0.00013873535,0.000008387585],"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":[0.000017490058,0.0000986121,0.00025550797,0.0001577407,0.0000271284,0.000008070054,0.00008609009,0.012087041,0.061965592,0.022892987,0.0005797382,0.901824],"study_design_scores_gemma":[0.000076787815,0.00015870536,0.000025878304,0.000019921163,0.000006961206,0.0000024651756,0.0000067852043,0.77209103,0.19369736,0.001278433,0.032527886,0.00010779888],"about_ca_topic_score_codex":0.00032655126,"about_ca_topic_score_gemma":0.00006421404,"teacher_disagreement_score":0.90171623,"about_ca_system_score_codex":0.000071839626,"about_ca_system_score_gemma":0.000074131145,"threshold_uncertainty_score":0.39200172},"labels":[],"label_agreement":null},{"id":"W4403071505","doi":"10.1007/978-3-031-72378-0_53","title":"Position-Guided Prompt Learning for Anomaly Detection in Chest X-Rays","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Anomaly detection; Position (finance); Anomaly (physics); Artificial intelligence; Computer vision; Medical physics; Physics","score_opus":0.016797487598930388,"score_gpt":0.2616671714768769,"score_spread":0.24486968387794653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403071505","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":[0.0002595767,0.0001791879,0.99468005,0.0008105347,0.0005972437,0.00096982584,0.000003150947,0.0005041877,0.0019962308],"genre_scores_gemma":[0.6932499,0.000034136796,0.30409518,0.0003914459,0.00043285717,0.0002758876,0.000008290826,0.00005435954,0.0014579848],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99710846,0.000017419201,0.0005362625,0.001439124,0.00041789655,0.00048082008],"domain_scores_gemma":[0.9985737,0.00022709412,0.00020980081,0.0006984305,0.00019362928,0.00009733974],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00069718086,0.0003895547,0.00035781052,0.0011260257,0.0003103603,0.00055920583,0.0013903609,0.00032974352,0.0000074240934],"category_scores_gemma":[0.000044523298,0.0003888265,0.00015552007,0.00096369244,0.00023584727,0.00044792422,0.0005176839,0.0008589977,0.00003734816],"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":[0.000007858507,0.00004894649,0.000045965324,0.00015263437,0.000013054362,0.00003447033,0.00055469706,0.0259242,0.004491095,0.063518815,0.00002245821,0.9051858],"study_design_scores_gemma":[0.00016415736,0.00029206675,0.00018121708,0.0003115127,0.000008931805,0.00009395605,2.7771387e-7,0.7431947,0.017395904,0.23174526,0.006073421,0.00053858175],"about_ca_topic_score_codex":0.000043218068,"about_ca_topic_score_gemma":0.00012294769,"teacher_disagreement_score":0.90464723,"about_ca_system_score_codex":0.00048190783,"about_ca_system_score_gemma":0.00024592233,"threshold_uncertainty_score":0.99985635},"labels":[],"label_agreement":null},{"id":"W4403090575","doi":"10.1007/978-3-031-72069-7_61","title":"Heteroscedastic Uncertainty Estimation Framework for Unsupervised Registration","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of British Columbia","funders":"National Institute of Neurological Disorders and Stroke; National Cancer Institute; National Heart, Lung, and Blood Institute; National Institutes of Health","keywords":"Heteroscedasticity; Computer science; Estimation; Artificial intelligence; Data mining; Machine learning; Systems engineering; Engineering","score_opus":0.021520903006297266,"score_gpt":0.2852284728215373,"score_spread":0.26370756981524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403090575","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000122142355,0.00014977902,0.99511224,0.0015244124,0.0009198188,0.0008099555,0.0000133514195,0.0005217276,0.000936501],"genre_scores_gemma":[0.102092676,0.000020856707,0.89628965,0.0005781321,0.000324972,0.00012724193,0.000015845431,0.000032343105,0.0005183083],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973989,0.000009461542,0.0004719835,0.0012664667,0.00048212882,0.00037104482],"domain_scores_gemma":[0.997937,0.00046054675,0.0002110113,0.0010850196,0.00020312458,0.0001032764],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00046649826,0.00036327317,0.0003015758,0.0004987958,0.0002828088,0.00073529116,0.0015400451,0.0003587649,0.000009025248],"category_scores_gemma":[0.00009872311,0.00034341303,0.00015363136,0.00055901054,0.0002971757,0.0003759879,0.00034940665,0.0005509456,0.000037491136],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000027656693,0.000010897427,7.6482377e-7,0.000060497146,0.0000050517233,0.000003268786,0.00012581788,0.047571428,0.0000906827,0.59632355,0.000023881626,0.3557814],"study_design_scores_gemma":[0.000037220452,0.0000909745,0.0000034489606,0.00020196466,0.0000057987836,0.000011042167,4.9874437e-8,0.50229603,0.0005762841,0.49564704,0.00093089626,0.00019922553],"about_ca_topic_score_codex":0.0000119572305,"about_ca_topic_score_gemma":0.000019981399,"teacher_disagreement_score":0.45472464,"about_ca_system_score_codex":0.0003133487,"about_ca_system_score_gemma":0.0002750126,"threshold_uncertainty_score":0.9999018},"labels":[],"label_agreement":null},{"id":"W4403342446","doi":"10.1080/23744731.2024.2411161","title":"Unsupervised identification of zone-level anomalies in VAV terminal units utilizing autoencoders and PCA","year":2024,"lang":"en","type":"article","venue":"Science and Technology for the Built Environment","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Terminal (telecommunication); Identification (biology); Computer science; Pattern recognition (psychology); Artificial intelligence; Telecommunications","score_opus":0.031174384319331738,"score_gpt":0.26267688485427576,"score_spread":0.23150250053494403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403342446","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":[0.30331483,0.00092843553,0.69047445,0.004664744,0.000055352044,0.00039610223,0.000006928145,0.00012364586,0.000035481567],"genre_scores_gemma":[0.9872694,0.00037359298,0.012088853,0.000024001332,0.00000374836,0.0001452822,3.678973e-7,0.0000033310414,0.0000914466],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991726,0.000006073238,0.0001805591,0.000347072,0.0001383314,0.0001553418],"domain_scores_gemma":[0.99950784,0.000051869214,0.000044903434,0.00035175687,0.000022145146,0.000021461032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005775492,0.00007271439,0.000075125434,0.0003247425,0.00024938156,0.0000708628,0.00053104194,0.000053480355,0.0000012372952],"category_scores_gemma":[0.000024710269,0.00005516248,0.000011700056,0.0009177464,0.0010222695,0.0002494133,0.00026480664,0.00008041249,0.000001383803],"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":[0.0000028802954,0.000033558925,0.00080331316,0.000039396964,0.000008110754,0.0000018492574,0.000532698,0.000049371352,0.14834213,0.26881486,0.00002892663,0.5813429],"study_design_scores_gemma":[0.000276538,0.00030350898,0.019162292,0.00007467775,0.00002926828,0.00009902255,0.0018677458,0.5432166,0.3430942,0.07610831,0.015467943,0.00029991876],"about_ca_topic_score_codex":0.000018703107,"about_ca_topic_score_gemma":0.0000063482876,"teacher_disagreement_score":0.68395454,"about_ca_system_score_codex":0.000038140257,"about_ca_system_score_gemma":0.000050322822,"threshold_uncertainty_score":0.37665945},"labels":[],"label_agreement":null},{"id":"W4403383360","doi":"10.1016/j.jag.2024.104185","title":"How can geostatistics help us understand deep learning? An exploratory study in SAR-based aircraft detection","year":2024,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"TD Bank Group","funders":"Shaanxi Key Science and Technology Innovation Team Project; Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Geostatistics; Geography; Remote sensing; Cartography; Data science; Computer science; Mathematics; Statistics","score_opus":0.020368755433142448,"score_gpt":0.2502538240855358,"score_spread":0.22988506865239336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403383360","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":[0.31621215,0.000021941154,0.682506,0.0006842098,0.00021017453,0.00018969383,0.000002800818,0.00008646867,0.00008654178],"genre_scores_gemma":[0.9876141,0.000041758558,0.011916671,0.00026768126,0.00009087633,0.000013597778,0.000020424905,0.0000082579,0.000026608705],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873316,0.000039672035,0.00048044036,0.00015909428,0.00047145327,0.00011618539],"domain_scores_gemma":[0.9991034,0.00006536741,0.0002899151,0.00011846439,0.00034738402,0.00007545783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005208204,0.00012406,0.00012610976,0.0005352074,0.00010589062,0.00077045406,0.0002648104,0.00006485791,0.0000055167275],"category_scores_gemma":[0.000029255858,0.000120849036,0.000037637983,0.00038696622,0.000025712794,0.0017643371,0.000037816633,0.00027863268,0.0000034951263],"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":[0.00015797943,0.00028770053,0.0045510987,0.00006182633,0.0000953294,0.00003434504,0.012317843,0.03289288,0.0016901722,0.0359992,0.000053364944,0.91185826],"study_design_scores_gemma":[0.0011234983,0.0006345966,0.04233061,0.00005264237,0.000017786067,0.000042009986,0.005226443,0.9309904,0.0033679602,0.005594206,0.0103546195,0.00026525618],"about_ca_topic_score_codex":0.000020019244,"about_ca_topic_score_gemma":0.00043351226,"teacher_disagreement_score":0.911593,"about_ca_system_score_codex":0.000117019335,"about_ca_system_score_gemma":0.00009681305,"threshold_uncertainty_score":0.7429506},"labels":[],"label_agreement":null},{"id":"W4403465936","doi":"10.1145/3689036","title":"A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions","year":2024,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":83,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Science Foundation of Ningbo; National Natural Science Foundation of China","keywords":"Computer science; Taxonomy (biology); Cluster analysis; Data science; Artificial intelligence; Information retrieval; Data mining","score_opus":0.13982884979208013,"score_gpt":0.34016127572519267,"score_spread":0.20033242593311254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403465936","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":[9.706716e-7,0.860485,0.13597953,0.00018000707,0.0009792796,0.00079322146,0.00003710515,0.0010134507,0.00053139566],"genre_scores_gemma":[0.00003585809,0.9846919,0.014168674,0.00005085671,0.0006235101,0.00017210699,0.00007270808,0.00006626809,0.000118119875],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99585253,0.001592124,0.00063565,0.0012910571,0.0002240138,0.00040461923],"domain_scores_gemma":[0.99621063,0.0013896838,0.00033446008,0.0017562798,0.0001486756,0.00016024538],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015539274,0.0006221405,0.0011997111,0.00041265337,0.0003715016,0.00034878612,0.0015323723,0.00038019358,0.000003584708],"category_scores_gemma":[0.000069523136,0.0005366908,0.00031929096,0.00090800325,0.000071415736,0.000099607074,0.0018387388,0.0008728841,0.00014148685],"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":[2.92444e-7,0.000029268598,0.0000012029602,0.001320531,0.000086153814,0.00000642632,0.00005807451,0.0000017857458,1.3451071e-8,0.0013237335,0.0006762606,0.99649626],"study_design_scores_gemma":[0.000045577683,0.00008326824,0.00097091263,0.001349365,0.0000761274,0.0000961241,0.000008303328,0.0021367753,1.1876681e-7,0.00021329332,0.99450624,0.00051389117],"about_ca_topic_score_codex":0.00016271931,"about_ca_topic_score_gemma":0.00019522084,"teacher_disagreement_score":0.99598235,"about_ca_system_score_codex":0.00012577238,"about_ca_system_score_gemma":0.00010504668,"threshold_uncertainty_score":0.9997085},"labels":[],"label_agreement":null},{"id":"W4403486961","doi":"10.3233/faia240853","title":"An Axiomatic Perspective on Anomaly Detection","year":2024,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Perspective (graphical); Anomaly detection; Anomaly (physics); Axiom; Computer science; Artificial intelligence; Mathematics; Physics; Geometry","score_opus":0.024804568384468383,"score_gpt":0.28865580224888787,"score_spread":0.2638512338644195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403486961","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":[0.000014038062,0.00049793307,0.8858266,0.0004524068,0.00019127528,0.0008026916,0.00001803955,0.00037496956,0.11182203],"genre_scores_gemma":[0.7673513,0.0027561553,0.10586218,0.0008522002,0.0016626974,0.0047967765,0.00006615222,0.0002699143,0.11638262],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99823153,0.000013320397,0.00045379944,0.0009013336,0.00018986255,0.00021013961],"domain_scores_gemma":[0.99882495,0.00003661729,0.00014116605,0.000782051,0.00010034981,0.00011488007],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015990999,0.00029470123,0.0002664456,0.00060723023,0.00023994136,0.0002712165,0.00058375107,0.00027491452,0.000017012042],"category_scores_gemma":[0.0000047881817,0.00031000606,0.00009959018,0.00032609602,0.00017469602,0.00022018548,0.00008213568,0.00046576836,0.00024008994],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030006024,0.000038236132,7.5007085e-7,0.000010630282,0.000011282362,0.0000018058685,0.00013011924,0.000042807646,0.00006707988,0.7663069,0.00008735848,0.23330007],"study_design_scores_gemma":[0.000008017483,0.00015382316,0.000005229919,0.00005715131,0.000021766542,0.000007957576,0.00028651257,0.035151336,0.0035534555,0.92668957,0.03374434,0.00032083894],"about_ca_topic_score_codex":0.000045878132,"about_ca_topic_score_gemma":0.00007960232,"teacher_disagreement_score":0.77996445,"about_ca_system_score_codex":0.0002603464,"about_ca_system_score_gemma":0.000048784077,"threshold_uncertainty_score":0.9999352},"labels":[],"label_agreement":null},{"id":"W4403487676","doi":"10.3233/faia240716","title":"Open-Set Multivariate Time-Series Anomaly Detection","year":2024,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Multivariate statistics; Series (stratigraphy); Anomaly detection; Anomaly (physics); Set (abstract data type); Computer science; Mathematics; Statistics; Artificial intelligence; Geology; Physics","score_opus":0.034297687218905464,"score_gpt":0.28449755042758107,"score_spread":0.2501998632086756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403487676","genre_codex":"methods","genre_gemma":"other","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":[0.0000023700604,0.00058204454,0.89428234,0.00057595625,0.00021485171,0.0011984067,0.000051131992,0.00030885366,0.10278404],"genre_scores_gemma":[0.021244243,0.0032233146,0.26317772,0.00041306193,0.0009024079,0.0049192864,0.0001389091,0.00020420733,0.7057769],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99808425,0.000013433689,0.0005894366,0.00091892225,0.0001541121,0.00023985571],"domain_scores_gemma":[0.99883896,0.00003589037,0.00018958727,0.00074709504,0.00008479191,0.000103676015],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023488197,0.0003144063,0.0003418435,0.00040025398,0.000307009,0.00063565624,0.0012066088,0.00030936877,0.000057008165],"category_scores_gemma":[0.0000063319953,0.00033488046,0.00009436079,0.00034280523,0.00020452848,0.00046348423,0.000650809,0.00044463735,0.00055010506],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005326986,0.000019339095,0.0000011612118,0.000013995816,0.000016955268,0.0000021022704,0.000067036264,0.00001618175,0.00017368657,0.7114711,0.0007758236,0.28743732],"study_design_scores_gemma":[0.00000970554,0.00004684714,0.0000036882898,0.000045782148,0.000018879568,0.000009555689,0.000037464004,0.011370025,0.0036055744,0.647613,0.33694747,0.00029199352],"about_ca_topic_score_codex":0.00007747621,"about_ca_topic_score_gemma":0.00008676801,"teacher_disagreement_score":0.63110465,"about_ca_system_score_codex":0.00011058952,"about_ca_system_score_gemma":0.000066422836,"threshold_uncertainty_score":0.9999103},"labels":[],"label_agreement":null},{"id":"W4403512878","doi":"10.1145/3689429","title":"A New Tensor Summary Statistic for Real-Time Detection of Stealthy Anomaly in Avatar Interaction","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"National Key Research and Development Program of China","keywords":"Computer science; Avatar; Anomaly detection; Statistic; Tensor (intrinsic definition); Anomaly (physics); Data mining; Human–computer interaction; Statistics; Mathematics; Physics","score_opus":0.0220094069706336,"score_gpt":0.3123839872359144,"score_spread":0.2903745802652808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403512878","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":[0.0010266246,0.00032588677,0.9949388,0.0013608385,0.00007672267,0.0013424052,0.00008412002,0.00053728354,0.00030731552],"genre_scores_gemma":[0.5382537,0.0008884465,0.45984247,0.000031003885,0.00004049983,0.00069370883,0.00003843071,0.000023954346,0.00018774174],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983322,0.00009322881,0.0006564702,0.0005360816,0.00014327993,0.00023871682],"domain_scores_gemma":[0.9957875,0.0019358088,0.00017333323,0.0018323127,0.00014361301,0.00012740317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035785083,0.00020880134,0.00025638027,0.00059035973,0.00045730962,0.00013632941,0.0010944061,0.000119053686,0.000009674944],"category_scores_gemma":[0.000029226452,0.00022708713,0.000113800095,0.0011726959,0.00011286053,0.00033583108,0.000084577565,0.000356899,0.00002507265],"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":[0.0000144023625,0.00022581064,0.000020263225,0.00007813569,0.00003907162,2.3510518e-7,0.00051561964,0.00036323591,0.013706628,0.010212319,0.00010444968,0.9747198],"study_design_scores_gemma":[0.0005245318,0.0002830556,0.0009016835,0.00022326427,0.000064612446,0.000023138382,0.00022925825,0.9568816,0.0067551755,0.0072897077,0.026450962,0.00037302493],"about_ca_topic_score_codex":0.0005870502,"about_ca_topic_score_gemma":0.00016033261,"teacher_disagreement_score":0.9743468,"about_ca_system_score_codex":0.00012591749,"about_ca_system_score_gemma":0.00014612325,"threshold_uncertainty_score":0.92603445},"labels":[],"label_agreement":null},{"id":"W4403528821","doi":"10.1016/j.ins.2024.121566","title":"Integrating granular computing with density estimation for anomaly detection in high-dimensional heterogeneous data","year":2024,"lang":"en","type":"article","venue":"Information Sciences","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Fundamental Research Funds for the Central Universities; Sichuan Province Science and Technology Support Program; National Natural Science Foundation of China","keywords":"Anomaly detection; Anomaly (physics); Granular computing; Computer science; Density estimation; Estimation; Data mining; Mathematics; Statistics; Physics","score_opus":0.023956480829232678,"score_gpt":0.2899104868076396,"score_spread":0.2659540059784069,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403528821","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":[0.13481613,0.000018578876,0.8640464,0.00032005037,0.00012742913,0.00029508027,0.000007891112,0.00030205084,0.00006638338],"genre_scores_gemma":[0.7849484,0.0000010451954,0.21486759,0.000110797846,0.000017862458,0.000026386517,0.00002297954,0.0000021706078,0.000002744328],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892426,0.000021134216,0.00032707595,0.00028737757,0.00027404327,0.0001660948],"domain_scores_gemma":[0.9993354,0.00012466912,0.00011130817,0.00029634297,0.000098995835,0.000033299217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008812036,0.00009705509,0.00009133831,0.00031987435,0.00038882665,0.0006826785,0.000587059,0.00004180675,0.0000017384549],"category_scores_gemma":[0.000049485017,0.000076196426,0.000022011025,0.0010154519,0.00007809294,0.0039047857,0.00015890098,0.000091464106,0.000014353601],"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":[0.000008638007,0.000018734454,0.0002479923,0.000054053686,0.0000065031613,0.0000018123786,0.0006177735,0.04232432,0.00069051335,0.038271677,0.000096894546,0.9176611],"study_design_scores_gemma":[0.00008133233,0.00010113435,0.00070896314,0.000046809426,0.0000029297898,0.000057522728,0.000036643185,0.9876219,0.007783072,0.0028703746,0.00058354577,0.00010574902],"about_ca_topic_score_codex":0.00022757167,"about_ca_topic_score_gemma":0.00013074862,"teacher_disagreement_score":0.9452976,"about_ca_system_score_codex":0.000055706627,"about_ca_system_score_gemma":0.00010223551,"threshold_uncertainty_score":0.65830845},"labels":[],"label_agreement":null},{"id":"W4403534926","doi":"10.1109/codit62066.2024.10708595","title":"Confusion Matrix Explainability to Improve Model Performance: Application to Network Intrusion Detection","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Intrusion detection system; Confusion; Computer science; Confusion matrix; Matrix (chemical analysis); Intrusion; Computer security; Artificial intelligence; Materials science; Geology; Psychology","score_opus":0.0062963459168268645,"score_gpt":0.26212436347482,"score_spread":0.2558280175579931,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403534926","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":[0.061989106,0.000015247255,0.93294245,0.0014381321,0.00019642952,0.000996901,0.0000019282272,0.0014092094,0.0010106171],"genre_scores_gemma":[0.88210183,0.000012661909,0.11546496,0.00041617162,0.00014579101,0.000874221,0.000002117917,0.000011827937,0.0009704257],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986508,0.000016996128,0.00026251798,0.00062709546,0.00019145248,0.00025112837],"domain_scores_gemma":[0.99901915,0.000026629039,0.000030016461,0.0006556669,0.00011039963,0.00015812708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038594447,0.00013792877,0.00010671667,0.00014371304,0.0002911503,0.00020107103,0.00040858015,0.00008984888,0.000014139048],"category_scores_gemma":[0.000009211615,0.00012362152,0.000055498265,0.0011124327,0.000011839305,0.0003747995,0.00036351956,0.00013822794,0.00030651264],"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":[0.0000122583515,0.000024346466,0.00001865835,0.000033595166,0.0000028816537,2.0511953e-7,0.00018793604,0.026444433,0.08561542,0.046600804,0.0015136569,0.8395458],"study_design_scores_gemma":[0.000029614324,0.00015019746,0.00018967869,0.000013117861,0.0000031959091,0.0000039927595,0.000008971371,0.9191994,0.05154622,0.0060769636,0.02262036,0.00015829012],"about_ca_topic_score_codex":0.00004806047,"about_ca_topic_score_gemma":0.000017862423,"teacher_disagreement_score":0.892755,"about_ca_system_score_codex":0.00018105417,"about_ca_system_score_gemma":0.000047780446,"threshold_uncertainty_score":0.5041139},"labels":[],"label_agreement":null},{"id":"W4403577844","doi":"10.1145/3627673.3679755","title":"Out-of-Distribution Aware Classification for Tabular Data","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Simon Fraser University","funders":"","keywords":"Computer science; Data mining","score_opus":0.10996733605566533,"score_gpt":0.3574037990781926,"score_spread":0.24743646302252725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403577844","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":[0.00007327486,0.000074061274,0.99682385,0.0015809385,0.00014979897,0.00020739045,0.00013503821,0.00048338267,0.00047229318],"genre_scores_gemma":[0.9127314,0.000032389442,0.08578386,0.000040532537,0.00006590072,0.000109457564,0.00045319667,0.000005315616,0.0007779392],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994588,0.0000059909676,0.0001341461,0.00026379898,0.00006902094,0.00006826593],"domain_scores_gemma":[0.99915427,0.000038023692,0.000028085933,0.0006983293,0.000060592556,0.000020716709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016470482,0.000042813484,0.000047599726,0.000028406386,0.000053792148,0.00007336029,0.0005700567,0.00003532368,0.000009832424],"category_scores_gemma":[0.000010885954,0.00003756634,0.000028281824,0.00020683289,0.000015467751,0.00030945713,0.000121431054,0.000033307504,0.000022227312],"study_design_candidate":"theoretical_or_conceptual","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":[5.970685e-7,0.000017480674,0.000007468543,0.000025983984,0.0000059061003,1.0867733e-7,0.00001603635,0.000001273079,0.002528649,0.81363326,0.037282776,0.14648049],"study_design_scores_gemma":[0.000021170174,0.000022584518,0.00011859217,0.000007804397,0.000004874247,6.5611556e-7,0.000011543234,0.5688007,0.009843206,0.007363767,0.4137557,0.000049422335],"about_ca_topic_score_codex":0.000006590099,"about_ca_topic_score_gemma":0.0000033438228,"teacher_disagreement_score":0.91265815,"about_ca_system_score_codex":0.000018700655,"about_ca_system_score_gemma":0.00003268187,"threshold_uncertainty_score":0.15319109},"labels":[],"label_agreement":null},{"id":"W4403596536","doi":"10.36227/techrxiv.172954131.15464807/v1","title":"Classification and Prediction in Data-Driven Analysis for Diverse Applications","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Data mining; Data science; Artificial intelligence; Machine learning","score_opus":0.07532284866128668,"score_gpt":0.3288045886882752,"score_spread":0.25348174002698853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403596536","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":[0.0006402463,0.000098662786,0.99448293,0.0013320273,0.000059616683,0.0012426819,0.00059961283,0.0005459358,0.0009982577],"genre_scores_gemma":[0.7219657,0.00022372877,0.27055883,0.00007010524,0.00011575799,0.004538066,0.0014460317,0.000013932685,0.0010678394],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984822,0.000018463737,0.00030582218,0.00097165524,0.00010952342,0.00011228684],"domain_scores_gemma":[0.99819416,0.000051237435,0.000112657435,0.0015193806,0.00007286637,0.000049675476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024121015,0.00013152383,0.00018368696,0.0005357865,0.000091520815,0.00024360484,0.00091495726,0.00017471056,0.000008096501],"category_scores_gemma":[0.000006661708,0.00013064347,0.00007951285,0.00090240326,0.000034134217,0.00015017319,0.0022040464,0.00024058153,0.000014815039],"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":[0.0000059379195,0.00022660062,0.0066761463,0.00036753088,0.00062536186,7.834629e-7,0.00033235314,0.003924614,0.00034970333,0.8492058,0.011797223,0.12648794],"study_design_scores_gemma":[0.00004582794,0.000008258621,0.008691446,0.000009986979,0.00020366386,7.305445e-7,0.000033017444,0.9407321,0.000034838864,0.034643408,0.015473163,0.00012351749],"about_ca_topic_score_codex":0.000093434086,"about_ca_topic_score_gemma":0.00021398233,"teacher_disagreement_score":0.9368075,"about_ca_system_score_codex":0.00007289748,"about_ca_system_score_gemma":0.00006402228,"threshold_uncertainty_score":0.53274864},"labels":[],"label_agreement":null},{"id":"W4403600544","doi":"10.1109/access.2024.3484270","title":"Efficient Observation Time Window Segmentation for Administrative Data Machine Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Anomaly Detection Techniques and Applications","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 Calgary","funders":"","keywords":"Computer science; Window (computing); Segmentation; Artificial intelligence; Machine learning; Operating system","score_opus":0.1257977195144193,"score_gpt":0.3915598023675483,"score_spread":0.26576208285312897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403600544","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":[0.015476711,0.00007639393,0.9822102,0.0008277538,0.00016789985,0.0004335851,0.000046110134,0.0005292179,0.00023212521],"genre_scores_gemma":[0.9459368,0.0000097694365,0.0523691,0.00014498786,0.00012862479,0.0002275343,0.00018531462,0.000012550757,0.000985294],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921215,0.00002274658,0.00015518202,0.00037911997,0.00012222084,0.00010860891],"domain_scores_gemma":[0.9993663,0.00011136783,0.000058965285,0.0003764151,0.00005531671,0.00003166758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022615444,0.00007933412,0.000067427725,0.00006627367,0.00017417152,0.00050933746,0.0008425167,0.000033507382,0.000016171565],"category_scores_gemma":[0.000017486578,0.00007365051,0.000028025413,0.00039014552,0.000014850071,0.00058412383,0.00014847903,0.00008794133,0.00004044819],"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":[0.00004953563,0.00037281215,0.0009868492,0.0003190177,0.00017661095,0.000014720293,0.0015356757,0.026229447,0.14461637,0.06275578,0.023545885,0.7393973],"study_design_scores_gemma":[0.00006786584,0.000056115874,0.00031012966,0.000016223521,0.000008837136,0.0000035569853,0.000005387626,0.9529202,0.033815067,0.0009190321,0.0117841,0.0000934601],"about_ca_topic_score_codex":0.000021092335,"about_ca_topic_score_gemma":0.0000044769577,"teacher_disagreement_score":0.9304601,"about_ca_system_score_codex":0.000036290723,"about_ca_system_score_gemma":0.0000515059,"threshold_uncertainty_score":0.4911553},"labels":[],"label_agreement":null},{"id":"W4403601156","doi":"10.1016/j.eswa.2024.125581","title":"Rethinking prediction-based video anomaly detection from local–global normality perspective","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"China Scholarship Council; Science and Technology Commission of Shanghai Municipality","keywords":"Normality; Perspective (graphical); Anomaly detection; Computer science; Anomaly (physics); Artificial intelligence; Data mining; Pattern recognition (psychology); Statistics; Mathematics","score_opus":0.011100133485326973,"score_gpt":0.25510862476355034,"score_spread":0.24400849127822336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403601156","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":[0.0006167516,0.0016662292,0.9891279,0.000978276,0.00024536473,0.001211536,0.00011950549,0.002943173,0.0030912817],"genre_scores_gemma":[0.96538234,0.00001677292,0.028368557,0.0001828933,0.0003846528,0.005514068,0.00002788918,0.000024305542,0.00009852967],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978786,0.0000837406,0.00040503437,0.0009644583,0.00039154192,0.00027660295],"domain_scores_gemma":[0.9981839,0.00013595381,0.0001284131,0.0010938309,0.00029882786,0.00015907211],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022956346,0.00025127473,0.00020797008,0.00013179285,0.0005746761,0.00052783143,0.00061558024,0.00016541667,0.000013855298],"category_scores_gemma":[0.0000075179137,0.00022109474,0.00010703759,0.0014643383,0.00012798316,0.0005373019,0.00007622297,0.0002464425,0.0001273055],"study_design_candidate":"theoretical_or_conceptual","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":[0.000046525558,0.00038384975,0.00086901843,0.0001201716,0.00031895793,0.000019240186,0.0033517948,0.008353309,0.005530646,0.9181466,0.0039457083,0.058914147],"study_design_scores_gemma":[0.00030708904,0.00018205224,0.0013206765,0.0001869628,0.000041601666,0.00008705845,0.00086531404,0.8447995,0.009235587,0.01517627,0.12722063,0.00057726144],"about_ca_topic_score_codex":0.0052920133,"about_ca_topic_score_gemma":0.00024913362,"teacher_disagreement_score":0.9647656,"about_ca_system_score_codex":0.00080563984,"about_ca_system_score_gemma":0.0002309413,"threshold_uncertainty_score":0.90159816},"labels":[],"label_agreement":null},{"id":"W4403674583","doi":"10.1109/ojemb.2024.3485535","title":"Generation of Seismocardiography Heartbeats Using a Wasserstein Generative Adversarial Network With Feature Control","year":2024,"lang":"en","type":"article","venue":"IEEE Open Journal of Engineering in Medicine and Biology","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"McGill University","keywords":"Feature (linguistics); Generative adversarial network; Adversarial system; Generative grammar; Computer science; Artificial intelligence; Control (management); Pattern recognition (psychology); Deep learning; Linguistics; Philosophy","score_opus":0.04830575407271324,"score_gpt":0.3097347810693128,"score_spread":0.26142902699659953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403674583","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":[0.06376959,0.0012280111,0.93366563,0.00091095356,0.00027450596,0.00011792293,7.286587e-7,0.000009088153,0.00002356358],"genre_scores_gemma":[0.9361764,0.00012532117,0.06306546,0.000093728006,0.0005256226,0.000004022977,5.3727416e-7,0.000003925008,0.000004974537],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995255,0.000035437755,0.00019892486,0.000106840154,0.000047283964,0.000086011314],"domain_scores_gemma":[0.99970317,0.00004995831,0.00007510351,0.000074787706,0.00006266576,0.000034292174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038868334,0.00007039986,0.00023840547,0.0001769977,0.000024370907,0.000025862802,0.00016177689,0.000053960397,0.000001153714],"category_scores_gemma":[0.000007025694,0.000045169345,0.000029789511,0.00039515432,0.000033152784,0.00014799026,0.000019163848,0.00015391466,3.882114e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016050026,0.000050928295,0.0029891215,0.000092421666,0.0006707873,0.00013194277,0.001163523,0.4078513,0.5306543,0.028265338,0.007469334,0.020500487],"study_design_scores_gemma":[0.0018402268,0.0024758768,0.00066228415,0.0006322961,0.000085421794,0.00066463556,0.000045033914,0.9764002,0.007855756,0.0006051085,0.0085416995,0.00019145595],"about_ca_topic_score_codex":0.000028089496,"about_ca_topic_score_gemma":0.000003071582,"teacher_disagreement_score":0.87240684,"about_ca_system_score_codex":0.00001280418,"about_ca_system_score_gemma":0.000039138373,"threshold_uncertainty_score":0.18419524},"labels":[],"label_agreement":null},{"id":"W4403687866","doi":"10.18280/isi.290534","title":"Deep Learning Based Multistage Approach for Anomaly Detection","year":2024,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Anomaly detection; Anomaly (physics); Computer science; Artificial intelligence; Deep learning; Machine learning; Physics","score_opus":0.012345330492139752,"score_gpt":0.23541226261168818,"score_spread":0.22306693211954842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403687866","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":[0.0014731982,0.000091928516,0.9930993,0.000031698473,0.00014736949,0.00054135144,0.000004618033,0.0016154792,0.0029950922],"genre_scores_gemma":[0.818761,0.000005688369,0.1803673,0.00006499038,0.000048061833,0.00059314875,0.000047557634,0.000010043918,0.00010220589],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990458,0.00003038866,0.00035634576,0.00020262941,0.00014824898,0.00021661578],"domain_scores_gemma":[0.9993637,0.00007439442,0.00011634377,0.0002384579,0.00015113436,0.00005592709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000363939,0.0001372272,0.00011134652,0.0003128315,0.00043605964,0.00076321216,0.0002705209,0.00010487925,0.000006035799],"category_scores_gemma":[0.00006443339,0.00013597135,0.00010696026,0.00062729506,0.000044190674,0.0026670306,0.000046559664,0.00014827012,0.000037315924],"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":[0.000010617646,0.000020533145,0.000044454933,0.00051724754,0.00001913939,5.717076e-7,0.0013483524,0.01254042,0.0014052107,0.025900906,0.00009859672,0.95809394],"study_design_scores_gemma":[0.000115124014,0.000101359474,0.00027222483,0.000026434065,0.0000073079304,0.00001634064,0.00011849663,0.96258897,0.010212935,0.0013488914,0.025028378,0.00016354157],"about_ca_topic_score_codex":0.000030698662,"about_ca_topic_score_gemma":0.00000248973,"teacher_disagreement_score":0.9579304,"about_ca_system_score_codex":0.00020385777,"about_ca_system_score_gemma":0.000044819757,"threshold_uncertainty_score":0.7359673},"labels":[],"label_agreement":null},{"id":"W4403765456","doi":"10.34293/sijash.v11is3-july.7914","title":"Humanactivity Recognition using Deep Learning","year":2024,"lang":"en","type":"article","venue":"Shanlax International Journal of Arts Science and Humanities","topic":"Anomaly Detection Techniques and Applications","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning","score_opus":0.06137672279471551,"score_gpt":0.31229575757059896,"score_spread":0.25091903477588345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403765456","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":[0.33361667,0.0006677221,0.65466785,0.0008126176,0.0013406884,0.00007514617,0.0000021719227,0.00014726238,0.008669875],"genre_scores_gemma":[0.9903001,0.00013302687,0.009059724,0.00011886315,0.00026874355,0.0000018484553,3.1716147e-7,0.0000035933633,0.00011378663],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990446,0.00001294436,0.00018979574,0.00014754961,0.00050182565,0.00010330603],"domain_scores_gemma":[0.9991324,0.000041592168,0.00011307832,0.00006026709,0.0006131422,0.000039524715],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00064145954,0.0000610523,0.000067890935,0.0005101835,0.00033969013,0.0010376148,0.0004751177,0.000017477145,0.000034235163],"category_scores_gemma":[0.00004363576,0.000053633925,0.00004161732,0.00019944947,0.00023601492,0.0022985737,0.00012514184,0.0001623583,0.000006411554],"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":[0.000008745062,0.000058624093,0.000107611515,0.00002054815,0.000056796856,0.000072567025,0.005069195,0.00012408342,0.020441227,0.34306532,0.00025915186,0.63071615],"study_design_scores_gemma":[0.00048915006,0.0010051854,0.0031777574,0.0008585741,0.000068964575,0.004046436,0.0045450698,0.4703481,0.06550784,0.26474053,0.18430053,0.0009118956],"about_ca_topic_score_codex":0.000017235594,"about_ca_topic_score_gemma":0.00000648625,"teacher_disagreement_score":0.65668344,"about_ca_system_score_codex":0.00012303867,"about_ca_system_score_gemma":0.00012558141,"threshold_uncertainty_score":0.9999994},"labels":[],"label_agreement":null},{"id":"W4403780041","doi":"10.48550/arxiv.2409.13652","title":"OATS: Outlier-Aware Pruning Through Sparse and Low Rank Decomposition","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"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":"Alliance de recherche numérique du Canada; Natural Sciences and Engineering Research Council of Canada; Government of Canada; Canadian Institute for Advanced Research","keywords":"Pruning; Outlier; Rank (graph theory); Decomposition; Artificial intelligence; Pattern recognition (psychology); Mathematics; Computer science; Statistics; Chemistry; Combinatorics; Biology; Horticulture","score_opus":0.05077375937497734,"score_gpt":0.2148835169835335,"score_spread":0.16410975760855617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403780041","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":[0.101556756,0.000118610085,0.8940036,0.00030112057,0.00023654789,0.00033937363,0.00001646882,0.00085188216,0.0025756226],"genre_scores_gemma":[0.9907168,0.00035589372,0.007697315,0.00012094937,0.00006826798,0.000006094379,0.000013775858,0.000019366513,0.0010014944],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998488,0.000056481196,0.00017244382,0.0009976858,0.00006511713,0.00022028825],"domain_scores_gemma":[0.99883336,0.00004383414,0.00014040818,0.000797711,0.00008756089,0.00009711639],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012272454,0.0002490534,0.00022209392,0.00017264666,0.00023041261,0.00023963168,0.0007384508,0.000244689,0.000015317753],"category_scores_gemma":[0.000004161539,0.00028848145,0.0001355182,0.00048592265,0.00009169456,0.0002915022,0.0020307289,0.00056221656,0.00008777201],"study_design_candidate":"theoretical_or_conceptual","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":[0.000025120164,0.00017654333,0.00054989185,0.00046290024,0.00017705628,0.0003235767,0.0010056628,0.023753367,0.00049157505,0.9622914,0.0022251206,0.00851776],"study_design_scores_gemma":[0.00024112887,0.00006493074,0.00032985618,0.0003159146,0.0000985557,0.000027112286,0.00009347256,0.6562032,0.0017094037,0.33763763,0.0026929465,0.0005858218],"about_ca_topic_score_codex":0.00011010738,"about_ca_topic_score_gemma":0.000014980786,"teacher_disagreement_score":0.8891601,"about_ca_system_score_codex":0.00013860833,"about_ca_system_score_gemma":0.000085937885,"threshold_uncertainty_score":0.9999567},"labels":[],"label_agreement":null},{"id":"W4403788260","doi":"10.1139/tcsme-2024-0063","title":"Comparisons of data-driven models for detecting slip occurrence and direction based on simulations of tactile sensing","year":2024,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Slip (aerodynamics); Acoustics; Computer science; Tactile sensor; Geology; Physics; Engineering; Artificial intelligence; Aerospace engineering; Robot","score_opus":0.03814162213223057,"score_gpt":0.26668103627721235,"score_spread":0.22853941414498177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403788260","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":[0.0009710487,0.000026990838,0.99759537,0.00027057392,0.00012076808,0.000363122,0.0005715862,0.00007490852,0.0000056602016],"genre_scores_gemma":[0.7805758,0.0000023407663,0.2193712,0.000011054066,0.000008363665,0.000015898462,0.0000050780873,0.000006976746,0.0000032509538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937433,0.0000064045817,0.00021265986,0.00019876612,0.00008536906,0.00012249574],"domain_scores_gemma":[0.9990939,0.00035603743,0.000055929548,0.00035950265,0.00007268296,0.000061936924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001603879,0.00007712717,0.00013130838,0.00007797044,0.00018643118,0.000022743368,0.00026524888,0.00006897426,0.0000010251232],"category_scores_gemma":[0.000020516969,0.00007425055,0.00019175603,0.00036848208,0.000025000669,0.00018326957,0.0000119794295,0.00011815311,2.8327516e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000028275877,0.000014485447,3.401035e-7,0.00022054109,0.00004797571,1.7040279e-8,0.00010536743,0.96751404,0.008398822,0.009850797,0.00005868015,0.013786087],"study_design_scores_gemma":[0.00007573153,0.00004876881,0.0000027727683,0.00009628122,0.000045799705,0.0000010781954,0.00001833214,0.9762809,0.022352686,0.00046486882,0.00054837356,0.00006439251],"about_ca_topic_score_codex":0.001367817,"about_ca_topic_score_gemma":0.0017740119,"teacher_disagreement_score":0.7796048,"about_ca_system_score_codex":0.00007721562,"about_ca_system_score_gemma":0.000134866,"threshold_uncertainty_score":0.30278495},"labels":[],"label_agreement":null},{"id":"W4403916386","doi":"10.1007/978-981-97-6671-0_15","title":"Artificial Intelligence and Its Application in Disaster Risk Reduction in the Agriculture Sector","year":2024,"lang":"en","type":"book-chapter","venue":"Disaster risk reduction","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Disaster risk reduction; Reduction (mathematics); Agriculture; Business; Risk analysis (engineering); Environmental resource management; Environmental science; Geography; Mathematics; Archaeology","score_opus":0.020792191279340694,"score_gpt":0.24756616454461783,"score_spread":0.22677397326527712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403916386","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":[0.10347099,0.0064131953,0.7963886,0.0061478266,0.0039423658,0.010684141,0.00035187943,0.0016125407,0.070988454],"genre_scores_gemma":[0.97681266,0.0021710338,0.0010212953,0.000029170671,0.0010905479,0.0007700385,0.00007377605,0.00005431984,0.017977146],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977892,0.00008525719,0.0006190997,0.00096836983,0.00030490724,0.00023315586],"domain_scores_gemma":[0.99883413,0.000034674747,0.00037882023,0.0006249658,0.00007185145,0.000055541383],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044930063,0.0003610474,0.00025562983,0.00035015933,0.00018348216,0.0002664289,0.00048889767,0.00037582364,0.000015554275],"category_scores_gemma":[0.000010679574,0.0002723024,0.00012188435,0.0004337431,0.00009289141,0.00043629084,0.00014776544,0.0010360129,0.00015821497],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032286745,0.00013817757,0.000024699828,0.000089127396,0.00003753013,0.0000043359964,0.010665276,0.0003665689,0.0014321095,0.566062,0.0006189923,0.42052895],"study_design_scores_gemma":[0.00011734882,0.00028339482,0.00064835505,0.00041674435,0.0001995919,0.0004626425,0.0034175622,0.02275514,0.0042582774,0.9097594,0.056350853,0.0013306605],"about_ca_topic_score_codex":0.00016126626,"about_ca_topic_score_gemma":0.00018192487,"teacher_disagreement_score":0.8733417,"about_ca_system_score_codex":0.00016456745,"about_ca_system_score_gemma":0.00002451239,"threshold_uncertainty_score":0.99997294},"labels":[],"label_agreement":null},{"id":"W4403918673","doi":"10.1109/sm63044.2024.10733482","title":"Bio-Inspired Intelligent Anomaly Diagnosis System for Health Monitoring in Connected Vehicles","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Anomaly detection; Computer science; Anomaly (physics); Artificial intelligence","score_opus":0.03284069221583372,"score_gpt":0.3122811141804246,"score_spread":0.27944042196459085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403918673","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":[0.03761527,0.00069043104,0.9553609,0.003592531,0.0003042244,0.00058245886,0.0000055307296,0.0016348838,0.0002137809],"genre_scores_gemma":[0.96122694,0.00011098378,0.037406806,0.00008505705,0.00007086828,0.0009895733,0.000001319969,0.000010058145,0.00009841471],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990508,0.000023657089,0.0002893575,0.00034622065,0.00008453955,0.00020540309],"domain_scores_gemma":[0.99945635,0.0001371896,0.000038191636,0.00026274935,0.000037310896,0.00006820838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023572327,0.00009360225,0.00013086021,0.00019254816,0.000103241866,0.0001833395,0.00030791532,0.00004483546,0.0000027008973],"category_scores_gemma":[0.000008608536,0.000084230254,0.00006382865,0.00065450615,0.000011852602,0.00017012339,0.00007215889,0.00006814678,0.00002099844],"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":[0.0000058799487,0.00012970419,0.010972266,0.0005659177,0.000032141823,0.000008592533,0.0007345585,0.000077248646,0.0017073142,0.49312302,0.0034737184,0.48916966],"study_design_scores_gemma":[0.0003211723,0.00058000727,0.011474282,0.0009988145,0.000010247483,0.000027031803,0.00064469647,0.38936937,0.5009023,0.0030589423,0.09198588,0.00062727113],"about_ca_topic_score_codex":0.00025149054,"about_ca_topic_score_gemma":0.0000357271,"teacher_disagreement_score":0.92361164,"about_ca_system_score_codex":0.00021493161,"about_ca_system_score_gemma":0.00006458563,"threshold_uncertainty_score":0.34348103},"labels":[],"label_agreement":null},{"id":"W4403944815","doi":"10.18280/ijsse.140501","title":"Classification of Physical Violence Actions Using Convolutional Neural Networks with Transfer Learning","year":2024,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Transfer of learning; Computer science; Artificial intelligence; Poison control; Machine learning; Medical emergency; Medicine","score_opus":0.011135776228751311,"score_gpt":0.25009182726088863,"score_spread":0.23895605103213732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403944815","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":[0.1710607,0.00018592116,0.8283047,0.00022178618,0.00013537225,0.000028278197,0.0000024150545,0.000038100785,0.000022711554],"genre_scores_gemma":[0.9958133,0.00015932372,0.0038445613,0.0000069156276,0.00016604393,0.0000013969182,0.000001266807,0.0000049154546,0.0000022879137],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941444,0.000012101513,0.00021104138,0.00009681405,0.00019871228,0.00006689597],"domain_scores_gemma":[0.99960256,0.000082158265,0.000053550477,0.000047172904,0.00017571216,0.000038856386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001212523,0.00006620981,0.000094352356,0.00013433762,0.00004351588,0.00005802192,0.00018504185,0.000030230973,0.0000025017919],"category_scores_gemma":[0.00000799853,0.00005736465,0.00006137722,0.00016702423,0.000031338324,0.00042567527,0.000024257948,0.00027029167,1.5217046e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043260046,0.000050252944,0.00023125557,0.000025968964,0.00012202176,0.000013224685,0.0005768373,0.734526,0.011668133,0.22952732,0.0000022610914,0.023213485],"study_design_scores_gemma":[0.000090923946,0.00006644639,0.0015677086,0.0001564006,0.0000116017745,0.00026247467,0.000026391614,0.99604094,0.0006913555,0.00033875383,0.00068834407,0.000058654532],"about_ca_topic_score_codex":0.0000060024367,"about_ca_topic_score_gemma":6.372018e-7,"teacher_disagreement_score":0.82475257,"about_ca_system_score_codex":0.000046953282,"about_ca_system_score_gemma":0.00003064538,"threshold_uncertainty_score":0.23392625},"labels":[],"label_agreement":null},{"id":"W4404030342","doi":"10.1109/icccnt61001.2024.10723970","title":"Comprehensive investigation on Deep learning models: Applications, Advantages, and Challenges","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"American Water (Canada)","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Data science","score_opus":0.07304477876139331,"score_gpt":0.2671518303614016,"score_spread":0.1941070516000083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404030342","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":[0.0004631164,0.0077811116,0.97933745,0.0032214888,0.000021195703,0.00023078646,4.5177694e-7,0.0011995044,0.0077448883],"genre_scores_gemma":[0.88695955,0.016318927,0.09490644,0.0004779338,0.000072619434,0.0005375645,0.000006137487,0.0000167453,0.00070409133],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993379,0.000024401676,0.000107218904,0.00034483135,0.00009403147,0.00009162941],"domain_scores_gemma":[0.99958634,0.00008482941,0.00002209029,0.00021384451,0.00003659894,0.000056269644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006522012,0.00008424473,0.00006427127,0.00010235228,0.00014356036,0.00013049976,0.00015727924,0.000043258136,0.000004152094],"category_scores_gemma":[0.0000017546536,0.00007567548,0.000021487433,0.00020043962,0.000034604203,0.0003239001,0.00007870276,0.00013311986,0.000046517423],"study_design_candidate":"theoretical_or_conceptual","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":[2.4166553e-7,0.0000041719522,0.0000021267492,0.000023735089,0.0000029838209,3.8018018e-7,0.000114399656,0.00021621957,0.00017721276,0.64922255,0.000041902935,0.35019407],"study_design_scores_gemma":[0.000036916972,0.000073136296,0.00014444363,0.000022261262,0.0000035112414,0.000016321168,0.00014852952,0.5870295,0.0015715049,0.13205837,0.2787581,0.00013739918],"about_ca_topic_score_codex":0.0000067002356,"about_ca_topic_score_gemma":0.0000016978582,"teacher_disagreement_score":0.8864964,"about_ca_system_score_codex":0.00001960381,"about_ca_system_score_gemma":0.0000104699875,"threshold_uncertainty_score":0.30859566},"labels":[],"label_agreement":null},{"id":"W4404038739","doi":"10.1007/978-981-97-6516-4_6","title":"Alarm Visual Analytics and Applications","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Visual analytics; Analytics; Computer science; ALARM; Data science; Artificial intelligence; Visualization; Engineering; Aerospace engineering","score_opus":0.013384228044317726,"score_gpt":0.2644992723872454,"score_spread":0.2511150443429277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404038739","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.0180616e-7,0.00022735786,0.51464415,0.00029211404,0.000017147371,0.00016360648,0.000004578126,0.00042296216,0.48422796],"genre_scores_gemma":[0.00094331737,0.0004607304,0.017816016,0.00025994514,0.00016003949,0.00011641895,0.000008382168,0.000025189995,0.98020995],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.999149,0.0000012843446,0.0001867724,0.00044309956,0.00012296023,0.00009687217],"domain_scores_gemma":[0.9993481,0.00002326493,0.000058317975,0.00043806472,0.00005355897,0.00007868257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000052355677,0.00016323634,0.00013812889,0.0001573964,0.00009295256,0.00017963572,0.0003153907,0.00016436554,0.00008620312],"category_scores_gemma":[5.5281055e-7,0.00015046644,0.00007569961,0.000077128396,0.00005645046,0.00006898159,0.0003020029,0.00021228933,0.000447811],"study_design_candidate":"theoretical_or_conceptual","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":[8.002717e-8,0.0000038105907,2.6956482e-7,0.000011080285,0.000015030497,0.0000010139421,0.0000037150237,3.5354125e-7,0.0000074624763,0.9606,0.0020679662,0.037289176],"study_design_scores_gemma":[0.000011445228,0.000019740384,0.0000014683917,0.000009980634,0.00002051143,0.000015716596,0.000001034771,0.0045419144,0.00009317246,0.2690894,0.7260553,0.00014028946],"about_ca_topic_score_codex":0.0000038989556,"about_ca_topic_score_gemma":0.000004092959,"teacher_disagreement_score":0.72398734,"about_ca_system_score_codex":0.000028748438,"about_ca_system_score_gemma":0.000031197775,"threshold_uncertainty_score":0.6135843},"labels":[],"label_agreement":null},{"id":"W4404038915","doi":"10.1007/978-981-97-6516-4_5","title":"Analysis of Industrial Alarm Floods","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"ALARM; Computer science; Environmental science; Engineering; Aerospace engineering","score_opus":0.03561540670534603,"score_gpt":0.2633704866008669,"score_spread":0.2277550798955209,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404038915","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000027871906,0.000065414926,0.32175443,0.00019450058,0.00008717193,0.00009497716,0.000019195373,0.00027633656,0.6775052],"genre_scores_gemma":[0.0059569078,0.00006630353,0.0074267033,0.00007629175,0.0001200676,0.000018168219,0.000017108865,0.00001563537,0.9863028],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990917,0.000002717379,0.00029438033,0.0003481272,0.00018459577,0.000078511],"domain_scores_gemma":[0.9991036,0.000024605057,0.00012173357,0.0006453286,0.000061293984,0.000043416712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009330417,0.00013675564,0.00029450006,0.0006111534,0.000027910346,0.00005343318,0.0005355233,0.00026979222,0.0005757618],"category_scores_gemma":[0.0000018183974,0.00012011511,0.00036952653,0.00039247086,0.000034626897,0.000050574603,0.00023944586,0.00022819602,0.000099905315],"study_design_candidate":"theoretical_or_conceptual","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":[3.047527e-7,0.000004289387,0.000002476612,0.000002435716,0.00044036403,0.0000011504778,0.0000082102915,0.000006049718,0.000008867273,0.97248626,0.0024574506,0.024582135],"study_design_scores_gemma":[0.000050308914,0.000071938586,0.0000062116123,0.000033511682,0.0012685457,0.000002625008,0.0000018851667,0.015838815,0.0011671939,0.11469985,0.8665561,0.00030303717],"about_ca_topic_score_codex":0.000026904894,"about_ca_topic_score_gemma":0.000011964179,"teacher_disagreement_score":0.8640986,"about_ca_system_score_codex":0.000027213036,"about_ca_system_score_gemma":0.000045432822,"threshold_uncertainty_score":0.6304188},"labels":[],"label_agreement":null},{"id":"W4404060023","doi":"10.1145/3702983","title":"Repairs and Breaks Prediction for Deep Neural Networks","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"JST-Mirai Program","keywords":"Computer science; Artificial neural network; Artificial intelligence; Deep neural networks; Data science","score_opus":0.04038717887472058,"score_gpt":0.2922085178703729,"score_spread":0.2518213389956523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404060023","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00096805283,0.00067983405,0.9958494,0.00034913188,0.00048666567,0.00017157287,0.000008470888,0.001484417,0.0000024801752],"genre_scores_gemma":[0.17698802,0.00016975362,0.82240057,0.00007089451,0.000062836705,0.0002266059,0.0000022646752,0.000014680608,0.000064353255],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993097,0.000036590267,0.0001347127,0.00032885346,0.00003829909,0.00015181226],"domain_scores_gemma":[0.9985947,0.0010386166,0.000014034968,0.00026629807,0.00002252129,0.00006384698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031372014,0.00010647289,0.00011821451,0.0001407679,0.00013438812,0.00005909238,0.00012619431,0.000114021364,0.0000022669342],"category_scores_gemma":[0.000088113055,0.00010418368,0.00005560277,0.00019856244,0.000025557125,0.00013609535,0.00001072251,0.0001879597,4.2732012e-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":[0.00000736028,0.000010702304,0.000013605165,0.000052719362,0.00003684942,0.0000016451093,0.00015777697,0.032507967,0.00019049426,0.006355327,0.000045704342,0.96061987],"study_design_scores_gemma":[0.00008886668,0.00017291124,0.0003014168,0.00001459684,0.000028047843,0.0001674341,0.00001012174,0.98698026,0.0005969357,0.0016906966,0.009835837,0.00011290161],"about_ca_topic_score_codex":0.000007361347,"about_ca_topic_score_gemma":0.0000016835817,"teacher_disagreement_score":0.960507,"about_ca_system_score_codex":0.000015244661,"about_ca_system_score_gemma":0.0000062248173,"threshold_uncertainty_score":0.42484874},"labels":[],"label_agreement":null},{"id":"W4404347030","doi":"10.48550/arxiv.2410.23706","title":"Complex trend inference for high-dimensional piecewise locally stationary time series","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"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":"University of Toronto; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Jump; Asynchronous communication; Dynamics (music); Computer science; Estimation; High dimensional; Statistical physics; Artificial intelligence; Physics; Engineering; Acoustics; Telecommunications","score_opus":0.054423702478852906,"score_gpt":0.21049615348286457,"score_spread":0.15607245100401168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404347030","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":[0.011554346,0.0000322075,0.9848894,0.0006958735,0.00019258593,0.00051763136,0.00027447197,0.0007997171,0.0010437877],"genre_scores_gemma":[0.9386696,0.000034342287,0.05222653,0.00014416488,0.00007000304,0.000022576069,0.00020735264,0.000021151815,0.00860424],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850553,0.00003893019,0.00021246713,0.000925461,0.00008780805,0.00022979917],"domain_scores_gemma":[0.9987129,0.00012285801,0.00015557959,0.0007115454,0.00018274838,0.000114390685],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011087516,0.00025721898,0.00024093379,0.00023759427,0.0002180188,0.00013942439,0.00092727604,0.00018967269,0.000102818514],"category_scores_gemma":[0.000010408738,0.00029108225,0.00015908133,0.00046482263,0.00012801547,0.00024202451,0.0016230986,0.00032000884,0.00018169072],"study_design_candidate":"theoretical_or_conceptual","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":[0.000030814146,0.0000784513,0.000018951196,0.00008451056,0.000070939335,0.000035990754,0.000055569442,0.04723914,0.00026940156,0.9416425,0.0072377613,0.0032359231],"study_design_scores_gemma":[0.00015362719,0.00009266553,0.000288256,0.00004662781,0.000041266583,0.000004934938,0.000007365168,0.5446558,0.00026983992,0.45067257,0.003453376,0.0003136747],"about_ca_topic_score_codex":0.000057845533,"about_ca_topic_score_gemma":0.000026970698,"teacher_disagreement_score":0.93266284,"about_ca_system_score_codex":0.00013649877,"about_ca_system_score_gemma":0.0002520876,"threshold_uncertainty_score":0.9999541},"labels":[],"label_agreement":null},{"id":"W4404446785","doi":"10.1016/j.compchemeng.2024.108933","title":"A semi-supervised learning algorithm for high and low-frequency variable imbalances in industrial data","year":2024,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Project 211; National Key Research and Development Program of China; National Natural Science Foundation of China; International Cooperation and Exchange Programme; Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project","keywords":"Variable (mathematics); Artificial intelligence; Computer science; Algorithm; Supervised learning; Semi-supervised learning; Pattern recognition (psychology); Machine learning; Mathematics; Artificial neural network","score_opus":0.01587833363308256,"score_gpt":0.2270507662520948,"score_spread":0.21117243261901225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404446785","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005961729,0.0002633515,0.992553,0.00017736401,0.00025423896,0.00017808899,0.000012031187,0.00058101903,0.000019155725],"genre_scores_gemma":[0.2867953,0.000022929675,0.71269166,0.000034212968,0.00029814467,0.00007925135,0.000045725672,0.000017296545,0.000015448324],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990356,0.0000065752943,0.0001940953,0.00046643105,0.00008193862,0.00021538963],"domain_scores_gemma":[0.99941415,0.00017644493,0.000018565885,0.0003084049,0.00001614595,0.00006630522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017183673,0.00012412132,0.00014973314,0.00009400473,0.00003615811,0.00019313284,0.0006010407,0.00009668819,0.0000019125744],"category_scores_gemma":[0.000028535502,0.00012992573,0.000022007483,0.00043217372,0.000015354864,0.00034821365,0.00037949943,0.0002809129,0.0000013207966],"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":[0.000003239934,0.00006110694,0.000059181773,0.00025222366,0.000059286547,0.000020115689,0.00021263282,0.004769618,0.10709845,0.045924976,0.0016481405,0.839891],"study_design_scores_gemma":[0.0002020906,0.00001969396,0.000008198384,0.00013038516,0.000004015918,0.000012457703,0.000001976169,0.98975956,0.0059867343,0.0008885381,0.0028346975,0.0001516732],"about_ca_topic_score_codex":0.000023485514,"about_ca_topic_score_gemma":8.6190106e-8,"teacher_disagreement_score":0.98498994,"about_ca_system_score_codex":0.00004655089,"about_ca_system_score_gemma":0.000035306733,"threshold_uncertainty_score":0.52982175},"labels":[],"label_agreement":null},{"id":"W4404516566","doi":"10.1016/j.asoc.2024.112377","title":"Adaptive deep learning models for efficient multivariate anomaly detection in IoT infrastructures","year":2024,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada)","funders":"","keywords":"Anomaly detection; Multivariate statistics; Computer science; Anomaly (physics); Artificial intelligence; Internet of Things; Deep learning; Machine learning; Multivariate analysis; Embedded system","score_opus":0.01568382607091543,"score_gpt":0.2503434834758336,"score_spread":0.23465965740491815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404516566","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":[0.03149226,0.000096604155,0.9657018,0.000036550282,0.00012923939,0.0005583852,0.0000010749534,0.0010749977,0.00090907543],"genre_scores_gemma":[0.80148053,0.0000010669465,0.19825712,0.000042592448,0.00007126852,0.000112019916,0.0000016763935,0.000019549474,0.000014188122],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857736,0.00002870468,0.00031854084,0.00059999875,0.00014461136,0.0003308],"domain_scores_gemma":[0.9992859,0.00029920452,0.00009051098,0.00021964473,0.000050259867,0.000054475033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035932183,0.00018014388,0.00017163028,0.0002556457,0.00032365552,0.00021206851,0.00034184405,0.000104865976,0.0000017549464],"category_scores_gemma":[0.000015284337,0.00018388657,0.000080985374,0.0006984619,0.000029622164,0.0000852979,0.0002067384,0.0003362089,0.000008846562],"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":[0.0000066264815,0.000013372962,0.0000043468303,0.00001610018,0.000008764682,0.0000010194725,0.0008930909,0.53348935,0.0041909493,0.07817447,0.0000043284676,0.38319758],"study_design_scores_gemma":[0.00016559927,0.000049537208,0.0003911838,0.000028065177,0.0000052474184,0.0000068519657,0.0000956815,0.9676963,0.006132862,0.024929821,0.00030156018,0.00019727353],"about_ca_topic_score_codex":0.00006514164,"about_ca_topic_score_gemma":0.000013195058,"teacher_disagreement_score":0.76998824,"about_ca_system_score_codex":0.00012421301,"about_ca_system_score_gemma":0.00003331535,"threshold_uncertainty_score":0.7498677},"labels":[],"label_agreement":null},{"id":"W4404562889","doi":"10.1109/ojim.2024.3502886","title":"Hydra-Mask-RCNN: An Adaptive HydraNet Architecture for Autonomous Aerial Vehicle Object Detection","year":2024,"lang":"en","type":"article","venue":"IEEE Open Journal of Instrumentation and Measurement","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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 Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lernaean Hydra; Architecture; Object detection; Computer science; Artificial intelligence; Computer vision; Object (grammar); Biology; Geography; Pattern recognition (psychology)","score_opus":0.04680575343935315,"score_gpt":0.2980250702009813,"score_spread":0.2512193167616281,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404562889","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":[0.074806124,0.00011080528,0.92269325,0.0006374027,0.00075138453,0.000653096,0.0000055867595,0.00006174323,0.00028060598],"genre_scores_gemma":[0.9704215,0.000029835583,0.029039938,0.00018067338,0.00020280272,0.000081202874,0.0000011652123,0.000010997693,0.00003190092],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99883187,0.000089482535,0.00038389012,0.00023846384,0.0003187765,0.00013754118],"domain_scores_gemma":[0.999303,0.000026992966,0.00020049358,0.00014949976,0.00019790199,0.0001221562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010414452,0.00012304241,0.0001571345,0.00015760072,0.00020034522,0.0006020412,0.00038250117,0.00004937611,0.000008612516],"category_scores_gemma":[0.000012391932,0.00010501793,0.000074264484,0.00018878044,0.000027741222,0.00093616097,0.000038295042,0.00014500723,0.0000023580078],"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":[0.00025226964,0.000088387715,0.000005083005,0.000024361056,0.00008161932,0.0000042911743,0.0011458935,0.00062690105,0.17438433,0.0034567819,0.00028942229,0.81964064],"study_design_scores_gemma":[0.0024903435,0.0053216335,0.0003659731,0.00022256667,0.000110166606,0.00043926126,0.00061951776,0.06380546,0.87167156,0.0148184085,0.03967616,0.00045894439],"about_ca_topic_score_codex":0.00004235078,"about_ca_topic_score_gemma":0.00006329605,"teacher_disagreement_score":0.89561534,"about_ca_system_score_codex":0.00017156718,"about_ca_system_score_gemma":0.00018876532,"threshold_uncertainty_score":0.5805497},"labels":[],"label_agreement":null},{"id":"W4404564541","doi":"10.1109/uemcon62879.2024.10754771","title":"Fitting and Filtering Functional Data for Use in Video Data Analysis","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"MacEwan University; University of Alberta","funders":"","keywords":"Computer science; Functional data analysis; Data mining; Machine learning","score_opus":0.16033419076972794,"score_gpt":0.3371630070763798,"score_spread":0.17682881630665187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404564541","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015175855,0.000060286988,0.9973848,0.00051864836,0.000032546475,0.00009019018,0.00009164717,0.0002078917,0.00009641772],"genre_scores_gemma":[0.3591804,0.000023660272,0.63970256,0.00013908948,0.000050984498,0.000038493527,0.0002639983,0.0000047463323,0.00059607136],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992328,0.000006668597,0.00012383892,0.0005010921,0.000055739732,0.0000798153],"domain_scores_gemma":[0.9986424,0.00017110445,0.0000144217875,0.0011372038,0.000012312733,0.00002256107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029851863,0.000045219524,0.00006227712,0.00015656452,0.000059579044,0.00036089992,0.0006082367,0.000019796154,0.000018288007],"category_scores_gemma":[0.00003003868,0.00004056758,0.000015731555,0.0006339751,0.000009674848,0.0011805703,0.0010640759,0.000044899647,0.000002586584],"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":[0.000008847311,0.00008149644,0.0074650645,0.00012922521,0.00044555747,0.000009529008,0.00012684692,0.00066422136,0.0028753316,0.23359275,0.06100782,0.6935933],"study_design_scores_gemma":[0.000024530773,0.0000050632243,0.0027059817,0.0000049115774,0.000022339194,0.0000028679,0.0000057752172,0.95956486,0.000103445316,0.0007872466,0.03672045,0.000052546005],"about_ca_topic_score_codex":0.00011633568,"about_ca_topic_score_gemma":0.0002016107,"teacher_disagreement_score":0.95890063,"about_ca_system_score_codex":0.000008719046,"about_ca_system_score_gemma":0.000015582013,"threshold_uncertainty_score":0.34801662},"labels":[],"label_agreement":null},{"id":"W4404623370","doi":"10.5220/0013063900003822","title":"Data-Driven Intrusion Detection in Vehicles: Integrating Unscented Kalman Filter (UKF) with Machine Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Kalman filter; Computer science; Intrusion detection system; Intrusion; Extended Kalman filter; Artificial intelligence; Real-time computing","score_opus":0.019360860579376294,"score_gpt":0.2655809294721313,"score_spread":0.246220068892755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404623370","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":[0.033741876,0.00005235,0.9632382,0.0005132786,0.00005190803,0.00016969614,0.0000034962266,0.001029372,0.0011998283],"genre_scores_gemma":[0.9487836,0.000021444566,0.050502405,0.00008626535,0.00003547981,0.000034779285,0.00002400229,0.00001220554,0.0004998375],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899966,0.000046463298,0.00019436522,0.00047064162,0.0001343624,0.0001544956],"domain_scores_gemma":[0.99938107,0.00004871206,0.000039344868,0.00046084757,0.000030360006,0.00003966918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002072518,0.00011227681,0.00009289865,0.00020442477,0.00013458211,0.0002235301,0.0005056454,0.000050142684,0.00003373051],"category_scores_gemma":[0.000014207928,0.000082672945,0.000021244023,0.00084342354,0.00002278526,0.00061863963,0.00032523842,0.0003735795,0.00003871856],"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":[0.000014588566,0.00007747971,0.00207021,0.000049792045,0.00002261883,0.000029891251,0.0006748713,0.0007019634,0.027468799,0.018796619,0.00034374578,0.9497494],"study_design_scores_gemma":[0.00008215613,0.00010745805,0.0005779367,0.000084352476,0.0000035252015,0.000028922523,0.00006305519,0.9714505,0.00950363,0.00029005576,0.017685918,0.00012245553],"about_ca_topic_score_codex":0.0003596058,"about_ca_topic_score_gemma":0.001121453,"teacher_disagreement_score":0.97074854,"about_ca_system_score_codex":0.00006214613,"about_ca_system_score_gemma":0.000025913654,"threshold_uncertainty_score":0.3371305},"labels":[],"label_agreement":null},{"id":"W4404686190","doi":"10.1007/s10489-024-05892-2","title":"Dirichlet and Liouville-based normality scores for deep anomaly detection using transformations: applications to images and beyond images","year":2024,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"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":"Computer science; Normality; Anomaly (physics); Anomaly detection; Artificial intelligence; Pattern recognition (psychology); Latent Dirichlet allocation; Dirichlet distribution; Topic model; Statistics; Mathematics; Physics; Mathematical analysis","score_opus":0.01799972045383175,"score_gpt":0.2885207132515042,"score_spread":0.27052099279767244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404686190","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":[0.003815964,0.0006362961,0.9923448,0.0005811991,0.000040734274,0.001430332,0.000035394296,0.00048080683,0.0006344546],"genre_scores_gemma":[0.77924013,0.0001019366,0.21879075,0.00027317315,0.000041555064,0.0015166245,0.0000049643613,0.000014278717,0.000016554333],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987389,0.000014358116,0.00032881912,0.000551045,0.00013118419,0.00023573144],"domain_scores_gemma":[0.9991713,0.00019832121,0.000055131564,0.00035204747,0.000098260025,0.0001249471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027137971,0.00018561629,0.00014988534,0.00027194552,0.00048037522,0.00044906785,0.00030275626,0.00007545853,0.0000052267496],"category_scores_gemma":[0.000011074472,0.00018353084,0.00005222882,0.0008124074,0.00012587014,0.00042297956,0.000083529776,0.000119173106,0.0000129111895],"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":[0.000015404723,0.00004865058,0.00003615331,0.0002083032,0.000020301854,5.371372e-7,0.00042632717,0.0007845986,0.048032977,0.118878275,0.00008257489,0.8314659],"study_design_scores_gemma":[0.00008204729,0.00013161851,0.0003649935,0.00003300778,0.000042536365,0.000029405659,0.00017085252,0.30838394,0.6462854,0.033288226,0.01074452,0.00044341412],"about_ca_topic_score_codex":0.000040000366,"about_ca_topic_score_gemma":0.000019267301,"teacher_disagreement_score":0.8310225,"about_ca_system_score_codex":0.00004956161,"about_ca_system_score_gemma":0.000041902516,"threshold_uncertainty_score":0.748417},"labels":[],"label_agreement":null},{"id":"W4404707788","doi":"10.1007/978-3-031-73030-6_24","title":"Unsupervised, Online and On-The-Fly Anomaly Detection for Non-stationary Image Distributions","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Anomaly detection; On the fly; Artificial intelligence; Image (mathematics); Pattern recognition (psychology); Computer vision; Operating system","score_opus":0.014335652502067125,"score_gpt":0.2571626024609905,"score_spread":0.24282694995892337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404707788","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00029605097,0.00011929962,0.99287426,0.004421385,0.0004098219,0.00081971847,0.00013942466,0.0002455441,0.00067451084],"genre_scores_gemma":[0.4177293,0.00012733146,0.5774354,0.001983481,0.0007449602,0.00034051327,0.00006665317,0.00006737171,0.0015050132],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978913,0.000010913975,0.00035104871,0.0010740971,0.00034472032,0.00032792077],"domain_scores_gemma":[0.9982029,0.0005395668,0.00012897498,0.0008023563,0.00022887009,0.00009729912],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037460055,0.0003339791,0.00023496774,0.000426127,0.00057324965,0.0005203329,0.0011369785,0.00018581416,0.000007676583],"category_scores_gemma":[0.00004168282,0.00026505237,0.00012274779,0.000581352,0.0005063724,0.00030933408,0.0004902831,0.00052924873,0.000023285302],"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":[0.000010044423,0.00006755594,0.0000049336263,0.00007658171,0.000021637314,0.00001429671,0.00023514793,0.000711476,0.0023633733,0.2769778,0.000181165,0.719336],"study_design_scores_gemma":[0.0001163289,0.00029546817,0.00012703922,0.00016290201,0.000014623484,0.00005671697,5.129717e-7,0.57779026,0.007179018,0.40601036,0.007847493,0.0003992636],"about_ca_topic_score_codex":0.00001434503,"about_ca_topic_score_gemma":0.00005905291,"teacher_disagreement_score":0.7189367,"about_ca_system_score_codex":0.00018715061,"about_ca_system_score_gemma":0.00017388111,"threshold_uncertainty_score":0.99998015},"labels":[],"label_agreement":null},{"id":"W4404742929","doi":"10.1109/tsmc.2024.3496332","title":"Landmark Block-Embedded Aggregation Autoencoder for Anomaly Detection","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Autoencoder; Landmark; Anomaly detection; Block (permutation group theory); Artificial intelligence; Anomaly (physics); Pattern recognition (psychology); Computer science; Mathematics; Combinatorics; Deep learning; Physics","score_opus":0.012760321569124346,"score_gpt":0.23947169906221535,"score_spread":0.226711377493091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404742929","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":[0.0046370686,0.00090544036,0.988883,0.00011860284,0.0023008033,0.0012747247,0.00004070724,0.0010651402,0.0007744705],"genre_scores_gemma":[0.991283,0.000113961476,0.0016562266,0.000026353402,0.00019623272,0.0011624073,0.0000024402725,0.00003649188,0.0055228723],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982862,0.00008707542,0.00047801816,0.0006111391,0.00026136162,0.00027623225],"domain_scores_gemma":[0.99900067,0.00014938213,0.00010889001,0.00048222064,0.00012774178,0.0001311054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003570985,0.0002468906,0.0002559128,0.00032358905,0.00036396482,0.00085084385,0.00025419012,0.0001986301,0.0000022995703],"category_scores_gemma":[0.0000022580743,0.00023080298,0.00013368693,0.00046360068,0.000043641357,0.0002784458,0.0000034433442,0.00019444303,0.000044736607],"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":[0.0001952551,0.0009843806,0.00004858979,0.0061117155,0.0013244211,0.00006204371,0.0065484317,0.13991901,0.056832083,0.23845255,0.010154459,0.5393671],"study_design_scores_gemma":[0.00027262807,0.00031375184,0.00001988456,0.0002743194,0.000055147044,0.00024679815,0.00017715356,0.9452317,0.010552432,0.00029716382,0.04221595,0.0003430705],"about_ca_topic_score_codex":0.0002342337,"about_ca_topic_score_gemma":0.00003524721,"teacher_disagreement_score":0.98722684,"about_ca_system_score_codex":0.00011592853,"about_ca_system_score_gemma":0.000045486395,"threshold_uncertainty_score":0.9411872},"labels":[],"label_agreement":null},{"id":"W4404776392","doi":"10.1049/itr2.12591","title":"Geo‐spatial traffic behaviour analysis and anomaly detection for ITS applications","year":2024,"lang":"en","type":"article","venue":"IET Intelligent Transport Systems","topic":"Anomaly Detection Techniques and Applications","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":"Esri (Canada)","funders":"Technische Universiteit Eindhoven; Rijksdienst voor Ondernemend Nederland; ITEA","keywords":"Anomaly detection; Computer science; Anomaly (physics); Transport engineering; Data mining; Engineering","score_opus":0.015371957442259849,"score_gpt":0.2620127390152522,"score_spread":0.24664078157299232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404776392","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":[0.022767602,0.0011122421,0.97327256,0.00011422655,0.00025737725,0.0013977519,0.00008448232,0.00090640876,0.0000873348],"genre_scores_gemma":[0.99422383,0.00013654506,0.002176068,0.000014949659,0.00014350454,0.0026215154,0.00004322812,0.000020119367,0.00062021584],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984489,0.000021002874,0.00048313677,0.0006342503,0.00018606559,0.00022661152],"domain_scores_gemma":[0.99921286,0.00007356451,0.000076571974,0.0003990685,0.00011991126,0.00011800512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028309276,0.00018562007,0.00024658727,0.00041380865,0.00022863688,0.00021758077,0.00033036558,0.0001294469,0.0000102376625],"category_scores_gemma":[0.000001942648,0.00017993408,0.000264989,0.0011744943,0.000029876588,0.000226296,0.000014673853,0.00012328803,0.00002515704],"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":[0.000050020928,0.00066435203,0.0050303056,0.0016885536,0.0022368487,0.000029965231,0.0030802232,0.015188186,0.015125076,0.1253274,0.0003702879,0.83120877],"study_design_scores_gemma":[0.00012551369,0.00026223465,0.0039961636,0.00006362104,0.00094433344,0.000068450754,0.00014001214,0.8415622,0.032588374,0.00027353177,0.119354814,0.00062072114],"about_ca_topic_score_codex":0.0002048269,"about_ca_topic_score_gemma":0.00037901624,"teacher_disagreement_score":0.9714562,"about_ca_system_score_codex":0.000060912396,"about_ca_system_score_gemma":0.00003726828,"threshold_uncertainty_score":0.73374987},"labels":[],"label_agreement":null},{"id":"W4404789475","doi":"10.1016/j.procir.2024.10.320","title":"Anomaly detection on MVTec AD using VQ-VAE-2","year":2024,"lang":"en","type":"article","venue":"Procedia CIRP","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Investment Agriculture Foundation; Agency for Science, Technology and Research","keywords":"Anomaly detection; Anomaly (physics); Pattern recognition (psychology); Artificial intelligence; Geology; Computer science; Mathematics; Physics","score_opus":0.020320279521997556,"score_gpt":0.2748372726133923,"score_spread":0.25451699309139475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404789475","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":[0.037745006,0.00023750104,0.9568553,0.0004549335,0.00040655726,0.00022591591,0.0000021903024,0.0015058231,0.0025667618],"genre_scores_gemma":[0.98280436,0.00002855078,0.016356746,0.00016136402,0.00018236997,0.00010147642,7.3321723e-7,0.000016453414,0.00034792596],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904245,0.000011518238,0.00015844037,0.0004292897,0.00016379371,0.00019452785],"domain_scores_gemma":[0.99948084,0.00003321244,0.00003930435,0.0003292022,0.000050906474,0.00006651634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000110750276,0.00012026778,0.00008525446,0.00018025018,0.00018197094,0.00024740692,0.00032602588,0.00007587198,0.000014274223],"category_scores_gemma":[0.000016140142,0.000114836635,0.000070852635,0.0006849649,0.000024110306,0.00033975847,0.000078806035,0.00017730467,0.00018811095],"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":[0.0000071085715,0.00008138552,0.00008016444,0.00008967676,0.000029949391,0.000016759552,0.0003946725,0.0000789093,0.08781805,0.040008854,0.0009713673,0.8704231],"study_design_scores_gemma":[0.00012837518,0.00030138975,0.00096895016,0.000109846376,0.000028257355,0.00017694866,0.000027741253,0.46497244,0.411852,0.018069336,0.10285,0.00051471207],"about_ca_topic_score_codex":0.00002065017,"about_ca_topic_score_gemma":0.000007133352,"teacher_disagreement_score":0.94505936,"about_ca_system_score_codex":0.00009209255,"about_ca_system_score_gemma":0.00006655588,"threshold_uncertainty_score":0.46829018},"labels":[],"label_agreement":null},{"id":"W4404818857","doi":"10.1016/j.aej.2024.11.024","title":"Real-time monitoring and analysis of track and field athletes based on edge computing and deep reinforcement learning algorithm","year":2024,"lang":"en","type":"article","venue":"Alexandria Engineering Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Reinforcement learning; Track and field athletics; Track (disk drive); Computer science; Field (mathematics); Enhanced Data Rates for GSM Evolution; Athletes; Artificial intelligence; Reinforcement; Algorithm; Real-time computing; Engineering; Mathematics; Structural engineering","score_opus":0.006075672647816456,"score_gpt":0.2331975967658453,"score_spread":0.22712192411802887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404818857","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":[0.06379828,0.00036782067,0.9354872,0.000068467285,0.000059893573,0.00004180657,3.1430318e-7,0.00010843644,0.00006778994],"genre_scores_gemma":[0.88323504,0.00046192302,0.116179414,0.0000060233115,0.00007298833,0.0000024718786,6.2940666e-7,0.000007088171,0.000034402525],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994099,0.000011870947,0.00019280097,0.00016213745,0.00010637777,0.00011687506],"domain_scores_gemma":[0.9995359,0.00022082636,0.000051691623,0.000090662645,0.000026590918,0.00007430301],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023178592,0.000091411384,0.0001484132,0.0003091212,0.00011192272,0.00019477605,0.0000799412,0.000038812737,0.0000057294033],"category_scores_gemma":[0.00001770857,0.000085642365,0.000045606856,0.0003409484,0.000011186477,0.00012676259,0.00004840041,0.00020012267,3.1161952e-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":[0.0000060156285,0.000019875406,0.0025516646,0.000113835325,0.00042279408,0.000028252301,0.0010043326,0.37218815,0.02524752,0.0020911999,0.000034953024,0.5962914],"study_design_scores_gemma":[0.00007528445,0.00012314701,0.003064075,0.00012136245,0.00006560123,0.000027247006,0.000009735399,0.9924783,0.0037560528,0.0000151061695,0.00017919212,0.00008488291],"about_ca_topic_score_codex":0.0000064718606,"about_ca_topic_score_gemma":6.130748e-8,"teacher_disagreement_score":0.8194368,"about_ca_system_score_codex":0.000017236596,"about_ca_system_score_gemma":0.0000086374575,"threshold_uncertainty_score":0.34923944},"labels":[],"label_agreement":null},{"id":"W4404869857","doi":"10.1016/j.aei.2024.102958","title":"Spatio-temporal attention-based hidden physics-informed neural network for remaining useful life prediction","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Artificial intelligence; Computer science; Machine learning","score_opus":0.01154729663356071,"score_gpt":0.2373368047712571,"score_spread":0.22578950813769638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404869857","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007065924,0.000045138237,0.9897092,0.00015758086,0.0005723168,0.00041989336,0.000018993243,0.0018868591,0.00012407464],"genre_scores_gemma":[0.46158668,0.00000761763,0.5376715,0.00012241412,0.00020659396,0.00025857502,0.00008951043,0.000018302542,0.000038804985],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989371,0.0000033606598,0.0004759454,0.00013436738,0.00016487778,0.0002843579],"domain_scores_gemma":[0.99926895,0.00013335512,0.000104522485,0.00032327225,0.0000770474,0.0000928304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013809708,0.00016303083,0.00014295717,0.00009343293,0.00014528338,0.00021367935,0.0002737023,0.000064957196,0.0000019976155],"category_scores_gemma":[0.000035656492,0.00016777779,0.00011064296,0.00052654074,0.000013046917,0.0012584211,0.000048567377,0.00015446146,0.000012817941],"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":[0.000004103674,0.000007220386,0.0002345257,0.000275007,0.000021062171,3.7519558e-7,0.00024243478,0.93478316,0.000031515083,0.025066148,0.0012114217,0.038123045],"study_design_scores_gemma":[0.0001600908,0.00007086191,0.00033321933,0.000108452754,0.000009480427,0.000003031892,0.000016417032,0.9562789,0.00024964428,0.000564956,0.042039,0.00016592746],"about_ca_topic_score_codex":0.0000014385386,"about_ca_topic_score_gemma":9.838135e-7,"teacher_disagreement_score":0.45452073,"about_ca_system_score_codex":0.00009482812,"about_ca_system_score_gemma":0.00009198694,"threshold_uncertainty_score":0.68417794},"labels":[],"label_agreement":null},{"id":"W4404892931","doi":"10.1007/978-3-031-78110-0_14","title":"Hybrid Human Action Anomaly Detection Based on Lightweight GNNs and Machine Learning","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Anomaly detection; Artificial intelligence; Action (physics); Machine learning","score_opus":0.017354435674233323,"score_gpt":0.25116807917056494,"score_spread":0.2338136434963316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404892931","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":[0.00040010456,0.00016801544,0.9935468,0.0005735114,0.00051552884,0.00033321467,0.0000041153567,0.0006648864,0.0037938647],"genre_scores_gemma":[0.95177674,0.000039144194,0.046121188,0.00046633932,0.00032115247,0.00003354993,0.0000067221736,0.000041285486,0.0011938863],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973502,0.000025417798,0.0003528582,0.0014041121,0.00051715266,0.00035024947],"domain_scores_gemma":[0.9986626,0.00015828574,0.00019714145,0.0007551788,0.00010023663,0.00012656594],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044124835,0.0004161468,0.00029885294,0.0010855407,0.00061379286,0.000641032,0.000899779,0.0002087241,0.000017694041],"category_scores_gemma":[0.000016788206,0.00038264613,0.00011038454,0.00046942703,0.00027976488,0.00027361582,0.00044127443,0.0011145495,0.000040097893],"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":[0.000007197334,0.000031537897,0.000035561123,0.000068058536,0.000010600287,0.000049162874,0.00009987995,0.010708971,0.003948939,0.036467224,0.000011249084,0.9485616],"study_design_scores_gemma":[0.000109125926,0.00045052476,0.00008083673,0.00019255585,0.000011185179,0.00007235372,5.6212087e-8,0.86659163,0.0397788,0.084376976,0.007879204,0.00045672574],"about_ca_topic_score_codex":0.000044843993,"about_ca_topic_score_gemma":0.000084970445,"teacher_disagreement_score":0.9513766,"about_ca_system_score_codex":0.0002778913,"about_ca_system_score_gemma":0.00009468553,"threshold_uncertainty_score":0.99986255},"labels":[],"label_agreement":null},{"id":"W4404985487","doi":"10.48550/arxiv.2411.14565","title":"Privacy-Preserving Video Anomaly Detection: A Survey","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; China Postdoctoral Science Foundation; Mitacs; National Natural Science Foundation of China","keywords":"Anomaly detection; Internet privacy; Computer science; Computer security; Data mining","score_opus":0.07994231064327328,"score_gpt":0.20677696346484686,"score_spread":0.12683465282157358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404985487","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":[0.14913015,0.00010788163,0.84463406,0.00017613602,0.0004813458,0.00035950926,0.000026057844,0.0014091424,0.0036757386],"genre_scores_gemma":[0.99326605,0.00010199475,0.0028317475,0.000066855755,0.00010359421,0.000009656685,0.000009987482,0.00002896783,0.0035811712],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977126,0.00017008049,0.00025300795,0.001434328,0.000101913094,0.00032807494],"domain_scores_gemma":[0.99714196,0.00012062134,0.00019817082,0.002164334,0.00020770726,0.00016721066],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004440128,0.00032430913,0.0002840476,0.0003756281,0.0002570859,0.00035924636,0.0028080286,0.00034992333,0.000048520677],"category_scores_gemma":[0.00005291532,0.0003826026,0.00026344688,0.0014292141,0.000074548865,0.00027223508,0.007812908,0.00089038373,0.00029817023],"study_design_candidate":"theoretical_or_conceptual","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":[0.0001780066,0.0008293737,0.017272048,0.0014726488,0.0012972583,0.0014656839,0.0013600034,0.085360035,0.0030722062,0.81940895,0.022475354,0.04580843],"study_design_scores_gemma":[0.00017641908,0.0000817152,0.009747539,0.00012115343,0.000073336385,0.000022352393,0.000020829,0.75569504,0.0031723646,0.22237378,0.0076882085,0.0008272454],"about_ca_topic_score_codex":0.0012361035,"about_ca_topic_score_gemma":0.00045318264,"teacher_disagreement_score":0.8441359,"about_ca_system_score_codex":0.00025657855,"about_ca_system_score_gemma":0.00018554193,"threshold_uncertainty_score":0.9998626},"labels":[],"label_agreement":null},{"id":"W4405022310","doi":"10.1109/ojcoms.2024.3511951","title":"Unsupervised Multivariate Time Series Data Anomaly Detection in Industrial IoT: A Confidence Adversarial Autoencoder Network","year":2024,"lang":"en","type":"article","venue":"IEEE Open Journal of the Communications Society","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Medical Science and Technology Foundation of Guangdong Province","keywords":"Autoencoder; Anomaly detection; Multivariate statistics; Computer science; Series (stratigraphy); Anomaly (physics); Time series; Artificial intelligence; Data mining; Confidence interval; Adversarial system; Machine learning; Pattern recognition (psychology); Statistics; Artificial neural network; Mathematics; Geology","score_opus":0.09876576825046417,"score_gpt":0.33488570223684827,"score_spread":0.2361199339863841,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405022310","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":[0.0032875657,0.0007684714,0.97241676,0.020050792,0.001431657,0.0010038399,0.00003702824,0.00015471636,0.00084917777],"genre_scores_gemma":[0.78341705,0.0004404658,0.21461402,0.0003311177,0.0004090103,0.00005408482,0.000005220914,0.000022356517,0.0007066742],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998328,0.00040142055,0.000602277,0.0002533961,0.0002272477,0.00018765868],"domain_scores_gemma":[0.9962798,0.00029832375,0.0003263855,0.0028739953,0.00016131291,0.000060222537],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.0020038006,0.00013226294,0.00021846966,0.000051026913,0.00057353114,0.0007084654,0.010562026,0.00013518764,0.000019153884],"category_scores_gemma":[0.00007379989,0.000102434344,0.00017671169,0.0012686026,0.00017644603,0.0016192202,0.003052132,0.00084522984,0.000015627036],"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":[0.00048580958,0.002015968,0.0016445094,0.00012368117,0.0023614538,0.000035241676,0.02642438,0.044735998,0.07875127,0.11851181,0.31268674,0.41222313],"study_design_scores_gemma":[0.000811297,0.00011444807,0.00072661834,0.00036786927,0.00007407139,0.00018088783,0.00021624826,0.882589,0.0021265903,0.009904403,0.10261202,0.00027653784],"about_ca_topic_score_codex":0.0002929676,"about_ca_topic_score_gemma":0.00013453503,"teacher_disagreement_score":0.837853,"about_ca_system_score_codex":0.00016959936,"about_ca_system_score_gemma":0.0005141999,"threshold_uncertainty_score":0.9947913},"labels":[],"label_agreement":null},{"id":"W4405079989","doi":"10.1007/978-981-97-8313-7_9","title":"Physics-Based Inverse Model Anomaly Detection in Light Commercial Buildings’ AHU Systems","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Anomaly (physics); Anomaly detection; Inverse; Physics; Computer science; Mathematics; Data mining; Geometry; Quantum mechanics","score_opus":0.011480974244381882,"score_gpt":0.21207141457222764,"score_spread":0.20059044032784576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405079989","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":[0.000091564674,0.00042667906,0.9901113,0.00016488662,0.0004589955,0.00042047675,0.000009533737,0.00073060516,0.007585965],"genre_scores_gemma":[0.98053795,0.000032588898,0.017203342,0.00012350039,0.00040715092,0.00021483764,0.000009002728,0.00012355628,0.0013480693],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983697,0.000007545959,0.00042294036,0.0006491475,0.00023701813,0.000313656],"domain_scores_gemma":[0.9990974,0.000096090385,0.00011085763,0.00058195216,0.000046319168,0.00006738116],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015863842,0.00043213912,0.00040033314,0.00061324413,0.00005288807,0.00016179969,0.00052079966,0.00049711874,0.000004797555],"category_scores_gemma":[0.000018167282,0.00047469354,0.00015931782,0.00039259056,0.00001926735,0.00016044147,0.00015049816,0.0010477578,0.000021201635],"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":[0.0000027341605,0.000012792784,0.000003957299,0.00018399589,0.000013433057,0.000017430057,0.000097498676,0.9317148,0.0022068594,0.054160647,0.000019322875,0.011566566],"study_design_scores_gemma":[0.00011598627,0.00004041238,0.0000045660795,0.0004959231,0.000013828193,0.00000854983,2.8144885e-7,0.9714184,0.0050359042,0.013302008,0.009106576,0.00045756862],"about_ca_topic_score_codex":0.0000938903,"about_ca_topic_score_gemma":0.000879863,"teacher_disagreement_score":0.9804464,"about_ca_system_score_codex":0.00045499025,"about_ca_system_score_gemma":0.00006744163,"threshold_uncertainty_score":0.99977046},"labels":[],"label_agreement":null},{"id":"W4405143142","doi":"10.2139/ssrn.5047563","title":"Clustering Based Approach for Enhanced Characterization of Anomalies in Traffic Flows,","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Cluster analysis; Computer science; Characterization (materials science); Data mining; Artificial intelligence; Physics","score_opus":0.010630302251101908,"score_gpt":0.24555659274134797,"score_spread":0.23492629049024608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405143142","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":[0.107025124,0.00031777957,0.89153445,0.0002473028,0.00014983481,0.00051368243,0.000010231586,0.0001240657,0.000077547826],"genre_scores_gemma":[0.9526356,0.0004014133,0.046319157,0.0000214367,0.00013057963,0.000285446,0.000033759203,0.000024409252,0.00014819144],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980907,0.000046549147,0.00049718213,0.00041115787,0.00016185877,0.00079257024],"domain_scores_gemma":[0.9991957,0.000022156091,0.00031455667,0.00033669616,0.000096214455,0.00003465588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009275372,0.00019474546,0.00028166213,0.0003859524,0.00011618076,0.00010878212,0.00072967535,0.00018718057,0.000001526896],"category_scores_gemma":[0.000009738876,0.0001930446,0.00019826704,0.0003233146,0.000019436213,0.000103723694,0.0003666249,0.001520899,8.455363e-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":[0.00013244025,0.0006276177,0.000020281537,0.0014762343,0.00029666527,0.000002045259,0.001972199,0.14841373,0.17366126,0.17810744,0.000014131641,0.49527594],"study_design_scores_gemma":[0.0002273825,0.00015720834,0.00003187587,0.0001347886,0.000020236343,0.000025242909,0.000064029045,0.94133925,0.012071995,0.04559996,0.00009743478,0.00023059589],"about_ca_topic_score_codex":0.000007980253,"about_ca_topic_score_gemma":0.00009781776,"teacher_disagreement_score":0.8456105,"about_ca_system_score_codex":0.000532945,"about_ca_system_score_gemma":0.0015538178,"threshold_uncertainty_score":0.787213},"labels":[],"label_agreement":null},{"id":"W4405174545","doi":"10.2139/ssrn.5049890","title":"An Investigation of Microbial Groundwater Contamination Seasonality and Extreme Weather Event Interruptions Using “Big Data”, Time-Series Analyses, and Unsupervised Machine Learning","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; University of Toronto; Public Health Ontario","funders":"","keywords":"Extreme weather; Series (stratigraphy); Environmental science; Event (particle physics); Contamination; Big data; Time series; Seasonality; Groundwater; Unsupervised learning; Meteorology; Climatology; Computer science; Machine learning; Data mining; Geography; Climate change; Engineering; Ecology; Oceanography; Geology; Geotechnical engineering","score_opus":0.068744656391172,"score_gpt":0.3151095497915246,"score_spread":0.2463648934003526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405174545","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":[0.436196,0.0016622923,0.5614207,0.00041666842,0.00006619365,0.00014799272,0.00001759891,0.00006680218,0.000005767185],"genre_scores_gemma":[0.98763776,0.0012621435,0.010518026,0.000019562143,0.00016234785,0.000009403679,0.00012000127,0.000020984322,0.00024976343],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981706,0.00025533576,0.000423134,0.0005229437,0.00018799774,0.00043998208],"domain_scores_gemma":[0.9989864,0.00002205101,0.0003175094,0.00045459042,0.0001387587,0.0000806567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001500216,0.00021099686,0.0002529864,0.00018839055,0.00026512635,0.00035070136,0.0005259405,0.00014177173,0.000006904189],"category_scores_gemma":[0.000013205988,0.0001943533,0.00006768892,0.00019235701,0.000117668285,0.0004623367,0.0008869767,0.001605292,0.0000013269455],"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":[0.00012683657,0.0003746085,0.015613575,0.0005527339,0.0015852798,0.000009320063,0.0046503996,0.001570287,0.5375006,0.10526837,0.000053903597,0.33269405],"study_design_scores_gemma":[0.00042640226,0.000536898,0.003491731,0.00034595537,0.00040610234,0.0009011878,0.00043175215,0.64085966,0.0050697457,0.3465665,0.00035931775,0.00060474174],"about_ca_topic_score_codex":0.00046968358,"about_ca_topic_score_gemma":0.0005594331,"teacher_disagreement_score":0.6392894,"about_ca_system_score_codex":0.00033790735,"about_ca_system_score_gemma":0.00071199704,"threshold_uncertainty_score":0.7925498},"labels":[],"label_agreement":null},{"id":"W4405232509","doi":"10.4271/2024-01-5225","title":"AI-Driven Diagnostic System for Vehicles: Leveraging AI for Accurate and Efficient Automotive Problem Solving","year":2024,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Automotive industry; Computer science; Class (philosophy); Paragraph; Artificial intelligence; Machine learning; Engineering","score_opus":0.013857428060558391,"score_gpt":0.26753906287335905,"score_spread":0.25368163481280065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405232509","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":[0.18858154,0.00651251,0.48079982,0.166754,0.0032378042,0.044282917,0.0014681845,0.09023484,0.018128375],"genre_scores_gemma":[0.94462013,0.00009552064,0.049473908,0.001704724,0.00015180589,0.0036689674,0.000021827545,0.000098870885,0.00016423306],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99590355,0.00008448873,0.0009400566,0.0016974467,0.0004855593,0.00088887126],"domain_scores_gemma":[0.9961845,0.0020015668,0.00018705029,0.0010482136,0.00026754124,0.00031108875],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00077691814,0.0006269759,0.0006815136,0.00029065603,0.0008613032,0.00070578756,0.0012009983,0.00049510505,0.000012035372],"category_scores_gemma":[0.0004815646,0.0005436757,0.00044290329,0.0009063968,0.0004472133,0.00064537063,0.00062541355,0.0007878099,0.000025124084],"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":[0.00006452491,0.00020342962,0.000018700799,0.00063347147,0.00007103721,0.000023757677,0.00016713458,0.00036343298,0.6800821,0.2935819,0.0055817645,0.019208767],"study_design_scores_gemma":[0.0036828062,0.009131829,0.53159285,0.008050604,0.00077652064,0.0010877216,0.001014088,0.0035500485,0.0075911297,0.066779315,0.3610224,0.0057207095],"about_ca_topic_score_codex":0.000019445406,"about_ca_topic_score_gemma":0.001188813,"teacher_disagreement_score":0.7560386,"about_ca_system_score_codex":0.00044042862,"about_ca_system_score_gemma":0.00016899273,"threshold_uncertainty_score":0.9997015},"labels":[],"label_agreement":null},{"id":"W4405268506","doi":"10.36227/techrxiv.173396130.09213575/v1","title":"Data Cleaning for Unsupervised Anomaly Detection","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Anomaly detection; Computer science; Data mining; Anomaly (physics); Benchmark (surveying); Data pre-processing; Artificial intelligence; Pattern recognition (psychology); Data set; Generalization; Similarity (geometry); Preprocessor; Contamination; Mathematics; Geography","score_opus":0.08149931435038517,"score_gpt":0.3297805017193006,"score_spread":0.24828118736891547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405268506","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":[0.00075369223,0.00015241369,0.9902017,0.001116274,0.00060003053,0.0007761448,0.00012598658,0.0022050487,0.004068688],"genre_scores_gemma":[0.58352023,0.000042327385,0.41320717,0.0002466797,0.00034935193,0.00063655403,0.00011719162,0.000036933634,0.0018435743],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823797,0.00001774779,0.00029848624,0.0011091847,0.00013819126,0.00019842286],"domain_scores_gemma":[0.99723643,0.00005608873,0.00009081646,0.0024608555,0.00009267789,0.00006313785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033288883,0.00019923015,0.00017923013,0.00016916187,0.00015622679,0.00053636404,0.0024165914,0.00022588421,0.000015429106],"category_scores_gemma":[0.000019623834,0.00019025114,0.000120387755,0.0002697041,0.000019784162,0.00017662867,0.006098965,0.00040600667,0.000070300375],"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":[0.0000071751615,0.00006642952,0.000007301522,0.00042987306,0.00010568153,0.00000284983,0.00012159458,0.00013904335,0.0041717323,0.17450793,0.016937852,0.80350256],"study_design_scores_gemma":[0.00006138149,0.000047077952,0.000034554967,0.000042950152,0.00003228083,0.000008837596,0.000014236231,0.8021099,0.011587147,0.10602334,0.079743385,0.0002948916],"about_ca_topic_score_codex":0.000096424155,"about_ca_topic_score_gemma":0.00008206941,"teacher_disagreement_score":0.80320764,"about_ca_system_score_codex":0.000060727714,"about_ca_system_score_gemma":0.00010845521,"threshold_uncertainty_score":0.7758216},"labels":[],"label_agreement":null},{"id":"W4405268798","doi":"10.36227/techrxiv.173396113.31607552/v1","title":"Impact of Inaccurate Contamination Ratios on Robust Unsupervised Anomaly Detection: Experimental Investigation","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Contamination; Anomaly detection; Anomaly (physics); Computer science; Benchmark (surveying); Misinformation; Data mining; Artificial intelligence; Geography; Biology; Ecology; Cartography","score_opus":0.03669432959578724,"score_gpt":0.30195535676412877,"score_spread":0.26526102716834155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405268798","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":[0.5887244,0.000075595926,0.40736237,0.00021540656,0.00026217793,0.00071923994,0.000015033,0.00063998165,0.001985777],"genre_scores_gemma":[0.989732,0.000013673262,0.009279066,0.00006277942,0.00009675289,0.00045076694,0.000031894153,0.000023676523,0.000309342],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998197,0.00008453167,0.00053633587,0.0007078425,0.00030249506,0.00017175761],"domain_scores_gemma":[0.9985147,0.000049703205,0.00030418372,0.00081186515,0.00022220788,0.00009735218],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020644671,0.00031814672,0.00028738743,0.00041960133,0.000115940544,0.0002686607,0.0005913876,0.0002833939,0.000058777958],"category_scores_gemma":[0.000016026037,0.00028259712,0.0002953507,0.00056387694,0.00006842549,0.0002421922,0.0006481091,0.0004362309,0.000054361197],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012494196,0.0011291095,0.0017598802,0.0005762062,0.00064766075,0.000019489575,0.0042181243,0.022951752,0.63519436,0.25595465,0.004258659,0.07316518],"study_design_scores_gemma":[0.0002474295,0.00086294475,0.011986845,0.00013154732,0.000025646319,0.000012711825,0.000064598935,0.22297159,0.7538533,0.009345493,0.000050351395,0.0004475045],"about_ca_topic_score_codex":0.0003735687,"about_ca_topic_score_gemma":0.000025750409,"teacher_disagreement_score":0.40100765,"about_ca_system_score_codex":0.0004325406,"about_ca_system_score_gemma":0.00024370795,"threshold_uncertainty_score":0.9999626},"labels":[],"label_agreement":null},{"id":"W4405268905","doi":"10.36227/techrxiv.173396102.26381681/v1","title":"Enhancing Security Anomaly Alert Prioritization through Calibrated Standard Deviation Uncertainty Estimation with an Ensemble of Auto-Encoders","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Anomaly detection; Prioritization; Data mining; Benchmark (surveying); Calibration; Artificial intelligence; Machine learning; Statistics; Engineering","score_opus":0.012797439207006297,"score_gpt":0.2757899181209466,"score_spread":0.2629924789139403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405268905","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":[0.041354913,0.00007797053,0.9540553,0.0004409085,0.0001402559,0.00089358364,0.000033716093,0.0013403939,0.0016629635],"genre_scores_gemma":[0.6729301,0.000029393752,0.32662094,0.000059537033,0.00003084651,0.00012816569,0.0001076447,0.000021419499,0.00007191584],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977365,0.00010279184,0.00061857555,0.0008527679,0.00046307896,0.00022628752],"domain_scores_gemma":[0.99808824,0.00004837158,0.00042860128,0.00086929,0.00048688034,0.000078586534],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003643491,0.00030892715,0.00036564277,0.0001886978,0.00015691627,0.00035192937,0.000543075,0.00030939767,0.000023662049],"category_scores_gemma":[0.000021008858,0.0002758101,0.000089721616,0.00079322257,0.000066581415,0.00071777083,0.00046508928,0.0004219814,0.0000064951323],"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":[0.00022334358,0.00058663246,0.00046646618,0.0031422284,0.00038955692,0.000019670835,0.015226173,0.17055176,0.013696563,0.6848179,0.0015972476,0.10928247],"study_design_scores_gemma":[0.000119967146,0.00022755766,0.00009994482,0.00028370446,0.0000414819,0.000007655604,0.00010529386,0.7974415,0.084144436,0.117059566,0.0001533881,0.00031553354],"about_ca_topic_score_codex":0.0011434681,"about_ca_topic_score_gemma":0.00060693565,"teacher_disagreement_score":0.6315752,"about_ca_system_score_codex":0.0002566268,"about_ca_system_score_gemma":0.0006583214,"threshold_uncertainty_score":0.9999694},"labels":[],"label_agreement":null},{"id":"W4405401847","doi":"10.1016/j.aei.2024.103038","title":"Self-supervised learning for remaining useful life prediction using simple triplet networks","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Science and Technology Council","keywords":"Simple (philosophy); Artificial intelligence; Machine learning; Computer science; Philosophy","score_opus":0.010678914079890765,"score_gpt":0.23281488586724863,"score_spread":0.22213597178735786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405401847","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005735238,0.0001150764,0.99062604,0.000021658247,0.00027981278,0.0003223112,0.0000032820121,0.0027951605,0.000101437916],"genre_scores_gemma":[0.32938948,0.000052011397,0.67025864,0.000048371465,0.0001058929,0.00009514898,0.000014035098,0.000021345859,0.000015085375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908376,0.0000050735566,0.0004003685,0.00013352616,0.00011407085,0.00026322244],"domain_scores_gemma":[0.9994402,0.000112915906,0.00006477567,0.00023840829,0.000057122743,0.000086583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020771734,0.00014070736,0.00013291831,0.00014723302,0.00017329874,0.00020800983,0.00023008318,0.00007909927,0.0000015551061],"category_scores_gemma":[0.00004683567,0.00014929769,0.00007536919,0.0005247186,0.0000060765624,0.001051109,0.00007354734,0.00022826149,0.000003444109],"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":[0.0000011101179,0.000003773544,0.00001324037,0.00014395674,0.000017832159,2.8203803e-7,0.0004953421,0.9756074,0.00016435698,0.008227858,0.00004523125,0.015279624],"study_design_scores_gemma":[0.00012351997,0.000053547006,0.000017630231,0.000055790657,0.000011222506,0.000011061148,0.00005343526,0.9345988,0.0002992507,0.00013410472,0.064495265,0.00014634866],"about_ca_topic_score_codex":5.519431e-7,"about_ca_topic_score_gemma":7.0122354e-8,"teacher_disagreement_score":0.32365423,"about_ca_system_score_codex":0.00009102953,"about_ca_system_score_gemma":0.00004022328,"threshold_uncertainty_score":0.60881835},"labels":[],"label_agreement":null},{"id":"W4405458571","doi":"10.3390/s24248039","title":"XAI GNSS—A Comprehensive Study on Signal Quality Assessment of GNSS Disruptions Using Explainable AI Technique","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Anomaly Detection Techniques and Applications","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":"École de Technologie Supérieure","funders":"","keywords":"GNSS applications; Computer science; Spoofing attack; SIGNAL (programming language); Multipath propagation; Identification (biology); Interference (communication); Jamming; Feature (linguistics); Artificial intelligence; Signal processing; Quality (philosophy); Data mining; Machine learning; Pattern recognition (psychology); Global Positioning System; Telecommunications; Computer security; Radar; Channel (broadcasting)","score_opus":0.07780400552907932,"score_gpt":0.410662196717968,"score_spread":0.3328581911888887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405458571","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":[0.31974927,0.000013287863,0.6779699,0.0002725417,0.00006963623,0.00068861473,0.000011746327,0.00042954404,0.00079548126],"genre_scores_gemma":[0.9673982,0.0000040868204,0.032106556,0.00007981828,0.000037126032,0.00016164705,0.0000020474818,0.00001416761,0.00019633802],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998466,0.00017885509,0.00037603092,0.00046110942,0.00031334625,0.00020464318],"domain_scores_gemma":[0.9989204,0.00014826401,0.0000980015,0.0006105397,0.00015725124,0.00006559219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028058552,0.00015891186,0.00021903962,0.0002205881,0.00022538826,0.00012311075,0.0003424173,0.00006710329,0.000021546131],"category_scores_gemma":[0.0000063877746,0.00014747093,0.00011589092,0.0007418698,0.000061721614,0.00018233842,0.00015948841,0.00026955883,0.000011253703],"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":[0.000021976995,0.00282883,0.002982245,0.00031839721,0.00027740872,0.00012191361,0.0036697446,0.01977954,0.27608302,0.6733911,0.0010827666,0.019443028],"study_design_scores_gemma":[0.00066583086,0.0025553426,0.04785532,0.00042845702,0.00012237058,0.0001314351,0.006012075,0.72611815,0.17343648,0.025085725,0.016093886,0.0014949444],"about_ca_topic_score_codex":0.00018933408,"about_ca_topic_score_gemma":0.0000053891918,"teacher_disagreement_score":0.7063386,"about_ca_system_score_codex":0.00013548533,"about_ca_system_score_gemma":0.00009409821,"threshold_uncertainty_score":0.601369},"labels":[],"label_agreement":null},{"id":"W4405489622","doi":"10.1109/embc53108.2024.10782608","title":"Interactive Explainable Deep Survival Analysis","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Science Foundation","keywords":"Computer science; Artificial intelligence","score_opus":0.00905347157417549,"score_gpt":0.27224943346297525,"score_spread":0.2631959618887998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405489622","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":[0.00058209314,0.00004571929,0.9429826,0.00062194513,0.00008382837,0.000048809674,5.205394e-7,0.0008455304,0.054788917],"genre_scores_gemma":[0.9431332,0.000009974184,0.05062474,0.00007990281,0.000025974607,0.000049241502,0.0000013687392,0.0000031860723,0.006072444],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947596,0.000014116767,0.00008931944,0.00024137898,0.00008172904,0.0000975282],"domain_scores_gemma":[0.9995881,0.000054672386,0.000012273184,0.00027599095,0.000033445634,0.000035572783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009719364,0.000053193187,0.0000715167,0.00020412316,0.00006701916,0.00023673657,0.00027767176,0.000021907495,0.00017801957],"category_scores_gemma":[0.0000035957312,0.000043963,0.00010126037,0.0015449101,0.000010195964,0.00036172598,0.00010125989,0.00006624262,0.0001688203],"study_design_candidate":"theoretical_or_conceptual","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":[6.739485e-7,0.00002285442,0.00007848355,0.000004173876,0.00017882627,0.000007713613,0.00024429936,0.00010190894,0.00026047227,0.90642476,0.0015665848,0.091109246],"study_design_scores_gemma":[0.000021648788,0.00003112183,0.0005757181,0.0000031510224,0.00005457088,0.0000053939657,0.00013466699,0.8616162,0.00852802,0.014628248,0.114256024,0.00014525595],"about_ca_topic_score_codex":0.00007498449,"about_ca_topic_score_gemma":0.000026265301,"teacher_disagreement_score":0.9425511,"about_ca_system_score_codex":0.000032496482,"about_ca_system_score_gemma":0.0000133888025,"threshold_uncertainty_score":0.22828563},"labels":[],"label_agreement":null},{"id":"W4405518089","doi":"10.1109/ojim.2024.3517614","title":"Semi-Siam: A Novel Intelligent Monitoring System With a Multibaseline Video Anomaly Detection","year":2024,"lang":"en","type":"article","venue":"IEEE Open Journal of Instrumentation and Measurement","topic":"Anomaly Detection Techniques and Applications","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":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Anomaly detection; Baseline (sea); Computer science; Anomaly (physics); Artificial intelligence; Computer vision; Video monitoring; Real-time computing; Geology; Physics","score_opus":0.05287494851388926,"score_gpt":0.29361650560013636,"score_spread":0.2407415570862471,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405518089","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":[0.06608064,0.00024326259,0.9323408,0.00027434263,0.0005068634,0.0003442007,0.0000012558595,0.00006538663,0.00014328008],"genre_scores_gemma":[0.9622353,0.00007553511,0.037462883,0.000033329794,0.00010986205,0.0000465423,1.9651802e-7,0.000009456956,0.000026929105],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987429,0.000036792284,0.0004385219,0.0002094828,0.00045842608,0.00011391994],"domain_scores_gemma":[0.99914783,0.000015992011,0.00022901518,0.0001468101,0.000348109,0.000112232556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091477844,0.000119782795,0.00014824381,0.00018906618,0.0001465396,0.00059527607,0.00031586993,0.000032158758,0.0000025581412],"category_scores_gemma":[0.0000071763297,0.00009137059,0.00004647594,0.00032348227,0.000018529572,0.00083686376,0.000048006503,0.0001450972,0.0000033873598],"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":[0.00012042477,0.00017897594,0.00041122508,0.00019795731,0.0002304078,0.00002859531,0.0015642502,0.0007113429,0.34310463,0.0030308072,0.00008967745,0.6503317],"study_design_scores_gemma":[0.0011571044,0.0010016272,0.0012707115,0.0016060037,0.000073904,0.0012399752,0.0021390226,0.04093947,0.94470906,0.00006627105,0.005520225,0.00027663156],"about_ca_topic_score_codex":0.00012815646,"about_ca_topic_score_gemma":0.00004176214,"teacher_disagreement_score":0.89615464,"about_ca_system_score_codex":0.00033889012,"about_ca_system_score_gemma":0.00014427931,"threshold_uncertainty_score":0.5740261},"labels":[],"label_agreement":null},{"id":"W4405663921","doi":"10.1016/j.ress.2024.110784","title":"Graph embedded patch-sense autoencoder with prior knowledge for multi-component system anomaly detection","year":2024,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Component (thermodynamics); Autoencoder; Anomaly detection; Computer science; Anomaly (physics); Graph; Artificial intelligence; Data mining; Theoretical computer science; Artificial neural network; Physics","score_opus":0.009495564695019333,"score_gpt":0.23238435032732033,"score_spread":0.222888785632301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405663921","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":[0.01423736,0.00034702892,0.9770303,0.00006164729,0.0007807841,0.0017110492,0.000029776284,0.0056770057,0.00012507185],"genre_scores_gemma":[0.8609796,0.0000067517717,0.13784683,0.000003237723,0.00011110132,0.0009056683,0.000004684782,0.000048231388,0.000093880466],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99773914,0.00006830284,0.00065303,0.0009012353,0.00022583336,0.00041248006],"domain_scores_gemma":[0.9982749,0.00020644267,0.0001007,0.0010250381,0.0002278199,0.00016508588],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008680229,0.00034421915,0.00039466514,0.00023716468,0.00027736733,0.0002075308,0.0003769921,0.00017336888,8.62355e-7],"category_scores_gemma":[0.000023400711,0.00029114258,0.00025424018,0.0008723046,0.00003083388,0.000307368,0.00007960887,0.00023834886,0.00003307739],"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":[0.00046031,0.0014304208,0.0005690903,0.060926013,0.0011120137,0.00015783797,0.007420718,0.30817145,0.061131123,0.4298021,0.00047475795,0.1283442],"study_design_scores_gemma":[0.0003038123,0.00013604533,0.0010945731,0.00060845976,0.000039528717,0.00013218763,0.0000926477,0.9845645,0.0061170748,0.000011495352,0.0065412596,0.0003584546],"about_ca_topic_score_codex":0.0000998959,"about_ca_topic_score_gemma":0.000019486777,"teacher_disagreement_score":0.8467423,"about_ca_system_score_codex":0.00083929463,"about_ca_system_score_gemma":0.00010119759,"threshold_uncertainty_score":0.99995404},"labels":[],"label_agreement":null},{"id":"W4405778752","doi":"10.1109/jiot.2024.3522863","title":"Device Identification and Anomaly Detection in IoT Environments","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vancouver Infectious Diseases Centre; University of New Brunswick","funders":"","keywords":"Computer science; Anomaly detection; Identification (biology); Internet of Things; Anomaly (physics); Computer security; Data mining","score_opus":0.011270494332622425,"score_gpt":0.25348604961156834,"score_spread":0.24221555527894592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405778752","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":[0.3176633,0.00018253931,0.6814935,0.00020903393,0.0002209246,0.000048048347,1.722382e-7,0.000036345013,0.00014610762],"genre_scores_gemma":[0.9933291,0.00007409981,0.0059559857,0.00005404437,0.000037019454,0.00000805391,1.0863682e-7,0.000005489732,0.0005360824],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992785,0.000027112754,0.0003012251,0.00017204773,0.0001336834,0.000087466935],"domain_scores_gemma":[0.9996841,0.00002626038,0.00011740607,0.000117412426,0.000016182776,0.000038632694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037169358,0.00006484451,0.00007401186,0.00021990694,0.000035262132,0.0002201032,0.00027616695,0.000046605972,0.0000073295696],"category_scores_gemma":[0.0000086684895,0.00006151581,0.000040087394,0.00017959927,0.000027894177,0.00052728516,0.000047987774,0.00022449873,0.000016270333],"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":[0.000008434117,0.00006081353,0.0005163796,0.000039472023,0.000030353176,0.000019621668,0.001967273,0.000037736067,0.48994705,0.0033910563,0.00032928417,0.5036525],"study_design_scores_gemma":[0.0001399514,0.00012086726,0.008844075,0.00017542977,0.000011886485,0.000704915,0.000042603133,0.19517696,0.7765044,0.009369409,0.008752837,0.00015665145],"about_ca_topic_score_codex":0.000055780572,"about_ca_topic_score_gemma":0.000006508011,"teacher_disagreement_score":0.6756658,"about_ca_system_score_codex":0.000068948044,"about_ca_system_score_gemma":0.000011733326,"threshold_uncertainty_score":0.2508542},"labels":[],"label_agreement":null},{"id":"W4405784969","doi":"10.1109/iros58592.2024.10802547","title":"Meta SAC-Lag: Towards Deployable Safe Reinforcement Learning via MetaGradient-based Hyperparameter Tuning","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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 Victoria","funders":"Mitacs","keywords":"Hyperparameter; Reinforcement learning; Lag; Computer science; Meta learning (computer science); Reinforcement; Artificial intelligence; Machine learning; Engineering; Operating system; Structural engineering","score_opus":0.038785579979602615,"score_gpt":0.2639766936641335,"score_spread":0.2251911136845309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405784969","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":[0.0005428938,0.0003754112,0.9816635,0.001114568,0.00013121947,0.00023528642,4.50535e-7,0.0018399725,0.014096669],"genre_scores_gemma":[0.7836546,0.000016377697,0.20663394,0.0005824753,0.00003099806,0.00022109965,0.0000037614752,0.000019093648,0.008837689],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849963,0.000051469837,0.00031783586,0.0005166948,0.00029263832,0.00032170027],"domain_scores_gemma":[0.9991459,0.0000860839,0.000056027005,0.0005307141,0.00006287976,0.000118360185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004261092,0.00019866954,0.00023493447,0.00022967008,0.0002903872,0.00041511908,0.0005405073,0.00006644476,0.000592354],"category_scores_gemma":[0.0000122111505,0.0001536735,0.00028298117,0.00073875877,0.000033549324,0.00043299675,0.00018544417,0.00025504798,0.00022723372],"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":[0.000016162201,0.00018138967,0.000094990755,0.00018519434,0.0023797909,0.00005851042,0.0007414257,0.043132387,0.030943047,0.3830212,0.0107503105,0.52849555],"study_design_scores_gemma":[0.00006212868,0.000108010514,0.000009089681,0.000009578161,0.00016757417,0.000011373329,0.0000096610975,0.8255093,0.04069125,0.0013396794,0.13188194,0.00020042936],"about_ca_topic_score_codex":0.00023599526,"about_ca_topic_score_gemma":0.000006182501,"teacher_disagreement_score":0.7831117,"about_ca_system_score_codex":0.00008105736,"about_ca_system_score_gemma":0.00007544057,"threshold_uncertainty_score":0.6485861},"labels":[],"label_agreement":null},{"id":"W4405786887","doi":"10.1109/iros58592.2024.10801781","title":"Towards Enhanced Fairness and Sample Efficiency in Traffic Signal Control","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; McGill University","funders":"","keywords":"Sample (material); Computer science; SIGNAL (programming language); Control (management); Traffic signal; Real-time computing; Artificial intelligence; Chemistry","score_opus":0.007862619707726703,"score_gpt":0.2469418322924056,"score_spread":0.2390792125846789,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405786887","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":[0.051513523,0.00011127341,0.9456457,0.0006550028,0.000038164,0.00015172706,0.000002195989,0.00048255688,0.0013998771],"genre_scores_gemma":[0.9806343,0.000013157372,0.019033857,0.00010674901,0.000016886253,0.00007182407,3.006533e-7,0.0000035407895,0.000119409706],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938434,0.000013956813,0.00012517601,0.00026736132,0.00008225626,0.00012689676],"domain_scores_gemma":[0.9996996,0.00009738202,0.000010791391,0.00013786186,0.000016175047,0.00003821299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013377066,0.00006591899,0.000077963385,0.000098542885,0.00007481987,0.0001117628,0.00019913721,0.000034662036,0.000043972603],"category_scores_gemma":[0.0000072071457,0.000054610267,0.000026018299,0.0004327687,0.000027635933,0.0001562643,0.00006047827,0.00007470334,0.000011012947],"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":[0.0000025223076,0.0000575439,0.000016823951,0.000020497982,0.000004809008,0.0000040115383,0.00066260295,0.0004681022,0.0070254193,0.27906886,0.00014051796,0.7125283],"study_design_scores_gemma":[0.00022691485,0.00011480198,0.00070399226,0.000020631374,0.0000031391762,0.0000110570245,0.000040609324,0.9553989,0.02503614,0.014019901,0.004235044,0.00018882436],"about_ca_topic_score_codex":0.000056707242,"about_ca_topic_score_gemma":0.000020330212,"teacher_disagreement_score":0.95493084,"about_ca_system_score_codex":0.0000202177,"about_ca_system_score_gemma":0.00003930855,"threshold_uncertainty_score":0.22269422},"labels":[],"label_agreement":null},{"id":"W4405796416","doi":"10.1021/acs.iecr.4c02832","title":"Improved Pearson Correlation Coefficient-Based Graph Neural Network for Dynamic Soft Sensor of Polypropylene Industries","year":2024,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Artificial Intelligence in Medicine (Canada)","funders":"Beijing University of Chemical Technology; National Natural Science Foundation of China","keywords":"Pearson product-moment correlation coefficient; Correlation coefficient; Artificial neural network; Soft sensor; Graph; Polypropylene; Materials science; Biological system; Computer science; Composite material; Statistics; Mathematics; Artificial intelligence; Theoretical computer science","score_opus":0.04131432042356799,"score_gpt":0.3128814571407807,"score_spread":0.2715671367172127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405796416","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":[0.08825102,0.0002909759,0.9085701,0.00079497055,0.0004139552,0.000826332,0.000060671337,0.0007411967,0.000050792074],"genre_scores_gemma":[0.99631613,0.0000044336857,0.0025341148,0.000004080459,0.00036459157,0.00020877457,0.00003607452,0.00002835165,0.00050347025],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984488,0.00002624322,0.00031812154,0.00041504978,0.0003374033,0.00045440282],"domain_scores_gemma":[0.9987627,0.00044271312,0.000055116656,0.00040609913,0.00022429616,0.0001090367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072104164,0.00015379014,0.00017078097,0.00015105841,0.00015078334,0.00018849241,0.00048887485,0.0003117556,0.000010583158],"category_scores_gemma":[0.00023998626,0.00015896892,0.0001034311,0.0016459726,0.00008220702,0.00012150045,0.00010539944,0.0007833237,0.0000018311476],"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":[0.000049441045,0.00005807626,0.000045686626,0.00020582012,0.00003750842,0.0000042606653,0.000038232894,0.16806398,0.79496163,0.00089714944,0.0020780875,0.03356012],"study_design_scores_gemma":[0.00019958612,0.00007756726,0.000010971035,0.000080053775,0.0000056189047,0.0000040713426,0.000009691237,0.72279984,0.27411658,0.000046990288,0.0025395032,0.00010955139],"about_ca_topic_score_codex":0.000030368896,"about_ca_topic_score_gemma":3.8454388e-7,"teacher_disagreement_score":0.9080651,"about_ca_system_score_codex":0.00012732018,"about_ca_system_score_gemma":0.00027705633,"threshold_uncertainty_score":0.6482564},"labels":[],"label_agreement":null},{"id":"W4405865884","doi":"10.1016/j.neunet.2024.107099","title":"Simplified PCNet with robustness","year":2024,"lang":"en","type":"article","venue":"Neural Networks","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Natural Science Foundation of China","keywords":"Robustness (evolution); Computer science; Artificial intelligence; Machine learning","score_opus":0.010537707453395773,"score_gpt":0.23349691315628193,"score_spread":0.22295920570288616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405865884","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":[0.0041303528,0.00020502725,0.9916753,0.0011747343,0.00017085088,0.00011926236,5.068258e-7,0.0011625515,0.0013614115],"genre_scores_gemma":[0.9897529,0.000016569104,0.009174593,0.00031951442,0.00017396187,0.000057296158,0.000002067294,0.000010540463,0.0004925626],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993513,0.000013250391,0.000093055416,0.00027978185,0.00009033501,0.00017224863],"domain_scores_gemma":[0.99954826,0.000039988605,0.000017934353,0.00031686062,0.000022455364,0.000054506938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000050605802,0.0000879607,0.00006898107,0.00003975971,0.00009456005,0.00024142188,0.00034192202,0.000048689388,0.00001576402],"category_scores_gemma":[8.84618e-7,0.00006495092,0.00003418368,0.000527777,0.000026334743,0.00021978698,0.00007717304,0.00017760381,0.000010735374],"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":[0.000008389745,0.000029984914,0.00009295189,0.000017909913,0.000021872305,0.00006871861,0.000051189272,0.2929247,0.000113747694,0.15671425,0.021562211,0.52839404],"study_design_scores_gemma":[0.000030891508,0.000053695192,0.00015973623,0.000010507881,0.0000033583035,0.000053899876,0.0000017984787,0.9844147,0.00015496819,0.0004252992,0.01459467,0.00009648817],"about_ca_topic_score_codex":0.0000059991085,"about_ca_topic_score_gemma":0.00000449603,"teacher_disagreement_score":0.9856225,"about_ca_system_score_codex":0.000013096763,"about_ca_system_score_gemma":0.000011364537,"threshold_uncertainty_score":0.26486215},"labels":[],"label_agreement":null},{"id":"W4405908938","doi":"10.1109/wf-iot62078.2024.10811419","title":"Work in Progress: Exploring Generative Modeling for Injection Attack Detection","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Dalhousie University","funders":"","keywords":"Computer science; Generative grammar; Work (physics); Artificial intelligence; Engineering","score_opus":0.11012157061788712,"score_gpt":0.32268131263536753,"score_spread":0.2125597420174804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405908938","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":[0.028985322,0.00014268368,0.96886533,0.00038426215,0.00021880286,0.00035993443,3.2980168e-7,0.0008438319,0.00019949407],"genre_scores_gemma":[0.86028314,0.000013920084,0.13772625,0.000026337395,0.00009990201,0.0016724011,7.014855e-7,0.0000081681665,0.00016917437],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993233,0.000012241893,0.00015655464,0.00030747475,0.00006838873,0.00013202072],"domain_scores_gemma":[0.99974346,0.000023216033,0.000015253639,0.00015343369,0.000040420786,0.00002422595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015348278,0.00007413713,0.000060882216,0.00017586244,0.000104358,0.00019678372,0.00013100794,0.000041240182,0.0000026293421],"category_scores_gemma":[0.000004714702,0.00006857324,0.00004610056,0.0007745769,0.00000872578,0.00061008276,0.000043780845,0.000090038804,0.000010807722],"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":[0.0000110538585,0.000038933595,0.000048590926,0.000034115288,0.00001175056,0.0000012248274,0.0009233961,0.037523326,0.0012154686,0.04873818,0.00025031678,0.9112036],"study_design_scores_gemma":[0.000039458915,0.000059999737,0.000020210502,0.000027476523,0.0000016444154,0.0000037370091,0.00004726,0.97802263,0.015457185,0.003743747,0.0024868443,0.000089802365],"about_ca_topic_score_codex":0.000014899721,"about_ca_topic_score_gemma":0.000028236913,"teacher_disagreement_score":0.9404993,"about_ca_system_score_codex":0.00008667557,"about_ca_system_score_gemma":0.00001831452,"threshold_uncertainty_score":0.27963355},"labels":[],"label_agreement":null},{"id":"W4405935030","doi":"10.1109/icm63406.2024.10815914","title":"Enhanced Deep Learning Model for Superior Multi-Class Classification Performance","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Class (philosophy); Artificial intelligence; Deep learning; Machine learning","score_opus":0.036985399616708034,"score_gpt":0.2889960955342128,"score_spread":0.25201069591750475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405935030","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":[0.006862734,0.00004423206,0.98959374,0.0004397229,0.000055580473,0.00027328817,0.0000010666365,0.0012539953,0.0014756517],"genre_scores_gemma":[0.76392365,0.00004725868,0.22944523,0.0000631288,0.000023734796,0.00041858185,0.000003859203,0.000008953421,0.006065615],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992834,0.0000075433736,0.00015205525,0.00032521336,0.000079584075,0.00015224465],"domain_scores_gemma":[0.99959904,0.000034388806,0.000023660708,0.00023725547,0.00006499551,0.00004064636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012241323,0.00008309785,0.000067244066,0.000074772615,0.00020231621,0.00017875808,0.00028985585,0.00005453292,0.000009052165],"category_scores_gemma":[0.00000859618,0.00007428383,0.00005926645,0.00019718744,0.000019150277,0.00041776145,0.000049912163,0.00010697047,0.00005140577],"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":[0.00000386711,0.000045429806,0.000016877246,0.00006859294,0.00001101292,1.6003429e-7,0.0007928509,0.010162465,0.09744467,0.20380923,0.0003207762,0.68732405],"study_design_scores_gemma":[0.000057501,0.00003652062,0.00011704141,0.000008361165,0.0000028785855,0.0000018356649,0.000020687352,0.9589009,0.032911662,0.00039951192,0.0074397926,0.000103297876],"about_ca_topic_score_codex":0.0000030665844,"about_ca_topic_score_gemma":0.0000060868892,"teacher_disagreement_score":0.94873846,"about_ca_system_score_codex":0.00004486538,"about_ca_system_score_gemma":0.000034102624,"threshold_uncertainty_score":0.30292067},"labels":[],"label_agreement":null},{"id":"W4405935473","doi":"10.23919/cnsm62983.2024.10814578","title":"Improving Real-Time Anomaly Detection using Multiple Instances of Micro-Cluster Detection","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Anomaly detection; Computer science; Cluster (spacecraft); Data mining; Anomaly (physics); Artificial intelligence; Real-time computing; Operating system","score_opus":0.01080262558548801,"score_gpt":0.24100237576063452,"score_spread":0.23019975017514652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405935473","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":[0.30044112,0.000045543064,0.6977872,0.00003027006,0.00013335451,0.00017032267,0.0000016000989,0.00065325724,0.00073728786],"genre_scores_gemma":[0.9074498,0.000015883881,0.09205511,0.000020209565,0.000060062193,0.00002867731,5.0218e-7,0.000012982192,0.00035677294],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989843,0.000031468615,0.00029609754,0.0003851604,0.00013330817,0.00016969418],"domain_scores_gemma":[0.99937373,0.00006855615,0.000097559896,0.0003349136,0.000082297716,0.000042919837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020327608,0.00012244236,0.00012451195,0.00023698313,0.00016134154,0.00015633577,0.00025281886,0.0000883545,0.000016349164],"category_scores_gemma":[0.0000130466115,0.000113091766,0.00009963882,0.0007314013,0.00003931362,0.0006584885,0.00011790436,0.00010377635,0.000027193184],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000035362805,0.000012159543,0.0000401687,0.000027457634,0.000007356893,6.0970524e-7,0.000054983368,0.000046274476,0.8480296,0.0003736444,0.0000069330563,0.15139727],"study_design_scores_gemma":[0.00004446199,0.000045781147,0.00014912973,0.000014247458,0.0000060053376,0.000020424699,0.00001238688,0.48325962,0.51565737,0.0002522635,0.000454341,0.00008399956],"about_ca_topic_score_codex":0.00094453484,"about_ca_topic_score_gemma":0.00017866882,"teacher_disagreement_score":0.6070087,"about_ca_system_score_codex":0.00010118303,"about_ca_system_score_gemma":0.000041221156,"threshold_uncertainty_score":0.46117485},"labels":[],"label_agreement":null},{"id":"W4405973741","doi":"10.2172/2484041","title":"Machine Learning Application for Fracture Analysis: Use Case","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"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":"National Energy Technology Laboratory; U.S. Department of Energy","keywords":"Computer science; Fracture (geology); Artificial intelligence; Machine learning; Engineering; Geotechnical engineering","score_opus":0.013810494679951688,"score_gpt":0.28365597565601014,"score_spread":0.2698454809760584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405973741","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":[0.0004932759,0.00011993861,0.9966699,0.0010450039,0.000016154807,0.0002315174,0.000004283596,0.0011117607,0.00030820124],"genre_scores_gemma":[0.89279443,0.0000125066545,0.104425445,0.00024548237,0.00003192815,0.000267527,0.000015477337,0.0000057643647,0.0022014268],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994477,0.000010221497,0.00011251484,0.00028486148,0.000056032986,0.00008865663],"domain_scores_gemma":[0.99952984,0.000089363115,0.00002540933,0.00027602992,0.000041939547,0.000037401474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011228298,0.00006434866,0.00007032748,0.00015941325,0.00015306043,0.000258856,0.00015161712,0.000043582426,0.000019995152],"category_scores_gemma":[0.000007593813,0.0000526728,0.00011125123,0.00090770185,0.0000074869545,0.00029447017,0.00003862562,0.00009620984,0.000021028143],"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":[0.0000026930943,0.000051083436,0.00058781507,0.000033010754,0.00029064232,0.00004389142,0.00026294976,0.005580758,0.001092059,0.69451463,0.0027768721,0.29476357],"study_design_scores_gemma":[0.00001507938,0.000015003683,0.000045995963,9.180083e-7,0.000045108336,0.00005584982,0.0000052618425,0.7218406,0.0010988413,0.0018504699,0.27496934,0.000057495603],"about_ca_topic_score_codex":0.00022502764,"about_ca_topic_score_gemma":0.000050424573,"teacher_disagreement_score":0.89230114,"about_ca_system_score_codex":0.000018519017,"about_ca_system_score_gemma":0.0000118650105,"threshold_uncertainty_score":0.24961543},"labels":[],"label_agreement":null},{"id":"W4405997808","doi":"10.59247/csol.v2i3.128","title":"Potential Applications and Limitations of Artificial Intelligence in Remote Sensing Data Interpretation: A Case Study","year":2024,"lang":"en","type":"article","venue":"Control Systems and Optimization Letters","topic":"Anomaly Detection Techniques and Applications","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":"Western University","funders":"","keywords":"Interpretation (philosophy); Computer science; Artificial intelligence; Data science","score_opus":0.030474710827986785,"score_gpt":0.27437234463981647,"score_spread":0.2438976338118297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405997808","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":[0.0027800635,0.00040206476,0.9946763,0.0010489465,0.00008931295,0.00086454017,0.000010495365,0.000108813,0.00001946104],"genre_scores_gemma":[0.9137361,0.000053120406,0.08605287,0.000073533185,0.000043441334,0.000023375691,0.0000065592653,0.0000068769937,0.0000041212966],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895483,0.00008226391,0.00040160454,0.00038002818,0.00009305666,0.00008822685],"domain_scores_gemma":[0.9992365,0.0001864248,0.000088230256,0.00038807414,0.00006193789,0.000038789465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031903325,0.00008907834,0.00013929792,0.00021723627,0.00011187631,0.00029662086,0.00016519573,0.00003467015,4.7905905e-7],"category_scores_gemma":[0.000023161414,0.00008879906,0.00001596427,0.00044601146,0.00004986554,0.00039794913,0.00009151215,0.000081020975,7.583299e-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":[0.0000073064125,0.000043899767,0.00001559166,0.00006518759,0.000041178282,0.00012439053,0.0015512386,0.36696061,0.0005303961,0.009068285,0.000030853887,0.62156105],"study_design_scores_gemma":[0.00006985039,0.000030322473,0.000011250863,0.000035404963,0.000023577162,0.00037156563,0.0008778667,0.9982148,0.000005863934,0.00015379286,0.00012373835,0.00008198516],"about_ca_topic_score_codex":0.0002942127,"about_ca_topic_score_gemma":0.000057705056,"teacher_disagreement_score":0.910956,"about_ca_system_score_codex":0.000017353754,"about_ca_system_score_gemma":0.000021668095,"threshold_uncertainty_score":0.36211205},"labels":[],"label_agreement":null},{"id":"W4406136923","doi":"10.1061/ciegag.0001745","title":"Flood Fixer","year":2024,"lang":"en","type":"article","venue":"Civil engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Golder Associates (Canada)","funders":"","keywords":"Flood myth; Engineering; Environmental science; Civil engineering; Hydrology (agriculture); Water resource management; Geology; Geotechnical engineering; Geography; Archaeology","score_opus":0.004930197843610891,"score_gpt":0.19888440181674058,"score_spread":0.19395420397312968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406136923","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":[0.00058344397,0.00035749085,0.9911199,0.00026137423,0.00016397021,0.000037022026,4.9177623e-7,0.001692114,0.005784145],"genre_scores_gemma":[0.9741291,0.000013650971,0.025184907,0.00002968512,0.00006672142,0.00003806917,3.718113e-7,0.000007937809,0.0005295825],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966437,0.0000012689713,0.00005857621,0.00013545771,0.000049698974,0.000090657566],"domain_scores_gemma":[0.99975586,0.000014939676,0.0000037096188,0.00018803566,0.0000073431383,0.00003009043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000042221043,0.000047877216,0.00003508001,0.00006397404,0.000023663253,0.00010285239,0.00018694477,0.000022251821,0.000023978875],"category_scores_gemma":[0.0000036057934,0.00004688161,0.000031443404,0.00028077996,0.0000029609437,0.00015155142,0.000052962176,0.00007096831,0.000105071565],"study_design_candidate":"theoretical_or_conceptual","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":[1.0893775e-7,0.00001132885,0.000009063617,0.000057958023,0.000016050079,0.000015273557,0.0001393854,0.0035035287,0.013626147,0.91991425,0.005637571,0.057069328],"study_design_scores_gemma":[0.000013895561,0.00000856241,0.0001388325,0.000024040266,0.0000017342344,0.0000188473,0.000001035713,0.65307033,0.009233639,0.0010216731,0.33637494,0.00009244506],"about_ca_topic_score_codex":0.0000016574111,"about_ca_topic_score_gemma":8.7716603e-7,"teacher_disagreement_score":0.9735456,"about_ca_system_score_codex":0.000015652791,"about_ca_system_score_gemma":0.000008700175,"threshold_uncertainty_score":0.19117765},"labels":[],"label_agreement":null},{"id":"W4406266453","doi":"10.1109/vtc2024-fall63153.2024.10757473","title":"Exploring Feature Importance and Explainability Towards Enhanced ML-Based DoS Detection in AI Systems","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Computer science; Feature (linguistics); Artificial intelligence","score_opus":0.033066367912101984,"score_gpt":0.26988799054635343,"score_spread":0.23682162263425144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406266453","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":[0.112592965,0.00021171225,0.88429666,0.0010332918,0.00018451254,0.00031926812,9.694489e-7,0.0007979679,0.00056263275],"genre_scores_gemma":[0.99339837,0.00003123148,0.005525446,0.000082334474,0.00004023097,0.000769941,5.20783e-7,0.0000074041864,0.00014452453],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905187,0.00002978816,0.00017885678,0.00045777246,0.00012182523,0.0001598603],"domain_scores_gemma":[0.99948937,0.000035220928,0.000026856469,0.00035246764,0.00004287948,0.00005319052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026095283,0.00010934193,0.000111867244,0.00013974622,0.000084143445,0.00024228518,0.00017526333,0.00006242033,0.0000038962858],"category_scores_gemma":[0.000012941085,0.00009603486,0.000036741425,0.0006675301,0.000023086724,0.0006783025,0.000055212153,0.00019155692,0.000004765749],"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":[0.000029052757,0.00016298857,0.0015127566,0.0008684268,0.00002589542,0.000043665965,0.0015142492,0.0009947874,0.07712044,0.16305636,0.00049483386,0.75417656],"study_design_scores_gemma":[0.00020192124,0.00014787455,0.0087968875,0.00012796123,0.000005339409,0.000025368367,0.0001948685,0.5793785,0.39899907,0.003820233,0.0079071345,0.00039487737],"about_ca_topic_score_codex":0.00015849344,"about_ca_topic_score_gemma":0.000096687225,"teacher_disagreement_score":0.8808054,"about_ca_system_score_codex":0.000099143595,"about_ca_system_score_gemma":0.00004683382,"threshold_uncertainty_score":0.39161882},"labels":[],"label_agreement":null},{"id":"W4406358932","doi":"10.36227/techrxiv.173396113.31607552/v2","title":"Impact of Inaccurate Contamination Ratios on Robust Unsupervised Anomaly Detection: Experimental Investigation","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Contamination; Anomaly detection; Anomaly (physics); Computer science; Environmental science; Artificial intelligence; Pattern recognition (psychology); Biology; Physics","score_opus":0.03582104492765254,"score_gpt":0.3056240092742931,"score_spread":0.2698029643466406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406358932","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":[0.34935817,0.000029123921,0.6462866,0.00014612827,0.00017002712,0.0007447101,0.000018079276,0.0003966485,0.0028505106],"genre_scores_gemma":[0.98353076,0.000015029396,0.015278319,0.00010070857,0.000051578845,0.00044844925,0.000038565653,0.0000097913,0.00052679953],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982782,0.00011016031,0.0005443877,0.0006381571,0.0002633129,0.00016580622],"domain_scores_gemma":[0.99823517,0.00007062803,0.00040235335,0.0009026608,0.00030809492,0.00008111204],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001921451,0.00030027618,0.0003098112,0.00042614588,0.00015608754,0.00015713002,0.0007067596,0.00029077329,0.000046661316],"category_scores_gemma":[0.000024862436,0.00028130002,0.00026363228,0.0005732194,0.000063963394,0.0003172387,0.00046161376,0.00031501573,0.00001032189],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003027438,0.0026383256,0.011316192,0.0006600671,0.0009346986,0.00001033664,0.0048778197,0.06781823,0.4490935,0.3223237,0.0052035516,0.13482082],"study_design_scores_gemma":[0.00041749646,0.00070763653,0.03315915,0.000119296936,0.000020198942,0.0000042270995,0.000057616315,0.17291917,0.7897524,0.0023576948,0.00006685915,0.00041823604],"about_ca_topic_score_codex":0.0004866936,"about_ca_topic_score_gemma":0.000031626772,"teacher_disagreement_score":0.6341726,"about_ca_system_score_codex":0.0004151297,"about_ca_system_score_gemma":0.00031083636,"threshold_uncertainty_score":0.99996394},"labels":[],"label_agreement":null},{"id":"W4406461799","doi":"10.1109/bigdata62323.2024.10825917","title":"Unsupervised Parameter-free Outlier Detection using HDBSCAN* Outlier Profiles","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Anomaly detection; Outlier; Computer science; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.0237836400262355,"score_gpt":0.26984187732199005,"score_spread":0.24605823729575454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406461799","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":[0.04884256,0.00009523669,0.94483227,0.0004754803,0.0003917666,0.0003175551,0.000004188771,0.002002885,0.0030380387],"genre_scores_gemma":[0.83602935,0.000010287622,0.16163628,0.00019472359,0.00014329044,0.00012317378,0.0000012242006,0.000021390742,0.0018403018],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987881,0.000024833382,0.0002418111,0.00050332595,0.00020027079,0.00024164128],"domain_scores_gemma":[0.9989385,0.000060700542,0.00003323723,0.0008207308,0.0000619873,0.00008482491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017384085,0.0001563121,0.000121415345,0.00019869224,0.00019314355,0.00040832153,0.00064043875,0.00009685911,0.000099683515],"category_scores_gemma":[0.000021816213,0.00013319797,0.00011533873,0.00073908945,0.00004075711,0.0005094916,0.00024911854,0.00016720717,0.00012454615],"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":[0.000010585249,0.00019396748,0.00036995718,0.00013085455,0.000121005294,0.00002815525,0.0007611679,0.00024825125,0.09254603,0.2053995,0.006346935,0.6938436],"study_design_scores_gemma":[0.00013079084,0.0001049412,0.0002983288,0.00003238129,0.000021145988,0.00006590583,0.000049205595,0.74206364,0.18689948,0.034113098,0.035850298,0.0003707618],"about_ca_topic_score_codex":0.000111961395,"about_ca_topic_score_gemma":0.000017083456,"teacher_disagreement_score":0.7871868,"about_ca_system_score_codex":0.00008439945,"about_ca_system_score_gemma":0.000053832075,"threshold_uncertainty_score":0.54316556},"labels":[],"label_agreement":null},{"id":"W4406482578","doi":"10.62441/nano-ntp.v20i7.4719","title":"Using Autoencoders for Anomaly and Drift Detection in Linguistic Segmentation on Product Review Platforms and Recommendation Systems","year":2024,"lang":"en","type":"article","venue":"Nanotechnology Perceptions","topic":"Anomaly Detection Techniques and Applications","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; Windsor Clinical Research","funders":"","keywords":"Anomaly (physics); Anomaly detection; Segmentation; Product (mathematics); Natural language processing; Computer science; Artificial intelligence; Physics; Mathematics; Condensed matter physics","score_opus":0.03662168663069268,"score_gpt":0.33213661443626974,"score_spread":0.2955149278055771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406482578","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":[0.06368795,0.0012384416,0.9315903,0.0015649424,0.00021224108,0.0010706974,0.0000083066425,0.00059209135,0.000035027613],"genre_scores_gemma":[0.95016146,0.002508606,0.04648269,0.00012212068,0.000043186134,0.000613711,0.000012454936,0.0000130744165,0.00004272179],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905586,0.000017927085,0.00026345215,0.0004557577,0.00005786709,0.0001491496],"domain_scores_gemma":[0.99959457,0.000057054505,0.0000611215,0.00021123212,0.00004930486,0.00002670277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028614354,0.00011758705,0.0001404386,0.00041002323,0.00022875544,0.00009222038,0.000104075014,0.00012861594,0.0000037419325],"category_scores_gemma":[0.00006446068,0.00011251864,0.000028220882,0.00053651596,0.00006191305,0.00024514092,0.000046328212,0.00018733596,0.000004642758],"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":[0.0000119130755,0.00011354141,0.00030654893,0.0016128346,0.000037249374,0.0000033642175,0.0008086502,0.0004579344,0.06956674,0.08624882,0.0003540939,0.8404783],"study_design_scores_gemma":[0.00027913932,0.0003881603,0.0007585752,0.0010868063,0.00005913037,0.00023453268,0.00024478295,0.97260153,0.004728276,0.009032565,0.010212846,0.00037367744],"about_ca_topic_score_codex":0.000047912137,"about_ca_topic_score_gemma":0.00003358816,"teacher_disagreement_score":0.9721436,"about_ca_system_score_codex":0.00012939368,"about_ca_system_score_gemma":0.000029220473,"threshold_uncertainty_score":0.4588377},"labels":[],"label_agreement":null},{"id":"W4406499925","doi":"10.1109/cascon62161.2024.10838205","title":"Resource Life-Cycle Aware Noise Detection via Kernel Event Monitoring","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Computer science; Kernel (algebra); Event (particle physics); Noise (video); Real-time computing; Artificial intelligence; Mathematics","score_opus":0.010346462249395456,"score_gpt":0.25809950384156555,"score_spread":0.2477530415921701,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406499925","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":[0.010466491,0.00014440971,0.98436534,0.0007288366,0.00024948386,0.00013379281,5.991257e-7,0.0019929635,0.0019180755],"genre_scores_gemma":[0.9897287,0.000015535867,0.008080515,0.00007609072,0.00023165303,0.00009938955,4.215562e-7,0.000012049724,0.0017556832],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915767,0.000017782346,0.00016615173,0.00034507763,0.00015408357,0.00015923714],"domain_scores_gemma":[0.99942476,0.000032547592,0.000025567413,0.00038748732,0.000030018404,0.00009964294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001246796,0.0000954077,0.00006769238,0.00010289344,0.00017266153,0.0002096171,0.0003248493,0.000058872844,0.000026842792],"category_scores_gemma":[0.0000059482986,0.0000874245,0.000077802826,0.0004888346,0.00001362478,0.00027539048,0.00014287834,0.00014103993,0.00021082768],"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":[0.0000030597653,0.00005600545,0.00016415621,0.00003770509,0.00002422546,0.000008416133,0.00024586119,0.0005789809,0.024749652,0.010905749,0.0012126062,0.9620136],"study_design_scores_gemma":[0.0000706706,0.00008644926,0.0024432729,0.00004607254,0.000009866041,0.000040397113,0.00006408038,0.66001296,0.23291017,0.0056078145,0.09841457,0.00029366862],"about_ca_topic_score_codex":0.00008191908,"about_ca_topic_score_gemma":0.0000029723813,"teacher_disagreement_score":0.9792622,"about_ca_system_score_codex":0.00006861239,"about_ca_system_score_gemma":0.000024197167,"threshold_uncertainty_score":0.35650676},"labels":[],"label_agreement":null},{"id":"W4406611623","doi":"10.1109/smc54092.2024.10831075","title":"Novel Post-Training Structure-Agnostic Weight Pruning Technique for Deep Neural Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; Brock University","funders":"","keywords":"Computer science; Pruning; Artificial neural network; Artificial intelligence; Training (meteorology); Deep neural networks; Training set; Machine learning; Pattern recognition (psychology)","score_opus":0.014585097438040593,"score_gpt":0.2566982081829201,"score_spread":0.2421131107448795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406611623","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":[0.00026795108,0.00013045638,0.99555784,0.0010324769,0.00026086872,0.0006560726,0.000007083981,0.001445488,0.00064176554],"genre_scores_gemma":[0.6112075,0.000002996615,0.387877,0.0003560041,0.00013220382,0.0002671537,0.0000063956286,0.000014850137,0.00013591655],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989704,0.000009586733,0.00021212222,0.00042336114,0.00009970488,0.00028483258],"domain_scores_gemma":[0.9992578,0.00020594036,0.000039600705,0.000340262,0.000082659106,0.00007376011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013177845,0.00014830752,0.000117629235,0.00012972025,0.00021353833,0.0002822612,0.0005119627,0.0001065127,0.000027586231],"category_scores_gemma":[0.000024234347,0.0001263509,0.00009346593,0.00048640312,0.000030262394,0.00033316168,0.00013817688,0.00020839885,0.000002646794],"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":[0.0000034189125,0.00002246302,0.000011761712,0.000039411116,0.000026236385,0.0000053626322,0.00034267915,0.0039177877,0.11063452,0.67081016,0.00055582746,0.21363035],"study_design_scores_gemma":[0.000051387236,0.00009049458,0.000054732882,0.000020633928,0.000008794063,0.00013336836,0.000017710983,0.9692467,0.018361604,0.006396238,0.0054252245,0.00019309972],"about_ca_topic_score_codex":0.000016108605,"about_ca_topic_score_gemma":0.000014082643,"teacher_disagreement_score":0.96532893,"about_ca_system_score_codex":0.000034789307,"about_ca_system_score_gemma":0.000033626307,"threshold_uncertainty_score":0.515244},"labels":[],"label_agreement":null},{"id":"W4406612920","doi":"10.1109/smc54092.2024.10831842","title":"MECNet: Multi-Scale Exposure-Consistency Learning via Fourier Transform for Exposure Correction","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Geomechanica (Canada)","funders":"","keywords":"Scale (ratio); Fourier transform; Consistency (knowledge bases); Computer science; Discrete Fourier transform (general); Artificial intelligence; Mathematics; Fourier analysis; Short-time Fourier transform; Physics; Mathematical analysis","score_opus":0.014240525438655985,"score_gpt":0.2576717683317233,"score_spread":0.2434312428930673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406612920","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":[0.0005505512,0.0002424316,0.990697,0.0008620153,0.00065213855,0.0004985441,0.0000019281663,0.0016439745,0.0048513995],"genre_scores_gemma":[0.6905635,0.00003666895,0.28319725,0.00012738834,0.00011621381,0.00052438764,0.000006057479,0.000022343287,0.025406195],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894464,0.000020749429,0.00025506073,0.000427432,0.00013051413,0.00022162846],"domain_scores_gemma":[0.99945176,0.000080599144,0.00003488156,0.00027888417,0.000080802594,0.00007309782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023461843,0.00013766917,0.00012370538,0.00011692661,0.00030707786,0.0001991649,0.00026301766,0.000106750194,0.00006191596],"category_scores_gemma":[0.000009003876,0.000120279416,0.00016600193,0.00038123567,0.000027861393,0.00039074378,0.000032778284,0.00018687524,0.000051845684],"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":[0.000004580599,0.000053203086,0.00008918311,0.000031166866,0.000020244883,0.0000012775313,0.0005529565,0.0000924608,0.0045167473,0.0073594693,0.002793734,0.984485],"study_design_scores_gemma":[0.00021433826,0.0004137144,0.00028939286,0.00002529423,0.000018537528,0.000051877836,0.000104315455,0.78896815,0.037033066,0.0030715815,0.1695474,0.0002623217],"about_ca_topic_score_codex":0.000043184344,"about_ca_topic_score_gemma":0.00007155901,"teacher_disagreement_score":0.98422265,"about_ca_system_score_codex":0.000044900888,"about_ca_system_score_gemma":0.00004493625,"threshold_uncertainty_score":0.49048522},"labels":[],"label_agreement":null},{"id":"W4406734187","doi":"10.1051/itmconf/20257003013","title":"Analysis of The Role of Deep Learning Models in Image Classification Applications","year":2025,"lang":"en","type":"article","venue":"ITM Web of Conferences","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Artificial intelligence; Deep learning; Computer science; Image (mathematics); Pattern recognition (psychology)","score_opus":0.018535536091835687,"score_gpt":0.26847979284085977,"score_spread":0.24994425674902407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406734187","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":[0.030783826,0.000109086504,0.9301179,0.00022722669,0.00000665991,0.00017923475,0.0000028641914,0.00002747119,0.038545724],"genre_scores_gemma":[0.99535567,0.000049851475,0.004432702,0.000007179011,0.0000015915887,0.00009269348,0.000001566947,0.0000010569793,0.000057664438],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934256,0.00004342238,0.0003031283,0.00014413026,0.00010980754,0.00005692959],"domain_scores_gemma":[0.9991234,0.00007699587,0.0002528299,0.0003593909,0.00017770598,0.00000967288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015304598,0.00004722133,0.0001581113,0.0002969773,0.00004170638,0.000013303999,0.00059439085,0.00003806063,0.000009266409],"category_scores_gemma":[0.0000121261655,0.00003775368,0.0000836243,0.001925521,0.00010204674,0.00010367952,0.000083083934,0.00006888985,3.3464502e-7],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000010521679,0.000046379315,0.022058029,0.0000077347895,0.000037366473,4.404745e-9,0.000107565225,0.001390179,0.016953949,0.89538306,0.0000015926214,0.064013086],"study_design_scores_gemma":[0.000040803912,0.000010654644,0.05877834,0.0000129016125,0.00005154045,4.3153907e-8,0.00029726556,0.8406814,0.036804717,0.062361117,0.0009214982,0.000039747512],"about_ca_topic_score_codex":0.00014893188,"about_ca_topic_score_gemma":0.000115597664,"teacher_disagreement_score":0.9645719,"about_ca_system_score_codex":0.0000092686305,"about_ca_system_score_gemma":0.0001597987,"threshold_uncertainty_score":0.15395506},"labels":[],"label_agreement":null},{"id":"W4406945945","doi":"10.2139/ssrn.5051654","title":"Unsupervised Detection of Anomalous Driving Patterns Using High Resolution Telematics Time Series Data","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Telematics; Series (stratigraphy); Computer science; Time series; High resolution; Data mining; Remote sensing; Telecommunications; Geography; Machine learning; Geology","score_opus":0.017324385882654145,"score_gpt":0.26221302139775193,"score_spread":0.2448886355150978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406945945","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":[0.08733558,0.0004671055,0.91115165,0.00021955285,0.00022935457,0.00030671534,0.000043127497,0.00018679677,0.000060122777],"genre_scores_gemma":[0.95839095,0.0023069456,0.038649604,0.000017250808,0.00020953339,0.000016853386,0.000036779857,0.000020976691,0.0003511265],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971807,0.00016784621,0.00072409015,0.00056674564,0.00034480516,0.0010158262],"domain_scores_gemma":[0.99730825,0.000048993214,0.00075272814,0.0015805643,0.0002503772,0.00005906091],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014350862,0.00027409173,0.00039125056,0.00036346656,0.0003689964,0.00018907839,0.0022916275,0.0002759005,0.0000068108247],"category_scores_gemma":[0.00004674675,0.00028449122,0.00014067674,0.00041453174,0.000045013796,0.0006557769,0.0019123943,0.002130065,0.0000031521167],"study_design_candidate":"theoretical_or_conceptual","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":[0.00013774021,0.0009902226,0.0034986802,0.001404829,0.0023511297,0.000020069743,0.0011055031,0.015509241,0.07206112,0.39375952,0.00017004977,0.5089919],"study_design_scores_gemma":[0.00049209385,0.0005015817,0.0010131563,0.0008700595,0.00030960795,0.0009114482,0.00027969576,0.64423794,0.0178629,0.3319369,0.0006324409,0.00095217465],"about_ca_topic_score_codex":0.0003197168,"about_ca_topic_score_gemma":0.00030769626,"teacher_disagreement_score":0.872502,"about_ca_system_score_codex":0.0009483702,"about_ca_system_score_gemma":0.002218549,"threshold_uncertainty_score":0.9999607},"labels":[],"label_agreement":null},{"id":"W4406992013","doi":"10.1007/978-3-031-76047-1_3","title":"Machine Learning for the Prediction of the Index of Effectiveness in Cycling","year":2025,"lang":"en","type":"book-chapter","venue":"Springer optimization and its applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Cycling; Index (typography); Computer science; Artificial intelligence; Geography; Forestry; World Wide Web","score_opus":0.011323799810993488,"score_gpt":0.23429792408802866,"score_spread":0.22297412427703517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406992013","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":[0.0000065627723,0.0006393219,0.98999476,0.00021466086,0.000036264326,0.0015788885,0.000029844306,0.000051458937,0.007448218],"genre_scores_gemma":[0.8702389,0.011586781,0.050350565,0.00017944166,0.00016099168,0.005499563,0.000113571514,0.000098768556,0.061771408],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992371,0.000018938308,0.00032472465,0.00024572012,0.00010395528,0.000069516085],"domain_scores_gemma":[0.9988468,0.00028240727,0.0003157628,0.0003615745,0.00017826885,0.00001517878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022336356,0.00011407042,0.00016237904,0.00013464679,0.00020616158,0.000020851285,0.0003724767,0.00012262056,0.000004840408],"category_scores_gemma":[0.000023274628,0.00008392091,0.00007905857,0.00022060554,0.000053694603,0.00006497119,0.00016325392,0.0001910936,2.4556238e-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":[0.000005057351,0.000024806308,0.00025845566,0.00017007545,0.000028929831,8.430673e-9,0.000045195906,0.11293958,0.00018820103,0.86925507,0.000009675608,0.017074918],"study_design_scores_gemma":[0.00021071346,0.000023143171,0.0007871914,0.00019460247,0.00004130558,8.3616936e-7,0.0000047669596,0.9598071,0.0019347648,0.0051226816,0.031774223,0.00009869447],"about_ca_topic_score_codex":0.00001140273,"about_ca_topic_score_gemma":0.0000045236634,"teacher_disagreement_score":0.9396442,"about_ca_system_score_codex":0.00002860755,"about_ca_system_score_gemma":0.000050885785,"threshold_uncertainty_score":0.34221953},"labels":[],"label_agreement":null},{"id":"W4407120710","doi":"10.1016/j.eswa.2025.126667","title":"Graph-enhanced anomaly detection framework in multivariate time series using Graph Attention and Enhanced Generative Adversarial Networks","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Université de Montréal","funders":"Key Science and Technology Program of Shaanxi Province; National Key Research and Development Program of China; National Natural Science Foundation of China; Department of Science and Technology of Sichuan Province; Organization Department of Sichuan Provincial Party Committee; Ministry of Science and Technology of the People's Republic of China","keywords":"Computer science; Multivariate statistics; Graph; Anomaly detection; Adversarial system; Generative grammar; Series (stratigraphy); Generative adversarial network; Anomaly (physics); Time series; Theoretical computer science; Artificial intelligence; Machine learning; Image (mathematics)","score_opus":0.006834604503249495,"score_gpt":0.25249767912061066,"score_spread":0.24566307461736117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407120710","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":[0.009560843,0.0003443387,0.98739964,0.00014438608,0.00014588881,0.001637971,0.0000037986879,0.00037072666,0.0003924182],"genre_scores_gemma":[0.89121294,0.00006503484,0.10476397,0.000066405766,0.00012198175,0.0035161918,0.000007719247,0.000015358126,0.0002303731],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983984,0.000107002656,0.00041765024,0.00067025464,0.0001426376,0.00026400242],"domain_scores_gemma":[0.99886566,0.00008220887,0.00022480277,0.0005952836,0.00015999936,0.00007204664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017296964,0.00023102964,0.00027025596,0.0003275788,0.00055132015,0.00020686773,0.000305409,0.00019782606,0.000002748711],"category_scores_gemma":[0.000009502674,0.00021870654,0.000055035318,0.0017559157,0.00010415728,0.0005034166,0.000097896984,0.00021220546,0.0000049289283],"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":[0.00018670247,0.00039366877,0.0004110178,0.00008275308,0.00022891107,0.0000023918046,0.0019335685,0.02736847,0.6993289,0.24515577,0.00015003623,0.024757802],"study_design_scores_gemma":[0.0022133144,0.00039497315,0.005931444,0.0007728092,0.00007750151,0.00005692564,0.0011664784,0.80865014,0.15909982,0.016023336,0.0039848434,0.0016284469],"about_ca_topic_score_codex":0.00046416544,"about_ca_topic_score_gemma":0.00007916474,"teacher_disagreement_score":0.88263565,"about_ca_system_score_codex":0.0001176731,"about_ca_system_score_gemma":0.00005355074,"threshold_uncertainty_score":0.8918594},"labels":[],"label_agreement":null},{"id":"W4407129318","doi":"10.1109/icit63607.2024.10859907","title":"VigilantAI: Real-time detection of anomalous activity from a video stream using deep learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":12,"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; Artificial intelligence; Deep learning; Computer vision; Real-time computing; Computer graphics (images)","score_opus":0.01123499906784591,"score_gpt":0.2546045622952905,"score_spread":0.24336956322744457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407129318","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":[0.39447206,0.00002729814,0.603139,0.000022804383,0.000037620175,0.00007430814,0.0000016860495,0.0006778548,0.0015473895],"genre_scores_gemma":[0.961917,0.000032932057,0.03766744,0.000007126536,0.000048791477,0.000014759261,0.00000131301,0.0000108859695,0.00029979018],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991672,0.000044155957,0.00016489597,0.0003516136,0.00013361467,0.00013851628],"domain_scores_gemma":[0.9994468,0.00009835114,0.00006907829,0.0002972258,0.000041519786,0.00004704305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000108990265,0.00010400288,0.0001295933,0.00012514617,0.00012676569,0.000119661534,0.00022202113,0.0000741154,0.000054687313],"category_scores_gemma":[0.000008278356,0.000098189135,0.00008171475,0.0005027993,0.000026452211,0.00038681604,0.00011874303,0.00014176112,0.000040604213],"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":[0.0000037934667,0.0000242494,0.000106262014,0.000008376916,0.000016043745,0.0000030940328,0.00008878418,0.00012252247,0.7694332,0.0014662977,0.0000071095983,0.22872025],"study_design_scores_gemma":[0.000034132656,0.00007167561,0.0013138073,0.00001893918,0.000010955945,0.000013992368,0.000012905306,0.641098,0.3555115,0.0011561007,0.00065355166,0.00010442739],"about_ca_topic_score_codex":0.0019673915,"about_ca_topic_score_gemma":0.000037297366,"teacher_disagreement_score":0.64097553,"about_ca_system_score_codex":0.00006974965,"about_ca_system_score_gemma":0.00003163956,"threshold_uncertainty_score":0.40040368},"labels":[],"label_agreement":null},{"id":"W4407270122","doi":"10.1016/j.comnet.2025.111098","title":"Machine learning approaches for predicting link failures in production networks","year":2025,"lang":"en","type":"article","venue":"Computer Networks","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Link (geometry); Production (economics); Artificial intelligence; Machine learning; Computer network","score_opus":0.018787687066132507,"score_gpt":0.23553449379358837,"score_spread":0.21674680672745586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407270122","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":[0.00027054903,0.0006075768,0.994954,0.002008441,0.0005910943,0.0006275272,2.68376e-7,0.0006193479,0.00032123053],"genre_scores_gemma":[0.84100306,0.00007174156,0.1569763,0.00021699005,0.0009421719,0.00036041415,0.000016187558,0.000013549984,0.0003995882],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987035,0.00006561391,0.00031244344,0.00054288463,0.00007228123,0.00030330347],"domain_scores_gemma":[0.9992855,0.00012355299,0.00011005971,0.0003864578,0.000055138175,0.000039297323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042635016,0.00015474643,0.0001806747,0.00016596171,0.00028851704,0.00016911153,0.0005117385,0.00013992505,9.125283e-7],"category_scores_gemma":[0.000015422733,0.00015648188,0.00007966395,0.00077462546,0.000031905336,0.00021521507,0.00029189698,0.0004154484,7.6527533e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000042602264,0.000021460504,0.0022582961,0.000010085435,0.000009082827,3.089478e-7,0.000038866416,0.59743977,0.0000010784764,0.01838814,0.00077098596,0.38105765],"study_design_scores_gemma":[0.00015968729,0.000051772968,0.0016267776,0.00006065147,0.0000052950154,0.0000038419766,0.0000044410435,0.9773965,0.000035044606,0.0020603945,0.018462168,0.00013345895],"about_ca_topic_score_codex":0.000020228224,"about_ca_topic_score_gemma":0.000022220309,"teacher_disagreement_score":0.8407325,"about_ca_system_score_codex":0.000050135153,"about_ca_system_score_gemma":0.000021512436,"threshold_uncertainty_score":0.6381146},"labels":[],"label_agreement":null},{"id":"W4407344104","doi":"10.21203/rs.3.rs-5975924/v1","title":"Deep Learning for Bifurcation Detection: Extending Early Warning Signals to Dynamical Systems with Coloured Noise","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Carleton University; University of Waterloo","funders":"","keywords":"Bifurcation; Noise (video); Warning system; Computer science; Artificial intelligence; Acoustics; Physics; Telecommunications; Nonlinear system; Image (mathematics)","score_opus":0.03903813923142154,"score_gpt":0.37424385286353506,"score_spread":0.33520571363211354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407344104","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":[0.009485373,0.00021370302,0.9845875,0.0005935095,0.00016437327,0.0036561906,0.000011707008,0.0007507792,0.00053688354],"genre_scores_gemma":[0.959483,0.000039667608,0.030913569,0.000016240067,0.00019446069,0.007335138,0.00002711605,0.00003140166,0.0019594007],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99651647,0.00045919282,0.00042015465,0.0011200146,0.00083327753,0.0006508755],"domain_scores_gemma":[0.99660105,0.0006600933,0.00019589988,0.00088009273,0.001415517,0.00024736047],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001805952,0.00027041964,0.00035823288,0.0009669544,0.0011274685,0.0009795226,0.0011830531,0.000351862,0.000006462568],"category_scores_gemma":[0.00031865254,0.00025980154,0.00014159083,0.0014544629,0.000061836065,0.00019874217,0.0011510728,0.0014578592,0.000028317829],"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":[0.00042515705,0.0003782929,0.0017550142,0.003907121,0.00030292184,0.000025883786,0.0038192882,0.5662302,0.01139617,0.04891078,0.0005612184,0.362288],"study_design_scores_gemma":[0.00023414083,0.000934345,0.0022227077,0.0010899928,0.00001679842,0.0000099656945,0.0003204828,0.98244464,0.0029492453,0.001387517,0.007972022,0.00041810967],"about_ca_topic_score_codex":0.0003197023,"about_ca_topic_score_gemma":0.00004494622,"teacher_disagreement_score":0.9536739,"about_ca_system_score_codex":0.0006445006,"about_ca_system_score_gemma":0.0003014697,"threshold_uncertainty_score":0.9999854},"labels":[],"label_agreement":null},{"id":"W4407357659","doi":"10.1016/j.bspc.2024.107471","title":"Fuse-Former: An interpretability analysis model for rs-fMRI based on multi-scale information fusion interaction","year":2025,"lang":"en","type":"article","venue":"Biomedical Signal Processing and Control","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Science and Technology Planning Project of Guangdong Province; Guangzhou Municipal Science and Technology Project; Canadian Institutes of Health Research; National Institutes of Health; Alzheimer's Disease Neuroimaging Initiative; National Natural Science Foundation of China; U.S. Department of Defense","keywords":"Interpretability; Fuse (electrical); Scale (ratio); Computer science; Artificial intelligence; Fusion; Machine learning; Scale analysis (mathematics); Data mining; Engineering","score_opus":0.011286862295975706,"score_gpt":0.28657315273136363,"score_spread":0.27528629043538794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407357659","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":[0.0029228055,0.000019985984,0.9951186,0.0012679637,0.000028766284,0.0003071595,0.000014862388,0.00021700874,0.00010285288],"genre_scores_gemma":[0.9598571,0.0000018712898,0.038663063,0.0011971393,0.000017812023,0.0002083911,0.000025713238,0.0000026198104,0.000026279136],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904853,0.000034114666,0.0003194043,0.0002779972,0.00016786199,0.00015206278],"domain_scores_gemma":[0.9993555,0.00007799829,0.00012298305,0.0001948628,0.00014266635,0.00010596487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037392968,0.00011451372,0.00017834255,0.00031060827,0.00030427793,0.00021162452,0.0002272881,0.00010827257,0.0000051200113],"category_scores_gemma":[0.00002595424,0.00009258969,0.000095184856,0.00057851477,0.000070313734,0.0006382795,0.000031493415,0.00011752768,0.0000012286229],"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":[0.00011540996,0.00021385521,0.00019441327,0.00004591744,0.000019277779,4.708033e-8,0.00017151587,0.0014887693,0.0013379464,0.00028631638,0.000042150892,0.9960844],"study_design_scores_gemma":[0.00070050923,0.00016034424,0.00052313646,0.00003244155,0.00007201422,3.069017e-7,0.00003383894,0.9958223,0.000455286,0.0007001786,0.0014008395,0.000098793935],"about_ca_topic_score_codex":0.000017403769,"about_ca_topic_score_gemma":0.000006874847,"teacher_disagreement_score":0.99598557,"about_ca_system_score_codex":0.000052300304,"about_ca_system_score_gemma":0.00009040476,"threshold_uncertainty_score":0.3775698},"labels":[],"label_agreement":null},{"id":"W4407366583","doi":"10.11834/jig.240061","title":"Double-pooling residual classification network based on feature reordering attention mechanism","year":2025,"lang":"en","type":"article","venue":"Journal of Image and Graphics","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"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":"Natural Science Foundation of Liaoning Province; National Natural Science Foundation of China","keywords":"Pooling; Residual; Computer science; Feature (linguistics); Artificial intelligence; Mechanism (biology); Pattern recognition (psychology); Algorithm","score_opus":0.01321936050736796,"score_gpt":0.26786501974755705,"score_spread":0.2546456592401891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407366583","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":[0.009665048,0.00010010071,0.9833099,0.00601471,0.00013372544,0.00008151782,4.2909926e-7,0.000044706496,0.0006498381],"genre_scores_gemma":[0.8811741,0.00031129146,0.11732899,0.0008543577,0.00015123154,0.0000074712384,0.0000011662146,0.000005742912,0.00016568654],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993799,0.000029553312,0.00021349643,0.00013093262,0.00014570696,0.00010041899],"domain_scores_gemma":[0.99931324,0.000054207954,0.00021781401,0.00019190706,0.00018476805,0.000038046026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043914234,0.00007383618,0.000102352766,0.0002246007,0.00022349581,0.00015033966,0.0002069995,0.00007412877,8.7758366e-7],"category_scores_gemma":[0.000013845197,0.000064294116,0.000069765534,0.00053376623,0.00002334079,0.00024521057,0.00003373451,0.00027330982,4.3403176e-7],"study_design_candidate":"theoretical_or_conceptual","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":[0.000072150644,0.00009152253,0.0005962753,0.000041352818,0.0000360692,0.000010404576,0.000046554076,0.00020458773,0.016568745,0.950181,0.0072288974,0.02492242],"study_design_scores_gemma":[0.0038746234,0.0011183759,0.063185796,0.0012038971,0.00019791108,0.00016879964,0.00020998392,0.4292561,0.036540277,0.42564815,0.037825152,0.0007709203],"about_ca_topic_score_codex":0.0000021879225,"about_ca_topic_score_gemma":0.0000019271097,"teacher_disagreement_score":0.871509,"about_ca_system_score_codex":0.000016284323,"about_ca_system_score_gemma":0.00004084849,"threshold_uncertainty_score":0.2621838},"labels":[],"label_agreement":null},{"id":"W4407412654","doi":"10.2514/6.2025-2226","title":"Developing A New Adaptive Optimal k-Nearest Neighbor Methodology for Flight Test Data Anomaly Detection - Application to Business Aircraft","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Anomaly detection; Computer science; k-nearest neighbors algorithm; Anomaly (physics); Data mining; Test (biology); Test data; Artificial intelligence; Software engineering; Geology","score_opus":0.09554295652117603,"score_gpt":0.35627747090030565,"score_spread":0.26073451437912964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407412654","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002467994,0.000027210694,0.9899823,0.007133163,0.00013785281,0.001230196,0.000017136977,0.00065471267,0.00057063706],"genre_scores_gemma":[0.0834671,0.000007711186,0.9133313,0.0010832814,0.000109809625,0.0006576128,0.000022815486,0.000012428694,0.0013079914],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983511,0.00004237259,0.00034143432,0.00088078756,0.0001064683,0.0002778469],"domain_scores_gemma":[0.9977288,0.00048312033,0.00010740097,0.0012492196,0.00033456928,0.000096882184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041362076,0.00017976789,0.00021351202,0.00027847505,0.00032467194,0.00014129735,0.0014826012,0.00012679878,0.0000054987977],"category_scores_gemma":[0.00024455617,0.00017647888,0.000041573952,0.0019044186,0.000028006622,0.0004940811,0.0007581698,0.00009478906,0.000032203487],"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":[0.000047496316,0.0000817652,0.00016469837,0.000025653366,0.000038662518,5.5717516e-7,0.000075580065,0.0005019721,0.024231067,0.32014546,0.005947913,0.64873916],"study_design_scores_gemma":[0.0005399913,0.00026372736,0.011381836,0.000041269068,0.000048381044,0.0000227812,0.00007144666,0.4905657,0.15470216,0.01764071,0.32409737,0.00062466797],"about_ca_topic_score_codex":0.0005227392,"about_ca_topic_score_gemma":0.00040883635,"teacher_disagreement_score":0.6481145,"about_ca_system_score_codex":0.00012832574,"about_ca_system_score_gemma":0.00036259287,"threshold_uncertainty_score":0.7196599},"labels":[],"label_agreement":null},{"id":"W4407450044","doi":"10.1109/tcyb.2025.3536165","title":"Personalizing Vision-Language Models With Hybrid Prompts for Zero-Shot Anomaly Detection","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"University of Toronto","funders":"South University of Science and Technology of China; Ministry of Industry and Information Technology of the People's Republic of China; China Scholarship Council","keywords":"Zero (linguistics); Anomaly detection; Shot (pellet); Computer science; Ground zero; Anomaly (physics); Artificial intelligence; Natural language processing; Physics; Linguistics; Chemistry","score_opus":0.01531708000535554,"score_gpt":0.2699115341578634,"score_spread":0.25459445415250787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407450044","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":[0.012543658,0.000046335343,0.9841335,0.00036656545,0.00017941822,0.0007632145,0.00002244028,0.00052379334,0.001421106],"genre_scores_gemma":[0.9221298,0.000023015313,0.074823335,0.00025018468,0.000018910436,0.0005147648,0.0000017641738,0.00002031073,0.0022178786],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998756,0.000027653863,0.00024397571,0.00050581765,0.00020105253,0.00026553727],"domain_scores_gemma":[0.99905807,0.000098850476,0.000080200516,0.0005428147,0.00014474028,0.00007529859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012216653,0.0002022134,0.00016727041,0.0002516815,0.00044226914,0.00015789096,0.00039272188,0.00008250133,0.000007698683],"category_scores_gemma":[0.000001924159,0.0001900619,0.00012467598,0.0005352168,0.0000610028,0.0003005204,0.000004108255,0.00020829857,0.000008747302],"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":[0.0002666824,0.0011604322,0.0000069385283,0.00016585855,0.0002504717,0.00001187264,0.0016612549,0.088922866,0.054417636,0.03761174,0.0010827734,0.81444144],"study_design_scores_gemma":[0.00056372257,0.0005709448,0.000030795203,0.000075307486,0.000057402336,0.000031865373,0.0000719945,0.58188343,0.4083477,0.0055376217,0.0025241326,0.00030505366],"about_ca_topic_score_codex":0.000063210355,"about_ca_topic_score_gemma":0.00007098652,"teacher_disagreement_score":0.9095862,"about_ca_system_score_codex":0.00010602391,"about_ca_system_score_gemma":0.00007723681,"threshold_uncertainty_score":0.7750499},"labels":[],"label_agreement":null},{"id":"W4407785856","doi":"10.1109/snams64316.2024.10883795","title":"A Novel Approach to Person-Job Fit Using Outlier Detection","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Anomaly detection; Computer science; Outlier; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.07085181722077652,"score_gpt":0.28872435229919907,"score_spread":0.21787253507842255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407785856","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":[0.0038512417,0.000021949594,0.9772215,0.00035360214,0.00011951773,0.00019987213,0.000001194988,0.001040206,0.0171909],"genre_scores_gemma":[0.6248744,6.665343e-7,0.37254205,0.00020328922,0.00006212836,0.00005823142,1.5855396e-7,0.0000075196067,0.0022515329],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992834,0.000006145045,0.00009381991,0.00035892313,0.00011534739,0.00014237504],"domain_scores_gemma":[0.9995864,0.000012109346,0.000011884324,0.000287896,0.00003157406,0.00007013628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000105480816,0.00008242903,0.000062105355,0.00014678715,0.00013028647,0.000275271,0.0002695163,0.000046790912,0.000012844769],"category_scores_gemma":[0.0000040224945,0.000072445255,0.00005883158,0.0007379118,0.000010507275,0.00024314856,0.00008415912,0.00009086276,0.000094237475],"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":[0.0000034658717,0.00017383102,0.000020718804,0.000056004294,0.00004185798,0.0000022236977,0.0018154723,0.0013589115,0.30831847,0.28255144,0.0032560613,0.40240154],"study_design_scores_gemma":[0.000031233125,0.000032992742,0.00014853147,0.000008315754,0.0000051521915,0.00007198547,0.000077036704,0.95575887,0.01979654,0.00033106987,0.02358419,0.00015406865],"about_ca_topic_score_codex":0.00013345758,"about_ca_topic_score_gemma":0.0000054477337,"teacher_disagreement_score":0.95439994,"about_ca_system_score_codex":0.000068940375,"about_ca_system_score_gemma":0.000024576115,"threshold_uncertainty_score":0.29542318},"labels":[],"label_agreement":null},{"id":"W4407891227","doi":"10.1007/978-981-96-1621-3_16","title":"ASTD Patterns for Integrated Continuous Anomaly Detection in Data Logs","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Anomaly detection; Anomaly (physics); Data mining","score_opus":0.021129983471791448,"score_gpt":0.2702787274616232,"score_spread":0.24914874398983172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407891227","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008491434,0.00013141736,0.99646187,0.000605188,0.0007295541,0.0009745333,0.000073219766,0.0002960279,0.00064327376],"genre_scores_gemma":[0.34664136,0.000079403995,0.64989626,0.0013650121,0.00031963747,0.00017959748,0.00008748902,0.000042803367,0.0013884156],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967232,0.000027632019,0.0005935409,0.0018113549,0.00035485468,0.000489468],"domain_scores_gemma":[0.9967862,0.00037106386,0.00028202683,0.0022249322,0.00025171784,0.000084077],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007998127,0.00041008103,0.00046964674,0.0010727977,0.0002099123,0.00041922528,0.004451794,0.0003610141,0.0000054840893],"category_scores_gemma":[0.00010499924,0.00039747733,0.00009348641,0.0009094994,0.00023334358,0.0005706524,0.0016599162,0.0006763244,0.000007152983],"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":[0.0000064449805,0.000044894274,0.00006900834,0.000027269656,0.0000063624543,0.000008848929,0.0000644335,0.000414755,0.00016013252,0.006460758,0.00006930161,0.9926678],"study_design_scores_gemma":[0.00047256227,0.0002114715,0.00023940673,0.0003398627,0.000011261514,0.00003386409,4.5861017e-7,0.91001946,0.0057985093,0.06465425,0.017579477,0.0006394049],"about_ca_topic_score_codex":0.00019994484,"about_ca_topic_score_gemma":0.001720998,"teacher_disagreement_score":0.9920284,"about_ca_system_score_codex":0.0003172834,"about_ca_system_score_gemma":0.00044661236,"threshold_uncertainty_score":0.9998477},"labels":[],"label_agreement":null},{"id":"W4407938766","doi":"10.1109/icsc63108.2024.10895186","title":"Unsupervised Insider Threat Detection Using Multi-Head Self-Attention Mechanisms","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Université Laval","funders":"","keywords":"Insider threat; Computer science; Head (geology); Artificial intelligence; Insider; Computer security","score_opus":0.035536149311442694,"score_gpt":0.29643192417555475,"score_spread":0.2608957748641121,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407938766","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":[0.037643686,0.000058604677,0.95826375,0.0002144677,0.00026277706,0.00023978687,7.662048e-7,0.0028996451,0.0004165373],"genre_scores_gemma":[0.66768014,0.000012413791,0.33179474,0.00009383599,0.00003109588,0.000040128834,7.596981e-7,0.000010706291,0.0003361663],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907637,0.00002697831,0.00018336452,0.00040278325,0.00014435363,0.00016616682],"domain_scores_gemma":[0.99949586,0.000018725668,0.000025713987,0.00034257444,0.0000602792,0.000056855493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013982508,0.000119880206,0.00008438452,0.00017175391,0.00021182616,0.00028002082,0.00022273361,0.00009039433,0.00003490697],"category_scores_gemma":[0.0000026507203,0.00010659439,0.000091364156,0.0006284313,0.000010910422,0.0005900199,0.00010242857,0.00011801965,0.00012472794],"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":[0.0000017443023,0.00013100376,0.000056315843,0.00004720041,0.000045005014,0.000009529303,0.00023026147,0.00005963952,0.6101285,0.24040543,0.000047249978,0.14883809],"study_design_scores_gemma":[0.00009176428,0.00004359256,0.0005612641,0.000018213725,0.000013634615,0.000055424072,0.000023894265,0.8612082,0.123979814,0.012395668,0.0014501271,0.00015838264],"about_ca_topic_score_codex":0.0001441644,"about_ca_topic_score_gemma":0.000039829218,"teacher_disagreement_score":0.8611486,"about_ca_system_score_codex":0.00010340509,"about_ca_system_score_gemma":0.000032573018,"threshold_uncertainty_score":0.4346793},"labels":[],"label_agreement":null},{"id":"W4407950596","doi":"10.1109/cdc56724.2024.10886153","title":"Aero-engines Anomaly Detection using an Unsupervised Fisher Autoencoder","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"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":"Autoencoder; Anomaly detection; Computer science; Pattern recognition (psychology); Artificial intelligence; Anomaly (physics); Artificial neural network; Physics","score_opus":0.029128329476053393,"score_gpt":0.2753654949228112,"score_spread":0.2462371654467578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407950596","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":[0.061842896,0.00005955842,0.9330784,0.00029517003,0.0002183544,0.00013020867,8.662162e-7,0.002268821,0.002105741],"genre_scores_gemma":[0.8617961,0.0000060147195,0.13687566,0.00016255397,0.00010530141,0.000035920344,9.838359e-7,0.000013767552,0.0010037497],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991426,0.000023277003,0.00015920916,0.00038669567,0.00012047694,0.00016773597],"domain_scores_gemma":[0.99942553,0.000018629293,0.00001714865,0.00041486003,0.00005017318,0.00007364117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000117767806,0.00011281284,0.00007923518,0.00015114158,0.00016089463,0.00040970097,0.00032580076,0.00007109211,0.000101118734],"category_scores_gemma":[0.0000032111122,0.000099032295,0.00006280676,0.0006758201,0.000019843315,0.0009856108,0.00007196179,0.00010621682,0.00005685646],"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":[0.000005632207,0.0002808989,0.0005086053,0.00009467917,0.00007434113,0.00003273906,0.0008783859,0.0036694543,0.22034533,0.1428153,0.0016131891,0.6296814],"study_design_scores_gemma":[0.000031877218,0.0000510853,0.0006319106,0.000007998056,0.0000059455006,0.000040974002,0.000015802592,0.95909,0.024559911,0.0022238921,0.013191171,0.0001494216],"about_ca_topic_score_codex":0.000190267,"about_ca_topic_score_gemma":0.000040926363,"teacher_disagreement_score":0.95542055,"about_ca_system_score_codex":0.000048460137,"about_ca_system_score_gemma":0.000031736934,"threshold_uncertainty_score":0.403842},"labels":[],"label_agreement":null},{"id":"W4407953083","doi":"10.1145/3701551.3703494","title":"Prospective Multi-Graph Cohesion for Multivariate Time Series Anomaly Detection","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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 Waterloo","funders":"","keywords":"Multivariate statistics; Cohesion (chemistry); Computer science; Anomaly detection; Series (stratigraphy); Graph; Artificial intelligence; Theoretical computer science; Machine learning; Geology; Physics","score_opus":0.008511234684036952,"score_gpt":0.2622854854853769,"score_spread":0.2537742508013399,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407953083","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":[0.0016122672,0.0000148814,0.99336016,0.00050734496,0.000094927425,0.00091541046,0.0000038076373,0.000975981,0.002515209],"genre_scores_gemma":[0.5250669,0.0000047845524,0.46125564,0.00014918348,0.000018884082,0.0007153443,0.0000016863971,0.000006914417,0.012780664],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999171,0.000020203628,0.00018466552,0.00039178276,0.000066876346,0.00016546772],"domain_scores_gemma":[0.99932647,0.000051871506,0.000064954045,0.00035478157,0.0001666873,0.000035265904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013736091,0.00011819178,0.00012094392,0.00016417635,0.00035013692,0.00010597792,0.0003071361,0.00008069384,0.000010495374],"category_scores_gemma":[0.000026960837,0.00010606974,0.00009169818,0.0005997549,0.000035876044,0.000396017,0.00010942365,0.000067671484,0.00003088012],"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":[0.00012214083,0.00047834046,0.0014857607,0.0000579967,0.00010628003,0.0000010959927,0.0002863342,0.00005533424,0.3041375,0.457636,0.0021768461,0.23345636],"study_design_scores_gemma":[0.0007261094,0.00026446616,0.022386367,0.000018039262,0.000016255157,0.0000053392077,0.000028832726,0.13109325,0.7977044,0.03157143,0.0159068,0.0002786942],"about_ca_topic_score_codex":0.00006743671,"about_ca_topic_score_gemma":0.00003581235,"teacher_disagreement_score":0.53210455,"about_ca_system_score_codex":0.000056281304,"about_ca_system_score_gemma":0.00003305521,"threshold_uncertainty_score":0.43253985},"labels":[],"label_agreement":null},{"id":"W4407980306","doi":"10.18280/ijsse.150112","title":"Real-Time Deep Learning-Driven Surveillance with Spatiotemporal Feature Extraction for Detection of Anomalous Human Behavior Across Dynamic Environments","year":2025,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Feature (linguistics); Feature extraction; Real-time computing; Pattern recognition (psychology)","score_opus":0.002888040109124917,"score_gpt":0.2501250481302675,"score_spread":0.24723700802114257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407980306","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":[0.35829818,0.000030315738,0.64135146,0.00011375576,0.00007872299,0.00008594037,0.0000063141165,0.00002194684,0.0000133642],"genre_scores_gemma":[0.9898579,0.00015585344,0.00985617,0.0000039049532,0.00003061658,0.000009532191,0.0000061859796,0.000005575851,0.00007423266],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938095,0.00001274185,0.00024832512,0.00011739143,0.00015839304,0.00008222857],"domain_scores_gemma":[0.99946773,0.00004603374,0.00025281805,0.00007696145,0.00012787554,0.000028605282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017235597,0.00008152773,0.00013034565,0.00010958681,0.00008834313,0.000040117466,0.00021105379,0.000062558836,0.0000013083546],"category_scores_gemma":[0.000012867509,0.00007933044,0.000056607092,0.00009235457,0.000022327158,0.00025798217,0.00004262547,0.0001678858,1.5360024e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053297594,0.00039184984,0.0128908735,0.0001125583,0.00043371582,0.000030779327,0.0014665956,0.036035284,0.8255455,0.005693759,0.00001186625,0.11685426],"study_design_scores_gemma":[0.0018411678,0.0008103283,0.36849177,0.00020270844,0.00004320001,0.0003325332,0.00010599727,0.5614246,0.05874133,0.00041906434,0.00722686,0.0003604455],"about_ca_topic_score_codex":0.000014041493,"about_ca_topic_score_gemma":0.000015285803,"teacher_disagreement_score":0.76680416,"about_ca_system_score_codex":0.00009432826,"about_ca_system_score_gemma":0.000011598453,"threshold_uncertainty_score":0.32350016},"labels":[],"label_agreement":null},{"id":"W4407989538","doi":"10.1016/j.future.2025.107779","title":"Entropy-based genetic feature engineering and multi-classifier fusion for anomaly detection in vehicle controller area networks","year":2025,"lang":"en","type":"article","venue":"Future Generation Computer Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Anomaly detection; Artificial intelligence; Classifier (UML); Pattern recognition (psychology); Entropy (arrow of time); Data mining","score_opus":0.009502587357635806,"score_gpt":0.21063995296458168,"score_spread":0.20113736560694587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407989538","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":[0.009121507,0.00091560115,0.9824797,0.00060311763,0.0056648552,0.0009789597,0.0000031333893,0.00022919364,0.000003928477],"genre_scores_gemma":[0.818933,0.000025055879,0.17360066,0.00039664592,0.0060531273,0.00077380054,0.000017030434,0.000016445523,0.00018422038],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988827,0.000059834412,0.0002993044,0.00045717054,0.00009663681,0.0002043687],"domain_scores_gemma":[0.9993872,0.000048929807,0.00010147893,0.00028292162,0.0001253366,0.000054108456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016698039,0.0001799562,0.00021265686,0.00023219797,0.00019947177,0.0003037679,0.00020370152,0.00020335251,4.5944176e-7],"category_scores_gemma":[0.000004343099,0.00017087845,0.0000644472,0.00041090563,0.000010002977,0.000136586,0.000056807017,0.00014482789,6.9358424e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005058986,0.00018585853,0.0014402991,0.00018400367,0.000073330375,0.00000520129,0.00016872292,0.6781088,0.063059025,0.013345433,0.026987728,0.216391],"study_design_scores_gemma":[0.0009402095,0.00006358106,0.003220616,0.000033163495,0.0000071225895,0.000005097918,0.0000049262444,0.96357876,0.0021548972,0.0000038694957,0.029833838,0.00015389599],"about_ca_topic_score_codex":0.000018329338,"about_ca_topic_score_gemma":0.00003871259,"teacher_disagreement_score":0.80981153,"about_ca_system_score_codex":0.00008550809,"about_ca_system_score_gemma":0.000030648578,"threshold_uncertainty_score":0.6968221},"labels":[],"label_agreement":null},{"id":"W4408034041","doi":"10.1007/978-3-031-80817-3_3","title":"Enhanced Public Safety: Real-Time Crime Detection with CNN-LSTM in Video Surveillance","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes on data engineering and communications technologies","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Computer science; Real-time computing; Computer security; Artificial intelligence","score_opus":0.01972967081605845,"score_gpt":0.23748446326483905,"score_spread":0.2177547924487806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408034041","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":[0.000047046567,0.0038290222,0.9627722,0.005859929,0.000053297652,0.0006005666,0.00016854503,0.0072333603,0.019436028],"genre_scores_gemma":[0.8658811,0.023476234,0.10680663,0.00006685948,0.000039361985,0.00037099954,0.00047340742,0.0001312296,0.0027542016],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984364,0.00001408241,0.00034414357,0.00078143534,0.0001703613,0.00025359076],"domain_scores_gemma":[0.9932823,0.00042342898,0.00013731,0.0060475003,0.00007385472,0.000035616773],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022019484,0.00039423676,0.00036016066,0.00065937475,0.00020740714,0.00025552287,0.003409443,0.00046329,0.0000040026193],"category_scores_gemma":[0.0001366086,0.00034324874,0.000041448675,0.00048638412,0.00017283077,0.00026095132,0.002042823,0.0011010835,0.00004005485],"study_design_candidate":"theoretical_or_conceptual","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":[0.000012969805,0.000047394533,0.0000027302299,0.0001161774,0.000103592705,0.0000059303625,0.000056220917,0.0006239934,0.0054778364,0.45180658,0.000108785855,0.5416378],"study_design_scores_gemma":[0.000577353,0.00086995715,0.00021464146,0.0018453301,0.00010554504,0.00016874162,0.000033142973,0.3159398,0.024932377,0.106780514,0.5453993,0.0031332946],"about_ca_topic_score_codex":0.000034106182,"about_ca_topic_score_gemma":0.0001650368,"teacher_disagreement_score":0.86583406,"about_ca_system_score_codex":0.00012889969,"about_ca_system_score_gemma":0.000047111153,"threshold_uncertainty_score":0.99990195},"labels":[],"label_agreement":null},{"id":"W4408175168","doi":"10.1007/s10618-024-01084-1","title":"Efficient outlier detection in numerical and categorical data","year":2025,"lang":"en","type":"article","venue":"Data Mining and Knowledge Discovery","topic":"Anomaly Detection Techniques and Applications","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 Alberta","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo; Carnegie Mellon University","keywords":"Categorical variable; Anomaly detection; Outlier; Computer science; Data mining; Artificial intelligence; Pattern recognition (psychology); Machine learning","score_opus":0.03590234063653296,"score_gpt":0.31659416447194305,"score_spread":0.2806918238354101,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408175168","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":[0.058186434,0.000779892,0.9392631,0.00023273852,0.00013705331,0.00010711085,0.00005233938,0.00008911567,0.001152223],"genre_scores_gemma":[0.9929909,0.000053718246,0.0064288774,0.000048147365,0.000029406068,0.000016495604,0.00007001287,0.000003843072,0.00035859353],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989729,0.000037351787,0.00017075623,0.0006249898,0.00005521134,0.00013878764],"domain_scores_gemma":[0.9986552,0.00010030183,0.00003172753,0.0011582656,0.000014745832,0.00003978653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030196883,0.000090489586,0.00012030734,0.00012673475,0.00013038517,0.00020905756,0.00077223824,0.000052073807,6.7047637e-7],"category_scores_gemma":[0.00006553371,0.000082285886,0.00000813364,0.00046286455,0.00005023259,0.0004014554,0.0022656368,0.000100445235,0.0000033421263],"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":[0.0000127581325,0.00023769724,0.0023950182,0.00004004362,0.000015728498,0.000004820749,0.0004959368,0.000008711675,0.00032745342,0.014092223,0.003452075,0.97891754],"study_design_scores_gemma":[0.00025464842,0.000034367054,0.009390613,0.000033726323,0.000013939252,0.000016211608,0.00022062802,0.9652631,0.0003276566,0.00042427893,0.023842711,0.00017811988],"about_ca_topic_score_codex":0.00007756984,"about_ca_topic_score_gemma":0.000084314335,"teacher_disagreement_score":0.97873944,"about_ca_system_score_codex":0.000020151245,"about_ca_system_score_gemma":0.0000558637,"threshold_uncertainty_score":0.33555213},"labels":[],"label_agreement":null},{"id":"W4408355665","doi":"10.1109/icassp49660.2025.10890321","title":"Harnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Anomaly detection; Computer science; Artificial intelligence; Series (stratigraphy); Transformation (genetics); Time series; Anomaly (physics); Artificial neural network; Pattern recognition (psychology); Machine learning","score_opus":0.005278056470116585,"score_gpt":0.24113857490189586,"score_spread":0.23586051843177927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408355665","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":[0.014369392,0.000023648307,0.9814558,0.0009296538,0.000032152344,0.00026392002,6.3903644e-7,0.00038175686,0.0025430366],"genre_scores_gemma":[0.97629917,0.0000063865127,0.021922477,0.00008455428,0.00001072926,0.000105021114,0.0000013939439,0.0000021832504,0.0015680871],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995959,0.0000155815,0.00011593057,0.00014781122,0.000034401146,0.00009039678],"domain_scores_gemma":[0.9997826,0.000045301054,0.000035043522,0.000058948506,0.00005951147,0.000018569293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009218837,0.00006023605,0.00006908004,0.000076543474,0.00033597104,0.00014223927,0.000070672286,0.00003687257,0.0000033503075],"category_scores_gemma":[0.000011493843,0.000056818284,0.000027063432,0.00019228067,0.000024831339,0.00059289194,0.000019848116,0.000056100787,0.0000014654808],"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":[0.000021850294,0.00001299579,0.0001169955,0.000025090967,0.000011083078,1.0622864e-7,0.0002498321,0.000053991436,0.045497473,0.07564278,0.000044827088,0.87832296],"study_design_scores_gemma":[0.00029193386,0.00016081946,0.0017370523,0.000013184773,0.000011308444,0.000011532575,0.00012285127,0.7883699,0.18643202,0.006380987,0.01633363,0.00013476737],"about_ca_topic_score_codex":0.000009390842,"about_ca_topic_score_gemma":0.0000065021195,"teacher_disagreement_score":0.9619298,"about_ca_system_score_codex":0.000016556867,"about_ca_system_score_gemma":0.000012214386,"threshold_uncertainty_score":0.25840515},"labels":[],"label_agreement":null},{"id":"W4408359404","doi":"10.1109/vcc63113.2024.10914482","title":"Generative Adversarial Networks for IoT Security: A Convolutional Neural Network Approach","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; Brock University","funders":"","keywords":"Computer science; Adversarial system; Convolutional neural network; Generative grammar; Artificial intelligence; Generative adversarial network; Internet of Things; Machine learning; Deep learning; Computer security","score_opus":0.01582010493479685,"score_gpt":0.25362176562144967,"score_spread":0.23780166068665282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408359404","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":[0.000077742865,0.00033111344,0.99252915,0.0012815786,0.00053164014,0.00045327924,0.00000654472,0.0008310753,0.0039579016],"genre_scores_gemma":[0.57429016,0.000011060912,0.4209276,0.00077287055,0.0019629544,0.0006046533,0.00002615439,0.000013687411,0.001390868],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999057,0.000025459345,0.00016463299,0.000401661,0.00010632539,0.00024491065],"domain_scores_gemma":[0.99953866,0.00008840723,0.000026942454,0.00022208632,0.0000594856,0.00006444576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017928219,0.00011215711,0.000103335806,0.00004120574,0.00023774408,0.00022492369,0.00031810955,0.00008337779,0.000020390813],"category_scores_gemma":[0.0000038861326,0.00009746963,0.000119424236,0.0004091926,0.000042323532,0.0001571225,0.00011964198,0.00013930761,0.000008227993],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000038819794,0.000017085637,0.0000043734526,0.0000051819375,0.000018854205,5.43569e-7,0.00005055265,0.015302832,0.000014963862,0.9079984,0.06941759,0.007165769],"study_design_scores_gemma":[0.00008541962,0.00005069337,0.000013477103,0.0000032334995,0.0000067257474,0.000014297115,0.000006645563,0.90433013,0.00006925995,0.03363306,0.0616715,0.00011553423],"about_ca_topic_score_codex":0.00001127385,"about_ca_topic_score_gemma":0.000004049333,"teacher_disagreement_score":0.8890273,"about_ca_system_score_codex":0.00004112645,"about_ca_system_score_gemma":0.0000728259,"threshold_uncertainty_score":0.3974696},"labels":[],"label_agreement":null},{"id":"W4408482362","doi":"10.19139/soic-2310-5070-2259","title":"Abnormal Behavior Detection in Surveillance Systems Using a Hybrid EfficientNet-Transformer Model","year":2025,"lang":"en","type":"article","venue":"Statistics Optimization & Information Computing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Transformer; Computer science; Engineering; Electrical engineering","score_opus":0.009189233084896655,"score_gpt":0.2591130718710099,"score_spread":0.24992383878611324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408482362","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":[0.0032268013,0.000012506064,0.99460584,0.000024738401,0.00030609677,0.0006378014,0.000059935144,0.0003137132,0.000812559],"genre_scores_gemma":[0.63780403,0.000009337044,0.36198896,0.00007021706,0.000010006606,0.000037472524,0.000057878155,0.000005161804,0.000016936496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984306,0.000050355116,0.0007960968,0.00021343713,0.00024038643,0.00026912865],"domain_scores_gemma":[0.9989075,0.00007462235,0.00030074693,0.00027904444,0.00038972916,0.000048336184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042008833,0.00016418776,0.00017677165,0.0005351033,0.00036993425,0.00039459404,0.00031648306,0.00007169244,0.0000030105487],"category_scores_gemma":[0.000044946275,0.00019061223,0.000034770557,0.0010052039,0.000032038097,0.001115103,0.0000740504,0.00017279855,0.0000058857186],"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":[0.0000032814035,0.000023225135,0.00015631101,0.000033193814,0.0000035448395,3.1247615e-7,0.00012995646,0.9601209,0.000037172318,0.022304723,0.00004226304,0.017145086],"study_design_scores_gemma":[0.00029257013,0.000016487933,0.0003388578,0.00003234467,0.0000061043665,0.0000094189345,0.000041674204,0.9983327,0.00045217844,0.00014217281,0.0001559537,0.00017957494],"about_ca_topic_score_codex":0.00009509388,"about_ca_topic_score_gemma":0.000008249163,"teacher_disagreement_score":0.6345772,"about_ca_system_score_codex":0.00022236681,"about_ca_system_score_gemma":0.00013845622,"threshold_uncertainty_score":0.77729416},"labels":[],"label_agreement":null},{"id":"W4408519581","doi":"10.1109/trpms.2025.3551946","title":"Generative Inpainting-Based Anomaly Detection for CT Liver Tumor Detection","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Radiation and Plasma Medical Sciences","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"National Institute of Biomedical Imaging and Bioengineering; National Cancer Institute","keywords":"Inpainting; Anomaly detection; Artificial intelligence; Anomaly (physics); Pattern recognition (psychology); Generative grammar; Computer science; Computer vision; Image (mathematics); Physics","score_opus":0.015566042883510706,"score_gpt":0.266098715260233,"score_spread":0.2505326723767223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408519581","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":[0.03711501,0.00003122983,0.9590256,0.002373876,0.00049781974,0.00042204975,0.000006571098,0.0002748085,0.00025301127],"genre_scores_gemma":[0.9883815,0.000044125492,0.009855759,0.0011928614,0.000041743046,0.00034006336,7.2104336e-7,0.0000043076852,0.00013889074],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986016,0.00009109432,0.00026915487,0.00048223525,0.00033714526,0.00021875829],"domain_scores_gemma":[0.99909407,0.00044963247,0.000099270044,0.00015988617,0.00006108199,0.00013607087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006854788,0.00014065053,0.00013366001,0.00038046855,0.0011658346,0.00018335081,0.0003018421,0.00007699412,0.000034170345],"category_scores_gemma":[0.000053060194,0.0001251809,0.000086405,0.0010297927,0.00021274625,0.00032160056,0.0000029768216,0.00018602521,0.0000075111216],"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":[0.000016977505,0.00010019442,0.00002355127,0.000017904662,0.000013304177,0.0000014554796,0.00005158402,0.0024282911,0.0014833035,0.004254392,0.00006957099,0.9915395],"study_design_scores_gemma":[0.00034715506,0.0002572861,0.00023312855,0.000018338977,0.000011095376,0.000012634458,0.000018827463,0.7438845,0.24954662,0.0007007342,0.004851756,0.00011794649],"about_ca_topic_score_codex":0.000093018825,"about_ca_topic_score_gemma":0.00033263976,"teacher_disagreement_score":0.9914215,"about_ca_system_score_codex":0.00007473854,"about_ca_system_score_gemma":0.00020003866,"threshold_uncertainty_score":0.89667755},"labels":[],"label_agreement":null},{"id":"W4408520919","doi":"10.1109/cw64301.2024.00046","title":"A Latent Feature Space Transformation For Identity-Aware Controllable De-Identification","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Identification (biology); Identity (music); Computer science; Transformation (genetics); Feature (linguistics); Space (punctuation); Artificial intelligence; Pattern recognition (psychology); Physics","score_opus":0.01088608320344055,"score_gpt":0.27665928483494945,"score_spread":0.2657732016315089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408520919","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":[0.0005278146,0.00014406566,0.9849856,0.011783642,0.00013034583,0.00052038743,0.00000872888,0.0008766197,0.001022828],"genre_scores_gemma":[0.9402541,0.00006045117,0.051764745,0.00021607724,0.000062248866,0.0005131834,0.000014705005,0.000009228608,0.0071052713],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994069,0.000011660947,0.00013459339,0.00020745205,0.00010473862,0.00013467357],"domain_scores_gemma":[0.9996014,0.000033223918,0.000027744192,0.00021483276,0.00008329358,0.000039475526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026574754,0.000068267225,0.00006534088,0.000092255614,0.00014511665,0.0005707544,0.0002538523,0.000062969804,0.000014914277],"category_scores_gemma":[0.000005282582,0.000060896255,0.00007479667,0.00037201017,0.0000094922525,0.0010505579,0.000017781107,0.00006658268,0.000055862907],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000023102,0.000015182709,0.000005173942,0.000051972136,0.000011086989,3.890058e-7,0.0002309417,0.000051178828,0.008650525,0.9569216,0.012793284,0.0212664],"study_design_scores_gemma":[0.00018229452,0.000048781745,0.00020385374,0.000023762077,0.000019290737,0.000018593744,0.00004841739,0.73119515,0.061162785,0.072034545,0.13490732,0.00015518442],"about_ca_topic_score_codex":0.00002115452,"about_ca_topic_score_gemma":0.000019975188,"teacher_disagreement_score":0.9397263,"about_ca_system_score_codex":0.0000681235,"about_ca_system_score_gemma":0.00004464396,"threshold_uncertainty_score":0.5503798},"labels":[],"label_agreement":null},{"id":"W4408566453","doi":"10.1016/j.knosys.2025.113318","title":"Mitigating covariance overfitting in out-of-distribution detection through intrinsic parameter learning","year":2025,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Overfitting; Covariance; Artificial intelligence; Computer science; Distribution (mathematics); Machine learning; Mathematics; Statistics; Artificial neural network; Mathematical analysis","score_opus":0.018201092854781956,"score_gpt":0.2818398798960486,"score_spread":0.26363878704126664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408566453","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":[0.026652979,0.000330328,0.9696409,0.000070005364,0.00069960806,0.0004009511,0.0000029206797,0.00032552987,0.0018767428],"genre_scores_gemma":[0.9920336,0.000004495609,0.0074427235,0.000020925032,0.000055503482,0.00021606727,0.00000679913,0.000007843639,0.00021202136],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844474,0.00023633626,0.00056804955,0.00039444343,0.00012505284,0.0002313701],"domain_scores_gemma":[0.99872446,0.00037499124,0.0002803642,0.00038712524,0.00020386385,0.000029166033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006314307,0.00014463192,0.0002511826,0.00014954129,0.00020607983,0.00010508955,0.0003273402,0.000130721,0.0000014096283],"category_scores_gemma":[0.00024841435,0.0001548442,0.000084023675,0.0012569806,0.000047435446,0.00024349334,0.00008495832,0.00029264297,0.000016906504],"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":[0.00008257793,0.00092938833,0.023286937,0.0023761475,0.00013235449,0.00001200411,0.003714503,0.031866502,0.105551146,0.34874853,0.0010465058,0.4822534],"study_design_scores_gemma":[0.0008077015,0.00014858301,0.0024972279,0.0010709912,0.000015589636,0.0000034463653,0.00023711727,0.76894325,0.20087768,0.0022868572,0.022758873,0.0003526948],"about_ca_topic_score_codex":0.00017417579,"about_ca_topic_score_gemma":0.0000482426,"teacher_disagreement_score":0.96538067,"about_ca_system_score_codex":0.00023811386,"about_ca_system_score_gemma":0.00012574022,"threshold_uncertainty_score":0.6314363},"labels":[],"label_agreement":null},{"id":"W4408581865","doi":"10.3847/1538-4357/adb623","title":"Rare Event Classification with Weighted Logistic Regression for Identifying Repeating Fast Radio Bursts","year":2025,"lang":"en","type":"article","venue":"The Astrophysical Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Statistical Sciences Institute","keywords":"Physics; Logistic regression; Event (particle physics); Astrophysics; Astronomy; Pattern recognition (psychology); Artificial intelligence; Medicine; Internal medicine; Computer science","score_opus":0.032063333364822394,"score_gpt":0.31127479680912734,"score_spread":0.2792114634443049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408581865","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":[0.027646553,0.000027582415,0.9692572,0.0025439954,0.000103226645,0.00022491155,0.0000011867029,0.00009710193,0.00009827382],"genre_scores_gemma":[0.87484455,0.000007614017,0.124417655,0.0000648275,0.00016893272,0.000065914915,0.0000017226436,0.0000061623564,0.00042262694],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991147,0.00006705801,0.00023490016,0.00021210685,0.00019042233,0.00018084615],"domain_scores_gemma":[0.9990795,0.00009435246,0.00023881957,0.00037699094,0.00015741633,0.0000528871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019601596,0.000104021085,0.00012084878,0.00006441224,0.0007576699,0.0002512297,0.00059304334,0.000030544117,0.0000021444837],"category_scores_gemma":[0.000016121912,0.00005958162,0.000093316274,0.00036299083,0.00006332819,0.0001990775,0.00008575287,0.00026942373,0.000004033055],"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":[0.00012054998,0.00020278271,0.00021086853,0.00003068009,0.00007867185,0.000007903633,0.00033725443,0.00033633853,0.12624024,0.28426763,0.0027217842,0.5854453],"study_design_scores_gemma":[0.0018658973,0.0009438737,0.045708075,0.0011258973,0.00017991636,0.0003572617,0.0005498179,0.7752406,0.06042529,0.10795514,0.00505664,0.00059156213],"about_ca_topic_score_codex":0.0000024489568,"about_ca_topic_score_gemma":3.8711906e-7,"teacher_disagreement_score":0.847198,"about_ca_system_score_codex":0.000083946754,"about_ca_system_score_gemma":0.00006627309,"threshold_uncertainty_score":0.5827461},"labels":[],"label_agreement":null},{"id":"W4408592929","doi":"10.1016/j.iot.2025.101561","title":"Intelligent multi-sensor fusion and anomaly detection in vehicles via deep learning","year":2025,"lang":"en","type":"article","venue":"Internet of Things","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"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 Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centre of Innovation","keywords":"Anomaly detection; Deep learning; Artificial intelligence; Computer science; Sensor fusion; Fusion; Anomaly (physics); Computer vision; Physics","score_opus":0.010236166749702343,"score_gpt":0.25442006181117804,"score_spread":0.2441838950614757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408592929","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":[0.30502388,0.00008717603,0.6943017,0.00012332691,0.000044608787,0.000085370055,4.360046e-8,0.000093844224,0.00024005827],"genre_scores_gemma":[0.96507883,0.000039321934,0.034099773,0.00010316903,0.0000046451883,0.000017600736,2.5409528e-7,0.0000037724442,0.0006526556],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993449,0.00003292664,0.00022848025,0.00022889758,0.00006779015,0.00009704117],"domain_scores_gemma":[0.9996456,0.00004574833,0.000090659,0.00014975657,0.00004583113,0.00002241886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017724479,0.000076131364,0.000105883446,0.00021310251,0.000041185904,0.000044752593,0.0002505529,0.00006368568,0.000004103737],"category_scores_gemma":[0.0000304637,0.0000755023,0.000035665533,0.00025980893,0.000035770092,0.00019661522,0.00023562621,0.00017071783,0.0000044548256],"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":[0.0000137264415,0.00007022399,0.0057882937,0.00003188687,0.000009983441,0.0000018301523,0.0016888331,0.000048600585,0.08745238,0.003074345,0.000009941043,0.90180993],"study_design_scores_gemma":[0.00013546241,0.00009146075,0.012037483,0.00007232274,0.0000035579826,0.000007650997,0.000088343106,0.57782316,0.40616554,0.0014514808,0.0020328213,0.00009070372],"about_ca_topic_score_codex":0.0007760512,"about_ca_topic_score_gemma":0.000068048175,"teacher_disagreement_score":0.9017193,"about_ca_system_score_codex":0.000040036623,"about_ca_system_score_gemma":0.0000057722727,"threshold_uncertainty_score":0.30788943},"labels":[],"label_agreement":null},{"id":"W4408609032","doi":"10.1109/tii.2025.3545106","title":"Adaptive Ordinal Sample-Weighted Meta-ResNet for Fault Severity Classification Under Class Imbalance","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Artificial intelligence; Pattern recognition (psychology); Class (philosophy); Residual neural network; Computer science; Sample (material); Statistics; Data mining; Mathematics; Artificial neural network","score_opus":0.10626245156890182,"score_gpt":0.30957674545846814,"score_spread":0.20331429388956632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408609032","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":[0.00059158716,0.0000056951144,0.99452364,0.00157804,0.00054953794,0.0010038742,0.0002746698,0.0004290774,0.0010438786],"genre_scores_gemma":[0.87435174,0.000026275316,0.122146755,0.0011035292,0.00007789327,0.0013097287,0.000024229683,0.000013569899,0.00094630744],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848133,0.000053335913,0.0007019818,0.00024010247,0.00024322131,0.00028000385],"domain_scores_gemma":[0.99832124,0.000441453,0.0002697527,0.0006172696,0.00026081206,0.000089472116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031427297,0.00023454252,0.00032369554,0.00028287334,0.000527895,0.00018877715,0.000596157,0.00031961207,0.000028577206],"category_scores_gemma":[0.000015981099,0.00021297559,0.00026780268,0.0010136673,0.00008272912,0.00066801754,0.000006483958,0.00047463304,0.000018897632],"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":[0.00057871256,0.0011134902,0.000015131742,0.00010899758,0.0027668278,5.524661e-7,0.0008576697,0.048260037,0.0005550586,0.5026676,0.04537337,0.39770254],"study_design_scores_gemma":[0.0012736989,0.0003210874,0.00003121384,0.00003129999,0.00044032614,0.000004732843,0.00036513712,0.90047705,0.02291429,0.012658582,0.061077617,0.00040495556],"about_ca_topic_score_codex":0.000056933335,"about_ca_topic_score_gemma":0.00001914741,"teacher_disagreement_score":0.8737601,"about_ca_system_score_codex":0.0002141209,"about_ca_system_score_gemma":0.0002406148,"threshold_uncertainty_score":0.86848927},"labels":[],"label_agreement":null},{"id":"W4408784152","doi":"10.1007/s44443-025-00024-3","title":"A novel anomaly detection method for multivariate time series based on spatial-temporal graph learning","year":2025,"lang":"en","type":"article","venue":"Journal of King Saud University - Computer and Information Sciences","topic":"Anomaly Detection Techniques and Applications","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":"Artificial Intelligence in Medicine (Canada)","funders":"Major Science and Technology Projects in Yunnan Province","keywords":"Anomaly detection; Multivariate statistics; Series (stratigraphy); Computer science; Graph; Anomaly (physics); Time series; Artificial intelligence; Pattern recognition (psychology); Data mining; Machine learning; Geology; Theoretical computer science; Physics","score_opus":0.009271093367766232,"score_gpt":0.2448203633149212,"score_spread":0.23554926994715497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408784152","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020686816,0.0000024227265,0.9963491,0.0007694487,0.00013349186,0.000108648645,0.000001701641,0.000054067837,0.0005124198],"genre_scores_gemma":[0.41104743,0.0000049143505,0.5885746,0.00029381737,0.00002416546,5.4922083e-7,7.373594e-7,9.902494e-7,0.00005279934],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936,0.00004442729,0.00022825526,0.000104246596,0.00016598795,0.00009707385],"domain_scores_gemma":[0.9991428,0.00014019411,0.00036491838,0.00007367741,0.00023832064,0.0000400667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007471681,0.00007489517,0.00012127901,0.0006790134,0.00062419364,0.00024838652,0.0003282399,0.000041849344,0.0000013830318],"category_scores_gemma":[0.00002473698,0.00006758604,0.00007627229,0.0006354484,0.00005833417,0.0026691617,0.000071967086,0.00010944958,7.1827304e-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":[0.0001797137,0.00009660849,0.0011563606,0.00006116131,0.00004927873,0.0000014039273,0.0011359786,0.0690596,0.0032635739,0.04633868,0.00027879616,0.87837887],"study_design_scores_gemma":[0.00042114736,0.0005102041,0.0047171153,0.00004480155,0.000008833101,0.000015046206,0.000072865456,0.96870494,0.0015414917,0.00048132683,0.023402043,0.00008021194],"about_ca_topic_score_codex":0.000104210114,"about_ca_topic_score_gemma":0.000006465118,"teacher_disagreement_score":0.8996453,"about_ca_system_score_codex":0.00003998883,"about_ca_system_score_gemma":0.00009930171,"threshold_uncertainty_score":0.4800856},"labels":[],"label_agreement":null},{"id":"W4408892511","doi":"10.61091/jcmcc125-01","title":"Anomalous pattern recognition based on image recognition in food safety detection","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Food safety; Computer science; Image (mathematics); Computer vision; Biology; Food science","score_opus":0.01300222881501107,"score_gpt":0.24550135363917236,"score_spread":0.2324991248241613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408892511","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":[0.22093181,0.00002892662,0.773479,0.00028004762,0.0037078888,0.00034411222,0.0000029552675,0.000089270856,0.001136004],"genre_scores_gemma":[0.9845294,0.000017275552,0.015072261,0.00009304788,0.0002611827,0.000009508712,0.0000018669535,0.000013936459,0.000001557287],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980163,0.0001372294,0.0009904903,0.00027005502,0.0003532224,0.00023271203],"domain_scores_gemma":[0.9979759,0.00051306566,0.0007134998,0.00027672472,0.00043672387,0.00008412919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012468866,0.00021723234,0.00042714376,0.0005595685,0.0002490204,0.00025396983,0.0003979784,0.00016716473,0.0000032260884],"category_scores_gemma":[0.0002374826,0.00021600717,0.00014094738,0.00076878053,0.000040232895,0.00030602235,0.00012361782,0.0004828473,0.000003593181],"study_design_candidate":"theoretical_or_conceptual","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":[0.00028086262,0.0024492522,0.00022110462,0.0003886172,0.000093315364,0.000031168893,0.000575836,0.00008012709,0.0027310394,0.15485011,0.00012846738,0.8381701],"study_design_scores_gemma":[0.0050171167,0.002143301,0.00065365434,0.0008990387,0.000039577782,0.000044176963,0.00009411352,0.117032565,0.01919401,0.8540964,0.00038932575,0.00039670407],"about_ca_topic_score_codex":0.000009950869,"about_ca_topic_score_gemma":0.000001694992,"teacher_disagreement_score":0.8377734,"about_ca_system_score_codex":0.000180824,"about_ca_system_score_gemma":0.00010773219,"threshold_uncertainty_score":0.8808517},"labels":[],"label_agreement":null},{"id":"W4409014847","doi":"10.1109/tim.2025.3551832","title":"AADC-Net: A Multimodal Deep Learning Framework for Automatic Anomaly Detection in Real-Time Surveillance","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Tellabs (Canada)","funders":"National Research Foundation of Korea","keywords":"Anomaly detection; Computer science; Artificial intelligence; Object detection; Deep learning; Computer vision; Real-time computing; Pattern recognition (psychology)","score_opus":0.016974583546768597,"score_gpt":0.2722687804763156,"score_spread":0.25529419692954697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409014847","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":[0.05498709,0.000014715284,0.9431394,0.0003636056,0.00020458526,0.00074070867,0.0000025172897,0.00032348407,0.00022391559],"genre_scores_gemma":[0.9284421,0.0000795568,0.070528835,0.00010775783,0.000009732204,0.0007255926,0.000001208124,0.000008837373,0.000096380456],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987881,0.00008613487,0.00033536722,0.00036882292,0.00022935244,0.0001921755],"domain_scores_gemma":[0.9994299,0.00009264775,0.00010216544,0.00021207746,0.00010979021,0.000053445114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043310213,0.00014868265,0.00015758937,0.0003194155,0.00035332236,0.00009164306,0.0001319628,0.00009616551,0.000014230817],"category_scores_gemma":[0.000014249087,0.00016159726,0.000066874156,0.0005322195,0.000029603143,0.00021155109,0.0000025335432,0.0001771012,0.00000760268],"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":[0.000040061375,0.0001757873,0.00018035124,0.000051247825,0.00003618856,2.7819746e-7,0.00036515848,0.0022039055,0.020691996,0.0034392416,0.00000616427,0.9728096],"study_design_scores_gemma":[0.0019255414,0.0004996439,0.016857477,0.00019056103,0.00003113616,0.000006099567,0.00028664587,0.79188776,0.1827275,0.004281691,0.000889065,0.00041690373],"about_ca_topic_score_codex":0.000117971205,"about_ca_topic_score_gemma":0.00024758046,"teacher_disagreement_score":0.97239274,"about_ca_system_score_codex":0.00027602518,"about_ca_system_score_gemma":0.000047523725,"threshold_uncertainty_score":0.65897447},"labels":[],"label_agreement":null},{"id":"W4409069510","doi":"10.1186/s40537-025-01122-9","title":"SPINEX-anomaly: similarity-based predictions with explainable neighbors exploration for anomaly and outlier detection","year":2025,"lang":"en","type":"article","venue":"Journal Of Big Data","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Clemson University","keywords":"Anomaly detection; Anomaly (physics); Outlier; Computer science; Similarity (geometry); Data mining; Computational Science and Engineering; Artificial intelligence; Pattern recognition (psychology); Machine learning; Image (mathematics)","score_opus":0.06406157203631375,"score_gpt":0.29009704088610744,"score_spread":0.2260354688497937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409069510","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":[0.0044153086,0.00008387558,0.9918205,0.0028925142,0.0002070479,0.00029307633,0.000038410602,0.00007294071,0.0001763305],"genre_scores_gemma":[0.85527724,0.00004076646,0.14397982,0.00030044606,0.00016724107,0.00005804913,0.000019225961,0.000008680406,0.00014852264],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990622,0.000030519936,0.00034484421,0.00027016713,0.0001556646,0.00013660571],"domain_scores_gemma":[0.9986086,0.000073542775,0.0002952071,0.0006711386,0.00028265882,0.00006884602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043128972,0.000108459615,0.00016183795,0.00031632165,0.0003125457,0.00019768869,0.0006045735,0.00006681051,0.0000015924217],"category_scores_gemma":[0.00006548352,0.000090104084,0.00004155068,0.00054338604,0.00004260776,0.0013305403,0.00014212808,0.00014649026,5.3327165e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009473196,0.0014739402,0.0068554305,0.00047874678,0.0006145846,0.000035800564,0.000506711,0.0049242214,0.020154402,0.056307312,0.041502476,0.8661991],"study_design_scores_gemma":[0.0032618293,0.0025846106,0.008819968,0.0002623007,0.00033182607,0.00015407187,0.00033071145,0.5778081,0.051746752,0.020002814,0.33414915,0.0005478757],"about_ca_topic_score_codex":0.000021260881,"about_ca_topic_score_gemma":0.0000594053,"teacher_disagreement_score":0.8656512,"about_ca_system_score_codex":0.000052004867,"about_ca_system_score_gemma":0.00018879985,"threshold_uncertainty_score":0.3674338},"labels":[],"label_agreement":null},{"id":"W4409075946","doi":"10.1007/978-3-031-82362-6_5","title":"Deep Learning for Network Anomaly Detection Under Data Contamination: Evaluating Robustness and Mitigating Performance Degradation","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Robustness (evolution); Anomaly detection; Contamination; Degradation (telecommunications); Artificial intelligence; Data mining; Real-time computing; Telecommunications","score_opus":0.034434955953586685,"score_gpt":0.289236039674537,"score_spread":0.2548010837209503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409075946","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006828972,0.0003182996,0.99702215,0.00026885423,0.0004239784,0.0007194096,0.0000017981956,0.00023109515,0.00033150672],"genre_scores_gemma":[0.3013712,0.00006625253,0.6975555,0.00023946093,0.00032090262,0.00007167979,0.00003281903,0.000019252606,0.0003229595],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973605,0.000036533318,0.00047400212,0.0013251846,0.00041669403,0.0003870747],"domain_scores_gemma":[0.99755484,0.00067266717,0.00040311555,0.0009364123,0.00036576224,0.000067209156],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015422347,0.00031229982,0.0003000491,0.000360667,0.0010264001,0.00051341875,0.0015102369,0.00023516499,0.0000027311921],"category_scores_gemma":[0.00013782359,0.00032708916,0.000046428926,0.00062840065,0.00023248172,0.00093676004,0.001099758,0.00052811514,9.761741e-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":[0.000002541917,0.000003856179,0.000056777903,0.0000417352,0.0000051857733,3.3959526e-7,0.000039330007,0.21098948,0.00006377812,0.0041878955,0.0000021574263,0.78460693],"study_design_scores_gemma":[0.00017150443,0.00017147511,0.000497041,0.00028693356,0.000017611406,0.000022277984,6.7419523e-7,0.9852169,0.00083448546,0.012152503,0.00029781222,0.0003307407],"about_ca_topic_score_codex":0.000011475673,"about_ca_topic_score_gemma":0.00010942084,"teacher_disagreement_score":0.7842762,"about_ca_system_score_codex":0.00020752884,"about_ca_system_score_gemma":0.00022163184,"threshold_uncertainty_score":0.9999181},"labels":[],"label_agreement":null},{"id":"W4409149437","doi":"10.1016/j.jfds.2025.100163","title":"Finding a needle in a haystack: A machine learning framework for anomaly detection in payment systems","year":2025,"lang":"en","type":"article","venue":"The Journal of Finance and Data Science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Bank of Canada","funders":"","keywords":"Haystack; Payment; Anomaly detection; Computer science; Anomaly (physics); Artificial intelligence; Machine learning; World Wide Web","score_opus":0.029643996272867908,"score_gpt":0.3219928109928626,"score_spread":0.2923488147199947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409149437","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":[0.1244816,0.0006635582,0.87400275,0.0005637752,0.00008850645,0.00015354634,0.000002819627,0.000008160154,0.000035267498],"genre_scores_gemma":[0.979989,0.00047319697,0.01942345,0.000055559187,0.00001392131,0.000010603318,1.7205116e-7,0.0000013426418,0.00003276457],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925417,0.000032548953,0.0002675755,0.00016874101,0.00012863577,0.00014831635],"domain_scores_gemma":[0.9992164,0.00016988657,0.00019715096,0.00034570848,0.000052836363,0.000018031573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024923594,0.000053711698,0.000108638786,0.00026689406,0.00024679105,0.00013278787,0.001125595,0.000025230362,2.2869767e-7],"category_scores_gemma":[0.00017147706,0.000037773843,0.000012379673,0.0013236551,0.00008095672,0.00085626333,0.000290572,0.00021839485,1.97376e-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":[0.00019180131,0.0002492755,0.014991692,0.00012651915,0.000014377136,0.000013207026,0.0033839806,0.011067841,0.032131996,0.35001156,0.0002212134,0.58759654],"study_design_scores_gemma":[0.00040227507,0.00027683252,0.021546682,0.00044650096,0.000007685107,0.00008621395,0.00042198153,0.9487179,0.0057663224,0.013974583,0.008222888,0.00013014695],"about_ca_topic_score_codex":0.00012329587,"about_ca_topic_score_gemma":0.000045172543,"teacher_disagreement_score":0.93765,"about_ca_system_score_codex":0.00005717695,"about_ca_system_score_gemma":0.00011404691,"threshold_uncertainty_score":0.20916541},"labels":[],"label_agreement":null},{"id":"W4409187585","doi":"10.1002/9781394294404.ch11","title":"Anomaly Detection and Evaluation Metrics","year":2025,"lang":"en","type":"other","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of British Columbia","funders":"","keywords":"Anomaly detection; Anomaly (physics); Computer science; Data mining; Physics","score_opus":0.016062095736707933,"score_gpt":0.28364648356681377,"score_spread":0.26758438783010585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409187585","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000013291343,0.0002492528,0.5365779,0.00006908102,0.000067578854,0.00021619847,0.0000012281391,0.0004920964,0.46232536],"genre_scores_gemma":[0.012126911,0.00028780982,0.08001314,0.00017318294,0.00007974967,0.00019886282,0.0000030963513,0.00004996156,0.9070673],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992892,0.000029013196,0.000105706706,0.00032552093,0.00017051608,0.00008002894],"domain_scores_gemma":[0.99941033,0.000021125792,0.00008836779,0.0003843526,0.00006568306,0.000030143434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018663138,0.00010613721,0.00010155826,0.00052810466,0.00005692827,0.000083032326,0.00022248327,0.00016705625,0.00025715583],"category_scores_gemma":[0.000019928802,0.000100526915,0.00003076789,0.0006632271,0.000015325866,0.00005916169,0.000113209106,0.00008079901,0.000031714575],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.7731164e-7,0.000013823082,0.0000116845595,0.000014807221,0.000015923852,1.5965965e-7,0.0000040464574,4.544897e-7,0.0000689161,0.04761389,0.091083005,0.86117303],"study_design_scores_gemma":[0.000103852086,0.000034527802,0.00022497948,0.000019567266,0.000031804222,0.000003804635,0.000003159207,0.046878945,0.0017934892,0.0052322247,0.94548166,0.00019196542],"about_ca_topic_score_codex":0.0002963967,"about_ca_topic_score_gemma":0.000116797586,"teacher_disagreement_score":0.86098105,"about_ca_system_score_codex":0.000038957518,"about_ca_system_score_gemma":0.0000542007,"threshold_uncertainty_score":0.40993688},"labels":[],"label_agreement":null},{"id":"W4409204231","doi":"10.23977/jaip.2025.080118","title":"E-MART: An Improved Misclassification Aware Adversarial Training with Entropy-Based Uncertainty Measure","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Adversarial system; Measure (data warehouse); Computer science; Artificial intelligence; Entropy (arrow of time); Statistics; Machine learning; Mathematics; Econometrics; Data mining","score_opus":0.04528000276109503,"score_gpt":0.33047002718029067,"score_spread":0.28519002441919566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409204231","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":[0.0014464371,0.000028049353,0.98770565,0.009596163,0.00032662638,0.00022166339,0.0000014694928,0.00008809965,0.0005858324],"genre_scores_gemma":[0.8656411,0.000012231609,0.1334655,0.00063778646,0.0001791041,0.00001748319,0.0000013588312,0.000007801965,0.000037596375],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823517,0.00021707323,0.000665323,0.00029203467,0.00037620615,0.00021421861],"domain_scores_gemma":[0.9969186,0.0004256255,0.00085575896,0.00044309258,0.0012308665,0.00012603085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00131122,0.00015211309,0.00020913835,0.0002461733,0.00029652903,0.00032370613,0.00075071317,0.00010019638,0.000017519094],"category_scores_gemma":[0.0005844627,0.00012553662,0.000101764905,0.00082174514,0.00008880468,0.0015232022,0.00003517917,0.00045396324,0.0000074480104],"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":[0.0011938147,0.0007881214,0.000024413992,0.000018658058,0.00012234102,0.00002602021,0.0016650722,0.015412599,0.034390192,0.14858957,0.00025693662,0.79751223],"study_design_scores_gemma":[0.00022401434,0.0014892819,0.00006393486,0.00013456219,0.00017813557,0.00011638976,0.005468964,0.8385878,0.10742637,0.015321682,0.030621476,0.0003673658],"about_ca_topic_score_codex":0.00005993579,"about_ca_topic_score_gemma":0.00004640469,"teacher_disagreement_score":0.8641947,"about_ca_system_score_codex":0.00014642757,"about_ca_system_score_gemma":0.00082124065,"threshold_uncertainty_score":0.5119235},"labels":[],"label_agreement":null},{"id":"W4409262152","doi":"10.1109/wacv61041.2025.00915","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Information and Communications Technology Council; National Research Foundation of Korea","keywords":"Anomaly detection; Computer science; Unsupervised learning; Artificial intelligence; Training (meteorology); Training set; Machine learning; Pattern recognition (psychology)","score_opus":0.04135908940490878,"score_gpt":0.2839425167875609,"score_spread":0.2425834273826521,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409262152","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":[0.0056600943,0.000027645357,0.9881532,0.00073263823,0.0000524067,0.00038754358,0.0000025094255,0.0008226313,0.004161284],"genre_scores_gemma":[0.81316155,0.0000059218837,0.18365327,0.00027843183,0.000023142375,0.00016662398,0.000010891285,0.000008160901,0.0026920105],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897754,0.000022062482,0.00017782203,0.000515467,0.00010189275,0.00020523481],"domain_scores_gemma":[0.9989116,0.00008266814,0.000059524828,0.0008067171,0.0000948029,0.000044643817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026358082,0.00011363093,0.00012151555,0.00013909503,0.00039299423,0.00016831486,0.0008477139,0.000058923375,0.000011559266],"category_scores_gemma":[0.000027322725,0.00009763561,0.00003510482,0.00063443504,0.000027307091,0.0004610845,0.00022616498,0.00012909899,0.000006593998],"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":[0.000029781204,0.00004481698,0.00019392464,0.000027087073,0.00003897041,8.6521834e-7,0.00021862322,0.00021412622,0.019273182,0.021505078,0.0005245504,0.957929],"study_design_scores_gemma":[0.0006859179,0.0004377902,0.0020081876,0.000033562035,0.00003130659,0.000017878445,0.00042897515,0.769898,0.038909033,0.002442592,0.18476427,0.00034250345],"about_ca_topic_score_codex":0.00004116589,"about_ca_topic_score_gemma":0.0001042926,"teacher_disagreement_score":0.95758647,"about_ca_system_score_codex":0.000029281297,"about_ca_system_score_gemma":0.00008717726,"threshold_uncertainty_score":0.39814645},"labels":[],"label_agreement":null},{"id":"W4409262526","doi":"10.1109/wacv61041.2025.00614","title":"Assessing Visually-Continuous Corruption Robustness of Neural Networks Relative to Human Performance","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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 Toronto","funders":"","keywords":"Robustness (evolution); Computer science; Artificial neural network; Artificial intelligence; Language change","score_opus":0.01966534667968344,"score_gpt":0.31760149622141026,"score_spread":0.2979361495417268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409262526","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":[0.20213813,0.0000066600455,0.7920047,0.0001974276,0.00007771749,0.00015983116,1.3395533e-7,0.0002036129,0.005211774],"genre_scores_gemma":[0.95957905,0.0000018422843,0.038913034,0.00015240702,0.000023157283,0.000040029037,0.0000013491447,0.0000033834217,0.0012857469],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993196,0.000024255301,0.00022458193,0.00021895413,0.00008400497,0.0001285911],"domain_scores_gemma":[0.9994335,0.00003243584,0.00008479397,0.0002935258,0.00012316725,0.000032551645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012342734,0.000077202785,0.000116822484,0.000117031144,0.00019345387,0.0001034401,0.0003483676,0.00005292553,0.000008050795],"category_scores_gemma":[0.000006756216,0.00007194957,0.00003952867,0.0005562673,0.000026284582,0.000591182,0.00016390588,0.00010579291,0.000001356028],"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":[0.000009149756,0.00018517535,0.00569248,0.00003777597,0.00002765643,8.165304e-7,0.00020992543,0.13722226,0.007907534,0.32679313,0.0017060575,0.52020806],"study_design_scores_gemma":[0.00006947717,0.00007812459,0.029186426,0.00003038445,0.000004658986,0.0000015214632,0.00002727968,0.9653037,0.004654657,0.00024927084,0.00030350513,0.00009097655],"about_ca_topic_score_codex":0.000024802097,"about_ca_topic_score_gemma":0.0000067042283,"teacher_disagreement_score":0.8280815,"about_ca_system_score_codex":0.000030953564,"about_ca_system_score_gemma":0.0000154259,"threshold_uncertainty_score":0.29340184},"labels":[],"label_agreement":null},{"id":"W4409263324","doi":"10.1109/wacv61041.2025.00720","title":"Robust Novelty Detection Through Style-Conscious Feature Ranking","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Novelty; Computer science; Artificial intelligence; Ranking (information retrieval); Style (visual arts); Novelty detection; Feature (linguistics); Pattern recognition (psychology); Machine learning; Data mining; Psychology","score_opus":0.01660168822541867,"score_gpt":0.24960228782948715,"score_spread":0.2330005996040685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409263324","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":[0.0011647694,0.00007709289,0.95710945,0.0026158935,0.00018512322,0.00020243633,7.33789e-7,0.0009895124,0.037655],"genre_scores_gemma":[0.797841,0.000027945749,0.19369273,0.0014345637,0.000041319956,0.00008143557,8.659191e-7,0.0000053809626,0.006874766],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99924123,0.000018692594,0.00013233774,0.00033817254,0.00010545837,0.00016412845],"domain_scores_gemma":[0.9993248,0.00003989144,0.000049878385,0.00048428169,0.000076647186,0.000024530533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000106666215,0.00010303608,0.000097640805,0.00008737292,0.00031907592,0.0001492533,0.0004443102,0.00010003674,0.000016743032],"category_scores_gemma":[0.0000129572345,0.00009252032,0.000068836525,0.00082597305,0.000029671257,0.00033122933,0.00015540294,0.00016857681,0.00002693846],"study_design_candidate":"theoretical_or_conceptual","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":[0.00000816129,0.00010609463,0.00018608476,0.000022288885,0.000039156716,0.0000025632398,0.00022714517,0.00032290665,0.018793443,0.5465803,0.015655179,0.41805667],"study_design_scores_gemma":[0.0007429656,0.00010388583,0.0037793836,0.000050923787,0.00002748357,0.000037949107,0.00012379963,0.15361051,0.3525378,0.038318176,0.45007947,0.000587655],"about_ca_topic_score_codex":0.00009396855,"about_ca_topic_score_gemma":0.000084735664,"teacher_disagreement_score":0.7966762,"about_ca_system_score_codex":0.00005653762,"about_ca_system_score_gemma":0.000034667763,"threshold_uncertainty_score":0.3772869},"labels":[],"label_agreement":null},{"id":"W4409263528","doi":"10.1109/wacv61041.2025.00416","title":"Graph-Jigsaw Conditioned Diffusion Model for Skeleton-Based Video Anomaly Detection","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"McGill University","funders":"","keywords":"Jigsaw; Computer science; Skeleton (computer programming); Anomaly detection; Graph; Artificial intelligence; Computer vision; Theoretical computer science; Mathematics; Programming language","score_opus":0.009966007387345321,"score_gpt":0.2585916027721452,"score_spread":0.2486255953847999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409263528","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":[0.0059930296,0.000011473829,0.98890847,0.0013912386,0.000090118556,0.00059962296,0.000008361168,0.0008488654,0.0021488136],"genre_scores_gemma":[0.87818605,0.0000037312816,0.11775079,0.0012567886,0.000016068087,0.0006819661,0.000009322877,0.0000068997565,0.0020883945],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904954,0.000017026137,0.00023616909,0.00040088364,0.00010915236,0.00018720949],"domain_scores_gemma":[0.9991432,0.00009196392,0.00007567554,0.00048789268,0.00014819462,0.00005310185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013149827,0.00012664951,0.00012194706,0.00027034382,0.00041839867,0.00010359116,0.00038344946,0.000098838274,0.000012970082],"category_scores_gemma":[0.00001680499,0.00012078426,0.00014244103,0.00063348346,0.000035358215,0.00022061137,0.00006624065,0.00007520955,0.000008742501],"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":[0.000046271856,0.0003596267,0.0001990389,0.00007322136,0.000032841996,5.2721964e-7,0.000055131666,0.0058784983,0.10902225,0.7320753,0.008962705,0.14329459],"study_design_scores_gemma":[0.000331878,0.000058617636,0.00042535574,0.000008262685,0.000007736945,6.280202e-7,0.000004194553,0.84076655,0.10372845,0.05176509,0.002782424,0.000120791745],"about_ca_topic_score_codex":0.00004708588,"about_ca_topic_score_gemma":0.00006256565,"teacher_disagreement_score":0.872193,"about_ca_system_score_codex":0.00005951387,"about_ca_system_score_gemma":0.00007635573,"threshold_uncertainty_score":0.4925439},"labels":[],"label_agreement":null},{"id":"W4409282063","doi":"10.3390/s25082388","title":"Ensemble Machine Learning Models Utilizing a Hybrid Recursive Feature Elimination (RFE) Technique for Detecting GPS Spoofing Attacks Against Unmanned Aerial Vehicles","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Anomaly Detection Techniques and Applications","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":"Queen's University","funders":"","keywords":"Spoofing attack; Computer science; Global Positioning System; Artificial intelligence; Machine learning; Random forest; Decision tree; Ensemble learning; Intrusion detection system; Data mining; Pattern recognition (psychology); Computer security","score_opus":0.020961872574740508,"score_gpt":0.26996949194549,"score_spread":0.24900761937074953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409282063","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":[0.07184395,0.00009849891,0.9231021,0.0010600142,0.00015193367,0.001031706,0.000007902661,0.0007772441,0.0019266484],"genre_scores_gemma":[0.8656672,0.000049888844,0.13297142,0.00017524151,0.00008204243,0.00031753242,0.000015526333,0.000023752385,0.0006973522],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984905,0.00009472032,0.0003012704,0.000581109,0.00016150968,0.00037085288],"domain_scores_gemma":[0.9989204,0.00021896826,0.000216458,0.00039515304,0.00018870084,0.000060352628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046660702,0.00022509617,0.00023278237,0.000312231,0.0007764509,0.00016619002,0.00042469054,0.00015522238,0.0000011414554],"category_scores_gemma":[0.00014544357,0.00024266416,0.00015296225,0.00059507013,0.000037570524,0.00029561587,0.00017227074,0.00040095655,0.000002325633],"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":[0.00018091609,0.00013360957,0.000071435265,0.00021441218,0.000100558995,0.000019066085,0.001359884,0.044515036,0.40390852,0.11188098,0.0013821336,0.43623343],"study_design_scores_gemma":[0.00024022456,0.000081228136,0.00001874235,0.000104244216,0.000014635262,0.00001438503,0.00015601987,0.5012388,0.48066616,0.013105214,0.0041362727,0.00022407822],"about_ca_topic_score_codex":0.00003378364,"about_ca_topic_score_gemma":0.000014386997,"teacher_disagreement_score":0.7938233,"about_ca_system_score_codex":0.00014503884,"about_ca_system_score_gemma":0.00005831849,"threshold_uncertainty_score":0.9895557},"labels":[],"label_agreement":null},{"id":"W4409321824","doi":"10.5220/0013367000003944","title":"A Comparative Study of Log-Based Anomaly Detection Methods in Real-World System Logs","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Anomaly detection; Computer science; Anomaly (physics); Data mining","score_opus":0.04525444307376378,"score_gpt":0.39929084202948084,"score_spread":0.35403639895571704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409321824","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":[0.068263836,0.000006988459,0.9118585,0.000056222503,0.0000564291,0.00061888964,4.1036492e-7,0.0003349689,0.018803751],"genre_scores_gemma":[0.86904633,4.699804e-7,0.13027628,0.000030261419,0.000003917904,0.0002305428,2.1641986e-7,0.0000023801588,0.00040958755],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988271,0.00025585075,0.00038622937,0.00032020503,0.000094077186,0.0001165555],"domain_scores_gemma":[0.99908864,0.00016320846,0.0001264464,0.0004983331,0.00009836396,0.000025005545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046589994,0.00009976566,0.00026282875,0.00052451884,0.00007836459,0.00003156853,0.0003845962,0.000040201085,0.000003960865],"category_scores_gemma":[0.0000053966605,0.00009056353,0.00004543854,0.0020658425,0.000026452828,0.000102818514,0.000096524825,0.000103153936,0.000003063124],"study_design_candidate":"theoretical_or_conceptual","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":[0.00015525742,0.004864023,0.044904646,0.00034150644,0.00020413296,0.000012537047,0.003202992,0.005984893,0.06359497,0.7134429,0.0005396109,0.16275251],"study_design_scores_gemma":[0.0009016361,0.0005834989,0.08390498,0.0000605122,0.00002189594,0.0000014972219,0.0018855896,0.54727703,0.36328465,0.0012753791,0.0005765521,0.00022677594],"about_ca_topic_score_codex":0.0023898683,"about_ca_topic_score_gemma":0.0039416696,"teacher_disagreement_score":0.8007825,"about_ca_system_score_codex":0.0001312578,"about_ca_system_score_gemma":0.000054554905,"threshold_uncertainty_score":0.36930737},"labels":[],"label_agreement":null},{"id":"W4409356433","doi":"10.1109/tip.2025.3558089","title":"CRCL: Causal Representation Consistency Learning for Anomaly Detection in Surveillance Videos","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Ministry of Education-China Mobile Research Fund Project; National Key Research and Development Program of China; National Natural Science Foundation of China; Shanghai Key Basic Research Program","keywords":"Anomaly detection; Computer science; Artificial intelligence; Representation (politics); Consistency (knowledge bases); Pattern recognition (psychology); Computer vision; Machine learning","score_opus":0.014795616016648309,"score_gpt":0.298702768909442,"score_spread":0.28390715289279367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409356433","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":[0.0082180565,0.000050257084,0.9894128,0.0005229489,0.0001656535,0.00047795227,0.0000021946903,0.0004966407,0.000653516],"genre_scores_gemma":[0.95782465,0.000019714891,0.040857032,0.000108600565,0.00001691126,0.0005664051,0.0000011555634,0.000012284916,0.0005932234],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987529,0.0000666114,0.00034792937,0.0004872122,0.000121120705,0.00022420613],"domain_scores_gemma":[0.9992304,0.00015236608,0.000121521254,0.00026399558,0.00019559795,0.00003611293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002624454,0.00014092443,0.00016386551,0.00039531558,0.00058192934,0.00023447943,0.00022980418,0.00008718993,0.000004216292],"category_scores_gemma":[0.000028052304,0.00015922228,0.00008554599,0.0011696627,0.000057311536,0.0006495667,0.0000032721746,0.00026497152,0.000005432496],"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":[0.00005216836,0.00015808859,0.0002079114,0.0001104333,0.000013721302,0.0000021600588,0.00024076996,0.0046528024,0.10624416,0.0003484184,0.000029940264,0.8879394],"study_design_scores_gemma":[0.00061181904,0.00011644958,0.0016865748,0.00009936436,0.000013570442,0.000012376536,0.00013550787,0.40117043,0.5928635,0.00234229,0.0006846827,0.00026344502],"about_ca_topic_score_codex":0.0000936346,"about_ca_topic_score_gemma":0.00015102395,"teacher_disagreement_score":0.9496066,"about_ca_system_score_codex":0.00012171663,"about_ca_system_score_gemma":0.00011107938,"threshold_uncertainty_score":0.6492896},"labels":[],"label_agreement":null},{"id":"W4409460571","doi":"10.1145/3720548","title":"The Comp-TSSs Scheme for Anomaly Detection in AI-Powered Autonomous Driving","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Autonomous and Adaptive Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Scheme (mathematics); Anomaly detection; Real-time computing; Artificial intelligence; Distributed computing","score_opus":0.013584605791569995,"score_gpt":0.2610005327847511,"score_spread":0.2474159269931811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409460571","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":[0.0037620834,0.00028039524,0.9916639,0.0016752385,0.0004531506,0.0011593138,0.000011656998,0.00034507684,0.0006491993],"genre_scores_gemma":[0.98579645,0.00006039898,0.011295966,0.00012406682,0.000027765192,0.0011979451,0.00000100631,0.000014160731,0.0014822131],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850714,0.0000797163,0.0004500779,0.0005288444,0.00011129142,0.00032292292],"domain_scores_gemma":[0.9985843,0.00037089305,0.00012883986,0.00072861946,0.000119300734,0.000068059046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039612563,0.0002109049,0.00024585205,0.00026276708,0.0010386199,0.0003379542,0.0006098044,0.0001286657,0.0000015153096],"category_scores_gemma":[0.000018879404,0.00017867851,0.00011524519,0.0005754507,0.00009108429,0.00027841673,0.000030100799,0.00027893507,0.0000054927673],"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":[0.000090592104,0.00032671486,0.000197223,0.000068032794,0.00017097792,0.0000030562908,0.000425165,0.0026826973,0.005057487,0.25352553,0.00016568419,0.73728687],"study_design_scores_gemma":[0.0014382016,0.00076557556,0.005147109,0.00021491137,0.000040053834,0.000043123997,0.000846721,0.8428775,0.014942442,0.012871247,0.12012487,0.0006882426],"about_ca_topic_score_codex":0.00032829063,"about_ca_topic_score_gemma":0.00039019505,"teacher_disagreement_score":0.9820344,"about_ca_system_score_codex":0.00023115908,"about_ca_system_score_gemma":0.00012104425,"threshold_uncertainty_score":0.79883295},"labels":[],"label_agreement":null},{"id":"W4409478867","doi":"10.1145/3729533","title":"Evaluating API-Level Deep Learning Fuzzers: A Comprehensive Benchmarking Study","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Benchmarking; Deep learning; Artificial intelligence; Software engineering; Machine learning; Data science","score_opus":0.16246850762860587,"score_gpt":0.392204384378163,"score_spread":0.22973587674955714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409478867","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031148257,0.00016147103,0.9672189,0.000121200734,0.0002841033,0.0002850941,0.0000011633836,0.00076593895,0.0000138927735],"genre_scores_gemma":[0.27406132,0.00003601756,0.725509,0.00008960723,0.000017599481,0.00016827972,7.750582e-7,0.00000937207,0.00010805516],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868834,0.00027032365,0.00024583988,0.00045664347,0.00011045632,0.0002283771],"domain_scores_gemma":[0.99730295,0.0020026097,0.00005521025,0.0004888744,0.000092870345,0.00005747941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006426266,0.0001723561,0.00024476738,0.0003395655,0.0003826034,0.000057752382,0.00036738988,0.00010155621,0.000008515324],"category_scores_gemma":[0.0003853727,0.00018276836,0.00006806474,0.0005683123,0.00002705402,0.00011507507,0.000046025852,0.0004457548,0.0000028518623],"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":[0.000009402515,0.00007822061,0.0002105447,0.000029109,0.00008987127,0.0000026159587,0.0008101516,0.06342825,0.001276885,0.0014072852,0.00000461475,0.93265307],"study_design_scores_gemma":[0.003530667,0.0043075182,0.055760343,0.00035720156,0.00046959805,0.0002380605,0.0034106765,0.8894279,0.01605471,0.009477514,0.015011156,0.0019546456],"about_ca_topic_score_codex":0.00003542308,"about_ca_topic_score_gemma":0.0000051926845,"teacher_disagreement_score":0.9306984,"about_ca_system_score_codex":0.0000454295,"about_ca_system_score_gemma":0.000029171253,"threshold_uncertainty_score":0.74530774},"labels":[],"label_agreement":null},{"id":"W4409507112","doi":"10.1145/3729222","title":"Networking Systems for Video Anomaly Detection: A Tutorial and Survey","year":2025,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":51,"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; University of Toronto; University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Computer science; Anomaly detection; Data science; Artificial intelligence","score_opus":0.06659610954253416,"score_gpt":0.3443571388472372,"score_spread":0.27776102930470303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409507112","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000015900665,0.482004,0.5138106,0.0000047359704,0.0028204487,0.0008958699,0.000023616929,0.0003908717,0.0000483151],"genre_scores_gemma":[0.0008576546,0.98436177,0.011190805,0.000030776155,0.002688732,0.0004491645,0.00007250923,0.00005694449,0.00029162716],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99562055,0.0017551534,0.00089915504,0.0010752769,0.0001950992,0.00045478705],"domain_scores_gemma":[0.99193317,0.005605979,0.00066127005,0.0014346403,0.0002600449,0.00010492056],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0061052623,0.00047310934,0.0013501582,0.00030059816,0.00064254215,0.00069338287,0.001691523,0.00034293393,4.0192097e-7],"category_scores_gemma":[0.0006199186,0.00045766847,0.00031930252,0.0013684485,0.000048213602,0.00013273043,0.0011696229,0.00037725366,0.0000053172116],"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":[8.570164e-7,0.000012490876,0.00003794282,0.0025829044,0.0000617238,0.0000010195487,0.0000075922126,0.00001058684,7.309283e-8,0.0005166367,0.00081474276,0.99595344],"study_design_scores_gemma":[0.00013808113,0.00007633455,0.00018047272,0.003207082,0.00009334739,0.000049145332,0.0000011959768,0.009451882,0.0000010159147,0.00021800121,0.98606336,0.0005200621],"about_ca_topic_score_codex":0.00064564554,"about_ca_topic_score_gemma":0.00009008889,"teacher_disagreement_score":0.9954334,"about_ca_system_score_codex":0.00012986462,"about_ca_system_score_gemma":0.00029058408,"threshold_uncertainty_score":0.9997875},"labels":[],"label_agreement":null},{"id":"W4409529050","doi":"10.1007/978-3-031-85933-5_13","title":"Mobility Anomaly Detection with Intelligent Video Surveillance","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Anomaly detection; Computer science; Anomaly (physics); Remote sensing; Artificial intelligence; Geography; Physics","score_opus":0.020313341529396838,"score_gpt":0.271366712868013,"score_spread":0.25105337133861616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409529050","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":[0.00005961529,0.00015603857,0.87663263,0.00048647844,0.00009674488,0.0005088129,0.000008770892,0.00022042831,0.121830486],"genre_scores_gemma":[0.614913,0.0041962177,0.3726701,0.0016283002,0.000054205288,0.0003876512,0.000051702966,0.000019606094,0.006079227],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99837387,0.000029833585,0.000633272,0.0004106542,0.00034303433,0.00020935944],"domain_scores_gemma":[0.9961934,0.00017460895,0.00033271348,0.00266306,0.0005479987,0.00008821221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009069294,0.00023381034,0.00024489372,0.0009146928,0.00057642313,0.0005238628,0.0026316703,0.00013310232,0.0000055654855],"category_scores_gemma":[0.0000256438,0.00021931894,0.0000473143,0.00085517124,0.00076340896,0.003604638,0.0016554493,0.0003813379,0.000019990475],"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":[0.0000036273377,0.000021258626,0.00007048937,0.000026069967,0.0000054872735,1.4942117e-7,0.00021923015,0.00009838735,0.000006348357,0.43543783,0.00009651445,0.5640146],"study_design_scores_gemma":[0.0002858049,0.00021464375,0.0042833537,0.0002536446,0.0000069969947,0.00006129562,0.000019896912,0.4848846,0.00048485142,0.015639564,0.4931848,0.0006805477],"about_ca_topic_score_codex":0.000038312315,"about_ca_topic_score_gemma":0.00010707743,"teacher_disagreement_score":0.6148534,"about_ca_system_score_codex":0.00024191808,"about_ca_system_score_gemma":0.00030966167,"threshold_uncertainty_score":0.89435667},"labels":[],"label_agreement":null},{"id":"W4409640667","doi":"10.1109/access.2025.3563158","title":"Real-Time Anomaly Detection in IoMT Networks Using Stacking Model and a Healthcare- Specific Dataset","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Anomaly detection; Stacking; Data modeling; Data mining; Anomaly (physics); Artificial intelligence; Database","score_opus":0.04342244837744393,"score_gpt":0.3415698963583335,"score_spread":0.2981474479808896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409640667","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":[0.16548847,0.00007141977,0.83366364,0.00020115278,0.00007896309,0.0002154857,0.000018890616,0.00014053979,0.00012145867],"genre_scores_gemma":[0.9856691,0.00020611897,0.0138123855,0.00018695166,0.00003869727,0.0000413375,0.000011471969,0.0000071893796,0.000026767231],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990054,0.000041433374,0.00024435454,0.0004110392,0.00008640166,0.00021140587],"domain_scores_gemma":[0.99931455,0.000036310532,0.00008672939,0.00047416778,0.000039597726,0.00004867458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021514468,0.00010749808,0.00013658738,0.00024571017,0.00019550894,0.00030793558,0.0005175259,0.0000817893,0.0000017069302],"category_scores_gemma":[0.0000027981662,0.00011706881,0.000020230964,0.0008982209,0.000029874378,0.00071325887,0.00023000616,0.00014846036,0.0000015502616],"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":[0.00011662445,0.00035216965,0.0072743488,0.00021098075,0.000056186527,0.000041369505,0.00042686274,0.28988305,0.1162269,0.03246514,0.009072686,0.5438737],"study_design_scores_gemma":[0.00010827522,0.000012741917,0.0017416105,0.00003233965,0.0000027116064,0.0000055736627,0.0000034152706,0.9892189,0.005008692,0.0032493242,0.00050235726,0.0001140798],"about_ca_topic_score_codex":0.0010538929,"about_ca_topic_score_gemma":0.00021375698,"teacher_disagreement_score":0.8201806,"about_ca_system_score_codex":0.000101139776,"about_ca_system_score_gemma":0.000047830046,"threshold_uncertainty_score":0.47739276},"labels":[],"label_agreement":null},{"id":"W4409787772","doi":"10.61091/jcmcc127a-528","title":"A deep learning-based action recognition and confrontation analysis system for sparring players","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Action (physics); Artificial intelligence; Psychology; Computer science","score_opus":0.02064685131559981,"score_gpt":0.2747281916719614,"score_spread":0.2540813403563616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409787772","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":[0.1291269,0.00007643209,0.86840695,0.00007062206,0.0018704958,0.0002578429,6.581966e-7,0.00008290444,0.00010719696],"genre_scores_gemma":[0.96321785,0.000012447033,0.03655345,0.000013911599,0.0001753992,0.000012352047,0.0000023042373,0.000008279289,0.0000039992906],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867845,0.000070834496,0.000664425,0.00020771706,0.0002206022,0.0001579531],"domain_scores_gemma":[0.99787635,0.0005528867,0.0008264607,0.00014325083,0.00052100664,0.000080044774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010072837,0.00015279015,0.00043492773,0.00045660255,0.00040485908,0.0003143275,0.00021711571,0.00011545268,5.543196e-7],"category_scores_gemma":[0.0001539707,0.00015117833,0.00015801688,0.0006226813,0.000031940945,0.00022391684,0.00007438871,0.00022307533,3.1145538e-7],"study_design_candidate":"theoretical_or_conceptual","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":[0.000101794314,0.00021793273,0.00029218453,0.00054689037,0.0004580812,0.0000024952026,0.0005707004,0.0015975725,0.00080160546,0.96041995,0.00002835061,0.034962464],"study_design_scores_gemma":[0.0024667128,0.00048129098,0.000222514,0.00023039602,0.0004920658,0.000016549273,0.0004970681,0.8054546,0.004300238,0.18533845,0.00029706594,0.0002030256],"about_ca_topic_score_codex":0.000010729328,"about_ca_topic_score_gemma":9.41215e-7,"teacher_disagreement_score":0.83409095,"about_ca_system_score_codex":0.00011214664,"about_ca_system_score_gemma":0.000069649504,"threshold_uncertainty_score":0.6164873},"labels":[],"label_agreement":null},{"id":"W4409807632","doi":"10.1007/978-3-031-89350-6_10","title":"Classifying Insider Threat Scenarios Through Explainable Articial Intelligence","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Insider; Computer science; Insider threat; Artificial intelligence; Epistemology; Philosophy","score_opus":0.031862209866124355,"score_gpt":0.282651684809829,"score_spread":0.2507894749437046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409807632","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000134069505,0.000319828,0.97916025,0.0014884756,0.00082053966,0.00052847766,0.0000027420974,0.0004512298,0.017215062],"genre_scores_gemma":[0.12707102,0.00020204177,0.86633265,0.0034762507,0.00045290252,0.00008622681,0.0000033936847,0.000034943016,0.002340582],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9964626,0.000025773983,0.0006062647,0.0015837325,0.0006611472,0.0006605187],"domain_scores_gemma":[0.9974214,0.00034960362,0.00025539543,0.0015742163,0.00028437882,0.00011501907],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00045732575,0.0004911614,0.0004693204,0.00059124123,0.0006060058,0.00061182666,0.0028953657,0.00038003136,0.00003683959],"category_scores_gemma":[0.00007680984,0.00046681025,0.00015825249,0.0011109698,0.0006200408,0.0008529599,0.0016847157,0.00090923253,0.000059090013],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000030036103,0.000030259078,0.000016245698,0.000032350454,0.000008300754,0.000030556395,0.0005051619,0.007532436,0.00009118518,0.31180888,0.000119906304,0.6798217],"study_design_scores_gemma":[0.00007420372,0.00011274478,0.00001764775,0.0003802952,0.0000096864305,0.000042214375,7.620944e-7,0.30952945,0.011040089,0.66718507,0.0109891305,0.00061872066],"about_ca_topic_score_codex":0.00007435359,"about_ca_topic_score_gemma":0.00008341386,"teacher_disagreement_score":0.679203,"about_ca_system_score_codex":0.0004205549,"about_ca_system_score_gemma":0.00068089116,"threshold_uncertainty_score":0.9997784},"labels":[],"label_agreement":null},{"id":"W4409814291","doi":"10.1016/j.procs.2025.03.058","title":"Transformer-Based Classification of Road Conditions Using Vehicular Sensor Data","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Acadia University","funders":"","keywords":"Computer science; Transformer; Data mining; Real-time computing; Automotive engineering; Electrical engineering; Voltage","score_opus":0.05023077047132253,"score_gpt":0.32855820822985066,"score_spread":0.27832743775852814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409814291","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":[0.02788163,0.000028302993,0.9702315,0.0008805741,0.0001658889,0.000299569,0.000013789305,0.00021491497,0.0002838359],"genre_scores_gemma":[0.6617142,0.0000027975557,0.33804378,0.00018267432,0.00001950641,0.000019538344,0.0000060159914,0.0000025194297,0.000008944845],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851596,0.000019426823,0.00030039318,0.0006334704,0.000312418,0.00021832311],"domain_scores_gemma":[0.9982321,0.000044057757,0.00012804018,0.0012047167,0.00032230778,0.00006873347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004570736,0.000107546846,0.00013210377,0.0003451026,0.0003635371,0.00014685602,0.002333348,0.000045189063,0.0000025917373],"category_scores_gemma":[0.000025697405,0.00010494421,0.00004002101,0.0022923443,0.0003605087,0.0008808016,0.00023607604,0.000094966614,0.000004316931],"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":[0.00000807123,0.0005707386,0.0019169986,0.00021587899,0.000029665316,0.0000026457103,0.00033422108,0.009405208,0.44390106,0.22865134,0.0009935835,0.3139706],"study_design_scores_gemma":[0.00010561514,0.000026041453,0.0053893006,0.000035071287,0.000008105306,0.0000047275626,0.0000056405247,0.9433687,0.048884436,0.0012986994,0.0007719928,0.000101713114],"about_ca_topic_score_codex":0.00002088812,"about_ca_topic_score_gemma":0.0000017284876,"teacher_disagreement_score":0.9339635,"about_ca_system_score_codex":0.000055668053,"about_ca_system_score_gemma":0.0006807823,"threshold_uncertainty_score":0.43359795},"labels":[],"label_agreement":null},{"id":"W4409909942","doi":"10.18280/ts.420225","title":"Surveillance Synthesis: Elevating Crowd Monitoring Using EERGAN with LSSVM Integration","year":2025,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Data mining; Environmental science","score_opus":0.016115704115325258,"score_gpt":0.2559595318599909,"score_spread":0.23984382774466562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409909942","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":[0.14234592,0.000027990813,0.8562911,0.0002559249,0.000049321952,0.0001630164,0.0000010959351,0.00026698707,0.00059864466],"genre_scores_gemma":[0.91206723,0.0000056350955,0.087663285,0.000050485967,0.00006298575,0.00009504911,6.884063e-7,0.0000061574956,0.000048457867],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991301,0.00003751019,0.00022132514,0.00027455107,0.00016689116,0.00016966209],"domain_scores_gemma":[0.9994715,0.00007560244,0.00009509071,0.00022885505,0.00009617249,0.000032801894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025062228,0.0001166707,0.00011110116,0.00011143739,0.00026291478,0.00016434485,0.00031873232,0.000035121397,0.000013543633],"category_scores_gemma":[0.000011933614,0.00010209956,0.00003459316,0.0005194303,0.000024150966,0.0002564878,0.000054596872,0.00009277187,0.0000027022613],"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":[0.000043599674,0.00027386867,0.038171224,0.00007112845,0.00013775504,0.000008410511,0.0006878171,0.0036917333,0.2944195,0.094837405,0.00031626242,0.5673413],"study_design_scores_gemma":[0.00035051996,0.00013611307,0.039159738,0.00036676176,0.000020545354,0.000012452958,0.0002579817,0.25875401,0.69744855,0.0016302417,0.0014127801,0.00045028518],"about_ca_topic_score_codex":0.000041424333,"about_ca_topic_score_gemma":0.0000071342556,"teacher_disagreement_score":0.7697213,"about_ca_system_score_codex":0.00009016501,"about_ca_system_score_gemma":0.000053548993,"threshold_uncertainty_score":0.41634992},"labels":[],"label_agreement":null},{"id":"W4409954131","doi":"10.18280/ts.420222","title":"A New DenseNet-Based Anomaly Detection Method Using Humerus and Shoulder X-Ray Images","year":2025,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Anomaly (physics); Humerus; Anomaly detection; Geology; Computer science; Artificial intelligence; Physics; Paleontology","score_opus":0.01641787538787677,"score_gpt":0.2957256173173895,"score_spread":0.27930774192951274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409954131","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":[0.012266622,0.00010416479,0.98613226,0.00057728676,0.000054110533,0.00027704757,0.0000017664593,0.00027769568,0.00030903102],"genre_scores_gemma":[0.58180493,0.0000030286915,0.41757384,0.00036455182,0.000034653716,0.000030609117,5.925876e-7,0.0000053909184,0.0001824162],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989999,0.00006522195,0.00022864058,0.00037722374,0.00014496311,0.00018405268],"domain_scores_gemma":[0.99948716,0.00006612935,0.00007601684,0.00023787428,0.000056132634,0.00007668725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023998374,0.00014021165,0.00013252026,0.00019755038,0.00025130843,0.00016773814,0.00023746313,0.00006173636,0.00004822205],"category_scores_gemma":[0.000004925383,0.0001390738,0.00006271963,0.00046410336,0.00002741416,0.00018772123,0.000076010634,0.000097531534,0.0000021451556],"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":[0.000021029364,0.000077661905,0.000542375,0.000027902131,0.000045979934,0.0000041672097,0.00008266206,0.0023651298,0.3311992,0.0069948183,0.00067697366,0.6579621],"study_design_scores_gemma":[0.0006628857,0.00014873277,0.008248574,0.000033153792,0.000059739396,0.00001275963,0.000023854333,0.541242,0.43781838,0.0030562424,0.00840734,0.00028633027],"about_ca_topic_score_codex":0.00019873887,"about_ca_topic_score_gemma":0.000018150979,"teacher_disagreement_score":0.6576758,"about_ca_system_score_codex":0.000054626053,"about_ca_system_score_gemma":0.00009321938,"threshold_uncertainty_score":0.5671265},"labels":[],"label_agreement":null},{"id":"W4410028579","doi":"10.1109/tpami.2025.3566620","title":"Graph Anomaly Detection in Time Series: A Survey","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Anomaly detection; Computer science; Time series; Artificial intelligence; Series (stratigraphy); Pattern recognition (psychology); Graph; Data mining; Machine learning; Theoretical computer science; Geology","score_opus":0.012149306362823476,"score_gpt":0.2606635989937058,"score_spread":0.24851429263088232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410028579","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":[0.010192591,0.000036800528,0.9889947,0.00024166715,0.00006324145,0.0001612765,0.000023637176,0.00015500536,0.00013105052],"genre_scores_gemma":[0.99750304,0.00022470689,0.0011293758,0.00025412705,0.000003721881,0.000086784996,0.0000031954705,0.0000057430148,0.0007893086],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99866945,0.000119469936,0.00037173045,0.0005096953,0.00014733544,0.00018234282],"domain_scores_gemma":[0.9991947,0.000113381844,0.00007328985,0.00049058866,0.000067221925,0.000060850194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038503986,0.00017384972,0.0002575602,0.0011392008,0.00019504929,0.00014092658,0.00042759167,0.00008234602,0.000059843314],"category_scores_gemma":[0.0000043452515,0.00016793673,0.00016420924,0.0037234954,0.000054539694,0.0002366661,0.0000080093,0.00023377089,0.000024125711],"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":[0.00002283467,0.00022680723,0.005284,0.00001106798,0.00023255924,0.00000306142,0.000088030465,0.0036173228,0.0010315864,0.000176646,0.000008545067,0.9892975],"study_design_scores_gemma":[0.00018112859,0.0002675009,0.08605105,0.000044042386,0.00025932165,0.000012987988,0.00003020656,0.25168234,0.6580272,0.002588225,0.0003004336,0.0005555987],"about_ca_topic_score_codex":0.004249262,"about_ca_topic_score_gemma":0.012609122,"teacher_disagreement_score":0.98874193,"about_ca_system_score_codex":0.000044833945,"about_ca_system_score_gemma":0.000018522735,"threshold_uncertainty_score":0.70361865},"labels":[],"label_agreement":null},{"id":"W4410041804","doi":"10.1007/978-3-031-87499-4_12","title":"Drone Anomaly Detection: Dataset and Unsupervised Machine Learning","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor; Research and Productivity Council; University of Victoria","funders":"","keywords":"Computer science; Anomaly detection; Drone; Artificial intelligence; Unsupervised learning; Machine learning","score_opus":0.011007468913122789,"score_gpt":0.23732606440724383,"score_spread":0.22631859549412103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410041804","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00003454027,0.00056421093,0.9964305,0.0005669505,0.00024568298,0.00032785966,0.000044367178,0.00031819256,0.0014677055],"genre_scores_gemma":[0.35576573,0.00062036415,0.63676125,0.0029301904,0.00048262763,0.00008603994,0.00015846654,0.000060828752,0.0031345293],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99750715,0.000029481602,0.00037747182,0.0013170546,0.00040039705,0.00036846605],"domain_scores_gemma":[0.9983102,0.00021881802,0.00017317657,0.0010569013,0.00011351412,0.0001273839],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047030728,0.00037787508,0.00035481623,0.0007078898,0.00056792086,0.00045415456,0.0016043229,0.00024759056,0.000022183945],"category_scores_gemma":[0.000039810635,0.00036943905,0.00006366066,0.00069369853,0.00043691613,0.00042032063,0.0014161032,0.00084440294,0.000015754284],"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":[0.0000034587363,0.0000151635095,0.000049375718,0.000031409065,0.000008639304,0.000016454493,0.000073943134,0.0012142147,0.00037488627,0.012156404,0.000043153963,0.9860129],"study_design_scores_gemma":[0.00028697532,0.0002766963,0.00014811347,0.00019046618,0.000016911996,0.00013544553,1.6327174e-7,0.87134886,0.0055841976,0.06008528,0.06115801,0.00076887256],"about_ca_topic_score_codex":0.000089041954,"about_ca_topic_score_gemma":0.00014561892,"teacher_disagreement_score":0.98524404,"about_ca_system_score_codex":0.00014181128,"about_ca_system_score_gemma":0.00018783346,"threshold_uncertainty_score":0.9998758},"labels":[],"label_agreement":null},{"id":"W4410048709","doi":"10.48175/ijarsct-3861i","title":"Real-Time UX Behavior Analytics using Flask, Javascript Event Listeners, and Heatmap Rendering for Interface Refinement","year":2022,"lang":"en","type":"article","venue":"International Journal of Advanced Research in Science Communication and Technology","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"ASTER","funders":"","keywords":"JavaScript; Computer science; Rendering (computer graphics); Analytics; Event (particle physics); Human–computer interaction; Computer graphics (images); Database; Programming language; Physics; Astrophysics","score_opus":0.08134009144529218,"score_gpt":0.44532906625750723,"score_spread":0.36398897481221504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410048709","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":[0.7442469,0.0012186862,0.22024119,0.03280516,0.00026495705,0.0007262668,0.000009902623,0.000095874304,0.00039105746],"genre_scores_gemma":[0.86098874,0.0009535072,0.13787346,0.000021643405,0.0000081820035,0.00008300647,7.392318e-7,0.000004387676,0.00006635067],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852407,0.000074411684,0.00040226054,0.00023170213,0.0005634335,0.00020414066],"domain_scores_gemma":[0.9983203,0.00010338856,0.00023777298,0.00043054254,0.0008449992,0.0000629861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027287675,0.00006798771,0.0001256275,0.0014293825,0.00052298966,0.00011776019,0.0020534943,0.00003327353,0.000005491568],"category_scores_gemma":[0.000120954945,0.000068182235,0.000026574264,0.0012220577,0.0005010207,0.00045064033,0.0018274541,0.0004298679,2.3352095e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006380295,0.0004063585,0.0013707763,0.000010293687,0.000030881816,0.000016512642,0.0006841128,0.0039451085,0.5306142,0.13612387,0.00015792991,0.32657614],"study_design_scores_gemma":[0.00426436,0.0030872002,0.006377748,0.0004269059,0.000029966743,0.002601833,0.010290778,0.63948286,0.11999759,0.1328582,0.07977519,0.0008073864],"about_ca_topic_score_codex":0.000037499904,"about_ca_topic_score_gemma":0.000009699567,"teacher_disagreement_score":0.63553774,"about_ca_system_score_codex":0.0005257822,"about_ca_system_score_gemma":0.00020496231,"threshold_uncertainty_score":0.40224668},"labels":[],"label_agreement":null},{"id":"W4410082631","doi":"10.20944/preprints202505.0199.v1","title":"A Critical Analysis on Anomaly Detection in High-Frequency Financial Data Using Deep Learning for Options","year":2025,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Anomaly detection; Anomaly (physics); Deep learning; Computer science; Artificial intelligence; Finance; Business; Physics","score_opus":0.12732739781209831,"score_gpt":0.39180997752704205,"score_spread":0.2644825797149437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410082631","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":[0.17437096,0.000037829355,0.8235582,0.00031195878,0.00025720295,0.0006605979,0.0000512401,0.0003718307,0.00038017338],"genre_scores_gemma":[0.92018425,0.00004582649,0.07871359,0.00007760148,0.00011590224,0.0006417734,0.0000838287,0.000013873061,0.00012336715],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966866,0.0001937349,0.00065906823,0.0018001948,0.00024503272,0.0004153984],"domain_scores_gemma":[0.9963433,0.00027896307,0.00025732518,0.002777508,0.0002504207,0.00009249749],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010072147,0.00030433154,0.0004844655,0.0010115632,0.00043373965,0.00012087013,0.002153594,0.0004193082,0.000039330087],"category_scores_gemma":[0.0010731352,0.00036583206,0.00026953846,0.0015681967,0.00007075005,0.00034508822,0.0035308409,0.0010389001,0.000030793235],"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":[0.00014750713,0.0017944551,0.28360397,0.0007594554,0.0009680976,0.00003064228,0.00093837426,0.27821922,0.016883366,0.3292876,0.000020521644,0.0873468],"study_design_scores_gemma":[0.00023627165,0.000048203674,0.19195083,0.00013151487,0.00038080395,0.0000037536643,0.00001677177,0.7661068,0.009496711,0.03054112,0.0005186623,0.0005685436],"about_ca_topic_score_codex":0.0011075437,"about_ca_topic_score_gemma":0.0004849116,"teacher_disagreement_score":0.74581325,"about_ca_system_score_codex":0.00033914417,"about_ca_system_score_gemma":0.00025323112,"threshold_uncertainty_score":0.99987936},"labels":[],"label_agreement":null},{"id":"W4410204421","doi":"10.1007/s44155-025-00221-5","title":"Recurrent neural network-based automated early detection of pandemic-prone diseases through symptoms analysis","year":2025,"lang":"en","type":"article","venue":"Discover Social Science and Health","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Université de Moncton","funders":"Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Pandemic; Artificial neural network; Coronavirus disease 2019 (COVID-19); Computer science; Artificial intelligence; Medicine; Disease; Internal medicine; Infectious disease (medical specialty)","score_opus":0.021048504975298876,"score_gpt":0.3459778127458909,"score_spread":0.32492930777059204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410204421","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":[0.21697709,0.00034822064,0.77932227,0.001978796,0.00016137215,0.00036853738,0.000014844643,0.0004901333,0.00033875107],"genre_scores_gemma":[0.99842036,0.000054236556,0.0007558676,0.0006814082,0.000031316773,0.0000382154,0.0000025993975,0.0000020460163,0.000013954384],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9987593,0.00004587502,0.00025082062,0.00036107126,0.00029777718,0.00028513145],"domain_scores_gemma":[0.9993991,0.00002349908,0.00017774923,0.0002063954,0.00012152529,0.00007168422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004130298,0.00008842689,0.0002069112,0.00015879881,0.0008901912,0.000099098965,0.00035157913,0.00003735749,0.0000012625046],"category_scores_gemma":[0.000010243151,0.000078718724,0.0000796716,0.004088889,0.00029642746,0.00045865323,0.00011278241,0.000081140504,4.390693e-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":[0.00003187006,0.00036018563,0.23964927,0.00011795779,0.00012974237,5.428178e-7,0.0019478789,0.0009011298,0.001033968,0.17606753,0.0011433125,0.5786166],"study_design_scores_gemma":[0.00016247774,0.00014012695,0.83182573,0.000018072766,0.00005065487,1.8407705e-7,0.00003771343,0.16413547,0.0003987492,0.002731355,0.00037949116,0.000119976925],"about_ca_topic_score_codex":0.0007744906,"about_ca_topic_score_gemma":0.00010195225,"teacher_disagreement_score":0.7814433,"about_ca_system_score_codex":0.00014570264,"about_ca_system_score_gemma":0.0004726361,"threshold_uncertainty_score":0.6846721},"labels":[],"label_agreement":null},{"id":"W4410297138","doi":"10.1109/isbi60581.2025.10980970","title":"Exploring the Use of Generative Adversarial Networks for Automated Dental Preparation Design","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"InterDigital (Canada); Polytechnique Montréal","funders":"","keywords":"Adversarial system; Computer science; Generative grammar; Generative adversarial network; Artificial intelligence; Deep learning","score_opus":0.14686212085906725,"score_gpt":0.3144776008676776,"score_spread":0.16761548000861037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410297138","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":[0.0024615384,0.0000044288695,0.9961615,0.00019220529,0.00013410882,0.00055501296,7.3096726e-7,0.000400832,0.00008961128],"genre_scores_gemma":[0.5711305,0.00000835117,0.42782858,0.00012460585,0.000027446707,0.0005373382,0.0000016435905,0.0000020923057,0.0003394107],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958986,0.00003379269,0.00013420232,0.00012882771,0.000044795444,0.0000685065],"domain_scores_gemma":[0.9995112,0.00015849869,0.000048144157,0.00020487898,0.00006706522,0.000010204368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010393856,0.000046372148,0.000053390497,0.000035778936,0.00015377779,0.00006752343,0.0001966278,0.00002029161,0.0000013987378],"category_scores_gemma":[0.000014350139,0.000033103188,0.000037851674,0.000262649,0.000019318231,0.00038285743,0.000060334947,0.000027036516,5.6783523e-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":[0.00006617482,0.00007640667,0.000044425477,0.000006026334,0.00006203104,1.3815394e-7,0.00039039517,0.3510239,0.005910477,0.5721079,0.029212262,0.041099824],"study_design_scores_gemma":[0.0000766636,0.00004437362,0.00015173749,0.0000033448007,0.0000050303247,3.551515e-7,0.000011506044,0.94082147,0.056176946,0.0004547592,0.0022209266,0.00003286103],"about_ca_topic_score_codex":0.000027753564,"about_ca_topic_score_gemma":0.000008668163,"teacher_disagreement_score":0.5897976,"about_ca_system_score_codex":0.000020719252,"about_ca_system_score_gemma":0.00002782497,"threshold_uncertainty_score":0.13499087},"labels":[],"label_agreement":null},{"id":"W4410524059","doi":"10.2139/ssrn.5261048","title":"Unsupervised, Robust, and Lightweight Detection of Data Pattern Anomalies and Outliers","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Outlier; Anomaly detection; Pattern recognition (psychology); Computer science; Artificial intelligence; Data mining","score_opus":0.019869770078424557,"score_gpt":0.2571072211651208,"score_spread":0.23723745108669625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410524059","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":[0.021997616,0.0055836607,0.97090065,0.00085951074,0.00012833858,0.00022444982,0.000028773047,0.00009694801,0.00018007691],"genre_scores_gemma":[0.9680137,0.02269237,0.008575821,0.000066293724,0.00012559336,0.000023284638,0.000010872629,0.000013043285,0.00047901957],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980554,0.00007937776,0.00039835405,0.00058872387,0.00018660624,0.0006915104],"domain_scores_gemma":[0.99854314,0.00004353673,0.0003215176,0.0009114027,0.000108524626,0.00007186653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000919899,0.00022123341,0.00028945252,0.0002739251,0.00023790171,0.00018809643,0.001286573,0.0002046122,0.0000019042129],"category_scores_gemma":[0.000014166964,0.00020641927,0.00006277911,0.0001996612,0.00007997987,0.00037358966,0.0018898142,0.001729158,6.2656216e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000122920155,0.000060425446,0.0012211091,0.00012741842,0.00025964854,0.0000017218255,0.00024381015,0.000049711653,0.00050019106,0.03820252,0.00006462229,0.95925653],"study_design_scores_gemma":[0.0014797363,0.0009870199,0.004497439,0.0005863268,0.00046588862,0.0015672611,0.0011456202,0.17232895,0.009423024,0.79290795,0.0129496,0.0016611759],"about_ca_topic_score_codex":0.00017424178,"about_ca_topic_score_gemma":0.00041587924,"teacher_disagreement_score":0.9623248,"about_ca_system_score_codex":0.00018539309,"about_ca_system_score_gemma":0.0008414571,"threshold_uncertainty_score":0.8417533},"labels":[],"label_agreement":null},{"id":"W4410545315","doi":"10.23977/jeis.2025.100116","title":"DAGAD: Dual Adversarial Learning Graph Anomaly Detection in Multivariate Time Series Data","year":2025,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multivariate statistics; Anomaly detection; Series (stratigraphy); Dual (grammatical number); Graph; Computer science; Adversarial system; Time series; Anomaly (physics); Artificial intelligence; Machine learning; Theoretical computer science; Geology; Physics","score_opus":0.00694633841918223,"score_gpt":0.25755425724416303,"score_spread":0.2506079188249808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410545315","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":[0.06552947,0.00009284453,0.93198395,0.00059437513,0.00014266065,0.0001082875,0.0000012455605,0.00003706842,0.0015100822],"genre_scores_gemma":[0.9903194,0.00028675498,0.009215709,0.0001085435,0.000018260815,0.0000023414636,0.0000012602709,0.0000012018103,0.00004654037],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990227,0.00002373097,0.000417909,0.00012015012,0.0002407597,0.00017473259],"domain_scores_gemma":[0.9991201,0.000032982593,0.00033040383,0.00022743008,0.0002458457,0.000043195425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015135728,0.000065635315,0.000104458464,0.00065749657,0.0002852341,0.0003229255,0.0006396025,0.000038133414,0.0000021632375],"category_scores_gemma":[0.00017040694,0.000058200112,0.00002121887,0.0015442542,0.00008605547,0.012070918,0.00025815147,0.000258521,0.0000025981944],"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":[0.00014879735,0.0000965082,0.00082063314,0.000031112897,0.000029333769,0.0000028149202,0.0015135749,0.0034173643,0.082454674,0.31985605,0.0003298788,0.59129924],"study_design_scores_gemma":[0.0012468849,0.0008379416,0.018942527,0.000065315806,0.00001714303,0.00022938324,0.0002477005,0.7588466,0.043968968,0.01132378,0.16396013,0.00031365146],"about_ca_topic_score_codex":0.00001469727,"about_ca_topic_score_gemma":0.000007000452,"teacher_disagreement_score":0.9247899,"about_ca_system_score_codex":0.000085105625,"about_ca_system_score_gemma":0.00039536238,"threshold_uncertainty_score":0.8751124},"labels":[],"label_agreement":null},{"id":"W4410589973","doi":"10.1007/978-3-031-92805-5_24","title":"SplatPose+: Real-Time Image-Based Pose-Agnostic 3D Anomaly Detection","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Computer vision; Anomaly detection; Artificial intelligence; Image (mathematics)","score_opus":0.007209697685370501,"score_gpt":0.23282249567697103,"score_spread":0.22561279799160053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410589973","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007828578,0.00007328337,0.9836271,0.00061172596,0.0007379553,0.0006866721,0.0000114702425,0.00088880653,0.013284702],"genre_scores_gemma":[0.09359714,0.000063027495,0.9021682,0.0012988948,0.000438032,0.000079515004,0.000012268616,0.000052528274,0.0022903755],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962067,0.000041957297,0.0006249894,0.001786847,0.0007165051,0.0006229916],"domain_scores_gemma":[0.9965922,0.0006226314,0.0003597804,0.0018671118,0.0003832846,0.00017499758],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005956699,0.00058768963,0.000520866,0.0013034078,0.00051129994,0.0006285991,0.0027792756,0.0004446731,0.00005577203],"category_scores_gemma":[0.000093009505,0.0005917289,0.00020609485,0.0012804187,0.0005686548,0.00053224247,0.00083176396,0.0007588526,0.00015284144],"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":[0.000012291852,0.00006694338,0.000017170738,0.00006546006,0.000015007292,0.00006274862,0.000072645424,0.0062521277,0.010368865,0.007944659,0.0001364899,0.9749856],"study_design_scores_gemma":[0.0002776765,0.00034765402,0.00020810291,0.00038355682,0.00002622372,0.00006027587,4.6241674e-8,0.8979038,0.043886185,0.052636404,0.0033397991,0.00093026954],"about_ca_topic_score_codex":0.000107000276,"about_ca_topic_score_gemma":0.00006312239,"teacher_disagreement_score":0.97405535,"about_ca_system_score_codex":0.00048212634,"about_ca_system_score_gemma":0.0007347596,"threshold_uncertainty_score":0.9996534},"labels":[],"label_agreement":null},{"id":"W4410604500","doi":"10.1017/asb.2025.14","title":"Assessing driving risk through unsupervised detection of anomalies in telematics time series data","year":2025,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Telematics; Series (stratigraphy); Computer science; Econometrics; Economics; Telecommunications; Geology","score_opus":0.018116247120355106,"score_gpt":0.2797682520344355,"score_spread":0.2616520049140804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410604500","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":[0.069665216,0.0000665211,0.9262279,0.001055251,0.000049951268,0.00017706312,0.000004906671,0.00020219637,0.0025509773],"genre_scores_gemma":[0.7422208,0.000037062815,0.25732532,0.000049159058,0.000015115039,0.000025476427,0.0000042729453,0.000006042173,0.0003167491],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99893355,0.000096939286,0.0003708186,0.0003315933,0.00011675959,0.00015031689],"domain_scores_gemma":[0.9986946,0.00019917221,0.00016854967,0.0008600549,0.00006129418,0.000016309643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042832195,0.00010655301,0.00017636055,0.00011428641,0.00015285033,0.00013461542,0.00078581757,0.00006454366,0.00003505021],"category_scores_gemma":[0.00020022583,0.00010875044,0.000029455585,0.0006377692,0.000061853,0.00041539257,0.0005481139,0.0001465394,0.000024274072],"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":[0.00003318196,0.0006697846,0.04463486,0.0005197067,0.00013614757,0.000013567021,0.0019730558,0.0010031987,0.12045925,0.053644065,0.00792849,0.7689847],"study_design_scores_gemma":[0.0011664983,0.00027615554,0.13783532,0.0011027467,0.00009549264,0.000041165775,0.0012449637,0.30092344,0.36881253,0.036870465,0.15051657,0.0011146383],"about_ca_topic_score_codex":0.00013876971,"about_ca_topic_score_gemma":0.000027947082,"teacher_disagreement_score":0.76787007,"about_ca_system_score_codex":0.000027251752,"about_ca_system_score_gemma":0.000041877334,"threshold_uncertainty_score":0.44347143},"labels":[],"label_agreement":null},{"id":"W4410730193","doi":"10.1007/978-3-031-91585-7_11","title":"Logit Disagreement: OoD Detection with Bayesian Neural Networks","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Logit; Artificial neural network; Bayesian probability; Artificial intelligence; Machine learning","score_opus":0.008365054723067977,"score_gpt":0.22318977658620748,"score_spread":0.2148247218631395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410730193","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":[0.000010285476,0.00015331422,0.99440545,0.0005684182,0.0005263649,0.00053109875,0.00000236647,0.00043259258,0.0033700848],"genre_scores_gemma":[0.7648298,0.00004464023,0.23243172,0.0014452805,0.00037106645,0.00006976104,0.0000048329302,0.000029273951,0.000773576],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99717736,0.000022069567,0.0003906981,0.0013633574,0.00053534575,0.00051114184],"domain_scores_gemma":[0.9979659,0.00016096607,0.00024718457,0.0013240911,0.00017464103,0.00012724749],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030178388,0.0004452101,0.00035150658,0.0005935587,0.0004303775,0.000503967,0.0021622125,0.00028201783,0.000014187386],"category_scores_gemma":[0.00001313995,0.0003760067,0.0001073118,0.0010122163,0.00043254983,0.00043298723,0.00080172357,0.0007764294,0.000006062354],"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":[0.0000050882586,0.00001747306,0.00002313525,0.000013383439,0.000008313493,0.000014046265,0.00004815994,0.03887855,0.000025616659,0.010368101,0.00001169619,0.95058644],"study_design_scores_gemma":[0.00013684358,0.00026206684,0.0000718854,0.00014873773,0.000010855265,0.0000506139,1.0046434e-7,0.9499389,0.001123211,0.046552416,0.0012495337,0.00045485716],"about_ca_topic_score_codex":0.000029948133,"about_ca_topic_score_gemma":0.00024781685,"teacher_disagreement_score":0.9501316,"about_ca_system_score_codex":0.00025233152,"about_ca_system_score_gemma":0.00017096837,"threshold_uncertainty_score":0.99986917},"labels":[],"label_agreement":null},{"id":"W4410730207","doi":"10.1007/978-3-031-91585-7_18","title":"The BRAVO Semantic Segmentation Challenge Results in UNCV2024","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"European Commission","keywords":"Computer science; Segmentation; Artificial intelligence; Natural language processing; Computer vision","score_opus":0.015062197627640904,"score_gpt":0.2642194236241006,"score_spread":0.2491572259964597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410730207","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000012283287,0.00040083454,0.9800899,0.005950471,0.0006326781,0.00054693787,0.000004128682,0.00017770725,0.012185053],"genre_scores_gemma":[0.43013588,0.0021581159,0.5501791,0.0033013616,0.00073383603,0.00025425295,0.00001995461,0.000066746215,0.0131506985],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99741286,0.00003296956,0.00055280834,0.0010823286,0.0005195941,0.00039945287],"domain_scores_gemma":[0.9978073,0.00055131427,0.00022341928,0.0012248127,0.0001326558,0.000060447048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009429502,0.0002967306,0.00024823146,0.0005758051,0.00039061008,0.00046680553,0.0023857916,0.00020821675,0.0000029873315],"category_scores_gemma":[0.000060210656,0.00023332049,0.00008231095,0.0008889518,0.00034622513,0.00027913568,0.0008660902,0.0006425058,0.00002424645],"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":[0.0000046280056,0.000017634362,0.0000047747253,0.000016738777,0.000003953342,0.00001166573,0.00031194114,0.0019437781,0.000046042107,0.0904096,0.00010350354,0.9071257],"study_design_scores_gemma":[0.0003661594,0.00018950622,0.00018362244,0.0005137729,0.0000068864892,0.000019688418,9.174701e-7,0.51081824,0.0026821834,0.4670761,0.017544352,0.00059856975],"about_ca_topic_score_codex":0.00004783308,"about_ca_topic_score_gemma":0.0004196323,"teacher_disagreement_score":0.90652716,"about_ca_system_score_codex":0.00029968587,"about_ca_system_score_gemma":0.00034158508,"threshold_uncertainty_score":0.9514534},"labels":[],"label_agreement":null},{"id":"W4410774452","doi":"10.1016/j.procs.2025.03.242","title":"An Intelligent Crime Surveillance Video System For Real-Time Applications","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Diabetes Action Canada","keywords":"Computer science; Real-time computing; Computer security; Artificial intelligence","score_opus":0.009655500179872163,"score_gpt":0.2802235906313833,"score_spread":0.27056809045151115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410774452","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":[0.000904237,0.000028908536,0.9944825,0.00039005015,0.00024346221,0.0011378375,0.000005809625,0.0013644723,0.0014427168],"genre_scores_gemma":[0.5365241,0.0000127839185,0.46180433,0.00020532515,0.00012646738,0.001185594,0.0000030737526,0.0000073657284,0.0001309271],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803174,0.000022902943,0.00034120216,0.00095185445,0.0002648146,0.00038747405],"domain_scores_gemma":[0.99789685,0.00012087472,0.00012843465,0.0011258947,0.0005656962,0.00016224709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080549344,0.00016470581,0.0001860689,0.00030631755,0.00069731165,0.0005057045,0.0026793145,0.000054812022,0.0000014182259],"category_scores_gemma":[0.000015511441,0.00015976967,0.00006617723,0.0021139185,0.00020615316,0.00067051005,0.0003660161,0.00007987566,0.00004671726],"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":[0.000003986964,0.0001343032,0.00032591366,0.00011349183,0.000007525417,3.8477975e-7,0.00017759355,0.00024073066,0.015705226,0.80613935,0.0011691066,0.17598239],"study_design_scores_gemma":[0.00015438593,0.00016292516,0.0027794098,0.000044562166,0.000006095389,0.000016906839,0.000017813287,0.92028177,0.052881878,0.00904859,0.014241612,0.00036406293],"about_ca_topic_score_codex":0.000017184515,"about_ca_topic_score_gemma":0.0000015248174,"teacher_disagreement_score":0.920041,"about_ca_system_score_codex":0.0001699076,"about_ca_system_score_gemma":0.00038832158,"threshold_uncertainty_score":0.6515218},"labels":[],"label_agreement":null},{"id":"W4410815597","doi":"10.1088/1748-0221/20/05/p05041","title":"AI-based particle track identification in scintillating fibres read out with imaging sensors","year":2025,"lang":"en","type":"article","venue":"Journal of Instrumentation","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute of Particle Physics","funders":"","keywords":"Track (disk drive); Particle identification; Particle (ecology); Identification (biology); Materials science; Optics; Computer science; Detector; Physics; Geology","score_opus":0.00998103517580309,"score_gpt":0.29269857550317807,"score_spread":0.282717540327375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410815597","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":[0.5209854,0.000008694601,0.47469524,0.0040338775,0.00005581017,0.00007457935,2.5440985e-7,0.000024271189,0.00012183315],"genre_scores_gemma":[0.9712816,0.0000036702727,0.028301414,0.0003338023,0.00001541941,0.0000068209347,5.004111e-7,0.000002946053,0.000053814656],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99920577,0.00003907445,0.00039983116,0.000112180256,0.00015688634,0.00008624805],"domain_scores_gemma":[0.99933714,0.000035163943,0.0003193425,0.00013366833,0.00015083032,0.00002388527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003055903,0.00005631296,0.00008246667,0.00020506854,0.00008077126,0.0001462473,0.00015692074,0.000017480093,0.000004387141],"category_scores_gemma":[0.00002190221,0.00004916702,0.000031814732,0.00041743304,0.00002349122,0.0006896184,0.000013704311,0.000098634766,0.000002280091],"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":[0.000107538726,0.00023280886,0.17033221,0.00005891706,0.000033871514,0.000015951524,0.0016433203,0.015252027,0.16098014,0.015511971,0.00057615404,0.6352551],"study_design_scores_gemma":[0.00095286325,0.00010902603,0.08676897,0.00021743412,0.00001613343,0.000027738102,0.0004943422,0.28392148,0.6238332,0.0030281749,0.00050462334,0.00012602727],"about_ca_topic_score_codex":0.00001461641,"about_ca_topic_score_gemma":0.0000151718505,"teacher_disagreement_score":0.6351291,"about_ca_system_score_codex":0.00010107787,"about_ca_system_score_gemma":0.000078825164,"threshold_uncertainty_score":0.20049728},"labels":[],"label_agreement":null},{"id":"W4410857093","doi":"10.1007/978-3-031-90921-4_73","title":"A Hybrid Deep Learning Approach for Anomaly Detection in Big Data Environments","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hog Administrative Marketing Services (Canada)","funders":"","keywords":"Anomaly detection; Anomaly (physics); Deep learning; Big data; Computer science; Artificial intelligence; Data science; Data mining; Physics","score_opus":0.024969516934514168,"score_gpt":0.22924751227681606,"score_spread":0.2042779953423019,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410857093","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":[0.000013116764,0.0042161043,0.9903885,0.000023683271,0.0002846744,0.001011273,0.000010254058,0.00008304065,0.0039693518],"genre_scores_gemma":[0.9798139,0.0011456085,0.010746075,0.000087732595,0.0006480827,0.0004456965,0.00020503058,0.000045529367,0.006862348],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99825716,0.00004777345,0.0004373559,0.00087206595,0.00012988324,0.00025574266],"domain_scores_gemma":[0.9987244,0.00019956382,0.00022657031,0.0007900935,0.000015207686,0.000044177734],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004254471,0.00028108136,0.00040143396,0.00025226682,0.0001459364,0.00016361816,0.00063370215,0.00037897498,7.5766707e-7],"category_scores_gemma":[0.0000229283,0.0002738717,0.00005710455,0.00010973493,0.00003677455,0.00009950275,0.00036822396,0.00060123514,5.717204e-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":[0.000014220706,0.000023071976,0.00011256414,0.000138263,0.00003333959,0.000004665776,0.000037362835,0.11826264,0.00002527552,0.005401731,0.00005020338,0.87589663],"study_design_scores_gemma":[0.00017880526,0.00005866798,0.000029690938,0.00014026006,0.000013834983,0.000020856387,0.0000011920763,0.950898,0.000023336495,0.0024924967,0.045886207,0.00025665673],"about_ca_topic_score_codex":0.00009520629,"about_ca_topic_score_gemma":0.00009613959,"teacher_disagreement_score":0.97980076,"about_ca_system_score_codex":0.00009997639,"about_ca_system_score_gemma":0.000019182133,"threshold_uncertainty_score":0.99997133},"labels":[],"label_agreement":null},{"id":"W4410887344","doi":"10.1109/syscon64521.2025.11014854","title":"Calibrated Unsupervised Anomaly Detection in Multivariate Time-Series Using Reinforcement Learning","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Anomaly detection; Multivariate statistics; Series (stratigraphy); Reinforcement learning; Computer science; Artificial intelligence; Unsupervised learning; Anomaly (physics); Time series; Pattern recognition (psychology); Machine learning; Geology; Physics","score_opus":0.011494488441099373,"score_gpt":0.2539032243105276,"score_spread":0.2424087358694282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410887344","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":[0.04077335,0.000008124194,0.9526612,0.00017399914,0.000039859613,0.00020960082,1.0357245e-7,0.000508466,0.0056252773],"genre_scores_gemma":[0.94389457,0.0000037850357,0.052052848,0.00015385615,0.00000802159,0.000042834636,0.0000010691974,0.0000050623908,0.003837929],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999215,0.00004897549,0.00024221354,0.00025502394,0.000075847274,0.00016294257],"domain_scores_gemma":[0.99961233,0.000024336683,0.000051517378,0.00023770898,0.000048461003,0.00002563105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014339543,0.00009717156,0.00010568295,0.00023214667,0.0001812116,0.00010712746,0.00023442405,0.000065336644,0.000040350365],"category_scores_gemma":[0.000016579197,0.00009545984,0.000034786386,0.00103697,0.000019413148,0.0004751004,0.00014781754,0.0001253299,0.000020002683],"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":[0.000054028358,0.0001239459,0.003219558,0.000037893944,0.00004551096,0.000007260351,0.00046022795,0.06478272,0.7467771,0.100060515,0.00009021166,0.08434101],"study_design_scores_gemma":[0.0001693429,0.000037470054,0.0015161255,0.000014269173,0.000002415714,0.0000020753282,0.000023377781,0.825546,0.16968925,0.00064147636,0.0022592335,0.00009895255],"about_ca_topic_score_codex":0.0008375509,"about_ca_topic_score_gemma":0.000044314835,"teacher_disagreement_score":0.90312123,"about_ca_system_score_codex":0.00008937973,"about_ca_system_score_gemma":0.000048914466,"threshold_uncertainty_score":0.38927394},"labels":[],"label_agreement":null},{"id":"W4410895359","doi":"10.2139/ssrn.5192526","title":"LSTM&amp;nbsp;&lt;span&gt;&amp;nbsp;Based Behavioural Recognition&lt;/span&gt;&lt;p&gt;&lt;/p&gt;","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Span (engineering); Endocrinology; Internal medicine; Psychology; Chemistry; Biology; Medicine; Engineering","score_opus":0.0286541541540788,"score_gpt":0.2768792462298939,"score_spread":0.2482250920758151,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410895359","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":[0.034526262,0.005020627,0.9424293,0.005590298,0.0020484743,0.0018783896,0.00032266352,0.0019461024,0.0062378757],"genre_scores_gemma":[0.81821764,0.017557628,0.10928873,0.0021393457,0.0031241567,0.0021639622,0.0011969532,0.00044107065,0.045870535],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98713744,0.0007044017,0.0024092742,0.0026340676,0.0018511316,0.005263677],"domain_scores_gemma":[0.99210817,0.00032590822,0.002120323,0.0032273673,0.0014309236,0.0007873343],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","research_integrity"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.0036833456,0.0016696957,0.0015578155,0.0016628776,0.0019179092,0.0014673847,0.0054139015,0.001510777,0.000463001],"category_scores_gemma":[0.00022954101,0.0017525632,0.0016772035,0.0019958275,0.00035279253,0.0008739073,0.0019043806,0.00880246,0.00064154866],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.00055112684,0.0038122898,0.0013518077,0.0005646115,0.002612669,0.000113274466,0.0011296571,0.00290291,0.026430195,0.34887695,0.054701697,0.55695283],"study_design_scores_gemma":[0.003656655,0.001199492,0.0019096326,0.0013879986,0.0011705649,0.0023154079,0.00009342275,0.0117420005,0.0056648473,0.48747498,0.47794977,0.005435232],"about_ca_topic_score_codex":0.00017610705,"about_ca_topic_score_gemma":0.0035731401,"teacher_disagreement_score":0.83314055,"about_ca_system_score_codex":0.0041743456,"about_ca_system_score_gemma":0.00960817,"threshold_uncertainty_score":0.9999673},"labels":[],"label_agreement":null},{"id":"W4410952971","doi":"10.1155/atr/6594290","title":"Effectiveness Evaluation of Signal Coordination Based on Spatially Sparse Trajectory Data","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Trajectory; SIGNAL (programming language); Computer science; Data mining; Algorithm; Artificial intelligence; Physics","score_opus":0.026048420219768782,"score_gpt":0.3183023985110012,"score_spread":0.29225397829123245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410952971","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":[0.14869153,0.000045853354,0.85049635,0.00017841223,0.00015895671,0.00028544405,0.000010761744,0.000024327379,0.00010833606],"genre_scores_gemma":[0.9610703,0.000008450623,0.038815252,0.000031097108,0.000018949017,0.00001744701,0.000029561243,0.0000044502767,0.000004489495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986236,0.00013815757,0.00046635824,0.00018993202,0.00051009865,0.000071838884],"domain_scores_gemma":[0.9981143,0.0001749058,0.0005095025,0.00035738354,0.0008157328,0.000028178305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013800056,0.00008694586,0.0001690982,0.00030248033,0.00005668508,0.000015374886,0.00045572087,0.000048990038,0.000008693316],"category_scores_gemma":[0.00004899126,0.00008303569,0.00006999517,0.0005121081,0.00002362566,0.00062666007,0.000004916362,0.00011845818,4.5902573e-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":[0.0002141944,0.0003000122,0.00065369613,0.000084619554,0.000034230074,0.0000017969849,0.00010671087,0.6755347,0.04998513,0.0069486294,0.000042512205,0.26609376],"study_design_scores_gemma":[0.0027994819,0.00068352727,0.4093996,0.00045658438,0.00019776839,0.0000018645776,0.00004334585,0.4044746,0.17277311,0.0085761,0.00042282822,0.00017119852],"about_ca_topic_score_codex":0.000007052906,"about_ca_topic_score_gemma":0.00001407567,"teacher_disagreement_score":0.81237876,"about_ca_system_score_codex":0.00008741992,"about_ca_system_score_gemma":0.00030417033,"threshold_uncertainty_score":0.3386097},"labels":[],"label_agreement":null},{"id":"W4411020796","doi":"10.1016/j.comnet.2025.111425","title":"Metaversal intelligence: Unifying human-AI interactions in human-in-the-loop AIB-Metaverse","year":2025,"lang":"en","type":"article","venue":"Computer Networks","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Computer science; Metaverse; Human-in-the-loop; Loop (graph theory); Possible world; Human–computer interaction; Cognitive science; Epistemology; Virtual reality; Psychology; Philosophy","score_opus":0.02705932915303069,"score_gpt":0.3333167116682958,"score_spread":0.30625738251526513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411020796","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":[0.00414531,0.00007199943,0.9909015,0.0008799983,0.00044282712,0.00035202518,3.9260888e-7,0.00022996591,0.0029759519],"genre_scores_gemma":[0.9807426,0.000023874898,0.016395343,0.0020265589,0.00016030246,0.00011643339,0.000005893565,0.000008611931,0.000520411],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839216,0.00015690854,0.00048546278,0.0004897578,0.00014304814,0.00033263196],"domain_scores_gemma":[0.99883145,0.00017433913,0.00009906677,0.0007901346,0.00006325156,0.00004173981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048664623,0.00019043108,0.00022639993,0.00044647968,0.0003404632,0.00030042208,0.0014403975,0.000083420346,0.000026623224],"category_scores_gemma":[0.000004361542,0.0001737736,0.00012481051,0.0017646181,0.000073427786,0.0003884486,0.00044411296,0.00068858685,0.000020198924],"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":[0.000005689141,0.00045011792,0.002342107,0.000022937038,0.000057534948,0.00005423923,0.000992324,0.093523175,0.00012563942,0.73509973,0.015619708,0.15170681],"study_design_scores_gemma":[0.00024279923,0.0000575746,0.004240459,0.000118158765,0.000016012043,0.000018115636,0.00009956915,0.9464806,0.0003794241,0.019831581,0.028236529,0.0002792106],"about_ca_topic_score_codex":0.00013519934,"about_ca_topic_score_gemma":0.0002162784,"teacher_disagreement_score":0.97659725,"about_ca_system_score_codex":0.00011071072,"about_ca_system_score_gemma":0.000031448188,"threshold_uncertainty_score":0.7086282},"labels":[],"label_agreement":null},{"id":"W4411043192","doi":"10.1038/s41598-025-03765-3","title":"Optimization of deep learning architecture based on multi-path convolutional neural network algorithm","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"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":"Computer science; Convolutional neural network; Path (computing); Artificial intelligence; Deep learning; Architecture; Algorithm; Machine learning; Computer network","score_opus":0.007630180766847761,"score_gpt":0.23913617921804903,"score_spread":0.23150599845120126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411043192","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041328912,0.000043594027,0.9969205,0.00020330165,0.0012683101,0.00021969831,6.566476e-7,0.00022138705,0.00070922927],"genre_scores_gemma":[0.39670432,7.084406e-7,0.60200703,0.000088277324,0.000029641555,0.000042888776,0.000024456604,0.0000048802926,0.0010977862],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862605,0.000056162924,0.0003329766,0.00052728096,0.00027631616,0.00018119792],"domain_scores_gemma":[0.99887985,0.0000463444,0.00025081614,0.00059129205,0.00018553474,0.00004613528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058747624,0.000095712065,0.000114331786,0.00019353585,0.00044049294,0.0001364644,0.00024391006,0.000055748966,0.000023192628],"category_scores_gemma":[0.000052562413,0.00008990548,0.00008316582,0.001142869,0.00012256873,0.00009469553,0.00009150712,0.00015370222,0.0000016673774],"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":[0.0000012384019,0.000046181525,0.00048221147,0.000004998389,0.0000031109378,0.000005766388,0.000018220444,0.9516706,0.00019310773,0.0011138342,0.0005428901,0.04591786],"study_design_scores_gemma":[0.00006795812,0.000026897818,0.00074255426,0.000022102618,0.000004003666,0.000012626507,0.0000034014265,0.9906598,0.001551418,0.0022700455,0.004561797,0.00007741522],"about_ca_topic_score_codex":0.000007942561,"about_ca_topic_score_gemma":0.000002015317,"teacher_disagreement_score":0.39629102,"about_ca_system_score_codex":0.00003532644,"about_ca_system_score_gemma":0.00009262382,"threshold_uncertainty_score":0.3666239},"labels":[],"label_agreement":null},{"id":"W4411125970","doi":"10.31234/osf.io/x8eaz_v1","title":"Unconscious processing of naturalistic scenes revealed by eye movement dynamics","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"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":"Canadian Institute for Advanced Research","keywords":"Unconscious mind; Movement (music); Dynamics (music); Eye movement; Computer science; Computer vision; Artificial intelligence; Psychology; Art; Aesthetics; Psychoanalysis","score_opus":0.007536018208930881,"score_gpt":0.27537653799881,"score_spread":0.2678405197898791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411125970","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":[0.001002288,0.00036794908,0.9902402,0.0014727745,0.00014814157,0.0004505729,0.00006299842,0.00044469573,0.0058103926],"genre_scores_gemma":[0.7214349,0.00015176364,0.25927994,0.0007965715,0.000030089997,0.00028593387,0.00006304614,0.000011480096,0.017946258],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987187,0.000025473171,0.00040401777,0.0004983843,0.00020078958,0.000152645],"domain_scores_gemma":[0.99865526,0.000029126502,0.00030098407,0.00072719215,0.00024414298,0.00004329697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014189965,0.00018675019,0.00026177304,0.00012767827,0.000113991926,0.00011258048,0.0010729812,0.00018466175,0.000008211538],"category_scores_gemma":[0.000015382366,0.00017141244,0.00009696426,0.00033263478,0.00006147429,0.000070414935,0.0011108542,0.00028416,0.0000016320644],"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":[0.000011583625,0.00055821385,0.002919852,0.0028831374,0.00015246245,0.000003711227,0.00033599668,0.0011713774,0.0023652914,0.46164373,0.029241612,0.49871302],"study_design_scores_gemma":[0.0001872682,0.000055265307,0.0005040686,0.0006206313,0.00004991443,0.0000018342309,0.000048815527,0.8608992,0.019132819,0.11491559,0.0029239182,0.0006606536],"about_ca_topic_score_codex":0.00024187971,"about_ca_topic_score_gemma":0.000053525346,"teacher_disagreement_score":0.85972786,"about_ca_system_score_codex":0.00015475157,"about_ca_system_score_gemma":0.00021595118,"threshold_uncertainty_score":0.69899964},"labels":[],"label_agreement":null},{"id":"W4411171734","doi":"10.1371/journal.pone.0321968","title":"Explainable one-class feature extraction by adaptive resonance for anomaly detection in quality assurance","year":2025,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Quality assurance; Feature (linguistics); Class (philosophy); Computer science; Anomaly detection; Feature extraction; Anomaly (physics); Artificial intelligence; Pattern recognition (psychology); Physics; Medicine; Pathology; External quality assessment; Linguistics","score_opus":0.038259334202345875,"score_gpt":0.2835435802032786,"score_spread":0.2452842460009327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411171734","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":[0.04648048,0.00038839006,0.94765496,0.0026474544,0.0000427925,0.00081939937,0.000018091425,0.00031578404,0.0016326569],"genre_scores_gemma":[0.9041093,0.000086622706,0.089242384,0.00025574,0.00003384093,0.0012827403,0.00000455343,0.0000085931815,0.0049762367],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99880964,0.00006651497,0.00024295048,0.0004684983,0.00018515188,0.00022723625],"domain_scores_gemma":[0.9990534,0.00018056881,0.00013622078,0.00042556386,0.00016882777,0.000035415458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003340153,0.00011974431,0.00020054585,0.00012399012,0.00021648937,0.000073415555,0.00032313124,0.00014185715,0.0000027738763],"category_scores_gemma":[0.00008740173,0.00013924882,0.000052438474,0.00075064995,0.000026292839,0.0004915682,0.000055291905,0.00022106041,0.0000060003345],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029696722,0.0034390767,0.0016597359,0.00030825983,0.00010800844,0.0000015587303,0.0002224226,0.000029872048,0.7753694,0.082075916,0.007897933,0.12859087],"study_design_scores_gemma":[0.00050843036,0.00019489157,0.016547572,0.00017942947,0.000018518314,8.0053695e-7,0.000049699305,0.029887667,0.92627937,0.00930025,0.016765116,0.00026824838],"about_ca_topic_score_codex":0.00015478891,"about_ca_topic_score_gemma":0.00022337218,"teacher_disagreement_score":0.85841256,"about_ca_system_score_codex":0.00020820023,"about_ca_system_score_gemma":0.000052697265,"threshold_uncertainty_score":0.5678402},"labels":[],"label_agreement":null},{"id":"W4411268806","doi":"10.1101/2025.06.11.25329022","title":"Beyond Benchmarks: Towards Robust Artificial Intelligence Bone Segmentation in Socio-Technical Systems","year":2025,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"RWTH Aachen University","keywords":"Artificial intelligence; Computer science; Segmentation; Machine learning","score_opus":0.03650084209652377,"score_gpt":0.30202402433700093,"score_spread":0.26552318224047716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411268806","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":[0.010163794,0.00031659112,0.9820196,0.0015456773,0.00081399316,0.0009433424,0.000025882116,0.00045167268,0.0037194719],"genre_scores_gemma":[0.9019473,0.0002640331,0.09566416,0.00013623264,0.00019199832,0.0013640238,0.000071070644,0.000014648863,0.00034653323],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974353,0.00014106573,0.0008472823,0.0008977639,0.0003741056,0.00030451606],"domain_scores_gemma":[0.99839544,0.00008189977,0.00029632731,0.0010100704,0.00013380003,0.00008248467],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00085926877,0.00028474655,0.00039445018,0.00040151054,0.00013703427,0.0002955892,0.0013044594,0.00045084473,0.0000313338],"category_scores_gemma":[0.000049548315,0.00029972754,0.0001560819,0.0007429252,0.000085488675,0.00016641674,0.0013032196,0.0008053294,0.000020389716],"study_design_candidate":"theoretical_or_conceptual","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":[0.00002060132,0.0006542169,0.001765512,0.000635236,0.000057276004,0.000050531966,0.0010451467,0.03506583,0.0035668719,0.7286319,0.0026446632,0.22586222],"study_design_scores_gemma":[0.00017495661,0.00019831464,0.006381942,0.0009339908,0.000076801676,0.000050898856,0.00058617425,0.6040473,0.034054935,0.3485931,0.0030241208,0.0018775065],"about_ca_topic_score_codex":0.0002961225,"about_ca_topic_score_gemma":0.000075881326,"teacher_disagreement_score":0.89178354,"about_ca_system_score_codex":0.00033250265,"about_ca_system_score_gemma":0.0002798744,"threshold_uncertainty_score":0.99994546},"labels":[],"label_agreement":null},{"id":"W4411300991","doi":"10.1007/978-981-96-8170-9_8","title":"Streaming Isolation Forest","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Isolation (microbiology); Biology","score_opus":0.011020265665701745,"score_gpt":0.24530088975787878,"score_spread":0.23428062409217704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411300991","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000015672354,0.00011272699,0.9838548,0.0006671036,0.00045678826,0.00032265368,0.0000028257928,0.0003374767,0.014229955],"genre_scores_gemma":[0.16981202,0.00004869947,0.8262007,0.0009950488,0.00027391684,0.000032068976,0.0000062469676,0.000019065837,0.0026121915],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978906,0.000010771211,0.00035517203,0.0010040452,0.00040786108,0.0003315836],"domain_scores_gemma":[0.9982579,0.00020036494,0.00019133766,0.0011030467,0.00017038867,0.00007697751],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031304694,0.00029285028,0.00025821783,0.0007211699,0.00029130548,0.00036292087,0.0019387079,0.00023850589,0.000011377234],"category_scores_gemma":[0.00003099963,0.00028714963,0.00009653778,0.0006589492,0.0002365822,0.00040938598,0.00082880486,0.0004685183,0.000022108972],"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":[8.0752113e-7,0.000010545709,0.00005802398,0.000013400172,0.0000033281108,0.0000051725788,0.000068914924,0.0045072623,0.00007031346,0.15471034,0.00004744472,0.84050447],"study_design_scores_gemma":[0.000075437325,0.00006959598,0.00020462685,0.00019521237,0.0000047050985,0.00001552397,5.1789616e-8,0.67408174,0.0011316511,0.31808627,0.005778057,0.00035711826],"about_ca_topic_score_codex":0.000026456246,"about_ca_topic_score_gemma":0.000084406616,"teacher_disagreement_score":0.8401473,"about_ca_system_score_codex":0.0002310528,"about_ca_system_score_gemma":0.0003332114,"threshold_uncertainty_score":0.99995804},"labels":[],"label_agreement":null},{"id":"W4411471974","doi":"10.1371/journal.pdig.0000874","title":"Efficient slice anomaly detection network for 3D brain MRI Volume","year":2025,"lang":"en","type":"article","venue":"PLOS Digital Health","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Vector Institute; Western University","funders":"Vector Institute","keywords":"Computer science; Benchmark (surveying); Feature (linguistics); Anomaly detection; Artificial intelligence; Pattern recognition (psychology); Volume (thermodynamics); Convolutional neural network; Identification (biology); Code (set theory); Data mining","score_opus":0.011202807839651567,"score_gpt":0.2659656474192372,"score_spread":0.2547628395795857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411471974","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":[0.0055390694,0.00010560171,0.98379564,0.006289917,0.00015792724,0.0008399175,0.000020204461,0.00067351595,0.0025782224],"genre_scores_gemma":[0.9369978,0.0000071162676,0.056828894,0.002898274,0.00013630881,0.00040065142,0.00000975547,0.000013497319,0.002707708],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987527,0.000016637568,0.00030804976,0.00041378808,0.00012997155,0.00037886275],"domain_scores_gemma":[0.99911356,0.00012376849,0.0001235532,0.000436165,0.00009289083,0.000110074085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020435487,0.00012285619,0.00016609112,0.00010177362,0.0004252525,0.00023866874,0.00036996024,0.000059855487,0.0000023649873],"category_scores_gemma":[0.00003275851,0.00012684893,0.000082322986,0.00075353857,0.000028158553,0.00016796887,0.00012244354,0.00010407547,0.000032573163],"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":[0.000034225148,0.0005721653,0.00072412984,0.0002451386,0.0000448983,7.145974e-7,0.00018446302,0.0030225408,0.00014226258,0.08976585,0.03618662,0.86907697],"study_design_scores_gemma":[0.00024079102,0.00041270672,0.0018759677,0.00006246409,0.0000043404975,0.0000051632837,0.000015757178,0.8105394,0.000697728,0.0062736752,0.17966753,0.00020443287],"about_ca_topic_score_codex":0.000037627473,"about_ca_topic_score_gemma":0.00002538538,"teacher_disagreement_score":0.9314587,"about_ca_system_score_codex":0.00016933515,"about_ca_system_score_gemma":0.00015684782,"threshold_uncertainty_score":0.5172749},"labels":[],"label_agreement":null},{"id":"W4411472091","doi":"10.1109/access.2025.3581839","title":"Analyzing Taiwanese Traffic Patterns on Consecutive Holidays Through Forecast Reconciliation and Prediction-Based Anomaly Detection Techniques","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"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":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Anomaly detection; Computer science; Anomaly (physics); Data mining","score_opus":0.021354337761179048,"score_gpt":0.2967989853481714,"score_spread":0.27544464758699233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411472091","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":[0.29461166,0.000020000538,0.7029728,0.00041803243,0.0001498244,0.0004000827,0.000019338351,0.0006489554,0.0007592546],"genre_scores_gemma":[0.9954892,0.000039477185,0.0034050283,0.0005401849,0.00005889192,0.00038884138,0.0000064684646,0.000010129637,0.00006182054],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99875957,0.00006635395,0.0003055897,0.00053259695,0.00013896529,0.00019694922],"domain_scores_gemma":[0.99905616,0.00012775909,0.00016743531,0.00044223326,0.00015990359,0.000046510413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019789665,0.00017979376,0.00017151628,0.0002997452,0.00036989708,0.00034496028,0.00045544447,0.00013546606,0.0000059354215],"category_scores_gemma":[0.000025172043,0.00017878579,0.00006951226,0.0007926871,0.000061352956,0.00080007705,0.000063513966,0.00019017232,0.000002958748],"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":[0.00013272857,0.00039964265,0.021536678,0.0001892952,0.00012398913,0.00001221225,0.00070060865,0.0022144932,0.012138234,0.008568648,0.0013961651,0.9525873],"study_design_scores_gemma":[0.0006378654,0.000494302,0.033814833,0.00024204492,0.000055559834,0.000017203676,0.00008139807,0.19647294,0.75870365,0.005109085,0.0038546193,0.00051652984],"about_ca_topic_score_codex":0.0001628137,"about_ca_topic_score_gemma":0.00013631624,"teacher_disagreement_score":0.9520708,"about_ca_system_score_codex":0.00013756998,"about_ca_system_score_gemma":0.00007271866,"threshold_uncertainty_score":0.7290673},"labels":[],"label_agreement":null},{"id":"W4411500983","doi":"10.1111/exsy.70086","title":"A Hybrid Spiking Model for Anomaly Detection in Multivariate Time Series","year":2025,"lang":"en","type":"article","venue":"Expert Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"China Scholarship Council; Natural Science Foundation of Hebei Province","keywords":"Computer science; Anomaly detection; Anomaly (physics); Multivariate statistics; Time series; Encoding (memory); Representation (politics); Artificial neural network; Artificial intelligence; Data mining; Spiking neural network; Series (stratigraphy); Recurrent neural network; Event (particle physics); Pattern recognition (psychology); Machine learning","score_opus":0.014333398116494749,"score_gpt":0.27232603227409835,"score_spread":0.2579926341576036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411500983","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":[0.0043810317,0.00014401511,0.99327075,0.00026443868,0.00020331502,0.00061353337,0.0000029910548,0.0003742237,0.00074567535],"genre_scores_gemma":[0.96320784,0.000005594647,0.031679288,0.000087373875,0.000048404512,0.0012127106,0.000001326819,0.000007937146,0.0037495343],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991713,0.000027467118,0.00025416716,0.00030769836,0.000070892784,0.00016845751],"domain_scores_gemma":[0.99947166,0.000034145913,0.0000692869,0.00034289205,0.00005641345,0.000025603573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019301054,0.000100681355,0.00014830727,0.00016797279,0.00015497505,0.00012142532,0.00029768012,0.000050747305,7.883385e-7],"category_scores_gemma":[0.000015896165,0.000099629564,0.00005427491,0.0002475369,0.000013004108,0.00031973302,0.00007575659,0.00005538064,0.000009129795],"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":[0.00012077585,0.00035999386,0.00018883197,0.00024292235,0.00008627748,0.00000939708,0.0039011757,0.019252094,0.5468001,0.30670565,0.007849553,0.11448326],"study_design_scores_gemma":[0.000156418,0.000025305415,0.000046929188,0.000042104093,0.0000010750689,0.000006974758,0.000022904103,0.9513425,0.038307145,0.0017847763,0.008160948,0.00010292722],"about_ca_topic_score_codex":0.00035377734,"about_ca_topic_score_gemma":0.0000150827145,"teacher_disagreement_score":0.9615915,"about_ca_system_score_codex":0.000099575016,"about_ca_system_score_gemma":0.000037036614,"threshold_uncertainty_score":0.40627757},"labels":[],"label_agreement":null},{"id":"W4411545027","doi":"10.1088/1748-9326/ade72d","title":"Disasters classification in a compound event perspective: insights from existing databases","year":2025,"lang":"en","type":"article","venue":"Environmental Research Letters","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"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":"European Commission","keywords":"Perspective (graphical); Database; Event (particle physics); Computer science; Data science; Artificial intelligence","score_opus":0.07535481398647627,"score_gpt":0.3789688207772461,"score_spread":0.30361400679076983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411545027","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":[0.60124487,0.00019202587,0.38925296,0.007055112,0.00004695406,0.00037294885,0.000012050767,0.000074350966,0.0017487537],"genre_scores_gemma":[0.99222195,0.000042736632,0.00689687,0.0004681911,0.00002496288,0.0001663357,0.000022577735,0.000005904757,0.0001504747],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9985166,0.00018738059,0.00019094169,0.00050670805,0.00036063397,0.00023773826],"domain_scores_gemma":[0.9991149,0.00024013824,0.000038536284,0.00054340466,0.000007283252,0.000055754048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002208517,0.00009473008,0.0000896127,0.00027883128,0.00025014704,0.000094787334,0.00055994646,0.000026689702,0.000016589718],"category_scores_gemma":[0.000031513082,0.00009715066,0.000035758985,0.00045507835,0.00017312879,0.0003319785,0.00042786676,0.00030449592,0.000060626706],"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":[0.000027325854,0.00059130124,0.012319065,0.000011032171,0.00003154085,0.000035509238,0.0017003195,0.00010554504,0.75880045,0.20379125,0.0034613223,0.019125346],"study_design_scores_gemma":[0.0011536524,0.00011680423,0.7878062,0.00023604748,0.000008883758,0.0000052252026,0.008260532,0.053143367,0.07226394,0.026204808,0.050149206,0.00065132027],"about_ca_topic_score_codex":0.0005181286,"about_ca_topic_score_gemma":0.000045929482,"teacher_disagreement_score":0.7754871,"about_ca_system_score_codex":0.00079172786,"about_ca_system_score_gemma":0.000022775741,"threshold_uncertainty_score":0.3961689},"labels":[],"label_agreement":null},{"id":"W4411551950","doi":"10.1109/icse55347.2025.00224","title":"Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Dalhousie University","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence; Deep neural networks; Pattern recognition (psychology); Machine learning","score_opus":0.014305138323774199,"score_gpt":0.26235900675922386,"score_spread":0.24805386843544966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411551950","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":[0.26875645,0.00006184477,0.73060316,0.00023926166,0.000018494304,0.00014149486,1.2199627e-7,0.0000470088,0.00013214609],"genre_scores_gemma":[0.9791745,0.000060153292,0.02059502,0.000058581787,0.000006447593,0.00008212212,2.5652176e-7,0.0000020889652,0.000020862262],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994635,0.000027503022,0.00016710434,0.00021159941,0.000035292614,0.00009501517],"domain_scores_gemma":[0.99967694,0.00006674966,0.000046812947,0.00015340062,0.000030396903,0.000025679248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010849948,0.00005658562,0.000082442384,0.00013176871,0.00008644693,0.000036062647,0.0000934041,0.00006020501,9.430437e-7],"category_scores_gemma":[0.000013761686,0.000054198408,0.000013912682,0.00042379656,0.0000429373,0.00020105756,0.00009700208,0.000095454074,3.4716994e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001606557,0.00007717136,0.019210285,0.000031708354,0.000006563451,4.8312734e-7,0.00012478272,0.00066877407,0.018350502,0.041840065,0.00000658328,0.919667],"study_design_scores_gemma":[0.00011400202,0.00003215044,0.02794092,0.000006357461,0.000002607053,0.0000026387318,0.000032579755,0.96150345,0.008383486,0.0018842142,0.000050008886,0.0000476087],"about_ca_topic_score_codex":0.00020142084,"about_ca_topic_score_gemma":0.00011286559,"teacher_disagreement_score":0.9608347,"about_ca_system_score_codex":0.000023562607,"about_ca_system_score_gemma":0.000007622878,"threshold_uncertainty_score":0.2210147},"labels":[],"label_agreement":null},{"id":"W4411552045","doi":"10.1109/icse55347.2025.00220","title":"Mock Deep Testing: Toward Separate Development of Data and Models for Deep Learning","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"National Science Foundation","keywords":"Computer science; Deep learning; Artificial intelligence; Data modeling; Data science; Machine learning; Software engineering","score_opus":0.09641629346456665,"score_gpt":0.33007178444085916,"score_spread":0.2336554909762925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411552045","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00092639524,0.000060757528,0.98955804,0.00015477282,0.0000099558065,0.00017693985,7.247939e-7,0.00016270598,0.008949695],"genre_scores_gemma":[0.34058616,0.000004909557,0.65844655,0.000035650155,0.000003355427,0.00004204342,0.0000026458094,0.0000019506308,0.0008767464],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941665,0.0000071995005,0.00017437976,0.00025801742,0.000050546554,0.00009323474],"domain_scores_gemma":[0.9994582,0.00007720852,0.000053017895,0.00030594354,0.00008103615,0.000024591713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001713737,0.000056697383,0.00008085028,0.000054128577,0.00013604989,0.000042228494,0.000483323,0.000027109276,0.0000014570351],"category_scores_gemma":[0.000021312688,0.000051973122,0.000009967679,0.00024129663,0.000014978132,0.00023733596,0.0004953647,0.000041465617,9.0529153e-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":[0.0000021802282,0.000023578767,0.00012783935,0.00003464465,0.000013645359,7.417359e-8,0.0003010934,0.0010449836,0.00086750416,0.086444564,0.00014213966,0.91099775],"study_design_scores_gemma":[0.00007005224,0.000017977514,0.00017113323,0.000007958085,0.0000032699456,9.0259806e-7,0.00003526382,0.9639683,0.008337419,0.019057883,0.008269405,0.00006046219],"about_ca_topic_score_codex":0.000007163305,"about_ca_topic_score_gemma":0.0000062602326,"teacher_disagreement_score":0.9629233,"about_ca_system_score_codex":0.000010097763,"about_ca_system_score_gemma":0.000051695853,"threshold_uncertainty_score":0.21194024},"labels":[],"label_agreement":null},{"id":"W4411552276","doi":"10.1109/icse55347.2025.00078","title":"Automated, Unsupervised, and Auto-Parameterized Inference of Data Patterns and Anomaly Detection","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Anomaly detection; Computer science; Parameterized complexity; Inference; Artificial intelligence; Anomaly (physics); Pattern recognition (psychology); Data mining; Algorithm","score_opus":0.027186502581582887,"score_gpt":0.30759539712763356,"score_spread":0.28040889454605067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411552276","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":[0.21727835,0.000035034383,0.7813311,0.00022561503,0.000022766933,0.00013968402,0.000009476551,0.00054437015,0.00041358898],"genre_scores_gemma":[0.9367069,0.000097482625,0.06293823,0.00011375709,0.0000036378458,0.00002288757,0.0000031703062,0.0000027048018,0.00011123568],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923754,0.00002691348,0.00020314816,0.00036604938,0.00006779021,0.000098552315],"domain_scores_gemma":[0.9990584,0.000079738886,0.000057694,0.00071990106,0.00004663633,0.000037630387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013836671,0.00008697983,0.00012903418,0.00012490564,0.00008860185,0.00009557897,0.00047364293,0.00005654674,0.00000696943],"category_scores_gemma":[0.000019885752,0.00007867368,0.000013330915,0.00032547762,0.000053053976,0.0004646691,0.0006756108,0.000060021743,0.0000012134933],"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":[0.000014319271,0.00010033168,0.016927328,0.00014798701,0.00005144829,0.0000015304222,0.00013199923,0.0000037963923,0.06328563,0.04574075,0.00019678053,0.8733981],"study_design_scores_gemma":[0.00031181375,0.00008908954,0.081420116,0.000032865322,0.000015286865,0.000012100277,0.000028307759,0.8779448,0.035958935,0.0024559195,0.0015820157,0.00014870695],"about_ca_topic_score_codex":0.0004051142,"about_ca_topic_score_gemma":0.00006950934,"teacher_disagreement_score":0.8779411,"about_ca_system_score_codex":0.000008021236,"about_ca_system_score_gemma":0.00002697148,"threshold_uncertainty_score":0.32082194},"labels":[],"label_agreement":null},{"id":"W4411599688","doi":"10.1109/iotm.001.2400162","title":"Leveraging Edge Intelligence and LLMs to Advance 6G-Enabled Internet of Automated Defense Vehicles","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Magazine","topic":"Anomaly Detection Techniques and Applications","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":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Enhanced Data Rates for GSM Evolution; The Internet; Computer security; Business intelligence; Computer science; Business; Telecommunications; World Wide Web; Knowledge management","score_opus":0.01284820026532324,"score_gpt":0.2665939369413274,"score_spread":0.25374573667600414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411599688","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":[0.15373768,0.00013143405,0.84250975,0.00053889834,0.00018971486,0.00022091363,0.0000014331042,0.00043464557,0.0022355157],"genre_scores_gemma":[0.9199877,0.000028539536,0.076367766,0.0005104197,0.000009458938,0.00003014914,8.152568e-7,0.000009287685,0.0030558822],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986895,0.00003258817,0.0005147829,0.0004136648,0.0001534941,0.00019600971],"domain_scores_gemma":[0.9989111,0.00011012283,0.00023384632,0.00046614808,0.0002095364,0.00006924315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002790306,0.00016710516,0.00028613524,0.0002932431,0.00002941887,0.00006762097,0.00089654105,0.00006786034,0.000011634693],"category_scores_gemma":[0.000076969336,0.00016367967,0.000068394926,0.0005832643,0.0000966419,0.00034426493,0.0004643523,0.00015723122,0.00002228077],"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":[0.00022391998,0.00056519243,0.002437457,0.0007916252,0.00025019658,0.000023177181,0.010829872,0.001145831,0.367926,0.15337881,0.047765758,0.41466215],"study_design_scores_gemma":[0.00015047498,0.00020258722,0.0009741907,0.0004763405,0.00001428225,0.000017555894,0.000039583036,0.29734856,0.685934,0.0064787166,0.008157709,0.0002060094],"about_ca_topic_score_codex":0.00017840447,"about_ca_topic_score_gemma":0.000006163514,"teacher_disagreement_score":0.76625,"about_ca_system_score_codex":0.00004485146,"about_ca_system_score_gemma":0.000030890624,"threshold_uncertainty_score":0.6674663},"labels":[],"label_agreement":null},{"id":"W4411617655","doi":"10.1007/978-981-96-6954-7_9","title":"An Innovative Approach to Detection of Steep Changes in Images","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Information retrieval; Remote sensing; Geology","score_opus":0.029235818374849603,"score_gpt":0.29909874477732046,"score_spread":0.26986292640247084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411617655","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006980056,0.000048953945,0.85670054,0.00036262302,0.000051710223,0.0005175125,0.000011934992,0.00007652679,0.14216039],"genre_scores_gemma":[0.4784038,0.0008691462,0.51862967,0.00094706967,0.00002080482,0.00026285578,0.000035428413,0.000008328909,0.00082289445],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987163,0.000029298462,0.00056486006,0.00029020628,0.00024821135,0.00015115121],"domain_scores_gemma":[0.99732715,0.00007804625,0.0002791516,0.0017421866,0.00051584194,0.00005760442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008243514,0.00015965958,0.00022693966,0.0022739966,0.00020170468,0.00021160053,0.0024966844,0.00011063971,9.851334e-7],"category_scores_gemma":[0.000023855106,0.0001690122,0.000019789288,0.0019328496,0.0003682675,0.002988494,0.0012752522,0.00030118495,0.0000035943567],"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":[0.0000017626184,0.00003497341,0.000017886307,0.000025113863,0.0000018713392,2.7843592e-8,0.0013708434,0.00015294275,0.00017325288,0.49889913,0.000027912361,0.49929428],"study_design_scores_gemma":[0.00054977584,0.00049283233,0.008967113,0.00055292214,0.000008052449,0.000018570141,0.00020490542,0.8348709,0.007631438,0.01754314,0.12818775,0.00097260193],"about_ca_topic_score_codex":0.00003745024,"about_ca_topic_score_gemma":0.000034566565,"teacher_disagreement_score":0.8347179,"about_ca_system_score_codex":0.00013133166,"about_ca_system_score_gemma":0.00017159349,"threshold_uncertainty_score":0.6892117},"labels":[],"label_agreement":null},{"id":"W4411653785","doi":"10.1007/978-3-031-92178-0_13","title":"Comparative Analysis of Machine Learning Classifiers for Yellow Fever Diagnosis Using Causative Data: Evaluating Naïve Bayes, KNN, RIPPER, and PART","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mount Royal University","funders":"","keywords":"Naive Bayes classifier; Artificial intelligence; Bayes' theorem; Computer science; Machine learning; Natural language processing; Support vector machine; Bayesian probability","score_opus":0.1741703264378278,"score_gpt":0.40548663044278616,"score_spread":0.23131630400495837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411653785","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021646282,0.00054017716,0.9875008,0.0002839173,0.00007648255,0.00070847676,0.00019559494,0.00006375214,0.010414359],"genre_scores_gemma":[0.13345912,0.0062064705,0.8577514,0.0004790959,0.000029557603,0.00024388416,0.0006749506,0.000010496087,0.0011449864],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982214,0.00006630707,0.0007960021,0.0004324037,0.00030883704,0.00017506316],"domain_scores_gemma":[0.9960522,0.0008018469,0.0006906478,0.0017687819,0.0006177968,0.00006873293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015122986,0.00020997913,0.00048066612,0.0014249937,0.0007751106,0.00031060178,0.0020614418,0.00011140928,0.0000050406306],"category_scores_gemma":[0.000096294156,0.00021397845,0.00006883806,0.001259647,0.0007887841,0.0032566395,0.0026275807,0.00033762679,8.3309584e-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":[0.00001165968,0.00006191009,0.0014163285,0.0001162938,0.00037252912,1.14802035e-7,0.0057673757,0.005970528,0.00002407857,0.58771574,0.0006110482,0.3979324],"study_design_scores_gemma":[0.00015375981,0.0000614968,0.000562798,0.00013273938,0.00013690707,0.0000015422531,0.00006688279,0.9544766,0.00003225941,0.0013164626,0.04286266,0.00019592402],"about_ca_topic_score_codex":0.00007343607,"about_ca_topic_score_gemma":0.000058073187,"teacher_disagreement_score":0.94850606,"about_ca_system_score_codex":0.00012749065,"about_ca_system_score_gemma":0.00027336142,"threshold_uncertainty_score":0.8725788},"labels":[],"label_agreement":null},{"id":"W4411799544","doi":"10.1109/tii.2025.3582142","title":"Retraction Notice: Guest Editorial: Special Section on Edge Intelligence for Industrial Internet of Things","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Notice; Special section; Section (typography); Internet of Things; Industrial Internet; Computer science; Computer security; Telecommunications; Business; Engineering; Political science; Law; Operating system","score_opus":0.0459329113971088,"score_gpt":0.29043606552197043,"score_spread":0.24450315412486162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411799544","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":[0.0007591365,7.4916136e-7,0.85441095,0.00027393692,0.14043412,0.00088341703,0.000025849486,0.00024030569,0.0029715453],"genre_scores_gemma":[0.78964096,0.000073020245,0.027216518,0.0009637441,0.1763704,0.001044349,0.000041381933,0.00006708681,0.0045825616],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979902,0.00005148167,0.0010691164,0.00023985177,0.0004012357,0.00024809374],"domain_scores_gemma":[0.99818665,0.00044170307,0.0004745149,0.0005049592,0.00031260628,0.00007954322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000643016,0.00023355664,0.00029293663,0.0005740883,0.00028484978,0.00018691206,0.00061084674,0.0009166284,0.000022592401],"category_scores_gemma":[0.00009385914,0.000237405,0.00020364218,0.0010724226,0.000083842315,0.0010196115,0.000008676106,0.0012547497,0.000023630892],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009836883,0.0008705514,0.0000059823033,0.00008715383,0.00018754962,3.6275407e-7,0.002120625,0.006860058,0.00043262637,0.027879236,0.5612724,0.3992998],"study_design_scores_gemma":[0.0016001152,0.001694328,0.0000027081012,0.00030205704,0.000101728656,0.000004973756,0.0004332889,0.04227436,0.4269889,0.0015959179,0.52455485,0.0004467757],"about_ca_topic_score_codex":0.00008432086,"about_ca_topic_score_gemma":0.000006784368,"teacher_disagreement_score":0.82719445,"about_ca_system_score_codex":0.00028703635,"about_ca_system_score_gemma":0.00024314242,"threshold_uncertainty_score":0.9681095},"labels":[{"model":"gemma","categories":["research_integrity"],"domain":null,"study_design":"not_applicable","genre":"editorial","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"gpt","categories":["research_integrity"],"domain":null,"study_design":"not_applicable","genre":"editorial","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"agree"},{"id":"W4411811655","doi":"10.1007/978-981-96-8889-0_32","title":"A-REACT: Adaptive Resampling and Active Classification for Thresholded Anomalies","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Resampling; Artificial intelligence; Pattern recognition (psychology); Computer vision","score_opus":0.03696965483662371,"score_gpt":0.28180242874856454,"score_spread":0.24483277391194083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411811655","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000035964953,0.00017545506,0.9927456,0.00089212303,0.00026293666,0.00083744054,0.000019259458,0.00021759825,0.0048136697],"genre_scores_gemma":[0.18375584,0.00009164198,0.81421745,0.00070390885,0.00020740811,0.00017379584,0.000009539752,0.000021498292,0.0008188904],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99779844,0.000011989766,0.00033867676,0.0012305158,0.00029510565,0.0003252946],"domain_scores_gemma":[0.99799275,0.00055507955,0.00025735778,0.000822494,0.00029155036,0.00008074691],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037356338,0.00031901736,0.0003222388,0.00059790065,0.00041860333,0.00033179641,0.0012327548,0.0002620933,0.0000029389741],"category_scores_gemma":[0.00005462316,0.00030673217,0.00008692838,0.00041819262,0.00044605965,0.0004280557,0.0005931775,0.00038916702,0.0000023289747],"study_design_candidate":"theoretical_or_conceptual","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":[0.000009417955,0.00001145661,0.000007480543,0.000017975808,0.00000760934,0.0000010687343,0.0002013798,0.00027652126,0.00032219407,0.298016,0.000035129222,0.70109373],"study_design_scores_gemma":[0.00018728492,0.00019262395,0.00030162852,0.00024373543,0.0000140658085,0.000014106889,0.0000012525684,0.506352,0.0058283,0.4819298,0.004456749,0.00047841016],"about_ca_topic_score_codex":0.000014027896,"about_ca_topic_score_gemma":0.000034683628,"teacher_disagreement_score":0.70061535,"about_ca_system_score_codex":0.00017806423,"about_ca_system_score_gemma":0.0003172957,"threshold_uncertainty_score":0.9999385},"labels":[],"label_agreement":null},{"id":"W4411949928","doi":"10.1109/iwcmc65282.2025.11059562","title":"Enhancing Network Anomaly Detection with Quantum GANs and Successive Data Injection for Multivariate Time Series","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Université de Sherbrooke; Polytechnique Montréal","funders":"","keywords":"Anomaly detection; Multivariate statistics; Series (stratigraphy); Computer science; Time series; Anomaly (physics); Data mining; Artificial intelligence; Pattern recognition (psychology); Machine learning; Geology; Physics","score_opus":0.01073050233633027,"score_gpt":0.260440038659818,"score_spread":0.24970953632348775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411949928","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":[0.0042372085,0.000027727086,0.99391603,0.0004136579,0.00007776127,0.00044022332,0.000005687225,0.00041126998,0.0004704596],"genre_scores_gemma":[0.79310864,0.000008556,0.20436776,0.00015419818,0.00007263716,0.00018974335,0.00001405728,0.000008605986,0.0020758125],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991494,0.00002306523,0.00016300938,0.00043948423,0.00005923855,0.0001658093],"domain_scores_gemma":[0.9991619,0.000100831494,0.00008515657,0.00052688643,0.00009340723,0.00003183161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000199851,0.00010599061,0.000113694114,0.00007665015,0.00042538726,0.00017131808,0.00033968367,0.00005764501,0.0000050937865],"category_scores_gemma":[0.00002179169,0.0000885271,0.000016415826,0.0004970524,0.000036244543,0.0008231323,0.00023403142,0.00006874132,0.0000031515608],"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":[0.0004729551,0.00020621679,0.0016466557,0.00021743089,0.000293513,0.0000034288596,0.000560307,0.0015952059,0.119537495,0.58433664,0.0035813341,0.28754878],"study_design_scores_gemma":[0.00054835255,0.0007297995,0.0031094356,0.00008127472,0.00004811315,0.000030560004,0.000109816676,0.8512349,0.11386506,0.019982766,0.009886724,0.0003731733],"about_ca_topic_score_codex":0.00024193121,"about_ca_topic_score_gemma":0.00061104767,"teacher_disagreement_score":0.8496397,"about_ca_system_score_codex":0.000024863597,"about_ca_system_score_gemma":0.000055676763,"threshold_uncertainty_score":0.36100304},"labels":[],"label_agreement":null},{"id":"W4412030696","doi":"10.1109/jiot.2025.3585884","title":"Deep Learning Advancements in Anomaly Detection: A Comprehensive Survey","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Anomaly detection; Deep learning; Artificial intelligence; Data science","score_opus":0.016040665378799793,"score_gpt":0.2843870404881003,"score_spread":0.26834637510930054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412030696","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":[0.1818362,0.00012865812,0.8167295,0.00008915995,0.0003484976,0.00007178266,1.3071651e-7,0.000050212777,0.0007458993],"genre_scores_gemma":[0.9860759,0.000060708957,0.012946016,0.00020093228,0.000019020948,0.000009787904,2.8200833e-7,0.0000047499057,0.0006825988],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893796,0.00012482304,0.00042310933,0.00019448725,0.00015740829,0.00016223449],"domain_scores_gemma":[0.9991752,0.00009785564,0.00028207997,0.00016967824,0.00023293046,0.00004226198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040491018,0.0001001759,0.00017379285,0.00032921237,0.00008521102,0.00010950928,0.00063247787,0.000056946305,0.000014422937],"category_scores_gemma":[0.000046017525,0.000099906174,0.00007559857,0.0005185771,0.000036248806,0.00048705042,0.000119966695,0.0004783109,0.000006517671],"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":[0.00032051178,0.00052646763,0.09797298,0.00009918054,0.0002710969,0.0000661929,0.003837097,0.00594961,0.06396263,0.0046934653,0.0012973648,0.8210034],"study_design_scores_gemma":[0.0018034445,0.0008604186,0.23419479,0.00060330465,0.000021487765,0.00044072207,0.00028734797,0.45311734,0.2737823,0.011993185,0.022292938,0.00060270255],"about_ca_topic_score_codex":0.0002976415,"about_ca_topic_score_gemma":0.0000649854,"teacher_disagreement_score":0.8204007,"about_ca_system_score_codex":0.000100632984,"about_ca_system_score_gemma":0.00003165269,"threshold_uncertainty_score":0.40740556},"labels":[],"label_agreement":null},{"id":"W4412045316","doi":"10.1007/s10489-025-06749-y","title":"Improving retail sales through unsupervised collective-contextual anomaly detection: a deep reconstruction autoencoder for network-wide sales analysis","year":2025,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institutes of Health Research; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Autoencoder; Computer science; Anomaly detection; Anomaly (physics); Artificial intelligence; Machine learning; Data mining; Pattern recognition (psychology); Data science; Deep learning","score_opus":0.019887485361882364,"score_gpt":0.2558557808389856,"score_spread":0.2359682954771032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412045316","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":[0.0022507038,0.00027162008,0.9908297,0.00027366777,0.00024018488,0.001038328,0.00000570943,0.0007407677,0.004349334],"genre_scores_gemma":[0.71917194,0.00006276194,0.27842903,0.0004170387,0.00008328247,0.0011505074,0.000010270012,0.000015477099,0.0006596859],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99764425,0.000053478427,0.0006645275,0.0009801323,0.0001856771,0.00047192696],"domain_scores_gemma":[0.99803776,0.00049729116,0.0002761247,0.00081835676,0.00029629533,0.00007419919],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038724224,0.00030234698,0.00042299894,0.00033070758,0.0008678826,0.000264307,0.0008640979,0.00020789249,0.000032157244],"category_scores_gemma":[0.000076282915,0.00031512041,0.00031053403,0.003855135,0.00018311095,0.00041747585,0.00020494203,0.00024351018,0.000029562309],"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":[0.000098671844,0.00008884244,0.0009432317,0.00005213344,0.0005571397,0.0000010042108,0.0006620262,0.007964299,0.0034658886,0.24512595,0.00033552002,0.7407053],"study_design_scores_gemma":[0.0002598889,0.000166782,0.0014001591,0.000032353983,0.0003743015,0.00001413076,0.00065054384,0.8207239,0.07226529,0.09682322,0.0066451393,0.0006442743],"about_ca_topic_score_codex":0.00011341203,"about_ca_topic_score_gemma":0.0005899394,"teacher_disagreement_score":0.81275964,"about_ca_system_score_codex":0.0002598,"about_ca_system_score_gemma":0.00019198828,"threshold_uncertainty_score":0.9999301},"labels":[],"label_agreement":null},{"id":"W4412108014","doi":"10.1130/g53365.1","title":"Palinspastic restoration of the Olympic orocline and its implications","year":2025,"lang":"en","type":"article","venue":"Geology","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Geology; Earth science; Paleontology; Seismology","score_opus":0.011111434686958174,"score_gpt":0.2646898882482599,"score_spread":0.2535784535613017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412108014","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":[0.07262879,0.0001608678,0.911227,0.01431583,0.00007877236,0.000157037,0.0000013070842,0.000071840055,0.001358549],"genre_scores_gemma":[0.9956724,0.000024036146,0.0032620346,0.00036234542,0.000008368335,0.000046863046,5.9871235e-7,9.397922e-7,0.00062243734],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99967253,0.00002294507,0.00011240135,0.00011447016,0.000022664117,0.000055013472],"domain_scores_gemma":[0.99955493,0.000045808345,0.000053704967,0.0002751167,0.000060239992,0.000010169893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005640705,0.000031467494,0.00005025639,0.000042229687,0.00010131628,0.0000061283777,0.00023862364,0.000037611586,0.0000033778163],"category_scores_gemma":[0.00003150561,0.00002405047,0.00001534117,0.00030576496,0.000042203093,0.00003889081,0.0001412456,0.000039372393,0.0000022830955],"study_design_candidate":"theoretical_or_conceptual","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":[6.5617564e-7,0.00001350209,0.0040324256,0.0000051284765,0.0000033432425,2.6208417e-8,0.000026502843,0.000021809978,0.0056798575,0.9800705,0.00039660462,0.009749666],"study_design_scores_gemma":[0.00025274148,0.000077184224,0.6569076,0.000015305619,0.000019007031,0.000019492627,0.000011642245,0.029846616,0.023211146,0.2581717,0.031350445,0.00011710132],"about_ca_topic_score_codex":0.0000132527875,"about_ca_topic_score_gemma":0.000020673408,"teacher_disagreement_score":0.9230436,"about_ca_system_score_codex":0.000006944196,"about_ca_system_score_gemma":0.000035054763,"threshold_uncertainty_score":0.09807497},"labels":[],"label_agreement":null},{"id":"W4412623196","doi":"10.1109/tnse.2025.3592574","title":"EMForecaster: A Deep Learning Framework for Time Series Forecasting in Wireless Networks With Distribution-Free Uncertainty Quantification","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"Anomaly Detection Techniques and Applications","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":"York University","funders":"","keywords":"Computer science; Series (stratigraphy); Time series; Wireless; Artificial intelligence; Wireless network; Distribution (mathematics); Deep learning; Machine learning; Telecommunications; Mathematics; Geology","score_opus":0.008104629607450278,"score_gpt":0.21361182121287559,"score_spread":0.2055071916054253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412623196","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":[0.0074582687,0.000032961394,0.99146056,0.00028676577,0.000150264,0.00032360412,0.000002746814,0.00024995572,0.00003484198],"genre_scores_gemma":[0.9178141,0.00003949184,0.08172393,0.000036131933,0.000032774078,0.00028665742,0.0000023730252,0.000008280413,0.000056305926],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988336,0.000011863128,0.00021060824,0.00040984107,0.00015561543,0.00037847072],"domain_scores_gemma":[0.9992263,0.00020677887,0.00005707211,0.0003260914,0.00011999391,0.00006378255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050748856,0.0001446508,0.0001449862,0.00016364499,0.0006603766,0.00022381858,0.00042442378,0.000078596284,0.0000015094824],"category_scores_gemma":[0.000023189737,0.00013794046,0.00003258417,0.002212757,0.000109854656,0.00046644657,0.00000988487,0.0002816431,6.4406925e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016342023,0.000014747351,0.000034716337,0.000013468844,0.0000054677707,3.9686307e-7,0.000051413608,0.92816323,0.00012957332,0.025010787,0.00001383733,0.046546042],"study_design_scores_gemma":[0.00013248164,0.000093677016,0.00019230596,0.00017381065,0.000007094676,0.000007878454,0.00002314205,0.99693435,0.0008828245,0.0010622246,0.00033849225,0.00015174226],"about_ca_topic_score_codex":0.0000114342465,"about_ca_topic_score_gemma":0.000025948255,"teacher_disagreement_score":0.9103558,"about_ca_system_score_codex":0.00011385009,"about_ca_system_score_gemma":0.00006669098,"threshold_uncertainty_score":0.5625049},"labels":[],"label_agreement":null},{"id":"W4412692904","doi":"10.1007/s44196-025-00888-3","title":"Improved Crime Prediction Using Hybrid Neural Architecture Search Together with Hyperparameter Tuning","year":2025,"lang":"en","type":"article","venue":"International Journal of Computational Intelligence Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"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":"Hyperparameter; Computer science; Hyperparameter optimization; Artificial intelligence; Machine learning; Artificial neural network; Pattern recognition (psychology); Data mining; Support vector machine","score_opus":0.025194905595206214,"score_gpt":0.3116027483846547,"score_spread":0.2864078427894485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412692904","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":[0.03274385,0.00015256976,0.9648326,0.00062503124,0.0009832846,0.00020930653,0.000012283336,0.000070195216,0.0003708515],"genre_scores_gemma":[0.91016644,0.000006722525,0.08926831,0.00018810586,0.00021503666,0.0000105737545,0.0000044346657,0.000009868969,0.0001305236],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99811906,0.000108597276,0.0006890656,0.00025254703,0.0006594425,0.0001712978],"domain_scores_gemma":[0.9974829,0.0002209229,0.000379402,0.00018975581,0.0016501584,0.00007689093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004282676,0.00015991204,0.00020713307,0.00057960185,0.00012761207,0.00038538795,0.0010657712,0.000052266696,0.000009384986],"category_scores_gemma":[0.00003460496,0.0001298516,0.00012211344,0.00038449833,0.00007017661,0.00051753246,0.00012520378,0.00034325855,0.0000033597908],"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":[0.00008195524,0.00009281281,0.00042024493,0.000016441705,0.00025067193,0.000028874418,0.00022460683,0.9317752,0.002679733,0.033609238,0.00013940991,0.030680792],"study_design_scores_gemma":[0.00013956435,0.00016340661,0.00027909692,0.00017052003,0.00001589436,0.0012124244,0.00011520677,0.98395747,0.005945154,0.007039359,0.0008402429,0.000121667814],"about_ca_topic_score_codex":0.00006493037,"about_ca_topic_score_gemma":7.794456e-7,"teacher_disagreement_score":0.8774226,"about_ca_system_score_codex":0.0001979432,"about_ca_system_score_gemma":0.00027240015,"threshold_uncertainty_score":0.52951944},"labels":[],"label_agreement":null},{"id":"W4412712956","doi":"10.1109/rfcon62306.2025.11085272","title":"Enhancing IoT Device Classifiers using Machine Learning Techniques","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Computer science; Internet of Things; Artificial intelligence; Machine learning; Embedded system","score_opus":0.01858104663520107,"score_gpt":0.2938694865618314,"score_spread":0.2752884399266303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412712956","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003523695,0.00005557413,0.9761327,0.00056951836,0.000042362786,0.00013652594,2.024934e-7,0.001415434,0.018124016],"genre_scores_gemma":[0.38858247,0.000013448489,0.6074666,0.0006499201,0.000018469531,0.000030843636,5.009576e-7,0.000005567554,0.0032321739],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992591,0.000031874777,0.00018882689,0.00027163772,0.00008534743,0.0001632263],"domain_scores_gemma":[0.99949807,0.00004333764,0.00005921704,0.00030225053,0.000060996765,0.000036122983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020192715,0.00009416456,0.00009972197,0.00017088877,0.00031406115,0.00010641779,0.00039824768,0.00006510214,0.000021285317],"category_scores_gemma":[0.000020064193,0.000088653505,0.00004952977,0.00070169324,0.000023898017,0.00013053234,0.00021376509,0.0001870729,0.000009086797],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025798809,0.00005047201,0.0015497729,0.00003196751,0.000023333703,0.0000024382991,0.00009908428,0.000057364552,0.46267796,0.38436738,0.0002907928,0.15084687],"study_design_scores_gemma":[0.000035563437,0.000021252112,0.00008390214,0.00002938684,0.0000050900894,0.0000058425976,0.000028506118,0.12029428,0.8123309,0.0026717882,0.0643682,0.00012529863],"about_ca_topic_score_codex":0.00022450193,"about_ca_topic_score_gemma":0.000041432155,"teacher_disagreement_score":0.3850588,"about_ca_system_score_codex":0.000072610914,"about_ca_system_score_gemma":0.000056287638,"threshold_uncertainty_score":0.3615185},"labels":[],"label_agreement":null},{"id":"W4412754243","doi":"10.2139/ssrn.5372224","title":"Evolution of Reid: From Early Methods to Llms Integration","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"History","score_opus":0.012043364435319438,"score_gpt":0.31917562989099396,"score_spread":0.3071322654556745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412754243","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":[0.007845862,0.001051456,0.98865557,0.0008465216,0.0003802309,0.00029940042,0.000011732037,0.00014286092,0.000766347],"genre_scores_gemma":[0.51224005,0.0006984139,0.4854529,0.00006082025,0.00020714122,0.00007930055,0.0000061600626,0.00001057898,0.0012446371],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978538,0.00021944621,0.00052204885,0.00043493928,0.00023742448,0.0007323413],"domain_scores_gemma":[0.9984287,0.000071675735,0.0004347977,0.000696875,0.00029571526,0.00007224678],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001611312,0.00019350387,0.00028856343,0.0003824009,0.00014380911,0.00012115168,0.0013491571,0.00024220966,0.0000065026347],"category_scores_gemma":[0.00006799035,0.00018543201,0.00021313992,0.0005092967,0.000022105321,0.00016922703,0.00057365774,0.002459724,0.000008082546],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001085289,0.00003713952,0.00006264199,0.000005009298,0.00007398347,1.6506426e-7,0.00019813629,0.00023580805,0.0037773694,0.68416244,0.00013114979,0.3113053],"study_design_scores_gemma":[0.00008655573,0.00018297292,0.0009259522,0.00010248003,0.0000343687,0.000012178828,0.00011052335,0.0068530207,0.01275588,0.97764367,0.0011057236,0.00018665464],"about_ca_topic_score_codex":0.0015287732,"about_ca_topic_score_gemma":0.00031968488,"teacher_disagreement_score":0.50439423,"about_ca_system_score_codex":0.0016541176,"about_ca_system_score_gemma":0.002620095,"threshold_uncertainty_score":0.99984163},"labels":[],"label_agreement":null},{"id":"W4412828160","doi":"10.1016/j.conengprac.2025.106485","title":"Real-time classification and early warning of industrial alarm floods using modified TF-IDF methods","year":2025,"lang":"en","type":"article","venue":"Control Engineering Practice","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"ALARM; Warning system; Computer science; False alarm; Data mining; Real-time computing; Reliability engineering; Engineering; Artificial intelligence; Telecommunications","score_opus":0.03273437176621574,"score_gpt":0.3290936591413625,"score_spread":0.2963592873751468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412828160","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":[0.0068284767,0.000081298575,0.9908209,0.00058011455,0.00011427223,0.00024179918,0.0000010419418,0.00026171716,0.0010704151],"genre_scores_gemma":[0.6320481,0.000022879121,0.36776006,0.000025664001,0.000038323516,0.000033953103,3.6564327e-7,0.000007375891,0.00006332871],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991024,0.0001214259,0.0002897855,0.0002487925,0.00009750215,0.00014007174],"domain_scores_gemma":[0.99851686,0.0007568098,0.00020010372,0.00035184564,0.00012865558,0.00004572992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089517183,0.000107686974,0.00019163117,0.00017581623,0.00008972475,0.00009171496,0.00023951489,0.000118390024,0.00000148446],"category_scores_gemma":[0.00067970605,0.00011763577,0.00004027368,0.000469801,0.000020044276,0.0005290727,0.00007493767,0.00022077584,0.0000010107743],"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":[0.000062896215,0.00009514974,0.00015545553,0.00004135242,0.0001902359,0.0000021582132,0.00030014434,0.057887197,0.67083097,0.15179569,0.00009010717,0.11854866],"study_design_scores_gemma":[0.00045321076,0.000035498728,0.00071284035,0.000031151718,0.000065612236,0.0000072582006,0.0000130034205,0.9894448,0.0059674648,0.00017092776,0.0029931215,0.00010512611],"about_ca_topic_score_codex":0.00009753661,"about_ca_topic_score_gemma":6.8093456e-8,"teacher_disagreement_score":0.9315576,"about_ca_system_score_codex":0.00003941902,"about_ca_system_score_gemma":0.00006333818,"threshold_uncertainty_score":0.47970474},"labels":[],"label_agreement":null},{"id":"W4412889746","doi":"10.18653/v1/2025.acl-long.1458","title":"Stochastic Chameleons: Irrelevant Context Hallucinations Reveal Class-Based (Mis)Generalization in LLMs","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research; Nvidia","keywords":"Generalization; Context (archaeology); Class (philosophy); Computer science; Cognitive psychology; Artificial intelligence; Psychology; Social psychology; Econometrics; Mathematics; Geography","score_opus":0.009901039363788106,"score_gpt":0.26794870818150035,"score_spread":0.25804766881771224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412889746","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":[0.0018112701,0.000056594472,0.98504764,0.006766631,0.000098148455,0.0003983639,0.0000024373942,0.0004046693,0.005414233],"genre_scores_gemma":[0.9712028,0.000004966184,0.023358444,0.0018582797,0.000013948398,0.00028913846,0.000008027317,0.0000056224435,0.0032587657],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989969,0.000043732623,0.00030357568,0.00034608625,0.00012860619,0.00018111114],"domain_scores_gemma":[0.99923193,0.00008500885,0.00006900348,0.00045043248,0.000119768774,0.000043846037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017635191,0.00011249764,0.00012965365,0.00034587484,0.00014481401,0.00009089702,0.00040715048,0.00007841348,0.00002936543],"category_scores_gemma":[0.000047001035,0.000110137145,0.000051371997,0.0011247506,0.00003506967,0.00019371646,0.000054577573,0.00011452952,0.000028489869],"study_design_candidate":"theoretical_or_conceptual","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":[0.000003114853,0.00013176959,0.00013802203,0.000012058866,0.00000542705,9.332486e-7,0.000071160684,0.003425541,0.0014248429,0.9553312,0.0026008398,0.036855087],"study_design_scores_gemma":[0.0005622939,0.0000677642,0.0034847388,0.00006474876,0.0000066902617,0.0000022422385,0.00006908245,0.9584899,0.0067677004,0.017832603,0.012413659,0.00023857059],"about_ca_topic_score_codex":0.00015585798,"about_ca_topic_score_gemma":0.00030774117,"teacher_disagreement_score":0.9693915,"about_ca_system_score_codex":0.00011653596,"about_ca_system_score_gemma":0.00014157199,"threshold_uncertainty_score":0.44912624},"labels":[],"label_agreement":null},{"id":"W4412905081","doi":"10.17072/1993-0550-2025-2-47-64","title":"An Algorithm for the Initial Detection of Malicious Traffic Based on the Autoencoder Reconstruction Error and a Variational Model: the Influence of the Error Distribution Density on the Performance Indicators of the Models","year":2025,"lang":"en","type":"article","venue":"Вестник Пермского университета Математика Механика Информатика","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Autoencoder; Algorithm; Computer science; Distribution (mathematics); Artificial intelligence; Mathematics; Artificial neural network","score_opus":0.01400314173683833,"score_gpt":0.2524198923661808,"score_spread":0.23841675062934245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412905081","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":[0.44722497,0.000024358389,0.5467529,0.003905002,0.00020735721,0.0016208747,0.00012491843,0.00007159689,0.00006798241],"genre_scores_gemma":[0.9964506,0.000025803436,0.0018911083,0.0008469595,0.00005743513,0.0006581421,0.000006144331,0.000020895468,0.00004290443],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997028,0.00048490413,0.0008083018,0.0005695218,0.0007538187,0.0003554887],"domain_scores_gemma":[0.9954095,0.0012170451,0.0009282747,0.0019334744,0.00046013284,0.000051611245],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0016593032,0.0003544348,0.00031993855,0.00014897126,0.0018952687,0.00010810375,0.0021347585,0.00023003439,0.000006291641],"category_scores_gemma":[0.00019200615,0.0001578372,0.00031259411,0.0016095965,0.0010689729,0.00037043446,0.00027909572,0.00064668246,9.837887e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018050226,0.00038140215,0.00036084183,0.00006128514,0.00014253928,1.353789e-7,0.0012466568,0.74345684,0.0032024593,0.11381805,0.00022738917,0.13692187],"study_design_scores_gemma":[0.00032655406,0.00021420587,0.018476736,0.000108082444,0.00009758439,0.000011540111,0.00017978161,0.9326762,0.040459957,0.0071748896,0.00011001428,0.0001644302],"about_ca_topic_score_codex":0.00012911667,"about_ca_topic_score_gemma":0.00009704694,"teacher_disagreement_score":0.5492256,"about_ca_system_score_codex":0.00014848699,"about_ca_system_score_gemma":0.00051171175,"threshold_uncertainty_score":0.99940413},"labels":[],"label_agreement":null},{"id":"W4412979458","doi":"10.1088/2632-2153/adf7fe","title":"Uncertainty quantification from ensemble variance scaling laws in deep neural networks","year":2025,"lang":"en","type":"article","venue":"Machine Learning Science and Technology","topic":"Anomaly Detection Techniques and Applications","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":"Vector Institute; University of Toronto","funders":"High Energy Physics","keywords":"Variance (accounting); Artificial neural network; Scaling law; Deep neural networks; Scaling; Law; Computer science; Artificial intelligence; Econometrics; Statistical physics; Mathematics; Political science; Economics; Physics; Accounting","score_opus":0.006595860381345152,"score_gpt":0.2530018814771641,"score_spread":0.24640602109581897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412979458","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":[0.10324641,0.00033722914,0.8903908,0.0049045705,0.000079598336,0.000119305936,2.8782884e-7,0.00046418587,0.0004576073],"genre_scores_gemma":[0.9846098,0.00004102948,0.015018771,0.00020407181,0.000008953641,0.000042874024,0.0000017063713,0.0000030562608,0.00006976498],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884623,0.000032785974,0.00019471293,0.0005425024,0.00012207641,0.00026166707],"domain_scores_gemma":[0.9993255,0.000071248134,0.00008156611,0.00038892456,0.00010281234,0.000029929968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005476082,0.00009472624,0.00012662308,0.000519169,0.0004901466,0.00015022118,0.00077495875,0.00010764575,0.0000024410404],"category_scores_gemma":[0.00018017055,0.00009126846,0.000013851727,0.0036101963,0.00033532787,0.00025146484,0.00032472584,0.00041785577,0.0000028126592],"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":[0.0000028320674,0.000027833054,0.020701542,0.0000028123013,0.0000023777777,0.0000023724072,0.000071302355,0.018717255,0.009782397,0.31735325,0.000008990554,0.633327],"study_design_scores_gemma":[0.00009680323,0.00002371468,0.0047463425,0.000010435485,0.0000018229496,0.0000039374268,0.00003513305,0.97151625,0.0020291351,0.019769983,0.0016784038,0.0000880358],"about_ca_topic_score_codex":0.0005061394,"about_ca_topic_score_gemma":0.00015546275,"teacher_disagreement_score":0.952799,"about_ca_system_score_codex":0.00005500727,"about_ca_system_score_gemma":0.000044270888,"threshold_uncertainty_score":0.3769861},"labels":[],"label_agreement":null},{"id":"W4413003197","doi":"10.1007/978-3-031-96625-5_10","title":"MAD-AD: Masked Diffusion for Unsupervised Brain Anomaly Detection","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Computer science; Anomaly detection; Diffusion; Artificial intelligence; Pattern recognition (psychology); Physics","score_opus":0.011782059464458334,"score_gpt":0.2467004645494136,"score_spread":0.23491840508495526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413003197","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000083756284,0.00014287865,0.9931577,0.0022572486,0.0008401491,0.0011937631,0.000012934962,0.0005242261,0.00178735],"genre_scores_gemma":[0.15843563,0.00007511105,0.8306162,0.003871918,0.00039831354,0.00029440582,0.00001667631,0.0000575043,0.006234274],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967762,0.0000259963,0.0005421121,0.0016155817,0.0005047996,0.00053531403],"domain_scores_gemma":[0.99737036,0.00052212283,0.00025606144,0.0014227424,0.00029519803,0.00013353558],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006007297,0.0004790101,0.00044477163,0.0010217407,0.0005597196,0.000433514,0.0024174626,0.00043371576,0.000015381942],"category_scores_gemma":[0.00008439117,0.00046680032,0.00023203349,0.00089800416,0.00030680583,0.0003956494,0.00091589516,0.0005225713,0.000014664249],"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":[0.000008917631,0.000027707914,0.0000054549596,0.00004435148,0.000006823911,0.0000038786616,0.00010404702,0.00050063495,0.0039771846,0.014140381,0.00007805011,0.9811026],"study_design_scores_gemma":[0.0004433665,0.00035525678,0.00014557476,0.00024970068,0.000014403779,0.00002990311,2.283679e-7,0.7277794,0.022615239,0.21270083,0.03489265,0.0007734758],"about_ca_topic_score_codex":0.000025783227,"about_ca_topic_score_gemma":0.0001054849,"teacher_disagreement_score":0.9803291,"about_ca_system_score_codex":0.00035125273,"about_ca_system_score_gemma":0.00034370285,"threshold_uncertainty_score":0.9997784},"labels":[],"label_agreement":null},{"id":"W4413067626","doi":"10.1109/ginotech63460.2025.11076822","title":"Real-Time Intelligent Surveillance with YOLOv8 for Threat Detection and DenseNet121 for Activity Recognition","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Computer science; Computer security; Artificial intelligence","score_opus":0.01639867851717115,"score_gpt":0.2660312790020946,"score_spread":0.24963260048492347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413067626","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":[0.0710502,0.0000068653344,0.92641926,0.00046927427,0.000030999236,0.00078978,0.0000068694294,0.0002977406,0.00092901866],"genre_scores_gemma":[0.8444259,0.00007410895,0.15319823,0.00006972096,0.000022393113,0.00077763584,0.0000038978965,0.0000065869895,0.001421536],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99941844,0.000014919943,0.00009801877,0.00030540745,0.000043065225,0.00012016346],"domain_scores_gemma":[0.99939895,0.00017291767,0.00005275709,0.0002072066,0.0001394609,0.00002871373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016078733,0.00008467453,0.00009963082,0.000070873546,0.00018931829,0.00006893956,0.00010362637,0.000053328433,0.000003005629],"category_scores_gemma":[0.000014850556,0.00007082494,0.000035048324,0.00021352735,0.000026248263,0.00013331645,0.000036322996,0.000032943077,0.000002507466],"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":[0.0001348789,0.00005933591,0.00022003372,0.000045127363,0.000028206956,1.12017474e-7,0.00002899824,0.000008475318,0.061760005,0.0074749556,0.00045985848,0.92978],"study_design_scores_gemma":[0.0004584399,0.00062882266,0.00515033,0.000024165372,0.000017106115,0.000012363334,0.000016921485,0.14302449,0.81175303,0.033285048,0.0053668967,0.00026235997],"about_ca_topic_score_codex":0.00007719574,"about_ca_topic_score_gemma":0.00016681121,"teacher_disagreement_score":0.9295176,"about_ca_system_score_codex":0.000038200826,"about_ca_system_score_gemma":0.00002463101,"threshold_uncertainty_score":0.28881574},"labels":[],"label_agreement":null},{"id":"W4413097588","doi":"10.4018/jdm.386132","title":"A Big Data Management and Analytics Framework for Supporting Machine Learning, OLAP, and Visualization on Big COVID-19 Data","year":2025,"lang":"en","type":"article","venue":"Journal of Database Management","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Big data; Data science; Computer science; Online analytical processing; Analytics; Data visualization; Business intelligence; Visualization; Variety (cybernetics); Visual analytics; Data analysis; Data warehouse; Data mining; Artificial intelligence","score_opus":0.10527804563174097,"score_gpt":0.3926301870058297,"score_spread":0.2873521413740887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413097588","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017719973,0.00024863952,0.9949292,0.003642689,0.00013811328,0.00040996584,0.00015143256,0.00005503965,0.0002477043],"genre_scores_gemma":[0.07991711,0.0045366525,0.90784854,0.00495391,0.00024221498,0.000040226132,0.0009269019,0.000025963196,0.0015084903],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986021,0.000052350977,0.000441614,0.00048546714,0.00025203053,0.00016642876],"domain_scores_gemma":[0.99787676,0.00015059083,0.00046325312,0.0013373486,0.00005121113,0.00012082063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015464276,0.00013182512,0.00017842074,0.00046408916,0.0002815308,0.00025453672,0.0013029522,0.00003317184,0.000003845093],"category_scores_gemma":[0.00016634559,0.000119320175,0.000024477287,0.0005153429,0.000032847012,0.00036870842,0.0030154882,0.00016944177,7.857171e-7],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057725283,0.00015046161,0.0008253711,0.0007311848,0.0003467563,0.000053284228,0.000063998974,0.00015109497,0.000017249104,0.47551748,0.049052857,0.47303256],"study_design_scores_gemma":[0.00070543657,0.00018642891,0.00037145228,0.00018681072,0.0002476875,0.000014775756,0.00021149241,0.23381129,0.000055847733,0.0061386386,0.75789714,0.00017299758],"about_ca_topic_score_codex":0.000012987182,"about_ca_topic_score_gemma":0.000010838499,"teacher_disagreement_score":0.7088443,"about_ca_system_score_codex":0.00004994276,"about_ca_system_score_gemma":0.000033124958,"threshold_uncertainty_score":0.48657355},"labels":[],"label_agreement":null},{"id":"W4413145953","doi":"10.1109/cvpr52734.2025.00952","title":"Beyond Clean Training Data: A Versatile and Model-Agnostic Framework for Out-of-Distribution Detection with Contaminated Training Data","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Training (meteorology); Computer science; Training set; Data modeling; Data mining; Artificial intelligence; Database","score_opus":0.07410539225919884,"score_gpt":0.32065925723558897,"score_spread":0.24655386497639015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413145953","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":[0.0035589354,0.000019133411,0.9948213,0.0002859966,0.00006672471,0.0004216474,0.00024939698,0.0002467558,0.00033008773],"genre_scores_gemma":[0.7465971,0.000007655264,0.25299567,0.00008011635,0.000013842376,0.000047597372,0.00020319776,0.000004975993,0.00004982834],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989815,0.000017414164,0.00021956484,0.00051947345,0.00009607479,0.00016597119],"domain_scores_gemma":[0.99845344,0.00028922208,0.00010866838,0.0010188263,0.00008710148,0.000042742344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003324456,0.00010723039,0.0001616375,0.00006493937,0.000212701,0.00008204045,0.00073741766,0.00008703283,0.0000017972293],"category_scores_gemma":[0.00014665577,0.000098191624,0.00001544072,0.0003343369,0.00006750443,0.0005679534,0.00034875196,0.00012145839,3.6392137e-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":[0.00004737899,0.000056119457,0.000029104614,0.000051021772,0.000063208194,6.0355285e-7,0.0013532548,0.0004508525,0.0011957645,0.25235185,0.0006967491,0.7437041],"study_design_scores_gemma":[0.00029383358,0.00014008266,0.00013719527,0.000059953505,0.000042176573,0.0000029096086,0.0005905571,0.9687148,0.0041108537,0.024560198,0.0012255071,0.00012196835],"about_ca_topic_score_codex":0.000028398299,"about_ca_topic_score_gemma":0.00011394306,"teacher_disagreement_score":0.9682639,"about_ca_system_score_codex":0.000026191337,"about_ca_system_score_gemma":0.000100551,"threshold_uncertainty_score":0.4004138},"labels":[],"label_agreement":null},{"id":"W4413146570","doi":"10.1109/cvpr52734.2025.00446","title":"PIAD: Pose and Illumination agnostic Anomaly Detection","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Simon Fraser University","funders":"Research and Development; Natural Sciences and Engineering Research Council of Canada; China Scholarship Council; Google","keywords":"Computer science; Anomaly detection; Artificial intelligence; Computer vision","score_opus":0.004164404903054871,"score_gpt":0.23129558094669364,"score_spread":0.22713117604363878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413146570","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":[0.042765222,0.00003822808,0.94469196,0.0010252969,0.000079429556,0.0001352578,3.1466584e-7,0.00039145918,0.010872834],"genre_scores_gemma":[0.9798742,0.000021763313,0.017763944,0.00033833645,0.000013623619,0.0000485136,4.1809054e-7,0.0000020192824,0.0019371633],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99954414,0.00001347583,0.000103811144,0.00020481793,0.000053577907,0.00008017048],"domain_scores_gemma":[0.99963295,0.000045695593,0.000028062263,0.00021492795,0.00005216122,0.000026210895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000803834,0.000056416164,0.00005076865,0.00013339808,0.0001499552,0.000081149774,0.00014824721,0.000041350402,0.00000838345],"category_scores_gemma":[0.000022963814,0.00005374468,0.000019034784,0.00041159368,0.000025724365,0.00021658059,0.00008999249,0.000048532536,0.000012397703],"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":[0.000001957979,0.000042932326,0.0004515131,0.000011232056,0.000007437628,8.3006427e-7,0.00007039552,0.0000049908685,0.02191422,0.3825655,0.0007619064,0.59416705],"study_design_scores_gemma":[0.0004976297,0.00027514304,0.11886874,0.00003542373,0.000027429856,0.00004875945,0.000120228084,0.13379633,0.5972829,0.093584776,0.054996327,0.0004663306],"about_ca_topic_score_codex":0.00007252234,"about_ca_topic_score_gemma":0.00003007986,"teacher_disagreement_score":0.937109,"about_ca_system_score_codex":0.000023382114,"about_ca_system_score_gemma":0.000014040683,"threshold_uncertainty_score":0.21916445},"labels":[],"label_agreement":null},{"id":"W4413156381","doi":"10.1109/iccea65460.2025.11103067","title":"A Dual-Network Architecture with Uncertainty-Aware Multimodal Fusion for Deepfake Detection","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Dual (grammatical number); Architecture; Fusion; Sensor fusion; Artificial intelligence; Machine learning; Distributed computing","score_opus":0.005110576087774684,"score_gpt":0.23721491893189783,"score_spread":0.23210434284412315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413156381","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":[0.003125855,0.000016051836,0.9927004,0.0011582572,0.00009133253,0.0007028792,0.000002350077,0.00071834,0.0014845291],"genre_scores_gemma":[0.79345596,0.000004204372,0.20404314,0.0005045402,0.00007804488,0.0004979341,0.000004374113,0.00000749081,0.0014043491],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910986,0.000021199212,0.00015561286,0.0003822857,0.00010181047,0.00022920627],"domain_scores_gemma":[0.99928373,0.00008688435,0.000058877013,0.0004070837,0.00011651327,0.00004693405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001245239,0.00012985367,0.00011650312,0.00010051673,0.00038966906,0.00008745935,0.0002580677,0.00008613276,0.000008291104],"category_scores_gemma":[0.000007898834,0.00009800781,0.00006785847,0.0006621598,0.000030288607,0.00009784696,0.000104784056,0.00012007767,0.000003940649],"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":[0.000102904625,0.00008877503,0.00014652051,0.00003503353,0.00003981051,0.0000015188247,0.0001172993,0.026366547,0.0032139374,0.08280801,0.002293108,0.88478655],"study_design_scores_gemma":[0.0009482129,0.00052973116,0.0012665495,0.000070264075,0.00002771757,0.000030270601,0.00005882202,0.8192753,0.032094132,0.043321524,0.10195949,0.00041796995],"about_ca_topic_score_codex":0.00007747018,"about_ca_topic_score_gemma":0.0004646325,"teacher_disagreement_score":0.88436854,"about_ca_system_score_codex":0.000049340142,"about_ca_system_score_gemma":0.000050046598,"threshold_uncertainty_score":0.39966425},"labels":[],"label_agreement":null},{"id":"W4413156672","doi":"10.1109/cvpr52734.2025.01429","title":"T-CIL: Temperature Scaling using Adversarial Perturbation for Calibration in Class-Incremental Learning","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Adversarial system; Scaling; Computer science; Perturbation (astronomy); Class (philosophy); Artificial intelligence; Physics; Mathematics; Geometry; Quantum mechanics","score_opus":0.012308834842591381,"score_gpt":0.27785946907791514,"score_spread":0.26555063423532377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413156672","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":[0.032047924,0.000011585674,0.96534467,0.0007960813,0.00011034267,0.0003326728,7.130934e-7,0.00021098844,0.0011450098],"genre_scores_gemma":[0.8974553,0.0000025719585,0.10110963,0.00030575917,0.00005022187,0.000058922626,0.000008683041,0.000004004103,0.0010049555],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936855,0.000036781414,0.00017674438,0.00023014036,0.00007452269,0.0001132505],"domain_scores_gemma":[0.99972326,0.000046467467,0.00004438697,0.00012336849,0.000044999204,0.000017510247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002165644,0.000068977075,0.00007336365,0.00014267156,0.00021466287,0.00013609265,0.00015616884,0.00008117934,0.0000061052524],"category_scores_gemma":[0.000028371836,0.00006829291,0.00004139292,0.00041761063,0.000010277392,0.00039097047,0.000058378962,0.00011006072,6.824606e-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":[0.000023144652,0.0000888083,0.0018289866,0.000030956176,0.000016789532,6.02868e-7,0.00035161834,0.016228195,0.24335012,0.70543647,0.0010178322,0.03162649],"study_design_scores_gemma":[0.00026736097,0.000022947308,0.00023481854,0.000018591008,0.0000033643596,9.3484545e-7,0.00008981922,0.94526297,0.04750706,0.0034906766,0.0030188384,0.00008260622],"about_ca_topic_score_codex":0.000046821093,"about_ca_topic_score_gemma":0.000015397183,"teacher_disagreement_score":0.92903477,"about_ca_system_score_codex":0.000115523915,"about_ca_system_score_gemma":0.00006257219,"threshold_uncertainty_score":0.2784904},"labels":[],"label_agreement":null},{"id":"W4413157644","doi":"10.1109/cvpr52734.2025.01778","title":"Correcting Deviations from Normality: A Reformulated Diffusion Model for Multi-Class Unsupervised Anomaly Detection","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Anomaly detection; Normality; Anomaly (physics); Computer science; Class (philosophy); Diffusion; Artificial intelligence; Large deviations theory; Pattern recognition (psychology); Mathematics; Statistics; Physics","score_opus":0.035180019979931684,"score_gpt":0.295088909399348,"score_spread":0.2599088894194163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413157644","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":[0.08876077,0.000013924719,0.9085415,0.00040995475,0.0001339042,0.0005987532,0.000012875224,0.0009060271,0.0006223011],"genre_scores_gemma":[0.80018485,0.0000045150746,0.19699022,0.00042519617,0.00001761871,0.00032063815,0.000014749146,0.000008157954,0.0020340392],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988479,0.00002607413,0.00035474234,0.00044977927,0.00010360038,0.00021789652],"domain_scores_gemma":[0.99900436,0.000094489966,0.00010387534,0.0005254612,0.00021313003,0.000058672358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015901921,0.00014767406,0.0001557259,0.00018539545,0.0005866582,0.00013858888,0.000422616,0.00013307104,0.000006792064],"category_scores_gemma":[0.000047960762,0.0001374958,0.000117052055,0.0006985048,0.000017324977,0.00039417995,0.00018038871,0.00012296952,0.000008305446],"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":[0.00007211279,0.000739902,0.0022609222,0.000058223868,0.00013135083,7.5944087e-7,0.0012297727,0.008738523,0.15565598,0.08729061,0.0005374293,0.7432844],"study_design_scores_gemma":[0.00044663984,0.000025246456,0.0013697408,0.000013969001,0.0000147998835,0.0000012895197,0.00003577318,0.9344726,0.05878418,0.0040792585,0.0006091817,0.00014731451],"about_ca_topic_score_codex":0.000676859,"about_ca_topic_score_gemma":0.00080438313,"teacher_disagreement_score":0.9257341,"about_ca_system_score_codex":0.00013913815,"about_ca_system_score_gemma":0.00007172313,"threshold_uncertainty_score":0.56069154},"labels":[],"label_agreement":null},{"id":"W4413223372","doi":"10.1007/978-981-95-0568-5_8","title":"Scene Invariant Cross Camera Anomaly Detection of Behaviours of Risk in People with Dementia","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; Toronto Rehabilitation Institute; University of Toronto","funders":"","keywords":"Invariant (physics); Anomaly detection; Dementia; Artificial intelligence; Computer science; Computer vision; Medicine; Mathematics; Internal medicine; Mathematical physics","score_opus":0.013818849608069441,"score_gpt":0.26842925449570754,"score_spread":0.2546104048876381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413223372","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":[0.006545308,0.00021343768,0.97237694,0.000093075425,0.00006065016,0.0004738133,0.000036826703,0.000052278363,0.020147644],"genre_scores_gemma":[0.8757336,0.0011753356,0.122858204,0.00005528678,0.000004431113,0.000043758995,0.000015648477,0.000004413324,0.00010928892],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984808,0.00003137117,0.0008143303,0.00023835561,0.00029081863,0.00014430727],"domain_scores_gemma":[0.9970767,0.000134313,0.0006879172,0.0015756271,0.0004817749,0.00004366001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007958165,0.00015733534,0.00027070014,0.0012682787,0.00023287196,0.00014746911,0.0018598745,0.00010855218,0.0000028463523],"category_scores_gemma":[0.000022965773,0.00015516022,0.000041030547,0.0010452966,0.0006387622,0.0024315037,0.0010911127,0.000321015,0.0000014155132],"study_design_candidate":"theoretical_or_conceptual","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":[0.000016468537,0.00013981208,0.017590467,0.0000916465,0.000021426093,2.5481765e-7,0.002491577,0.0009851307,0.00007342409,0.55491734,0.000016792319,0.42365566],"study_design_scores_gemma":[0.0012347293,0.00047300162,0.42189977,0.00096083235,0.000059648683,0.000032534033,0.000073041556,0.5560933,0.0037316082,0.008704422,0.0059583923,0.0007787658],"about_ca_topic_score_codex":0.00054405595,"about_ca_topic_score_gemma":0.0006650031,"teacher_disagreement_score":0.8691883,"about_ca_system_score_codex":0.00009346053,"about_ca_system_score_gemma":0.00029709018,"threshold_uncertainty_score":0.632725},"labels":[],"label_agreement":null},{"id":"W4413244843","doi":"10.1007/978-3-032-00630-1_7","title":"Towards Robust Artificial Intelligence: Self-supervised Learning Approach for Out-of-Distribution Detection","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"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":"Computer science; Artificial intelligence; Machine learning","score_opus":0.032686812850764455,"score_gpt":0.26924124676590155,"score_spread":0.23655443391513709,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413244843","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000011008582,0.00006912941,0.996371,0.00017008132,0.00066618493,0.00085629936,0.0000138432315,0.00042580106,0.0014166649],"genre_scores_gemma":[0.12021957,0.00003468829,0.87908727,0.0000888788,0.00026121948,0.00010664593,0.00003222211,0.000017453223,0.00015202632],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973327,0.000030920335,0.0006260967,0.0011424866,0.00047684065,0.00039094008],"domain_scores_gemma":[0.9981716,0.00019380116,0.00033276813,0.0007376688,0.00048306372,0.00008109787],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00084509223,0.00035135524,0.00040887386,0.0005133235,0.00042030838,0.0002611264,0.0016423458,0.00038779268,0.0000044610006],"category_scores_gemma":[0.000095586234,0.0003529215,0.00020459817,0.00080729456,0.00027274442,0.00028656013,0.00054220547,0.0006379835,0.000003253932],"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":[0.0000060854677,0.000040690218,0.0000013014342,0.00006682115,0.000009026743,5.5162e-7,0.00020265572,0.046281062,0.00025591216,0.049329933,0.0000057039897,0.90380025],"study_design_scores_gemma":[0.00004309133,0.00020302781,0.0000039168835,0.0000703466,0.000013826428,0.00000490998,7.129112e-7,0.88237834,0.028000712,0.08797757,0.0009996637,0.00030387423],"about_ca_topic_score_codex":0.000016239776,"about_ca_topic_score_gemma":0.000013258946,"teacher_disagreement_score":0.9034964,"about_ca_system_score_codex":0.00033530843,"about_ca_system_score_gemma":0.00042624422,"threshold_uncertainty_score":0.9998923},"labels":[],"label_agreement":null},{"id":"W4413372669","doi":"10.1007/978-981-96-5784-1_14","title":"Optimizing Deep Learning Models for Knee Structure Detection: A Comparative Study of U-Net and Its Variants","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Deep learning; Artificial intelligence; Net (polyhedron); Computer science; Machine learning; Mathematics","score_opus":0.0285223937846496,"score_gpt":0.2574262412569362,"score_spread":0.2289038474722866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413372669","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":[0.0004249369,0.0064989068,0.9899054,0.000010306504,0.00019669105,0.0015901424,0.000007673217,0.00007779788,0.0012881205],"genre_scores_gemma":[0.99540526,0.00013387203,0.0033269732,0.000015583662,0.00013343312,0.00013664042,0.0000050853723,0.00001649497,0.00082668586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986413,0.00005701101,0.00044288574,0.00055166817,0.00012755422,0.00017958955],"domain_scores_gemma":[0.99889,0.00033446358,0.00032558292,0.00026397803,0.00013955029,0.000046420468],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017670664,0.0002883528,0.0006021639,0.0001915515,0.00022227188,0.00013231138,0.00020824472,0.00038652675,0.0000012504337],"category_scores_gemma":[0.000009403821,0.0002603708,0.00005044653,0.00013949902,0.000026041955,0.00010887649,0.00015094281,0.00050337304,5.642299e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024821933,0.00001789476,0.000009512648,0.00014314814,0.000103293554,0.0000019293132,0.0017185733,0.92847204,0.000024601619,0.029768514,0.0000056999475,0.039709955],"study_design_scores_gemma":[0.00033316185,0.00044167403,0.000009692073,0.0002442054,0.00003781128,0.000016434476,0.000048874917,0.9901052,0.000022191109,0.008044354,0.0004692731,0.00022712407],"about_ca_topic_score_codex":0.000036366233,"about_ca_topic_score_gemma":0.00023813681,"teacher_disagreement_score":0.9949803,"about_ca_system_score_codex":0.000035714085,"about_ca_system_score_gemma":0.00001845727,"threshold_uncertainty_score":0.99998486},"labels":[],"label_agreement":null},{"id":"W4413386198","doi":"10.1016/j.jobe.2025.113784","title":"Occupancy estimation and activity recognition in smart buildings using open set domain adaptation","year":2025,"lang":"en","type":"article","venue":"Journal of Building Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Occupancy; Domain adaptation; Adaptation (eye); Computer science; Estimation; Set (abstract data type); Domain (mathematical analysis); Building automation; Artificial intelligence; Architectural engineering; Engineering; Mathematics; Psychology; Systems engineering; Programming language","score_opus":0.030627140097473426,"score_gpt":0.3034120487375829,"score_spread":0.27278490864010946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413386198","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":[0.4195767,0.00002444988,0.5801278,0.00010798442,0.000057389105,0.000069792986,3.6029377e-7,0.000020111584,0.000015399666],"genre_scores_gemma":[0.56874764,0.000009881611,0.43121243,0.000011827603,0.00000959684,0.0000039761308,1.2143332e-7,0.000002621585,0.0000019062171],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994553,0.00001761973,0.00023812675,0.00011600863,0.000081704165,0.00009124692],"domain_scores_gemma":[0.99959755,0.000056673274,0.00016374658,0.00009367605,0.0000590771,0.000029253892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004900395,0.00006986409,0.00012884587,0.0003644159,0.00005862365,0.00016402795,0.0002313708,0.00004297337,5.2111295e-7],"category_scores_gemma":[0.00005613264,0.00007442525,0.000026072878,0.00047298733,0.0000059733984,0.0009817001,0.000109013934,0.00015272737,1.1587548e-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":[0.00004393763,0.000095086674,0.0016903469,0.00014201687,0.000049747727,0.000019591958,0.0007039249,0.20167418,0.2523043,0.020072078,0.00007666266,0.52312815],"study_design_scores_gemma":[0.0002695946,0.000031467025,0.004859288,0.0003096347,0.0000072150824,0.00007010408,0.000018499679,0.9693979,0.017952545,0.006766083,0.00022708777,0.00009058736],"about_ca_topic_score_codex":0.000044375352,"about_ca_topic_score_gemma":0.0000021834205,"teacher_disagreement_score":0.76772374,"about_ca_system_score_codex":0.00012001859,"about_ca_system_score_gemma":0.000042115156,"threshold_uncertainty_score":0.30349734},"labels":[],"label_agreement":null},{"id":"W4413423583","doi":"10.1016/b978-0-443-30046-2.00006-5","title":"Health applications of shaped charge learning","year":2025,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Charge (physics); Computer science; Physics","score_opus":0.012165029949751896,"score_gpt":0.2618497143587892,"score_spread":0.2496846844090373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413423583","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[9.626401e-8,0.0005646469,0.21963002,0.00031239854,0.00003592348,0.0008649241,0.00001330887,0.000318587,0.7782601],"genre_scores_gemma":[0.00039381758,0.0002607869,0.013555556,0.0003629313,0.00007102908,0.00058112555,0.000015135116,0.000022734748,0.98473686],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99857557,0.000021289581,0.00051965786,0.0004820558,0.00020771966,0.00019373142],"domain_scores_gemma":[0.9983709,0.000047991274,0.0005217723,0.00083170366,0.00013814718,0.00008947076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021721517,0.00022947136,0.00043120928,0.00025420458,0.00023358582,0.00003769534,0.0008073266,0.0001797675,0.000057085505],"category_scores_gemma":[0.000004350469,0.00024260466,0.00020092948,0.00006422885,0.000068134024,0.000041097373,0.00029831502,0.00044276626,0.00008257513],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.3818574e-7,0.0000045388147,5.780391e-7,0.00005002749,0.00001265536,1.4042723e-7,0.000028120572,1.4043037e-7,0.000012725851,0.3358158,0.00015847532,0.66391647],"study_design_scores_gemma":[0.000051560844,0.000048559134,0.0000026477303,0.0001458039,0.000010978411,0.0000033761235,0.000001264781,0.00034681006,0.00012202315,0.01989434,0.97919774,0.00017491181],"about_ca_topic_score_codex":0.0000017206111,"about_ca_topic_score_gemma":0.000003830368,"teacher_disagreement_score":0.97903925,"about_ca_system_score_codex":0.000076169024,"about_ca_system_score_gemma":0.00027420386,"threshold_uncertainty_score":0.98931307},"labels":[],"label_agreement":null},{"id":"W4413451031","doi":"10.1016/j.inffus.2025.103627","title":"DLBN: A deep logic belief network for fault diagnosis of dust removal fans","year":2025,"lang":"en","type":"article","venue":"Information Fusion","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Major Science and Technology Projects of China; Shanghai Jiao Tong University; National Natural Science Foundation of China","keywords":"Computer science; Fault (geology); Artificial intelligence; Computer security; Real-time computing; Aeronautics; Geology; Seismology; Engineering","score_opus":0.010286793643303448,"score_gpt":0.2548901515156677,"score_spread":0.24460335787236423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413451031","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":[0.002630154,0.000038883216,0.9873453,0.0015781504,0.0001527706,0.00046769326,0.000007737927,0.00020419451,0.007575166],"genre_scores_gemma":[0.5741045,0.00026990334,0.4203119,0.0035581763,0.000100287616,0.0009623179,0.00007622675,0.000006281322,0.0006104386],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992822,0.000011707238,0.00036101358,0.00009840576,0.000116317315,0.00013037783],"domain_scores_gemma":[0.9992141,0.00008029044,0.00018361135,0.00029532288,0.00019907045,0.000027601494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019105752,0.000075619646,0.00010340398,0.00012726053,0.00021123937,0.000059201884,0.0003384202,0.0000875578,0.000015801234],"category_scores_gemma":[0.00004234257,0.00006936494,0.00007381745,0.0006017995,0.000020718651,0.00059265894,0.00013589048,0.000060716342,0.000017390048],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012533541,0.000045560275,0.00031822978,0.00005970959,0.00000931735,1.4657667e-7,0.00034709196,0.0026133412,0.000108695196,0.44751424,0.024604257,0.5243669],"study_design_scores_gemma":[0.00041393086,0.00014368516,0.0023152812,0.00007471857,0.000012671391,0.000005873167,0.00008017909,0.27717254,0.0071660606,0.03096632,0.6814775,0.00017123437],"about_ca_topic_score_codex":0.000022775486,"about_ca_topic_score_gemma":0.000007193149,"teacher_disagreement_score":0.6568732,"about_ca_system_score_codex":0.00003211142,"about_ca_system_score_gemma":0.00003344958,"threshold_uncertainty_score":0.282862},"labels":[],"label_agreement":null},{"id":"W4413487367","doi":"10.64628/aam.wjt7xqnwr","title":"What Roblox’s safety updates mean for its users","year":2024,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science","score_opus":0.01833184362619823,"score_gpt":0.28016737923785046,"score_spread":0.26183553561165224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413487367","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":[0.00017259241,0.00027345962,0.9869729,0.008373271,0.00029516153,0.00025006186,0.000002953798,0.0011507439,0.0025088417],"genre_scores_gemma":[0.66938907,0.00071847084,0.29877007,0.002328188,0.00019798148,0.00043872042,0.00001244616,0.000028037475,0.028116984],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99942726,0.0000057316793,0.000116424904,0.00026059418,0.00006707962,0.00012291152],"domain_scores_gemma":[0.99960065,0.000051417013,0.000013646264,0.00025111312,0.000039957977,0.00004323365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000120404846,0.00006548497,0.000056233996,0.000055437762,0.00010436792,0.00051272457,0.00033789955,0.000034474044,0.000059080685],"category_scores_gemma":[0.000002967345,0.00005375903,0.00006657812,0.00025588114,0.0000104252085,0.0008937643,0.000079873826,0.000044898505,0.000121393],"study_design_candidate":"theoretical_or_conceptual","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":[9.636212e-7,0.000009135867,0.0000016933809,0.000013509124,0.000008467834,4.6653025e-7,0.00011685391,0.000014083521,0.0010347095,0.90937054,0.01775053,0.071679026],"study_design_scores_gemma":[0.00004561985,0.000045787936,0.000018821758,0.000023426668,0.0000049867012,0.000008862082,0.00006310378,0.17871484,0.06341087,0.017715525,0.73981214,0.00013600127],"about_ca_topic_score_codex":0.0000034907791,"about_ca_topic_score_gemma":0.000010393639,"teacher_disagreement_score":0.891655,"about_ca_system_score_codex":0.000024032777,"about_ca_system_score_gemma":0.000022849841,"threshold_uncertainty_score":0.49442148},"labels":[],"label_agreement":null},{"id":"W4413513820","doi":"10.1109/isie62713.2025.11124715","title":"Log-Based Anomaly Detection Without Ground-Truth: Evaluating Weakly Supervised, Semi-Supervised, and Unsupervised Deep Learning Approaches","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Ground truth; Anomaly detection; Artificial intelligence; Computer science; Supervised learning; Pattern recognition (psychology); Unsupervised learning; Anomaly (physics); Deep learning; Semi-supervised learning; Machine learning; Artificial neural network; Physics","score_opus":0.03814150079227615,"score_gpt":0.27475502168045096,"score_spread":0.2366135208881748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413513820","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":[0.23033367,0.00023028122,0.76356363,0.0005028569,0.00010460939,0.00050223694,8.517601e-7,0.0010992952,0.003662551],"genre_scores_gemma":[0.90723103,0.000026013438,0.0912338,0.00034065085,0.000052390205,0.00033305504,0.000007954171,0.000026111697,0.000749007],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973583,0.00025755135,0.00052602706,0.0010296509,0.0003580457,0.00047040218],"domain_scores_gemma":[0.9984478,0.00030008325,0.00012965444,0.00077840633,0.0001897857,0.00015432097],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00081040827,0.0003660182,0.00035364382,0.0004223795,0.00096849253,0.00052272214,0.00071223534,0.00022497165,0.00004572091],"category_scores_gemma":[0.0001150703,0.00035338668,0.00014791242,0.0013117971,0.00013509372,0.00064961717,0.00033658047,0.00043955602,0.000023452423],"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":[0.00006799953,0.00022815213,0.02648313,0.00018656472,0.00009699678,0.0000031554378,0.0005344156,0.0035519153,0.030080896,0.028465992,0.000029167886,0.91027164],"study_design_scores_gemma":[0.00072914554,0.00024011714,0.0075179883,0.000041331117,0.00004537477,0.000015798756,0.0004121587,0.9711492,0.016621517,0.002162848,0.00065843103,0.00040608482],"about_ca_topic_score_codex":0.00031763056,"about_ca_topic_score_gemma":0.00013570966,"teacher_disagreement_score":0.9675973,"about_ca_system_score_codex":0.00012387315,"about_ca_system_score_gemma":0.00011762983,"threshold_uncertainty_score":0.9998918},"labels":[],"label_agreement":null},{"id":"W4413602668","doi":"10.64628/aam.9fmqnrtd4","title":"We need to prepare for the public safety hazards posed by artificial intelligence","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Risk analysis (engineering); Computer science; Business","score_opus":0.07098528375071642,"score_gpt":0.3242725051132704,"score_spread":0.253287221362554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413602668","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":[0.00003053821,0.000043185784,0.9025827,0.09265735,0.00051285414,0.0018610407,0.000106815794,0.0014757391,0.0007297669],"genre_scores_gemma":[0.5232404,0.00056735217,0.43350068,0.0036587378,0.0008688081,0.011619733,0.0001635712,0.00013097795,0.02624977],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981159,0.000034226116,0.00046440828,0.00080042507,0.0002616684,0.00032334853],"domain_scores_gemma":[0.99765825,0.00028618547,0.0001457561,0.0015567146,0.0002258652,0.0001272074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005476215,0.00022333831,0.00020559176,0.0001344928,0.0004346437,0.000648562,0.0025509552,0.00019479061,0.00004657891],"category_scores_gemma":[0.00010014817,0.00016679219,0.00020650799,0.0006443498,0.00004436165,0.00010457508,0.001962499,0.0002472756,0.00013972909],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011703932,0.00005559906,0.000001542152,0.00002745004,0.000041369814,3.199863e-7,0.00037571546,0.0005286247,0.0002741778,0.38778332,0.13421343,0.47668678],"study_design_scores_gemma":[0.000025791638,0.00008880745,0.000012086137,0.000027214644,0.000014437882,0.0000025246231,0.00027977268,0.30301118,0.014358414,0.15130131,0.53046006,0.00041841614],"about_ca_topic_score_codex":0.00012040008,"about_ca_topic_score_gemma":0.00019599835,"teacher_disagreement_score":0.52320987,"about_ca_system_score_codex":0.00009861059,"about_ca_system_score_gemma":0.00019735457,"threshold_uncertainty_score":0.6801588},"labels":[],"label_agreement":null},{"id":"W4413759308","doi":"10.1016/j.patcog.2025.112332","title":"LayerMix: Enhanced Data Augmentation for Robust Deep Learning","year":2025,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"InStream Fisheries Research (Canada); University of Windsor","funders":"Mitacs","keywords":"Artificial intelligence; Computer science; Deep learning; Machine learning","score_opus":0.06046658496043765,"score_gpt":0.3148666183039309,"score_spread":0.25440003334349326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413759308","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":[0.004155847,0.00001845609,0.9932944,0.00047307374,0.0001302541,0.00035772714,0.000016050202,0.00032387747,0.0012302819],"genre_scores_gemma":[0.86175466,0.000051241223,0.13618888,0.0005174027,0.00007315278,0.00048358963,0.0005549127,0.000008422871,0.00036775012],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992845,0.000028800543,0.00016142978,0.000341026,0.000067461224,0.00011674699],"domain_scores_gemma":[0.99938756,0.00007275164,0.00008310918,0.00034531197,0.000089380694,0.000021908141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016682394,0.0000707555,0.00006828235,0.000089314126,0.00019446353,0.000102253725,0.00039267496,0.00004449873,0.000025356987],"category_scores_gemma":[0.00003075626,0.00007762695,0.000028850834,0.00021702852,0.000009789537,0.00038964374,0.00013945966,0.000076659155,0.000047498263],"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":[0.00000230595,0.000021259559,0.00006725772,0.000016752692,0.0000068808517,9.185953e-8,0.0000318286,0.000033673776,0.0018684078,0.00013874384,0.00033064676,0.9974821],"study_design_scores_gemma":[0.0007751802,0.0001246226,0.0025033655,0.00010148244,0.00004193558,0.000003823755,0.00012821933,0.8398582,0.13153118,0.014841085,0.00975434,0.00033658458],"about_ca_topic_score_codex":0.000019023686,"about_ca_topic_score_gemma":0.000023654342,"teacher_disagreement_score":0.9971456,"about_ca_system_score_codex":0.000030533713,"about_ca_system_score_gemma":0.000014205773,"threshold_uncertainty_score":0.31655353},"labels":[],"label_agreement":null},{"id":"W4413772255","doi":"10.3390/s25175331","title":"GICEDCam: A Geospatial Internet of Things Framework for Complex Event Detection in Camera Streams","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Geospatial analysis; STREAMS; Event (particle physics); The Internet; Computer science; Remote sensing; World Wide Web; Geography; Computer network; Physics","score_opus":0.013307528580364051,"score_gpt":0.2885387308388215,"score_spread":0.27523120225845743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413772255","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":[0.14898808,0.000008200329,0.84981376,0.00037268052,0.00008389773,0.00028935724,0.0000019954227,0.000100081044,0.0003419643],"genre_scores_gemma":[0.867086,0.0000041659214,0.1324635,0.00015874361,0.000013827484,0.00007079965,0.0000014308017,0.0000037918003,0.00019774829],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993132,0.00002389063,0.0002386863,0.00022262326,0.00007673659,0.0001248906],"domain_scores_gemma":[0.9994547,0.00010129003,0.000100765385,0.00026211457,0.00005992619,0.000021236521],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011512359,0.00007761479,0.00012656051,0.00014217665,0.0000429843,0.000024675222,0.0002700698,0.000076763085,0.0000072126645],"category_scores_gemma":[0.000038635208,0.00007811471,0.00006937309,0.00039663908,0.000027293185,0.00007440318,0.0000779888,0.000112976006,0.0000020765533],"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":[0.00003980113,0.00016076019,0.00048779565,0.00006222928,0.000027993978,9.63392e-7,0.0014896464,0.00067061925,0.0063457848,0.2611132,0.0002780895,0.72932315],"study_design_scores_gemma":[0.0002500614,0.00015872125,0.0034104004,0.00008231649,0.000007535373,0.000002506497,0.00012903634,0.8296252,0.10355952,0.05852047,0.0041129086,0.00014135141],"about_ca_topic_score_codex":0.0006581343,"about_ca_topic_score_gemma":0.00008709882,"teacher_disagreement_score":0.8289546,"about_ca_system_score_codex":0.000050987623,"about_ca_system_score_gemma":0.000020191841,"threshold_uncertainty_score":0.31854254},"labels":[],"label_agreement":null},{"id":"W4413830730","doi":"10.1145/3764936","title":"Decentralized Model Selection for Test-Time Adaptation in Heterogeneous Connected Systems","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on the Web","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Guangdong Provincial Pearl River Talents Program","keywords":"Computer science; Adaptation (eye); Selection (genetic algorithm); Test (biology); Distributed computing; Artificial intelligence","score_opus":0.02027805220109835,"score_gpt":0.2551290940245798,"score_spread":0.23485104182348143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413830730","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":[0.0066701164,0.000022657798,0.9887911,0.0030175652,0.000077667064,0.00090949034,0.00001969523,0.00038696424,0.00010473191],"genre_scores_gemma":[0.97080606,0.000046078858,0.026612852,0.00032656195,0.0000064860496,0.0011544757,0.000002531532,0.000008899984,0.0010360538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915725,0.000052828294,0.00024215844,0.00027362184,0.00009619463,0.00017792155],"domain_scores_gemma":[0.9988438,0.0004910643,0.000060731796,0.0004792165,0.0000976812,0.000027543407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001891788,0.000111167516,0.00011825565,0.00018676138,0.00030268577,0.00009668255,0.00050186855,0.00008056394,0.000008761605],"category_scores_gemma":[0.000050811956,0.0000941131,0.00007979907,0.0007239431,0.00002107223,0.00013319001,0.000006702933,0.00012229983,0.000014121143],"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":[0.00010541489,0.0004946272,0.000017330989,0.000042463453,0.00006373108,4.0946998e-7,0.00032582006,0.8314912,0.05430566,0.062565535,0.0009970904,0.049590718],"study_design_scores_gemma":[0.000297516,0.00006108117,0.000012603093,0.000020725112,0.000013562154,0.000004662053,0.000018862107,0.962704,0.028623637,0.007210438,0.0009428815,0.000090007816],"about_ca_topic_score_codex":0.000053065858,"about_ca_topic_score_gemma":0.0000670971,"teacher_disagreement_score":0.96413594,"about_ca_system_score_codex":0.000113470334,"about_ca_system_score_gemma":0.00009731184,"threshold_uncertainty_score":0.3837821},"labels":[],"label_agreement":null},{"id":"W4413848782","doi":"10.53941/tai.2025.100012","title":"Editorial: Special Issue on Artificial Intelligence for Security","year":2025,"lang":"en","type":"editorial","venue":"Transactions on Artificial Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"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":"University of Toronto; Innovation and Technology Commission; National Natural Science Foundation of China; Beijing University of Posts and Telecommunications; University of Sydney; City University of Hong Kong","keywords":"Software deployment; Vulnerability (computing); Computer science; Computer security; Engineering ethics; Artificial intelligence; Engineering","score_opus":0.02788459004601883,"score_gpt":0.3247557693711103,"score_spread":0.29687117932509144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413848782","genre_codex":"methods","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.9097015e-7,0.000009154989,0.51257646,0.00073998433,0.48317525,0.0009300571,0.00036165357,0.00050058635,0.0017065705],"genre_scores_gemma":[0.00057680655,0.0002160933,0.009379821,0.00017373008,0.98597825,0.0012628897,0.00008321105,0.00007121301,0.0022579886],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99415296,0.00015910467,0.0015738249,0.0019377061,0.0013494042,0.0008270145],"domain_scores_gemma":[0.99406266,0.0023608953,0.00046799675,0.0018199251,0.0010217486,0.0002667841],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00088860263,0.00084165775,0.0007875622,0.00081633247,0.0011862159,0.00075916224,0.002622012,0.0016179228,0.00047887594],"category_scores_gemma":[0.00040283735,0.0009164903,0.00070587377,0.0015804006,0.0002769896,0.0003694889,0.000053331,0.0022558023,0.0010140423],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016610394,0.00048332,3.6176042e-9,0.000045878118,0.000036519472,0.000001324243,0.00017327814,0.0008699615,0.000020794343,0.0557088,0.6202811,0.3222129],"study_design_scores_gemma":[0.000019661482,0.0006555435,2.2300066e-8,0.0001310072,0.0000586519,6.1295947e-7,0.00008129989,0.010004872,0.049190473,0.16539559,0.7738164,0.0006458468],"about_ca_topic_score_codex":0.00014073444,"about_ca_topic_score_gemma":0.00035320283,"teacher_disagreement_score":0.5031966,"about_ca_system_score_codex":0.00047337558,"about_ca_system_score_gemma":0.0007317659,"threshold_uncertainty_score":0.9997638},"labels":[],"label_agreement":null},{"id":"W4413968650","doi":"10.1016/j.mlwa.2025.100728","title":"CNN-CCA: A deep learning approach for anomaly detection in metro rail sensor time-series data","year":2025,"lang":"en","type":"article","venue":"Machine Learning with Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Queen's University","funders":"Mitacs","keywords":"Anomaly detection; Deep learning; Anomaly (physics); Series (stratigraphy); Computer science; Time series; Artificial intelligence; Real-time computing; Data mining; Machine learning; Geology","score_opus":0.011026601456883714,"score_gpt":0.2546715695148593,"score_spread":0.24364496805797559,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413968650","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":[0.0011834531,0.00016482394,0.9932464,0.0006602677,0.00001241194,0.0012906177,0.000009506359,0.00085639965,0.0025761705],"genre_scores_gemma":[0.540187,0.000025334968,0.4521842,0.000093959185,0.00004883184,0.0033231326,0.0002205596,0.000029265566,0.0038877116],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981916,0.00010680374,0.0003467901,0.00087657815,0.00015553876,0.00032267903],"domain_scores_gemma":[0.9983522,0.00017600511,0.00020041433,0.0010812042,0.00012398072,0.00006613823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052237837,0.00022319166,0.00025693886,0.00038856338,0.0007597764,0.00019982814,0.0010598766,0.00010769558,0.000009036352],"category_scores_gemma":[0.00008955638,0.00021239318,0.000055949193,0.001797321,0.000074033356,0.00043412307,0.00036799954,0.00043349838,0.00002351249],"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":[0.00023745326,0.00085249037,0.03700677,0.0003133791,0.00023445254,0.0000026454184,0.00058539974,0.06455748,0.01510885,0.06887348,0.00034442174,0.8118832],"study_design_scores_gemma":[0.00047312168,0.00015626645,0.0021381616,0.00001323298,0.000034490993,0.000009877417,0.000117703166,0.8669712,0.0027657598,0.0006217928,0.12641148,0.00028690585],"about_ca_topic_score_codex":0.00014572366,"about_ca_topic_score_gemma":0.00013486079,"teacher_disagreement_score":0.8115963,"about_ca_system_score_codex":0.000088266956,"about_ca_system_score_gemma":0.00006204237,"threshold_uncertainty_score":0.86611426},"labels":[],"label_agreement":null},{"id":"W4414076960","doi":"10.21203/rs.3.rs-7160738/v1","title":"MalScore: A Quality Assessment Framework for Visual Malware Datasets Using No-Reference Image Quality Metrics","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Malware; Ranking (information retrieval); Set (abstract data type); Quality (philosophy); Obfuscation; The Internet","score_opus":0.3089725723004142,"score_gpt":0.5880369290188462,"score_spread":0.279064356718432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414076960","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020296453,0.0001602608,0.9864449,0.00089182454,0.00024461883,0.002797902,0.006146642,0.00046711558,0.0008170963],"genre_scores_gemma":[0.11610676,0.0002083719,0.8795313,0.00013559744,0.00024047785,0.0020933638,0.0012630536,0.000030603864,0.00039043665],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9931307,0.0013730552,0.0009887585,0.0018169119,0.0017632378,0.00092737825],"domain_scores_gemma":[0.9908924,0.002765259,0.00046654415,0.0032141476,0.0023601404,0.00030148588],"candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00701485,0.00041997383,0.00067739666,0.00079592643,0.00091113383,0.0011582403,0.0032845119,0.0007242213,0.00008882788],"category_scores_gemma":[0.002291025,0.0004259543,0.00032170414,0.0019935798,0.00020905914,0.00037371094,0.007034745,0.0027434325,0.00004201653],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008835204,0.0011421234,0.0016920838,0.006260976,0.00016807768,0.000011759767,0.00016074031,0.00013747446,0.0010968926,0.92980695,0.011260935,0.04817362],"study_design_scores_gemma":[0.000962382,0.00090627425,0.025152106,0.0031570087,0.0000836312,0.000007242708,0.0005353809,0.31008226,0.0083218245,0.5765425,0.071444154,0.002805256],"about_ca_topic_score_codex":0.0013215974,"about_ca_topic_score_gemma":0.000032309774,"teacher_disagreement_score":0.35326448,"about_ca_system_score_codex":0.00089341926,"about_ca_system_score_gemma":0.0017701242,"threshold_uncertainty_score":0.99987864},"labels":[],"label_agreement":null},{"id":"W4414122916","doi":"10.1175/aies-d-24-0114.1","title":"Evaluating the Robustness of PCMCI+ for Causal Discovery of Flood Drivers","year":2025,"lang":"en","type":"article","venue":"Artificial Intelligence for the Earth Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"HORIZON EUROPE European Research Council; Helmholtz Artificial Intelligence Cooperation Unit","keywords":"Robustness (evolution); Causal inference; Spurious relationship; Causal structure; Flood myth; Causal model; Conditional independence; Multivariate statistics","score_opus":0.10476902776923586,"score_gpt":0.37583284711653975,"score_spread":0.2710638193473039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414122916","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":[0.017529635,0.00013412269,0.9795862,0.00048735118,0.00070295285,0.0014346321,0.000021393947,0.000039333216,0.00006436812],"genre_scores_gemma":[0.9913467,0.000008881246,0.0075135976,0.000016479305,0.00008910358,0.0004601506,0.0000012832608,0.0000050957924,0.00055869657],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989449,0.000053650125,0.00048903754,0.0002013471,0.0001584572,0.00015262299],"domain_scores_gemma":[0.99798495,0.00093946425,0.00024995656,0.0005449937,0.00026731915,0.00001331318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010145715,0.00008580164,0.000161907,0.000057450525,0.00031868336,0.00009390244,0.00078772806,0.000043869506,0.0000011802741],"category_scores_gemma":[0.0001236408,0.00005287354,0.00014141985,0.0004239729,0.00013069289,0.00014874736,0.00008757129,0.00005852825,0.0000012815871],"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":[0.000026790454,0.000042773772,0.000020879705,0.00008457498,0.000050124312,2.3870646e-8,0.00031977595,0.094892904,0.0077012107,0.83466065,0.00012814507,0.06207213],"study_design_scores_gemma":[0.000022238768,0.00017809356,0.000025564432,0.0000556811,0.000029181676,8.682148e-7,0.00078826613,0.82089585,0.16637324,0.010918565,0.00065331056,0.000059141523],"about_ca_topic_score_codex":0.00014465666,"about_ca_topic_score_gemma":0.000046391695,"teacher_disagreement_score":0.97381705,"about_ca_system_score_codex":0.000010953686,"about_ca_system_score_gemma":0.000084482686,"threshold_uncertainty_score":0.24510871},"labels":[],"label_agreement":null},{"id":"W4414360079","doi":"10.24963/ijcai.2025/102","title":"Template-based Uncertainty Multimodal Fusion Network for RGBT Tracking","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Reliability (semiconductor); Focus (optics); Computation; Fusion; Quality (philosophy); Eye tracking; Sensor fusion; Tracking (education)","score_opus":0.01631948585212069,"score_gpt":0.28657983509899754,"score_spread":0.27026034924687686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414360079","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":[0.0010486628,0.000015366923,0.99324185,0.0013963494,0.00018228238,0.00049285026,0.0000015386346,0.0005697316,0.0030513727],"genre_scores_gemma":[0.6678755,0.0000021510532,0.32941878,0.00093832135,0.00009057426,0.00037445317,0.0000044146655,0.00000400297,0.0012917445],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933726,0.000012878563,0.00015632836,0.0002563099,0.000062407125,0.00017482015],"domain_scores_gemma":[0.9994046,0.0001221638,0.000041980788,0.00031877166,0.000080869635,0.00003161163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016644236,0.00007766602,0.000085082436,0.00005624752,0.0003288183,0.000086868306,0.00035781265,0.000060111313,0.000015190151],"category_scores_gemma":[0.000010555783,0.00006828936,0.00007617172,0.00039676004,0.00001679216,0.00009500804,0.000061879204,0.000058558962,0.0000056993795],"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":[0.00002418553,0.00010636172,0.00069558347,0.000028264407,0.000014157991,4.7794174e-7,0.000031720545,0.030627102,0.0019822302,0.5395956,0.046740416,0.38015392],"study_design_scores_gemma":[0.00025682498,0.000042727967,0.0006552562,0.000020447795,0.0000040279497,3.933271e-7,0.0000072009466,0.8718333,0.009147532,0.019169368,0.09875369,0.00010921522],"about_ca_topic_score_codex":0.00008434217,"about_ca_topic_score_gemma":0.00003551447,"teacher_disagreement_score":0.8412062,"about_ca_system_score_codex":0.00003364143,"about_ca_system_score_gemma":0.00005349064,"threshold_uncertainty_score":0.27847594},"labels":[],"label_agreement":null},{"id":"W4414416854","doi":"10.1002/cjce.70101","title":"Industrial prediction method based on graph sampling and aggregation of temporal features","year":2025,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Soft sensor; Robustness (evolution); Normalization (sociology); Encoder; Pattern recognition (psychology); Graph; Feature extraction; Inference","score_opus":0.014784633643475808,"score_gpt":0.23797448573950972,"score_spread":0.2231898520960339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414416854","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":[0.035290174,0.00006173281,0.96303135,0.0013723832,0.00010367179,0.000060107635,0.0000026994417,0.000016807482,0.000061052124],"genre_scores_gemma":[0.964844,7.4543743e-7,0.035034403,0.00006352122,0.000047643836,0.000002285025,4.2691212e-7,0.0000023187013,0.0000046741093],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996394,0.000010702056,0.00015471109,0.000055817163,0.00007093027,0.00006845829],"domain_scores_gemma":[0.9996149,0.000088325294,0.00007281568,0.00009857209,0.000052020445,0.00007334626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002538784,0.000046073405,0.00007665508,0.00017859976,0.00004545908,0.000033553373,0.00018067814,0.00005599182,9.152071e-7],"category_scores_gemma":[0.00008356804,0.00003617708,0.000037057653,0.00027879374,0.00001889291,0.00005420563,0.000008141671,0.0002080223,3.0107948e-8],"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":[0.000076772136,0.000054566743,0.0034021456,0.00012229891,0.00018625526,0.000009402012,0.0005450011,0.29139155,0.23702562,0.18553317,0.0031444582,0.27850875],"study_design_scores_gemma":[0.00049189245,0.0000978156,0.0016274222,0.0003287028,0.000031693646,0.00004360299,0.000007974731,0.36493263,0.6239699,0.0055546463,0.0027905775,0.00012316537],"about_ca_topic_score_codex":0.0002705263,"about_ca_topic_score_gemma":0.00001640631,"teacher_disagreement_score":0.9295538,"about_ca_system_score_codex":0.0000499503,"about_ca_system_score_gemma":0.00014895147,"threshold_uncertainty_score":0.14752585},"labels":[],"label_agreement":null},{"id":"W4414715898","doi":"10.30632/pjv66n5-2025a11","title":"Automatic Fracture Identifications From Image Logs With Machine-Learning Approaches: A Contest Summary","year":2025,"lang":"en","type":"article","venue":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Petrophysics; Fracture (geology); Consistency (knowledge bases); Feature (linguistics); Borehole; Identification (biology); Data set; Well logging; Scalability; Big data","score_opus":0.0315279243083011,"score_gpt":0.26388377468009494,"score_spread":0.23235585037179385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414715898","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":[0.0850058,0.0004314393,0.90829504,0.0051754187,0.00009013517,0.00038669218,0.000004495669,0.0000758475,0.0005351306],"genre_scores_gemma":[0.9804456,0.00016930963,0.01876784,0.00019930025,0.000060736606,0.00006185446,0.00004347045,0.0000076278106,0.00024422593],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983151,0.0003201844,0.00052830175,0.00016855444,0.00054535986,0.00012249834],"domain_scores_gemma":[0.99797195,0.0001289222,0.0007323272,0.0003522892,0.0007626623,0.000051827777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011373343,0.00014088092,0.00017828404,0.00023833143,0.0005849873,0.00050100527,0.00045370255,0.00006150887,0.000017736624],"category_scores_gemma":[0.0001071389,0.00009225497,0.00008743985,0.0006161182,0.00007674898,0.0020603605,0.000069549766,0.0004006394,0.000006806734],"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":[0.00047350588,0.0017799825,0.004452662,0.0006260211,0.0012680076,0.0000110819965,0.020631908,0.09438752,0.07638192,0.16279572,0.023650955,0.6135407],"study_design_scores_gemma":[0.00059238507,0.0001077838,0.008082172,0.000121778176,0.00012464763,0.000026660047,0.0006693365,0.96646416,0.0019645696,0.019033583,0.0026980974,0.00011483837],"about_ca_topic_score_codex":0.000043626405,"about_ca_topic_score_gemma":0.000016550479,"teacher_disagreement_score":0.89543986,"about_ca_system_score_codex":0.0001440101,"about_ca_system_score_gemma":0.00013123693,"threshold_uncertainty_score":0.48312056},"labels":[],"label_agreement":null},{"id":"W4414801884","doi":"10.1016/j.jprocont.2025.103563","title":"Real-time identification of most critical alarms for alarm flood reduction","year":2025,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"ALARM; Identification (biology); Process (computing); Construct (python library); Prioritization; Flood myth; Markov process; False alarm","score_opus":0.0059795978242270734,"score_gpt":0.30270476106012656,"score_spread":0.2967251632358995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414801884","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":[0.008418026,0.00007751166,0.98695374,0.003900991,0.00017784305,0.00024273187,0.000005838214,0.000040667648,0.00018267037],"genre_scores_gemma":[0.9848369,0.000029516372,0.0146810375,0.0000481111,0.00012687143,0.000055420453,5.8400127e-7,0.000004413865,0.00021719834],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989767,0.00002555633,0.00059840054,0.00013350295,0.00016368132,0.00010216621],"domain_scores_gemma":[0.99814314,0.00009954805,0.00041963317,0.00018757096,0.0011088472,0.000041279494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004751267,0.00006677528,0.00018992418,0.00017722741,0.00009486597,0.000063136045,0.00039955077,0.000059232403,0.0000034558316],"category_scores_gemma":[0.00021313273,0.000059624934,0.00009932114,0.00036497824,0.00004264304,0.0004069681,0.00001528754,0.00009094663,0.0000014383211],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012687584,0.00043754288,0.00008131714,0.00019726739,0.00011068771,0.0000011636272,0.0002557442,0.00032754202,0.8452283,0.10174423,0.0032436952,0.048245646],"study_design_scores_gemma":[0.0018806726,0.0004989864,0.001203282,0.00014882463,0.00019264028,0.000097516844,0.00013520113,0.09575456,0.7854435,0.11249381,0.0019583495,0.00019264712],"about_ca_topic_score_codex":0.0000022644108,"about_ca_topic_score_gemma":1.3133753e-7,"teacher_disagreement_score":0.9764188,"about_ca_system_score_codex":0.000031935524,"about_ca_system_score_gemma":0.0001545661,"threshold_uncertainty_score":0.24314342},"labels":[],"label_agreement":null},{"id":"W4414809591","doi":"10.1007/978-3-032-05461-6_24","title":"Adaptive Isolation Forest","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Anomaly detection; Benchmark (surveying); Stability (learning theory); Novelty detection; Change detection; Anomaly (physics); Isolation (microbiology); Data stream","score_opus":0.01397789772431564,"score_gpt":0.24452927588441054,"score_spread":0.23055137816009488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414809591","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000003935823,0.000118682685,0.9785041,0.0008073151,0.00047255502,0.00039671033,0.0000036622014,0.00032658977,0.019366425],"genre_scores_gemma":[0.12422876,0.000042740485,0.8712905,0.0014312172,0.00025264724,0.000041572264,0.000004353805,0.00001792026,0.0026902799],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99780846,0.000013285688,0.00034982275,0.0010635802,0.00043108437,0.00033374364],"domain_scores_gemma":[0.9981968,0.00020558618,0.00020041737,0.0010941104,0.00022047386,0.000082614686],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032087843,0.00030922913,0.00027699867,0.0007138409,0.00027845803,0.00028729878,0.0020369594,0.0002580163,0.000012586702],"category_scores_gemma":[0.000026087733,0.00029951843,0.00010673146,0.00071074517,0.0003062604,0.00041752387,0.0008648257,0.0005166564,0.000033728087],"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":[0.0000019256124,0.000012134496,0.000020422727,0.000008241987,0.0000044463304,0.0000056278654,0.00007972263,0.0055940174,0.000031000185,0.28594443,0.00010401394,0.708194],"study_design_scores_gemma":[0.00006432638,0.00009357528,0.000093394585,0.00012652065,0.0000039082975,0.000011858905,4.507938e-8,0.6173506,0.00062489894,0.37670502,0.004634681,0.00029117183],"about_ca_topic_score_codex":0.000024929204,"about_ca_topic_score_gemma":0.00007889803,"teacher_disagreement_score":0.70790285,"about_ca_system_score_codex":0.00025697146,"about_ca_system_score_gemma":0.00039819503,"threshold_uncertainty_score":0.9999457},"labels":[],"label_agreement":null},{"id":"W4414830786","doi":"10.1093/jas/skaf300.497","title":"PSIV-10 Towards automated anemia detection: AI models for accurate FAMACHA classification in outdoor environments.","year":2025,"lang":"en","type":"article","venue":"Journal of Animal Science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Support vector machine; Cohen's kappa; Pattern recognition (psychology); Kappa; Artificial neural network; Robustness (evolution); Backpropagation","score_opus":0.031378924131947136,"score_gpt":0.329126567218658,"score_spread":0.29774764308671087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414830786","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":[0.045385085,0.000036330082,0.95128554,0.0021515205,0.00012473288,0.00021389888,0.0000017825221,0.00008946358,0.00071165554],"genre_scores_gemma":[0.97689867,0.0000262798,0.022607934,0.00025277757,0.000029844461,0.000031227723,1.8679485e-7,0.0000033703006,0.00014973298],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986813,0.000023451012,0.00046229377,0.00028970398,0.00031442556,0.00022879132],"domain_scores_gemma":[0.9991354,0.000032200573,0.00031556838,0.00024618572,0.00018626679,0.000084395964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008682676,0.00009887587,0.00014682548,0.00042291195,0.0002493912,0.00024295409,0.0012030692,0.00006309181,0.0000060161897],"category_scores_gemma":[0.000062321065,0.00008701618,0.00007470358,0.0014259869,0.00014113954,0.0018576522,0.00015861823,0.00016656517,0.0000044521385],"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":[0.00008568409,0.0001460164,0.00008710731,0.000013849888,0.0000062824283,0.0000028613322,0.0001555882,0.0016979693,0.8818581,0.039505195,0.00080153986,0.075639784],"study_design_scores_gemma":[0.00027918606,0.00035393008,0.015089829,0.00002368892,0.0000066285893,0.000024657944,0.00004450566,0.8370794,0.13564163,0.0076708435,0.0036762014,0.0001094624],"about_ca_topic_score_codex":0.00001250869,"about_ca_topic_score_gemma":0.0000036641197,"teacher_disagreement_score":0.93151355,"about_ca_system_score_codex":0.00029796647,"about_ca_system_score_gemma":0.00032315552,"threshold_uncertainty_score":0.35484168},"labels":[],"label_agreement":null},{"id":"W4414888745","doi":"10.48550/arxiv.2506.00188","title":"Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"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":"Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México; Institute for Catastrophic Loss Reduction","keywords":"Anomaly detection; Benchmark (surveying); Series (stratigraphy); False positive paradox; Time series; Multivariate statistics; Embedding; Anomaly (physics)","score_opus":0.02750161693196363,"score_gpt":0.2943868169891777,"score_spread":0.26688520005721406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414888745","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":[0.21826632,0.000034814482,0.7782377,0.0011768234,0.00038893239,0.0010501695,0.00013209,0.00058413297,0.00012900085],"genre_scores_gemma":[0.9058708,0.000042385906,0.085754395,0.00039313512,0.00025787798,0.001336275,0.00012353346,0.000031143907,0.006190482],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981295,0.00007277018,0.00049296714,0.0008662094,0.00012610563,0.000312443],"domain_scores_gemma":[0.99845874,0.000105534746,0.00024396114,0.00094042945,0.00018520208,0.00006613122],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023177739,0.00030905774,0.00035559357,0.00029667406,0.00017649842,0.00010266031,0.000851223,0.00039373577,0.000012119535],"category_scores_gemma":[0.000049985007,0.00032644378,0.00017607614,0.0004233429,0.000045207682,0.00026610921,0.0011085185,0.00049111905,0.000034909233],"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":[0.0012400799,0.006142525,0.1350187,0.0043839165,0.0012790404,0.00009994345,0.0055531035,0.017380686,0.08924106,0.015774371,0.011370689,0.7125159],"study_design_scores_gemma":[0.0019865013,0.00063080195,0.26082066,0.00070794753,0.00012775569,0.000035738933,0.00008462462,0.5473001,0.11783842,0.01343711,0.054779503,0.0022508488],"about_ca_topic_score_codex":0.0005395634,"about_ca_topic_score_gemma":0.00051370915,"teacher_disagreement_score":0.71026504,"about_ca_system_score_codex":0.00017406282,"about_ca_system_score_gemma":0.0001579845,"threshold_uncertainty_score":0.99991876},"labels":[],"label_agreement":null},{"id":"W4414907684","doi":"10.21203/rs.3.rs-7380471/v1","title":"A machine learning approach: a classification model for overdose","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Random forest; Resampling; Opioid overdose; Data pre-processing; Preprocessor; Receiver operating characteristic; Missing data; Drug overdose","score_opus":0.16720600624870197,"score_gpt":0.42286876378815047,"score_spread":0.2556627575394485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414907684","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":[0.00007611166,0.00026868636,0.9863798,0.0011752617,0.000034735,0.0019923756,0.00010101826,0.00060065894,0.00937133],"genre_scores_gemma":[0.5525751,0.00053665874,0.41499382,0.00007010539,0.00012995837,0.008985254,0.00029846904,0.000030474337,0.022380166],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976461,0.00019634934,0.0002849246,0.00091519544,0.0005397183,0.00041769294],"domain_scores_gemma":[0.99772453,0.0002047353,0.000120392666,0.0011932668,0.0006477503,0.00010929694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001342745,0.00018569673,0.0002209979,0.0004957575,0.0005690868,0.00037403364,0.0014907921,0.00031492556,0.0000047507883],"category_scores_gemma":[0.00018504242,0.00018585354,0.00019572697,0.00061359187,0.000066822155,0.00012839044,0.0019245818,0.0014003231,0.000011144818],"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":[0.000029852668,0.00035021952,0.000112634676,0.0014036837,0.000043165088,6.675069e-7,0.00084196177,0.049431667,0.0003119678,0.85618496,0.004708751,0.086580455],"study_design_scores_gemma":[0.00010973873,0.00005050545,0.000081140526,0.000090856876,0.000004342727,8.186108e-7,0.000028344533,0.93862027,0.00012515084,0.047817368,0.012915422,0.0001560163],"about_ca_topic_score_codex":0.000082670864,"about_ca_topic_score_gemma":0.0000047679,"teacher_disagreement_score":0.88918865,"about_ca_system_score_codex":0.00025863247,"about_ca_system_score_gemma":0.00052981335,"threshold_uncertainty_score":0.75788873},"labels":[],"label_agreement":null},{"id":"W4414996522","doi":"10.1080/14763141.2025.2569580","title":"Machine learning-based classification of ice hockey skating tasks using kinematic data","year":2025,"lang":"en","type":"article","venue":"Sports Biomechanics","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Kinematics; Ice hockey; Body segment; Trunk; Support vector machine; Random forest; Kinematic chain","score_opus":0.04088488800473407,"score_gpt":0.30411924983984556,"score_spread":0.2632343618351115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414996522","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":[0.0048775366,0.00007955261,0.9939104,0.000278749,0.000101169215,0.00022186466,0.000014181521,0.00026483304,0.0002517223],"genre_scores_gemma":[0.8454522,0.000011702934,0.15425394,0.00008168753,0.000011403163,0.000011664373,0.00006315253,0.000007592161,0.000106650754],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988526,0.000029329258,0.0003821836,0.00038920832,0.00020492282,0.00014173606],"domain_scores_gemma":[0.99826956,0.00004861967,0.00035424586,0.0011834651,0.00010957843,0.000034531015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046680844,0.00011037854,0.00015790483,0.00025995067,0.00016411839,0.0000461011,0.00086813816,0.00007814211,0.000014094035],"category_scores_gemma":[0.00006182308,0.00011115999,0.00004071016,0.0012005365,0.000022500533,0.0001902226,0.00031435365,0.00012977798,0.000002208695],"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":[0.000016964328,0.0005438747,0.0013571412,0.00060068094,0.00006434588,0.0000080949,0.000190882,0.0032912118,0.41715732,0.21854617,0.0004179681,0.35780534],"study_design_scores_gemma":[0.00008476331,0.00002174471,0.00012579623,0.000066263354,0.000017557597,0.0000018461177,0.000022594204,0.97188795,0.021868862,0.0026798479,0.0031245304,0.00009821956],"about_ca_topic_score_codex":0.000090864116,"about_ca_topic_score_gemma":0.0000064185638,"teacher_disagreement_score":0.96859676,"about_ca_system_score_codex":0.000040773502,"about_ca_system_score_gemma":0.00012540506,"threshold_uncertainty_score":0.4532973},"labels":[],"label_agreement":null},{"id":"W4415014456","doi":"10.1016/j.asoc.2025.114015","title":"Optimizing deep learning predictive models: A comprehensive review of RNN and its variant architectures","year":2025,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro-Québec; Université du Québec à Chicoutimi","funders":"Canada Excellence Research Chairs, Government of Canada; Canada Research Chairs","keywords":"Hyperparameter; Deep learning; Artificial neural network; Recurrent neural network; Deep neural networks; Prognostics","score_opus":0.013200387050013479,"score_gpt":0.25379032549242897,"score_spread":0.2405899384424155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415014456","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":[0.0017806443,0.011306577,0.9819774,0.000181772,0.000030370205,0.0004727929,7.634338e-7,0.0003676283,0.0038820482],"genre_scores_gemma":[0.8454631,0.0010136579,0.1527572,0.0006833444,0.000017584418,0.00004111962,0.0000016625501,0.000008382844,0.00001396882],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988181,0.00005367411,0.00035899229,0.0004282337,0.00013677156,0.00020424183],"domain_scores_gemma":[0.9990228,0.0003008207,0.0002151066,0.0002691909,0.00014241342,0.000049705955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024285968,0.00015612089,0.0002995875,0.00012334702,0.00028791284,0.000042857853,0.0003958886,0.000057913254,0.000001530479],"category_scores_gemma":[0.000028158887,0.00015503628,0.00006106123,0.00052917236,0.000046570494,0.000057723628,0.0005498708,0.00028410973,0.000001222114],"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":[0.000013726555,0.00006653592,0.000013864186,0.0027814826,0.0001159194,0.000003663247,0.0020654872,0.23094077,0.0025917718,0.41197163,0.000081191894,0.34935394],"study_design_scores_gemma":[0.00014453115,0.0000341888,0.00008325835,0.0011200871,0.000021823216,0.000012949753,0.00007123256,0.98329383,0.0015216095,0.012972443,0.0005844455,0.00013957231],"about_ca_topic_score_codex":0.000006943593,"about_ca_topic_score_gemma":2.6729631e-7,"teacher_disagreement_score":0.84368247,"about_ca_system_score_codex":0.000024175428,"about_ca_system_score_gemma":0.000038179893,"threshold_uncertainty_score":0.6322196},"labels":[],"label_agreement":null},{"id":"W4415151362","doi":"10.2139/ssrn.5600037","title":"From Classical Models to Attention-Based Transformers: A Comparative Study on Injury Prediction Pipelines in Female Varsity Soccer","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Support vector machine; Jumping; Jump; Transformer; Motion capture; Athletes","score_opus":0.03926303811910966,"score_gpt":0.32217592662242256,"score_spread":0.2829128885033129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415151362","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":[0.2538127,0.000042681786,0.7433383,0.001327396,0.00018767755,0.0008778776,0.000069091024,0.00014076146,0.0002035195],"genre_scores_gemma":[0.9963036,0.000083633226,0.0026491717,0.00016839152,0.00019998451,0.00025758543,0.000020172238,0.000010029537,0.00030742213],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99709696,0.000228998,0.0005979814,0.0007436977,0.0004234043,0.0009089772],"domain_scores_gemma":[0.9989547,0.00007464571,0.00021475946,0.00047328707,0.00017664685,0.00010596523],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00087641797,0.00030927447,0.0004236788,0.00051511946,0.00025671392,0.00017988667,0.00093733816,0.00022455733,0.0000051720554],"category_scores_gemma":[0.000006595294,0.000299804,0.00023447111,0.0005377025,0.000028545302,0.00022712603,0.000203227,0.0032988035,0.00001100571],"study_design_candidate":"theoretical_or_conceptual","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":[0.0027008853,0.021559795,0.017485015,0.00011172135,0.0025961746,0.00003087393,0.026043901,0.30936342,0.0017517192,0.29184586,0.003689897,0.32282072],"study_design_scores_gemma":[0.0015186962,0.0022724574,0.008861163,0.00034719892,0.00012480473,0.0000068552126,0.0036628398,0.4543336,0.0007702583,0.5271436,0.00023853825,0.0007200017],"about_ca_topic_score_codex":0.0004682457,"about_ca_topic_score_gemma":0.0013840781,"teacher_disagreement_score":0.74249095,"about_ca_system_score_codex":0.0015481296,"about_ca_system_score_gemma":0.0021906593,"threshold_uncertainty_score":0.9999454},"labels":[],"label_agreement":null},{"id":"W4415189903","doi":"10.1007/978-981-95-3453-1_9","title":"Federated Spatio-Temporal Attention for Time Series Anomaly Detection","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Université de Montréal","funders":"","keywords":"Anomaly detection; Leverage (statistics); Discriminative model; Graph; Time series; Anomaly (physics); Architecture","score_opus":0.009936095344302365,"score_gpt":0.23787684504739806,"score_spread":0.2279407497030957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415189903","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007029509,0.00006204945,0.9951139,0.00084272854,0.0006117261,0.00091134437,0.0000133665935,0.00051875145,0.0018558393],"genre_scores_gemma":[0.2138703,0.000028427463,0.77295524,0.0008158817,0.00047854136,0.00024227738,0.00006446197,0.00004546334,0.011499408],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975409,0.000020946685,0.0004801301,0.0011961497,0.0003753569,0.00038650108],"domain_scores_gemma":[0.9983893,0.0001496127,0.00030111198,0.0006560453,0.00042068923,0.00008324451],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00046029966,0.00037906633,0.000361164,0.00070709974,0.00062477146,0.0006452398,0.0011105045,0.00032889293,0.000016447384],"category_scores_gemma":[0.000040537383,0.00038412178,0.00016501489,0.000702992,0.00026531628,0.00068121677,0.0004493777,0.0003528207,0.000029052186],"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":[0.000019947103,0.000030076959,0.000033327713,0.00005390717,0.000015294156,0.000004720902,0.00004876422,0.00084915326,0.001502251,0.013951738,0.00015284322,0.983338],"study_design_scores_gemma":[0.0002762132,0.0004473723,0.00016840566,0.00022570872,0.000017328717,0.000044655062,1.3062706e-7,0.8300577,0.023588592,0.12812267,0.016307048,0.0007441764],"about_ca_topic_score_codex":0.000033398468,"about_ca_topic_score_gemma":0.00018122944,"teacher_disagreement_score":0.9825938,"about_ca_system_score_codex":0.00029543345,"about_ca_system_score_gemma":0.00034724682,"threshold_uncertainty_score":0.99986106},"labels":[],"label_agreement":null},{"id":"W4415222043","doi":"10.1109/tfuzz.2025.3621833","title":"PAC-X: Fuzzy Explainable AI for Multiclass Malware Detection","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Anomaly Detection Techniques and Applications","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":"Defence Research and Development Canada; McGill University","funders":"Canada Research Chairs; Defence Research and Development Canada","keywords":"Malware; Adversarial system; Exploit; Robustness (evolution); Embedding; Fuzzy logic; Cluster analysis; Artificial neural network","score_opus":0.013000825761761457,"score_gpt":0.2631470465305248,"score_spread":0.2501462207687633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415222043","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":[0.0006756532,0.00006582768,0.9916883,0.00085465313,0.0016764075,0.0014014994,0.00003395519,0.0010233783,0.0025802911],"genre_scores_gemma":[0.9828984,0.000018829052,0.005935592,0.00026410978,0.00006613536,0.0028638914,0.0000016722936,0.000020313773,0.007931022],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99839723,0.00007296497,0.000408939,0.0005684316,0.00019820023,0.0003542237],"domain_scores_gemma":[0.99861676,0.00015728905,0.00010479889,0.0007789154,0.00024477163,0.000097474294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002707255,0.0002227359,0.00024436,0.0003936573,0.00077945343,0.00026892606,0.0004992858,0.00018980564,0.0000024304036],"category_scores_gemma":[0.0000060956395,0.0002250951,0.00020395515,0.000893103,0.000038774724,0.00041908005,0.000004095089,0.00024162697,0.00004418153],"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":[0.0002722289,0.0016400127,0.000038052993,0.0012294464,0.00050991867,0.00001176259,0.00093043706,0.074058436,0.07408662,0.15774545,0.015476716,0.6740009],"study_design_scores_gemma":[0.0018308539,0.00071827666,0.00008710473,0.00028632733,0.000111415786,0.000059316277,0.0006797221,0.38358435,0.44397536,0.008684135,0.15903404,0.0009490828],"about_ca_topic_score_codex":0.0002486467,"about_ca_topic_score_gemma":0.00008868502,"teacher_disagreement_score":0.98575276,"about_ca_system_score_codex":0.00023444313,"about_ca_system_score_gemma":0.000079517245,"threshold_uncertainty_score":0.9179111},"labels":[],"label_agreement":null},{"id":"W4415230414","doi":"10.1609/aies.v8i1.36596","title":"Incident Analysis for AI Agents","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI/ACM Conference on AI Ethics and Society","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute on Governance","funders":"","keywords":"Documentation; Incident report; Incident management; Private information retrieval; Information system; Incident response","score_opus":0.06055215157506705,"score_gpt":0.35728052859633175,"score_spread":0.2967283770212647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415230414","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":[0.06638153,0.000049944025,0.7830817,0.1436772,0.00014314523,0.0008811284,0.000018634346,0.0002054692,0.0055612475],"genre_scores_gemma":[0.9760599,0.00025707006,0.015787665,0.006782554,0.000012437598,0.000093168026,6.423733e-7,0.0000035173664,0.0010030614],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999111,0.000008470617,0.00021035426,0.00031327255,0.00021564397,0.00014124437],"domain_scores_gemma":[0.998686,0.00013051865,0.00016010329,0.0002867225,0.00069773424,0.000038884722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007660441,0.000106988075,0.00018028182,0.000058694186,0.0005065417,0.00020105383,0.0010460102,0.00017055594,0.0000040404157],"category_scores_gemma":[0.00020837814,0.000080858335,0.00026919667,0.0007617976,0.00013277575,0.00013909963,0.00052143226,0.0004960753,5.5893355e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027413425,0.00003489272,0.0024968649,0.00006395107,0.00012609948,6.9650246e-9,0.0014740725,0.000003674557,0.001280576,0.9864706,0.0059250267,0.0021215],"study_design_scores_gemma":[0.00050386996,0.00017821412,0.03359194,0.00018948785,0.00036603832,0.0000010418822,0.00106339,0.2005195,0.041571613,0.70243454,0.019176904,0.00040344056],"about_ca_topic_score_codex":0.000033459797,"about_ca_topic_score_gemma":0.0000033675788,"teacher_disagreement_score":0.90967834,"about_ca_system_score_codex":0.00003083786,"about_ca_system_score_gemma":0.00010756556,"threshold_uncertainty_score":0.38959602},"labels":[],"label_agreement":null},{"id":"W4415232908","doi":"10.21203/rs.3.rs-7862447/v1","title":"Multimodal Anomaly Detection for Urban Safety: A Real-World Implementation in Large-Scale Surveillance Systems","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Toronto","funders":"","keywords":"Software deployment; Anomaly detection; Robustness (evolution); Artificial neural network; Field (mathematics); Event (particle physics); Feature (linguistics)","score_opus":0.03399347703042941,"score_gpt":0.40605779294573746,"score_spread":0.37206431591530803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415232908","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":[0.008316958,0.00021769886,0.98226583,0.0005976524,0.0003423607,0.005315964,0.00064406457,0.00047917129,0.0018202915],"genre_scores_gemma":[0.9813682,0.00027250062,0.009706676,0.000016632757,0.00021594243,0.00639596,0.00020032866,0.000023168197,0.001800588],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99641895,0.0005140355,0.00066571025,0.0010764418,0.00060170563,0.00072314043],"domain_scores_gemma":[0.99736524,0.00042284094,0.0002056344,0.0011733274,0.00071683235,0.000116129275],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0030371442,0.0002469406,0.00038468235,0.0011700413,0.00047309842,0.00039249682,0.0010479938,0.0002643947,0.000012550877],"category_scores_gemma":[0.000058559464,0.00026987755,0.00017436633,0.0015444749,0.000044750526,0.0001964894,0.0011338205,0.0008360382,0.000010956922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008777947,0.0021286942,0.28707916,0.01283829,0.0003694443,0.000037382662,0.007383644,0.012496029,0.0038792437,0.2414492,0.020751733,0.41070938],"study_design_scores_gemma":[0.0015395768,0.000378481,0.16170526,0.0006430715,0.000008974646,0.0000029940916,0.00091983186,0.7636529,0.0043894756,0.0051868022,0.060791697,0.0007809144],"about_ca_topic_score_codex":0.0077258996,"about_ca_topic_score_gemma":0.02083752,"teacher_disagreement_score":0.97305125,"about_ca_system_score_codex":0.0009821327,"about_ca_system_score_gemma":0.00044798132,"threshold_uncertainty_score":0.9999753},"labels":[],"label_agreement":null},{"id":"W4415302549","doi":"10.18280/isi.300802","title":"Ensemble CNN with Feature Selection and Soft Voting for Document Classification","year":2025,"lang":"","type":"article","venue":"Ingénierie des systèmes d information","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Mustansiriyah University","keywords":"Feature selection; Feature (linguistics); Pattern recognition (psychology); Selection (genetic algorithm); Voting; Ensemble learning","score_opus":0.011090739284292946,"score_gpt":0.2462046227108012,"score_spread":0.23511388342650827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415302549","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":[0.010693445,0.00018567283,0.98233944,0.0011363993,0.00020475451,0.0018601527,0.000010872443,0.000384339,0.0031849355],"genre_scores_gemma":[0.92637104,0.00007371549,0.07133828,0.00032328445,0.000066046865,0.0008737858,0.00004380648,0.000011990625,0.0008980423],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99825144,0.000054462238,0.0006768307,0.00038643173,0.00023716819,0.00039365471],"domain_scores_gemma":[0.99804366,0.00011402779,0.00063142227,0.00032916464,0.0007896569,0.00009208947],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005629819,0.00030661072,0.00027238656,0.00045425305,0.0013834029,0.0013253711,0.00029809875,0.00027290126,0.000005651783],"category_scores_gemma":[0.00009912693,0.00029656253,0.00007915593,0.0012706412,0.0001376744,0.003951549,0.00011330768,0.00024284092,0.00001163439],"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":[0.000093268296,0.000035656114,0.00071624253,0.0008287956,0.00007407263,1.1468674e-7,0.0019946506,0.00044201585,0.00080491725,0.22302602,0.0016467304,0.7703375],"study_design_scores_gemma":[0.0011652942,0.00075421407,0.010594808,0.000848545,0.00015031768,0.00007021439,0.0012791026,0.8868593,0.01478587,0.019205907,0.0635975,0.00068894797],"about_ca_topic_score_codex":0.00005272778,"about_ca_topic_score_gemma":0.000033508655,"teacher_disagreement_score":0.9156776,"about_ca_system_score_codex":0.000506072,"about_ca_system_score_gemma":0.00027450573,"threshold_uncertainty_score":0.9999486},"labels":[],"label_agreement":null},{"id":"W4415303157","doi":"10.18280/isi.300818","title":"Noise-Resilient Human Activity Recognition via A Hybrid CNN–InceptionV3 Model","year":2025,"lang":"","type":"article","venue":"Ingénierie des systèmes d information","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"SRM Institute of Science and Technology","keywords":"Activity recognition; Pattern recognition (psychology); Feature (linguistics); Noise (video); Matching (statistics)","score_opus":0.0195064122075948,"score_gpt":0.2606519880184093,"score_spread":0.24114557581081447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415303157","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":[0.11586035,0.000050634233,0.86138684,0.00026608707,0.00040210655,0.0012696068,0.000079465695,0.00072412664,0.019960772],"genre_scores_gemma":[0.9812377,0.00010601592,0.016373008,0.00051752804,0.00008505046,0.0006728787,0.00012740842,0.00001793927,0.0008624481],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967785,0.00015499721,0.0013539593,0.0005626586,0.00051851146,0.0006314052],"domain_scores_gemma":[0.9968662,0.000057241392,0.00093125046,0.0010517724,0.00090736744,0.00018616565],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00075758825,0.00050037995,0.00044798566,0.00091145467,0.0019785452,0.0011168687,0.0008637901,0.00030281785,0.000102215934],"category_scores_gemma":[0.00008730479,0.0005796181,0.00029742153,0.0015371533,0.00036137964,0.007281705,0.00046286554,0.0004989133,0.00045847267],"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":[0.000048525268,0.00021604792,0.00006357237,0.00047379258,0.00006351206,0.0000011932709,0.0015513048,0.003721567,0.0048189596,0.012199182,0.0020216978,0.9748207],"study_design_scores_gemma":[0.000519276,0.00018296544,0.001822954,0.00042521884,0.00007018488,0.000031918964,0.000115476345,0.884577,0.032798193,0.07672538,0.0021307284,0.00060070434],"about_ca_topic_score_codex":0.00031460766,"about_ca_topic_score_gemma":0.000023944995,"teacher_disagreement_score":0.9742199,"about_ca_system_score_codex":0.0011968815,"about_ca_system_score_gemma":0.00037006495,"threshold_uncertainty_score":0.99992007},"labels":[],"label_agreement":null},{"id":"W4415310281","doi":"10.46298/mbj.16165","title":"A new methodological approach for analyzing bouldering fall kinematics: preliminary study on 6 athletes","year":2025,"lang":"en","type":"article","venue":"Multidisciplinary Biomechanics Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Mitacs; École de technologie supérieure","keywords":"Athletes; Perspective (graphical); Elite athletes; Statistical analysis; Work (physics)","score_opus":0.08658245684168937,"score_gpt":0.37396343320806524,"score_spread":0.2873809763663759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415310281","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011311301,0.00014304552,0.9859905,0.0007866413,0.00022588116,0.0010571342,0.0000032808114,0.0002639705,0.00021819143],"genre_scores_gemma":[0.19662833,0.000025221267,0.8024703,0.000060889746,0.00012248433,0.00018239958,0.0000024136586,0.00001522013,0.00049271696],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980054,0.00018562132,0.00059588626,0.00058052817,0.00025147825,0.00038109685],"domain_scores_gemma":[0.9984524,0.00035182975,0.0002795398,0.00060816336,0.00013560729,0.00017241528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015280785,0.0002621226,0.00038123684,0.00056567136,0.00089746213,0.00032213397,0.001186071,0.00014141618,0.000003901829],"category_scores_gemma":[0.000107088854,0.0002139168,0.00025847697,0.0009184742,0.00001902725,0.0002480002,0.0006986768,0.00042028338,0.0000029201424],"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":[0.00064953434,0.005391877,0.0011422959,0.0002987186,0.00080096145,0.00007531607,0.0055916454,0.0047255554,0.057794183,0.18051887,0.0059804358,0.7370306],"study_design_scores_gemma":[0.0014170695,0.003707269,0.0012387156,0.00015665642,0.00010127311,0.00011083991,0.0012883274,0.89586544,0.013435809,0.08170193,0.00047438286,0.00050230377],"about_ca_topic_score_codex":0.000005404766,"about_ca_topic_score_gemma":5.994977e-7,"teacher_disagreement_score":0.89113986,"about_ca_system_score_codex":0.00011716375,"about_ca_system_score_gemma":0.00010416228,"threshold_uncertainty_score":0.87232745},"labels":[],"label_agreement":null},{"id":"W4415342019","doi":"10.22541/essoar.176086852.28048212/v1","title":"Hierarchical Deep Learning Architectures for Multiclass Violation Detection in Urban Surveillance Systems Authors","year":2025,"lang":"","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Toronto","funders":"","keywords":"Deep learning; Artificial neural network; Key (lock); Class (philosophy); Deep neural networks","score_opus":0.015074259079620208,"score_gpt":0.2791023426943145,"score_spread":0.26402808361469426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415342019","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":[0.029808791,0.00029442355,0.9621494,0.00041041226,0.0013340317,0.0039932495,0.000023451359,0.00088994595,0.0010963109],"genre_scores_gemma":[0.95714927,0.00008404156,0.036355615,0.000047321595,0.0003259717,0.0029403113,0.000028916887,0.00003483966,0.0030337328],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99433905,0.0008513177,0.0015369607,0.0020268043,0.00045868766,0.0007871555],"domain_scores_gemma":[0.99644244,0.0011140398,0.00070280256,0.00109479,0.0004289296,0.00021699653],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0019241651,0.0006729536,0.00082516496,0.0010677006,0.0007910031,0.000645494,0.0012534652,0.0009956098,0.000006085159],"category_scores_gemma":[0.00042018807,0.0007105294,0.00042662892,0.0013588194,0.00014271028,0.00010201028,0.0008973785,0.002235554,0.000007852283],"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":[0.0001640746,0.00023107338,0.0067700003,0.0007767879,0.00008407058,0.0000019334157,0.0015363953,0.46125934,0.001250061,0.018658226,0.000036218902,0.5092318],"study_design_scores_gemma":[0.00047238724,0.00022576112,0.014759115,0.00024667362,0.00001813665,0.000010795729,0.00008457157,0.9702247,0.0023376974,0.0036746205,0.0072672004,0.0006783563],"about_ca_topic_score_codex":0.0011368601,"about_ca_topic_score_gemma":0.00092309783,"teacher_disagreement_score":0.92734045,"about_ca_system_score_codex":0.0005623419,"about_ca_system_score_gemma":0.00022321225,"threshold_uncertainty_score":0.9995346},"labels":[],"label_agreement":null},{"id":"W4415359990","doi":"10.59934/jaiea.v5i1.1547","title":"Implementation of Isolation Forest-Based Machine Learning in Batch Anomaly Detection on Zeek Log Data (Case Study: Langkat Regency Communication and Information Agency)","year":2025,"lang":"","type":"article","venue":"Journal of Artificial Intelligence and Engineering Applications (JAIEA)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Isolation (microbiology); Anomaly detection; Context (archaeology); Proxy (statistics); Network security; F1 score; Intrusion detection system; Information security","score_opus":0.03348309721170299,"score_gpt":0.31944677414549877,"score_spread":0.28596367693379576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415359990","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":[0.26180074,0.0003415567,0.7366204,0.0004052752,0.000057581066,0.00070932956,0.000013102754,0.000035024175,0.000016955299],"genre_scores_gemma":[0.9873343,0.0007119334,0.0117574,0.000026925907,0.000034893055,0.00009601428,0.000022617523,0.000010561122,0.0000053565377],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977018,0.00011010164,0.0014690083,0.00029879328,0.00023515522,0.00018512298],"domain_scores_gemma":[0.9977958,0.00024764167,0.00083128375,0.00069119583,0.00035977806,0.00007428583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012693966,0.00021442682,0.00030096603,0.0010782154,0.00031757588,0.00020044018,0.0005156608,0.00012701176,0.0000067072883],"category_scores_gemma":[0.00008098571,0.00023496767,0.00005589044,0.0014669055,0.00006673379,0.0014361059,0.00019574525,0.00048945635,0.0000022274003],"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":[0.000068347756,0.00036379692,0.005360683,0.00015963949,0.000055429067,0.0000053164117,0.0016510461,0.053008907,0.0022709223,0.010192445,0.0000082906845,0.9268552],"study_design_scores_gemma":[0.00021823046,0.0008199186,0.009615959,0.00016470783,0.000105184125,0.00008240266,0.0030646138,0.9726134,0.010691336,0.0014463641,0.00094587496,0.00023204398],"about_ca_topic_score_codex":0.0011727744,"about_ca_topic_score_gemma":0.00075740286,"teacher_disagreement_score":0.9266231,"about_ca_system_score_codex":0.0001127316,"about_ca_system_score_gemma":0.00012964613,"threshold_uncertainty_score":0.9581703},"labels":[],"label_agreement":null},{"id":"W4415451460","doi":"10.1016/j.asoc.2025.114114","title":"Fuzzy natural neighbors for outlier detection","year":2025,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Fondo Nacional de Desarrollo Científico y Tecnológico; Fondo de Fomento al Desarrollo Científico y Tecnológico; Agencia Nacional de Investigación y Desarrollo; Instituto de Sistemas Complejos de Ingeniería","keywords":"Outlier; Fuzzy logic; Anomaly detection; Ambiguity; Benchmarking; Flexibility (engineering)","score_opus":0.006518451004324022,"score_gpt":0.2520087350476332,"score_spread":0.2454902840433092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415451460","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":[0.0047367564,0.000041266543,0.98350114,0.00034878403,0.0003587567,0.0004951698,6.549585e-7,0.0009550274,0.009562452],"genre_scores_gemma":[0.87462026,0.000001010333,0.12420223,0.00070648437,0.00007423438,0.000101962294,0.000001995214,0.000008456918,0.00028336965],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989772,0.000010234693,0.00024379292,0.00041611912,0.00009581437,0.0002568412],"domain_scores_gemma":[0.9992293,0.0001796655,0.00009833689,0.0003784748,0.000077352175,0.000036872654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021023166,0.00013256,0.0001396645,0.00013752472,0.000499034,0.00013818673,0.00049587106,0.00007636821,8.0017713e-7],"category_scores_gemma":[0.000019625331,0.0001363026,0.000086149186,0.0005870313,0.000028672392,0.000090672016,0.00021299736,0.0001626329,0.000013096058],"study_design_candidate":"theoretical_or_conceptual","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":[0.000005257483,0.000021919248,0.000026473645,0.000015509522,0.000014651683,1.2987618e-7,0.00008470853,0.000221754,0.007540683,0.39820752,0.0005150242,0.59334636],"study_design_scores_gemma":[0.00093249424,0.00007245722,0.0022853105,0.000037995094,0.000030265077,0.000008334873,0.00012668388,0.60524684,0.11532021,0.21382815,0.061459593,0.00065166043],"about_ca_topic_score_codex":0.000008254271,"about_ca_topic_score_gemma":0.0000036214174,"teacher_disagreement_score":0.8698835,"about_ca_system_score_codex":0.00006306405,"about_ca_system_score_gemma":0.000034889854,"threshold_uncertainty_score":0.5558259},"labels":[],"label_agreement":null},{"id":"W4415536964","doi":"10.1145/3746027.3754500","title":"EventVAD: Training-Free Event-Aware Video Anomaly Detection","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Queen's University; Queen's University Belfast","keywords":"Anomaly detection; Thresholding; Leverage (statistics); Graph; Pattern recognition (psychology); Event (particle physics); Noise (video)","score_opus":0.011751332295551505,"score_gpt":0.26049116275425066,"score_spread":0.24873983045869916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415536964","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":[0.002534062,0.000028265995,0.97466826,0.0021952884,0.00018430746,0.00021270498,0.0000015571101,0.0010052917,0.019170286],"genre_scores_gemma":[0.97286034,0.000008128395,0.018541496,0.00070303597,0.00003538892,0.00016166265,9.476545e-7,0.0000056482363,0.007683361],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999039,0.000029098606,0.00023017208,0.0003747093,0.0001273951,0.0001996545],"domain_scores_gemma":[0.99892104,0.000039882812,0.00006040608,0.0008498815,0.000072878254,0.00005589387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001715954,0.000114752744,0.00011257714,0.00017975234,0.0002472587,0.000102813516,0.0008600093,0.00008165507,0.000052773452],"category_scores_gemma":[0.000024744912,0.0001103299,0.00009794237,0.00080828124,0.000026771424,0.00029286116,0.0002566463,0.000119521894,0.0000461458],"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":[0.0000056612657,0.00009244753,0.00023417045,0.000014306032,0.00003192493,0.000002360633,0.00016140763,0.00003999078,0.00544759,0.28882134,0.010792972,0.69435585],"study_design_scores_gemma":[0.0010854787,0.00029522585,0.013230044,0.000067812114,0.00003676269,0.000052833766,0.0003291422,0.19372714,0.2587859,0.2080487,0.3235242,0.0008167429],"about_ca_topic_score_codex":0.00007470379,"about_ca_topic_score_gemma":0.00010487987,"teacher_disagreement_score":0.97032624,"about_ca_system_score_codex":0.000060796363,"about_ca_system_score_gemma":0.000069820824,"threshold_uncertainty_score":0.44991228},"labels":[],"label_agreement":null},{"id":"W4415538738","doi":"10.1145/3746027.3755576","title":"ACMamba: Fast Unsupervised Anomaly Detection via An Asymmetrical Consensus State Space Model","year":2025,"lang":"","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Natural Science Basic Research Program of Shaanxi Province; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Anomaly detection; Key (lock); Property (philosophy); Anomaly (physics); Feature (linguistics); Unsupervised learning; Pattern recognition (psychology); Computation; Perspective (graphical)","score_opus":0.01563967350595667,"score_gpt":0.2699503602487381,"score_spread":0.2543106867427814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415538738","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":[0.038319018,0.00014327893,0.9435576,0.0037093966,0.00038302687,0.0010317838,0.000023775568,0.0012151299,0.011616971],"genre_scores_gemma":[0.88689816,0.00010139507,0.09950388,0.0008696603,0.000060584982,0.00015056563,0.0000037698776,0.00003367929,0.012378311],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956909,0.00024399305,0.0010063065,0.0016838283,0.0005057666,0.0008692041],"domain_scores_gemma":[0.99647605,0.00016814131,0.00027681727,0.0019724164,0.0006359675,0.0004706105],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00058472174,0.000597798,0.00055586966,0.0010673972,0.0010335111,0.0006985322,0.001370476,0.00041217243,0.00007315883],"category_scores_gemma":[0.00006121135,0.00062793586,0.00030291636,0.004993316,0.00030311928,0.00060029,0.00068057014,0.00067939283,0.00013607364],"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":[0.00011750518,0.0009196112,0.00030297422,0.00006385302,0.00012061531,0.000012643336,0.00032761964,0.00548852,0.03360474,0.04276688,0.0012160613,0.915059],"study_design_scores_gemma":[0.0005701399,0.00042599902,0.0015443945,0.000022104628,0.00005708843,0.000028880771,0.00007299214,0.8871761,0.09241139,0.014896991,0.00222235,0.0005715811],"about_ca_topic_score_codex":0.0007338918,"about_ca_topic_score_gemma":0.0002711384,"teacher_disagreement_score":0.9144874,"about_ca_system_score_codex":0.00035829056,"about_ca_system_score_gemma":0.00046213093,"threshold_uncertainty_score":0.9996172},"labels":[],"label_agreement":null},{"id":"W4415680882","doi":"10.1016/j.neunet.2025.108267","title":"Multi-teacher knowledge distillation framework for lightweight anomaly detection","year":2025,"lang":"en","type":"article","venue":"Neural Networks","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Iran National Science Foundation","keywords":"Undersampling; Anomaly detection; Robustness (evolution); Inference; Process (computing); Benchmark (surveying); Intrusion detection system; Resampling; Oversampling","score_opus":0.01724369453870204,"score_gpt":0.2966870594862686,"score_spread":0.27944336494756655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415680882","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":[0.0033766294,0.00032394245,0.9933731,0.0005502691,0.00063389214,0.0004970273,0.0000010701785,0.00060745043,0.00063662365],"genre_scores_gemma":[0.9045421,0.000017102173,0.09352406,0.0001687471,0.00025127493,0.00029476383,0.0000038669755,0.000010389318,0.0011877266],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990482,0.000038975566,0.000228479,0.0003956951,0.00005736702,0.00023128776],"domain_scores_gemma":[0.999168,0.00014159152,0.00008876162,0.00042590813,0.00012317776,0.000052564916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011580745,0.00014070985,0.00013130117,0.000098928394,0.0003430135,0.00012951704,0.00037833324,0.0001976054,0.000006235715],"category_scores_gemma":[0.000036219142,0.00013235323,0.000101768645,0.00082106265,0.00002933978,0.00020094654,0.000107713546,0.00022488812,0.000006881675],"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":[0.000026113012,0.00018070475,0.0013415726,0.000024834799,0.000029887233,6.160285e-7,0.00008985698,0.002821634,0.0008531373,0.14031151,0.0024090374,0.85191107],"study_design_scores_gemma":[0.00014577653,0.000050819148,0.0063969926,0.000016532842,0.000010663577,0.0000018453817,0.000002953223,0.9623783,0.002040697,0.006113165,0.022705356,0.00013684962],"about_ca_topic_score_codex":0.000007702709,"about_ca_topic_score_gemma":0.000027838349,"teacher_disagreement_score":0.9595567,"about_ca_system_score_codex":0.00005839662,"about_ca_system_score_gemma":0.00001609397,"threshold_uncertainty_score":0.53972083},"labels":[],"label_agreement":null},{"id":"W4415707948","doi":"10.1109/icme59968.2025.11209270","title":"AnoCLIP: Text-Guided Zero-shot Anomaly Localization via Self-Supervised Adaptation","year":2025,"lang":"","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Anomaly (physics); Anomaly detection; Adaptation (eye); Task (project management); Pattern recognition (psychology)","score_opus":0.0255922886296217,"score_gpt":0.27435432942178295,"score_spread":0.24876204079216124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415707948","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":[0.0012952294,0.00035123058,0.9370045,0.0020871742,0.0005969372,0.0018427786,0.0000059759946,0.0014795897,0.055336557],"genre_scores_gemma":[0.8810325,0.00023655541,0.10985467,0.0024972612,0.00009410968,0.00041392664,0.000029986004,0.000031141935,0.0058098407],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995819,0.00027436178,0.0012435691,0.0013944074,0.000645997,0.0006226936],"domain_scores_gemma":[0.99664664,0.00010890869,0.0003500129,0.0015792436,0.0010940704,0.00022112337],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007755593,0.00052323175,0.00044951326,0.000629344,0.0010033048,0.0006715755,0.001270631,0.0004357586,0.00065783213],"category_scores_gemma":[0.000050589068,0.00056239933,0.00027309536,0.0038732712,0.00013565746,0.0010682072,0.0004606569,0.00031032,0.00026592598],"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":[0.000027957336,0.0010373413,0.001386847,0.00018061962,0.0002184577,0.000004781655,0.0012764989,0.0058454205,0.003176624,0.26658085,0.02794007,0.6923245],"study_design_scores_gemma":[0.00069602224,0.00014823474,0.00080772856,0.00006696691,0.00013929524,0.000014126132,0.000099489866,0.93824637,0.013648269,0.0120511195,0.033553656,0.000528743],"about_ca_topic_score_codex":0.00061411003,"about_ca_topic_score_gemma":0.00008749142,"teacher_disagreement_score":0.93240094,"about_ca_system_score_codex":0.00042383108,"about_ca_system_score_gemma":0.0006807219,"threshold_uncertainty_score":0.9996827},"labels":[],"label_agreement":null},{"id":"W4415719725","doi":"10.18280/ts.420514","title":"Automatic Early Warning and Visual Analysis Framework for Sudden Health Incidents in Public Spaces Based on Multi-Source Video Streams and Behavior Recognition Algorithms","year":2025,"lang":"","type":"article","venue":"Traitement du signal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Warning system; STREAMS; Data stream mining; Visualization; Early warning system; Pattern recognition (psychology)","score_opus":0.03515065955660658,"score_gpt":0.3305167826278008,"score_spread":0.29536612307119425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415719725","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":[0.42061824,0.00007625873,0.5770501,0.00095410843,0.000035695946,0.0011249441,0.000029662451,0.00010800557,0.0000029730118],"genre_scores_gemma":[0.87922245,0.000046918107,0.11904462,0.000588598,0.0000393211,0.0009704683,0.000042441,0.000016819236,0.000028339036],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970174,0.00026555613,0.0008599938,0.0009482121,0.00038673973,0.0005221216],"domain_scores_gemma":[0.9984083,0.0004750981,0.0004748435,0.0002642032,0.00014211421,0.00023539388],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011492628,0.0003514932,0.00053556554,0.0013390833,0.000636073,0.00086875504,0.0002908782,0.0001999974,0.00007159231],"category_scores_gemma":[0.00006639384,0.00038127028,0.0001614326,0.0020954183,0.00011825818,0.00042832992,0.00014173082,0.0003459639,0.0000023970465],"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":[0.000024966635,0.0010322444,0.032238852,0.00013704073,0.00013757471,0.0000019586312,0.0013684247,0.00017965047,0.00004888774,0.00031606347,0.00001809767,0.96449625],"study_design_scores_gemma":[0.0010437894,0.00087303075,0.15889019,0.0003460168,0.00028712934,0.0000010674486,0.00031943072,0.837258,0.00022215789,0.0004100204,0.00006704826,0.00028213524],"about_ca_topic_score_codex":0.0006284521,"about_ca_topic_score_gemma":0.00016666252,"teacher_disagreement_score":0.9642141,"about_ca_system_score_codex":0.0002162128,"about_ca_system_score_gemma":0.00018230264,"threshold_uncertainty_score":0.9998639},"labels":[],"label_agreement":null},{"id":"W4415883248","doi":"10.1109/jsyst.2025.3619401","title":"Distributed Anomaly Detection With Attention-Guided Diffusion Models and Client-Side Defect Generation","year":2025,"lang":"en","type":"article","venue":"IEEE Systems Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Anomaly detection; Modular design; Inference; Data modeling; Process (computing); Domain (mathematical analysis); Distributed database; Modularity (biology)","score_opus":0.021389268214762485,"score_gpt":0.2553661889209643,"score_spread":0.23397692070620182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415883248","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":[0.3071464,0.00013264384,0.69171226,0.000105374565,0.00034871564,0.00022070073,0.0000026400573,0.00012043719,0.00021082554],"genre_scores_gemma":[0.9962971,0.00006098617,0.0030773033,0.00004073188,0.00015728043,0.0000691776,0.0000030995552,0.0000075133457,0.00028676164],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880767,0.00011057821,0.0003835604,0.00029768722,0.00021662662,0.00018388659],"domain_scores_gemma":[0.9991174,0.000023745177,0.00024093548,0.00028407923,0.00024037412,0.00009347873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035202532,0.00013861226,0.00016181846,0.00021519673,0.0006619002,0.00049421506,0.0002165592,0.00008553538,7.679733e-7],"category_scores_gemma":[0.0000073761557,0.00011123465,0.00007515697,0.00047069692,0.000030138683,0.0005584345,0.000042920754,0.00019000834,0.0000029345688],"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":[0.00008557336,0.00043929872,0.012045428,0.00021209105,0.00045327732,0.00006304791,0.00038912467,0.1554115,0.6870872,0.04017128,0.0077071125,0.095935114],"study_design_scores_gemma":[0.00076205144,0.00020124244,0.005039595,0.0001456796,0.000050554794,0.0012371307,0.000038523525,0.9757406,0.01285146,0.0020639952,0.0016041306,0.00026499626],"about_ca_topic_score_codex":0.00008684845,"about_ca_topic_score_gemma":0.000034650388,"teacher_disagreement_score":0.8203291,"about_ca_system_score_codex":0.00012900558,"about_ca_system_score_gemma":0.00005762893,"threshold_uncertainty_score":0.50908685},"labels":[],"label_agreement":null},{"id":"W4416117870","doi":"10.48550/arxiv.2504.04911","title":"IterMask3D: Unsupervised Anomaly Detection and Segmentation with Test-Time Iterative Mask Refinement in 3D Brain MR","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Canadian Institutes of Health Research; Avid Radiopharmaceuticals; National Institutes of Health; University of Oxford; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Meso Scale Diagnostics; Department of Health and Social Care; National Institute for Health and Care Research; Northern California Institute for Research and Education; BioClinica; Biogen; Pfizer; Novartis Pharmaceuticals Corporation; Wellcome Trust; University of Southern California; UK Research and Innovation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Alzheimer's Disease Neuroimaging Initiative; Alzheimer's Association","keywords":"Segmentation; Iterative reconstruction; Pattern recognition (psychology); Anomaly detection; Image segmentation; Process (computing); Feature (linguistics); Image (mathematics)","score_opus":0.01426190354241507,"score_gpt":0.2524771708589169,"score_spread":0.23821526731650183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416117870","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":[0.66043925,0.000045679397,0.33569616,0.0018322554,0.000063866864,0.00093331886,0.000026816675,0.0003263639,0.00063626625],"genre_scores_gemma":[0.96091014,0.000056498437,0.035283558,0.00087620545,0.00005004689,0.0008655951,0.000035260055,0.0000164479,0.0019062569],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981315,0.000102237645,0.00042887445,0.00089609186,0.00020033817,0.00024096627],"domain_scores_gemma":[0.9987463,0.0001463858,0.00023363238,0.0006893997,0.000112706475,0.00007158935],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002510336,0.00031904993,0.0002943704,0.00036375268,0.00017518665,0.0001939349,0.0004343244,0.00019604835,0.000021947408],"category_scores_gemma":[0.000027492588,0.00030377688,0.000055425524,0.00055386056,0.000058833564,0.00030777423,0.0006086835,0.00042943333,0.000018571865],"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":[0.00021184029,0.001025679,0.47280627,0.00076871226,0.00029085318,0.00007549729,0.008149527,0.0014645307,0.25844625,0.0017233102,0.0006606904,0.25437686],"study_design_scores_gemma":[0.0021962803,0.0010688365,0.6074406,0.0008314006,0.00009480069,0.00004271305,0.00020031666,0.15959474,0.22142775,0.001310659,0.004152017,0.0016398724],"about_ca_topic_score_codex":0.00020727095,"about_ca_topic_score_gemma":0.00014637709,"teacher_disagreement_score":0.30047086,"about_ca_system_score_codex":0.00019338168,"about_ca_system_score_gemma":0.00010592462,"threshold_uncertainty_score":0.9999414},"labels":[],"label_agreement":null},{"id":"W4416231290","doi":"10.3390/e27111151","title":"An Outlier Suppression and Adversarial Learning Model for Anomaly Detection in Multivariate Time Series","year":2025,"lang":"en","type":"article","venue":"Entropy","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Anomaly detection; Overfitting; Outlier; Robustness (evolution); Multivariate statistics; Time series; Adversarial system; Generalization","score_opus":0.00634037957887684,"score_gpt":0.25950213966753477,"score_spread":0.25316176008865793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416231290","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":[0.054245174,0.000009038755,0.9449411,0.00021295989,0.000057452784,0.00024308091,0.0000016867701,0.00018701832,0.00010246216],"genre_scores_gemma":[0.9175829,0.000004706484,0.08114902,0.00003566005,0.000023464894,0.00009963569,0.0000024230326,0.0000045971683,0.0010975475],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942183,0.00003326276,0.00012696261,0.00025028206,0.000046968205,0.000120702316],"domain_scores_gemma":[0.99970853,0.000025627538,0.00004283911,0.00016490601,0.000029422179,0.000028663955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000120357174,0.000071276074,0.00008676416,0.000105603336,0.00017194478,0.00007409305,0.00011965972,0.000060410912,0.000002924408],"category_scores_gemma":[0.000020662557,0.00006986691,0.000024976836,0.00014804245,0.000017478524,0.0003961224,0.0000653597,0.000087542605,0.0000024436617],"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":[0.00027973307,0.00018079657,0.0015681947,0.000030170262,0.000019486051,0.0000013163293,0.0014508187,0.019932913,0.7517596,0.10325108,0.00022979856,0.12129613],"study_design_scores_gemma":[0.00035920908,0.00007799299,0.0015583095,0.0000077174045,0.0000034143272,8.243976e-7,0.000014317069,0.9473195,0.042484164,0.0068273596,0.0012760502,0.00007113109],"about_ca_topic_score_codex":0.00003334545,"about_ca_topic_score_gemma":0.000008153312,"teacher_disagreement_score":0.9273866,"about_ca_system_score_codex":0.000030865718,"about_ca_system_score_gemma":0.000020489493,"threshold_uncertainty_score":0.28490898},"labels":[],"label_agreement":null},{"id":"W4416250848","doi":"10.1109/ijcnn64981.2025.11229407","title":"Shared Knowledge Base for Multi Deep Learning in Defect Detection","year":2025,"lang":"","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Anomaly detection; Deep learning; Anomaly (physics); Knowledge base; Baseline (sea); Base (topology); Visualization; Pattern recognition (psychology)","score_opus":0.030774618409984505,"score_gpt":0.315913304856236,"score_spread":0.28513868644625145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416250848","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":[0.0022197994,0.0006764592,0.98978573,0.00027356957,0.00033033485,0.0013598758,0.000002523563,0.0005922354,0.0047594844],"genre_scores_gemma":[0.8978101,0.000059216858,0.09063224,0.00013817917,0.000045807497,0.0010494541,0.000002970776,0.000016234968,0.010245795],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978973,0.00013214801,0.00054970104,0.0008676239,0.00008632442,0.00046685096],"domain_scores_gemma":[0.99878174,0.00024014452,0.00013351609,0.0005105102,0.00023523455,0.00009885693],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00069323723,0.00025463084,0.0002677061,0.00059140817,0.00058904913,0.00027708805,0.00055282266,0.0002379933,0.00006583649],"category_scores_gemma":[0.00021120836,0.00028033648,0.0003013975,0.0019707466,0.00005308632,0.00037356734,0.00029920516,0.0003929194,0.0000685809],"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":[0.000026737694,0.00033427664,0.0006285121,0.00011081919,0.000024216391,7.223474e-7,0.0003248402,0.0004193356,0.00423801,0.018849136,0.00013810952,0.9749053],"study_design_scores_gemma":[0.0008025458,0.00019296179,0.0031491814,0.00007906118,0.000023470593,0.0000024888698,0.00010398565,0.9352647,0.042758383,0.0011915377,0.01615917,0.0002725352],"about_ca_topic_score_codex":0.00013862968,"about_ca_topic_score_gemma":0.0016088445,"teacher_disagreement_score":0.97463274,"about_ca_system_score_codex":0.0002869737,"about_ca_system_score_gemma":0.00015633555,"threshold_uncertainty_score":0.9999649},"labels":[],"label_agreement":null},{"id":"W4416551965","doi":"10.1049/ipr2.70247","title":"Video Anomaly Detection With Probabilistic Modelling and Ensemble Learning on Deep Spatiotemporal Features","year":2025,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Anomaly detection; Probabilistic logic; Benchmark (surveying); Ensemble learning; Anomaly (physics); Feature (linguistics); Pattern recognition (psychology); Ensemble forecasting","score_opus":0.007920257535288841,"score_gpt":0.23927638713262273,"score_spread":0.23135612959733387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416551965","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":[0.03641747,0.00016325944,0.9600313,0.00020737258,0.000022680886,0.0002024993,1.7710092e-7,0.0004428495,0.0025123945],"genre_scores_gemma":[0.84638596,0.000007673031,0.15314536,0.00009282042,0.000025021476,0.000062932326,7.6985754e-7,0.0000091474585,0.00027031434],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991089,0.000029150182,0.00014881964,0.00041470368,0.00012510078,0.00017329745],"domain_scores_gemma":[0.9994533,0.000039678853,0.00009790775,0.00019183857,0.00017701108,0.00004022987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014446944,0.0001350821,0.000115976225,0.00014222023,0.0005099001,0.00044155266,0.00016841848,0.000059509624,7.8561794e-7],"category_scores_gemma":[0.000033406013,0.00011796068,0.000022015021,0.000493204,0.000058392114,0.0004920506,0.00006352092,0.00024349577,0.000002429305],"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":[0.00009275854,0.00013126002,0.0007542129,0.0003877231,0.000023533821,0.000009286275,0.001125774,0.036060933,0.021601928,0.005776207,0.000061299135,0.9339751],"study_design_scores_gemma":[0.00020120485,0.00017950864,0.0007904958,0.00016920757,0.000015552816,0.000019392257,0.0000659541,0.9118292,0.077837825,0.008061492,0.0006082589,0.00022193418],"about_ca_topic_score_codex":0.000038086124,"about_ca_topic_score_gemma":0.000022484095,"teacher_disagreement_score":0.93375313,"about_ca_system_score_codex":0.000044026034,"about_ca_system_score_gemma":0.00005536507,"threshold_uncertainty_score":0.48102966},"labels":[],"label_agreement":null},{"id":"W4416566860","doi":"10.1016/j.patcog.2025.112759","title":"One-shot unsupervised industrial anomaly detection: Enhanced performance under extreme data scarcity","year":2025,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Innovation and Technology Commission","keywords":"Pattern recognition (psychology); Anomaly detection; Invariant (physics); Artificial neural network; Feature (linguistics); Graph; Feature extraction","score_opus":0.26102294279360494,"score_gpt":0.30639945184503004,"score_spread":0.045376509051425096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416566860","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":[0.28113264,0.000014525533,0.71555984,0.0004545955,0.00025076728,0.0003016201,0.000025758523,0.00036169888,0.001898562],"genre_scores_gemma":[0.994983,0.000057362035,0.0037355996,0.0006358691,0.00017833067,0.00015595507,0.00011440498,0.000009612937,0.00012985083],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99851215,0.00007300004,0.0003237291,0.0006514963,0.00020063842,0.00023898581],"domain_scores_gemma":[0.9985942,0.000053877055,0.00011498313,0.001033747,0.00014074994,0.00006243612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027373678,0.00015500463,0.00015708152,0.00019695182,0.00031328082,0.00017595178,0.00091237994,0.00015335415,0.000098700926],"category_scores_gemma":[0.000019718836,0.00017794322,0.00005097178,0.00074642996,0.00004362711,0.0008588931,0.00040238607,0.00026163022,0.00012464134],"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":[0.00001023354,0.00007979467,0.00068406825,0.0000162028,0.00001822536,2.7740563e-7,0.000019818279,0.0000034666282,0.0089215245,0.000033738263,0.00012491815,0.99008775],"study_design_scores_gemma":[0.001852269,0.00028534821,0.060726266,0.00030283612,0.00009180694,0.000015697748,0.00007011263,0.11689437,0.80925906,0.0060772942,0.0035559575,0.0008689758],"about_ca_topic_score_codex":0.00010338715,"about_ca_topic_score_gemma":0.00009254766,"teacher_disagreement_score":0.9892188,"about_ca_system_score_codex":0.0000822835,"about_ca_system_score_gemma":0.00007043801,"threshold_uncertainty_score":0.72563136},"labels":[],"label_agreement":null},{"id":"W4416984649","doi":"10.23977/jaip.2025.080320","title":"Empowering Security Surveillance with Machine Vision: A Survey of Anomaly Detection Technologies","year":2025,"lang":"","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Anomaly detection; Intrusion detection system; Object detection; Constant false alarm rate; Path (computing); Sensor fusion; ALARM; False alarm","score_opus":0.024972744917274685,"score_gpt":0.35731769480697906,"score_spread":0.33234494988970437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416984649","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":[0.02843233,0.0020436766,0.96443206,0.003011958,0.00083680014,0.0003753957,0.000014478317,0.00011584773,0.0007374302],"genre_scores_gemma":[0.9745681,0.001659748,0.023583129,0.00007076942,0.000054625376,0.000009817346,5.853112e-7,0.000015788879,0.00003738154],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9956504,0.000660477,0.002087326,0.0005352386,0.00068853074,0.00037801045],"domain_scores_gemma":[0.9900094,0.0018688561,0.003320371,0.00087470055,0.003833781,0.000092850314],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0052401293,0.00034933788,0.0006993792,0.0007797218,0.00039959987,0.0003711103,0.0014256579,0.00029244315,0.00002346615],"category_scores_gemma":[0.0037216747,0.0003063885,0.00019916747,0.0042546014,0.0005861856,0.001987162,0.00039941797,0.0012330774,0.00000975312],"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":[0.0025742047,0.0015755577,0.001848233,0.00017593298,0.00038120183,0.000080041005,0.0011924473,0.0016044003,0.009860495,0.012082092,0.00009515763,0.96853024],"study_design_scores_gemma":[0.00020726466,0.006174804,0.006112234,0.0009392738,0.00025010196,0.0012789045,0.0048790043,0.18395872,0.7692911,0.017427096,0.008621839,0.00085962063],"about_ca_topic_score_codex":0.0012068978,"about_ca_topic_score_gemma":0.0010264792,"teacher_disagreement_score":0.9676706,"about_ca_system_score_codex":0.00018557861,"about_ca_system_score_gemma":0.0005539606,"threshold_uncertainty_score":0.99993885},"labels":[],"label_agreement":null},{"id":"W4417090763","doi":"10.1109/icmla66185.2025.00041","title":"Sliced-Wasserstein Distance-based Data Selection","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Benchmark (surveying); Anomaly detection; Selection (genetic algorithm); Scalability; Data point; Model selection; Feature selection; Euclidean distance; Data modeling","score_opus":0.021961308288099068,"score_gpt":0.29369711631543627,"score_spread":0.2717358080273372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417090763","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":[0.00020107771,0.000015159993,0.97328997,0.0028773127,0.000053343985,0.000118699994,0.0000022992551,0.00071154255,0.02273057],"genre_scores_gemma":[0.7993487,0.0000042996753,0.19455655,0.0010217575,0.000018164272,0.000047806636,0.000010007294,0.0000029563234,0.0049897484],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993756,0.00001490819,0.0001133617,0.00032051848,0.000074692514,0.000100941375],"domain_scores_gemma":[0.99899703,0.000028674023,0.000030396912,0.0008755986,0.00004357427,0.00002474603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011540514,0.00005803707,0.00005604611,0.000078242076,0.00014634962,0.00010339501,0.0009251819,0.00003417767,0.000023183165],"category_scores_gemma":[0.000009361872,0.000053593674,0.000020151987,0.0007863736,0.000014627331,0.0002709724,0.00018472431,0.00006192469,0.000023993527],"study_design_candidate":"theoretical_or_conceptual","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":[0.0000024825497,0.000059708185,0.00059582153,0.000008872499,0.000007739946,2.4662413e-7,0.0000052954238,0.000051930965,0.0022225012,0.90136117,0.034497555,0.0611867],"study_design_scores_gemma":[0.00012340717,0.000022864864,0.001023528,0.000009339631,0.0000049470327,7.956902e-7,0.000007518784,0.6204034,0.03648544,0.005869192,0.33593875,0.000110795954],"about_ca_topic_score_codex":0.000058411464,"about_ca_topic_score_gemma":0.00007720739,"teacher_disagreement_score":0.89549196,"about_ca_system_score_codex":0.000035812904,"about_ca_system_score_gemma":0.00007247589,"threshold_uncertainty_score":0.21854867},"labels":[],"label_agreement":null},{"id":"W4417284537","doi":"10.1109/pimrc62392.2025.11274556","title":"WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks","year":2025,"lang":"","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Generative grammar; Generative adversarial network; Anomaly detection; Anomaly (physics); Transformer; Network packet; Encoding (memory); Network architecture","score_opus":0.013939667375759416,"score_gpt":0.2704861563239498,"score_spread":0.2565464889481904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417284537","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":[0.025199538,0.00034540537,0.9652459,0.0040461062,0.00040692714,0.0018589841,0.000007935341,0.00027333072,0.0026158723],"genre_scores_gemma":[0.872519,0.00009937861,0.11633926,0.0008535938,0.00018877436,0.00067313615,0.0000072920293,0.000020362731,0.009299208],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99733716,0.00009924296,0.00076514645,0.0010263819,0.00015481249,0.0006172814],"domain_scores_gemma":[0.9985134,0.00021845456,0.00024914977,0.0007111039,0.00021949752,0.00008841537],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00053792156,0.0003709624,0.0004039474,0.00048097677,0.0005605259,0.00043583193,0.000661764,0.00031411738,0.000033937984],"category_scores_gemma":[0.000048047197,0.0003770052,0.0002719431,0.0019904377,0.00019254303,0.00029202676,0.00039712107,0.00046779017,0.000011667015],"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":[0.000083652674,0.00034546605,0.0007574794,0.00015820835,0.00010361485,0.0000032499127,0.00088018953,0.15519547,0.0075231725,0.14174694,0.002359411,0.69084316],"study_design_scores_gemma":[0.0006322104,0.00022038995,0.0022092564,0.000170662,0.000029259638,0.0000021442258,0.00023407834,0.9710133,0.009701019,0.010787972,0.0046157585,0.00038393534],"about_ca_topic_score_codex":0.000106666885,"about_ca_topic_score_gemma":0.00065109204,"teacher_disagreement_score":0.84890664,"about_ca_system_score_codex":0.00016825626,"about_ca_system_score_gemma":0.0001271714,"threshold_uncertainty_score":0.9998682},"labels":[],"label_agreement":null},{"id":"W4417288161","doi":"10.1016/j.engappai.2025.113488","title":"Spatio-temporal traffic accidents detection via graph based generative adversarial network","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Research Grants Council, University Grants Committee; City University of Hong Kong","keywords":"Discriminator; Adversarial system; Anomaly detection; Generative grammar; Field (mathematics); Context (archaeology); Graph; Deep learning; Scarcity","score_opus":0.010807132292860263,"score_gpt":0.252006326730256,"score_spread":0.24119919443739576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417288161","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":[0.0036092058,0.0000534766,0.99479556,0.00021476932,0.00021866229,0.00057495566,0.000003387593,0.00044998972,0.00007999541],"genre_scores_gemma":[0.8272341,0.000006580461,0.17201833,0.000039772578,0.0001003707,0.00055960234,0.000009011327,0.0000086693035,0.00002357022],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872726,0.000023874923,0.0004944313,0.00038168297,0.0001620168,0.00021071994],"domain_scores_gemma":[0.9989065,0.00010449826,0.00014855116,0.0006060145,0.00017702578,0.00005742737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022833543,0.00015894143,0.00016679527,0.0002920314,0.00022450229,0.00006644234,0.0006587484,0.000100965175,0.000011557154],"category_scores_gemma":[0.000024677829,0.00018405578,0.00010977229,0.0019322932,0.000052355088,0.0001787296,0.00008109454,0.00016525495,0.000019972058],"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":[0.000010573728,0.00010321867,0.000045110242,0.00001754366,0.000023308567,2.2154896e-7,0.00004753145,0.614009,0.0045887255,0.10050031,0.00006385134,0.2805906],"study_design_scores_gemma":[0.000021591779,0.000036092748,0.00014998438,0.0000160978,0.000011370231,7.3859104e-7,0.000009161773,0.78610504,0.19966283,0.011468688,0.0023727966,0.00014560633],"about_ca_topic_score_codex":0.000058641916,"about_ca_topic_score_gemma":0.000052534444,"teacher_disagreement_score":0.8236249,"about_ca_system_score_codex":0.00006476138,"about_ca_system_score_gemma":0.000063682935,"threshold_uncertainty_score":0.75055766},"labels":[],"label_agreement":null},{"id":"W4417439175","doi":"10.1109/tim.2025.3644534","title":"An Explainable Attention-Augmented LSTM Model for Robust Real-Time Detection of Driving Behavior Patterns and Road Anomalies in Smart Transportation Networks","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"","keywords":"Interpretability; Anomaly detection; Global Positioning System; Calibration; Intelligent transportation system; Reliability (semiconductor); Consistency (knowledge bases); Vehicle dynamics; Software deployment","score_opus":0.02512356465725961,"score_gpt":0.26017368080097425,"score_spread":0.23505011614371463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417439175","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":[0.36068,0.000023046812,0.6374818,0.00008354933,0.00018741262,0.0014133574,0.00004381485,0.00007214321,0.00001483387],"genre_scores_gemma":[0.9883759,0.0007837096,0.008814159,0.0000491687,0.000012069163,0.0017655873,0.00002365603,0.00002306509,0.00015270605],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99747986,0.0001170871,0.0009490905,0.0007394243,0.00038636103,0.0003281942],"domain_scores_gemma":[0.9988161,0.000031029704,0.00032736958,0.00035511074,0.00034273212,0.00012770704],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006364065,0.00033316502,0.00035132802,0.0006292925,0.0006006133,0.00016842684,0.00016863906,0.00020358425,0.00001259986],"category_scores_gemma":[0.0000024417222,0.00039948273,0.00012500031,0.0005564505,0.000094609226,0.00082986784,0.000002471178,0.0001992354,4.0206507e-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":[0.0002988455,0.0017656806,0.0049170363,0.0003599064,0.00014347078,9.2109036e-7,0.0015635999,0.13326202,0.13391042,0.00054679514,0.0000066768603,0.72322464],"study_design_scores_gemma":[0.0016997157,0.00053561316,0.05895482,0.0003340318,0.00022792017,0.0000020972916,0.000653536,0.88503736,0.052178733,0.00007912015,0.0000063018806,0.00029077264],"about_ca_topic_score_codex":0.00068099605,"about_ca_topic_score_gemma":0.0035379545,"teacher_disagreement_score":0.7517753,"about_ca_system_score_codex":0.00033437027,"about_ca_system_score_gemma":0.00011122458,"threshold_uncertainty_score":0.9998457},"labels":[],"label_agreement":null},{"id":"W5129082","doi":"10.4018/978-1-60566-242-8.ch059","title":"Outlying Subspace Detection for High-Dimensional Data","year":2009,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University; Dalhousie University","funders":"","keywords":"Cluster analysis; Zhàng; Computer science; Outlier; Sander; Artificial intelligence; Data mining; Geography; Engineering","score_opus":0.036500487371734724,"score_gpt":0.2763823639310432,"score_spread":0.23988187655930848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W5129082","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000009393086,0.00010051379,0.7211779,0.00025044772,0.00027098705,0.0006103104,0.00018878501,0.0006495881,0.27674207],"genre_scores_gemma":[0.35804448,0.000019446014,0.36802697,0.0029078699,0.0016253778,0.00031744342,0.00014316541,0.0001372666,0.26877797],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982008,0.000008884841,0.00032464668,0.00089919637,0.00029651873,0.00026995808],"domain_scores_gemma":[0.99773216,0.000036258232,0.00026692706,0.0016987567,0.0001505519,0.00011533892],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016311678,0.000315871,0.0002872186,0.00007544979,0.00029381632,0.00015768883,0.0013715624,0.00034078173,0.0000055175137],"category_scores_gemma":[0.000011077699,0.00033056032,0.00012851696,0.000034567507,0.000043865875,0.00015644934,0.00050651,0.00021398635,0.00006167343],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006049147,0.000005279407,1.307982e-7,0.0000057083366,0.00001654668,0.0000023450502,0.0000024305386,0.0000032319422,0.00010051234,0.7998107,0.002217602,0.19782943],"study_design_scores_gemma":[0.00021701361,0.00016680043,0.0000145675585,0.00005856611,0.000046590674,0.000060301423,7.0802224e-7,0.004653634,0.0014400283,0.82398564,0.16887565,0.00048049563],"about_ca_topic_score_codex":0.000057915437,"about_ca_topic_score_gemma":0.000089466164,"teacher_disagreement_score":0.3580351,"about_ca_system_score_codex":0.00018155121,"about_ca_system_score_gemma":0.00012115855,"threshold_uncertainty_score":0.99991465},"labels":[],"label_agreement":null},{"id":"W62212937","doi":"10.1007/978-3-642-30353-1_16","title":"Clustering Based One-Class Classification for Compliance Verification of the Comprehensive Nuclear-Test-Ban Treaty","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Cluster analysis; Computer science; Class (philosophy); Artificial intelligence; Data mining; Credence; Machine learning","score_opus":0.06428016657807852,"score_gpt":0.27747068347663945,"score_spread":0.21319051689856094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W62212937","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":[0.00007235406,0.0000988589,0.995811,0.0015721669,0.0003745716,0.0010270652,0.000022437196,0.0001650975,0.00085646735],"genre_scores_gemma":[0.64348656,0.000020913654,0.3555097,0.0006217867,0.00015174306,0.00006932908,0.0000069476946,0.000030223106,0.00010279429],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977384,0.000028798187,0.0005172964,0.0008657305,0.00048669285,0.00036307602],"domain_scores_gemma":[0.9966417,0.000513395,0.0006283268,0.0016866412,0.0004407103,0.000089219575],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003293795,0.00031863837,0.00035863664,0.0002658515,0.00041039352,0.00016181523,0.0024046106,0.00024418678,0.000009431916],"category_scores_gemma":[0.00004727685,0.00027635685,0.00018343472,0.00052466156,0.00060970866,0.0003060356,0.0004250543,0.00036626175,0.000010785759],"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":[0.000016854088,0.00017971883,0.00013796464,0.0002249354,0.000015419395,4.1735424e-7,0.00039128595,0.008954752,0.021419413,0.077143036,0.00005996888,0.89145625],"study_design_scores_gemma":[0.00022396061,0.00012534615,0.003349806,0.00031602476,0.00001683398,0.000007843474,4.2609776e-7,0.9665271,0.009807641,0.012340859,0.0069100736,0.0003741096],"about_ca_topic_score_codex":0.000013102335,"about_ca_topic_score_gemma":0.000024669656,"teacher_disagreement_score":0.95757234,"about_ca_system_score_codex":0.0002868937,"about_ca_system_score_gemma":0.00020416552,"threshold_uncertainty_score":0.9999689},"labels":[],"label_agreement":null},{"id":"W6888954540","doi":"10.25316/ir-10572","title":"Nanaimo Free Press [Saturday, August 29, 1896]","year":2019,"lang":"en","type":"other","venue":"VIURRSpace (Vancouver Island University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"","score_opus":0.008849896221481655,"score_gpt":0.20038046132340143,"score_spread":0.19153056510191976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6888954540","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.5357881e-7,0.00013635794,0.25258088,0.00016827958,0.0006854757,0.00039377683,0.000091410075,0.0009035887,0.74504006],"genre_scores_gemma":[0.00015130297,0.00042536893,0.008441823,0.00008815247,0.00019198227,0.0000043596656,6.791344e-7,0.00014021185,0.9905561],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99835825,0.00005542604,0.00012037839,0.00078302773,0.00031182865,0.00037107122],"domain_scores_gemma":[0.9976087,0.000036512673,0.00028722404,0.001859471,0.00007061807,0.0001374608],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00005536872,0.00036977002,0.0003621542,0.00055801147,0.00015081311,0.000103999504,0.002221532,0.00045558417,0.0002477711],"category_scores_gemma":[0.000007927383,0.0003847875,0.000187837,0.0006887052,0.00008111255,0.00027407266,0.00083708647,0.000395741,0.00022327565],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004579986,0.000037560683,5.207271e-7,0.000033945653,0.000048470072,0.000026796355,0.00003826803,0.00000848602,0.000009509522,0.023984164,0.97466695,0.001140739],"study_design_scores_gemma":[0.00049072556,0.00006110071,0.0000017574881,0.00007825872,0.00003920977,0.0000036639303,0.000046512556,0.0003064256,0.00012452719,0.00021359896,0.99815553,0.0004786776],"about_ca_topic_score_codex":0.0012779381,"about_ca_topic_score_gemma":0.0013009475,"teacher_disagreement_score":0.24551603,"about_ca_system_score_codex":0.00014266426,"about_ca_system_score_gemma":0.0001343709,"threshold_uncertainty_score":0.9998604},"labels":[],"label_agreement":null},{"id":"W6891751509","doi":"10.48550/arxiv.0807.0422","title":"Finding Local Low-mass Supermassive Black Holes","year":2008,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Herzberg Institute of Astrophysics","funders":"","keywords":"Supermassive black hole; Intermediate-mass black hole; Binary black hole; Galaxy; Black hole (networking); Stellar black hole","score_opus":0.0538625269543609,"score_gpt":0.19045702067382522,"score_spread":0.13659449371946433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6891751509","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":[0.08543966,0.000015956866,0.9098986,0.00011860315,0.00014966745,0.00026411575,0.000014973146,0.0005999332,0.003498496],"genre_scores_gemma":[0.9933746,0.0004729849,0.004162782,0.00010182143,0.00007189028,0.0000036748636,0.000013102936,0.00001817088,0.0017809616],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982847,0.00006588114,0.00019766868,0.0010293163,0.00009302763,0.00032941988],"domain_scores_gemma":[0.9982477,0.00005765436,0.00020036788,0.0011773235,0.00014544747,0.00017152552],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008444994,0.0002915918,0.00027004047,0.00025432356,0.00028615008,0.000096453,0.0017339783,0.00033996213,0.000022106775],"category_scores_gemma":[0.000008294578,0.00034756586,0.00025453532,0.000606449,0.00050457666,0.00030380033,0.001280765,0.00060771615,0.00022039269],"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":[0.000017902319,0.0002071339,0.001415711,0.00011259982,0.00013261694,0.00069021806,0.0006256571,0.36143664,0.0004983216,0.6271515,0.0037213403,0.003990377],"study_design_scores_gemma":[0.0003079693,0.000057969053,0.0005808954,0.00010751121,0.00004082533,0.000017147608,0.00015215526,0.9284523,0.0057102614,0.059986603,0.0037651265,0.00082123204],"about_ca_topic_score_codex":0.000048798964,"about_ca_topic_score_gemma":0.0000046446503,"teacher_disagreement_score":0.90793496,"about_ca_system_score_codex":0.0002600749,"about_ca_system_score_gemma":0.00017714057,"threshold_uncertainty_score":0.99989766},"labels":[],"label_agreement":null},{"id":"W6892797307","doi":"10.5281/zenodo.12395578","title":"Iso 3669 pdf","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Standardization; Enhanced Data Rates for GSM Evolution; Normative; Dual purpose; International standard","score_opus":0.022498653466897233,"score_gpt":0.2465654354315839,"score_spread":0.22406678196468668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6892797307","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[5.9084635e-7,0.00019659451,0.2506435,0.0006071747,0.00011426467,0.00027772525,0.000065499786,0.00423402,0.74386066],"genre_scores_gemma":[0.00075745216,0.00022129997,0.0053687245,0.0001906054,0.00033600617,1.7965137e-7,0.00032281075,0.0054813465,0.98732156],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99858534,0.00009238269,0.00018153715,0.0006041577,0.00027459863,0.00026198063],"domain_scores_gemma":[0.9987439,0.0000055938704,0.000105851126,0.0008854437,0.00013188542,0.00012736011],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00023898428,0.00017438218,0.00014366976,0.00042499645,0.00061171583,0.0012188172,0.0020112835,0.00014471837,0.05354475],"category_scores_gemma":[0.00004578289,0.00018186009,0.00007790922,0.0007435734,0.000089201,0.00011285892,0.001746731,0.000333763,0.23133709],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.7223024e-7,0.000025528117,1.2617087e-8,0.000042933574,0.000019826219,0.000005721997,0.00006342953,4.18046e-7,0.000104053135,0.041882247,0.89230937,0.065545715],"study_design_scores_gemma":[0.00006161789,0.000069436726,0.0000019052885,0.000058948368,0.000008679316,0.00007046861,0.000013627707,0.0003400818,0.00012202275,0.001616878,0.9974418,0.000194499],"about_ca_topic_score_codex":0.000022283515,"about_ca_topic_score_gemma":3.10525e-7,"teacher_disagreement_score":0.24527477,"about_ca_system_score_codex":0.00008465945,"about_ca_system_score_gemma":0.000004194366,"threshold_uncertainty_score":0.999818},"labels":[],"label_agreement":null},{"id":"W6894238803","doi":"10.5281/zenodo.7918058","title":"Amblyaspis tatika Awad & Krogmann & Talamas 2023, comb. nov.","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Clade; Holotype; DNA barcoding; Tree (set theory); Phylogenetic tree","score_opus":0.034519728492906306,"score_gpt":0.2578404682137789,"score_spread":0.22332073972087257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6894238803","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":[0.014901383,0.000064939006,0.7659633,0.007548043,0.00025883838,0.0010265844,0.00016472625,0.01306545,0.19700672],"genre_scores_gemma":[0.9792934,0.0002234802,0.0071515357,0.00060098636,0.0002421221,8.043847e-7,0.0010363762,0.0012132481,0.010238061],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99838734,0.00013869246,0.00024419522,0.00049344735,0.00035532378,0.00038099702],"domain_scores_gemma":[0.99858797,0.000026398846,0.000100601625,0.0007533807,0.0003664557,0.00016519922],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005397556,0.00013398637,0.00012300404,0.00030905646,0.0022068499,0.00090724597,0.0018243459,0.00006432587,0.0020451157],"category_scores_gemma":[0.0001387137,0.00014554028,0.00006896795,0.002014259,0.00011446562,0.00039245712,0.0017349231,0.00022858685,0.027387206],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000066130597,0.000103545564,0.0000051052007,0.000026357828,0.000026222257,0.00001719702,0.000615732,0.00006736729,0.006796234,0.10640147,0.6424626,0.24347156],"study_design_scores_gemma":[0.000184799,0.00013954575,0.0007685867,0.0000105580275,0.000003991691,0.000048872393,0.00008994522,0.0064438777,0.0017863841,0.0020334735,0.9883137,0.0001762849],"about_ca_topic_score_codex":0.000022464374,"about_ca_topic_score_gemma":3.777499e-7,"teacher_disagreement_score":0.964392,"about_ca_system_score_codex":0.00008425843,"about_ca_system_score_gemma":0.0000045209495,"threshold_uncertainty_score":0.99909216},"labels":[],"label_agreement":null},{"id":"W6901779841","doi":"10.60692/cz9sv-haj54","title":"Multi-Modal Anomaly Detection by Using Audio and Visual Cues","year":2021,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Anomaly detection; Anomaly (physics); Pattern recognition (psychology); Cepstrum; Inference; Feature extraction; Audio visual","score_opus":0.025888347046491143,"score_gpt":0.2383464316777938,"score_spread":0.21245808463130264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6901779841","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":[0.3350059,0.0000060603625,0.66430086,0.000020496005,0.00008446516,0.0001126955,0.000010177731,0.0003178312,0.0001414733],"genre_scores_gemma":[0.9811542,3.1770873e-7,0.018589437,0.000092669026,0.000029230767,0.00004190547,0.0000043387704,0.000005432606,0.00008248094],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908686,0.000042249067,0.0003529236,0.000199059,0.00015865397,0.0001602614],"domain_scores_gemma":[0.9993223,0.0000040613604,0.00018398948,0.00024157987,0.0001719543,0.000076097094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014230049,0.00012419952,0.00013251074,0.00012605671,0.00027993612,0.00039825094,0.00012886788,0.000091001806,0.000003281926],"category_scores_gemma":[0.0000063381367,0.00012006684,0.000045059925,0.00034682069,0.000021229353,0.0011690392,0.00012608554,0.00007425972,0.00005790696],"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":[0.00015266944,0.00016389087,0.3457855,0.0027632285,0.00063793064,0.00006565143,0.18158467,0.0010881011,0.07235315,0.007979132,0.0010150715,0.386411],"study_design_scores_gemma":[0.00068558555,0.0000652837,0.019356165,0.00005460027,0.000019564146,0.0004198277,0.0029469794,0.8302686,0.14436138,0.0000035821913,0.0014163379,0.00040209768],"about_ca_topic_score_codex":0.000017159577,"about_ca_topic_score_gemma":5.7673424e-7,"teacher_disagreement_score":0.8291805,"about_ca_system_score_codex":0.00008548464,"about_ca_system_score_gemma":0.000027452299,"threshold_uncertainty_score":0.48961836},"labels":[],"label_agreement":null},{"id":"W6911392026","doi":"10.5281/zenodo.10563550","title":"Deep Unsupervised Anomaly Detection in High-Frequency Markets - JFDS","year":2024,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Nucleofection; Gestational period; TSG101; Dysgeusia; Liquation; Diafiltration; Triacetin; Emperipolesis; Demotion","score_opus":0.016517484489922515,"score_gpt":0.22982356247084199,"score_spread":0.21330607798091947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6911392026","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":[0.025295518,0.00018690944,0.9315891,0.0011339698,0.00016079833,0.00044735492,0.000018169152,0.0034533145,0.037714813],"genre_scores_gemma":[0.99474,0.000083477215,0.004233866,0.00008164815,0.00007693347,4.0550145e-7,0.00008041614,0.0003853415,0.00031791942],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985009,0.0001896921,0.00025247026,0.0005274827,0.00023899386,0.00029047762],"domain_scores_gemma":[0.9991155,0.000024188623,0.00004063296,0.0005370758,0.00017547193,0.00010710962],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005363609,0.00012587063,0.00010382158,0.0004518821,0.0008769068,0.0011686955,0.0011518381,0.000076247714,0.0017575395],"category_scores_gemma":[0.0000871882,0.00013528194,0.00005132531,0.0016306235,0.00006314435,0.0006142387,0.0006374713,0.00027710389,0.0028053648],"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":[0.000013043419,0.00010556069,0.000006324989,0.00006631104,0.000020707283,0.0000379499,0.00057758816,0.000041579253,0.020404762,0.08613896,0.00521735,0.8873699],"study_design_scores_gemma":[0.00045307243,0.00032334094,0.006648462,0.000076994176,0.000011947075,0.00031290637,0.00010227426,0.08808705,0.00959455,0.011573047,0.8823268,0.00048955024],"about_ca_topic_score_codex":0.000052454765,"about_ca_topic_score_gemma":0.0000023079938,"teacher_disagreement_score":0.96944445,"about_ca_system_score_codex":0.00020536395,"about_ca_system_score_gemma":0.0000051152933,"threshold_uncertainty_score":0.9998682},"labels":[],"label_agreement":null},{"id":"W6911940822","doi":"10.5281/zenodo.13839446","title":"Machine Learning in Time Series Anomaly Detection","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"","keywords":"Anomaly detection; Series (stratigraphy); Anomaly (physics); Time series; Support vector machine; Computational learning theory","score_opus":0.014657201159475811,"score_gpt":0.21402753855833187,"score_spread":0.19937033739885607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6911940822","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":[0.04753057,0.00016612063,0.82886267,0.0035816513,0.00015440161,0.0012008375,0.00010097149,0.008632961,0.109769836],"genre_scores_gemma":[0.9950179,0.000024075209,0.0016094424,0.000095053154,0.000030262367,4.8315957e-7,0.00015516358,0.00031571626,0.0027519036],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99873006,0.00029943313,0.00017139234,0.00032802823,0.00026437797,0.00020671492],"domain_scores_gemma":[0.99943125,0.00001075783,0.00007733167,0.0003211656,0.00009864738,0.00006081937],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006049595,0.00008103877,0.00008110767,0.0002766342,0.0028403844,0.0004477371,0.0011714116,0.000024194404,0.0036137453],"category_scores_gemma":[0.00007032006,0.00009767838,0.00003284092,0.0010737168,0.00004045979,0.0003491547,0.0019162897,0.00035220137,0.0012272971],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000101027785,0.0004484134,0.00008196998,0.000031919295,0.000029328246,0.000037531867,0.0020913514,0.0027774305,0.057834253,0.04023022,0.014849429,0.88148713],"study_design_scores_gemma":[0.00016353083,0.0003146209,0.0005846677,0.000002378894,0.0000015507204,0.00018496902,0.00005887811,0.018091196,0.002161701,0.00061011827,0.97770447,0.00012195251],"about_ca_topic_score_codex":0.000031036274,"about_ca_topic_score_gemma":5.3434843e-7,"teacher_disagreement_score":0.962855,"about_ca_system_score_codex":0.00017604977,"about_ca_system_score_gemma":0.000002615529,"threshold_uncertainty_score":0.99955034},"labels":[],"label_agreement":null},{"id":"W6912870704","doi":"10.5281/zenodo.7378419","title":"Language Models for Novelty Detection in Kernel Traces","year":2023,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Novelty detection; Novelty; Kernel (algebra); Language model; Kernel method; Web application","score_opus":0.01554248652980256,"score_gpt":0.2530205557107239,"score_spread":0.23747806918092135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6912870704","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":[0.06521904,0.00013498955,0.92881393,0.0018551884,0.00006613374,0.00092265196,0.000024243036,0.002674251,0.00028959464],"genre_scores_gemma":[0.8730597,0.00008538354,0.12280072,0.00048384312,0.00006388894,0.0026240484,0.00001146658,0.000034053908,0.0008369328],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99829906,0.00004438844,0.00037551837,0.0005193699,0.00020770324,0.0005539373],"domain_scores_gemma":[0.9987877,0.00012003128,0.00013653155,0.00075334974,0.00007055528,0.000131848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064662745,0.00021117348,0.00022192585,0.00064710394,0.00022011033,0.00017523265,0.00075597916,0.00021224335,0.0000033770634],"category_scores_gemma":[0.00005751362,0.00022362318,0.00014583423,0.0016173567,0.000034244833,0.0006404482,0.00019238607,0.00023935248,0.000019670066],"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":[0.000064526306,0.0003581603,0.0010884968,0.00007985138,0.000032821557,0.000035638182,0.0019080606,0.025540996,0.10620306,0.26889148,0.002550544,0.59324634],"study_design_scores_gemma":[0.00027498428,0.00008330362,0.0049085105,0.0000150107935,0.000004444863,0.000022538828,0.0001101553,0.8959138,0.071291044,0.02579032,0.0013315848,0.00025430054],"about_ca_topic_score_codex":0.0029660326,"about_ca_topic_score_gemma":0.0027975244,"teacher_disagreement_score":0.87037283,"about_ca_system_score_codex":0.00023967942,"about_ca_system_score_gemma":0.000077960525,"threshold_uncertainty_score":0.91190886},"labels":[],"label_agreement":null},{"id":"W6920454356","doi":"10.60692/wk1fv-jcv66","title":"Multi-Modal Anomaly Detection by Using Audio and Visual Cues","year":2021,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Anomaly detection; Anomaly (physics); Pattern recognition (psychology); Cepstrum; Inference; Feature extraction; Audio visual","score_opus":0.025888347046491143,"score_gpt":0.2383464316777938,"score_spread":0.21245808463130264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920454356","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":[0.3350059,0.0000060603625,0.66430086,0.000020496005,0.00008446516,0.0001126955,0.000010177731,0.0003178312,0.0001414733],"genre_scores_gemma":[0.9811542,3.1770873e-7,0.018589437,0.000092669026,0.000029230767,0.00004190547,0.0000043387704,0.000005432606,0.00008248094],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908686,0.000042249067,0.0003529236,0.000199059,0.00015865397,0.0001602614],"domain_scores_gemma":[0.9993223,0.0000040613604,0.00018398948,0.00024157987,0.0001719543,0.000076097094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014230049,0.00012419952,0.00013251074,0.00012605671,0.00027993612,0.00039825094,0.00012886788,0.000091001806,0.000003281926],"category_scores_gemma":[0.0000063381367,0.00012006684,0.000045059925,0.00034682069,0.000021229353,0.0011690392,0.00012608554,0.00007425972,0.00005790696],"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":[0.00015266944,0.00016389087,0.3457855,0.0027632285,0.00063793064,0.00006565143,0.18158467,0.0010881011,0.07235315,0.007979132,0.0010150715,0.386411],"study_design_scores_gemma":[0.00068558555,0.0000652837,0.019356165,0.00005460027,0.000019564146,0.0004198277,0.0029469794,0.8302686,0.14436138,0.0000035821913,0.0014163379,0.00040209768],"about_ca_topic_score_codex":0.000017159577,"about_ca_topic_score_gemma":5.7673424e-7,"teacher_disagreement_score":0.8291805,"about_ca_system_score_codex":0.00008548464,"about_ca_system_score_gemma":0.000027452299,"threshold_uncertainty_score":0.48961836},"labels":[],"label_agreement":null},{"id":"W6926336567","doi":"10.25384/sage.c.5697902","title":"A Quality Improvement Project on Pain Management at a Tertiary Pediatric Hospital","year":2021,"lang":"en","type":"other","venue":"Sage Journals Data","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Quality management; Pain management; Chart; Intervention (counseling); Pain assessment; MEDLINE; Acute pain","score_opus":0.03771945736841172,"score_gpt":0.3299141173494549,"score_spread":0.2921946599810432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6926336567","genre_codex":"methods","genre_gemma":"other","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":[0.000022670458,0.010738131,0.9076735,0.0014667863,0.00043223015,0.0016907146,0.0008931697,0.0006218179,0.076460995],"genre_scores_gemma":[0.0009913747,0.067594595,0.15589735,0.003869737,0.0025851235,0.0009020388,0.0007959571,0.000479265,0.76688457],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99759996,0.00020977597,0.00045322455,0.000907673,0.00053039246,0.00029895268],"domain_scores_gemma":[0.99612874,0.000046746747,0.00056671025,0.0031356132,0.000026087157,0.00009613218],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013780888,0.0002752387,0.0002739039,0.00033254316,0.00014772241,0.0002596273,0.00249474,0.0001490706,0.0014926986],"category_scores_gemma":[0.000027718777,0.00024279862,0.00011596994,0.0004805874,0.000017107846,0.00012977688,0.0027532624,0.00031806054,0.000057594938],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.177139e-7,0.00016666893,0.00009158465,0.00009053562,0.00006289899,0.000046323352,0.000013938671,9.360103e-8,0.00001607725,0.00008012399,0.5644418,0.434989],"study_design_scores_gemma":[0.00028389366,0.00020615433,0.0011641592,0.000647844,0.00004701758,0.000012486706,0.00003154292,0.00039182283,0.00006533253,0.00033419012,0.99625313,0.0005624107],"about_ca_topic_score_codex":0.00012041866,"about_ca_topic_score_gemma":0.000039925224,"teacher_disagreement_score":0.75177616,"about_ca_system_score_codex":0.00012910266,"about_ca_system_score_gemma":0.00006899791,"threshold_uncertainty_score":0.99942005},"labels":[],"label_agreement":null},{"id":"W6931527953","doi":"10.5683/sp3/nrgmmb","title":"Fully Annotated Gas Prices of America Dataset for Multi-Metric Extraction in the Wild","year":2022,"lang":"en","type":"dataset","venue":"Borealis","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Context (archaeology); Extraction (chemistry); Information extraction; Feature extraction; Mainland China","score_opus":0.03571624167284711,"score_gpt":0.3293489774343086,"score_spread":0.2936327357614615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931527953","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000012310766,0.00006410356,0.18877785,0.00029486304,0.000037559403,0.0006245231,0.8101497,0.00003802563,0.000012134474],"genre_scores_gemma":[0.000014237541,0.00037429732,0.018959822,0.0004180186,0.000031079486,0.0009935519,0.9791947,0.000007434934,0.0000068823347],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986728,0.00008651915,0.00038407225,0.00039724068,0.00028035045,0.00017902606],"domain_scores_gemma":[0.9979812,0.00022845958,0.00051283534,0.001186561,0.000060927036,0.000030054878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039982572,0.000159956,0.00021828014,0.0004237711,0.00015973003,0.00007366866,0.0016959804,0.000092493916,0.000087604785],"category_scores_gemma":[0.000082009545,0.00012915263,0.00008162174,0.0015078643,0.00004816972,0.00018394178,0.00020248951,0.00027661386,0.0000013638562],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000060099806,0.00017439712,0.0000012005551,0.00002718008,0.000008963773,0.000002579456,0.000025179332,0.000016530912,0.0000065650215,0.0000970755,0.98811257,0.0115217585],"study_design_scores_gemma":[0.00012744674,0.00015218982,0.00020725807,0.000005985934,0.000021510636,0.000011696411,0.000041900905,0.0027928008,0.000040823696,0.00008062873,0.9963798,0.00013797454],"about_ca_topic_score_codex":0.026486171,"about_ca_topic_score_gemma":0.001232351,"teacher_disagreement_score":0.16981803,"about_ca_system_score_codex":0.000052882882,"about_ca_system_score_gemma":0.000069467125,"threshold_uncertainty_score":0.97999656},"labels":[],"label_agreement":null},{"id":"W6931797794","doi":"10.5281/zenodo.7988919","title":"Integrated interpretation applied to the Nardoo prospect, QLD.","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mira Geoscience (Canada)","funders":"","keywords":"Petrophysics; Interpretation (philosophy); Outcrop; Process (computing); Basement; A priori and a posteriori; Economic geology; Rock magnetism","score_opus":0.023939947441143767,"score_gpt":0.24494472333705555,"score_spread":0.22100477589591178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931797794","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":[0.002829661,0.000006915659,0.9203216,0.00572587,0.00007766394,0.0008247374,0.00003053136,0.0044785216,0.0657045],"genre_scores_gemma":[0.9936899,0.000016692418,0.0034155878,0.0005088705,0.00007867883,0.0000011691625,0.00026273486,0.00037549724,0.0016508951],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988916,0.00009337598,0.00017551504,0.0003631545,0.00024051663,0.00023585536],"domain_scores_gemma":[0.9989498,0.000018501363,0.000058122463,0.00059577066,0.0002823929,0.000095413045],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00054217177,0.00009379457,0.00007662662,0.00021920448,0.0017329099,0.00087058835,0.0016606584,0.000034264,0.0005852697],"category_scores_gemma":[0.00013427455,0.00007716751,0.00003520165,0.002112632,0.00005138794,0.00016820438,0.0011826718,0.00017847843,0.01546383],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016518925,0.000039723407,6.227491e-7,0.000009823181,0.000014909004,0.0000020390903,0.0021005564,0.00027383596,0.0075166635,0.12109654,0.31490535,0.55402344],"study_design_scores_gemma":[0.00008212978,0.00009273559,0.00032680292,0.000008042897,0.0000024511705,0.000018977425,0.00019244745,0.014365273,0.0022353365,0.001314351,0.9812562,0.00010529275],"about_ca_topic_score_codex":0.000007205479,"about_ca_topic_score_gemma":3.9913877e-7,"teacher_disagreement_score":0.9908602,"about_ca_system_score_codex":0.0000894547,"about_ca_system_score_gemma":0.0000032547787,"threshold_uncertainty_score":0.9995667},"labels":[],"label_agreement":null},{"id":"W6931881215","doi":"10.5281/zenodo.8352337","title":"Quality of life among patients with atrial fibrillation","year":2023,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vancouver General Hospital; University of British Columbia","funders":"","keywords":"Atrial fibrillation; Quality of life (healthcare); Population; Analysis of variance; Mental health; Multilevel model; Recreation; Variance (accounting)","score_opus":0.030702142172528042,"score_gpt":0.2554932078498813,"score_spread":0.22479106567735324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931881215","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018118151,0.00006363148,0.4361111,0.00022433841,0.00019262204,0.0016587594,0.00056267204,0.0103757065,0.54899937],"genre_scores_gemma":[0.6154527,0.00090454286,0.03012439,0.00020834575,0.0030662839,6.255294e-7,0.0069633806,0.053835284,0.28944445],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99859923,0.00016492055,0.0002741173,0.0003890134,0.00040207844,0.00017065607],"domain_scores_gemma":[0.998513,0.00002331843,0.0003903245,0.0006417613,0.0003291469,0.00010244477],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00031207374,0.00013010297,0.00019028017,0.0002896388,0.00045563903,0.00023709699,0.0009304148,0.00011096068,0.0020215784],"category_scores_gemma":[0.00021600074,0.00012531603,0.000074873344,0.0007226872,0.00011063109,0.00015149286,0.00069837377,0.00013610198,0.0012105758],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049746508,0.00006265485,0.00043980975,0.00013121823,0.00014017533,7.8483095e-7,0.00021253384,0.00007929091,0.000029654413,0.023105936,0.7918998,0.18384838],"study_design_scores_gemma":[0.00035706203,0.00014338107,0.00907631,0.00004108465,0.0000064845126,6.933753e-7,0.000008772784,0.00024567777,0.000017970735,0.00010995418,0.9898205,0.00017211365],"about_ca_topic_score_codex":0.000113084105,"about_ca_topic_score_gemma":0.0000032060038,"teacher_disagreement_score":0.6136409,"about_ca_system_score_codex":0.000056095545,"about_ca_system_score_gemma":0.000005449544,"threshold_uncertainty_score":0.9995671},"labels":[],"label_agreement":null},{"id":"W6964222859","doi":"10.25318/9810043501-fra","title":"Situation d’activité, selon la minorité visible, le plus haut niveau de scolarité, le principal domaine d’études (STIM et SACHES, général) et le statut d’immigrant : Canada, provinces et territoires, régions métropolitaines de recensement et agglomérations de recensement y compris les parties","year":2022,"lang":"fr","type":"dataset","venue":"Statistics Canada Dissemination","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Context (archaeology); Identity (music); Subject (documents); Field (mathematics)","score_opus":0.012965419625818255,"score_gpt":0.26726661418246794,"score_spread":0.2543011945566497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6964222859","genre_codex":"methods","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030302925,0.0004449923,0.59988225,0.030117357,0.00024450728,0.0015244592,0.3643953,0.00014019715,0.00022063016],"genre_scores_gemma":[0.30851322,0.0024155076,0.13808705,0.0016067774,0.00010851778,0.0023653,0.5449364,0.00012835249,0.0018388268],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.993937,0.0016850169,0.0011014536,0.0010274883,0.0012865751,0.0009624519],"domain_scores_gemma":[0.99575675,0.0014142579,0.001018412,0.0008261393,0.00056134176,0.0004231049],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0016485547,0.00070648687,0.00061308406,0.00031142455,0.002394113,0.0006764345,0.00088968256,0.00027014303,0.00010289296],"category_scores_gemma":[0.000535334,0.00088695047,0.00009591513,0.0006545939,0.00026877297,0.00060940854,0.00046107618,0.0010531023,6.555727e-7],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.000041780175,0.0005732001,0.00017820853,0.00031300526,0.00013629532,0.00012214284,0.001545103,0.005934678,0.00068883144,0.37887087,0.60710096,0.0044949176],"study_design_scores_gemma":[0.00072979723,0.0003310083,0.012854023,0.00038384664,0.00022258372,0.000145452,0.0057525034,0.027589628,0.002206488,0.003509572,0.945076,0.0011990964],"about_ca_topic_score_codex":0.99171627,"about_ca_topic_score_gemma":0.9990785,"teacher_disagreement_score":0.4617952,"about_ca_system_score_codex":0.0062664687,"about_ca_system_score_gemma":0.037553202,"threshold_uncertainty_score":0.9993581},"labels":[],"label_agreement":null},{"id":"W6967980149","doi":"10.5281/zenodo.13839445","title":"Machine Learning in Time Series Anomaly Detection","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"","keywords":"Anomaly detection; Series (stratigraphy); Anomaly (physics); Time series; Support vector machine; Computational learning theory","score_opus":0.014657201159475811,"score_gpt":0.21402753855833187,"score_spread":0.19937033739885607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6967980149","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":[0.04753057,0.00016612063,0.82886267,0.0035816513,0.00015440161,0.0012008375,0.00010097149,0.008632961,0.109769836],"genre_scores_gemma":[0.9950179,0.000024075209,0.0016094424,0.000095053154,0.000030262367,4.8315957e-7,0.00015516358,0.00031571626,0.0027519036],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99873006,0.00029943313,0.00017139234,0.00032802823,0.00026437797,0.00020671492],"domain_scores_gemma":[0.99943125,0.00001075783,0.00007733167,0.0003211656,0.00009864738,0.00006081937],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006049595,0.00008103877,0.00008110767,0.0002766342,0.0028403844,0.0004477371,0.0011714116,0.000024194404,0.0036137453],"category_scores_gemma":[0.00007032006,0.00009767838,0.00003284092,0.0010737168,0.00004045979,0.0003491547,0.0019162897,0.00035220137,0.0012272971],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000101027785,0.0004484134,0.00008196998,0.000031919295,0.000029328246,0.000037531867,0.0020913514,0.0027774305,0.057834253,0.04023022,0.014849429,0.88148713],"study_design_scores_gemma":[0.00016353083,0.0003146209,0.0005846677,0.000002378894,0.0000015507204,0.00018496902,0.00005887811,0.018091196,0.002161701,0.00061011827,0.97770447,0.00012195251],"about_ca_topic_score_codex":0.000031036274,"about_ca_topic_score_gemma":5.3434843e-7,"teacher_disagreement_score":0.962855,"about_ca_system_score_codex":0.00017604977,"about_ca_system_score_gemma":0.000002615529,"threshold_uncertainty_score":0.99955034},"labels":[],"label_agreement":null},{"id":"W6977172557","doi":"10.6084/m9.figshare.25248935","title":"Additional file 2 of Strategies for engaging older adults and informal caregivers in health policy development: A scoping review","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Public policy; Qualitative research; Health policy; Health care; MEDLINE; Government (linguistics)","score_opus":0.0272416623706665,"score_gpt":0.3119508369528118,"score_spread":0.2847091745821453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977172557","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000011283406,0.0028468003,0.003092091,0.0002807425,0.000004992695,0.00069643976,0.9922648,0.000137813,0.000675192],"genre_scores_gemma":[0.0054763425,0.0002824405,0.06270458,0.0012457749,0.000094447525,0.011662537,0.91827273,0.000018566378,0.00024259512],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995431,0.0000067011365,0.0001672687,0.00012628343,0.00005749519,0.000099158926],"domain_scores_gemma":[0.99960655,0.00019588428,0.00005684179,0.00007207287,0.000040063453,0.000028569902],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000029234965,0.00005064847,0.0000782913,0.00009777592,0.00007154377,0.000050325925,0.00011131105,0.000017873994,0.16988769],"category_scores_gemma":[0.00011076479,0.000050696628,0.000021099084,0.00027560064,0.0000035066348,0.00031444436,0.00006575627,0.00005217673,0.000019958621],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.627892e-7,0.0000026727373,2.917967e-8,0.008799099,0.0000015151533,2.8316214e-7,0.000493358,0.0000011034516,5.1675897e-8,0.00080685515,0.8927968,0.09709793],"study_design_scores_gemma":[0.000050297847,0.000019854231,0.000116097915,0.39172712,2.9855784e-7,0.0000066142607,0.00011870824,0.0024175886,0.000023664174,0.00007809775,0.6053561,0.00008556695],"about_ca_topic_score_codex":0.00001194361,"about_ca_topic_score_gemma":0.000023367611,"teacher_disagreement_score":0.382928,"about_ca_system_score_codex":0.00002663845,"about_ca_system_score_gemma":0.00083945936,"threshold_uncertainty_score":0.83087116},"labels":[],"label_agreement":null},{"id":"W6997412643","doi":"","title":"Winter Quarter Grad Prevails in Global Simulation Competition","year":2018,"lang":"en","type":"article","venue":"Calhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Quarter (Canadian coin); Competition (biology); Measure (data warehouse); Global imbalances; Imperfect competition","score_opus":0.017289915065180416,"score_gpt":0.28220420232498217,"score_spread":0.26491428725980176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6997412643","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":[0.20756437,0.00020161075,0.75770766,0.018580772,0.0015524834,0.0023149813,0.00038517953,0.0010775563,0.010615408],"genre_scores_gemma":[0.98521805,0.000076991215,0.0103988275,0.002350377,0.0010813274,0.00022404904,0.0001890155,0.00003927332,0.00042210316],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99447244,0.000601184,0.0012610115,0.0014334208,0.0011636077,0.0010683273],"domain_scores_gemma":[0.9964729,0.00026554262,0.00042886028,0.0014565226,0.0007295952,0.000646591],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010111707,0.0007354955,0.00054811616,0.0004220207,0.0014385245,0.00068014604,0.0021995413,0.00023344955,0.000093630544],"category_scores_gemma":[0.0004287266,0.00059917296,0.00045187172,0.0017854277,0.0012526808,0.0018617722,0.00069450197,0.0011677506,0.0027894918],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016275645,0.0018440135,0.023652213,0.00016511089,0.00049903523,0.0002121522,0.0013565095,0.016339906,0.012544377,0.86799747,0.031573765,0.042187873],"study_design_scores_gemma":[0.0037659353,0.0016871863,0.3042642,0.0006001209,0.00013239171,0.0007889153,0.00018533078,0.18950221,0.0036112536,0.4418014,0.051260207,0.0024008679],"about_ca_topic_score_codex":0.00053611165,"about_ca_topic_score_gemma":0.0004519612,"teacher_disagreement_score":0.7776537,"about_ca_system_score_codex":0.00081053685,"about_ca_system_score_gemma":0.00066677504,"threshold_uncertainty_score":0.9998615},"labels":[],"label_agreement":null},{"id":"W6998689723","doi":"","title":"An Anomaly Detection System for Smart Manufacturing Using Deep Learning","year":2021,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Research Council Canada","keywords":"Anomaly detection; Autoencoder; Deep learning; Residual; Convolutional neural network; Thresholding; Focus (optics); Anomaly (physics)","score_opus":0.06768313866499705,"score_gpt":0.30325969376512796,"score_spread":0.23557655510013092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6998689723","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":[0.5047102,0.000015777438,0.494484,0.000016976976,0.00010710525,0.00014962266,0.0000020869768,0.00047588506,0.00003834059],"genre_scores_gemma":[0.99070567,0.0000073757883,0.008434184,0.000051948755,0.00008700101,0.000006167045,0.000008960439,0.000030839943,0.00066783995],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99820215,0.0002113435,0.0002369353,0.0007449204,0.0002143286,0.0003903544],"domain_scores_gemma":[0.9986097,0.000059476773,0.00020544186,0.00069717335,0.00022220158,0.00020596635],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028534624,0.00023039764,0.00022965281,0.00032865535,0.00083479384,0.00058526004,0.0007899799,0.00018361455,0.0000033165645],"category_scores_gemma":[0.0000116224155,0.000290566,0.00015677688,0.0005014287,0.000033740394,0.0022933073,0.00028140095,0.00035856097,0.000022304437],"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":[0.0001140765,0.00032843824,0.66184676,0.00043228647,0.00019705297,0.00049014745,0.0008032741,0.004005214,0.26519406,0.002880055,1.3811159e-7,0.06370848],"study_design_scores_gemma":[0.00070473575,0.00023639444,0.18277954,0.00011457195,0.00008116318,0.00033828508,0.00059913564,0.0015237042,0.8098252,0.00016232792,0.0029876663,0.0006473102],"about_ca_topic_score_codex":0.0000072626813,"about_ca_topic_score_gemma":0.00041283667,"teacher_disagreement_score":0.5446311,"about_ca_system_score_codex":0.00036551547,"about_ca_system_score_gemma":0.00006948764,"threshold_uncertainty_score":0.99995464},"labels":[],"label_agreement":null},{"id":"W7024093641","doi":"","title":"The role of socio-economic strategies in the childbearing decisions of Anglophone women in Montreal, Quebec /","year":2007,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Generosity; Affect (linguistics); Welfare; Welfare reform; Rhetoric; Social Welfare; Single mothers","score_opus":0.008713753084914677,"score_gpt":0.2496466150065022,"score_spread":0.24093286192158753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024093641","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":[0.96448535,0.00036255136,0.000028832894,0.000020040807,0.00012129423,0.0005681058,0.000072483585,0.00006520509,0.034276128],"genre_scores_gemma":[0.9977821,0.00048250516,0.0008084885,0.000024525985,0.000013006869,0.0002629056,0.000025222498,0.000028144548,0.0005731027],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99745286,0.0001904985,0.0009888764,0.00052233436,0.0003498203,0.0004955939],"domain_scores_gemma":[0.9975145,0.0006522934,0.0006467331,0.0009958941,0.00011413996,0.000076466284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018324292,0.00030251849,0.00044209516,0.00040676442,0.0005193034,0.00012165166,0.0019321014,0.0003389012,0.000012769456],"category_scores_gemma":[0.00011220486,0.00023427139,0.00018554067,0.00090478075,0.0000891328,0.00056005904,0.00016011452,0.0008179092,0.000016927084],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055171266,0.00019146867,0.00013287301,0.000025543713,0.000041092437,0.000004679435,0.0003809079,0.0002569661,0.007071115,0.46951768,0.0000023291614,0.5223202],"study_design_scores_gemma":[0.0009364784,0.0002805265,0.120701194,0.0004420748,0.000034803274,0.000018979425,0.030503508,0.0008988292,0.07791986,0.75483876,0.012394366,0.0010306173],"about_ca_topic_score_codex":0.017043125,"about_ca_topic_score_gemma":0.11288255,"teacher_disagreement_score":0.5212896,"about_ca_system_score_codex":0.0005923863,"about_ca_system_score_gemma":0.00013847464,"threshold_uncertainty_score":0.9895025},"labels":[],"label_agreement":null},{"id":"W7024317643","doi":"","title":"Sledování více osob ve videu z jedné kamery","year":2016,"lang":"sk","type":"dissertation","venue":"Brno University of Technology Digital Library (Brno University of Technology)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Nautical Research Society","funders":"","keywords":"","score_opus":0.003974069820403734,"score_gpt":0.1676744871896657,"score_spread":0.16370041736926197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024317643","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":[0.71699667,0.0019973947,0.13041185,0.025939247,0.0009078006,0.0034556054,0.0055790287,0.013387068,0.10132534],"genre_scores_gemma":[0.9011944,0.002856364,0.037705954,0.00005495896,0.00004888816,0.0000016324283,0.00052699924,0.00012388974,0.0574869],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99547976,0.00006200861,0.0007923171,0.002053518,0.0005805359,0.0010318841],"domain_scores_gemma":[0.9940091,0.00015238878,0.0021912062,0.0027912306,0.0005818053,0.00027431245],"candidate_categories":["metaepi_narrow","sts","open_science","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00012206782,0.0009928263,0.0016323228,0.0054534115,0.001039423,0.00009312172,0.008139343,0.003445585,0.0005254111],"category_scores_gemma":[0.000084823165,0.0012788854,0.00091862626,0.0051355995,0.0041804267,0.0037173978,0.0037650529,0.0014991895,0.0005229915],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011576568,0.002257869,0.017664662,0.0008107428,0.0018888848,0.0010119496,0.0007998335,0.0000142981,0.013553465,0.23252314,0.027681863,0.7006356],"study_design_scores_gemma":[0.0055445693,0.0038131322,0.0074564093,0.0036886293,0.0011196978,0.00037528857,0.023840977,0.0007156094,0.10376919,0.07450968,0.77011055,0.0050562737],"about_ca_topic_score_codex":0.00012244872,"about_ca_topic_score_gemma":0.000030573967,"teacher_disagreement_score":0.74242866,"about_ca_system_score_codex":0.00024060464,"about_ca_system_score_gemma":0.0007874196,"threshold_uncertainty_score":0.9989661},"labels":[],"label_agreement":null},{"id":"W7027029673","doi":"","title":"A Class of Augmented Convolutional Networks Architectures for Efficient Visual Anomaly Detection","year":2021,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Blackberry (Canada)","funders":"","keywords":"Anomaly detection; Autoencoder; Task (project management); Focus (optics); Generative grammar; Class (philosophy); Anomaly (physics); Convolutional neural network","score_opus":0.0065492980175727465,"score_gpt":0.21385697216866562,"score_spread":0.20730767415109286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7027029673","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":[0.608759,0.00007754665,0.39035916,0.00010336268,0.00016000397,0.00036498698,0.000021666337,0.00008813486,0.00006613946],"genre_scores_gemma":[0.96058446,0.000015863192,0.013647502,0.000010573978,0.000040404095,0.000008307678,0.00020391199,0.000015208304,0.025473744],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988988,0.000046601534,0.00018770999,0.0004300583,0.00023152132,0.00020531066],"domain_scores_gemma":[0.9987541,0.000059830745,0.0004111799,0.00030959505,0.00040079176,0.000064511114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000118309894,0.00018196492,0.00031870228,0.00028095534,0.00024205617,0.000020933989,0.00045745255,0.00025660676,0.00002243066],"category_scores_gemma":[0.000008364761,0.00021936753,0.00028586393,0.0004055081,0.00007636039,0.00004093695,0.000091727816,0.0001783307,0.000001666659],"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":[0.0030097798,0.0035288434,0.0009203851,0.0035379787,0.0022895471,0.00005785879,0.09219113,0.12799692,0.38158196,0.0411665,0.002816986,0.3409021],"study_design_scores_gemma":[0.0012117389,0.0007397717,0.0112745045,0.0002489428,0.00020932543,0.000014478701,0.014351387,0.8607633,0.109090246,0.0005021149,0.00094902207,0.00064515555],"about_ca_topic_score_codex":0.0028130508,"about_ca_topic_score_gemma":0.0054487633,"teacher_disagreement_score":0.7327664,"about_ca_system_score_codex":0.00007970966,"about_ca_system_score_gemma":0.00009995943,"threshold_uncertainty_score":0.89455485},"labels":[],"label_agreement":null},{"id":"W7030248880","doi":"","title":"Mental health portal : The student’s psychoemotional support","year":2023,"lang":"en","type":"other","venue":"Unimas Institutional Repository (Universiti Malaysia Sarawak)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"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":"Japan Society for the Promotion of Science; National Health and Medical Research Council; Florey Medical Research Foundation; Lakehead University; Griffith University; Rosetrees Trust; Universität Bremen; Medical Research Council; Brisbane Catholic Education; Ministry of Education, India; De La Salle University; British Psychological Society; Universität Konstanz; Sigma Theta Tau International; University of Hong Kong; Wellcome Trust","keywords":"Usability; Mental health; Scale (ratio); Heuristic evaluation; Web usability; System usability scale; Pluralistic walkthrough","score_opus":0.010081657952796712,"score_gpt":0.25125558284531335,"score_spread":0.24117392489251663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7030248880","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016460392,0.00023050161,0.09901599,0.0055659,0.0023405608,0.00093864586,0.0002011422,0.003021444,0.8885212],"genre_scores_gemma":[0.03864839,0.00038117415,0.007923658,0.0011580526,0.0009381352,0.00008196305,0.00024897774,0.00031874655,0.95030093],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99772125,0.00007977017,0.00032282562,0.0007289607,0.0007735892,0.00037360677],"domain_scores_gemma":[0.99843913,0.00003056804,0.00041582546,0.000846751,0.00007831516,0.00018942736],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019805587,0.0003356299,0.00027889348,0.0004560061,0.0012830299,0.00015315962,0.0015208328,0.00021581337,0.00042754257],"category_scores_gemma":[0.0000035166051,0.00031312695,0.00021744643,0.00070877624,0.0004007224,0.00034927254,0.00043267914,0.0003902367,0.00041085487],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000035091996,0.00010816327,0.00011127888,0.0000125122915,0.000116524134,0.00011649117,0.00013077138,0.00013009858,0.000022514487,0.4179782,0.58000493,0.0012650102],"study_design_scores_gemma":[0.00028075598,0.0001205008,0.0013394853,0.00009990995,0.000018528042,0.0005840436,0.00018574587,0.001759753,0.0000191929,0.00034706478,0.9948775,0.0003675305],"about_ca_topic_score_codex":0.00023822133,"about_ca_topic_score_gemma":0.000039844952,"teacher_disagreement_score":0.41763115,"about_ca_system_score_codex":0.00044851,"about_ca_system_score_gemma":0.000813895,"threshold_uncertainty_score":0.9999321},"labels":[],"label_agreement":null},{"id":"W7089218699","doi":"10.1109/actea66485.2025.11189958","title":"Evaluation of a GAN-Based Method for Anomaly Detection in Open RAN Based on Experimental 5G Data","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Université Laval; Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Anomaly detection; Discriminative model; Discriminator; Context (archaeology); Precision and recall; Intrusion detection system; Set (abstract data type); Data set; Flexibility (engineering)","score_opus":0.11846107723374755,"score_gpt":0.45145122035366925,"score_spread":0.3329901431199217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7089218699","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":[0.001904793,0.000012307537,0.9896579,0.00043775918,0.000047068443,0.0014815837,0.00000879233,0.0000772117,0.006372603],"genre_scores_gemma":[0.7565209,1.5667463e-7,0.24243346,0.00030828852,0.0000049620853,0.00066936685,0.00001055239,0.0000033621131,0.000048912043],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884224,0.0001710538,0.00024057052,0.00042242673,0.00022856604,0.00009515107],"domain_scores_gemma":[0.9985991,0.0001912979,0.00008625614,0.0009553709,0.00014804312,0.000019925636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024892997,0.00007905527,0.00012198966,0.00022887888,0.000078958576,0.000072660165,0.0010950506,0.00004894431,0.000026481652],"category_scores_gemma":[0.00009337238,0.0000760892,0.000035040037,0.00058262626,0.000013153434,0.0002456388,0.0000861771,0.000045699897,0.0000012335322],"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":[0.00016117765,0.00093227654,0.00010581032,0.000026582113,0.000019499399,1.7047182e-7,0.00006793026,0.008614692,0.1928649,0.02197457,0.00078395027,0.77444845],"study_design_scores_gemma":[0.0006323494,0.00009332468,0.00023147101,0.0000095848445,0.000007437031,7.682159e-8,0.000018533427,0.53717846,0.4606164,0.0008074616,0.00036414101,0.000040734125],"about_ca_topic_score_codex":0.00032716914,"about_ca_topic_score_gemma":0.00014812188,"teacher_disagreement_score":0.7744077,"about_ca_system_score_codex":0.00012023284,"about_ca_system_score_gemma":0.00023799868,"threshold_uncertainty_score":0.31028277},"labels":[],"label_agreement":null},{"id":"W7089457906","doi":"10.23977/jeeem.2025.080116","title":"Research on a Control Strategy for Dual Active Bridge Converters Combining Super-Twisting Sliding Mode and Model Predictive Control","year":2025,"lang":"en","type":"article","venue":"Journal of Electrotechnology Electrical Engineering and Management","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Texas Space Grant Consortium","keywords":"Model predictive control; Control theory (sociology); Converters; Compensation (psychology); Renewable energy; Stability (learning theory); Power (physics); Dual (grammatical number); Mode (computer interface)","score_opus":0.01814912511404532,"score_gpt":0.2979034457800346,"score_spread":0.27975432066598926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7089457906","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":[0.013736748,0.00021386985,0.9836246,0.0015768355,0.000026446538,0.0005370084,0.0000022571035,0.00013691386,0.00014536227],"genre_scores_gemma":[0.9916667,0.00024068379,0.007726174,0.00013652755,0.000017450924,0.00016230062,2.419719e-7,0.000009171905,0.00004074647],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882406,0.000034668454,0.0003001866,0.00028013776,0.00016199918,0.00039893735],"domain_scores_gemma":[0.9991949,0.00032840896,0.000097971184,0.00015089479,0.00016383562,0.00006395021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005473267,0.00013936122,0.00027786565,0.0010232656,0.00020769575,0.000069931106,0.0002645035,0.00011831735,1.2199801e-7],"category_scores_gemma":[0.00007629316,0.00013237855,0.000054828488,0.00067412754,0.00004226232,0.00012957057,0.000066973626,0.00067734666,1.1361821e-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":[0.0002068985,0.0000787168,0.0000058025607,0.00004535614,0.00029061965,0.00001544846,0.000030107376,0.048057977,0.008256545,0.8981446,0.00027874453,0.044589184],"study_design_scores_gemma":[0.0014501915,0.0013818146,0.000102952334,0.000055335528,0.000043698758,0.00005046574,0.00002579436,0.97926474,0.004542031,0.012767363,0.00021295133,0.000102655555],"about_ca_topic_score_codex":0.0000029156342,"about_ca_topic_score_gemma":2.7565815e-7,"teacher_disagreement_score":0.97792995,"about_ca_system_score_codex":0.00015589989,"about_ca_system_score_gemma":0.00004784375,"threshold_uncertainty_score":0.53982407},"labels":[],"label_agreement":null},{"id":"W7093347908","doi":"10.2139/ssrn.5650370","title":"Enhancing Time Series Anomaly Detection with Residuals Stationarity Intervention on State-Space Models","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Anomaly detection; Residual; Time series; Bayesian probability; Series (stratigraphy); False alarm; Kalman filter; Anomaly (physics); Filter (signal processing)","score_opus":0.007879223958851852,"score_gpt":0.24881927290124117,"score_spread":0.24094004894238932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7093347908","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":[0.0354143,0.0007126639,0.95938385,0.0013517034,0.0003174008,0.001147147,0.00004580417,0.00038309552,0.0012440666],"genre_scores_gemma":[0.94329053,0.0066111977,0.016016938,0.0001081234,0.00027930332,0.0003270683,0.000028908875,0.00006878159,0.033269145],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99260217,0.0006749041,0.0014479241,0.001484781,0.0009944754,0.0027957447],"domain_scores_gemma":[0.99560237,0.00016596483,0.0017884228,0.0011481107,0.0010717746,0.00022334367],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0040548146,0.0008399444,0.00074772636,0.00091268984,0.0016137878,0.0010325593,0.0014886312,0.0004561772,0.00004575237],"category_scores_gemma":[0.00005975142,0.00082362804,0.00049454847,0.0011604175,0.00017141164,0.0018076946,0.00061936124,0.0067367028,0.00006491186],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.0022293932,0.0013305926,0.00007976445,0.00038395647,0.0019154181,0.000019942578,0.0022603918,0.118173674,0.005855662,0.5299915,0.00012963255,0.33763006],"study_design_scores_gemma":[0.0009974124,0.004585118,0.00021482923,0.0011349663,0.00020074821,0.00061444397,0.0008530961,0.05895964,0.05995014,0.87040377,0.0009327348,0.0011531134],"about_ca_topic_score_codex":0.00063000235,"about_ca_topic_score_gemma":0.0049408623,"teacher_disagreement_score":0.9433669,"about_ca_system_score_codex":0.0044444273,"about_ca_system_score_gemma":0.0058708326,"threshold_uncertainty_score":0.999765},"labels":[],"label_agreement":null},{"id":"W7093713505","doi":"","title":"À la croisée de l’IA et de l’analyse multivariée pour l’identification d’atmosphères complexes par des réseaux de capteurs","year":2023,"lang":"fr","type":"other","venue":"Open Repository and Bibliography (University of Liège)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"GDG Environnement","funders":"","keywords":"Context (archaeology); Yield (engineering); Product (mathematics)","score_opus":0.028765332346438622,"score_gpt":0.2903731977639614,"score_spread":0.2616078654175228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7093713505","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":[0.18269712,0.0009566323,0.7456056,0.0017996088,0.00013622628,0.0008822941,0.000093885974,0.00065817183,0.06717045],"genre_scores_gemma":[0.87039965,0.005831068,0.097282544,0.000060607894,0.000048214017,0.000009232583,0.000007442413,0.00006673497,0.026294513],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979388,0.0005906773,0.00026137504,0.0006506947,0.00019794735,0.00036051823],"domain_scores_gemma":[0.99815375,0.00027007793,0.00051324716,0.000600121,0.0002034927,0.00025931068],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00096547353,0.0002743159,0.00037526654,0.0028008341,0.001013048,0.0006824608,0.0014510795,0.00037084194,0.00009611205],"category_scores_gemma":[0.0000022106803,0.00033765653,0.00031369843,0.0071973703,0.00085635966,0.00076359103,0.00064405054,0.00033386532,0.000014563006],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000074890886,0.000672676,0.83091915,0.0003109945,0.00081516564,0.00026750853,0.004455683,0.00013577822,0.043613035,0.05178669,0.06108059,0.00586782],"study_design_scores_gemma":[0.00041138715,0.00011938683,0.9674799,0.00035483742,0.00034268326,0.00018366116,0.0017328177,0.004599898,0.0053671338,0.004934418,0.013986558,0.00048731075],"about_ca_topic_score_codex":0.054378804,"about_ca_topic_score_gemma":0.001221144,"teacher_disagreement_score":0.68770254,"about_ca_system_score_codex":0.00007231642,"about_ca_system_score_gemma":0.0002856846,"threshold_uncertainty_score":0.99990755},"labels":[],"label_agreement":null},{"id":"W7106481835","doi":"10.1609/aaaiss.v7i1.36943","title":"Evaluating Uncertainty in Deep Q-Network Ensembles forTrustworthy Anomaly Detection in Medical Imaging","year":2025,"lang":"","type":"article","venue":"Proceedings of the AAAI Symposium Series","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute","funders":"","keywords":"Anomaly detection; Trustworthiness; Anomaly (physics); Usability; Medical imaging; Software deployment","score_opus":0.009250932867616976,"score_gpt":0.27869043535183247,"score_spread":0.2694395024842155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7106481835","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":[0.9352047,0.0018093653,0.027746646,0.020245153,0.0013527704,0.002378874,0.0000045103366,0.00032221436,0.010935761],"genre_scores_gemma":[0.99461186,0.0005043043,0.0035325733,0.0003819372,0.0001507316,0.00036580357,6.5749344e-7,0.000024112818,0.0004280254],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99613374,0.000072427196,0.001381698,0.00089503295,0.0007667335,0.0007503447],"domain_scores_gemma":[0.99810976,0.00016184866,0.0007051034,0.00043175396,0.0004909118,0.000100635356],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0024881754,0.00040740447,0.0005666458,0.000458245,0.000558749,0.0003009027,0.0018384688,0.00028144268,0.000027414222],"category_scores_gemma":[0.0003630029,0.00036767498,0.00023279594,0.0041020755,0.00041794492,0.0011222038,0.0013941203,0.0007455755,0.000002403884],"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":[0.0006935923,0.0006661234,0.2699611,0.0015327996,0.00014268655,0.000007883132,0.006046887,0.012847529,0.08821591,0.14325067,0.0005516803,0.47608313],"study_design_scores_gemma":[0.0009161683,0.0002289993,0.025815133,0.0022374713,0.000082480015,0.00008342775,0.0009739186,0.84851384,0.08080447,0.038128827,0.0015927377,0.00062256056],"about_ca_topic_score_codex":0.0006866702,"about_ca_topic_score_gemma":0.0011801183,"teacher_disagreement_score":0.8356663,"about_ca_system_score_codex":0.0003598328,"about_ca_system_score_gemma":0.00034718114,"threshold_uncertainty_score":0.9998775},"labels":[],"label_agreement":null},{"id":"W7106484071","doi":"10.1609/aaaiss.v7i1.36902","title":"Quantum Variational Rewinding for Time Series Anomaly Detection","year":2025,"lang":"","type":"article","venue":"Proceedings of the AAAI Symposium Series","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Bank of Canada; Agnostiq (Canada)","funders":"","keywords":"Series (stratigraphy); Anomaly (physics); Anomaly detection; Quantum; Transmon; Parameterized complexity; Noise (video); Qubit; Quantum computer","score_opus":0.006799866180215701,"score_gpt":0.22477121029056518,"score_spread":0.2179713441103495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7106484071","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":[0.11431107,0.0007562881,0.7387995,0.093344964,0.00533286,0.008805874,0.0003426738,0.001737019,0.036569767],"genre_scores_gemma":[0.945638,0.0001565219,0.02378031,0.0002458141,0.00029878618,0.00074363966,0.0000037447799,0.000041743297,0.02909144],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974869,0.000019226409,0.0008760036,0.0007635719,0.00037667423,0.00047765952],"domain_scores_gemma":[0.9973545,0.00010418347,0.0008683562,0.00042001027,0.0011801796,0.00007274834],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0006808848,0.0003943817,0.0004542018,0.00031860627,0.0015439655,0.0005893366,0.0016038371,0.0002800446,0.000028705097],"category_scores_gemma":[0.00017113195,0.00035406815,0.00039031848,0.0017636551,0.0003663212,0.0022423088,0.00079608324,0.00026656734,0.000012240382],"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":[0.00020655298,0.00010781387,0.0005225101,0.00043778945,0.00012564116,5.0232448e-8,0.00067824253,0.000022940078,0.46733585,0.5266659,0.0022176052,0.0016791082],"study_design_scores_gemma":[0.00036948232,0.00057843555,0.0019337952,0.0003357776,0.0002047071,0.000042238178,0.00019790746,0.027643679,0.856182,0.07583261,0.036223687,0.0004557056],"about_ca_topic_score_codex":0.00003553098,"about_ca_topic_score_gemma":0.000004450511,"teacher_disagreement_score":0.83132696,"about_ca_system_score_codex":0.00019464531,"about_ca_system_score_gemma":0.00023288565,"threshold_uncertainty_score":0.9998911},"labels":[],"label_agreement":null},{"id":"W7115717146","doi":"10.3390/computers14120561","title":"Beyond the Norm: Unsupervised Anomaly Detection in Telecommunications with Mahalanobis Distance","year":2025,"lang":"en","type":"article","venue":"Computers","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Q & T Research","funders":"","keywords":"Mahalanobis distance; Anomaly detection; Dimensionality reduction; Troubleshooting; Key (lock); Overhead (engineering); Outlier; Reliability (semiconductor); Preprocessor; Performance indicator","score_opus":0.005782901952124114,"score_gpt":0.22327529740903573,"score_spread":0.21749239545691162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7115717146","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":[0.017590549,0.00009041366,0.97288966,0.0046830066,0.00008999032,0.00031871165,0.0000011745533,0.0002564195,0.0040800697],"genre_scores_gemma":[0.9539405,0.000029126664,0.04472057,0.0008650934,0.000012598816,0.00018553228,0.000002164264,0.0000054686857,0.0002389582],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992104,0.000059009668,0.00019237488,0.00027623068,0.000091481954,0.00017051354],"domain_scores_gemma":[0.9987396,0.00011339963,0.000059471116,0.0010006859,0.000057440677,0.000029406185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013347824,0.00011018298,0.0001047843,0.00014913947,0.00031208043,0.00013647675,0.0011654473,0.000041597414,0.0000023182452],"category_scores_gemma":[0.0000044708718,0.000082176906,0.00004018974,0.0013810906,0.00008383123,0.00020637293,0.00021804745,0.0001867415,0.000008360393],"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":[0.000029818959,0.0003220652,0.011015395,0.000035291163,0.00006599004,0.0000065920635,0.0009498151,0.0021514706,0.001626546,0.34660164,0.0031932285,0.63400215],"study_design_scores_gemma":[0.001462676,0.00030008517,0.24288905,0.00015812113,0.000031146796,0.000054688568,0.0002152993,0.5854137,0.018504946,0.027086884,0.12306626,0.0008171175],"about_ca_topic_score_codex":0.000102967286,"about_ca_topic_score_gemma":0.00047680584,"teacher_disagreement_score":0.9363499,"about_ca_system_score_codex":0.000084087565,"about_ca_system_score_gemma":0.000051424326,"threshold_uncertainty_score":0.33510768},"labels":[],"label_agreement":null},{"id":"W7116674004","doi":"10.23919/cnsm67658.2025.11297558","title":"Unsupervised Anomaly Detection for Wi-Fi Networks using RFFI","year":2025,"lang":"","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Anomaly detection; Pattern recognition (psychology); Noise (video); Feature (linguistics); Artificial neural network","score_opus":0.021692082521080056,"score_gpt":0.2924806311303572,"score_spread":0.27078854860927715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116674004","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":[0.0065591824,0.00041192223,0.9857411,0.00074267446,0.001034866,0.0016256694,0.00000714319,0.00064614834,0.0032313264],"genre_scores_gemma":[0.88734657,0.00009994058,0.10670485,0.0009474063,0.00026189556,0.00030675513,0.000003232745,0.000024524621,0.004304858],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99743307,0.00007409683,0.0007165731,0.0009909585,0.00017033133,0.0006149789],"domain_scores_gemma":[0.99806976,0.00015491608,0.00020848325,0.0010228388,0.00040278313,0.00014124488],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043905285,0.0003605939,0.0003562231,0.0003550746,0.001231784,0.0005444249,0.0008658348,0.00037841758,0.0000783129],"category_scores_gemma":[0.000029277135,0.0003886562,0.00034665674,0.002171343,0.00011782845,0.0005229563,0.00035164616,0.00028034375,0.0000124520675],"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":[0.00007586077,0.000289893,0.00026635663,0.00011360832,0.0001386762,0.0000012200765,0.00009259235,0.0094916085,0.012779828,0.09903217,0.0015860504,0.87613213],"study_design_scores_gemma":[0.00045581855,0.00018866894,0.00041328548,0.000061962375,0.00008226332,0.000008933605,0.000042924457,0.935936,0.033999827,0.0038972974,0.024544712,0.0003683121],"about_ca_topic_score_codex":0.00024873202,"about_ca_topic_score_gemma":0.00009301696,"teacher_disagreement_score":0.9264444,"about_ca_system_score_codex":0.00023827751,"about_ca_system_score_gemma":0.0002247986,"threshold_uncertainty_score":0.99985653},"labels":[],"label_agreement":null},{"id":"W7116856121","doi":"10.18280/mmep.121113","title":"MedGRU-SVC: A Hybrid ConvGRU and Support Vector Clustering Framework for Interpretable Anomaly Detection in Medical Radiographs","year":2025,"lang":"","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Anomaly detection; Cluster analysis; Pattern recognition (psychology); Support vector machine; Feature (linguistics); Radiography","score_opus":0.012918284158527919,"score_gpt":0.24344579310553444,"score_spread":0.23052750894700652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116856121","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":[0.011576211,0.0007737657,0.9855508,0.00054929144,0.00022601661,0.00090244476,0.000005618158,0.00028501576,0.00013079021],"genre_scores_gemma":[0.8168132,0.0004602221,0.18199517,0.000062644125,0.00004223038,0.0005009501,0.0000012796311,0.000029642737,0.00009467549],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997634,0.000027290052,0.00082472974,0.0007243906,0.00023159526,0.0005580064],"domain_scores_gemma":[0.99865544,0.00052531145,0.00010225454,0.00039726234,0.000066569,0.00025318388],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00093583873,0.0003614851,0.00056947715,0.00044109146,0.00019754792,0.00033555445,0.0003516613,0.00033077304,0.000016327835],"category_scores_gemma":[0.00015582793,0.00037335543,0.00012321776,0.0005767428,0.000112902926,0.00023195571,0.00026588482,0.00058117614,0.000002232083],"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":[0.0001059209,0.00060057745,0.00008680757,0.012911239,0.0003423354,0.000022091372,0.0044283965,0.27572513,0.0013127226,0.5137043,0.00003631642,0.19072416],"study_design_scores_gemma":[0.00032560187,0.00015244789,0.000013483917,0.0018117152,0.000040961128,0.000065718465,0.000019915056,0.9095258,0.0010229404,0.08624729,0.00046180477,0.00031233398],"about_ca_topic_score_codex":0.000028974431,"about_ca_topic_score_gemma":0.0000054089714,"teacher_disagreement_score":0.805237,"about_ca_system_score_codex":0.00006323916,"about_ca_system_score_gemma":0.00005794916,"threshold_uncertainty_score":0.99987185},"labels":[],"label_agreement":null},{"id":"W7117357636","doi":"10.1016/j.metip.2025.100225","title":"Group-based trajectory modeling under non-random attrition: A sensitivity analysis and application to frailty trajectories","year":2025,"lang":"en","type":"article","venue":"Methods in Psychology","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Attrition; Trajectory; Dropout (neural networks); Monte Carlo method; Sensitivity (control systems); Constant (computer programming)","score_opus":0.04419897702253756,"score_gpt":0.42815151141618846,"score_spread":0.3839525343936509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117357636","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0135751525,0.00006882665,0.9835186,0.001724573,0.00009157125,0.00036259685,0.0000026540151,0.00016321383,0.00049280806],"genre_scores_gemma":[0.45798576,0.000008316657,0.54050696,0.0012271582,0.000012386996,0.00024039864,0.0000022805007,0.0000033441643,0.000013429628],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834955,0.00045800616,0.00029926005,0.00063916517,0.000072266346,0.00018173826],"domain_scores_gemma":[0.9988556,0.0003679431,0.00005427781,0.0006089953,0.000059085614,0.000054117707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013844612,0.0001293635,0.00030154936,0.0006987787,0.00012729909,0.000040017956,0.00021993859,0.00014513604,0.0000036060644],"category_scores_gemma":[0.00003512872,0.00013754355,0.000092518996,0.002693154,0.000056385707,0.00008586444,0.000059459246,0.00017901456,0.0000016228565],"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":[0.00014020213,0.00045336838,0.0050453064,0.00004181306,0.0001998905,0.0000039274887,0.00034352162,0.031230053,0.15012892,0.05137157,0.00014895733,0.7608925],"study_design_scores_gemma":[0.0007532523,0.00006304511,0.04767907,0.000011041817,0.00007016117,0.000004118131,0.000029102159,0.9149189,0.005686394,0.029683175,0.0008776708,0.00022408826],"about_ca_topic_score_codex":0.00016768691,"about_ca_topic_score_gemma":0.0002222738,"teacher_disagreement_score":0.8836888,"about_ca_system_score_codex":0.00004531076,"about_ca_system_score_gemma":0.00002720687,"threshold_uncertainty_score":0.5608863},"labels":[],"label_agreement":null},{"id":"W7117542592","doi":"10.18280/ts.420644","title":"Terminal Abnormal Behavior Detection and Spatiotemporal Interpretability Analysis Based on Multimodal Visual-Behavioral Joint Representation","year":2025,"lang":"","type":"article","venue":"Traitement du signal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Interpretability; Joint (building); Representation (politics); Pattern recognition (psychology); Terminal (telecommunication)","score_opus":0.02048923808360446,"score_gpt":0.3186853573070912,"score_spread":0.2981961192234867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117542592","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":[0.45138773,0.0000143220295,0.54692566,0.00022702031,0.00014284281,0.0009833453,0.000028354923,0.00016524146,0.00012549534],"genre_scores_gemma":[0.99217993,0.0000084829635,0.0065084454,0.0002433483,0.000086787586,0.0007993961,0.00004495298,0.000018067707,0.00011060595],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99573684,0.0003912846,0.00127448,0.0014518049,0.0006537145,0.0004918854],"domain_scores_gemma":[0.99809676,0.00010533563,0.0005023385,0.0007759073,0.00029176942,0.00022789407],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008090845,0.0005215565,0.0006050635,0.0012279551,0.0007241286,0.0005742335,0.00042642196,0.0002690686,0.00042193226],"category_scores_gemma":[0.000018840277,0.00056192226,0.0005424082,0.002214817,0.00029552742,0.00064701017,0.0002456409,0.00048820124,0.00001046642],"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":[0.0005175079,0.0041136676,0.09573279,0.00009738392,0.00027655918,0.000029709485,0.00067418476,0.0033350014,0.01703538,0.0004653871,0.000036794354,0.8776856],"study_design_scores_gemma":[0.000706241,0.00095561077,0.32488203,0.00004330304,0.0008834448,0.00000439494,0.000071471055,0.6373891,0.03461607,0.000058874684,0.00006299676,0.00032641817],"about_ca_topic_score_codex":0.00096991466,"about_ca_topic_score_gemma":0.00021809965,"teacher_disagreement_score":0.8773592,"about_ca_system_score_codex":0.0003940448,"about_ca_system_score_gemma":0.00016251663,"threshold_uncertainty_score":0.9996832},"labels":[],"label_agreement":null},{"id":"W7117708092","doi":"10.18280/ts.420620","title":"Enhancing Deep Feature Learning and Visual Mining for Effective Detection of Suspicious Activities in Video Surveillance","year":2025,"lang":"","type":"article","venue":"Traitement du signal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Feature (linguistics); Deep learning; Feature extraction; Pattern recognition (psychology); Object detection","score_opus":0.004629740525013719,"score_gpt":0.26005630361431903,"score_spread":0.25542656308930534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117708092","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":[0.38873342,0.0004395827,0.6096028,0.000111897614,0.00008725432,0.00088918646,0.0000029531054,0.00006057757,0.0000723653],"genre_scores_gemma":[0.99466014,0.000083045816,0.004397133,0.00004523238,0.0000728434,0.00055901456,0.000002498873,0.0000143470015,0.00016574557],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981586,0.00020855805,0.00048507418,0.0006010929,0.00018757324,0.00035912707],"domain_scores_gemma":[0.9985657,0.0008174717,0.0003171745,0.00011698143,0.00013084206,0.000051884806],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008632655,0.00026357349,0.0004144674,0.0004143124,0.00038899246,0.0001286092,0.00017712598,0.00017456317,0.000008553353],"category_scores_gemma":[0.00006939162,0.0002968952,0.00011730462,0.00075205124,0.000116164054,0.00028257637,0.00013635017,0.0003043623,2.983527e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024302898,0.00016942469,0.0058008046,0.00041507583,0.000082600214,0.0000014857858,0.0020145017,0.0005031736,0.29313073,0.0008373386,0.000009646343,0.6967922],"study_design_scores_gemma":[0.0014795447,0.0016714385,0.032663252,0.00042746027,0.000044074615,0.0000070291594,0.0014636972,0.34593174,0.6144636,0.00040434857,0.0010470211,0.00039676006],"about_ca_topic_score_codex":0.000058799265,"about_ca_topic_score_gemma":0.0004637832,"teacher_disagreement_score":0.69639546,"about_ca_system_score_codex":0.00016854255,"about_ca_system_score_gemma":0.00007019565,"threshold_uncertainty_score":0.9999483},"labels":[],"label_agreement":null},{"id":"W7118166196","doi":"10.23977/cpcs.2025.090114","title":"ACMAN: Adaptive Cross-Modal Anomaly Network","year":2025,"lang":"","type":"article","venue":"Computing Performance and Communication systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Anomaly detection; Anomaly (physics); Inference; Feature (linguistics); Representation (politics); Generative grammar; Quality (philosophy)","score_opus":0.019556140233221966,"score_gpt":0.28809829246778185,"score_spread":0.2685421522345599,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7118166196","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":[0.21680266,0.015520447,0.7274738,0.00073631975,0.0010564344,0.0012175983,0.0000055715595,0.0006309241,0.03655627],"genre_scores_gemma":[0.98189974,0.0025214248,0.010788211,0.0003145906,0.00023289415,0.00011357891,0.000008456643,0.000020555119,0.0041005444],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99688417,0.0003469352,0.0011228931,0.00076784054,0.00027606063,0.00060208834],"domain_scores_gemma":[0.9958898,0.00027971726,0.00065740367,0.0025188175,0.0005182797,0.00013601671],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0014437401,0.00040708744,0.0005296566,0.00021816495,0.0030666408,0.0013884988,0.0022062284,0.0002988452,0.00000744955],"category_scores_gemma":[0.000016154805,0.00044130592,0.00012398936,0.0014306462,0.00044893415,0.0005871301,0.0021044235,0.0006674493,0.000057218847],"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":[0.00008954125,0.00026955138,0.080074675,0.00050734705,0.00026700052,0.0000014358664,0.0018474096,0.0475032,0.000042251562,0.48934034,0.01005079,0.37000647],"study_design_scores_gemma":[0.00037582483,0.00015682331,0.06429459,0.0010062995,0.000030634885,0.000028906014,0.00016066653,0.8908266,0.000058184876,0.0005168933,0.042147335,0.00039727963],"about_ca_topic_score_codex":0.00022365383,"about_ca_topic_score_gemma":0.000008101984,"teacher_disagreement_score":0.84332335,"about_ca_system_score_codex":0.00015197287,"about_ca_system_score_gemma":0.00020993006,"threshold_uncertainty_score":0.9998039},"labels":[],"label_agreement":null},{"id":"W7118246782","doi":"10.18280/ijsse.151017","title":"A Comprehensive Survey of Transformer-Based Models for Video Anomaly Detection in Surveillance Systems","year":2025,"lang":"","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Anomaly detection; Anomaly (physics); Poison control","score_opus":0.014267272938587636,"score_gpt":0.25696873161254985,"score_spread":0.2427014586739622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7118246782","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":[0.08139825,0.001754634,0.91521645,0.0002857887,0.00086105237,0.00031620284,0.00012088,0.000015564101,0.000031205025],"genre_scores_gemma":[0.99717766,0.00073294603,0.0019757047,0.000033580178,0.000048627124,0.000014158389,0.00000409381,0.000008450932,0.0000047671256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981243,0.00007578787,0.0011330062,0.00021719447,0.00028114204,0.00016854255],"domain_scores_gemma":[0.99741685,0.00057090085,0.00044253745,0.00013509407,0.0013743265,0.000060311842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095138414,0.00017636643,0.00044657013,0.00061746,0.000055131324,0.00009147342,0.00048586391,0.000115871866,0.0000011576374],"category_scores_gemma":[0.00008361844,0.00019307192,0.00015373157,0.00050346367,0.000045925124,0.00042547923,0.00003697978,0.0002778849,7.79395e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013307219,0.000270604,0.0017611677,0.00068918284,0.00036402437,0.000009857844,0.0005760425,0.9489053,0.0048183636,0.017922567,0.000013041063,0.023339145],"study_design_scores_gemma":[0.0012177835,0.00017704164,0.01972605,0.00049679383,0.00001161491,0.00003359288,0.00003391703,0.9719459,0.004237578,0.00060957536,0.0013641315,0.00014601828],"about_ca_topic_score_codex":0.0005751177,"about_ca_topic_score_gemma":0.0001791678,"teacher_disagreement_score":0.9157794,"about_ca_system_score_codex":0.00016690689,"about_ca_system_score_gemma":0.0001727215,"threshold_uncertainty_score":0.7873244},"labels":[],"label_agreement":null},{"id":"W7118504750","doi":"10.71465/csb168","title":"Adaptive Token Pruning for Transformers in Real-Time Monitoring Applications","year":2025,"lang":"","type":"article","venue":"Computer Science Bulletin","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Security token; Anomaly detection; Transformer; Computational complexity theory; Thresholding; Pruning; Margin (machine learning)","score_opus":0.016724023638549964,"score_gpt":0.2844225062261195,"score_spread":0.2676984825875695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7118504750","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015348849,0.00015224374,0.98624736,0.0040827543,0.00050488167,0.0027889102,0.000009945449,0.00035264198,0.0043263696],"genre_scores_gemma":[0.4020533,0.00024462517,0.59340215,0.00027259922,0.00029467812,0.0021961655,0.0000021313188,0.000022014605,0.0015123498],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99609506,0.00006412121,0.00082013046,0.0016050951,0.00044313766,0.0009724795],"domain_scores_gemma":[0.9978595,0.00028011162,0.00023086496,0.00093458506,0.00047035268,0.00022460843],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0015630202,0.00037598325,0.00041778415,0.0009664175,0.0013731358,0.00071574724,0.0027542314,0.0001669457,0.000024849001],"category_scores_gemma":[0.000020530375,0.0004286689,0.00019159721,0.0043261037,0.0007411761,0.0004896844,0.000640669,0.00033992185,0.00009792362],"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":[0.000027070437,0.00026515097,0.00022029024,0.00006920766,0.00001895234,0.0000018192814,0.00084574596,0.0025794795,0.004899978,0.04645968,0.0014305622,0.94318205],"study_design_scores_gemma":[0.00080708356,0.00037371428,0.0028965077,0.000484762,0.000028839037,0.000008393691,0.0001357542,0.8878931,0.016921373,0.003985791,0.08572162,0.00074307283],"about_ca_topic_score_codex":0.000114483344,"about_ca_topic_score_gemma":0.000002553873,"teacher_disagreement_score":0.942439,"about_ca_system_score_codex":0.0005074965,"about_ca_system_score_gemma":0.00079556665,"threshold_uncertainty_score":0.9999269},"labels":[],"label_agreement":null},{"id":"W7124455713","doi":"10.52202/083087-0103","title":"Robust TLE Outlier Detection Algorithm with Manoeuvre Detection Capabilities","year":2025,"lang":"","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Canadian Military Institute","funders":"","keywords":"Anomaly detection; Outlier; Pattern recognition (psychology); Noise (video); Robustness (evolution)","score_opus":0.011170272318377243,"score_gpt":0.22121526789144894,"score_spread":0.21004499557307169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7124455713","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":[0.0035215851,0.00017012063,0.96783745,0.0008503142,0.00075870054,0.0010647229,0.000009259841,0.001133986,0.024653865],"genre_scores_gemma":[0.89373827,0.00007292889,0.07464126,0.0003744053,0.00011623055,0.0005164246,0.0000016605866,0.000031817435,0.030507011],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969714,0.00012573083,0.00065543933,0.0012413199,0.0004058274,0.00060027203],"domain_scores_gemma":[0.9976976,0.00008441634,0.00023451027,0.0013390767,0.00048607934,0.00015831135],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037815436,0.00048476356,0.0003835541,0.00054059806,0.0011921015,0.000618462,0.00071321847,0.00036260963,0.00028319116],"category_scores_gemma":[0.00002095477,0.0004404229,0.00020411739,0.0022728203,0.0003195556,0.00080937316,0.0002973638,0.000542856,0.00013226633],"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":[0.000026959431,0.00018936812,0.00005290968,0.00005290773,0.00008166289,0.000002455964,0.00028283987,0.00035964645,0.001679047,0.007712344,0.00023511087,0.98932475],"study_design_scores_gemma":[0.00078938965,0.0012518584,0.0023842072,0.00013461993,0.00013604997,0.0000975853,0.0011707346,0.5850883,0.35666192,0.0055689695,0.045728944,0.0009874075],"about_ca_topic_score_codex":0.00070293713,"about_ca_topic_score_gemma":0.0007741309,"teacher_disagreement_score":0.98833734,"about_ca_system_score_codex":0.00042235843,"about_ca_system_score_gemma":0.00021176718,"threshold_uncertainty_score":0.99980474},"labels":[],"label_agreement":null},{"id":"W7125483262","doi":"10.1109/iceamst67459.2025.11335662","title":"Deep Learning Models for Detecting Anomalies in Flight Operation Data","year":2025,"lang":"","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Deep learning; Artificial neural network; Feature (linguistics); Noise (video); Pattern recognition (psychology)","score_opus":0.05722698423672942,"score_gpt":0.3121852336221023,"score_spread":0.2549582493853729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125483262","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":[0.00071390206,0.00051072746,0.9873631,0.0015383124,0.00018824862,0.0010028285,0.0000042042457,0.00030796468,0.008370719],"genre_scores_gemma":[0.7856208,0.00017415704,0.20970152,0.00025791366,0.00005582185,0.00027269742,0.000018607145,0.000012056424,0.0038863951],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977099,0.00009114337,0.0006445297,0.001030801,0.00013802834,0.00038564534],"domain_scores_gemma":[0.99822366,0.0002089374,0.00014102766,0.0012243092,0.00015002134,0.00005205533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087401306,0.00022000237,0.00025240995,0.0003611363,0.00077736744,0.0006157492,0.0015302012,0.0001742753,0.000030166568],"category_scores_gemma":[0.00010499639,0.00023439075,0.000066509354,0.0010272128,0.000048810976,0.0017090417,0.0011258424,0.00030374582,0.000010121936],"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":[0.000015072029,0.00009451432,0.00022233905,0.00007081154,0.000022164691,6.752516e-7,0.0005082689,0.07982514,0.0009647862,0.2608289,0.00021432721,0.657233],"study_design_scores_gemma":[0.00028032434,0.000080068094,0.00008845318,0.000059607628,0.000015207155,0.0000024860017,0.00026098615,0.97132504,0.0076838387,0.011829599,0.008140171,0.00023423889],"about_ca_topic_score_codex":0.00019569379,"about_ca_topic_score_gemma":0.0005290854,"teacher_disagreement_score":0.8914999,"about_ca_system_score_codex":0.00010952318,"about_ca_system_score_gemma":0.0001461363,"threshold_uncertainty_score":0.95581776},"labels":[],"label_agreement":null},{"id":"W7125630179","doi":"10.1109/cascon66301.2025.00061","title":"Anomaly Detection in Time Series Data: a Comparative Study of Time-Series Clustering and Recurring Neural Networks","year":2025,"lang":"","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"IBM (Canada); York University","funders":"","keywords":"Series (stratigraphy); Cluster analysis; Anomaly detection; Artificial neural network; Pattern recognition (psychology); Time series","score_opus":0.027225665838041976,"score_gpt":0.2935008051739376,"score_spread":0.26627513933589564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125630179","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":[0.38724318,0.00022385357,0.60987175,0.00015339837,0.00013896463,0.0011873079,0.000006837922,0.00018028323,0.0009944335],"genre_scores_gemma":[0.9930012,0.00007944761,0.005059288,0.00003345818,0.00003833568,0.000095848096,0.0000037392317,0.000010053101,0.0016786343],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975783,0.00021408731,0.00080267404,0.0009044713,0.00018541631,0.0003150404],"domain_scores_gemma":[0.99835867,0.000103824306,0.0002706401,0.0010793993,0.000121940735,0.0000655037],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00050288474,0.00030049693,0.00054631947,0.00033906265,0.0003800215,0.00036311665,0.00097093475,0.0001265934,0.00002706412],"category_scores_gemma":[0.000016896445,0.00031570543,0.00003962091,0.0015578306,0.00019059998,0.0018311954,0.002371356,0.00033147694,0.000004435554],"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":[0.002897551,0.006653396,0.06843385,0.00072848477,0.0010993237,0.000058368485,0.02653886,0.09524772,0.017086143,0.0048042764,0.0013139915,0.775138],"study_design_scores_gemma":[0.00047084474,0.0009726372,0.011357407,0.000090786096,0.000038132774,0.000019483565,0.0011013672,0.98223543,0.003094153,0.00012794137,0.00022420812,0.00026762893],"about_ca_topic_score_codex":0.00057522487,"about_ca_topic_score_gemma":0.002301447,"teacher_disagreement_score":0.8869877,"about_ca_system_score_codex":0.000061602084,"about_ca_system_score_gemma":0.000039973373,"threshold_uncertainty_score":0.9999295},"labels":[],"label_agreement":null},{"id":"W7126265460","doi":"10.18280/isi.301211","title":"Review of Domain-Based Novelty Detection Using One Class Support Vector Machine","year":2025,"lang":"","type":"article","venue":"Ingénierie des systèmes d information","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Novelty detection; Support vector machine; Class (philosophy); Pattern recognition (psychology); Novelty","score_opus":0.019022645704046635,"score_gpt":0.2670762256146611,"score_spread":0.24805357991061447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126265460","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":[0.004804041,0.0029272754,0.98406786,0.00038674852,0.0005149618,0.001460269,0.00008855488,0.00029659068,0.005453713],"genre_scores_gemma":[0.9559119,0.0017322458,0.039619867,0.0023431696,0.000053139927,0.00019030343,0.00007246711,0.000017093293,0.000059824437],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996514,0.00015811558,0.0020748496,0.00035219762,0.00046823994,0.0004325801],"domain_scores_gemma":[0.99613434,0.0000924392,0.001543427,0.0009844382,0.0011268812,0.00011847347],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001232816,0.00038150998,0.00063929294,0.0007290363,0.00062637957,0.0002659453,0.00071015407,0.0002826323,0.00012743023],"category_scores_gemma":[0.00021007341,0.00042538927,0.00031241702,0.002908668,0.00029554422,0.002359443,0.00023241689,0.0003411869,0.000042987605],"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":[0.00013454554,0.00043938504,0.00032089427,0.056994807,0.00024850923,0.0000013725431,0.001159886,0.00096182746,0.011415819,0.040027276,0.00078432326,0.8875114],"study_design_scores_gemma":[0.0015676033,0.00085213495,0.003030078,0.03267374,0.00040098696,0.00006459643,0.00017362087,0.7122327,0.15871255,0.007054281,0.08201495,0.0012227839],"about_ca_topic_score_codex":0.0002852877,"about_ca_topic_score_gemma":0.00001911511,"teacher_disagreement_score":0.95110786,"about_ca_system_score_codex":0.0009158831,"about_ca_system_score_gemma":0.00077139086,"threshold_uncertainty_score":0.9998198},"labels":[],"label_agreement":null},{"id":"W7127301292","doi":"10.1109/ccece64018.2025.11364456","title":"A Fourier Graph Attention Network for Satellite Anomaly Detection","year":2025,"lang":"","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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 Manitoba; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Anomaly detection; Graph; Satellite; Anomaly (physics); Time series; Multivariate statistics; Pattern recognition (psychology); Attention network","score_opus":0.011101836431543981,"score_gpt":0.2605924382942919,"score_spread":0.24949060186274793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127301292","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":[0.0025387658,0.0011525876,0.9813704,0.0019388358,0.001260911,0.0017707924,0.000006832024,0.0006380033,0.009322889],"genre_scores_gemma":[0.7790562,0.0005461383,0.1930012,0.0010886847,0.0004113779,0.00108025,0.000008124323,0.000024191439,0.024783844],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974431,0.000080585705,0.0007091033,0.0009536464,0.00019633946,0.0006172419],"domain_scores_gemma":[0.9981382,0.00013053241,0.00026022596,0.0009719981,0.00037922902,0.00011983431],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006379858,0.00032162608,0.00030333994,0.0003361367,0.001113865,0.00048869755,0.00064032467,0.0003049283,0.000046635898],"category_scores_gemma":[0.000023645016,0.00034071325,0.0004852216,0.0025018463,0.00010922045,0.00047841851,0.00022187136,0.00022062259,0.00004734515],"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":[0.00003964921,0.00010082082,0.001130774,0.00006003731,0.0000805956,3.9247584e-7,0.00002250663,0.00007423848,0.0015034971,0.21019277,0.003734665,0.7830601],"study_design_scores_gemma":[0.00088234333,0.00060477573,0.035871763,0.00016700606,0.0002160646,0.0000137443785,0.00004606269,0.25765923,0.017833494,0.16721,0.5187081,0.0007874069],"about_ca_topic_score_codex":0.000113352275,"about_ca_topic_score_gemma":0.00018733008,"teacher_disagreement_score":0.7883692,"about_ca_system_score_codex":0.00011642973,"about_ca_system_score_gemma":0.00011029548,"threshold_uncertainty_score":0.9999045},"labels":[],"label_agreement":null},{"id":"W7127343677","doi":"10.1109/ccece64018.2025.11364423","title":"Systematic Exploration of Feature Engineering and Machine Learning Algorithms for HVAC Fault Detection","year":2025,"lang":"","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Random forest; Artificial neural network; Fault detection and isolation; Reliability (semiconductor); Feature (linguistics); Replicate; Feature engineering; Set (abstract data type)","score_opus":0.016394434361254054,"score_gpt":0.25166290311141737,"score_spread":0.23526846875016333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127343677","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":[0.0004798964,0.0010650611,0.99581885,0.00056473556,0.00020224784,0.0014620578,0.00000449772,0.00025461407,0.00014804577],"genre_scores_gemma":[0.90721345,0.00028832865,0.08739307,0.00003135897,0.000041791096,0.0006494946,0.0000039617794,0.000014391445,0.0043641585],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987727,0.0000468704,0.00048541484,0.0003882888,0.00013065398,0.00017609097],"domain_scores_gemma":[0.99898314,0.00017733609,0.00025191146,0.00030294838,0.00023624657,0.00004839102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041531952,0.00019257424,0.0003434574,0.0002946115,0.00028377768,0.00015631832,0.00021268771,0.00016441334,0.0000026017356],"category_scores_gemma":[0.00013179381,0.00018427828,0.00010658498,0.0007531296,0.000025031894,0.0005493199,0.00011944129,0.00020995768,0.0000011361057],"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":[0.000076548284,0.00034626407,0.00006657623,0.08757469,0.0006522096,0.0000013877416,0.0028496762,0.022561153,0.13478239,0.21666114,0.00025245768,0.5341755],"study_design_scores_gemma":[0.00019732799,0.00017723908,0.000025055766,0.0010721848,0.000065924585,0.000005215833,0.00011791059,0.8862986,0.11042317,0.0008335756,0.00064452976,0.00013927535],"about_ca_topic_score_codex":0.000035920388,"about_ca_topic_score_gemma":0.000011743307,"teacher_disagreement_score":0.9084258,"about_ca_system_score_codex":0.00005431209,"about_ca_system_score_gemma":0.000028502713,"threshold_uncertainty_score":0.751465},"labels":[],"label_agreement":null},{"id":"W7127451634","doi":"10.18280/ijsse.151113","title":"Adaptive Clustering Approaches for Domain Name System Anomaly Detection: Comparative Performance Analysis","year":2025,"lang":"","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cluster analysis; Domain (mathematical analysis); Anomaly detection; Anomaly (physics); Domain Name System","score_opus":0.018517222919202162,"score_gpt":0.2449330319513825,"score_spread":0.22641580903218034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127451634","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":[0.034792993,0.0009570033,0.96212614,0.00046303723,0.0008613059,0.00029067133,0.000035942372,0.000054958313,0.00041796354],"genre_scores_gemma":[0.9732839,0.00031913162,0.026038403,0.000024419014,0.000253659,0.000027154256,0.000002802428,0.000008150216,0.000042405925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980755,0.00004559326,0.000977674,0.00033579813,0.0003380979,0.00022737213],"domain_scores_gemma":[0.9982942,0.00019148213,0.00055542367,0.00019373637,0.0006562695,0.0001089326],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00072724797,0.00026028,0.00056633947,0.00086502783,0.00026948997,0.0002608736,0.0007093014,0.00014179842,0.000003959],"category_scores_gemma":[0.000017704913,0.00027164404,0.00037793195,0.0008013857,0.00006821919,0.00067072327,0.00022724271,0.00039061488,7.2225424e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017940125,0.00030131813,0.0006310827,0.00070174003,0.011274458,0.000027102951,0.008559956,0.6765953,0.00055207364,0.18293345,0.000027436918,0.11660213],"study_design_scores_gemma":[0.0006401194,0.00021972338,0.00261318,0.00034436298,0.0002892063,0.00012962286,0.0008376131,0.9894581,0.001511296,0.00023868094,0.0034942334,0.0002239071],"about_ca_topic_score_codex":0.00001505712,"about_ca_topic_score_gemma":0.000017613245,"teacher_disagreement_score":0.93849087,"about_ca_system_score_codex":0.00041334194,"about_ca_system_score_gemma":0.00008723755,"threshold_uncertainty_score":0.9999736},"labels":[],"label_agreement":null},{"id":"W7130391898","doi":"10.1109/iconstem65670.2025.11374356","title":"Deep Learning-Driven Anomaly Detection in Wearable HealthTechnology for Real-Time Patient Monitoring and Predictive Diagnostics","year":2025,"lang":"","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Wearable computer; Anomaly detection; Wearable technology; Remote patient monitoring; Anomaly (physics); Motion (physics); Patient data","score_opus":0.006719366786159124,"score_gpt":0.25335531764301744,"score_spread":0.24663595085685833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7130391898","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":[0.118251845,0.0007213154,0.8767764,0.0007809957,0.00028943588,0.0016963354,0.000004595975,0.00046600835,0.0010130525],"genre_scores_gemma":[0.9597088,0.007344478,0.030960612,0.000038216145,0.00005122437,0.0011433184,0.0000016921575,0.000018469955,0.0007331914],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976897,0.00010699144,0.0006471587,0.00087435835,0.00013968814,0.0005420546],"domain_scores_gemma":[0.99848247,0.0004752194,0.00024973167,0.000449759,0.00023376138,0.000109087836],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026761118,0.00027204715,0.0003665618,0.00058059674,0.0006361485,0.00014399133,0.00034323087,0.00040525498,0.0000082949255],"category_scores_gemma":[0.00019737257,0.00030954755,0.000077727556,0.0012304926,0.00014763238,0.00028890962,0.00038793,0.0004815704,0.000009007269],"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":[0.00011509132,0.00034251015,0.018949198,0.00015388402,0.000055932956,0.0000037965622,0.00079204515,0.004875647,0.005045718,0.006778584,0.00007649283,0.9628111],"study_design_scores_gemma":[0.00057781,0.0023440009,0.019874876,0.00022954223,0.000037355177,0.000009191731,0.00040058309,0.9139955,0.055059135,0.0045116586,0.0026366825,0.00032363986],"about_ca_topic_score_codex":0.00045538342,"about_ca_topic_score_gemma":0.00008648064,"teacher_disagreement_score":0.96248746,"about_ca_system_score_codex":0.0003413787,"about_ca_system_score_gemma":0.00011068369,"threshold_uncertainty_score":0.9999357},"labels":[],"label_agreement":null},{"id":"W7130716064","doi":"10.1109/swc65939.2025.00240","title":"SPEAR: Soft Prompt Enhanced Anomaly Recognition for Time Series Data","year":2025,"lang":"","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Anomaly detection; Leverage (statistics); Series (stratigraphy); Anomaly (physics); Time series; Vector quantization","score_opus":0.03262644635862121,"score_gpt":0.2942497132887075,"score_spread":0.2616232669300863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7130716064","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00050651294,0.000114793234,0.96247613,0.004588804,0.0002871284,0.0019141126,0.00023527561,0.0006840968,0.029193133],"genre_scores_gemma":[0.13412446,0.00026633253,0.6407115,0.0015368604,0.00036739203,0.0011294824,0.00048592532,0.000041858435,0.22133622],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99749523,0.000050083796,0.0006023892,0.0012569801,0.0001699446,0.0004253388],"domain_scores_gemma":[0.9970603,0.00012785385,0.00021060434,0.0021249603,0.00038158658,0.000094668794],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048405802,0.00028162767,0.0003068435,0.00019525005,0.000605953,0.00052957697,0.0019061787,0.00018956554,0.0007130493],"category_scores_gemma":[0.000101192236,0.00029372147,0.00011850051,0.00097460684,0.00014282677,0.0015614507,0.0011053623,0.00016202487,0.000506917],"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":[0.000070376744,0.0002747861,0.000010215093,0.00014557102,0.00008392023,5.7910637e-7,0.00009269566,0.0000033332453,0.015079495,0.018660387,0.06378686,0.9017918],"study_design_scores_gemma":[0.00065328984,0.0005705751,0.00023866644,0.00022157512,0.00013255748,0.00001299563,0.000069205955,0.232638,0.39590517,0.058661178,0.31011397,0.000782807],"about_ca_topic_score_codex":0.00004177512,"about_ca_topic_score_gemma":0.000031575426,"teacher_disagreement_score":0.90100896,"about_ca_system_score_codex":0.00004896633,"about_ca_system_score_gemma":0.00032564448,"threshold_uncertainty_score":0.9999515},"labels":[],"label_agreement":null},{"id":"W7130720418","doi":"10.1109/swc65939.2025.00038","title":"Automatic Defect Detection of Chain Link Fences Using Artificial Intelligence","year":2025,"lang":"","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Raytheon Technologies (Canada); University of Calgary","funders":"","keywords":"Process (computing); Segmentation; Task (project management); Anomaly detection; Categorization; Damages; Autoencoder; Enhanced Data Rates for GSM Evolution","score_opus":0.03875716386074521,"score_gpt":0.31345907819550345,"score_spread":0.2747019143347582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7130720418","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":[0.04181318,0.00026985237,0.9540356,0.0006079085,0.0005360875,0.0005721258,0.0000019210295,0.0003386842,0.0018246493],"genre_scores_gemma":[0.92607933,0.000063726235,0.07343829,0.000093951974,0.00007687545,0.000043701635,2.7214736e-7,0.000007701816,0.00019617629],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997538,0.00013764984,0.0011276447,0.0006074863,0.000265706,0.00032354827],"domain_scores_gemma":[0.9983203,0.00017953772,0.00043969328,0.00073776656,0.00024997437,0.00007272633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007264848,0.0002430801,0.0003362451,0.0005068465,0.00047913095,0.00020843274,0.00079022016,0.00021219766,0.00010988139],"category_scores_gemma":[0.00009713231,0.0002447339,0.00029018224,0.0028599477,0.000258204,0.00030667987,0.00031909134,0.000247972,0.000027512448],"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":[0.0000036174342,0.00007942388,0.000017147322,0.00007815442,0.000028555132,4.2239697e-7,0.000114540504,0.0007343429,0.019192338,0.15263025,0.0000029905948,0.8271182],"study_design_scores_gemma":[0.000013096961,0.0001075409,0.00010272161,0.000104031846,0.000034261146,0.000004429073,0.00009696229,0.5786515,0.36702016,0.05364335,0.000093458126,0.00012849316],"about_ca_topic_score_codex":0.00048874575,"about_ca_topic_score_gemma":0.000090066074,"teacher_disagreement_score":0.88426614,"about_ca_system_score_codex":0.00012173275,"about_ca_system_score_gemma":0.00026224987,"threshold_uncertainty_score":0.9979959},"labels":[],"label_agreement":null},{"id":"W7131259250","doi":"10.1109/vcip67698.2025.11396919","title":"M <sup>2</sup> S <sup>2</sup> L: Mamba-based Multi-Scale Spatial-temporal Learning for Video Anomaly Detection","year":2025,"lang":"","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Anomaly detection; Benchmark (surveying); Encoder; Inference; Task (project management); Feature (linguistics); Motion (physics); Task analysis; Object detection","score_opus":0.01650861080206411,"score_gpt":0.2688046898298811,"score_spread":0.252296079027817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131259250","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":[0.029559487,0.00021276808,0.96077216,0.0019153593,0.0003478644,0.0034781597,0.00004489659,0.0021405653,0.0015287356],"genre_scores_gemma":[0.8283015,0.000032360505,0.15598701,0.0013576768,0.00032646736,0.001652218,0.000049929527,0.000099462275,0.012193408],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99284714,0.0004583482,0.0018964297,0.0026347777,0.0006779406,0.0014853335],"domain_scores_gemma":[0.99532366,0.00058999093,0.00072085497,0.0019577672,0.0009040555,0.0005036693],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0013708276,0.0010808762,0.0010164339,0.001115123,0.0026334228,0.0011332493,0.0017673433,0.0007646417,0.0004344952],"category_scores_gemma":[0.00025162468,0.0011811175,0.0010169436,0.002832215,0.0003934765,0.0009946292,0.0006649607,0.0011660014,0.00023159069],"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":[0.0005108708,0.0020025934,0.011086125,0.00070593064,0.00032052782,0.000018387755,0.0012393034,0.22371064,0.005921899,0.0031290848,0.0042917333,0.7470629],"study_design_scores_gemma":[0.002101447,0.0009087232,0.00085809355,0.00016382434,0.00015155195,0.0000167179,0.00041014006,0.84402627,0.06741563,0.0005490661,0.08242231,0.0009762259],"about_ca_topic_score_codex":0.003692292,"about_ca_topic_score_gemma":0.0007209949,"teacher_disagreement_score":0.80478513,"about_ca_system_score_codex":0.00054251804,"about_ca_system_score_gemma":0.0007667951,"threshold_uncertainty_score":0.9999037},"labels":[],"label_agreement":null},{"id":"W7132924193","doi":"","title":"Out of Distribution Detection via Normalizing Flows for Open World Machine Learning","year":2024,"lang":"","type":"dissertation","venue":"TSpace","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Feature (linguistics); Set (abstract data type); Task (project management); Sample (material); Training set; Feature vector; Open set; Unsupervised learning; Pattern recognition (psychology)","score_opus":0.02881087111061361,"score_gpt":0.35587589885230986,"score_spread":0.32706502774169627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132924193","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":[0.0050156107,0.00086409156,0.98774135,0.00033985617,0.0014152023,0.0023884529,0.00006609191,0.00038056375,0.0017887533],"genre_scores_gemma":[0.93857616,0.00019670872,0.016860643,0.000026523729,0.00026304307,0.0013160148,0.0012666727,0.00008977513,0.041404437],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99711394,0.00011077521,0.0008939396,0.0010591729,0.00036159065,0.00046060496],"domain_scores_gemma":[0.9975882,0.00016487898,0.0009246288,0.00067422417,0.0005080932,0.00013993478],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008115252,0.00049826154,0.00060947594,0.00036869702,0.00082149083,0.0006437517,0.0013343683,0.00033267197,0.000090017485],"category_scores_gemma":[0.00008169412,0.0005486379,0.00039883866,0.0014228424,0.00004736824,0.00053562876,0.00048063637,0.0008074311,0.000081951745],"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":[0.0006040405,0.0004699464,0.00012475609,0.0027633547,0.0004317968,0.0000055273517,0.016782844,0.002170897,0.17120507,0.024105625,0.0012389768,0.7800971],"study_design_scores_gemma":[0.00033498378,0.00051827874,0.0001530047,0.00048037947,0.0002306757,0.000007731739,0.000685688,0.6447958,0.22928172,0.0023123592,0.12055691,0.00064249785],"about_ca_topic_score_codex":0.0018506756,"about_ca_topic_score_gemma":0.0042908066,"teacher_disagreement_score":0.97088075,"about_ca_system_score_codex":0.000278472,"about_ca_system_score_gemma":0.00016591196,"threshold_uncertainty_score":0.9996965},"labels":[],"label_agreement":null},{"id":"W7133062637","doi":"","title":"Unsupervised Multivariate Time Series Anomaly Detection via Transformer-based models and Time Series Encoding","year":2021,"lang":"","type":"dissertation","venue":"TSpace","topic":"Anomaly Detection Techniques and Applications","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 Toronto","funders":"Centre for Management of Technology and Entrepreneurship, University of Toronto","keywords":"Gramian matrix; Autoencoder; Anomaly detection; Series (stratigraphy); Multivariate statistics; Pattern recognition (psychology); Time series","score_opus":0.016473717280376426,"score_gpt":0.27687502049856544,"score_spread":0.260401303218189,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133062637","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":[0.066507615,0.0005274382,0.92504084,0.0009539328,0.0002885646,0.0014482961,0.00003540711,0.0008815434,0.004316383],"genre_scores_gemma":[0.9029752,0.00052284246,0.061566997,0.00013928614,0.00018786956,0.0006962211,0.00036199822,0.00016540026,0.033384193],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9957748,0.00026586305,0.0009176805,0.0017032238,0.0005776936,0.00076075504],"domain_scores_gemma":[0.99735755,0.00013522492,0.0005448439,0.0010107221,0.0006131322,0.00033855197],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0004334637,0.0009750647,0.00093935477,0.0004702176,0.0014714947,0.00083708984,0.00068258366,0.0008067742,0.00050283887],"category_scores_gemma":[0.000029640754,0.0011163352,0.00038380196,0.001442391,0.00021268117,0.0020676642,0.00009301076,0.00070283725,0.00012912168],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044649828,0.00026980956,0.000012528565,0.00053296227,0.0001897528,0.000027045437,0.013491316,0.0015686558,0.90293384,0.0020135758,0.000031977484,0.07848202],"study_design_scores_gemma":[0.00063657947,0.0006297558,0.00034887716,0.00031046759,0.00018984488,0.00008681975,0.0012492136,0.48255545,0.50988513,0.001655,0.0012260361,0.0012268164],"about_ca_topic_score_codex":0.00077658385,"about_ca_topic_score_gemma":0.0001976477,"teacher_disagreement_score":0.86347383,"about_ca_system_score_codex":0.00019407798,"about_ca_system_score_gemma":0.00043558638,"threshold_uncertainty_score":0.99982846},"labels":[],"label_agreement":null},{"id":"W7133357431","doi":"10.1109/tps-isa67132.2025.00021","title":"Anomaly Detection in Graphs via Topology-Aware Attention Mechanisms","year":2025,"lang":"","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","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":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Anomaly detection; Topological data analysis; Embedding; Graph; Persistent homology; Computation; Topology (electrical circuits); Adversarial system","score_opus":0.007513977587061194,"score_gpt":0.2517626322810102,"score_spread":0.24424865469394902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133357431","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":[0.01495594,0.00009344035,0.9745112,0.0020705312,0.00090936734,0.00080872816,0.0000025400263,0.000525648,0.0061226184],"genre_scores_gemma":[0.978515,0.000105339444,0.015024807,0.0006639673,0.000024368508,0.000299198,0.0000028515587,0.0000124523385,0.005351972],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974035,0.00015192476,0.0007460913,0.0009983436,0.0002040664,0.0004960615],"domain_scores_gemma":[0.9985987,0.00005081096,0.00019803247,0.00088501483,0.00017566317,0.00009178796],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00046153137,0.0003128674,0.00032009094,0.0010192198,0.00046741666,0.0001963253,0.00075420155,0.00041967968,0.00019494988],"category_scores_gemma":[0.000012069695,0.00034462975,0.00023386072,0.0031753264,0.0001224677,0.00051423156,0.00038047705,0.00041536876,0.00010334529],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017348983,0.00026930418,0.0006657485,0.000039431863,0.00002934207,0.000005091536,0.00005057592,0.000018165354,0.03428835,0.4964853,0.00014904416,0.4679823],"study_design_scores_gemma":[0.0007003373,0.0004393532,0.027207153,0.000096734504,0.00004834571,0.000037891758,0.00018655972,0.32069847,0.1729729,0.47486907,0.002133333,0.0006098552],"about_ca_topic_score_codex":0.0010282106,"about_ca_topic_score_gemma":0.0013408397,"teacher_disagreement_score":0.9635591,"about_ca_system_score_codex":0.00022759642,"about_ca_system_score_gemma":0.00009703134,"threshold_uncertainty_score":0.9999006},"labels":[],"label_agreement":null},{"id":"W7134174218","doi":"10.1109/bigdata66926.2025.11401894","title":"Interpretable Multivariate Anomaly Detector Selection for Automatic Marine Data Quality Control","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Reach Technologies (Canada)","funders":"","keywords":"Selection (genetic algorithm); Anomaly detection; Multivariate statistics; Data quality; Quality (philosophy); Detector; Pattern recognition (psychology)","score_opus":0.02896562805842805,"score_gpt":0.3450778936459841,"score_spread":0.31611226558755606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7134174218","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":[0.0021948183,0.0000073085857,0.99181575,0.001021425,0.00012543445,0.00071183476,0.000019128755,0.0009982638,0.003106029],"genre_scores_gemma":[0.69961154,0.0000012401467,0.29663527,0.00044968483,0.000019572553,0.00026975147,0.000009410846,0.000004590258,0.0029989546],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998866,0.00005200086,0.00033570823,0.00047435655,0.0000811001,0.00019082871],"domain_scores_gemma":[0.9985092,0.0002570399,0.00010457152,0.0009712958,0.00011682935,0.000041051724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051381404,0.00011249064,0.00017740905,0.00010901467,0.00019805656,0.00015564161,0.0010362094,0.000062876374,0.000068171656],"category_scores_gemma":[0.0001364526,0.0001011554,0.000055517503,0.0004555274,0.000019306019,0.00045311163,0.00045571532,0.000078458266,0.0000107688775],"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":[0.000046402038,0.00028616935,0.004232405,0.00012256851,0.00017278567,2.0522096e-7,0.000055618646,0.000055302608,0.025741328,0.34219322,0.010579266,0.61651474],"study_design_scores_gemma":[0.00045841816,0.000051383067,0.006776783,0.000009379126,0.00001642217,0.0000016640371,0.000004837108,0.9652793,0.00769801,0.008267414,0.011313073,0.0001233583],"about_ca_topic_score_codex":0.00071122963,"about_ca_topic_score_gemma":0.00022104464,"teacher_disagreement_score":0.96522397,"about_ca_system_score_codex":0.000059017264,"about_ca_system_score_gemma":0.00007797703,"threshold_uncertainty_score":0.41249976},"labels":[],"label_agreement":null},{"id":"W7140117717","doi":"10.1109/iconscept66142.2025.11437045","title":"Intelligent CCTV Surveillance System For Real Time Suspicious Activity Detection Using Deep Learning","year":2025,"lang":"","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Deep learning; Feature (linguistics); Artificial neural network; Object detection; Noise (video); Focus (optics)","score_opus":0.015715599169975607,"score_gpt":0.280084226404432,"score_spread":0.26436862723445637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7140117717","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":[0.07682086,0.000079884936,0.91536635,0.00012154765,0.00057661487,0.0013063652,0.0000037208101,0.001104538,0.004620144],"genre_scores_gemma":[0.97794807,0.00007103625,0.017340846,0.000026474103,0.00013798074,0.00019974589,0.0000016972654,0.00002539964,0.004248744],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974134,0.00023668958,0.0005975571,0.00097268226,0.0002257903,0.0005538845],"domain_scores_gemma":[0.9980505,0.00029907734,0.000381628,0.000715818,0.0004311505,0.000121796584],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0008833695,0.00034603252,0.0004468666,0.00032225123,0.0013394407,0.00041500342,0.00060272013,0.00028847574,0.000022710885],"category_scores_gemma":[0.000060477443,0.00037543086,0.00028125636,0.0013156817,0.00008493231,0.0003235337,0.00027071175,0.0003502864,0.000047572055],"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":[0.00010545846,0.00016899501,0.00048461382,0.00036319782,0.00011047325,0.0000018685497,0.00017503688,0.0057718507,0.09495828,0.0145315565,0.0000319235,0.8832967],"study_design_scores_gemma":[0.0001781093,0.00021547017,0.000724312,0.00007756156,0.000029770063,0.000014391645,0.00011313121,0.82837206,0.1675788,0.00016496173,0.0022295278,0.00030191411],"about_ca_topic_score_codex":0.0009390863,"about_ca_topic_score_gemma":0.00016328198,"teacher_disagreement_score":0.9011272,"about_ca_system_score_codex":0.0008935654,"about_ca_system_score_gemma":0.00015549923,"threshold_uncertainty_score":0.99996066},"labels":[],"label_agreement":null},{"id":"W7163424452","doi":"10.32628/cseit2342441","title":"Anomaly Detection in Financial Time-Series Data : A Conceptual Model for Healthcare and Banking Applications","year":2023,"lang":"","type":"article","venue":"International Journal of Scientific Research in Computer Science Engineering and Information Technology","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"PricewaterhouseCoopers (Canada)","funders":"","keywords":"EWMA chart; Anomaly detection; Control chart; Outlier; Payment; Anomaly (physics); Autoencoder; Statistical process control; Conceptual model; Data modeling","score_opus":0.053448293104366745,"score_gpt":0.3446283220877376,"score_spread":0.2911800289833708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7163424452","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":[0.04335693,0.00012720321,0.9490439,0.0061319363,0.0006719283,0.0005413927,0.000040780527,0.00007918952,0.000006689674],"genre_scores_gemma":[0.9365156,0.00037128,0.06281439,0.000039011575,0.00011784608,0.000104460065,0.000011740619,0.000006053377,0.000019583626],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970971,0.000032111515,0.0009371541,0.00049460377,0.00092199334,0.000517081],"domain_scores_gemma":[0.99723214,0.00016973393,0.0003058189,0.00049953605,0.0016631688,0.00012961155],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0068217535,0.00014330278,0.00022396429,0.007888933,0.00048344216,0.0011922487,0.0025883408,0.00016444191,0.0000010694649],"category_scores_gemma":[0.0003939295,0.00015290041,0.000030053534,0.005928787,0.0012561433,0.0067038205,0.0016921852,0.00062236964,0.000006728824],"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":[0.000028059247,0.00004388601,0.00014215858,0.000060260936,0.000008135501,0.0000050650488,0.0015290854,0.030191272,0.0021979103,0.069467224,0.0001507626,0.89617616],"study_design_scores_gemma":[0.00035865026,0.00017344145,0.0005147302,0.00015271621,0.0000013355748,0.000096820775,0.00010796187,0.98098516,0.0014289268,0.009227151,0.0068256604,0.00012745803],"about_ca_topic_score_codex":0.000016484257,"about_ca_topic_score_gemma":0.000014484872,"teacher_disagreement_score":0.95079386,"about_ca_system_score_codex":0.0002710044,"about_ca_system_score_gemma":0.0008050984,"threshold_uncertainty_score":0.9998446},"labels":[],"label_agreement":null},{"id":"W785218702","doi":"10.1007/s00500-015-1757-7","title":"Relative density degree induced boundary detection for one-class SVM","year":2015,"lang":"en","type":"article","venue":"Soft Computing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina; University of Calgary","funders":"","keywords":"Support vector machine; Outlier; Class (philosophy); Degree (music); Computer science; Boundary (topology); Data set; Artificial intelligence; Set (abstract data type); Training set; Decision boundary; Pattern recognition (psychology); One-class classification; Data mining; Mathematics; Mathematical analysis","score_opus":0.0934051911808412,"score_gpt":0.2973101661553134,"score_spread":0.20390497497447221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W785218702","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":[0.09811584,0.000018913493,0.8988986,0.0003600951,0.0002524605,0.0003403028,8.2395394e-7,0.0007309354,0.0012820314],"genre_scores_gemma":[0.86617076,3.846435e-7,0.13337483,0.00018049976,0.00015302954,0.000026360874,0.0000015540063,0.000010863538,0.000081746264],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891543,0.00004760012,0.00023229021,0.00038368517,0.00017769387,0.00024331227],"domain_scores_gemma":[0.9988998,0.00015500012,0.00016742153,0.00036742885,0.00029259967,0.0001177174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049635343,0.00012167936,0.00014476378,0.00009178262,0.0004892249,0.00014850889,0.0003787384,0.000104346174,7.401662e-7],"category_scores_gemma":[0.00013867818,0.00013282373,0.000079539364,0.0004023461,0.00003577505,0.00034546875,0.00024557987,0.00020180082,0.000027927399],"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":[0.000022702081,0.0001130977,0.00056272035,0.000021323911,0.0000468566,0.000002157704,0.0014573506,0.00014721324,0.011318892,0.057089526,0.000716172,0.92850196],"study_design_scores_gemma":[0.0009839669,0.00065023574,0.0061103413,0.00005620965,0.000030280193,0.00006274353,0.00016982974,0.7092985,0.12322985,0.14009304,0.018618992,0.0006960254],"about_ca_topic_score_codex":0.000049469018,"about_ca_topic_score_gemma":0.000025513964,"teacher_disagreement_score":0.92780596,"about_ca_system_score_codex":0.00015357265,"about_ca_system_score_gemma":0.000109465196,"threshold_uncertainty_score":0.54163945},"labels":[],"label_agreement":null},{"id":"W8065818","doi":"10.1007/978-3-662-43813-8_7","title":"Anomaly Detection for Mobile Device Comfort","year":2014,"lang":"en","type":"book-chapter","venue":"IFIP advances in information and communication technology","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University; Carleton University","funders":"","keywords":"Anomaly detection; Computer science; Anomaly (physics); Artificial intelligence; Physics","score_opus":0.006860157689114918,"score_gpt":0.2546611446255987,"score_spread":0.24780098693648378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W8065818","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":[0.000035879093,0.0031046562,0.91600335,0.0004733053,0.00007263469,0.0010273353,0.000010921256,0.00068322365,0.07858868],"genre_scores_gemma":[0.57089704,0.0728623,0.3107619,0.0025054778,0.00009886891,0.010207426,0.00044357937,0.00011017314,0.032113254],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987426,0.000015154309,0.00069789554,0.00024718075,0.00012035351,0.00017682707],"domain_scores_gemma":[0.99777645,0.00012130205,0.00062732265,0.0012135215,0.00022212176,0.000039302475],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029587673,0.00022945636,0.00029080134,0.0008068026,0.0002782064,0.000096322525,0.0010462266,0.0004924258,0.000009406772],"category_scores_gemma":[0.000029188765,0.00025266182,0.00006321069,0.00022575431,0.00018886116,0.0011556766,0.00034591794,0.00040643016,0.00003192301],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000028834818,0.0000036174686,0.0000056918598,0.000026179308,0.0000027508738,2.0799716e-8,0.000029237728,0.000017013961,0.0000042673014,0.5098877,0.000029310568,0.48999134],"study_design_scores_gemma":[0.00021438932,0.00012381066,0.0000074164104,0.000054087002,0.0000058709697,0.000016263293,0.00003860436,0.005517604,0.0007290736,0.18090239,0.81216717,0.00022334319],"about_ca_topic_score_codex":0.000006372466,"about_ca_topic_score_gemma":0.00008901346,"teacher_disagreement_score":0.81213784,"about_ca_system_score_codex":0.00010495567,"about_ca_system_score_gemma":0.00003598915,"threshold_uncertainty_score":0.99999255},"labels":[],"label_agreement":null},{"id":"W835534531","doi":"10.1007/s40846-015-0018-7","title":"Detection of Abnormalities in Type II Diabetic Patients Using Particle Filters","year":2015,"lang":"en","type":"article","venue":"Journal of Medical and Biological Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial pancreas; Pancreas; Adipose tissue; Type 2 diabetes; Insulin; Diabetes mellitus; Internal medicine; Particle filter; Endocrinology; Medicine; Biology; Type 1 diabetes; Computer science; Filter (signal processing)","score_opus":0.03339649033746788,"score_gpt":0.24546371814605492,"score_spread":0.21206722780858703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W835534531","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":[0.8638751,0.0001262844,0.13577995,0.00011321792,0.00007309725,0.000017653614,1.3290902e-7,0.000009296886,0.000005273754],"genre_scores_gemma":[0.9968912,0.000056181787,0.0029991155,0.000024346904,0.000026512484,6.0915573e-7,6.085096e-8,8.3347675e-7,0.0000011122485],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950415,0.000014336481,0.00020809098,0.000045230696,0.00015775545,0.00007041859],"domain_scores_gemma":[0.9997205,0.00003134764,0.000061551254,0.000035140954,0.00005584849,0.00009561254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003429845,0.000035108522,0.000094896386,0.000039314047,0.000013398221,0.0000065649488,0.000120497185,0.000054089745,0.0000028806332],"category_scores_gemma":[0.00015137995,0.000022864377,0.000018708566,0.000149731,0.000022421731,0.000078697834,0.00007162181,0.00010128276,1.218352e-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":[0.00021436604,0.0016099392,0.1248642,0.0001996326,0.0001109347,0.00010174078,0.0022606547,0.011650243,0.14079912,0.03183538,0.00011128787,0.6862425],"study_design_scores_gemma":[0.0018951001,0.004088009,0.10192,0.0002949113,0.000013118178,0.00020171536,0.00017576436,0.80471355,0.08192364,0.0014623689,0.0029788793,0.000332965],"about_ca_topic_score_codex":0.0000069886905,"about_ca_topic_score_gemma":2.692176e-7,"teacher_disagreement_score":0.7930633,"about_ca_system_score_codex":0.00001522204,"about_ca_system_score_gemma":0.000014447077,"threshold_uncertainty_score":0.09323822},"labels":[],"label_agreement":null},{"id":"W889267531","doi":"10.1007/s11760-015-0797-x","title":"Time-domain period detection in short-duration videos","year":2015,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Autocorrelation; Similarity (geometry); Discrete-time signal; Frequency domain; Time domain; Computer science; Cluster analysis; Algorithm; SIGNAL (programming language); Sequence (biology); Artificial intelligence; Noise (video); Domain (mathematical analysis); Autocorrelation technique; Pattern recognition (psychology); Function (biology); Mathematics; Computer vision; Image (mathematics); Statistics; Telecommunications; Signal transfer function","score_opus":0.016984695272062717,"score_gpt":0.2698493732863185,"score_spread":0.2528646780142558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W889267531","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":[0.085625954,0.00014425047,0.9125934,0.00038223577,0.000014691754,0.00014725134,4.2699776e-7,0.00020455032,0.00088723167],"genre_scores_gemma":[0.9670232,0.000004093883,0.032664716,0.00010397872,0.000054290333,0.00006052442,0.0000014030285,0.000007897936,0.000079906764],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913955,0.00004611441,0.00022085336,0.00029327397,0.0001420816,0.00015814813],"domain_scores_gemma":[0.9996176,0.000014153696,0.000044165008,0.00013472146,0.00010350622,0.00008585409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043078032,0.0001015205,0.00010377837,0.00012806186,0.00016606197,0.00032395075,0.0001555215,0.00005734382,0.000005531003],"category_scores_gemma":[0.000020827738,0.00009830315,0.00002181947,0.0004029118,0.000056453548,0.0012265122,0.00007354992,0.00011789867,0.000018255734],"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":[0.000013704774,0.00004574133,0.00011230059,0.000022690117,0.0000025405877,0.000011549048,0.0015868137,0.000016166607,0.36956552,0.00025599796,0.00011951027,0.6282475],"study_design_scores_gemma":[0.0005402034,0.00028130758,0.00089297607,0.000103846134,0.000010888162,0.00021833596,0.00088901044,0.5630106,0.41217977,0.017783461,0.003542102,0.000547518],"about_ca_topic_score_codex":0.000018054861,"about_ca_topic_score_gemma":0.000013085357,"teacher_disagreement_score":0.88139725,"about_ca_system_score_codex":0.00005143541,"about_ca_system_score_gemma":0.00006495355,"threshold_uncertainty_score":0.40086862},"labels":[],"label_agreement":null},{"id":"W96556717","doi":"10.1007/978-3-642-35527-1_59","title":"Modeling Outlier Score Distributions","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Outlier; Anomaly detection; Computer science; Artificial intelligence; Probabilistic logic; Local outlier factor; Pattern recognition (psychology); Data mining; Object (grammar); Probability density function; Statistics; Mathematics","score_opus":0.027089130155039498,"score_gpt":0.2570276324457522,"score_spread":0.22993850229071272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W96556717","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":[0.00004416083,0.00043394527,0.99505675,0.00061855384,0.00054619496,0.00034429342,0.000012480519,0.00042306187,0.0025205638],"genre_scores_gemma":[0.50523907,0.00008282983,0.49316278,0.0005247447,0.0005170628,0.00004259773,0.000012043349,0.000031250653,0.0003875871],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99737704,0.000013266834,0.00042708393,0.0010323409,0.00055781193,0.000592436],"domain_scores_gemma":[0.9979095,0.000086800035,0.00014683699,0.0014317755,0.00022191645,0.00020315842],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047089346,0.00037810244,0.00032115204,0.00047118912,0.00044583846,0.00036188305,0.0023786328,0.00029119864,0.000031883563],"category_scores_gemma":[0.000025257888,0.00035682938,0.00014421668,0.0005770541,0.00034673707,0.0005999335,0.0010930408,0.0007051966,0.000097009804],"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":[0.000001004931,0.000033120898,0.000026416243,0.000012126517,0.000006257296,0.000006042868,0.00015850553,0.023796214,0.00009995436,0.21321781,0.000023105042,0.76261944],"study_design_scores_gemma":[0.00006222023,0.00003937375,0.000022692964,0.00009484178,0.0000071455247,0.000043378266,5.2416247e-8,0.7942597,0.00091118197,0.20076989,0.003334899,0.00045461967],"about_ca_topic_score_codex":0.000023639215,"about_ca_topic_score_gemma":0.000023083223,"teacher_disagreement_score":0.77046347,"about_ca_system_score_codex":0.00030086839,"about_ca_system_score_gemma":0.00026606952,"threshold_uncertainty_score":0.99988836},"labels":[],"label_agreement":null}]}