{"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01384521,0.00014712317,0.9850348,0.000057870042,0.000026449068,0.000008133033,0.000018819195,0.0003697551,0.0004918545],"genre_scores_gemma":[0.6081933,0.000497853,0.385698,0.00009910189,0.00009518192,0.000040005823,0.00024640257,0.00024185482,0.004888312],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895597,0.00021828503,0.000045187906,0.00027986392,0.00038334884,0.000117377764],"domain_scores_gemma":[0.99818295,0.00075647025,0.00019278881,0.00032010704,0.0004617076,0.00008594227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010783471,0.00080206414,0.0012418086,0.0011067304,0.00034250383,0.0013040972,0.0016783943,0.0012268984,0.00089529477],"category_scores_gemma":[0.0050209686,0.0005867805,0.0010271946,0.0014311943,0.00091663335,0.0024807635,0.0026323549,0.001650571,0.00050181337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049368956,0.00015769278,0.003433956,0.00017354016,0.00033005173,0.00021671057,0.00018507658,0.31983006,0.043127786,0.06516588,0.003407834,0.56347775],"study_design_scores_gemma":[0.0000036003262,0.000018192422,0.00031175945,0.0000027030235,0.00001234562,0.00008160202,0.000006137084,0.9831498,0.0028876087,0.013128699,0.0003862008,0.000011292518],"about_ca_topic_score_codex":0.001910945,"about_ca_topic_score_gemma":0.0016791663,"teacher_disagreement_score":0.001910945,"about_ca_system_score_codex":0.0006549119,"about_ca_system_score_gemma":0.00053110934,"threshold_uncertainty_score":0.005702913},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13773903,0.0004580761,0.84248805,0.00081638625,0.00024776172,0.0003526839,0.002653701,0.012142325,0.003101945],"genre_scores_gemma":[0.75156945,0.00016370523,0.23933017,0.00027705161,0.00027037627,0.0003601534,0.0050186017,0.00047992525,0.0025304519],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99634004,0.0011564784,0.00023200196,0.0005482086,0.001414019,0.00030933195],"domain_scores_gemma":[0.9849871,0.007810514,0.0014822512,0.0026223855,0.002280431,0.0008173456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023656995,0.0007624348,0.0014338366,0.0054110247,0.0009510964,0.0014730737,0.002514923,0.0017295067,0.0036719716],"category_scores_gemma":[0.015374781,0.00027372403,0.0008149007,0.0036770832,0.0014061788,0.0032194653,0.0019374182,0.0012894904,0.0010425692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003990827,0.0010321726,0.06838613,0.00073734537,0.0008933362,0.0012313914,0.0005850936,0.12598954,0.031059219,0.03297216,0.030616803,0.70250595],"study_design_scores_gemma":[0.00019302854,0.0007741986,0.013825558,0.0000479747,0.00011633513,0.0008448638,0.00036190916,0.9035599,0.018783268,0.056074716,0.0053210426,0.000097198674],"about_ca_topic_score_codex":0.0019246358,"about_ca_topic_score_gemma":0.0018473186,"teacher_disagreement_score":0.0054110247,"about_ca_system_score_codex":0.0007374012,"about_ca_system_score_gemma":0.0011770402,"threshold_uncertainty_score":0.012511134},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08722242,0.00019880658,0.9116452,0.00010299825,0.000062603525,0.000029826346,0.00004655987,0.00019997775,0.00049163593],"genre_scores_gemma":[0.8884519,0.00015518801,0.11028027,0.000031822427,0.000077930425,0.000054298565,0.0001735921,0.00002699066,0.0007480351],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995523,0.00007780913,0.00003891664,0.00013789517,0.00014876986,0.000044178814],"domain_scores_gemma":[0.9993211,0.00026019628,0.00012510658,0.00008074515,0.00017875356,0.000034068536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006362679,0.00042835603,0.0010363562,0.00089038495,0.00032715796,0.0007224848,0.001043898,0.0006487027,0.0005063532],"category_scores_gemma":[0.0020157383,0.0002166093,0.00066377677,0.0008374051,0.00034414922,0.0011672709,0.0007116863,0.0006609783,0.000120871315],"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":[0.00039399348,0.00030223286,0.011552468,0.00016898004,0.00015580583,0.00019362372,0.00018470023,0.5240063,0.023669263,0.008050374,0.0011673074,0.43015495],"study_design_scores_gemma":[0.000002638173,0.000026093592,0.0006946476,0.0000015929068,0.0000064949486,0.000029335744,0.000008914846,0.9968035,0.00084240787,0.0014252665,0.00015455113,0.0000044455637],"about_ca_topic_score_codex":0.0023032213,"about_ca_topic_score_gemma":0.001996062,"teacher_disagreement_score":0.0023032213,"about_ca_system_score_codex":0.00035258968,"about_ca_system_score_gemma":0.00040845238,"threshold_uncertainty_score":0.0045796633},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018477514,0.0003901824,0.96564406,0.00014276369,0.000101736434,0.000086436274,0.0006518404,0.014150343,0.00035507508],"genre_scores_gemma":[0.07856859,0.0002059341,0.9186646,0.00006299128,0.000035582914,0.00009115709,0.0011772291,0.0004454937,0.0007484421],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903286,0.00011796902,0.00007451688,0.0001687414,0.0005152341,0.00009073621],"domain_scores_gemma":[0.998432,0.0005913933,0.00012671412,0.00041665888,0.00035316608,0.00008010262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011827406,0.0012477605,0.002215639,0.0024278888,0.0007350056,0.0012630648,0.0025603191,0.0011401344,0.0021824173],"category_scores_gemma":[0.004776061,0.0007011854,0.00080094254,0.0037926356,0.0005891386,0.0021134415,0.0024057534,0.0010708418,0.0009145849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045428984,0.00011096928,0.004602284,0.00023444902,0.00010790216,0.00021088535,0.00013719914,0.059668276,0.009223968,0.0024509495,0.024071833,0.89872706],"study_design_scores_gemma":[0.00007674547,0.00007042211,0.0011502268,0.000013733171,0.000025837931,0.0003034934,0.00008122799,0.97315896,0.010308487,0.009089966,0.00569453,0.000026419802],"about_ca_topic_score_codex":0.00829636,"about_ca_topic_score_gemma":0.008666915,"teacher_disagreement_score":0.00829636,"about_ca_system_score_codex":0.0004337506,"about_ca_system_score_gemma":0.0009267269,"threshold_uncertainty_score":0.016496181},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059458338,0.00039470248,0.93731666,0.00017292678,0.000112210124,0.000041898842,0.00009711338,0.0013255457,0.0010806865],"genre_scores_gemma":[0.71895576,0.00024171606,0.27817708,0.00010670399,0.00013872777,0.000070872105,0.00046607235,0.00011453128,0.0017284952],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987016,0.00030962014,0.00009998601,0.00031275224,0.0004618631,0.000114213326],"domain_scores_gemma":[0.99848026,0.00032798527,0.00015697499,0.00024394445,0.0007222324,0.00006861282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016700313,0.0007630359,0.0013407258,0.0017599437,0.00042967885,0.0009941823,0.0010456383,0.00074803154,0.00092631596],"category_scores_gemma":[0.0034249886,0.00030408986,0.000999657,0.001015449,0.00023410434,0.0015239517,0.00090451614,0.001201545,0.0006041762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019954928,0.00022002251,0.0072894916,0.000074665666,0.00027391178,0.00012797488,0.00011749629,0.25439298,0.013630667,0.0027015514,0.003722382,0.7172494],"study_design_scores_gemma":[0.0000034638545,0.000033476834,0.0009107045,0.000003765185,0.00001955288,0.000038717582,0.000014671927,0.99516726,0.0023236775,0.00082775526,0.00064557587,0.000011365743],"about_ca_topic_score_codex":0.0019627176,"about_ca_topic_score_gemma":0.0016693351,"teacher_disagreement_score":0.0019627176,"about_ca_system_score_codex":0.00033148113,"about_ca_system_score_gemma":0.0004963994,"threshold_uncertainty_score":0.008832097},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09072823,0.00018182906,0.9059617,0.00006513564,0.000052170366,0.00007092646,0.00020572358,0.0016188297,0.0011154915],"genre_scores_gemma":[0.59760296,0.00021358469,0.39920017,0.000034779565,0.00005086107,0.0000728331,0.00070081424,0.00023291628,0.0018911127],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996259,0.000041679024,0.00003096761,0.00007364636,0.0001892448,0.000038495804],"domain_scores_gemma":[0.99917173,0.00033635032,0.00010700935,0.00009440247,0.00025034937,0.000040192856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006046904,0.00053695927,0.000861148,0.0022745556,0.00029677284,0.0009815234,0.00088849803,0.00037363815,0.0013772051],"category_scores_gemma":[0.0015705865,0.00024862445,0.00061052834,0.0021693048,0.00028573387,0.0009653934,0.00057536253,0.00046406966,0.00039815422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070450146,0.0004273647,0.010412457,0.00019602667,0.00019373994,0.0002940186,0.00014158023,0.06476546,0.10085952,0.0067789634,0.0020178598,0.8132085],"study_design_scores_gemma":[0.000014525413,0.00012827724,0.012780023,0.0000115034845,0.00005780805,0.0003999467,0.00007411645,0.9437533,0.03425431,0.007251443,0.0012389789,0.000035707388],"about_ca_topic_score_codex":0.0012222065,"about_ca_topic_score_gemma":0.0017826825,"teacher_disagreement_score":0.0022745556,"about_ca_system_score_codex":0.00029612618,"about_ca_system_score_gemma":0.00034976788,"threshold_uncertainty_score":0.0046072006},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18374807,0.003796906,0.8076602,0.0013305141,0.000133792,0.00007011232,0.0001675422,0.00078161934,0.002311294],"genre_scores_gemma":[0.860686,0.0013766023,0.1355805,0.00014744446,0.00015981351,0.000081255916,0.00023744248,0.000047558882,0.0016834225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990588,0.00033368872,0.00010272706,0.00020024598,0.00022307503,0.00008141802],"domain_scores_gemma":[0.9944224,0.0041189655,0.0005932643,0.00013930083,0.000638657,0.00008749304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003361979,0.0006378654,0.00087066233,0.0018636999,0.00037739857,0.0012906017,0.0010892599,0.0013706159,0.0006034749],"category_scores_gemma":[0.012851523,0.00044463732,0.0005094354,0.0017568968,0.00066579843,0.0023313311,0.0007015965,0.0012824227,0.0001913866],"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":[0.00038029018,0.0002141502,0.017400224,0.000121348145,0.00017848423,0.00011930774,0.00012874496,0.7379762,0.0015652445,0.008052844,0.0011549713,0.23270822],"study_design_scores_gemma":[0.0000049509945,0.000016346488,0.0008095372,0.000008206278,0.000007291301,0.000014941273,0.000009715038,0.994896,0.00024671343,0.0038825374,0.00009895626,0.000004795663],"about_ca_topic_score_codex":0.009166923,"about_ca_topic_score_gemma":0.004724363,"teacher_disagreement_score":0.009166923,"about_ca_system_score_codex":0.0012684284,"about_ca_system_score_gemma":0.0005847026,"threshold_uncertainty_score":0.01822716},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014750058,0.00014539115,0.9827626,0.00009372524,0.00006451176,0.000035774883,0.00005702587,0.0016861287,0.0004046857],"genre_scores_gemma":[0.30818662,0.00021305525,0.6882945,0.00014667475,0.00010355813,0.00012759106,0.0005233629,0.00026378778,0.0021409085],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981937,0.00027189145,0.00009216315,0.00053579744,0.0007344094,0.00017201767],"domain_scores_gemma":[0.9977076,0.0005630152,0.00026810364,0.00047536806,0.00087947125,0.00010646886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014171947,0.0011091945,0.0017544249,0.0022614056,0.0008914626,0.0011414319,0.0024187095,0.001345788,0.0007745359],"category_scores_gemma":[0.00471545,0.00041725632,0.0012636087,0.0019981766,0.0008757589,0.0019271529,0.0020195413,0.0016142474,0.00089326344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031717404,0.00017529314,0.009063236,0.00015990119,0.00021570123,0.00025825071,0.00043585862,0.08040442,0.050407995,0.0058430186,0.0048876945,0.8478314],"study_design_scores_gemma":[0.000016336824,0.0001311639,0.0030372746,0.000014635858,0.00005066154,0.0004103701,0.00009948663,0.96094555,0.023571035,0.00751988,0.004124944,0.00007868682],"about_ca_topic_score_codex":0.004559755,"about_ca_topic_score_gemma":0.0045908643,"teacher_disagreement_score":0.004559755,"about_ca_system_score_codex":0.00076300505,"about_ca_system_score_gemma":0.0011537408,"threshold_uncertainty_score":0.009066403},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16679184,0.0011510592,0.8180067,0.00076901825,0.00020627043,0.000120790035,0.00038841378,0.0059596836,0.00660624],"genre_scores_gemma":[0.88218343,0.0006409992,0.111699864,0.00017971086,0.00010362195,0.00006555573,0.00056234404,0.00013449865,0.0044300114],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898976,0.00011494769,0.0000675374,0.00023276186,0.00051206123,0.00008288251],"domain_scores_gemma":[0.9978211,0.00094773865,0.0003703365,0.00028665978,0.0005185587,0.000055714278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005876729,0.0005849428,0.00044865717,0.0017538896,0.0003925446,0.0010329876,0.00067963434,0.0008506337,0.0020265838],"category_scores_gemma":[0.0051940503,0.00024331892,0.000260115,0.00086537184,0.0005277319,0.0017604588,0.00087198697,0.0006797846,0.0009370459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003030086,0.00018737164,0.039403334,0.00041669843,0.000090778005,0.0008947018,0.00063412706,0.06654903,0.07875742,0.01712944,0.01118978,0.7844444],"study_design_scores_gemma":[0.000035607907,0.00047617778,0.0255119,0.00011065728,0.00008019929,0.0025662938,0.0005251836,0.81018394,0.0939723,0.033842605,0.032627158,0.00006800683],"about_ca_topic_score_codex":0.0010738218,"about_ca_topic_score_gemma":0.00087637873,"teacher_disagreement_score":0.0020265838,"about_ca_system_score_codex":0.00042618858,"about_ca_system_score_gemma":0.00040086376,"threshold_uncertainty_score":0.006779611},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08461565,0.001006015,0.9067271,0.00024243248,0.000117100615,0.00012105353,0.00010398986,0.0040262016,0.0030405237],"genre_scores_gemma":[0.7455907,0.00020504805,0.25050136,0.0002688612,0.00012307584,0.00014280697,0.00038321523,0.00018373293,0.0026011993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880755,0.00049638975,0.000049312315,0.00020313873,0.00031284388,0.00013090191],"domain_scores_gemma":[0.9952252,0.0029863028,0.00020421314,0.0007644454,0.00067319005,0.00014673389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044825803,0.0009999989,0.0015345212,0.0009825573,0.00043220815,0.0005783629,0.0015603735,0.0011889708,0.0019807958],"category_scores_gemma":[0.008684257,0.000552247,0.00056257425,0.00061484333,0.0006363167,0.0014763972,0.0014586458,0.0018272976,0.00093430316],"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":[0.0004058625,0.00025888614,0.0024740458,0.00011474191,0.00011842655,0.00006834122,0.00007367008,0.54637384,0.006873683,0.0044959383,0.005636315,0.43310624],"study_design_scores_gemma":[0.000013045377,0.00004190076,0.00020552709,0.000004342539,0.000007897908,0.000021703814,0.0000044104486,0.9947404,0.0019809837,0.0024489881,0.0005265285,0.0000041593344],"about_ca_topic_score_codex":0.0017253918,"about_ca_topic_score_gemma":0.0018874365,"teacher_disagreement_score":0.0044825803,"about_ca_system_score_codex":0.0005771008,"about_ca_system_score_gemma":0.00063737406,"threshold_uncertainty_score":0.023706436},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.099902555,0.0006710763,0.8941203,0.00018783548,0.00008231185,0.00006610954,0.00063068455,0.0036427188,0.0006964587],"genre_scores_gemma":[0.43774152,0.00053383404,0.55479884,0.00005495329,0.000164428,0.00008436418,0.0039239763,0.00044040452,0.0022577103],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987779,0.00011702871,0.000117984484,0.0002626189,0.0005826767,0.00014176201],"domain_scores_gemma":[0.99768627,0.0009336562,0.00037708983,0.0003735253,0.000541363,0.000087988745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010675474,0.0011605014,0.0019894312,0.002862326,0.0005907859,0.0017573937,0.00243974,0.0010723098,0.0009547445],"category_scores_gemma":[0.0044479193,0.0006232867,0.0012860603,0.004072537,0.00057386706,0.0017527296,0.0017679527,0.0011895201,0.0009821487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006658121,0.0002666663,0.026946839,0.0003484744,0.00029783518,0.00080423633,0.00027899176,0.2361158,0.033665445,0.0065769916,0.00886354,0.6851694],"study_design_scores_gemma":[0.0000140197735,0.000061076054,0.0022787384,0.000010938351,0.000047152906,0.00034661355,0.00012399392,0.9787555,0.009472815,0.0066909804,0.0021845305,0.0000137289535],"about_ca_topic_score_codex":0.003790121,"about_ca_topic_score_gemma":0.0045818193,"teacher_disagreement_score":0.003790121,"about_ca_system_score_codex":0.00049684325,"about_ca_system_score_gemma":0.00084469107,"threshold_uncertainty_score":0.0075361133},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010636645,0.0025669946,0.48287663,0.0016285898,0.0013390743,0.00090568763,0.049972683,0.38632265,0.063751094],"genre_scores_gemma":[0.04791506,0.0012889901,0.69727033,0.001113069,0.00029649684,0.00070285593,0.10391111,0.02131018,0.12619185],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989479,0.00018526944,0.00009460517,0.00035064275,0.00032094316,0.00010067096],"domain_scores_gemma":[0.9986082,0.0004398103,0.00006137993,0.0005057648,0.00029321873,0.000091643014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013783829,0.0018895423,0.0013443129,0.0022907865,0.0009891923,0.002176925,0.0024035652,0.0012574232,0.09064579],"category_scores_gemma":[0.004948974,0.0011460083,0.0015629126,0.0025637078,0.0003712518,0.002583493,0.0020700947,0.0016845883,0.0892672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034306024,0.00014628975,0.0010791437,0.00051474205,0.00014444924,0.00010211248,0.00007119136,0.0055920915,0.002137493,0.004565449,0.48456064,0.50074345],"study_design_scores_gemma":[0.0003209695,0.0002154386,0.0014108444,0.00027243214,0.00015831077,0.0006178521,0.00021814823,0.26942536,0.021255411,0.059374064,0.6466421,0.00008907172],"about_ca_topic_score_codex":0.0024654076,"about_ca_topic_score_gemma":0.006771201,"teacher_disagreement_score":0.09064579,"about_ca_system_score_codex":0.00046160695,"about_ca_system_score_gemma":0.0021119295,"threshold_uncertainty_score":0},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018245451,0.00017275623,0.9937697,0.00006594123,0.00009258375,0.00004107968,0.00009828411,0.0030284754,0.0009064944],"genre_scores_gemma":[0.018689817,0.00025266502,0.97512937,0.00009638531,0.00006648003,0.00017261558,0.00048123836,0.0005876092,0.0045237895],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991485,0.00012570046,0.00003746549,0.00025183443,0.00038341654,0.000053082233],"domain_scores_gemma":[0.9994708,0.00011863348,0.00003516129,0.000107636646,0.00023757842,0.000030219544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007792235,0.001021489,0.0013343157,0.0013147703,0.0011029021,0.0013911898,0.0023547,0.0014728465,0.0036231447],"category_scores_gemma":[0.0017498054,0.00075438624,0.00091708015,0.0020539742,0.00070364913,0.001560919,0.002013174,0.0012933563,0.004315071],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025500957,0.000082132996,0.00092000247,0.00024833446,0.00018489866,0.000094468254,0.00021468224,0.0822016,0.025749639,0.021372186,0.043032397,0.82564473],"study_design_scores_gemma":[0.00006274068,0.0000647776,0.00069770025,0.000030327896,0.000048200825,0.0002211553,0.000072835166,0.89785874,0.020540392,0.030018013,0.05031822,0.00006689342],"about_ca_topic_score_codex":0.0042370763,"about_ca_topic_score_gemma":0.005467983,"teacher_disagreement_score":0.0042370763,"about_ca_system_score_codex":0.0005287913,"about_ca_system_score_gemma":0.001313809,"threshold_uncertainty_score":0.012120664},"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":"software","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068453033,0.00027555006,0.48685795,0.0004557501,0.00013735902,0.00037888877,0.008963561,0.49175507,0.004330605],"genre_scores_gemma":[0.16038838,0.0010475504,0.7582196,0.0011977501,0.00027028588,0.002297889,0.038534116,0.023659687,0.014384862],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887127,0.00020051259,0.00009682683,0.00024072986,0.00050807925,0.00008255856],"domain_scores_gemma":[0.9956696,0.0014603792,0.0002791436,0.0009814822,0.0010681938,0.0005411087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001941413,0.002373132,0.0014082164,0.0042527732,0.00082976156,0.0023854054,0.0029740357,0.0014157911,0.023998545],"category_scores_gemma":[0.005068665,0.0006660142,0.0010165321,0.0022967893,0.0004238304,0.003280662,0.0039603286,0.0019934466,0.007829001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021290225,0.001015603,0.008395466,0.0014944166,0.0005528456,0.0021582788,0.0017748397,0.014055061,0.060851082,0.008555119,0.38786265,0.5111556],"study_design_scores_gemma":[0.0005365873,0.00045915594,0.01067982,0.00032997268,0.00017115152,0.0014395774,0.00062204694,0.6106759,0.0675819,0.026168156,0.28077132,0.0005643189],"about_ca_topic_score_codex":0.003397986,"about_ca_topic_score_gemma":0.004231732,"teacher_disagreement_score":0.023998545,"about_ca_system_score_codex":0.0005243809,"about_ca_system_score_gemma":0.0012671266,"threshold_uncertainty_score":0.080283165},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006363909,0.00033380798,0.99203473,0.00009928901,0.00003500963,0.000043051987,0.000058200967,0.0004057996,0.00062624173],"genre_scores_gemma":[0.37621677,0.0010253618,0.61779416,0.00012238845,0.00023157104,0.00025406608,0.00057304004,0.00019589017,0.0035868105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984579,0.0006044456,0.000076888784,0.0003397703,0.00041353202,0.00010754315],"domain_scores_gemma":[0.99784434,0.001273181,0.0003116341,0.00018462143,0.0003051173,0.00008113947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023107359,0.0012868666,0.0014406417,0.0025298765,0.0011405791,0.0015371485,0.0014414281,0.0013162023,0.0007510168],"category_scores_gemma":[0.006055401,0.00088608515,0.0015904314,0.002149123,0.0011430422,0.0018066501,0.0018001077,0.0012333044,0.00060676323],"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":[0.00010302285,0.00005684983,0.0021118838,0.00013215082,0.00016781516,0.000115429204,0.00032713445,0.88251,0.0016334603,0.018900665,0.0017891444,0.092152454],"study_design_scores_gemma":[0.000003433438,0.000009975398,0.00032023955,0.000009571579,0.000009972773,0.000021384136,0.00002856502,0.98513687,0.00047431426,0.013338513,0.00063108787,0.000016012955],"about_ca_topic_score_codex":0.021106351,"about_ca_topic_score_gemma":0.012672016,"teacher_disagreement_score":0.021106351,"about_ca_system_score_codex":0.0014723571,"about_ca_system_score_gemma":0.00160278,"threshold_uncertainty_score":0.041967034},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.083183445,0.00053750153,0.91445106,0.00009051204,0.00008415547,0.000060234353,0.00006354279,0.00060470315,0.00092478376],"genre_scores_gemma":[0.8122352,0.00030496728,0.18547702,0.00009634543,0.00010347916,0.00007633357,0.00029357214,0.00006208574,0.001351034],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990012,0.00020896061,0.00007307335,0.00027484068,0.0003266832,0.000115233066],"domain_scores_gemma":[0.99819535,0.0004786151,0.00019888603,0.00036699892,0.00067138317,0.000088891786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019802556,0.00078509643,0.0012196022,0.00093444076,0.0005624056,0.0006181028,0.0008564505,0.00066006265,0.00059687014],"category_scores_gemma":[0.004055351,0.00030971807,0.0007616869,0.0009312162,0.00039421147,0.0013724399,0.0014141842,0.00088832027,0.00035622664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029829727,0.00021414785,0.0072503868,0.000059270467,0.00020705389,0.00013633008,0.00027408949,0.2931241,0.039772343,0.0036533838,0.0016964101,0.6533142],"study_design_scores_gemma":[0.000003925999,0.00010779255,0.0017473372,0.0000063332773,0.000033351327,0.00008688005,0.000031480424,0.9858415,0.008997945,0.0023554727,0.000767371,0.000020741278],"about_ca_topic_score_codex":0.0013582478,"about_ca_topic_score_gemma":0.0016677346,"teacher_disagreement_score":0.0019802556,"about_ca_system_score_codex":0.00040487768,"about_ca_system_score_gemma":0.0004413339,"threshold_uncertainty_score":0.010472715},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20100644,0.0010040287,0.7886424,0.0005863898,0.00014032157,0.00012960297,0.0010917698,0.004796996,0.0026020692],"genre_scores_gemma":[0.6786255,0.00046227968,0.31458795,0.00021678745,0.00022337613,0.00015031114,0.0024845633,0.00021036503,0.0030388846],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994203,0.00013973562,0.00005573836,0.00018526922,0.00015097688,0.000048043607],"domain_scores_gemma":[0.9979103,0.0012270296,0.00023714981,0.00029925755,0.0002579887,0.00006815496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002320973,0.0009838847,0.0011504578,0.003328446,0.00058966276,0.001120902,0.001185578,0.00087725424,0.0019073487],"category_scores_gemma":[0.006955237,0.0003759479,0.00096369284,0.0015838909,0.00038415327,0.0018110865,0.0009869777,0.00084030227,0.00059225026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012177756,0.00058646733,0.040830426,0.0002466546,0.0006623173,0.00030015846,0.00019301315,0.19416258,0.0151632065,0.009860602,0.0073406682,0.729436],"study_design_scores_gemma":[0.000023272063,0.00013850612,0.0070008147,0.000013320467,0.000038332717,0.00017534415,0.000027716285,0.9822447,0.003983231,0.0045169736,0.0018125701,0.000025273997],"about_ca_topic_score_codex":0.0025583727,"about_ca_topic_score_gemma":0.003305332,"teacher_disagreement_score":0.003328446,"about_ca_system_score_codex":0.00036666892,"about_ca_system_score_gemma":0.0006340342,"threshold_uncertainty_score":0.012274623},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068528834,0.005822618,0.98496544,0.0005667657,0.00022056715,0.00001712604,0.00012441466,0.00030488646,0.0011252309],"genre_scores_gemma":[0.32640985,0.014392668,0.6464239,0.000590406,0.0022043185,0.00013038357,0.0010026086,0.00035481906,0.008491037],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991371,0.00015486627,0.000071225004,0.0003375401,0.00025313903,0.000046196117],"domain_scores_gemma":[0.9977933,0.0013601384,0.0001933164,0.00030340286,0.0002606425,0.00008930113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001246061,0.0009475897,0.0019473638,0.0012678818,0.0003426405,0.0027849518,0.0023889258,0.0015233343,0.0020959927],"category_scores_gemma":[0.005087364,0.000541984,0.0010499096,0.0028110943,0.0014215554,0.004196559,0.0014340011,0.0020066346,0.00089434464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020531424,0.000115329116,0.004373999,0.00092276454,0.00027492558,0.00021241141,0.0002806412,0.036373597,0.022288332,0.09405043,0.009865649,0.8310365],"study_design_scores_gemma":[0.000023437775,0.00019573743,0.0031812345,0.00012390758,0.0000941438,0.0006796485,0.00011763206,0.6787615,0.0075827423,0.2945319,0.014618043,0.0000899911],"about_ca_topic_score_codex":0.0005775952,"about_ca_topic_score_gemma":0.00049972674,"teacher_disagreement_score":0.0027849518,"about_ca_system_score_codex":0.00039941524,"about_ca_system_score_gemma":0.0004101064,"threshold_uncertainty_score":0.007011831},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007612833,0.00015856551,0.9895968,0.000098966826,0.000036544712,0.000021552272,0.00015856234,0.0020506473,0.0002655027],"genre_scores_gemma":[0.3898743,0.00030568914,0.60390997,0.00016641077,0.00019340953,0.00015930264,0.0020770652,0.00069038064,0.0026235373],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99454665,0.001283432,0.00047477934,0.0011755767,0.0021634581,0.00035600847],"domain_scores_gemma":[0.98253804,0.008572688,0.0009880649,0.0056078704,0.0020338097,0.00025950145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042461352,0.00087596336,0.0022213357,0.0018979984,0.00056501117,0.0018338583,0.004229306,0.0014599742,0.0016050608],"category_scores_gemma":[0.023201171,0.0007204377,0.0013959436,0.0027076064,0.0012858766,0.003969803,0.0028173712,0.0031513898,0.0010768648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007256525,0.00025884103,0.010773917,0.00031858342,0.00027970874,0.0002650168,0.00020269195,0.15669106,0.022350477,0.04117403,0.01004056,0.7569194],"study_design_scores_gemma":[0.000016041422,0.00007425406,0.0010281041,0.000014820773,0.000024105291,0.00034337444,0.000025009043,0.91150117,0.007960887,0.07698382,0.0019935574,0.000034855053],"about_ca_topic_score_codex":0.0012657733,"about_ca_topic_score_gemma":0.0012599102,"teacher_disagreement_score":0.0042461352,"about_ca_system_score_codex":0.0007829496,"about_ca_system_score_gemma":0.0010201032,"threshold_uncertainty_score":0.02245593},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014290599,0.00050923007,0.99673235,0.00019358603,0.0000764673,0.000045860896,0.00015735686,0.00043041422,0.000425723],"genre_scores_gemma":[0.08027495,0.00095668394,0.90806407,0.00034222487,0.0005390894,0.0005998511,0.002590728,0.00047390602,0.006158482],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9851386,0.00833087,0.00086725963,0.0028852823,0.0021102314,0.0006677471],"domain_scores_gemma":[0.9539431,0.031542756,0.0015627737,0.008216662,0.004245366,0.0004894441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023978714,0.0018299512,0.0059728036,0.003247056,0.0023218102,0.0045788256,0.015518807,0.0053316667,0.00833361],"category_scores_gemma":[0.051050022,0.0023144116,0.0051297387,0.0062013627,0.0019306,0.0067350557,0.004864291,0.009184911,0.004639033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050509226,0.0005143141,0.0047486695,0.00058252976,0.001149832,0.00030499636,0.0007193303,0.20153895,0.00082685717,0.13443796,0.018500183,0.6361714],"study_design_scores_gemma":[0.00003669563,0.000038473267,0.0005986816,0.00006456075,0.000095539806,0.0001195671,0.0000554769,0.81643593,0.00039335823,0.17926516,0.0028518578,0.000044689357],"about_ca_topic_score_codex":0.005950589,"about_ca_topic_score_gemma":0.007823521,"teacher_disagreement_score":0.023978714,"about_ca_system_score_codex":0.0017616441,"about_ca_system_score_gemma":0.0020430554,"threshold_uncertainty_score":0.12681305},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025637406,0.00022378279,0.99533904,0.00003634458,0.00007591654,0.00003339377,0.00013415352,0.0010508484,0.00054280466],"genre_scores_gemma":[0.020231633,0.00033248813,0.97460926,0.0000699688,0.00010390591,0.00009498729,0.00044762314,0.0001837027,0.0039263996],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999438,0.000036756115,0.000031243337,0.00014121483,0.00031409724,0.000038696664],"domain_scores_gemma":[0.9994307,0.00013847573,0.000045564702,0.00008496929,0.0002648989,0.000035424087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042071176,0.0008913602,0.001072072,0.001771221,0.0003713428,0.0010684417,0.0013897867,0.0010544409,0.0042362553],"category_scores_gemma":[0.0010865683,0.00058879954,0.0007862546,0.0017865007,0.0004606237,0.0012472348,0.0010388325,0.00096829596,0.0032500112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002048721,0.000101117446,0.00054149283,0.00025163963,0.0000879855,0.00012501229,0.00007059525,0.0062921736,0.256898,0.002240815,0.0051396745,0.7280467],"study_design_scores_gemma":[0.000063023464,0.00017614452,0.004710041,0.000042672207,0.00015015804,0.0012745954,0.00006113391,0.73688656,0.21993269,0.0038900124,0.032688584,0.00012451127],"about_ca_topic_score_codex":0.0020187506,"about_ca_topic_score_gemma":0.0043492797,"teacher_disagreement_score":0.0042362553,"about_ca_system_score_codex":0.00033681438,"about_ca_system_score_gemma":0.0006121989,"threshold_uncertainty_score":0.01417166},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010392549,0.00017481482,0.98788005,0.00017500213,0.000021250586,0.00004363636,0.00012232157,0.00076231733,0.00042801967],"genre_scores_gemma":[0.29377708,0.00059018313,0.70210385,0.00014265523,0.0001670969,0.00015436746,0.0012594546,0.00030078876,0.0015044595],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973506,0.00068473874,0.00018833818,0.0005879933,0.00093777856,0.00025054548],"domain_scores_gemma":[0.99142647,0.0042269477,0.0014128032,0.0013513501,0.0013263422,0.0002561326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021140883,0.0015764349,0.0013997456,0.004746236,0.0010122061,0.002530348,0.0018128444,0.0017660764,0.0019324761],"category_scores_gemma":[0.012871523,0.0006004442,0.001792865,0.0043348223,0.0017845961,0.003541418,0.003155752,0.0023627945,0.0009669127],"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":[0.0005003942,0.00022411156,0.018194469,0.00044591716,0.00027632416,0.0007327285,0.0010253508,0.31682467,0.017405009,0.07661588,0.009059815,0.5586954],"study_design_scores_gemma":[0.000029333822,0.00006625292,0.0019615681,0.000033086093,0.000039474744,0.0003139067,0.00025616286,0.8983876,0.00628761,0.087935604,0.0046266797,0.00006279619],"about_ca_topic_score_codex":0.0036431886,"about_ca_topic_score_gemma":0.003289657,"teacher_disagreement_score":0.004746236,"about_ca_system_score_codex":0.00081486616,"about_ca_system_score_gemma":0.0014579168,"threshold_uncertainty_score":0.01118052},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05064686,0.0002477898,0.94271415,0.0014910896,0.0000838129,0.00010980862,0.0002275359,0.00044723856,0.004031738],"genre_scores_gemma":[0.87244326,0.00047922312,0.1184444,0.00058162014,0.00016999766,0.00020473661,0.00040184925,0.00016247702,0.0071123904],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99303883,0.003288177,0.0002747021,0.0013344686,0.0013355552,0.00072817283],"domain_scores_gemma":[0.95523345,0.032128748,0.0032186715,0.006508198,0.0015470574,0.001363771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0077883364,0.0015130687,0.001661118,0.0012125139,0.001979616,0.0025927322,0.0026335947,0.0026122823,0.0024748493],"category_scores_gemma":[0.03210144,0.0008683794,0.0010706006,0.0010797689,0.00495191,0.0058769584,0.007434482,0.004467061,0.0006685035],"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":[0.0004957008,0.00009437492,0.0021679502,0.00012338145,0.00015431612,0.00048146173,0.00064327917,0.6060884,0.0036330235,0.35509986,0.0048852484,0.026133085],"study_design_scores_gemma":[0.000027110485,0.000047319325,0.00024932026,0.000018768678,0.000020205218,0.00010874071,0.00009645743,0.79200023,0.0014518452,0.20476301,0.0011876059,0.000029395615],"about_ca_topic_score_codex":0.0020139646,"about_ca_topic_score_gemma":0.0017360193,"teacher_disagreement_score":0.0077883364,"about_ca_system_score_codex":0.0021555566,"about_ca_system_score_gemma":0.0013364014,"threshold_uncertainty_score":0.041189134},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29432434,0.00082950125,0.48431545,0.0015459114,0.0005912093,0.01330228,0.034633268,0.13525344,0.035204582],"genre_scores_gemma":[0.68647206,0.00041286318,0.23269568,0.000826867,0.00021772696,0.008185375,0.03976965,0.00091975456,0.030500047],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981408,0.0003757708,0.0002889257,0.00034447102,0.0007450898,0.0001049801],"domain_scores_gemma":[0.9970873,0.0003457975,0.0001914304,0.00023616823,0.0017028876,0.00043634538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026985481,0.0008801105,0.0016621677,0.0023566228,0.00084967783,0.0009979574,0.0011432568,0.0010600772,0.010451856],"category_scores_gemma":[0.004469301,0.00025099624,0.0003282574,0.0008700788,0.0002755183,0.0012537087,0.0014639826,0.0005258519,0.0070138676],"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":[0.006059045,0.0031652546,0.06856037,0.0008050232,0.00034824226,0.0009152746,0.0006340853,0.013134222,0.044340562,0.0036848614,0.1572829,0.7010702],"study_design_scores_gemma":[0.0017106321,0.0053016264,0.12639102,0.0002781383,0.0004890901,0.0023381223,0.0006107887,0.6858143,0.057594214,0.00642896,0.11247269,0.0005704372],"about_ca_topic_score_codex":0.004077898,"about_ca_topic_score_gemma":0.003403334,"teacher_disagreement_score":0.010451856,"about_ca_system_score_codex":0.0006923982,"about_ca_system_score_gemma":0.0012551066,"threshold_uncertainty_score":0.03496492},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10576235,0.00038598463,0.88219035,0.00022203653,0.00018774683,0.00009998898,0.00037283904,0.009981121,0.0007976634],"genre_scores_gemma":[0.3245183,0.00028544138,0.67134166,0.00011293469,0.00007054223,0.00007802811,0.0011794806,0.0010363816,0.0013772361],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982887,0.00029329132,0.00020235624,0.0003463286,0.00071640336,0.00015296706],"domain_scores_gemma":[0.99011886,0.0037374333,0.000705548,0.0026308044,0.0026070422,0.0002002576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002751601,0.0011653373,0.0010200313,0.003302047,0.00068248465,0.0014266899,0.0013634748,0.0011909311,0.0020627866],"category_scores_gemma":[0.015046954,0.00039107035,0.0009908307,0.0028508448,0.00044697596,0.0012315067,0.0010702682,0.0016160312,0.001647918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011016667,0.00044077073,0.02072673,0.0003633379,0.0002945657,0.00049512024,0.00078561186,0.05100116,0.27671757,0.004286423,0.005952954,0.637834],"study_design_scores_gemma":[0.00007464161,0.00033849117,0.025245074,0.000041069274,0.0001488734,0.0009720457,0.00032116627,0.68171513,0.26538378,0.008351277,0.017271882,0.00013655581],"about_ca_topic_score_codex":0.0027642972,"about_ca_topic_score_gemma":0.003001221,"teacher_disagreement_score":0.003302047,"about_ca_system_score_codex":0.00038974112,"about_ca_system_score_gemma":0.00097373436,"threshold_uncertainty_score":0.014552057},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004708734,0.00017936366,0.99402857,0.000109996494,0.0000560613,0.000014709144,0.000034736964,0.0004702724,0.00039752017],"genre_scores_gemma":[0.24223998,0.00056952203,0.7540846,0.00016761875,0.0003792851,0.000075381125,0.0002923663,0.00042789395,0.0017633945],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952657,0.0009418246,0.00027719527,0.0010575865,0.0022530775,0.00020469644],"domain_scores_gemma":[0.99363977,0.003260902,0.0007336725,0.000965668,0.0011930529,0.00020698603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031147166,0.0010372145,0.0025657243,0.0039772345,0.000766555,0.0030997174,0.0026977935,0.0014979993,0.0011392859],"category_scores_gemma":[0.012719702,0.0005242459,0.0014711322,0.0047005,0.0016742256,0.0033174765,0.0028537551,0.0028271421,0.0005531117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039299758,0.0002612394,0.010375918,0.00032923598,0.0005827174,0.00036982808,0.00053552666,0.058870398,0.024925314,0.06846079,0.0052601304,0.8296359],"study_design_scores_gemma":[0.000036917274,0.00013030757,0.0029047697,0.000032486612,0.00014254278,0.0007005958,0.00013272392,0.894026,0.011065549,0.0846141,0.0061259903,0.00008803331],"about_ca_topic_score_codex":0.0015561567,"about_ca_topic_score_gemma":0.0012117801,"teacher_disagreement_score":0.0039772345,"about_ca_system_score_codex":0.00056541234,"about_ca_system_score_gemma":0.0010345096,"threshold_uncertainty_score":0.0164724},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9990119,0.00003744615,0.00036808723,0.00006752358,0.000023617262,0.000030655483,0.000022463864,0.000009896027,0.00042854174],"genre_scores_gemma":[0.9986436,0.000038032445,0.0006456319,0.00010916068,0.000011001091,0.000014014043,0.00003659586,0.0000046182213,0.00049741747],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99861217,0.00051414117,0.00014629829,0.00022210983,0.00037881624,0.00012649283],"domain_scores_gemma":[0.990457,0.0054017166,0.002029681,0.00090409024,0.00078319124,0.0004244184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030388462,0.0003289902,0.00040726358,0.0002418516,0.00033633033,0.0005847483,0.00032389318,0.00088454183,0.0023968676],"category_scores_gemma":[0.016831178,0.0002527729,0.00018027656,0.00010797674,0.0007063733,0.00052078353,0.00039906547,0.00050656806,0.00051609514],"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":[0.058155116,0.0032602018,0.5795478,0.0005330761,0.00025886061,0.0018181353,0.009112672,0.00048133585,0.26826644,0.0017997324,0.0015310876,0.0752356],"study_design_scores_gemma":[0.00082469155,0.04635957,0.83192825,0.0001924702,0.00065249327,0.005315768,0.00784387,0.008196475,0.09108593,0.0026277495,0.004838257,0.00013447995],"about_ca_topic_score_codex":0.00088133453,"about_ca_topic_score_gemma":0.0010053272,"teacher_disagreement_score":0.0030388462,"about_ca_system_score_codex":0.00019506257,"about_ca_system_score_gemma":0.00030311325,"threshold_uncertainty_score":0.0160712},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17404494,0.00021985313,0.82246655,0.00021353345,0.00004174505,0.00008334337,0.0003352582,0.0013829911,0.001211773],"genre_scores_gemma":[0.9092232,0.0001613007,0.08895229,0.00003897555,0.00006191351,0.000060060735,0.0004255554,0.0000765736,0.0010002436],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986904,0.00014661174,0.000091530106,0.00037198138,0.00057008787,0.0001293057],"domain_scores_gemma":[0.9946661,0.0020235192,0.0015275648,0.0005037792,0.0010045165,0.00027447133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007164641,0.0006182934,0.00091568794,0.0034004287,0.00033435444,0.001007237,0.0012265332,0.0007642767,0.0008522913],"category_scores_gemma":[0.0058874562,0.00024109252,0.0006574287,0.0017374958,0.00063851924,0.001715921,0.00069302006,0.0007818029,0.00036974892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010073531,0.0006476806,0.12073975,0.00029874293,0.00036151244,0.0032661124,0.0009003542,0.2507027,0.09141319,0.018333105,0.0037745223,0.50855494],"study_design_scores_gemma":[0.000007921359,0.00007194917,0.014086144,0.000010016334,0.000033567565,0.0007258678,0.00006184816,0.96964574,0.009942355,0.004471475,0.0009115429,0.000031622225],"about_ca_topic_score_codex":0.004409322,"about_ca_topic_score_gemma":0.0049680946,"teacher_disagreement_score":0.004409322,"about_ca_system_score_codex":0.0006407483,"about_ca_system_score_gemma":0.00057769817,"threshold_uncertainty_score":0.008767307},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037212644,0.0038425217,0.98521173,0.0035884602,0.00008220869,0.00035943065,0.000073208976,0.00078838493,0.0023328585],"genre_scores_gemma":[0.061924823,0.0040063416,0.9315762,0.0006286606,0.00034944015,0.00072795415,0.00008091389,0.00017378126,0.000531873],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.8877077,0.08314134,0.005930282,0.006381381,0.015701579,0.0011376425],"domain_scores_gemma":[0.77244353,0.19137505,0.00925232,0.009985032,0.01603376,0.0009102462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.11340559,0.0041767843,0.0041767186,0.014304318,0.0018855769,0.012126994,0.005757609,0.007310169,0.0017428735],"category_scores_gemma":[0.2555363,0.0030103778,0.0028948986,0.010568977,0.013701027,0.011448758,0.0060804435,0.009121822,0.001763506],"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":[0.00025138594,0.000450407,0.015969733,0.0029792497,0.0011201263,0.0007284187,0.0060654567,0.0908015,0.0042966376,0.3208745,0.013745005,0.5427175],"study_design_scores_gemma":[0.00012741347,0.00047962007,0.008704486,0.0013006619,0.00019386332,0.0012431195,0.0010654178,0.22772264,0.0049736896,0.7294648,0.024182796,0.0005415359],"about_ca_topic_score_codex":0.0035330865,"about_ca_topic_score_gemma":0.0020412314,"teacher_disagreement_score":0.11340559,"about_ca_system_score_codex":0.0036698182,"about_ca_system_score_gemma":0.005113613,"threshold_uncertainty_score":0.59975326},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.499275,0.002079367,0.49027193,0.00021739904,0.00032845704,0.00017947142,0.00067406485,0.0014740119,0.0055002323],"genre_scores_gemma":[0.81646293,0.0016796321,0.17719938,0.0000714725,0.00011607983,0.000058475467,0.0009867939,0.00006128322,0.0033639208],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998392,0.000020298205,0.000009097392,0.00003689479,0.00007287843,0.00002176961],"domain_scores_gemma":[0.99966717,0.000070294045,0.00006974522,0.000021895152,0.00013795732,0.00003290116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018127545,0.00044345303,0.00039088845,0.0014340797,0.00021387756,0.00042853047,0.00025255655,0.00034389307,0.0008928618],"category_scores_gemma":[0.0009816067,0.00013964898,0.0001993657,0.00054094195,0.00019464918,0.0005695117,0.00027130358,0.00040093507,0.00041922508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075189915,0.00015650694,0.00925551,0.0003177764,0.00006772303,0.0005023749,0.00020130837,0.007150355,0.49661288,0.0011223202,0.0018397268,0.4820217],"study_design_scores_gemma":[0.0000456896,0.0011599499,0.11434995,0.00015433435,0.0001380109,0.0019718527,0.00044063615,0.48075068,0.38811862,0.0027002073,0.0100690285,0.00010099858],"about_ca_topic_score_codex":0.0017566484,"about_ca_topic_score_gemma":0.002547,"teacher_disagreement_score":0.0017566484,"about_ca_system_score_codex":0.00019221577,"about_ca_system_score_gemma":0.00021043817,"threshold_uncertainty_score":0.0034928918},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20251517,0.0056019793,0.7418029,0.008226775,0.0013693544,0.0005829363,0.0011063949,0.007088177,0.03170633],"genre_scores_gemma":[0.935761,0.002244278,0.05040542,0.00032425032,0.00032657865,0.00015481633,0.0006558403,0.00011632367,0.010011388],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99844104,0.0005924478,0.00010533213,0.00012625592,0.00062550313,0.00010943412],"domain_scores_gemma":[0.99335396,0.0026451251,0.0009110004,0.0012035066,0.0015560155,0.0003304652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019696483,0.0007341313,0.0004862174,0.0010838747,0.00091987394,0.0010758204,0.0012154614,0.0012952369,0.00829796],"category_scores_gemma":[0.008626955,0.00022360818,0.0003817995,0.0005952479,0.0005904461,0.0017211583,0.0013573213,0.0007881878,0.0016496786],"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":[0.002636751,0.00032934456,0.043997355,0.0011474454,0.00022258102,0.0015250117,0.0008441192,0.04259842,0.070796035,0.014031251,0.032485742,0.789386],"study_design_scores_gemma":[0.00055621855,0.0065567913,0.05166302,0.0017108948,0.000860109,0.005791405,0.0029779577,0.58034533,0.12420393,0.079996526,0.14508599,0.0002518403],"about_ca_topic_score_codex":0.0012901014,"about_ca_topic_score_gemma":0.0012382806,"teacher_disagreement_score":0.00829796,"about_ca_system_score_codex":0.00052635744,"about_ca_system_score_gemma":0.0010751294,"threshold_uncertainty_score":0.027759433},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004079941,0.0004889453,0.9941673,0.0002933859,0.000022267606,0.000018519002,0.000050979615,0.00005262503,0.0008260486],"genre_scores_gemma":[0.5498307,0.0031481236,0.44047925,0.00046129394,0.0007770188,0.00024531808,0.00040679725,0.00012362603,0.0045277807],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998738,0.00043147797,0.00008676489,0.00024926584,0.0004236121,0.00007082185],"domain_scores_gemma":[0.99539185,0.0034359375,0.00030901516,0.00029193558,0.00048106207,0.0000902824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026507932,0.001154737,0.0023156758,0.0035110782,0.0006874921,0.0026189997,0.0019800758,0.0015765227,0.0015831408],"category_scores_gemma":[0.007992576,0.000829658,0.0018260761,0.0018952417,0.0022398236,0.0029190872,0.0017272555,0.0014775434,0.00035340895],"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":[0.00024045553,0.00024881156,0.0020678136,0.00043132156,0.0004991217,0.00033338324,0.00018475935,0.5374126,0.010510081,0.20696612,0.0036520618,0.2374535],"study_design_scores_gemma":[0.0000079788815,0.00006038048,0.0004339934,0.000017728262,0.000068137386,0.00010945031,0.000018821886,0.9279597,0.001117835,0.06970354,0.00047805655,0.000024471861],"about_ca_topic_score_codex":0.0018495788,"about_ca_topic_score_gemma":0.0013821819,"teacher_disagreement_score":0.0035110782,"about_ca_system_score_codex":0.0013539322,"about_ca_system_score_gemma":0.0011321504,"threshold_uncertainty_score":0.014018953},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012772466,0.00014165275,0.9862163,0.00007807491,0.000023218467,0.000028859055,0.00005121626,0.00042062585,0.00026757354],"genre_scores_gemma":[0.39224547,0.0002706156,0.60477775,0.00018419109,0.00015025168,0.00020747252,0.0007147711,0.00021223266,0.0012372386],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99646086,0.0011812387,0.00024991125,0.0006896215,0.0011254859,0.00029290316],"domain_scores_gemma":[0.9892005,0.0051118275,0.0008953003,0.0023179287,0.0021163633,0.00035813462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043975944,0.0010854289,0.0020832901,0.0016426342,0.0008993128,0.0013556444,0.0015561507,0.0012120706,0.0010287865],"category_scores_gemma":[0.018228186,0.0006535212,0.0013829733,0.001465161,0.00093878753,0.002345606,0.002714256,0.0020971568,0.000645107],"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":[0.0002942238,0.00018156707,0.006641787,0.00013634119,0.0003136851,0.00018578739,0.00037063906,0.58420706,0.018793697,0.02997193,0.0039634397,0.35493988],"study_design_scores_gemma":[0.0000077283385,0.000049522183,0.0004534175,0.000008404496,0.000017241828,0.000062599225,0.000024911318,0.98184764,0.0036614893,0.012739029,0.0011139299,0.000014072384],"about_ca_topic_score_codex":0.0019138996,"about_ca_topic_score_gemma":0.0027330767,"teacher_disagreement_score":0.0043975944,"about_ca_system_score_codex":0.0006497186,"about_ca_system_score_gemma":0.0010463672,"threshold_uncertainty_score":0.023257017},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17880774,0.00079688703,0.8163911,0.00030268394,0.000119519835,0.00015997609,0.0005144753,0.0022260686,0.0006815177],"genre_scores_gemma":[0.55517185,0.00028481174,0.4421778,0.000076379445,0.00011777805,0.000071229806,0.0013898924,0.00013315096,0.0005769672],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970118,0.00033141146,0.00027554535,0.00066008046,0.0014110983,0.00031010868],"domain_scores_gemma":[0.99532133,0.0017746709,0.00088811555,0.00056169694,0.0012326773,0.00022158065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028867566,0.0011273442,0.0019967488,0.005049569,0.0010038768,0.0014584513,0.0022453205,0.0015496291,0.00072611443],"category_scores_gemma":[0.015083483,0.0003945634,0.0011281327,0.0041678906,0.00086502347,0.0019793164,0.0013331319,0.0013415228,0.00064596377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014451351,0.0006498828,0.076681964,0.0004957546,0.0004980443,0.0009299132,0.00058036577,0.076391704,0.050550185,0.0040931753,0.007899131,0.7797847],"study_design_scores_gemma":[0.00007340296,0.00025969636,0.01967377,0.000036263296,0.00019852888,0.00113597,0.00030882793,0.9387363,0.031053115,0.005598107,0.0028541496,0.00007188108],"about_ca_topic_score_codex":0.0038116921,"about_ca_topic_score_gemma":0.0034512188,"teacher_disagreement_score":0.005049569,"about_ca_system_score_codex":0.00037168915,"about_ca_system_score_gemma":0.0011030681,"threshold_uncertainty_score":0.015266836},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.117189996,0.0014107799,0.87090254,0.0005179625,0.00017257703,0.00024026899,0.00040902095,0.0017232104,0.007433624],"genre_scores_gemma":[0.739341,0.00041641164,0.2566504,0.00021484458,0.00019412054,0.00022345097,0.0012081788,0.00049649685,0.0012550624],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9671565,0.0134086525,0.0025210998,0.003497284,0.012503729,0.00091269705],"domain_scores_gemma":[0.8937322,0.060791496,0.0119796265,0.010373763,0.021309068,0.001813804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0296303,0.0020672616,0.002103505,0.0061747977,0.0015762307,0.004374473,0.0021432696,0.0020847656,0.0011549213],"category_scores_gemma":[0.123481035,0.00042748186,0.0011655516,0.0035903,0.0036875147,0.004551691,0.0043294686,0.0023054301,0.0006252841],"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":[0.0014425757,0.0005904784,0.07266627,0.00091027014,0.0007835301,0.00024915143,0.001112568,0.31405634,0.018737715,0.051041473,0.0061038095,0.5323059],"study_design_scores_gemma":[0.000071570685,0.0011785548,0.02091331,0.00026864218,0.00015551674,0.00048506077,0.00059971627,0.896088,0.032063786,0.044062886,0.0039256047,0.00018726476],"about_ca_topic_score_codex":0.0016876574,"about_ca_topic_score_gemma":0.0018372228,"teacher_disagreement_score":0.0296303,"about_ca_system_score_codex":0.0018697948,"about_ca_system_score_gemma":0.0019039288,"threshold_uncertainty_score":0.15670192},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1294395,0.00042728448,0.8677279,0.00015779401,0.00006133133,0.00006297309,0.0003118615,0.0007591787,0.0010522517],"genre_scores_gemma":[0.7604855,0.00020314258,0.23792781,0.000024575902,0.000037544676,0.000056800247,0.0004907434,0.000056273206,0.0007176651],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994276,0.00014566956,0.00004885754,0.000094641124,0.00022460453,0.000058629383],"domain_scores_gemma":[0.99854517,0.00062633253,0.00018126963,0.00011996008,0.0004793939,0.000047914225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010434963,0.000282501,0.0004373457,0.002462043,0.0003017123,0.00075029465,0.0004879539,0.00027608604,0.0010133736],"category_scores_gemma":[0.0031652988,0.00013875184,0.000342002,0.0022656012,0.0003582626,0.0007869206,0.0005689966,0.0003549828,0.00025247326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032331987,0.000109723136,0.018572338,0.00022473781,0.00012708834,0.00021185944,0.0007087337,0.17914644,0.0290845,0.018964084,0.0039804634,0.7485467],"study_design_scores_gemma":[0.000014226197,0.000057788504,0.009733633,0.000017629913,0.000017620565,0.00010665426,0.00020704453,0.973175,0.0061961166,0.0085515985,0.0018935819,0.00002908278],"about_ca_topic_score_codex":0.0048825787,"about_ca_topic_score_gemma":0.0023275276,"teacher_disagreement_score":0.0048825787,"about_ca_system_score_codex":0.00044820044,"about_ca_system_score_gemma":0.00048184858,"threshold_uncertainty_score":0.009708285},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28220937,0.005607798,0.70330375,0.00049532246,0.0005700357,0.00049617485,0.0014054164,0.0036614577,0.0022506586],"genre_scores_gemma":[0.7434167,0.000857094,0.2505944,0.00012198583,0.0005143128,0.00015346281,0.0017219987,0.00011728612,0.002502752],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99680316,0.0004327219,0.0002509916,0.0004818528,0.0017502089,0.0002811222],"domain_scores_gemma":[0.99354124,0.0022331397,0.00075423793,0.00054701284,0.0025875072,0.0003367767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028452196,0.0008333538,0.0022535352,0.010202398,0.00090600096,0.0017354211,0.002004243,0.0013179209,0.0015231292],"category_scores_gemma":[0.007502964,0.00028353633,0.0010854743,0.0054535507,0.0005672886,0.0021362344,0.0011062388,0.0010888459,0.00091429264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020550953,0.00094419916,0.023269515,0.00052862585,0.00042443073,0.000442796,0.00019160993,0.013643695,0.086364925,0.003531099,0.00883805,0.85976595],"study_design_scores_gemma":[0.00008970207,0.0006962908,0.016231786,0.000034033175,0.00024751364,0.0012193079,0.00017021228,0.9294191,0.04451414,0.003970868,0.0033153757,0.000091729344],"about_ca_topic_score_codex":0.0037207152,"about_ca_topic_score_gemma":0.00547128,"teacher_disagreement_score":0.010202398,"about_ca_system_score_codex":0.00093404367,"about_ca_system_score_gemma":0.0013671943,"threshold_uncertainty_score":0.015047133},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011807788,0.00012937083,0.98611546,0.00009448811,0.000033752294,0.000059187318,0.000065353386,0.0014593533,0.0002353165],"genre_scores_gemma":[0.11459669,0.0001118941,0.8835968,0.00005110067,0.000048869304,0.0001385368,0.00036485086,0.00013296271,0.0009583011],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902177,0.00012556472,0.00008780774,0.00024901776,0.0004184764,0.00009737977],"domain_scores_gemma":[0.9985575,0.00037478394,0.00021033442,0.00027962986,0.00048679838,0.000090966416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013055053,0.00076637644,0.0014474012,0.0014103639,0.00089996203,0.00080458797,0.0021129146,0.0007757994,0.0009387016],"category_scores_gemma":[0.003355194,0.0004477781,0.0010228953,0.002209613,0.0005183134,0.0015825084,0.0015033088,0.0010988011,0.0006802704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006102659,0.0002276578,0.004944066,0.00013429722,0.00020968681,0.00032008335,0.00022975187,0.22290176,0.02976382,0.0044151964,0.0051517584,0.73109174],"study_design_scores_gemma":[0.000041468767,0.000056497163,0.000524689,0.0000036280303,0.000018820223,0.00013114067,0.000028441005,0.98992175,0.0055910842,0.002277026,0.0013928502,0.0000125544875],"about_ca_topic_score_codex":0.0045262333,"about_ca_topic_score_gemma":0.0047947257,"teacher_disagreement_score":0.0045262333,"about_ca_system_score_codex":0.000540226,"about_ca_system_score_gemma":0.0013834343,"threshold_uncertainty_score":0.008999765},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22515802,0.0011097142,0.7697055,0.00013824973,0.00014066261,0.0000805111,0.00018265122,0.0014169521,0.0020677433],"genre_scores_gemma":[0.7725861,0.00059027656,0.22497799,0.00006954895,0.0001141784,0.00005337077,0.00023426412,0.00003841913,0.0013358054],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994947,0.00010769764,0.000032647054,0.000093499664,0.00023837072,0.000033064254],"domain_scores_gemma":[0.99921334,0.00021087639,0.00019729686,0.000057115412,0.00026903627,0.00005226969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004717345,0.00044172895,0.0005388475,0.0018077245,0.00020486054,0.00044386595,0.00040909453,0.00057949463,0.00044018833],"category_scores_gemma":[0.0013900462,0.0001915593,0.00026297718,0.00081059395,0.00021483652,0.00076808943,0.0003106641,0.00032466266,0.00019697643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071361114,0.00026881276,0.023235617,0.0003669469,0.00013007999,0.0004993505,0.00030944435,0.020516237,0.21634306,0.0020367778,0.0019632517,0.73361677],"study_design_scores_gemma":[0.00005652329,0.0009754821,0.05749664,0.00009184668,0.0001525905,0.00214138,0.00031091057,0.7935582,0.13563374,0.0023476868,0.0071467035,0.00008840006],"about_ca_topic_score_codex":0.0012231044,"about_ca_topic_score_gemma":0.0017964705,"teacher_disagreement_score":0.0018077245,"about_ca_system_score_codex":0.00025208888,"about_ca_system_score_gemma":0.0001968835,"threshold_uncertainty_score":0.002494812},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6128554,0.0013249096,0.37772253,0.0011191993,0.00013019424,0.00014907538,0.0020449033,0.0019668671,0.0026869688],"genre_scores_gemma":[0.83870775,0.0006391647,0.1554639,0.00017263588,0.00018844736,0.00008904442,0.003435644,0.0002063196,0.0010971318],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962114,0.00042632502,0.0005044695,0.00066596555,0.0019351871,0.00025665524],"domain_scores_gemma":[0.97268265,0.0121404035,0.005294499,0.004979481,0.004311825,0.00059104554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024793018,0.0007806382,0.0012203169,0.005686093,0.0010441093,0.002504597,0.0014119699,0.0009756576,0.0007559061],"category_scores_gemma":[0.02586422,0.0004243968,0.0009766672,0.006963037,0.0011595925,0.0050398493,0.0019852982,0.0018191749,0.00034556683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00085509685,0.00036277244,0.4227242,0.00068956177,0.00044469387,0.004577211,0.002760668,0.021389347,0.02432215,0.026758723,0.005293347,0.48982227],"study_design_scores_gemma":[0.00006128171,0.0005341182,0.12549852,0.0004401062,0.0007220343,0.0062814406,0.0039611957,0.59356356,0.0394789,0.2039662,0.025355102,0.00013760693],"about_ca_topic_score_codex":0.0016582436,"about_ca_topic_score_gemma":0.0024486363,"teacher_disagreement_score":0.005686093,"about_ca_system_score_codex":0.0006256874,"about_ca_system_score_gemma":0.0012731786,"threshold_uncertainty_score":0.013111949},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026505949,0.00025016174,0.97124195,0.00014693367,0.000043825254,0.000053744847,0.000101809725,0.0013161469,0.0003396134],"genre_scores_gemma":[0.5868176,0.00036610395,0.41046932,0.00010535019,0.0001503094,0.00011555524,0.0006945028,0.00015658229,0.0011246552],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977615,0.00031799456,0.00017564141,0.0004832643,0.0011182206,0.00014335079],"domain_scores_gemma":[0.9931646,0.0031178538,0.0010894705,0.0010479403,0.001279365,0.0003007245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001782854,0.001058144,0.0016390434,0.0018490332,0.0006515619,0.0015786407,0.0025703681,0.0009874156,0.00052012666],"category_scores_gemma":[0.0117657725,0.00042630112,0.00067442,0.0023998807,0.0007211586,0.0029590912,0.0021844867,0.001642994,0.00041958693],"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":[0.00051742664,0.0003397706,0.013222564,0.00026776647,0.0001996851,0.00048101306,0.0003379146,0.51793647,0.028802963,0.010905592,0.0036427511,0.42334607],"study_design_scores_gemma":[0.000008461243,0.000049740156,0.00074858055,0.000005292043,0.0000075361927,0.00009432131,0.000050726616,0.98992425,0.0036071707,0.0049233935,0.00056949194,0.000010946613],"about_ca_topic_score_codex":0.0025677592,"about_ca_topic_score_gemma":0.002331716,"teacher_disagreement_score":0.0025703681,"about_ca_system_score_codex":0.0007274096,"about_ca_system_score_gemma":0.0010233525,"threshold_uncertainty_score":0.00942874},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012455682,0.0005155175,0.9801701,0.0010993668,0.000119039774,0.000090842324,0.00035240562,0.00030413384,0.004892922],"genre_scores_gemma":[0.68479836,0.0015203878,0.3060969,0.00047542944,0.0005641101,0.00039681565,0.00088970014,0.0001966786,0.0050616823],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9967393,0.0011549235,0.00019682411,0.0004910258,0.0010771283,0.00034088735],"domain_scores_gemma":[0.9921779,0.0035405757,0.0017519,0.0006605226,0.0014594183,0.0004096394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009358127,0.0021727413,0.0009842471,0.004541272,0.0009725324,0.004948202,0.0030001155,0.0015468474,0.0023823744],"category_scores_gemma":[0.021070221,0.0005148928,0.0011266839,0.002342826,0.0030214523,0.008358039,0.003346145,0.0026907658,0.00039374296],"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":[0.000069688555,0.0000673479,0.006757511,0.0001580767,0.00011952482,0.00024707682,0.0002921673,0.5371595,0.001031165,0.4148062,0.004709768,0.034581944],"study_design_scores_gemma":[0.0000074416957,0.00006867525,0.0017379595,0.000069402115,0.000023698518,0.00014702618,0.00026266152,0.7899839,0.00062639377,0.20204553,0.0049668066,0.000060436523],"about_ca_topic_score_codex":0.0062626777,"about_ca_topic_score_gemma":0.004347411,"teacher_disagreement_score":0.009358127,"about_ca_system_score_codex":0.0023185317,"about_ca_system_score_gemma":0.002849682,"threshold_uncertainty_score":0.049491048},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8203231,0.00088254106,0.12836096,0.00019250005,0.00043073497,0.0022477661,0.030652067,0.004844332,0.012065909],"genre_scores_gemma":[0.8189474,0.0008771935,0.1450302,0.00006285658,0.0001216346,0.0008520302,0.026805986,0.00046113253,0.0068416237],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99870396,0.00016574898,0.0001630825,0.00020838965,0.0006585438,0.00010034677],"domain_scores_gemma":[0.99465585,0.0010197402,0.000472474,0.0004095646,0.0033441004,0.00009827535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012786491,0.0006340242,0.00046086765,0.005493574,0.0003434869,0.0007724378,0.0004560183,0.0003705086,0.0028382419],"category_scores_gemma":[0.005091507,0.00013744582,0.00041945695,0.0043549575,0.0002140826,0.00042529733,0.0002798911,0.00039645215,0.0016633907],"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":[0.002097959,0.00063239,0.16406086,0.0013538506,0.00024512556,0.0025995022,0.0018335356,0.0133734085,0.10456008,0.0014908849,0.0134312585,0.69432116],"study_design_scores_gemma":[0.00013170262,0.0010981789,0.7000781,0.00038280143,0.0004059877,0.001813641,0.0028553458,0.106526256,0.14139834,0.0006628771,0.04442982,0.0002168903],"about_ca_topic_score_codex":0.010318721,"about_ca_topic_score_gemma":0.0072164745,"teacher_disagreement_score":0.010318721,"about_ca_system_score_codex":0.0005385143,"about_ca_system_score_gemma":0.00085216097,"threshold_uncertainty_score":0.02051735},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16955103,0.0006499306,0.82679284,0.00019408879,0.000118676115,0.00007049684,0.00013677774,0.0009847039,0.0015014956],"genre_scores_gemma":[0.88203084,0.0001577027,0.1154005,0.0000497374,0.00006192807,0.00007660993,0.00022989637,0.00005980596,0.0019330103],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954957,0.00010628961,0.000023553926,0.00015807817,0.00010496858,0.00005749111],"domain_scores_gemma":[0.9992539,0.00036690652,0.000060863178,0.00008888428,0.00019196057,0.000037528665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009427992,0.00045987216,0.0011983572,0.0008317822,0.0004475125,0.00082636625,0.0009215956,0.00089705683,0.0012309701],"category_scores_gemma":[0.0024490675,0.00023240724,0.00065778656,0.00060687104,0.00035239226,0.0008872832,0.00064175884,0.0007970637,0.00041313845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010913928,0.00035093605,0.016874336,0.00014981741,0.0002458476,0.0001976123,0.00015546057,0.05636204,0.039828114,0.004690811,0.0037962392,0.8762575],"study_design_scores_gemma":[0.000017387714,0.00011129776,0.0045053787,0.000007350264,0.0000374397,0.00017146385,0.000036740632,0.9837923,0.0070570577,0.0036783998,0.0005709643,0.0000142445915],"about_ca_topic_score_codex":0.0014790996,"about_ca_topic_score_gemma":0.0014074235,"teacher_disagreement_score":0.0014790996,"about_ca_system_score_codex":0.0002803502,"about_ca_system_score_gemma":0.00042334505,"threshold_uncertainty_score":0.0049860477},"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":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012843409,0.08331681,0.8752796,0.007615119,0.0023577448,0.00008553543,0.00089171884,0.0010704331,0.028098643],"genre_scores_gemma":[0.0813342,0.17909834,0.67160237,0.0064453403,0.008603344,0.0005822966,0.0021729583,0.0007066232,0.049454525],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991448,0.00015657097,0.00008123887,0.00019397298,0.00036948052,0.000053952368],"domain_scores_gemma":[0.99862957,0.0008443053,0.000053270334,0.00016070795,0.00024663383,0.000065601605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012334707,0.0010809669,0.0008029018,0.0012638728,0.00040132512,0.0022245138,0.001534088,0.0020013019,0.008464095],"category_scores_gemma":[0.0032796192,0.0008288066,0.0008969034,0.0024513085,0.0014015437,0.0031578152,0.0014931884,0.0049443366,0.0037747591],"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":[0.000053046097,0.00008284067,0.0010477462,0.0012479768,0.00014028464,0.00020990302,0.00014651107,0.024923068,0.002182453,0.38562125,0.067288585,0.51705635],"study_design_scores_gemma":[0.00001474582,0.000061110164,0.00061868,0.00055056455,0.000030430952,0.00035873306,0.000034386634,0.043612275,0.0015550245,0.57383615,0.37926966,0.000058230315],"about_ca_topic_score_codex":0.0020035822,"about_ca_topic_score_gemma":0.0020463364,"teacher_disagreement_score":0.008464095,"about_ca_system_score_codex":0.0012757909,"about_ca_system_score_gemma":0.0011523997,"threshold_uncertainty_score":0.028315246},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012819706,0.00018621511,0.98511237,0.000108029366,0.0000650809,0.00007550747,0.000038950293,0.0008177279,0.00077631895],"genre_scores_gemma":[0.39695117,0.00043333287,0.59807533,0.00013246955,0.00015116208,0.00029125644,0.00052802835,0.00013083399,0.003306508],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963638,0.00041752748,0.00033097283,0.0010226445,0.0016523346,0.0002126979],"domain_scores_gemma":[0.9980052,0.0004565959,0.00022562766,0.00021558038,0.0010074112,0.00008954876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014421145,0.0008978547,0.0017138541,0.0029726687,0.0009781674,0.0016101695,0.0020615887,0.0012067553,0.0013250741],"category_scores_gemma":[0.006217479,0.0002997029,0.0009663841,0.0029943006,0.0007596014,0.0022662198,0.0016306324,0.0011397896,0.0006703473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026491767,0.00019118573,0.0065065543,0.0002064549,0.00020726907,0.00030309852,0.0003221841,0.08342635,0.028576816,0.02144768,0.004755267,0.85379225],"study_design_scores_gemma":[0.00004225301,0.00020020781,0.002119999,0.00001848136,0.000065306136,0.00055989786,0.00010731283,0.95502114,0.022962257,0.014536211,0.0043130987,0.00005380263],"about_ca_topic_score_codex":0.0017724601,"about_ca_topic_score_gemma":0.00086817826,"teacher_disagreement_score":0.0029726687,"about_ca_system_score_codex":0.00061673904,"about_ca_system_score_gemma":0.0012387194,"threshold_uncertainty_score":0.007626772},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018480243,0.00023635925,0.9969892,0.00007328894,0.000041341562,0.000021772223,0.000034703862,0.00026527134,0.000490072],"genre_scores_gemma":[0.12731984,0.00096793217,0.8679482,0.00015294449,0.00025589674,0.0003089855,0.00039862204,0.0003347642,0.0023127862],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99575007,0.0012608969,0.00035120547,0.0008024493,0.0016923947,0.00014294752],"domain_scores_gemma":[0.99358106,0.00301201,0.0006850381,0.001523216,0.0010847889,0.00011386616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030190514,0.0009502663,0.0010788343,0.0021103644,0.00059793284,0.0023785334,0.0011457175,0.0009953537,0.0035077797],"category_scores_gemma":[0.020010833,0.00046006395,0.0012508235,0.0027887523,0.002065987,0.0030151529,0.0021240038,0.0025425884,0.0024706374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020773809,0.000075309756,0.0006906005,0.00025440118,0.00007314685,0.000109881345,0.00020306387,0.1714048,0.011744884,0.45246857,0.005048508,0.35771906],"study_design_scores_gemma":[0.000037156944,0.00016510615,0.0004295051,0.00004107043,0.00002042242,0.0002098688,0.00005772608,0.6877971,0.011210425,0.27567193,0.02430836,0.00005128157],"about_ca_topic_score_codex":0.0010078422,"about_ca_topic_score_gemma":0.0005612262,"teacher_disagreement_score":0.0035077797,"about_ca_system_score_codex":0.0009224007,"about_ca_system_score_gemma":0.0008243223,"threshold_uncertainty_score":0.015966475},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071942843,0.00015095023,0.95151496,0.00010030906,0.00008125692,0.00011665254,0.0004203444,0.039838176,0.0005831086],"genre_scores_gemma":[0.12370628,0.00025897866,0.8689968,0.00030728997,0.00016165609,0.00043501225,0.0021732252,0.0012125467,0.0027480382],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987233,0.00017864304,0.000116222414,0.0002950288,0.000600686,0.000086176144],"domain_scores_gemma":[0.9969727,0.000898462,0.00039413694,0.0005552494,0.0009206232,0.00025893823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002034396,0.0011188611,0.0018545196,0.0024222732,0.00067182444,0.0015345257,0.0033324347,0.0016731069,0.004121991],"category_scores_gemma":[0.0051024924,0.0005828014,0.0007135893,0.0018276141,0.0006421688,0.0033364282,0.002336942,0.0014214693,0.0028811386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018951681,0.0003477283,0.007930783,0.0007046349,0.00039263835,0.00079000747,0.0006072183,0.027610416,0.071108304,0.008463391,0.044454765,0.8356949],"study_design_scores_gemma":[0.00013097929,0.00033838875,0.002382466,0.000035642028,0.00006420749,0.00069076585,0.000107927786,0.91863436,0.05354432,0.005949194,0.018011363,0.00011032928],"about_ca_topic_score_codex":0.0011149569,"about_ca_topic_score_gemma":0.0011986323,"teacher_disagreement_score":0.004121991,"about_ca_system_score_codex":0.00043449277,"about_ca_system_score_gemma":0.0008563285,"threshold_uncertainty_score":0.013789415},"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":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008357667,0.031888574,0.9557455,0.00083938637,0.00023103126,0.00010553602,0.00021387447,0.0009865152,0.0016319298],"genre_scores_gemma":[0.14588901,0.079474404,0.7671575,0.000796696,0.0015108039,0.00037022942,0.0021607631,0.0004143598,0.002226245],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99252605,0.00208858,0.0009426077,0.0013323259,0.0028796082,0.00023078061],"domain_scores_gemma":[0.9831366,0.0097522335,0.0012652454,0.0018656678,0.0037637833,0.00021654562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005236827,0.0014622658,0.0034026222,0.0063918596,0.00069477345,0.0031774682,0.0036508786,0.0021284372,0.0010046771],"category_scores_gemma":[0.023527477,0.00087462686,0.0021032458,0.011766427,0.0014846185,0.0040211147,0.0018364987,0.0018035871,0.0012258283],"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":[0.00013341391,0.00020417244,0.009923363,0.0030635404,0.00043332804,0.00028347835,0.00033958213,0.03874382,0.003552331,0.021697154,0.008114599,0.9135113],"study_design_scores_gemma":[0.000074002775,0.00050351594,0.014192147,0.0017910168,0.00038043514,0.0035120558,0.00075014256,0.69752085,0.022324441,0.11125819,0.14742187,0.00027134895],"about_ca_topic_score_codex":0.0010503958,"about_ca_topic_score_gemma":0.0007322573,"teacher_disagreement_score":0.0063918596,"about_ca_system_score_codex":0.0007522406,"about_ca_system_score_gemma":0.0014161491,"threshold_uncertainty_score":0.027695298},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045716178,0.0005116245,0.95114887,0.00019597939,0.00006049768,0.00006107931,0.0003080335,0.0014662062,0.0005315813],"genre_scores_gemma":[0.5891176,0.0005765044,0.40731114,0.00009509855,0.00008618782,0.00009036731,0.0011450326,0.00016222522,0.0014158412],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927884,0.00015666799,0.000056234894,0.00019896126,0.0002524909,0.00005676791],"domain_scores_gemma":[0.9977574,0.0012229866,0.00022475242,0.00018517734,0.0005203168,0.000089282075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084600167,0.0007723549,0.0010518513,0.004163944,0.00068773545,0.0010240556,0.0012930311,0.0010864064,0.0009427933],"category_scores_gemma":[0.0034635025,0.00024197706,0.0010968795,0.0039034605,0.000537481,0.0013550719,0.0007484141,0.0010278377,0.00028130846],"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":[0.00037349502,0.0004360091,0.01111235,0.00028557086,0.00031710597,0.0004738886,0.0002518119,0.4845674,0.010126896,0.014954751,0.0039083976,0.47319221],"study_design_scores_gemma":[0.000005190044,0.000029740639,0.00090824324,0.0000043288906,0.00001956751,0.00005540573,0.000027158949,0.99313956,0.000883873,0.0044440795,0.00047351842,0.000009373715],"about_ca_topic_score_codex":0.01543002,"about_ca_topic_score_gemma":0.0116740195,"teacher_disagreement_score":0.01543002,"about_ca_system_score_codex":0.00074809854,"about_ca_system_score_gemma":0.0008501075,"threshold_uncertainty_score":0.030680418},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22672676,0.00029405465,0.769336,0.000067005974,0.000094973126,0.000026975851,0.00015871669,0.002323252,0.0009723039],"genre_scores_gemma":[0.70428973,0.00027431617,0.2932157,0.000025933492,0.00006956656,0.000019821742,0.00041349544,0.00040805005,0.0012833604],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997038,0.000054811895,0.000015213908,0.0000782719,0.000102164,0.000045721823],"domain_scores_gemma":[0.9982735,0.00068168406,0.00022390508,0.00015568329,0.00056560454,0.00009953206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000583413,0.0008880711,0.0006291962,0.0025858558,0.00027277053,0.0011976543,0.0006529251,0.000731723,0.00092401134],"category_scores_gemma":[0.0053305062,0.00038389643,0.00049378077,0.0010994351,0.00024308055,0.00082931935,0.0006133485,0.0010593503,0.0007516705],"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":[0.0015817342,0.00015095639,0.014759333,0.00025009012,0.00012677081,0.00036153823,0.00030156042,0.097394355,0.29769704,0.002830731,0.0019117851,0.58263415],"study_design_scores_gemma":[0.000039344617,0.00014088546,0.013941963,0.000023057622,0.00007428625,0.0003459369,0.00008121277,0.9247809,0.056498878,0.0023808095,0.0016552891,0.00003743471],"about_ca_topic_score_codex":0.004189974,"about_ca_topic_score_gemma":0.0048884293,"teacher_disagreement_score":0.004189974,"about_ca_system_score_codex":0.00040247448,"about_ca_system_score_gemma":0.0008224067,"threshold_uncertainty_score":0.00833118},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21665984,0.00048110823,0.7787952,0.000121699835,0.00004553211,0.00019750331,0.00048521147,0.002606431,0.0006074683],"genre_scores_gemma":[0.4629867,0.00027466763,0.53239954,0.00005973883,0.00007901484,0.00019943228,0.0026131063,0.00023297036,0.0011548012],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988049,0.00017267875,0.00014204979,0.00025809737,0.00051976903,0.000102527454],"domain_scores_gemma":[0.9965257,0.0012983509,0.0009196069,0.0002795574,0.0008472753,0.00012956402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010271872,0.0009854884,0.0011843053,0.0074968026,0.0007957872,0.0010467632,0.0008668565,0.0008803566,0.0004647709],"category_scores_gemma":[0.005664177,0.00029290805,0.0008806977,0.0049275043,0.00046987925,0.0018911841,0.0008151209,0.0006794284,0.0005617997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007150316,0.0005031708,0.04985481,0.00038422475,0.00028062938,0.0007560771,0.00066498976,0.040858977,0.05282581,0.006034095,0.0033956082,0.8437266],"study_design_scores_gemma":[0.00003750684,0.000261167,0.014652147,0.00004089038,0.00010507814,0.00070040906,0.0004917871,0.9412483,0.021111894,0.017644294,0.0036415963,0.00006490392],"about_ca_topic_score_codex":0.0032121164,"about_ca_topic_score_gemma":0.004567861,"teacher_disagreement_score":0.0074968026,"about_ca_system_score_codex":0.0005136259,"about_ca_system_score_gemma":0.0007492105,"threshold_uncertainty_score":0.006386876},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013763017,0.00005517771,0.98532987,0.0000626901,0.0000095781115,0.000023670338,0.000042019125,0.0005566178,0.00015726253],"genre_scores_gemma":[0.24178782,0.00008413121,0.75714386,0.000048086335,0.000042350654,0.00010266814,0.00027791897,0.00013722097,0.0003758938],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99558973,0.0014558749,0.00046400723,0.00069227174,0.0016492333,0.00014886876],"domain_scores_gemma":[0.9836039,0.0082826875,0.003350046,0.0019993398,0.0024903752,0.00027354396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003792778,0.0007021122,0.0013973375,0.005028392,0.0005405917,0.001681871,0.001580231,0.0008842554,0.0007548652],"category_scores_gemma":[0.018710956,0.00046433098,0.0010664094,0.0040819105,0.001590889,0.0020033352,0.0016677894,0.00112451,0.00044146247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000412765,0.0002109068,0.038895488,0.00040666803,0.00047466572,0.0006772351,0.00077324384,0.4075882,0.03305298,0.032258194,0.0025842679,0.48266536],"study_design_scores_gemma":[0.000019432062,0.000074283635,0.004447314,0.0000214433,0.00004304011,0.00029176002,0.00014163462,0.9630095,0.010332809,0.019375356,0.0021800478,0.000063355124],"about_ca_topic_score_codex":0.002338422,"about_ca_topic_score_gemma":0.0018556815,"teacher_disagreement_score":0.005028392,"about_ca_system_score_codex":0.00076153077,"about_ca_system_score_gemma":0.0010489858,"threshold_uncertainty_score":0.020058393},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98623604,0.00007132617,0.01248415,0.00009214268,0.000007341152,0.000012723968,0.0004120528,0.000051131974,0.00063305884],"genre_scores_gemma":[0.9976126,0.000038214886,0.001817081,0.0000027248961,0.000005314496,0.0000047926237,0.0003035244,0.0000050012704,0.00021068276],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99989355,0.000023953173,0.0000064158617,0.000028664532,0.000023550485,0.00002378788],"domain_scores_gemma":[0.99902225,0.0004955732,0.00019019825,0.000046529607,0.00018041712,0.00006502844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002893255,0.00028630134,0.00025221752,0.001314096,0.0002870902,0.00046288103,0.00039375797,0.00030016815,0.0008623366],"category_scores_gemma":[0.0018690972,0.00018107453,0.00021896741,0.0010917182,0.00023264313,0.0004503411,0.00029501534,0.00033700242,0.00007905847],"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":[0.00054943166,0.00016036729,0.15371567,0.00010441141,0.00017443133,0.00076748035,0.00024480975,0.7845154,0.01118173,0.0054399935,0.0012518544,0.041894436],"study_design_scores_gemma":[0.0000044878775,0.000014630526,0.033886645,0.0000023542877,0.000012139083,0.00003450428,0.00007308569,0.9645363,0.0005178769,0.0007662014,0.00014660829,0.0000051384236],"about_ca_topic_score_codex":0.014404166,"about_ca_topic_score_gemma":0.013914335,"teacher_disagreement_score":0.014404166,"about_ca_system_score_codex":0.00047488912,"about_ca_system_score_gemma":0.0003065352,"threshold_uncertainty_score":0.028640687},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93341386,0.00026949888,0.062513225,0.0011449617,0.000084066334,0.00015682027,0.00057811406,0.00056640623,0.0012731081],"genre_scores_gemma":[0.9710192,0.0001538995,0.027805269,0.00006381996,0.00003679174,0.000037043752,0.00030334404,0.000037411493,0.0005431442],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99808717,0.0005597952,0.00016879421,0.00027501248,0.00076234364,0.00014685151],"domain_scores_gemma":[0.98363197,0.012263708,0.0009935347,0.0012003813,0.0014255873,0.00048484368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024802738,0.00067385583,0.00064234686,0.0013566266,0.00079958024,0.0012132198,0.0015330046,0.0025992475,0.000614795],"category_scores_gemma":[0.0137858065,0.0003035068,0.00053262943,0.0018339979,0.00096726185,0.0014965708,0.00082517543,0.0011638901,0.00021170806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025290188,0.0037190586,0.3127148,0.00092437904,0.00061039167,0.039698437,0.0035857672,0.2971223,0.03059889,0.0076911314,0.01175187,0.2890539],"study_design_scores_gemma":[0.00018031217,0.0008207372,0.038916428,0.000043208565,0.00015457005,0.01185324,0.0019970704,0.91605943,0.018809708,0.007113389,0.003957935,0.00009406044],"about_ca_topic_score_codex":0.003838811,"about_ca_topic_score_gemma":0.0045388457,"teacher_disagreement_score":0.003838811,"about_ca_system_score_codex":0.00053067843,"about_ca_system_score_gemma":0.00048920664,"threshold_uncertainty_score":0.013117075},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.707201,0.00028656417,0.2910269,0.00023222902,0.000030057698,0.000049330192,0.00010499164,0.00035187442,0.00071702595],"genre_scores_gemma":[0.9886663,0.00006170704,0.01078766,0.000017542116,0.000008555812,0.000021561887,0.000107016785,0.00000865018,0.00032089226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99865353,0.0003713959,0.0000651073,0.0003953295,0.00031651297,0.00019801548],"domain_scores_gemma":[0.9914352,0.005693819,0.0013142318,0.0005125778,0.00071681506,0.00032729356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030553145,0.00055991445,0.0008730586,0.0009434088,0.000613101,0.0007309188,0.0018432742,0.0010921546,0.0005138061],"category_scores_gemma":[0.009080089,0.0005830398,0.00056756014,0.0006463166,0.001684484,0.0022786388,0.0016614848,0.00084408565,0.00016438785],"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":[0.00085555547,0.00015646579,0.049007826,0.000117843745,0.00013156804,0.00044946672,0.00046124143,0.89322424,0.008853543,0.0075072395,0.0004723736,0.03876272],"study_design_scores_gemma":[0.00000936229,0.000080225574,0.002377301,0.000004515972,0.0000073693195,0.00008525385,0.00006221212,0.9933188,0.0014310754,0.002548872,0.0000670414,0.000007944839],"about_ca_topic_score_codex":0.007729607,"about_ca_topic_score_gemma":0.0066043,"teacher_disagreement_score":0.007729607,"about_ca_system_score_codex":0.0012845198,"about_ca_system_score_gemma":0.0006880193,"threshold_uncertainty_score":0.016158283},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6506591,0.00041794407,0.3473097,0.00014168136,0.0001197092,0.00008790227,0.00010110081,0.00050277566,0.0006601875],"genre_scores_gemma":[0.92974824,0.00013463492,0.06923622,0.000032619984,0.000044988927,0.00003555667,0.00013267912,0.000023485929,0.0006116379],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991431,0.00009830055,0.000041710893,0.00013818304,0.00052411645,0.00005459429],"domain_scores_gemma":[0.9976661,0.0008513704,0.00043851236,0.00020877033,0.00076835393,0.00006699411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007019917,0.00058627763,0.0006017903,0.0010313533,0.0001586521,0.00042310377,0.0006664082,0.00044149844,0.00068143086],"category_scores_gemma":[0.0031862147,0.00021300289,0.00046524787,0.00044931308,0.00036389678,0.0006788963,0.00047067163,0.00044536314,0.00022738088],"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":[0.0007687114,0.00025198466,0.01938442,0.0003021844,0.00009942865,0.00040570932,0.0002740835,0.014757264,0.621349,0.0005169142,0.00049407774,0.34139615],"study_design_scores_gemma":[0.0000614141,0.0026054436,0.113904424,0.000043444335,0.00013549729,0.0021279883,0.00028112385,0.422508,0.45419866,0.001289327,0.0027266822,0.0001179448],"about_ca_topic_score_codex":0.00039283492,"about_ca_topic_score_gemma":0.00067981955,"teacher_disagreement_score":0.0010313533,"about_ca_system_score_codex":0.0002541077,"about_ca_system_score_gemma":0.00018521673,"threshold_uncertainty_score":0.003712535},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53226453,0.000070848386,0.46368873,0.000048281378,0.000025502402,0.00085466524,0.000849446,0.00068573497,0.0015122158],"genre_scores_gemma":[0.86194897,0.000057181824,0.13521199,0.000021605823,0.000013036858,0.00068007596,0.0014205985,0.00004551717,0.00060104724],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99693584,0.0016435257,0.00021550142,0.0003867676,0.0006284174,0.0001899879],"domain_scores_gemma":[0.99285203,0.004099797,0.00072797295,0.0011486071,0.001040282,0.00013130064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041132327,0.00045476505,0.00065713614,0.0023146614,0.00049292797,0.0006827166,0.00044494963,0.00021032142,0.0023551076],"category_scores_gemma":[0.017112557,0.00027448582,0.0010034976,0.0021661227,0.00035957617,0.0006950602,0.00092180853,0.00050076575,0.0004999733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011098217,0.0005570514,0.57109237,0.00015511525,0.0008452154,0.00024443583,0.0016981389,0.048489947,0.012930884,0.015790459,0.0023825439,0.34470406],"study_design_scores_gemma":[0.0001277243,0.00159434,0.40461135,0.00004745187,0.00035344443,0.00034650392,0.0007159373,0.5637673,0.007045854,0.017146364,0.004126112,0.00011757629],"about_ca_topic_score_codex":0.0111465575,"about_ca_topic_score_gemma":0.00698639,"teacher_disagreement_score":0.0111465575,"about_ca_system_score_codex":0.0005410242,"about_ca_system_score_gemma":0.0015845937,"threshold_uncertainty_score":0.022163332},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10917236,0.008159468,0.5017521,0.3287941,0.001161232,0.0005604576,0.00078190316,0.0027842126,0.046834275],"genre_scores_gemma":[0.49954736,0.0067094075,0.47429723,0.01316649,0.00077737804,0.00026770643,0.00034559378,0.00021064666,0.0046781986],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951251,0.0030326766,0.00027583318,0.00039480525,0.000921282,0.0002503448],"domain_scores_gemma":[0.985622,0.0107290065,0.0009674808,0.0009848437,0.0010920941,0.00060455565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011917256,0.00074284186,0.000617048,0.0018841488,0.0008867718,0.004838055,0.0009599764,0.0024333852,0.0024073836],"category_scores_gemma":[0.044615716,0.0006189092,0.0007386362,0.0012574543,0.0012524298,0.007053946,0.0015178698,0.0028975895,0.0014686112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001436919,0.0009969934,0.10195216,0.0007567657,0.0004510327,0.0006074103,0.00786086,0.026771097,0.003925805,0.059158757,0.059954595,0.7374208],"study_design_scores_gemma":[0.00021916759,0.00082263205,0.047558766,0.0018606686,0.00036813127,0.002358145,0.014509792,0.1807708,0.008771061,0.54752916,0.19468713,0.0005444616],"about_ca_topic_score_codex":0.0025981585,"about_ca_topic_score_gemma":0.0034158782,"teacher_disagreement_score":0.011917256,"about_ca_system_score_codex":0.000886105,"about_ca_system_score_gemma":0.001828727,"threshold_uncertainty_score":0.06302518},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00260718,0.00016886777,0.99634486,0.000053829503,0.000039236413,0.000017778024,0.000035477042,0.00033140648,0.00040136074],"genre_scores_gemma":[0.05391819,0.00045041458,0.9417635,0.00006646049,0.000077270335,0.00007711854,0.00028680157,0.00023302688,0.0031271114],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999368,0.00015857426,0.000038768714,0.00013751232,0.00024602658,0.000051143477],"domain_scores_gemma":[0.9987544,0.0005268024,0.00008049306,0.0002193318,0.00036896102,0.00005001375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086209463,0.0006264025,0.0011166827,0.0013299823,0.0004287748,0.00088060956,0.0011368478,0.0010262649,0.0035517986],"category_scores_gemma":[0.0029017874,0.00045316477,0.0005663975,0.0014973853,0.0005164468,0.0013832814,0.0010915712,0.0015424627,0.0022509915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023928584,0.00012602986,0.0011878615,0.00015017144,0.0000885389,0.00007721513,0.0001221817,0.061408386,0.043904193,0.026224157,0.0056168246,0.8608552],"study_design_scores_gemma":[0.000013653,0.00006091717,0.0007096714,0.00001212372,0.000016511623,0.00017479839,0.000019454706,0.9746968,0.009904057,0.008975624,0.0053939167,0.000022555161],"about_ca_topic_score_codex":0.002274205,"about_ca_topic_score_gemma":0.0025516127,"teacher_disagreement_score":0.0035517986,"about_ca_system_score_codex":0.00024580874,"about_ca_system_score_gemma":0.0007113581,"threshold_uncertainty_score":0.011882007},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09954255,0.00028220267,0.89673364,0.00016117006,0.000033989872,0.0000620286,0.00029699176,0.0021488564,0.0007385584],"genre_scores_gemma":[0.8154059,0.00020552347,0.18175994,0.00008491988,0.00006478781,0.000055366552,0.00081905676,0.000118774195,0.0014857412],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993267,0.00008982619,0.000031681226,0.00026695686,0.00017625095,0.00010860401],"domain_scores_gemma":[0.9986773,0.0005268084,0.00026919422,0.0001857944,0.0002306408,0.00011032694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059503346,0.00086830446,0.0009923398,0.0014642362,0.0003032303,0.00067191914,0.0017233175,0.00063104375,0.000593664],"category_scores_gemma":[0.0027141052,0.00039395524,0.0006013443,0.00082680734,0.00049024366,0.0015948237,0.0009258831,0.0010620437,0.00022856421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043287483,0.0005467176,0.02276342,0.00019623442,0.00015253412,0.0008862723,0.00034914544,0.18854813,0.0449694,0.0099953655,0.005045916,0.726114],"study_design_scores_gemma":[0.0000035588382,0.000037549864,0.0013685725,0.000003855787,0.00000933913,0.00009016028,0.000028316887,0.99015486,0.0040026987,0.0038173026,0.00047714397,0.0000066907282],"about_ca_topic_score_codex":0.007298275,"about_ca_topic_score_gemma":0.009445293,"teacher_disagreement_score":0.007298275,"about_ca_system_score_codex":0.0006268462,"about_ca_system_score_gemma":0.00084797817,"threshold_uncertainty_score":0.014511585},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064486535,0.00010981446,0.99277264,0.00006148775,0.00001656231,0.0000136360295,0.000023125274,0.00033175488,0.00022232308],"genre_scores_gemma":[0.50228554,0.0005150054,0.4935247,0.00013767312,0.000108441316,0.00022704883,0.00046250797,0.00016797431,0.0025709989],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992766,0.00025331348,0.00005099727,0.00012544592,0.0002448763,0.0000488136],"domain_scores_gemma":[0.9983589,0.0009707652,0.00015063961,0.000108732514,0.0003761751,0.00003467725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014629582,0.00062600145,0.0010695262,0.0007079837,0.00032443463,0.0005072158,0.0009308604,0.000709401,0.0013372685],"category_scores_gemma":[0.0043762075,0.0004252185,0.0008044151,0.0010149563,0.0003956921,0.0009468226,0.00064596004,0.0015416294,0.00035044324],"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":[0.00018820842,0.00009625662,0.0018427927,0.00016203165,0.0001203784,0.00014880805,0.00009930067,0.72236526,0.011780485,0.0090686055,0.002311565,0.25181633],"study_design_scores_gemma":[0.0000021050469,0.0000091454585,0.00007272794,0.0000014658223,0.000002795031,0.000008965962,0.0000016490304,0.99884665,0.0004694749,0.00045586113,0.0001263871,0.000002702802],"about_ca_topic_score_codex":0.0038948152,"about_ca_topic_score_gemma":0.0025719297,"teacher_disagreement_score":0.0038948152,"about_ca_system_score_codex":0.00034636786,"about_ca_system_score_gemma":0.0008658775,"threshold_uncertainty_score":0.0077442527},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13542837,0.0012180923,0.85958207,0.00045404801,0.00006539828,0.00018607934,0.00079354976,0.0016107426,0.0006617375],"genre_scores_gemma":[0.60323125,0.00082893006,0.3904301,0.00020459658,0.00017876916,0.00026387206,0.0033780066,0.00019655457,0.0012879384],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99462575,0.00052589603,0.0006371163,0.0013125965,0.0024306523,0.00046797007],"domain_scores_gemma":[0.991939,0.0030545886,0.0014721515,0.001288818,0.0018411671,0.00040421035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002861082,0.0015067626,0.003217261,0.0039622835,0.0021321177,0.002666819,0.004373354,0.0016179674,0.0005943343],"category_scores_gemma":[0.014292343,0.00074343185,0.0017511426,0.006773093,0.001158047,0.003898117,0.002830603,0.0017041214,0.00062401965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014955846,0.000618444,0.074923694,0.000875465,0.00066052657,0.0028735646,0.0014243311,0.33110103,0.021004139,0.014335573,0.009600716,0.5410869],"study_design_scores_gemma":[0.00003614336,0.00018858736,0.0057740537,0.000045146808,0.00009574447,0.0008995708,0.0007869731,0.95590127,0.009355199,0.023124212,0.0037499154,0.000043216947],"about_ca_topic_score_codex":0.0038642813,"about_ca_topic_score_gemma":0.005395483,"teacher_disagreement_score":0.004373354,"about_ca_system_score_codex":0.0007983236,"about_ca_system_score_gemma":0.0013183502,"threshold_uncertainty_score":0.015131056},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2045959,0.0017209605,0.78858614,0.00013632093,0.00011822508,0.00008252843,0.0003889015,0.0029704073,0.0014006062],"genre_scores_gemma":[0.8758522,0.00059702207,0.12208848,0.00003681401,0.00006205801,0.000037343852,0.0004625988,0.00004409499,0.0008192806],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999701,0.000048569542,0.000016381227,0.00007410691,0.00013787164,0.00002217636],"domain_scores_gemma":[0.99935037,0.0002787106,0.00013382045,0.0000503397,0.00015349542,0.000033362747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030926723,0.00039443985,0.00035448468,0.0012688542,0.00016255977,0.00042484718,0.0004677425,0.0003974023,0.00052277214],"category_scores_gemma":[0.0016197945,0.00014909488,0.00020600799,0.0008131027,0.0001853344,0.0005324934,0.000258237,0.00029953266,0.00021058007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000690935,0.00016495962,0.009654761,0.00025078154,0.00006302281,0.00031719176,0.0001634318,0.051760215,0.21070388,0.0017666642,0.0019665128,0.7224976],"study_design_scores_gemma":[0.000022025266,0.00045042764,0.018185977,0.000029687315,0.000044232664,0.00073574664,0.00011569396,0.84043634,0.1323342,0.0027701168,0.004842143,0.000033345033],"about_ca_topic_score_codex":0.0019542184,"about_ca_topic_score_gemma":0.001649677,"teacher_disagreement_score":0.0019542184,"about_ca_system_score_codex":0.0003257714,"about_ca_system_score_gemma":0.00021071846,"threshold_uncertainty_score":0.0038856268},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22918051,0.0023988222,0.38247117,0.006473977,0.0021873564,0.002638015,0.039100185,0.30225357,0.03329638],"genre_scores_gemma":[0.5824484,0.0012674413,0.3460465,0.0015755746,0.0008557481,0.0010472693,0.030423202,0.0028743988,0.033461504],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991636,0.00013410086,0.000088398985,0.00015804215,0.00035143126,0.0001045435],"domain_scores_gemma":[0.99747795,0.0008679243,0.00020587347,0.00060237816,0.00063704443,0.00020887314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014271316,0.0009798439,0.0006581628,0.0036725944,0.0011035374,0.0016390465,0.0011333574,0.00088483904,0.007034748],"category_scores_gemma":[0.004829891,0.00042446115,0.00049862143,0.0016211469,0.00032522867,0.003985106,0.0020024164,0.0005945621,0.004664755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014249297,0.00031366904,0.0335928,0.0005231855,0.00022311427,0.0006571109,0.0014342556,0.0029881555,0.034530282,0.0035728286,0.14444675,0.7762929],"study_design_scores_gemma":[0.00021772074,0.00094331853,0.040681276,0.00022504364,0.00025174048,0.0013665776,0.0025800655,0.4481069,0.13092904,0.018131193,0.3562158,0.00035131862],"about_ca_topic_score_codex":0.0060199485,"about_ca_topic_score_gemma":0.0132240355,"teacher_disagreement_score":0.007034748,"about_ca_system_score_codex":0.0007817105,"about_ca_system_score_gemma":0.00077097287,"threshold_uncertainty_score":0.023533583},"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":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016567783,0.0007478768,0.5634452,0.0011459807,0.000989887,0.00079122715,0.038686354,0.24323407,0.13439165],"genre_scores_gemma":[0.23974892,0.0017308793,0.4073431,0.0018235844,0.0007210996,0.0021705246,0.16522557,0.02888065,0.15235563],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986168,0.00017985387,0.000107187225,0.00050622126,0.0004372565,0.0001527447],"domain_scores_gemma":[0.9972282,0.00039002305,0.00013114366,0.0013695138,0.000748068,0.00013299943],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012035295,0.0011610609,0.0007386309,0.001948011,0.00079227553,0.0034096618,0.0019279161,0.00090185355,0.054136872],"category_scores_gemma":[0.0057321647,0.0006613863,0.0013325048,0.001288561,0.0005542501,0.0038329042,0.0028126729,0.0015702673,0.059243564],"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":[0.0019918294,0.0004202886,0.00985324,0.0016780285,0.0004935096,0.0007150488,0.00079970475,0.016140899,0.022199353,0.10959423,0.3920754,0.44403842],"study_design_scores_gemma":[0.0001370959,0.0002336978,0.002655872,0.00027374984,0.00017905983,0.0011440818,0.0003532583,0.118492074,0.029217213,0.071267344,0.77591544,0.00013119113],"about_ca_topic_score_codex":0.0019454391,"about_ca_topic_score_gemma":0.0024337475,"teacher_disagreement_score":0.9458631,"about_ca_system_score_codex":0.0008050964,"about_ca_system_score_gemma":0.0018811999,"threshold_uncertainty_score":0},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27337563,0.0030284123,0.7036833,0.0008592121,0.00035891484,0.00049448217,0.0023594028,0.001693819,0.014146902],"genre_scores_gemma":[0.8775635,0.0017455951,0.10667232,0.0001607646,0.00048444283,0.000095838586,0.0018680731,0.00032789377,0.01108166],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99943525,0.000071609196,0.000030108238,0.00009002103,0.00025036623,0.00012259181],"domain_scores_gemma":[0.99913126,0.00022944833,0.00012278228,0.00023242725,0.00019553847,0.00008845414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068615313,0.00075962313,0.0007332688,0.0014752685,0.00052200904,0.001716783,0.0009682578,0.0005681387,0.0041954233],"category_scores_gemma":[0.001724153,0.000426111,0.0010623655,0.0018895639,0.00048573423,0.0015463178,0.0013714998,0.0007946278,0.00079742185],"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":[0.00047310095,0.0005626739,0.12467345,0.000494841,0.00031123956,0.00077173935,0.0002650237,0.34942228,0.06877508,0.02008532,0.008322283,0.42584297],"study_design_scores_gemma":[0.000022922584,0.00028162246,0.10945009,0.00004282298,0.00012523979,0.0007384151,0.00030306226,0.8329924,0.01958388,0.026552597,0.009803115,0.00010384814],"about_ca_topic_score_codex":0.0055351174,"about_ca_topic_score_gemma":0.0064148316,"teacher_disagreement_score":0.0055351174,"about_ca_system_score_codex":0.00064719317,"about_ca_system_score_gemma":0.0012541123,"threshold_uncertainty_score":0.014035106},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033160478,0.00003477948,0.9963223,0.000022715187,0.000011827155,0.000010427111,0.000007847546,0.00021317373,0.000060854014],"genre_scores_gemma":[0.14287943,0.00013108323,0.85572904,0.00006700122,0.000068567686,0.000086457905,0.00016054834,0.00013140992,0.0007464291],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976707,0.00034808574,0.00013087456,0.0004768011,0.001242016,0.000131503],"domain_scores_gemma":[0.9969168,0.0010465521,0.00068144366,0.00045525425,0.0007795761,0.00012040716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011969107,0.0010136511,0.0015404138,0.0012877426,0.0004252868,0.0006973835,0.0018214857,0.0012481742,0.0007814104],"category_scores_gemma":[0.006440748,0.0006273175,0.0011089282,0.0011004301,0.00091552,0.0013833967,0.0016450485,0.0019459539,0.00064493984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004671031,0.00018801577,0.0032884683,0.00025192997,0.00021122635,0.000505205,0.0003272889,0.3227891,0.108294465,0.00890616,0.0020293256,0.5527417],"study_design_scores_gemma":[0.000020660977,0.000151926,0.00085142296,0.00000980812,0.000021992571,0.00027372877,0.000023984552,0.969397,0.025174769,0.0024439665,0.0015830508,0.00004769789],"about_ca_topic_score_codex":0.001437971,"about_ca_topic_score_gemma":0.001069135,"teacher_disagreement_score":0.0018214857,"about_ca_system_score_codex":0.00043529016,"about_ca_system_score_gemma":0.00094387634,"threshold_uncertainty_score":0.006329894},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48722255,0.0045349468,0.40986043,0.003996931,0.00042900426,0.001417832,0.07539415,0.0060064537,0.011137763],"genre_scores_gemma":[0.8136439,0.0011748134,0.14344288,0.00016914924,0.00009882321,0.0006982915,0.03975726,0.00007242089,0.0009425358],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99679035,0.0005334438,0.00070971256,0.00075854326,0.0009838191,0.00022408836],"domain_scores_gemma":[0.9872671,0.005446102,0.0025683143,0.0012196033,0.0031483315,0.0003506222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033274798,0.0014565145,0.0014277202,0.013374622,0.0006652194,0.0019302901,0.0014162388,0.000885726,0.0009269022],"category_scores_gemma":[0.020278422,0.00033217174,0.0012944734,0.0091965655,0.00050711544,0.0019785857,0.0013659813,0.0012049719,0.00062003895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000668601,0.00063969,0.24628937,0.002060057,0.0011361915,0.0014396678,0.0008996453,0.116937466,0.006969149,0.010202127,0.01928352,0.59347445],"study_design_scores_gemma":[0.0001141982,0.0006221251,0.13987967,0.0007716863,0.000613262,0.0014054566,0.002799969,0.7246203,0.03209435,0.052583147,0.044218984,0.00027692068],"about_ca_topic_score_codex":0.0038645898,"about_ca_topic_score_gemma":0.003426794,"teacher_disagreement_score":0.013374622,"about_ca_system_score_codex":0.0011489497,"about_ca_system_score_gemma":0.0014301874,"threshold_uncertainty_score":0.017597556},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07682174,0.0007822347,0.9179316,0.00043851483,0.00012495849,0.00008013093,0.0004501748,0.002477393,0.0008932923],"genre_scores_gemma":[0.7678694,0.00045754248,0.2299121,0.00013465708,0.00014838803,0.000084340936,0.0008228507,0.0001269597,0.0004436752],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997274,0.00054576434,0.0002002336,0.0006224212,0.001169609,0.00018809058],"domain_scores_gemma":[0.9942497,0.002498112,0.00084808964,0.0011192105,0.0010942325,0.00019075822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020075392,0.0007723362,0.0010649357,0.0022767272,0.00069820473,0.0013429571,0.001441809,0.0009146028,0.00035018512],"category_scores_gemma":[0.011473929,0.0004255591,0.00064950384,0.0027367845,0.0008748857,0.0022804174,0.0017793772,0.0011696428,0.00018336851],"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":[0.0005371139,0.00028902016,0.053273946,0.00048203208,0.00037312048,0.0012596564,0.00068656914,0.501477,0.024751656,0.023197336,0.005944473,0.387728],"study_design_scores_gemma":[0.000007044739,0.000044633085,0.0049791355,0.000014642351,0.000021514736,0.00022956515,0.00014800836,0.96857107,0.005230717,0.018572785,0.0021636928,0.000017193643],"about_ca_topic_score_codex":0.00412205,"about_ca_topic_score_gemma":0.005691455,"teacher_disagreement_score":0.00412205,"about_ca_system_score_codex":0.00087656826,"about_ca_system_score_gemma":0.00079008506,"threshold_uncertainty_score":0.010617018},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040062345,0.0011808838,0.99328893,0.00017159774,0.00007993594,0.00001994094,0.00006131666,0.00088320667,0.00030794617],"genre_scores_gemma":[0.13967614,0.002535899,0.8541574,0.00009179443,0.0003632563,0.00012421941,0.0005627967,0.00027945108,0.0022090233],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974463,0.0005715375,0.00020046195,0.00044726356,0.0012377782,0.0000966532],"domain_scores_gemma":[0.9940037,0.002686643,0.00066082465,0.00068706437,0.0017876143,0.00017419153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020910713,0.0014098233,0.00232931,0.0038231777,0.00097032683,0.002238905,0.0032631396,0.0025373877,0.0015349429],"category_scores_gemma":[0.011467537,0.0006284287,0.0007691493,0.007980758,0.001465115,0.0030767734,0.0023344506,0.002692567,0.0015022898],"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":[0.00030024874,0.00016227679,0.003354048,0.00025917616,0.00010254101,0.00012678654,0.00015831023,0.10367799,0.005953399,0.026071036,0.0071506696,0.8526835],"study_design_scores_gemma":[0.00003844649,0.00007562499,0.00081282743,0.000031830674,0.000041286457,0.00035684736,0.00008732695,0.9452694,0.0063670217,0.04053836,0.006332429,0.00004853649],"about_ca_topic_score_codex":0.002487089,"about_ca_topic_score_gemma":0.0015702187,"teacher_disagreement_score":0.0038231777,"about_ca_system_score_codex":0.0007716829,"about_ca_system_score_gemma":0.0007714336,"threshold_uncertainty_score":0.011058748},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90038306,0.0001074185,0.09686435,0.000059883667,0.000026333702,0.000064025604,0.00074065913,0.00058529305,0.0011689528],"genre_scores_gemma":[0.9846415,0.000021821725,0.0146266455,0.00000820541,0.000011648023,0.000020666166,0.00036897068,0.00001542777,0.00028525715],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988226,0.00032313674,0.000058938844,0.00031148718,0.0004149261,0.00006888047],"domain_scores_gemma":[0.9922042,0.004127288,0.0008444398,0.0015522753,0.0010861844,0.00018573264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016520347,0.00026173374,0.00039529678,0.0014750775,0.000351316,0.0006293284,0.0005468386,0.00033590273,0.0015308573],"category_scores_gemma":[0.012090904,0.0001684766,0.00037761632,0.00108371,0.0006294421,0.0006185513,0.0005491567,0.00029630552,0.00048409912],"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":[0.0009636182,0.00037201066,0.4718206,0.00030272864,0.00044963672,0.0014114776,0.0012435261,0.033911653,0.06975029,0.0039313203,0.002804432,0.41303873],"study_design_scores_gemma":[0.000024018778,0.0004497982,0.6362927,0.000021684367,0.00008736108,0.0020836655,0.0006761372,0.33249944,0.019531064,0.00602805,0.0022109512,0.000095092735],"about_ca_topic_score_codex":0.0016518986,"about_ca_topic_score_gemma":0.0021759009,"teacher_disagreement_score":0.0016520347,"about_ca_system_score_codex":0.00021781237,"about_ca_system_score_gemma":0.00021387693,"threshold_uncertainty_score":0.008736908},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8290065,0.00035133515,0.16868886,0.00010686763,0.00013708732,0.00002856648,0.00012696341,0.0006004048,0.0009534914],"genre_scores_gemma":[0.9681553,0.00014017291,0.031029223,0.000014004563,0.00003736791,0.000006406805,0.00015094396,0.000037537036,0.00042897696],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9997067,0.000023638442,0.000018194898,0.000068410736,0.00012334078,0.0000596767],"domain_scores_gemma":[0.99905425,0.00029949294,0.00015056104,0.00007881686,0.00028803217,0.00012897367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042904177,0.0003743043,0.0005296867,0.0031181062,0.0002957825,0.00072708767,0.0005402363,0.0006599485,0.00066302944],"category_scores_gemma":[0.00244265,0.00026275337,0.00025025642,0.0014368664,0.00031081136,0.00077899115,0.00051917025,0.0005534395,0.00020737294],"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":[0.0015083292,0.00028993064,0.096842214,0.00015642273,0.00020349312,0.0006221515,0.000381586,0.019504432,0.29523262,0.002861947,0.001850946,0.5805459],"study_design_scores_gemma":[0.000053791806,0.0004301775,0.113702275,0.000027839194,0.0001395486,0.0013858252,0.0003722533,0.79030925,0.08647947,0.0040991004,0.0029409605,0.000059531307],"about_ca_topic_score_codex":0.0016488332,"about_ca_topic_score_gemma":0.0012425055,"teacher_disagreement_score":0.0031181062,"about_ca_system_score_codex":0.00026239865,"about_ca_system_score_gemma":0.00035331628,"threshold_uncertainty_score":0.0032784939},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10083178,0.0008115717,0.89166427,0.00075557147,0.000095885,0.00016729676,0.0008176184,0.003276644,0.001579411],"genre_scores_gemma":[0.7372157,0.00058057596,0.25786313,0.00020581155,0.00013911782,0.00012933156,0.0018923117,0.00015479699,0.0018192491],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.991869,0.0012236766,0.0005701744,0.0016021017,0.0042040767,0.00053104426],"domain_scores_gemma":[0.98270607,0.0059377044,0.004859521,0.002935609,0.0030344864,0.0005266327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030201522,0.0009544138,0.0020043822,0.0047207032,0.0009958181,0.0023481103,0.0030746916,0.0016774973,0.0009732336],"category_scores_gemma":[0.017002499,0.00036715178,0.0011284782,0.0060464987,0.0007696691,0.0035537416,0.0026217739,0.0020914208,0.0008948458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004049399,0.0008089794,0.18559572,0.00058937067,0.0004948723,0.0017044218,0.00079239695,0.09716713,0.025298405,0.017958399,0.0108169755,0.6583684],"study_design_scores_gemma":[0.000022534707,0.0003084081,0.02216433,0.000056061257,0.00010075919,0.0016201525,0.00048946583,0.9237812,0.018901078,0.023807388,0.008665029,0.000083615036],"about_ca_topic_score_codex":0.0018612072,"about_ca_topic_score_gemma":0.0020735068,"teacher_disagreement_score":0.0047207032,"about_ca_system_score_codex":0.00073528447,"about_ca_system_score_gemma":0.0009960732,"threshold_uncertainty_score":0.015972257},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042852843,0.00010333106,0.9790125,0.00006924093,0.000019267269,0.00016859623,0.000055808407,0.015423088,0.0008629501],"genre_scores_gemma":[0.1308933,0.0005425494,0.86118007,0.00018041338,0.00004502002,0.00051059,0.0007755111,0.0019598017,0.00391275],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989214,0.00014878971,0.00013330595,0.0002740041,0.00040564832,0.000116972864],"domain_scores_gemma":[0.9989146,0.00029293052,0.00010740709,0.00029101537,0.00027772266,0.00011630466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022425991,0.0010098052,0.00091752503,0.0010823368,0.0004589761,0.0021413728,0.002882243,0.0010362403,0.0021808064],"category_scores_gemma":[0.0031510137,0.0009086044,0.0010468628,0.00053895696,0.0010835971,0.0022875562,0.0019555078,0.0018814658,0.0013299703],"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":[0.00059997523,0.00051487633,0.008397354,0.00083965564,0.000376716,0.0012067737,0.0022791254,0.15956655,0.11102772,0.071908206,0.011590528,0.6316925],"study_design_scores_gemma":[0.00012390547,0.00032753212,0.0022369102,0.00019090438,0.00019650112,0.0006422352,0.00012877864,0.86418176,0.040811263,0.027261648,0.06375205,0.00014657584],"about_ca_topic_score_codex":0.00385212,"about_ca_topic_score_gemma":0.002450749,"teacher_disagreement_score":0.00385212,"about_ca_system_score_codex":0.00068913784,"about_ca_system_score_gemma":0.0015402504,"threshold_uncertainty_score":0.011860132},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012785002,0.0029383833,0.9758567,0.0058359373,0.00020162102,0.000031355445,0.00011202043,0.00019152153,0.0020473977],"genre_scores_gemma":[0.6994204,0.0056729494,0.28638625,0.0017737792,0.0032804215,0.00015211206,0.00025082676,0.00020750705,0.0028557242],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99295205,0.0023589996,0.0005071025,0.001803861,0.0019242084,0.00045376772],"domain_scores_gemma":[0.970076,0.016573131,0.0025225512,0.004989086,0.0047830455,0.0010561876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0071131424,0.0011560186,0.004196495,0.0050363187,0.0020908278,0.008104287,0.005604001,0.0041166996,0.0015228812],"category_scores_gemma":[0.028991701,0.00088579266,0.0027952937,0.0071481713,0.009988977,0.015383106,0.00696527,0.0060321754,0.0003173563],"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":[0.00019434556,0.00016743956,0.011761806,0.00046476626,0.00051726605,0.0010040484,0.0010612431,0.11918788,0.0024996267,0.7001902,0.0064352737,0.1565161],"study_design_scores_gemma":[0.00002378635,0.00008779946,0.0011253302,0.00006865357,0.00013103371,0.00037787855,0.00033217546,0.39620656,0.000793275,0.5962981,0.004484469,0.00007100717],"about_ca_topic_score_codex":0.0067240656,"about_ca_topic_score_gemma":0.0056234887,"teacher_disagreement_score":0.008104287,"about_ca_system_score_codex":0.002001858,"about_ca_system_score_gemma":0.001991178,"threshold_uncertainty_score":0.03761834},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043819126,0.00037397753,0.9457466,0.0002654562,0.00010538667,0.00013211291,0.0015715326,0.0057740007,0.0022117451],"genre_scores_gemma":[0.52640057,0.00038568422,0.46814102,0.00013542728,0.0000914982,0.00022054547,0.0035432195,0.0002217401,0.000860342],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952231,0.0007422709,0.0004138229,0.0009749056,0.0024130447,0.00023293904],"domain_scores_gemma":[0.99391925,0.0019940508,0.0011118522,0.0013058037,0.0014937335,0.00017526063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002220447,0.0013117921,0.0013360451,0.005366517,0.0006513775,0.0025296176,0.0017836476,0.0010256666,0.0010858636],"category_scores_gemma":[0.013327154,0.00039663873,0.0012616453,0.0053298213,0.00088886044,0.0036860732,0.0023490654,0.0013004323,0.00069308345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00096754276,0.0005358695,0.058127236,0.00078240054,0.0006514504,0.0011579993,0.0009891503,0.12490956,0.04145577,0.047029443,0.011287119,0.71210647],"study_design_scores_gemma":[0.000033149106,0.00019601862,0.007577973,0.000073175805,0.00010464141,0.0006166985,0.0003803785,0.88164836,0.033183806,0.062332477,0.013770928,0.00008233648],"about_ca_topic_score_codex":0.0017005882,"about_ca_topic_score_gemma":0.0017691087,"teacher_disagreement_score":0.005366517,"about_ca_system_score_codex":0.0006464121,"about_ca_system_score_gemma":0.0010894305,"threshold_uncertainty_score":0.011743009},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009568805,0.00013126137,0.9870821,0.00016401886,0.00003241584,0.000102849874,0.00006999286,0.0021528797,0.0006956503],"genre_scores_gemma":[0.1109489,0.00014764626,0.8834728,0.00018376924,0.00006133144,0.0008271805,0.0004152001,0.00032304283,0.0036202173],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99748707,0.00077066245,0.00016203013,0.00042145138,0.0009853977,0.00017330293],"domain_scores_gemma":[0.996357,0.0022912668,0.0001859323,0.00018773957,0.0008695527,0.00010848417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036824835,0.0023684225,0.001684613,0.0022453098,0.0010383056,0.0022154662,0.0018990365,0.0028113679,0.0068158414],"category_scores_gemma":[0.007301668,0.0006369857,0.0013178674,0.0011684563,0.0011088728,0.0018030186,0.0019165366,0.001879428,0.0019528908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021267792,0.00044703917,0.004032926,0.00032938863,0.00032260147,0.00053473515,0.0007743982,0.19972987,0.026937569,0.022469997,0.008612146,0.7336826],"study_design_scores_gemma":[0.00017851833,0.00018708987,0.00085798616,0.000026526486,0.000046414727,0.00021291581,0.00005776857,0.97659665,0.011086083,0.0055211177,0.0051936638,0.00003532107],"about_ca_topic_score_codex":0.0043290965,"about_ca_topic_score_gemma":0.0029340351,"teacher_disagreement_score":0.0068158414,"about_ca_system_score_codex":0.00096292695,"about_ca_system_score_gemma":0.0010393373,"threshold_uncertainty_score":0.02280128},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027255697,0.004847516,0.96340686,0.0027483208,0.00013216306,0.00004250123,0.00014922103,0.00094637106,0.00047143697],"genre_scores_gemma":[0.27874908,0.0045144293,0.71262807,0.00043749233,0.0010925664,0.000120353994,0.0007359012,0.00033164732,0.0013904286],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99143046,0.0033334089,0.000658816,0.0013764706,0.002931431,0.00026952283],"domain_scores_gemma":[0.93289816,0.048427284,0.0030990862,0.009165387,0.0053110206,0.0010989881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011843909,0.001541212,0.004350826,0.0031748281,0.0014625981,0.006851447,0.003087921,0.004166258,0.0016816588],"category_scores_gemma":[0.05218237,0.0010304541,0.0011734734,0.0049439026,0.003372387,0.011052349,0.0041548824,0.005085617,0.0011687067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006335271,0.00031069678,0.016106145,0.0005342786,0.0002904817,0.00016405598,0.0005113992,0.05490437,0.008648601,0.024703702,0.010403298,0.8827895],"study_design_scores_gemma":[0.0000536512,0.00017657186,0.0041660257,0.0000535115,0.00007739431,0.00066019583,0.00036195226,0.87242955,0.0047988994,0.1129695,0.0041551143,0.000097754426],"about_ca_topic_score_codex":0.0029224409,"about_ca_topic_score_gemma":0.002151246,"teacher_disagreement_score":0.011843909,"about_ca_system_score_codex":0.00083235506,"about_ca_system_score_gemma":0.0013705884,"threshold_uncertainty_score":0.06263733},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09269412,0.00055732584,0.9039235,0.00080164586,0.00006563814,0.000045729947,0.00072366535,0.0009220492,0.00026633515],"genre_scores_gemma":[0.7005854,0.0006555378,0.29615366,0.00013652233,0.0001263372,0.00007947512,0.001848817,0.00007452999,0.0003396406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99647766,0.0010663085,0.00046604677,0.0006522859,0.0011675217,0.0001702107],"domain_scores_gemma":[0.98589826,0.009681831,0.0016196783,0.0013386946,0.0012349427,0.00022658826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038159075,0.00061838794,0.0012189193,0.002739304,0.00063291215,0.0017244351,0.0015734822,0.001122097,0.0002218661],"category_scores_gemma":[0.01684444,0.000394431,0.0010149818,0.0040765037,0.000997083,0.0034133159,0.0015116553,0.0016266822,0.00012304068],"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":[0.00049734255,0.00057197886,0.05370834,0.00045455282,0.00045345764,0.0013181819,0.0009776767,0.55810094,0.02547445,0.043284472,0.004383806,0.31077468],"study_design_scores_gemma":[0.0000066222606,0.00004109035,0.0022786246,0.000012933067,0.000020321537,0.00016986301,0.00010647378,0.97393835,0.0030294918,0.01938223,0.0009984126,0.000015462287],"about_ca_topic_score_codex":0.004890657,"about_ca_topic_score_gemma":0.0045202407,"teacher_disagreement_score":0.004890657,"about_ca_system_score_codex":0.0009665558,"about_ca_system_score_gemma":0.00093569997,"threshold_uncertainty_score":0.020180643},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8963499,0.00028739046,0.098243095,0.00022191725,0.00008342254,0.0014135336,0.00023025095,0.0016490648,0.0015213406],"genre_scores_gemma":[0.7936974,0.00037716332,0.20092572,0.00027483606,0.000058750207,0.0009332901,0.00095390656,0.000097003525,0.002681935],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99879396,0.00046279383,0.00012004857,0.00019051091,0.000341166,0.00009158632],"domain_scores_gemma":[0.99768686,0.00094716524,0.000069277725,0.00018893428,0.0009201635,0.00018757288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025952132,0.0006252972,0.000654979,0.00035122354,0.00027436466,0.00041759212,0.0011984913,0.0008511949,0.0015421214],"category_scores_gemma":[0.005788281,0.00029960464,0.00023202952,0.00020962331,0.00041300652,0.00094429916,0.00042373367,0.0003558704,0.0005929908],"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":[0.004980673,0.009757276,0.056930747,0.0020874245,0.0003641133,0.001312868,0.0028157183,0.020445123,0.3411377,0.0011109974,0.0036471286,0.55541027],"study_design_scores_gemma":[0.0033193156,0.11222597,0.18237823,0.0002993941,0.00084402703,0.0037718469,0.0014189127,0.28019515,0.38988397,0.00086046965,0.024515672,0.00028699136],"about_ca_topic_score_codex":0.0014299047,"about_ca_topic_score_gemma":0.001158564,"teacher_disagreement_score":0.0025952132,"about_ca_system_score_codex":0.00018841565,"about_ca_system_score_gemma":0.00055070245,"threshold_uncertainty_score":0.013724983},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011060399,0.0004910747,0.9860313,0.00017173546,0.000118999094,0.00013257911,0.000111402755,0.00089364644,0.0009888284],"genre_scores_gemma":[0.2115653,0.00033249697,0.78457963,0.00028787428,0.00009709241,0.0002905876,0.00052548863,0.0001761725,0.0021454426],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961021,0.00084640726,0.00029148796,0.00069705414,0.0018557883,0.00020722508],"domain_scores_gemma":[0.99517965,0.0014979995,0.0004844279,0.0008355867,0.0018066841,0.00019575036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030076155,0.00069994363,0.0015525107,0.0021656107,0.0011683844,0.001361492,0.002800795,0.0015245099,0.0020410595],"category_scores_gemma":[0.0117808515,0.00043514062,0.0010569158,0.0023337933,0.0011129571,0.0026188653,0.0020404942,0.0013185511,0.0011196545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005548337,0.00022677661,0.010334216,0.00034171555,0.00014710115,0.00019694353,0.00030780002,0.1147689,0.00930887,0.019062296,0.010206564,0.83454406],"study_design_scores_gemma":[0.00006900104,0.00021186187,0.0020267377,0.00003217761,0.000031978303,0.0004856184,0.00009450889,0.96448976,0.0057713427,0.017448252,0.009272783,0.000065985085],"about_ca_topic_score_codex":0.0062543564,"about_ca_topic_score_gemma":0.0071261837,"teacher_disagreement_score":0.0062543564,"about_ca_system_score_codex":0.0012144893,"about_ca_system_score_gemma":0.0016220657,"threshold_uncertainty_score":0.015906036},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55357456,0.00068089366,0.4362573,0.0008579536,0.00013338517,0.00010525374,0.001983683,0.0037483058,0.0026586822],"genre_scores_gemma":[0.94886637,0.00016296029,0.048752967,0.000027414006,0.000064128835,0.000038144735,0.0014234505,0.00002083361,0.000643744],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993525,0.00020529896,0.000057523426,0.00012676299,0.00018177762,0.000076133656],"domain_scores_gemma":[0.99737954,0.0014017123,0.0004117165,0.00020385033,0.0004927101,0.00011051844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070377724,0.0007687734,0.0010142467,0.0032671867,0.00030754684,0.0006748539,0.000750141,0.0008426885,0.0011169055],"category_scores_gemma":[0.0039382065,0.0002418306,0.0005407417,0.0022732432,0.00023145504,0.0011290852,0.00048414618,0.0007128929,0.0008263879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005902013,0.0006416498,0.11986266,0.00023783626,0.00035406084,0.0005987306,0.00010789496,0.35093564,0.0060647465,0.0020102211,0.00906429,0.509532],"study_design_scores_gemma":[0.000008558074,0.00006339182,0.0057705063,0.000008208365,0.000015076776,0.00006505144,0.000036858033,0.99003744,0.0019025618,0.001687071,0.00039434122,0.000010881549],"about_ca_topic_score_codex":0.003365829,"about_ca_topic_score_gemma":0.0032052991,"teacher_disagreement_score":0.003365829,"about_ca_system_score_codex":0.00027438116,"about_ca_system_score_gemma":0.00035221223,"threshold_uncertainty_score":0.006692469},"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":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011685315,0.05837675,0.91323364,0.002163061,0.00060334196,0.00022448931,0.0013386647,0.0030008967,0.009373953],"genre_scores_gemma":[0.16885899,0.14678276,0.66831446,0.0011517828,0.0015307871,0.0004991374,0.0049180076,0.0004693225,0.0074747796],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99587566,0.0005229542,0.00046014483,0.0004631293,0.002549689,0.00012842484],"domain_scores_gemma":[0.99536586,0.001914159,0.0005968254,0.0005148387,0.00151597,0.00009233544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021743102,0.0012619825,0.0017510026,0.0056321314,0.0008214806,0.0023486798,0.0019425269,0.00089531916,0.002124123],"category_scores_gemma":[0.0086569935,0.0003666418,0.0013056869,0.011763432,0.0006979001,0.003203409,0.0011153101,0.0010166377,0.0017400217],"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":[0.000086676024,0.000096528245,0.005899849,0.0018111452,0.0001388164,0.00018254503,0.000104039005,0.008737466,0.0027017633,0.011853136,0.009955735,0.9584323],"study_design_scores_gemma":[0.000118178774,0.00087339577,0.022867072,0.0030132066,0.000592262,0.00792382,0.0012762067,0.32643485,0.02570822,0.1787441,0.43199715,0.00045154608],"about_ca_topic_score_codex":0.0013797028,"about_ca_topic_score_gemma":0.0016659026,"teacher_disagreement_score":0.0056321314,"about_ca_system_score_codex":0.0004486959,"about_ca_system_score_gemma":0.0013829167,"threshold_uncertainty_score":0.011498928},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00867813,0.00003553914,0.9851999,0.00007558725,0.000029309711,0.00004106132,0.000057148878,0.004535542,0.0013477281],"genre_scores_gemma":[0.10196165,0.00004213561,0.8954715,0.000054745116,0.000010652961,0.00012421294,0.0001967931,0.00012630844,0.0020119466],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997631,0.000043028922,0.000022093844,0.000051317136,0.00009603409,0.000024451854],"domain_scores_gemma":[0.9992754,0.00035778084,0.00003650189,0.00014975126,0.00015018188,0.000030420768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004073451,0.00036828563,0.000569002,0.00058200397,0.00039598247,0.0007955756,0.0015969541,0.0007722427,0.0073161745],"category_scores_gemma":[0.0020517393,0.00028294546,0.00044650133,0.00043323133,0.00032561985,0.00073745876,0.00079112547,0.00046699136,0.0015621597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047686527,0.00012543988,0.002436018,0.00019748208,0.00012303713,0.00037391338,0.0001939332,0.27225164,0.08692455,0.016486311,0.007288142,0.61312264],"study_design_scores_gemma":[0.000038037884,0.00006424334,0.00038882447,0.000005417548,0.000011360016,0.00009258658,0.000024423842,0.9755053,0.015844192,0.0042909314,0.0037211012,0.000013698509],"about_ca_topic_score_codex":0.0028705143,"about_ca_topic_score_gemma":0.002758109,"teacher_disagreement_score":0.0073161745,"about_ca_system_score_codex":0.00049502274,"about_ca_system_score_gemma":0.00066635705,"threshold_uncertainty_score":0.024475038},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004022422,0.00049145846,0.99333465,0.0002686388,0.00011400285,0.000033732038,0.00011666661,0.0006720388,0.0009464174],"genre_scores_gemma":[0.32852703,0.0015767064,0.6613675,0.00066295394,0.0010851256,0.00037768902,0.0018477815,0.0006825387,0.0038726449],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9940708,0.0021415208,0.0003911561,0.0014042312,0.0016112378,0.00038097028],"domain_scores_gemma":[0.98400635,0.006598711,0.0014665865,0.0041843355,0.0031570944,0.0005868931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055647874,0.0014226604,0.0025774536,0.0031494084,0.0014646916,0.0028752936,0.0026512532,0.001954368,0.0026599497],"category_scores_gemma":[0.024562167,0.0008705167,0.002108628,0.0027751958,0.0019107868,0.0041618627,0.004794029,0.0049526435,0.0018419359],"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":[0.00020614348,0.00015378639,0.009718353,0.0003650739,0.0007180205,0.000245107,0.000540842,0.41262805,0.0071772137,0.18400909,0.015161695,0.36907664],"study_design_scores_gemma":[0.000008656581,0.000060992472,0.0008924107,0.000051202354,0.000038921396,0.00013031559,0.00006205337,0.8451937,0.002080007,0.14411709,0.007320357,0.00004427313],"about_ca_topic_score_codex":0.0017406888,"about_ca_topic_score_gemma":0.0022718648,"teacher_disagreement_score":0.0055647874,"about_ca_system_score_codex":0.0009753072,"about_ca_system_score_gemma":0.0010754659,"threshold_uncertainty_score":0.029429793},"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":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022021106,0.040768582,0.65195566,0.07490723,0.020708745,0.0011395682,0.065421596,0.02043765,0.10263992],"genre_scores_gemma":[0.14956105,0.0162994,0.26962277,0.0047911946,0.0034596063,0.0007413486,0.12239063,0.004362692,0.42877135],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99683976,0.00089149753,0.00014040823,0.0007214515,0.0010676484,0.000339193],"domain_scores_gemma":[0.98674357,0.003724508,0.00016729553,0.0021915932,0.006282018,0.00089112535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009098388,0.0021572618,0.0028122505,0.0028395527,0.0040418804,0.007669208,0.0036012016,0.0022768073,0.06538365],"category_scores_gemma":[0.023265444,0.0012985757,0.0017133296,0.002645617,0.0022755468,0.0030759107,0.002885343,0.005010864,0.013545159],"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":[0.00028218585,0.00021913934,0.0029136103,0.000211584,0.00022366954,0.00014339008,0.0002974732,0.0142568825,0.0006678171,0.018485626,0.758129,0.20416953],"study_design_scores_gemma":[0.00028344872,0.000090383204,0.010555727,0.0007157042,0.00035945026,0.00018044887,0.0008054391,0.23483561,0.0036632733,0.09959562,0.64869916,0.00021572177],"about_ca_topic_score_codex":0.8024696,"about_ca_topic_score_gemma":0.90222883,"teacher_disagreement_score":0.8024696,"about_ca_system_score_codex":0.01977352,"about_ca_system_score_gemma":0.033303052,"threshold_uncertainty_score":0.39738733},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031171711,0.00016235362,0.9653487,0.00005169642,0.000028589091,0.000043586508,0.000038761613,0.0017007837,0.0014537728],"genre_scores_gemma":[0.58141166,0.00016808754,0.41448396,0.000115921735,0.0000308912,0.00009860835,0.00017946624,0.00010281131,0.0034085705],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957424,0.000048230424,0.000017107513,0.00016664533,0.00013175678,0.00006188794],"domain_scores_gemma":[0.99963725,0.0001032161,0.000055275214,0.000070647795,0.00010047753,0.00003314996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041966233,0.00044098002,0.00048671468,0.0008199024,0.00024183569,0.00042422005,0.0013728079,0.0004905821,0.0012907087],"category_scores_gemma":[0.00079748075,0.00026691623,0.00055732235,0.00037642283,0.00029728768,0.00080999976,0.0005189498,0.00038957695,0.00040012214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032752389,0.00016967724,0.0031536287,0.00013127005,0.00013145646,0.00035065756,0.00022534476,0.033726268,0.18955648,0.007958713,0.0022263224,0.76204264],"study_design_scores_gemma":[0.000022574315,0.0000894647,0.003945369,0.000009189727,0.0000709554,0.00029275627,0.000041226096,0.91819286,0.06874082,0.0055685653,0.0029954775,0.000030766954],"about_ca_topic_score_codex":0.0033406294,"about_ca_topic_score_gemma":0.0041576256,"teacher_disagreement_score":0.0033406294,"about_ca_system_score_codex":0.00053338264,"about_ca_system_score_gemma":0.00053598,"threshold_uncertainty_score":0.006642401},"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":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.084180176,0.0012627549,0.9056063,0.0006018679,0.00021034197,0.0004417465,0.0014323934,0.0010312892,0.0052330354],"genre_scores_gemma":[0.81390136,0.00039625523,0.17952551,0.0003857387,0.0003972318,0.0012496074,0.0019875574,0.00033344323,0.0018232452],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9753661,0.011534256,0.0016109117,0.0054033888,0.0052118506,0.0008734977],"domain_scores_gemma":[0.7801465,0.18598087,0.010518119,0.016259175,0.005443921,0.0016514926],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.021249143,0.0016557951,0.0030574212,0.004750508,0.001133652,0.0029146636,0.0031098374,0.002970873,0.0056316857],"category_scores_gemma":[0.1356511,0.00050916016,0.0034025689,0.003720008,0.006435269,0.005948469,0.004082587,0.0040071616,0.001107703],"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":[0.0026414322,0.0005410953,0.13779101,0.0023551162,0.003684933,0.0019054312,0.0013357276,0.13549206,0.0171893,0.26270458,0.008765377,0.42559394],"study_design_scores_gemma":[0.00030684186,0.003719746,0.0582518,0.00038553376,0.00066448125,0.002584459,0.0011502182,0.44354013,0.017043341,0.4606972,0.0113279885,0.0003282736],"about_ca_topic_score_codex":0.0004413093,"about_ca_topic_score_gemma":0.0002975366,"teacher_disagreement_score":0.9787509,"about_ca_system_score_codex":0.0010563557,"about_ca_system_score_gemma":0.0012580252,"threshold_uncertainty_score":0.112377584},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007630281,0.00017286661,0.99126583,0.00006705582,0.000022514521,0.000022101447,0.00004662031,0.00043318653,0.000339498],"genre_scores_gemma":[0.31506795,0.00040234774,0.6811437,0.0001284157,0.00023956655,0.00015713547,0.0007693473,0.00034738073,0.0017441557],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961153,0.0011878986,0.00020506431,0.00064918265,0.0015916773,0.0002508],"domain_scores_gemma":[0.9933924,0.0030147182,0.0009673649,0.0010752204,0.0013448846,0.0002053435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033359192,0.0010599736,0.0020310122,0.0039050053,0.0008307501,0.0020606322,0.0017559596,0.00081101427,0.0010839939],"category_scores_gemma":[0.011876718,0.00037659195,0.0013077378,0.0031537004,0.0010814179,0.0025465193,0.0017567916,0.0018330747,0.0008021539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022177807,0.0002254066,0.0057331994,0.00020119922,0.00029257793,0.000114755276,0.00030067988,0.13092217,0.011496647,0.03412032,0.0045117494,0.8118595],"study_design_scores_gemma":[0.000021497628,0.00010388048,0.002722607,0.000023345874,0.00003916781,0.00014938672,0.00007241418,0.949925,0.0053077554,0.038409647,0.0031515248,0.000073775685],"about_ca_topic_score_codex":0.0020332586,"about_ca_topic_score_gemma":0.0025214278,"teacher_disagreement_score":0.0039050053,"about_ca_system_score_codex":0.00079673185,"about_ca_system_score_gemma":0.0014013542,"threshold_uncertainty_score":0.01764226},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0529301,0.00017357702,0.92310035,0.0005690628,0.000053351014,0.00034346952,0.0004019284,0.013574395,0.008853876],"genre_scores_gemma":[0.27480578,0.00027733963,0.72147566,0.00010917845,0.000021195385,0.00027938225,0.0005432224,0.00044516585,0.0020431234],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995036,0.00020664107,0.00003849396,0.000084630075,0.00012786782,0.000038810453],"domain_scores_gemma":[0.99827754,0.0010215059,0.00011267745,0.0001301577,0.00036259025,0.00009551336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013174139,0.0009151371,0.0003060945,0.0016059047,0.0003214491,0.002293996,0.00075028464,0.00050277455,0.003269071],"category_scores_gemma":[0.005644252,0.00037240563,0.00043835974,0.0005523865,0.00040759682,0.0022891543,0.0014273212,0.00086957205,0.0006864707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073534594,0.0005550794,0.009735335,0.0011811337,0.00015004913,0.0010237034,0.01314235,0.07204945,0.07912992,0.045198336,0.022912722,0.75418663],"study_design_scores_gemma":[0.00016992287,0.00055912277,0.008315442,0.0005565824,0.00019884783,0.000977012,0.0037447673,0.7744388,0.07529501,0.055785272,0.07975201,0.00020722463],"about_ca_topic_score_codex":0.0016125046,"about_ca_topic_score_gemma":0.0013249915,"teacher_disagreement_score":0.003269071,"about_ca_system_score_codex":0.00050701346,"about_ca_system_score_gemma":0.00049953314,"threshold_uncertainty_score":0.010936141},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39825842,0.00057732174,0.5833275,0.00018429941,0.0001268187,0.00020079703,0.0028350968,0.009089842,0.0053999303],"genre_scores_gemma":[0.77705854,0.00027345636,0.21677381,0.000051972285,0.00007177752,0.00006495508,0.0036785505,0.00017716296,0.0018497601],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993876,0.000072574454,0.000028816716,0.00013935019,0.00029240816,0.000079216916],"domain_scores_gemma":[0.998681,0.000306419,0.00025199616,0.00019852315,0.0004490193,0.00011289436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031949708,0.00045295985,0.00058914366,0.0027309228,0.00056212826,0.0005112257,0.00096327753,0.00055261876,0.0012248404],"category_scores_gemma":[0.0015850714,0.0002819491,0.0003721336,0.0011857473,0.00024148708,0.00061762286,0.0006089961,0.0004980813,0.00093439216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006354841,0.00024608083,0.10276369,0.0004252451,0.00018705492,0.0016889765,0.00087519473,0.028295774,0.11259871,0.0023537865,0.012786268,0.7371437],"study_design_scores_gemma":[0.000045644185,0.00023756652,0.20385613,0.00011307144,0.00012283004,0.0040451568,0.00083595025,0.6750029,0.0878014,0.0054307715,0.022355478,0.0001530211],"about_ca_topic_score_codex":0.006593778,"about_ca_topic_score_gemma":0.01114722,"teacher_disagreement_score":0.006593778,"about_ca_system_score_codex":0.00026533147,"about_ca_system_score_gemma":0.00063583266,"threshold_uncertainty_score":0.013110757},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05018119,0.00022202481,0.9379113,0.00018407592,0.00015797128,0.0008059179,0.0016617508,0.007007591,0.0018681501],"genre_scores_gemma":[0.18727951,0.0002880159,0.80411863,0.000077153636,0.000040795498,0.0012420111,0.0049443212,0.00033852068,0.0016711046],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790645,0.00026668145,0.0002958642,0.00046253038,0.00093944056,0.0001290355],"domain_scores_gemma":[0.99583566,0.0010932009,0.00039410742,0.00077125034,0.0018305208,0.00007521607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012731937,0.00121861,0.0010474974,0.002967529,0.0008896836,0.0012223282,0.001299727,0.0005348981,0.0023842347],"category_scores_gemma":[0.007911583,0.0004098432,0.00091309816,0.0036850858,0.00052287977,0.0014757842,0.0010065257,0.0013740899,0.0021454797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037365349,0.0005690707,0.008485021,0.0005842611,0.0001397603,0.00024331805,0.0005239665,0.021175114,0.07762094,0.0040963944,0.008139063,0.8780495],"study_design_scores_gemma":[0.000078186895,0.0009542084,0.0434317,0.00015375408,0.00012056872,0.0009808746,0.00095260766,0.58589065,0.30023012,0.010274966,0.05667888,0.00025340746],"about_ca_topic_score_codex":0.002342916,"about_ca_topic_score_gemma":0.0025097402,"teacher_disagreement_score":0.002967529,"about_ca_system_score_codex":0.0006526887,"about_ca_system_score_gemma":0.0014751091,"threshold_uncertainty_score":0.007976115},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33816501,0.003204747,0.6292047,0.0009939473,0.00070624566,0.00068144436,0.009834702,0.0035995606,0.013609639],"genre_scores_gemma":[0.70268,0.0011452778,0.27383482,0.00025417082,0.00027624393,0.00045438245,0.01593205,0.00014590764,0.0052770847],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.997981,0.0003471178,0.00025958134,0.00041615602,0.0008209992,0.00017511834],"domain_scores_gemma":[0.9929756,0.002192013,0.0008736746,0.0009902741,0.0025554085,0.00041299863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012798468,0.000507919,0.0008035279,0.005379891,0.0006706055,0.001267694,0.0010935924,0.0010044599,0.0033804884],"category_scores_gemma":[0.010551495,0.00012857985,0.0005861678,0.0047097537,0.0005645701,0.0018494425,0.00091124827,0.0008132264,0.0017786092],"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":[0.0009328845,0.00032440206,0.07056905,0.00094376854,0.0001056438,0.0007812492,0.0006813394,0.021822935,0.023307512,0.02182099,0.011404081,0.84730613],"study_design_scores_gemma":[0.00008657341,0.0009004259,0.06764404,0.00039073703,0.000112362264,0.0034460379,0.0025404545,0.7205885,0.028139878,0.10960964,0.06634796,0.00019337737],"about_ca_topic_score_codex":0.001700686,"about_ca_topic_score_gemma":0.0015100763,"teacher_disagreement_score":0.005379891,"about_ca_system_score_codex":0.00070602936,"about_ca_system_score_gemma":0.00077693316,"threshold_uncertainty_score":0.011308849},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04848084,0.0009675084,0.94541585,0.0014186734,0.00006323633,0.00009411418,0.00039625418,0.0017264077,0.0014370427],"genre_scores_gemma":[0.5292632,0.000826155,0.46743217,0.0002241384,0.00016838966,0.00014850842,0.0008248018,0.00019680212,0.0009157824],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98985684,0.004410428,0.00086280203,0.0015146713,0.0030011993,0.0003540853],"domain_scores_gemma":[0.92983675,0.04826687,0.0037456267,0.011562516,0.0056166584,0.000971498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023465926,0.0010107524,0.001525185,0.0037581942,0.0013990232,0.005631189,0.0025258728,0.0023554796,0.0016682873],"category_scores_gemma":[0.08315993,0.0006009713,0.0010292762,0.0035891167,0.0018242681,0.009106221,0.004438931,0.0024824159,0.000672808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010752862,0.00047718722,0.025114663,0.0007794263,0.0005804109,0.00031881302,0.0008515539,0.27094954,0.012862398,0.10749846,0.0071244384,0.57236785],"study_design_scores_gemma":[0.000038146125,0.00016123576,0.0020167404,0.00006107907,0.00007445203,0.0002885987,0.00020128387,0.87546504,0.0069127884,0.11177493,0.0029606896,0.000045090004],"about_ca_topic_score_codex":0.0021097239,"about_ca_topic_score_gemma":0.0016389779,"teacher_disagreement_score":0.023465926,"about_ca_system_score_codex":0.0013439264,"about_ca_system_score_gemma":0.0018182894,"threshold_uncertainty_score":0.12410116},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1844246,0.00015931085,0.81169546,0.00007792965,0.00003989743,0.00017652544,0.00021164192,0.0021285124,0.0010860824],"genre_scores_gemma":[0.707013,0.00007226439,0.291693,0.000032817312,0.000013152632,0.00016819354,0.00020965245,0.000034624987,0.0007633169],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970144,0.00007266415,0.000027947495,0.000060543534,0.00012091968,0.000016324422],"domain_scores_gemma":[0.999166,0.00040419702,0.00008581028,0.00008817507,0.00023196012,0.000023995259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058690045,0.00044363717,0.0004311189,0.00062980177,0.00016549442,0.000293162,0.00035513804,0.00030458948,0.0011718264],"category_scores_gemma":[0.002289736,0.00020680032,0.00020713461,0.000302541,0.00014833905,0.0003322905,0.00018323673,0.00023993335,0.00028909856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009673208,0.00033250227,0.01750922,0.00029066016,0.000118232274,0.0002215325,0.00017030106,0.07535658,0.12487734,0.0016263583,0.0015572642,0.7769726],"study_design_scores_gemma":[0.000058890815,0.00047747325,0.022235429,0.000026993155,0.00003653672,0.00030236106,0.000039701707,0.9264545,0.047274195,0.0014618863,0.0015996608,0.0000323288],"about_ca_topic_score_codex":0.0014470539,"about_ca_topic_score_gemma":0.00387575,"teacher_disagreement_score":0.0014470539,"about_ca_system_score_codex":0.00024241305,"about_ca_system_score_gemma":0.0003507807,"threshold_uncertainty_score":0.003920138},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045454226,0.00025124612,0.9518411,0.00052356377,0.00004357878,0.000060251925,0.00029436263,0.0005249576,0.0010067668],"genre_scores_gemma":[0.79739577,0.00034272228,0.19515507,0.00031507245,0.00022058243,0.00021302853,0.0016784172,0.00012365697,0.004555685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982168,0.00055997627,0.00009452619,0.0005102631,0.00041476463,0.00020363307],"domain_scores_gemma":[0.99253255,0.004911166,0.0007902754,0.0007318066,0.0008487551,0.00018549808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026732923,0.00085547543,0.0017227862,0.0021315282,0.00080693676,0.001348716,0.0024591275,0.0020041065,0.0024620863],"category_scores_gemma":[0.015514707,0.0008275068,0.0016326212,0.0015759786,0.0009441372,0.0026156276,0.0015315551,0.0021337029,0.0005303275],"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":[0.00022026313,0.00013253925,0.013385327,0.00014304872,0.00023310597,0.0004155706,0.00026868636,0.76799273,0.001161678,0.041136324,0.0058196904,0.16909105],"study_design_scores_gemma":[0.000008079821,0.000012169894,0.0004363803,0.0000064244696,0.000013259546,0.000053323718,0.000018485865,0.97459275,0.00018052348,0.024296544,0.00037451435,0.000007576144],"about_ca_topic_score_codex":0.007615698,"about_ca_topic_score_gemma":0.012476732,"teacher_disagreement_score":0.007615698,"about_ca_system_score_codex":0.001098097,"about_ca_system_score_gemma":0.0013947502,"threshold_uncertainty_score":0.015142739},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041772146,0.00020358873,0.9562693,0.000097222706,0.000044569868,0.00009824124,0.00013691718,0.00079931633,0.00057872554],"genre_scores_gemma":[0.26434687,0.00018964993,0.73414373,0.00002969395,0.000030877174,0.00012235685,0.00035990248,0.000066993045,0.00070994586],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984542,0.00021256214,0.00014706615,0.0004039418,0.0006950621,0.00008710083],"domain_scores_gemma":[0.9982627,0.0005695552,0.00019184734,0.00016162886,0.0007629672,0.000051402618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017998421,0.000796902,0.0009812667,0.0040556435,0.0012443981,0.00128243,0.0013972138,0.0013002452,0.00071201264],"category_scores_gemma":[0.0053540324,0.00030347874,0.0011098365,0.003164061,0.00064268,0.0010070086,0.0006590202,0.0011184962,0.00035609287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042934832,0.00030016596,0.00819532,0.00029915475,0.000311396,0.0003879816,0.0006460529,0.27091965,0.046245746,0.011784542,0.0030486425,0.657432],"study_design_scores_gemma":[0.000007130153,0.000041401385,0.0025882372,0.000015704816,0.000027840544,0.00008852483,0.000058625683,0.9844583,0.008841263,0.0028822676,0.00095246214,0.000038216334],"about_ca_topic_score_codex":0.015297697,"about_ca_topic_score_gemma":0.009502838,"teacher_disagreement_score":0.015297697,"about_ca_system_score_codex":0.0010587671,"about_ca_system_score_gemma":0.0015260398,"threshold_uncertainty_score":0.030417323},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003368808,0.00040866656,0.99451244,0.00006697419,0.000050479714,0.000050637667,0.0000409968,0.0009596373,0.0005413222],"genre_scores_gemma":[0.2262581,0.001028121,0.7669253,0.00023034748,0.0003208668,0.0002573675,0.00080740213,0.0005765501,0.0035960677],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9924609,0.0015856808,0.00045275348,0.0016194718,0.003428152,0.00045299146],"domain_scores_gemma":[0.9949587,0.0015205048,0.0005680324,0.0009131293,0.0018257492,0.00021398724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046219807,0.002883478,0.0041977405,0.0073161786,0.0011301673,0.0027883758,0.0040016137,0.0022402988,0.0019754374],"category_scores_gemma":[0.008756068,0.0013862633,0.00402045,0.006881385,0.00095455704,0.0040232576,0.0034722101,0.003196923,0.0022328536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006479314,0.00041227703,0.01236495,0.00044854218,0.0017801089,0.00057672855,0.00034175513,0.15256071,0.023259448,0.010463882,0.005440988,0.7917026],"study_design_scores_gemma":[0.000022015824,0.00020420415,0.002315211,0.000028869898,0.00028813662,0.0006635424,0.00006816019,0.976267,0.0069898916,0.007981416,0.005066764,0.00010488167],"about_ca_topic_score_codex":0.0029152844,"about_ca_topic_score_gemma":0.00328835,"teacher_disagreement_score":0.0073161786,"about_ca_system_score_codex":0.0008715355,"about_ca_system_score_gemma":0.0011278522,"threshold_uncertainty_score":0.024443686},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.761029,0.00041785036,0.23021924,0.00019741808,0.00007150902,0.0000950547,0.0007521354,0.0043713734,0.0028464827],"genre_scores_gemma":[0.96151733,0.00006696997,0.037227552,0.000017993585,0.000018765963,0.00001988848,0.0006266538,0.000026083344,0.00047876764],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949646,0.00008828527,0.000028117978,0.00014245062,0.00017109732,0.00007351894],"domain_scores_gemma":[0.99900264,0.00026390862,0.00025122537,0.00011081554,0.0002711983,0.00010021666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000460176,0.0004883026,0.0006056992,0.0023445738,0.00025628167,0.0005895231,0.0006360178,0.00056789187,0.0005865034],"category_scores_gemma":[0.0017656712,0.00013948623,0.0002929818,0.00090715714,0.00040766347,0.0010455198,0.0005717378,0.00035291922,0.00038073488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013792831,0.0007966415,0.08020247,0.00031343635,0.0001775339,0.0007443662,0.0007603095,0.050146103,0.13996772,0.0031589724,0.0046960227,0.7176571],"study_design_scores_gemma":[0.000033999393,0.0004499205,0.078607805,0.000021862657,0.00006797327,0.0008553761,0.0006378122,0.8614404,0.05178943,0.0033032254,0.0027267144,0.00006547657],"about_ca_topic_score_codex":0.002532207,"about_ca_topic_score_gemma":0.004817647,"teacher_disagreement_score":0.002532207,"about_ca_system_score_codex":0.0003499345,"about_ca_system_score_gemma":0.0003890182,"threshold_uncertainty_score":0.005034864},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016194092,0.00066462165,0.9820436,0.00043474016,0.00003960835,0.00003709823,0.00012732265,0.00019547707,0.0002633677],"genre_scores_gemma":[0.5163894,0.0017841216,0.4789557,0.00027476304,0.00064168166,0.00016793438,0.0010631256,0.0001322828,0.00059098244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98979694,0.0030261434,0.00076293055,0.0019415139,0.0040940554,0.0003785402],"domain_scores_gemma":[0.9456075,0.041148167,0.0056254365,0.0040847296,0.002971779,0.00056240574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009036145,0.001498901,0.0034827192,0.004774712,0.0012536902,0.004191897,0.0036594083,0.0025374566,0.0005138409],"category_scores_gemma":[0.060724158,0.0010174409,0.0018084834,0.0055231038,0.0027542296,0.0076903985,0.0028160133,0.0034071933,0.0002873216],"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":[0.00027045023,0.00015488465,0.021780845,0.0005843385,0.00048356078,0.0008395862,0.0005476521,0.69734544,0.0027969102,0.04646427,0.0026267439,0.22610529],"study_design_scores_gemma":[0.000011050015,0.000037922626,0.0015031605,0.00005088672,0.00004396057,0.00029657612,0.0001430642,0.9214098,0.0016568719,0.073625185,0.0011828386,0.00003875114],"about_ca_topic_score_codex":0.003620249,"about_ca_topic_score_gemma":0.0020271423,"teacher_disagreement_score":0.009036145,"about_ca_system_score_codex":0.0013128377,"about_ca_system_score_gemma":0.0010836177,"threshold_uncertainty_score":0.047788262},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36305046,0.00040525946,0.6169004,0.0010888376,0.00017112226,0.0004915092,0.0005381503,0.010514615,0.006839645],"genre_scores_gemma":[0.88497525,0.00011753098,0.11217321,0.00022987979,0.000043333996,0.00008073035,0.00031298868,0.00005685861,0.0020102914],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968004,0.00049013953,0.00027101886,0.0007344895,0.0013134601,0.0003904373],"domain_scores_gemma":[0.9913669,0.0020317163,0.0017037443,0.0026616831,0.0018690721,0.0003668573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022675162,0.0007431807,0.0011040132,0.0033621087,0.0008835218,0.0022200868,0.0018795934,0.0011590503,0.0013699712],"category_scores_gemma":[0.011154575,0.000438796,0.00075506227,0.0014096972,0.0007824133,0.0026926026,0.004092277,0.0012463061,0.0008278784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007500381,0.00085693033,0.07616997,0.00023983348,0.00037682056,0.00076716783,0.0007445374,0.061649583,0.05203388,0.011896596,0.0075803665,0.78693414],"study_design_scores_gemma":[0.000043922635,0.00054906466,0.022230081,0.000054656142,0.00017205413,0.0014937257,0.0003895353,0.8707913,0.07682346,0.016969532,0.010331887,0.0001507479],"about_ca_topic_score_codex":0.00198873,"about_ca_topic_score_gemma":0.0015642846,"teacher_disagreement_score":0.0033621087,"about_ca_system_score_codex":0.0010226035,"about_ca_system_score_gemma":0.0011629786,"threshold_uncertainty_score":0.0119918585},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66127586,0.00036075845,0.3342062,0.00011387138,0.000043451644,0.00009745249,0.0008962808,0.0012737548,0.0017324665],"genre_scores_gemma":[0.9612366,0.00014283451,0.03739339,0.000022411494,0.000019846339,0.00003004345,0.00065597816,0.000022241815,0.00047672546],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996648,0.000041842464,0.000020731544,0.00012373982,0.000113297356,0.000035543457],"domain_scores_gemma":[0.9995291,0.00014861285,0.00014262547,0.000033563498,0.000101703874,0.000044353274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025979875,0.00029583974,0.00038687172,0.0017801685,0.00018686039,0.00034216294,0.0003511521,0.00036162813,0.00044290465],"category_scores_gemma":[0.0013604674,0.00017479333,0.00020325034,0.00073570636,0.00019409726,0.00034076898,0.00032901403,0.00022545633,0.0001868734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00097329105,0.00026065874,0.16880684,0.00028729532,0.00016823215,0.00045380817,0.00049777946,0.0966478,0.15090106,0.0019048256,0.0020694695,0.57702893],"study_design_scores_gemma":[0.000025389141,0.00041982895,0.2655356,0.000034555287,0.000056841138,0.00072278804,0.00028651426,0.7037874,0.02362438,0.0021351203,0.0033075244,0.00006411662],"about_ca_topic_score_codex":0.0037448548,"about_ca_topic_score_gemma":0.0067951274,"teacher_disagreement_score":0.0037448548,"about_ca_system_score_codex":0.00025424606,"about_ca_system_score_gemma":0.00027739763,"threshold_uncertainty_score":0.0074461102},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026570011,0.0002966749,0.9932903,0.00014054336,0.000048526683,0.00006793653,0.0002854776,0.002754059,0.0004593838],"genre_scores_gemma":[0.19615303,0.00051613245,0.7993755,0.0002238052,0.00022112332,0.00028860886,0.0016240807,0.00032833323,0.001269414],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99794704,0.00037492652,0.0001433225,0.00047340468,0.00091365125,0.00014766994],"domain_scores_gemma":[0.9978415,0.0006730731,0.00026910487,0.0003895077,0.0007005615,0.0001263293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024778116,0.0011248576,0.0013015753,0.0031005384,0.00074241945,0.0019327234,0.0029641802,0.0010498127,0.0014314164],"category_scores_gemma":[0.0068101147,0.00043220172,0.0014413135,0.0023652203,0.00075568963,0.0021083911,0.0022327823,0.0019023718,0.0006145571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041637564,0.00041234537,0.011424246,0.0006550093,0.00054250023,0.0007695776,0.00054582255,0.39876622,0.014724746,0.10413959,0.020050544,0.447553],"study_design_scores_gemma":[0.00001293809,0.000041185565,0.000972289,0.000018194753,0.000036358164,0.00014132388,0.00006426867,0.9535026,0.0028016751,0.034650277,0.007732538,0.000026257327],"about_ca_topic_score_codex":0.008479136,"about_ca_topic_score_gemma":0.011398731,"teacher_disagreement_score":0.008479136,"about_ca_system_score_codex":0.0011635221,"about_ca_system_score_gemma":0.0019335441,"threshold_uncertainty_score":0.016859591},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7658917,0.036997072,0.07389209,0.0735198,0.0032200357,0.00029910062,0.0010767711,0.0019571637,0.043146286],"genre_scores_gemma":[0.9724591,0.0058927266,0.0071292715,0.0102797095,0.0009839103,0.00003593472,0.00016158515,0.00017194975,0.0028858145],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99202543,0.002835533,0.0007823187,0.0010322452,0.0027866873,0.0005378259],"domain_scores_gemma":[0.9635151,0.02191198,0.008184353,0.0015546652,0.0040192218,0.0008147249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007093906,0.0008885158,0.0008891963,0.0013164387,0.00052799954,0.0019844766,0.0014834589,0.004157541,0.0043340735],"category_scores_gemma":[0.045232136,0.00072636537,0.0009484274,0.0014818276,0.0016846322,0.002979919,0.0009715141,0.0018878704,0.0019966909],"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":[0.0018803297,0.00028238576,0.46615243,0.0028323052,0.0006095869,0.021617161,0.0048018815,0.0013625359,0.030590411,0.0052701873,0.035515454,0.4290853],"study_design_scores_gemma":[0.00031672977,0.004967467,0.3908414,0.003076368,0.0016516624,0.2843066,0.015724981,0.006704613,0.10927756,0.026272342,0.15649658,0.00036367812],"about_ca_topic_score_codex":0.0014044164,"about_ca_topic_score_gemma":0.001425754,"teacher_disagreement_score":0.007093906,"about_ca_system_score_codex":0.00074226275,"about_ca_system_score_gemma":0.0004573364,"threshold_uncertainty_score":0.037516594},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00453523,0.00008366265,0.9944904,0.000032814798,0.000024436054,0.00002451654,0.000043382173,0.0006234101,0.00014209186],"genre_scores_gemma":[0.09930131,0.00015427885,0.8985596,0.0000516496,0.00009548801,0.00012642024,0.00039380617,0.0001234291,0.0011941455],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99719894,0.000505368,0.00019729233,0.00051577354,0.0014116039,0.00017094269],"domain_scores_gemma":[0.9959747,0.0016727063,0.00039882204,0.00065116736,0.0011636296,0.00013895711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028636095,0.0012852424,0.0025750569,0.002350761,0.00091864384,0.0014994643,0.002959557,0.0016129006,0.0011832645],"category_scores_gemma":[0.009312647,0.0006839494,0.001552124,0.003389658,0.0010755248,0.001988851,0.002794556,0.0019434053,0.0009910766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005008201,0.00028964767,0.0039838543,0.00021194016,0.0002147401,0.00022910887,0.00014875691,0.11531232,0.027808737,0.0070484336,0.0045704264,0.83968115],"study_design_scores_gemma":[0.000021263964,0.00008939496,0.0008527682,0.0000085042675,0.000028388546,0.00017196444,0.000028912262,0.9854439,0.006452512,0.005465944,0.0014110069,0.000025412623],"about_ca_topic_score_codex":0.0023556007,"about_ca_topic_score_gemma":0.0032315135,"teacher_disagreement_score":0.002959557,"about_ca_system_score_codex":0.00046362376,"about_ca_system_score_gemma":0.0012073973,"threshold_uncertainty_score":0.015144408},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.070861325,0.0017471743,0.9245643,0.00074957823,0.000115000345,0.0000826381,0.00010806889,0.0009608854,0.00081101],"genre_scores_gemma":[0.6586077,0.0015120452,0.33728424,0.00015232815,0.0004365511,0.00008398315,0.0003689695,0.00009660632,0.0014576064],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981,0.0004837774,0.00012159153,0.00040704667,0.00073793414,0.00014963889],"domain_scores_gemma":[0.99077004,0.005521079,0.0011678286,0.000489098,0.0017828679,0.00026910193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015535902,0.0008476736,0.0012074601,0.0019813427,0.00075836433,0.0012840531,0.0018932996,0.0021685625,0.00083604804],"category_scores_gemma":[0.011801771,0.00037804403,0.0005016135,0.0021539808,0.0008911709,0.0025871252,0.000963905,0.0015655705,0.00039957155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006540939,0.0002941039,0.015234813,0.00052805664,0.00016987219,0.0010577033,0.00042522376,0.22278252,0.05723728,0.02003077,0.0070921606,0.6744934],"study_design_scores_gemma":[0.000012173671,0.00015032916,0.0018802538,0.000012183847,0.00002228387,0.0005623439,0.00006601415,0.97481155,0.009482769,0.011161031,0.001815806,0.00002331213],"about_ca_topic_score_codex":0.0022787515,"about_ca_topic_score_gemma":0.0013064514,"teacher_disagreement_score":0.0022787515,"about_ca_system_score_codex":0.0007317049,"about_ca_system_score_gemma":0.0005139489,"threshold_uncertainty_score":0.0082163215},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0072855824,0.00034335107,0.9903972,0.000083711995,0.00003335147,0.00001312703,0.00002145531,0.00026972225,0.0015524384],"genre_scores_gemma":[0.6567606,0.0011046681,0.33424526,0.000097907054,0.0001330684,0.00010552247,0.00023469642,0.00012478014,0.0071934625],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878496,0.0002139529,0.00005047981,0.00021454197,0.00062545313,0.00011064726],"domain_scores_gemma":[0.9985078,0.0007987013,0.00016023965,0.0001735031,0.00032187614,0.00003788252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001632921,0.0009886854,0.0015759944,0.0014965271,0.00055043667,0.0012131649,0.0014118112,0.0016012455,0.0020037051],"category_scores_gemma":[0.00503646,0.0008190922,0.0010899237,0.0013461392,0.0011838992,0.0025786553,0.0016605945,0.0013418014,0.00081564113],"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":[0.0005111513,0.00009753986,0.0019686623,0.00017647874,0.00016350001,0.0001726509,0.00014463447,0.5096223,0.019210571,0.03941232,0.002290026,0.42623016],"study_design_scores_gemma":[0.000009307797,0.000052495478,0.0007250946,0.000012854379,0.000028945698,0.00011658644,0.000014644281,0.9661063,0.004468216,0.027579103,0.0008578267,0.000028562345],"about_ca_topic_score_codex":0.0020303812,"about_ca_topic_score_gemma":0.0018389252,"teacher_disagreement_score":0.0020303812,"about_ca_system_score_codex":0.0007224449,"about_ca_system_score_gemma":0.00060374767,"threshold_uncertainty_score":0.008635759},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014225691,0.00025542855,0.9843077,0.00010017169,0.00003336028,0.000019885481,0.00003492874,0.00046344503,0.0005593307],"genre_scores_gemma":[0.474607,0.0004501202,0.52233565,0.0001357053,0.00015161617,0.00012256936,0.00043209866,0.00020831148,0.0015569563],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862957,0.0003741203,0.000077266035,0.00022718892,0.0005879214,0.00010391344],"domain_scores_gemma":[0.9972566,0.0013145023,0.00029349286,0.0003291431,0.00071201235,0.00009425891],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002013971,0.00077488174,0.001312692,0.0014613026,0.0004465128,0.0011726057,0.0012132035,0.0009197483,0.00094632513],"category_scores_gemma":[0.0067868647,0.0003765429,0.00066559063,0.0014106628,0.0007257675,0.0017525837,0.0013146495,0.0014596059,0.00049040024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000120056866,0.0001167231,0.0027842992,0.000128616,0.00007557391,0.00010649613,0.00011083682,0.2436331,0.011546764,0.015091161,0.0029341457,0.7233521],"study_design_scores_gemma":[0.00000563237,0.000024830098,0.00044649938,0.000009410414,0.000007757113,0.00004173312,0.000013802158,0.9874949,0.0025279128,0.008655736,0.00075525517,0.000016545146],"about_ca_topic_score_codex":0.0019273895,"about_ca_topic_score_gemma":0.0023389638,"teacher_disagreement_score":0.002013971,"about_ca_system_score_codex":0.00044865717,"about_ca_system_score_gemma":0.0011939751,"threshold_uncertainty_score":0.010650992},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032982596,0.00015681582,0.99206984,0.0001473307,0.000023199504,0.00018431325,0.00077978603,0.0028430417,0.0004974111],"genre_scores_gemma":[0.045833595,0.00011802286,0.95124453,0.00008982707,0.000018664528,0.00024855346,0.0018882361,0.0001316266,0.0004267845],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963406,0.0005600167,0.00043941318,0.00084338,0.0016193916,0.00019722516],"domain_scores_gemma":[0.9950557,0.0015807238,0.000474389,0.0012503457,0.0013298424,0.00030902456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045189965,0.0009934215,0.0013936344,0.005684511,0.0011176692,0.002647286,0.0026547015,0.0010691732,0.0016970607],"category_scores_gemma":[0.011167252,0.00070766895,0.0016116097,0.0031774081,0.0012951987,0.0033237261,0.0038707512,0.0016583699,0.0007430122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003931943,0.00034246172,0.03394204,0.001186151,0.00031251187,0.0008895268,0.0010865314,0.030969702,0.02345844,0.055325344,0.023884887,0.8282092],"study_design_scores_gemma":[0.00012076495,0.00023324697,0.011382346,0.00018008637,0.00016338343,0.0034377347,0.0008099617,0.7763753,0.029373582,0.07425536,0.103429064,0.00023914973],"about_ca_topic_score_codex":0.005440085,"about_ca_topic_score_gemma":0.00482842,"teacher_disagreement_score":0.005684511,"about_ca_system_score_codex":0.0011084203,"about_ca_system_score_gemma":0.0020041515,"threshold_uncertainty_score":0.023899019},"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":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011849541,0.0030081982,0.99419594,0.000081727216,0.000046409594,0.000028207372,0.000083175815,0.0010244611,0.00034695645],"genre_scores_gemma":[0.046397634,0.00675345,0.94296783,0.00016834827,0.00029601375,0.00016653203,0.000825361,0.00047118688,0.0019537448],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99775726,0.0005396,0.00021397669,0.00070361875,0.0006841307,0.000101402155],"domain_scores_gemma":[0.9931879,0.0035869924,0.00036603268,0.001653571,0.0010970351,0.00010833917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002418055,0.0013950452,0.0027582939,0.002459379,0.00085373246,0.0018347662,0.00492011,0.0014519175,0.0027542962],"category_scores_gemma":[0.011864307,0.0011285293,0.0019382115,0.003793414,0.00069140806,0.003042211,0.0017370592,0.0030053686,0.0025562502],"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":[0.000100898484,0.0000876877,0.0012327206,0.00048837473,0.00015203429,0.00005359235,0.00012211186,0.034067612,0.004634015,0.008086788,0.0034549492,0.9475193],"study_design_scores_gemma":[0.000035529163,0.00016029534,0.002388691,0.0002064898,0.00023424324,0.0007363917,0.00013133703,0.9124162,0.016147157,0.04475805,0.02265811,0.00012756456],"about_ca_topic_score_codex":0.0062418804,"about_ca_topic_score_gemma":0.0070282193,"teacher_disagreement_score":0.0062418804,"about_ca_system_score_codex":0.00080722006,"about_ca_system_score_gemma":0.0013696834,"threshold_uncertainty_score":0.012788057},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21039511,0.0005164539,0.7798368,0.00014222556,0.00005520374,0.00029518694,0.0020323535,0.0028316835,0.0038950506],"genre_scores_gemma":[0.6145234,0.0004236827,0.38036132,0.000037852173,0.000056369565,0.0001668085,0.0029489694,0.00009225985,0.0013894122],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994011,0.000126143,0.000028621738,0.00010106821,0.0002893628,0.000053660235],"domain_scores_gemma":[0.9985453,0.0005489734,0.0001813274,0.00017699086,0.0004978689,0.000049519796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048765418,0.00046749826,0.00039182525,0.0029490788,0.00025976953,0.00059314736,0.00054335623,0.000370211,0.00088871684],"category_scores_gemma":[0.0026506155,0.00018374427,0.0002535037,0.0015298671,0.00023610856,0.00072380004,0.000504663,0.00032063894,0.000377014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037498388,0.00017102974,0.017864078,0.00025229037,0.00007490363,0.00029034598,0.00023946611,0.06140994,0.07910556,0.003076133,0.00363662,0.8335047],"study_design_scores_gemma":[0.00004086031,0.0002748297,0.037180614,0.000055233093,0.000052457173,0.00034789476,0.0003505172,0.87876904,0.0681655,0.0052852444,0.00941955,0.000058243713],"about_ca_topic_score_codex":0.005659498,"about_ca_topic_score_gemma":0.005957408,"teacher_disagreement_score":0.005659498,"about_ca_system_score_codex":0.00044193445,"about_ca_system_score_gemma":0.0006002548,"threshold_uncertainty_score":0.0112531185},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015925143,0.00023925207,0.9814683,0.0005845083,0.000043813838,0.00009858048,0.00017526955,0.00057898415,0.00088603416],"genre_scores_gemma":[0.658607,0.0006560908,0.3338744,0.0003491281,0.00012797573,0.0006399666,0.0005685788,0.0001922298,0.0049846475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.994584,0.0012546695,0.0005525631,0.0013320425,0.0017052687,0.0005715166],"domain_scores_gemma":[0.9906328,0.0034355742,0.0013864503,0.0016409084,0.0025266467,0.00037773733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006550616,0.0014274272,0.0015045782,0.0013598709,0.0008421214,0.004044267,0.00457445,0.0018813969,0.0022769612],"category_scores_gemma":[0.013102625,0.00094747415,0.0015842151,0.0013566525,0.0014347784,0.009098935,0.0027892457,0.0023084437,0.00064656825],"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":[0.00041881984,0.00027330974,0.007973742,0.0003058178,0.00023760195,0.00023143746,0.00036562944,0.8495326,0.011008597,0.05573702,0.0023833453,0.07153201],"study_design_scores_gemma":[0.000023063045,0.000054904354,0.00044554757,0.00001235788,0.000043762633,0.0000383829,0.000015988428,0.9857691,0.0014835908,0.011189759,0.0008990079,0.000024657975],"about_ca_topic_score_codex":0.0071146544,"about_ca_topic_score_gemma":0.007159134,"teacher_disagreement_score":0.0071146544,"about_ca_system_score_codex":0.0025674466,"about_ca_system_score_gemma":0.0030474667,"threshold_uncertainty_score":0.034643352},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017554564,0.9428575,0.043127377,0.0042186216,0.00073447905,0.000079090176,0.00009476332,0.00014901882,0.0069836653],"genre_scores_gemma":[0.030248757,0.9008917,0.063503005,0.0013374937,0.0017711519,0.00014273346,0.00032109217,0.000069586225,0.0017144418],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99455863,0.0013109156,0.00060421845,0.0009705671,0.0023621246,0.0001936325],"domain_scores_gemma":[0.9751872,0.016582891,0.0012552226,0.0010026728,0.0056857425,0.00028633594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075704134,0.0009878263,0.002039017,0.0138268,0.0010928338,0.0054046875,0.0043342873,0.0023082318,0.0021876718],"category_scores_gemma":[0.018694013,0.00060211617,0.0012287239,0.018106721,0.002839114,0.008651802,0.0015866138,0.002646051,0.0014443852],"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":[0.000044772838,0.00009328951,0.0014037642,0.0074722953,0.00008570268,0.00011244915,0.00048444927,0.0014657597,0.00046793884,0.048525434,0.0142879775,0.92555606],"study_design_scores_gemma":[0.000034037243,0.0002316562,0.004626488,0.01935005,0.00026212813,0.0022112937,0.0020516391,0.018243691,0.002702632,0.15529504,0.7947737,0.00021775684],"about_ca_topic_score_codex":0.0038086458,"about_ca_topic_score_gemma":0.002109236,"teacher_disagreement_score":0.0138268,"about_ca_system_score_codex":0.0027276026,"about_ca_system_score_gemma":0.0036709113,"threshold_uncertainty_score":0.04003668},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047607697,0.0015721257,0.9304909,0.0011951259,0.0007168557,0.00038163888,0.0047446582,0.019237107,0.03690088],"genre_scores_gemma":[0.14478856,0.0021222695,0.735836,0.0024399953,0.001199064,0.0011460681,0.02564131,0.004455762,0.0823709],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971035,0.00033823666,0.00020856546,0.0006498533,0.001344405,0.0003553829],"domain_scores_gemma":[0.9959002,0.00110259,0.00029576087,0.0013331191,0.0012240745,0.00014421274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026942564,0.0014255403,0.0011877273,0.004829217,0.0018137947,0.0036786469,0.0026734625,0.0024944262,0.07034544],"category_scores_gemma":[0.013886158,0.00047847716,0.0016199896,0.0041016955,0.0011141766,0.005126272,0.0027148137,0.0021741353,0.050086793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035662903,0.00020253408,0.003929155,0.00041798662,0.00008228906,0.00031970075,0.00021487389,0.011751341,0.0048197773,0.06885535,0.13557279,0.77347755],"study_design_scores_gemma":[0.00012508048,0.00029257004,0.0031781548,0.00023467088,0.00008453245,0.0020246753,0.00037710564,0.20038846,0.013505581,0.15270224,0.62691957,0.00016723879],"about_ca_topic_score_codex":0.0052188826,"about_ca_topic_score_gemma":0.0065624053,"teacher_disagreement_score":0.07034544,"about_ca_system_score_codex":0.0012002616,"about_ca_system_score_gemma":0.0017717363,"threshold_uncertainty_score":0},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15234783,0.00012350657,0.8450707,0.0002213385,0.000049909355,0.0000859549,0.00014435801,0.0012880388,0.00066836015],"genre_scores_gemma":[0.82522684,0.000056193196,0.17308332,0.00012624047,0.00004354486,0.00012588521,0.00038586708,0.00005399194,0.00089815224],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985335,0.0004219599,0.000108043205,0.0004397378,0.00036478532,0.00013186387],"domain_scores_gemma":[0.98658437,0.009487001,0.001261932,0.0012263465,0.0011137807,0.00032654376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004775202,0.00058477506,0.001042169,0.0017855368,0.0006517712,0.0010840363,0.0014371702,0.0012569586,0.0018848625],"category_scores_gemma":[0.02026021,0.00040803145,0.00049802125,0.00081295666,0.00117943,0.0017860213,0.0011013567,0.0015470145,0.00060828286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009802904,0.00053617114,0.024911748,0.00020788182,0.00010530087,0.00047198043,0.00044503866,0.35174242,0.024508089,0.010698542,0.002492689,0.58289987],"study_design_scores_gemma":[0.000006655725,0.000054383843,0.0016625379,0.0000047088533,0.0000043980453,0.000047606012,0.000019381914,0.9934407,0.0029322458,0.0016379908,0.00018200098,0.000007431948],"about_ca_topic_score_codex":0.004075929,"about_ca_topic_score_gemma":0.004292496,"teacher_disagreement_score":0.004775202,"about_ca_system_score_codex":0.0009403882,"about_ca_system_score_gemma":0.0010517611,"threshold_uncertainty_score":0.025253952},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03617132,0.00058122387,0.95509535,0.00031810557,0.00015314092,0.00012675962,0.00009425449,0.0064792577,0.0009805285],"genre_scores_gemma":[0.3268101,0.00032804455,0.66918325,0.00028337075,0.00027289745,0.00016468976,0.000475237,0.00045335497,0.0020291382],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99656373,0.0008967738,0.00027994878,0.000613799,0.0013467768,0.00029900885],"domain_scores_gemma":[0.9846513,0.0100333225,0.0006511713,0.0018159546,0.0025203703,0.0003278332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004035384,0.0016178902,0.002067366,0.002397739,0.0007015694,0.0022021835,0.0029428604,0.0018368108,0.006112793],"category_scores_gemma":[0.017738966,0.0010403596,0.0010042761,0.0015918396,0.0006508851,0.0048510367,0.0020249304,0.0031978,0.0036411397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044785943,0.00016527051,0.0018551681,0.00015492774,0.00015106487,0.00014967674,0.00015627171,0.044446807,0.019108985,0.0035982383,0.0031750554,0.92659056],"study_design_scores_gemma":[0.0000679739,0.00014289177,0.0007498713,0.000025012281,0.00006414854,0.00015510489,0.000047600053,0.9672426,0.019980574,0.008127222,0.003363106,0.000033893128],"about_ca_topic_score_codex":0.0031281388,"about_ca_topic_score_gemma":0.0026366487,"teacher_disagreement_score":0.006112793,"about_ca_system_score_codex":0.00062942674,"about_ca_system_score_gemma":0.001320259,"threshold_uncertainty_score":0.021341383},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002238644,0.000080019316,0.9966852,0.00026962438,0.000028851575,0.00003511744,0.00008607363,0.0003402409,0.00023626015],"genre_scores_gemma":[0.19406842,0.0003912654,0.8013304,0.00059851306,0.0004892404,0.0005164908,0.00093282375,0.00030646264,0.001366449],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97948223,0.005660283,0.0011850885,0.0051342365,0.007669318,0.00086891744],"domain_scores_gemma":[0.95740604,0.019609816,0.0053073154,0.010604912,0.005887156,0.0011846916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0146319335,0.0015638847,0.002697637,0.006547932,0.0016984596,0.005498307,0.008069301,0.003768755,0.0016901394],"category_scores_gemma":[0.06878651,0.0012798718,0.0038458067,0.008993352,0.0050917394,0.009271709,0.0074738394,0.004983823,0.0010222943],"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":[0.00022626345,0.00032866054,0.029372923,0.00033149484,0.0006616885,0.00068007497,0.0016561953,0.17538409,0.005900919,0.5115572,0.0077528986,0.26614758],"study_design_scores_gemma":[0.00003299282,0.00013627922,0.0015195793,0.000046158348,0.00007785682,0.00042252603,0.000121104924,0.7223732,0.0025057094,0.2650739,0.007614759,0.00007595498],"about_ca_topic_score_codex":0.00307802,"about_ca_topic_score_gemma":0.0018657012,"teacher_disagreement_score":0.0146319335,"about_ca_system_score_codex":0.0019858435,"about_ca_system_score_gemma":0.0025739244,"threshold_uncertainty_score":0.07738197},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041451706,0.00005224643,0.9952402,0.000053857584,0.000013790571,0.00003429644,0.000015678354,0.00027812872,0.00016657759],"genre_scores_gemma":[0.06198435,0.000094103736,0.9370337,0.00006598565,0.0000263665,0.00014980667,0.0001091812,0.000041616233,0.0004949047],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980586,0.00046602287,0.00012866207,0.00043661133,0.00081373396,0.000096476346],"domain_scores_gemma":[0.9976174,0.0010671138,0.00027690455,0.000266768,0.0006820839,0.00008979244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019433404,0.00092086487,0.0015680692,0.0023083878,0.0006841534,0.001233724,0.0022500954,0.0016309696,0.0006427918],"category_scores_gemma":[0.0052095633,0.0005011389,0.0011876272,0.0022778877,0.0010185767,0.0015888005,0.00091872696,0.0015905504,0.00028428718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001451861,0.00030048421,0.0039948993,0.0001967649,0.00030174977,0.00025513608,0.00028150188,0.429765,0.022373509,0.019425338,0.00237569,0.5205847],"study_design_scores_gemma":[0.000034442204,0.000068087946,0.00049379974,0.000011111363,0.000029951827,0.00016793946,0.000025852614,0.98830265,0.004742031,0.004650782,0.0014461502,0.000027152386],"about_ca_topic_score_codex":0.0046205465,"about_ca_topic_score_gemma":0.003644901,"teacher_disagreement_score":0.0046205465,"about_ca_system_score_codex":0.0009771623,"about_ca_system_score_gemma":0.0018469115,"threshold_uncertainty_score":0.01027745},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01842291,0.00005254495,0.97922117,0.00007042257,0.000018219587,0.000020274985,0.00008017239,0.0016822146,0.00043202637],"genre_scores_gemma":[0.48657718,0.00013554994,0.5105,0.000052536245,0.000051157796,0.00009059452,0.00060067035,0.00028341127,0.0017089555],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982358,0.0003417821,0.00011244914,0.00026140473,0.000915374,0.00013317291],"domain_scores_gemma":[0.9957728,0.0019855886,0.00047669985,0.00046892656,0.0011682863,0.00012770886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014331704,0.0005973109,0.00061265967,0.0025811545,0.00045359845,0.00085161877,0.0012195227,0.00067922415,0.0013209751],"category_scores_gemma":[0.01009751,0.00030944918,0.0007198176,0.0016143572,0.00069631427,0.0015909472,0.00095479446,0.0012053548,0.00040695115],"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":[0.00018646137,0.00011052476,0.011611918,0.000100953934,0.00012324285,0.00032390095,0.00026673084,0.5434058,0.028568838,0.03338294,0.0033718052,0.3785469],"study_design_scores_gemma":[0.0000037603606,0.000017741702,0.0008253216,0.0000027694443,0.000003971384,0.00008215361,0.0000121914145,0.9873904,0.00272847,0.007821344,0.0011018411,0.000010005352],"about_ca_topic_score_codex":0.0054872157,"about_ca_topic_score_gemma":0.0036502872,"teacher_disagreement_score":0.0054872157,"about_ca_system_score_codex":0.0007741654,"about_ca_system_score_gemma":0.00079166994,"threshold_uncertainty_score":0.010910511},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047003172,0.0006570068,0.93654865,0.0002918404,0.00011928502,0.0005159301,0.0020512037,0.011955926,0.00085705274],"genre_scores_gemma":[0.0987727,0.00026640578,0.8906405,0.00015055758,0.000054112465,0.0006199568,0.0067170393,0.00047220127,0.0023065328],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890506,0.000113377755,0.0001444185,0.00024186377,0.00049582595,0.000099451056],"domain_scores_gemma":[0.99773335,0.0008475015,0.00032516028,0.0002711833,0.0007198958,0.00010287902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011436729,0.001351384,0.0014322562,0.00562531,0.0011189007,0.0014373199,0.002466266,0.0019449309,0.0014527335],"category_scores_gemma":[0.0065362174,0.0006347207,0.0009490801,0.0036508073,0.00064864894,0.0025989334,0.0018157035,0.0012785597,0.001867382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006255551,0.00040993985,0.019778287,0.00043376454,0.00029917667,0.0006656326,0.00034953386,0.031407554,0.02188433,0.0031388078,0.027667502,0.8933399],"study_design_scores_gemma":[0.00014311825,0.0002065312,0.004695228,0.00006213535,0.00007576275,0.0015970975,0.0002635015,0.9367577,0.029304875,0.0087610595,0.018059518,0.00007353549],"about_ca_topic_score_codex":0.0027595575,"about_ca_topic_score_gemma":0.0049363147,"teacher_disagreement_score":0.00562531,"about_ca_system_score_codex":0.00050661154,"about_ca_system_score_gemma":0.0014388196,"threshold_uncertainty_score":0.006048441},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01427855,0.000106498,0.98451936,0.0001148376,0.0000076754495,0.0000579718,0.00013176861,0.00033544385,0.00044784576],"genre_scores_gemma":[0.63780105,0.00025707728,0.3601949,0.00006443622,0.000029111121,0.00026814983,0.00059409486,0.000069992435,0.0007212658],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990638,0.00038539764,0.00004026946,0.00020804092,0.00024089892,0.000061609775],"domain_scores_gemma":[0.99732566,0.0020268718,0.00026527053,0.00013097274,0.00017183252,0.0000794308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014679701,0.0008430845,0.0009117351,0.0013462873,0.00038453046,0.00092290365,0.0011097965,0.0006796518,0.0012718637],"category_scores_gemma":[0.0059067835,0.0005639956,0.0009370101,0.0008176881,0.0008541519,0.0014128145,0.00093413045,0.0011768198,0.00018095787],"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":[0.00016652068,0.00011678476,0.0033051658,0.00012802087,0.00010415134,0.00016199987,0.0002103385,0.88728416,0.0024438004,0.019794505,0.0008474755,0.08543702],"study_design_scores_gemma":[0.0000047874337,0.00001050293,0.00021340887,0.0000044096,0.0000033410583,0.000007688925,0.000008710674,0.99010205,0.0004339528,0.00906986,0.00013661906,0.00000469549],"about_ca_topic_score_codex":0.013135203,"about_ca_topic_score_gemma":0.010541453,"teacher_disagreement_score":0.013135203,"about_ca_system_score_codex":0.0014317926,"about_ca_system_score_gemma":0.0011032295,"threshold_uncertainty_score":0.026117504},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008653005,0.000044969132,0.99094385,0.000040057566,0.0000070682877,0.000011932292,0.000032220665,0.00016546856,0.00010133448],"genre_scores_gemma":[0.40005505,0.00025379946,0.59747213,0.00006154369,0.00003498563,0.00016124238,0.0004906211,0.0001415469,0.0013290115],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926394,0.00027190533,0.000051217416,0.00018536889,0.0001792322,0.000048252437],"domain_scores_gemma":[0.9941076,0.004766149,0.00040453326,0.0003728331,0.00026578005,0.00008300179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014496822,0.00066092407,0.00074127346,0.00083585375,0.0004634057,0.00079757086,0.0012289996,0.00097024045,0.0016878294],"category_scores_gemma":[0.009777452,0.00077733933,0.0007971715,0.00063605205,0.0009612106,0.0019915323,0.0011223475,0.0018145044,0.00038223524],"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":[0.00006781009,0.000043098273,0.0014887138,0.00006628192,0.00004010804,0.00010514929,0.00013744966,0.94183797,0.0018439216,0.020897858,0.00024062805,0.03323102],"study_design_scores_gemma":[0.0000030118142,0.0000052735013,0.000085354586,0.000003333806,0.000002807662,0.000007827668,0.0000048430325,0.9902148,0.00043232302,0.009096501,0.0001399741,0.000003917925],"about_ca_topic_score_codex":0.0063761794,"about_ca_topic_score_gemma":0.007315894,"teacher_disagreement_score":0.0063761794,"about_ca_system_score_codex":0.00078519364,"about_ca_system_score_gemma":0.0012058555,"threshold_uncertainty_score":0.012678146},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062484697,0.00020427049,0.9358361,0.0001323635,0.000025168376,0.000040061273,0.00008239071,0.0007119997,0.00048292227],"genre_scores_gemma":[0.78277993,0.00020871617,0.2156816,0.000029929777,0.000031594904,0.00007131765,0.00029452992,0.00006592025,0.0008364587],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994863,0.00021217378,0.000029959572,0.000105441606,0.0001237129,0.00004245519],"domain_scores_gemma":[0.9977391,0.0014892041,0.0002288779,0.00019655742,0.00027653427,0.0000698162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009490131,0.0005371612,0.0006360957,0.0013267398,0.0004889351,0.00056641607,0.00072937546,0.0006828425,0.00049567956],"category_scores_gemma":[0.0047605103,0.0004600057,0.000584111,0.0012234622,0.00044592496,0.00068673113,0.0005847809,0.0006859021,0.00015921434],"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":[0.00006575013,0.0000335076,0.0017881724,0.000024137358,0.0000440589,0.000048047063,0.00009713608,0.942557,0.0013523635,0.0029910805,0.00027318712,0.050725587],"study_design_scores_gemma":[0.0000016027994,0.0000052272358,0.00017947801,0.0000016431421,0.0000020354132,0.0000075403386,0.0000059115637,0.9977356,0.00032274565,0.0016516338,0.00008326952,0.000003285568],"about_ca_topic_score_codex":0.016493658,"about_ca_topic_score_gemma":0.010670049,"teacher_disagreement_score":0.016493658,"about_ca_system_score_codex":0.0010192522,"about_ca_system_score_gemma":0.0009653672,"threshold_uncertainty_score":0.03279531},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054696415,0.0019220834,0.94016814,0.00032738174,0.00033313766,0.00013788493,0.0002676928,0.0008834419,0.001263888],"genre_scores_gemma":[0.5962158,0.00064574456,0.40096,0.00023068833,0.0004469051,0.0002879471,0.00045680426,0.00007063544,0.0006856078],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953548,0.0007204516,0.00062709744,0.0006528458,0.002479371,0.00016536904],"domain_scores_gemma":[0.9907657,0.0043744254,0.0016272039,0.0005522619,0.0022590896,0.00042142178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029183102,0.0013379782,0.0014434624,0.0043701907,0.0005195559,0.0020089,0.00150441,0.0014619653,0.0006134611],"category_scores_gemma":[0.015314563,0.00026813138,0.0007279852,0.0023377123,0.0010684091,0.003634593,0.0015881145,0.0012120312,0.00032831967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011956133,0.00079395203,0.037805386,0.0011983123,0.0006836734,0.00051716,0.0005703204,0.07717689,0.08587746,0.025619356,0.0053242887,0.76323754],"study_design_scores_gemma":[0.00008198108,0.0021751102,0.025915843,0.00014322088,0.00031454655,0.0022297597,0.00025924627,0.900759,0.043765713,0.01441282,0.0096794395,0.00026335943],"about_ca_topic_score_codex":0.0004769608,"about_ca_topic_score_gemma":0.0003819644,"teacher_disagreement_score":0.0043701907,"about_ca_system_score_codex":0.0009784866,"about_ca_system_score_gemma":0.0007735146,"threshold_uncertainty_score":0.015433669},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061997373,0.0002399404,0.9353173,0.000048643244,0.00005570384,0.0000661461,0.00019545862,0.0009752843,0.0011041875],"genre_scores_gemma":[0.5670256,0.0002927305,0.42983076,0.000029723116,0.000054613574,0.00009144902,0.0008429062,0.000105180705,0.0017270508],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988876,0.00012838033,0.000107436244,0.00035702484,0.00042559628,0.00009399532],"domain_scores_gemma":[0.9989951,0.00021963798,0.00018012097,0.0001787544,0.00037561244,0.000050767194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048759187,0.0008748156,0.0009113147,0.0032180073,0.00040362717,0.00093640946,0.0011499856,0.00055515126,0.0007224741],"category_scores_gemma":[0.002162956,0.00023237997,0.0007536967,0.002590835,0.00033879772,0.0012126629,0.0009753202,0.00065465044,0.00050239346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033502342,0.00029032305,0.025223266,0.00035177017,0.00032942998,0.00037378058,0.00037775186,0.10638611,0.0590333,0.011828586,0.0030026268,0.79246795],"study_design_scores_gemma":[0.000015384421,0.0001391865,0.011989641,0.00001555842,0.000050970524,0.0003975853,0.00013702441,0.95400566,0.023304846,0.0048822677,0.005013364,0.000048446615],"about_ca_topic_score_codex":0.003002267,"about_ca_topic_score_gemma":0.0025188818,"teacher_disagreement_score":0.0032180073,"about_ca_system_score_codex":0.0005317296,"about_ca_system_score_gemma":0.0005418636,"threshold_uncertainty_score":0.005969584},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039056214,0.00008525762,0.99252737,0.000047526784,0.000020488167,0.00004008872,0.000090804744,0.002963704,0.00031916483],"genre_scores_gemma":[0.1692626,0.00012453295,0.8280066,0.000085063824,0.000054413547,0.00024758893,0.0008850155,0.00036699962,0.0009671071],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860066,0.00058965496,0.00007175385,0.00035678886,0.00028529123,0.00009575097],"domain_scores_gemma":[0.99361044,0.0036814664,0.00033713243,0.0012520544,0.000963646,0.00015525575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002722919,0.0009766053,0.0014381497,0.00095999014,0.0006776381,0.00070971745,0.0027783632,0.0011008804,0.0034178575],"category_scores_gemma":[0.009013897,0.0009208276,0.00086896826,0.0011124918,0.00072579936,0.0018344678,0.0011431527,0.0020118705,0.0015852918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026739002,0.0001991759,0.0019156487,0.0002057302,0.00010985574,0.00016010692,0.00016710743,0.45876876,0.01038447,0.0067338236,0.009695947,0.511392],"study_design_scores_gemma":[0.000010887815,0.000017655502,0.00022328814,0.0000063799207,0.0000056296585,0.000032746706,0.0000067151873,0.9933634,0.0016316926,0.0038388267,0.00085371756,0.000009171048],"about_ca_topic_score_codex":0.0055385963,"about_ca_topic_score_gemma":0.0076533523,"teacher_disagreement_score":0.0055385963,"about_ca_system_score_codex":0.00056033256,"about_ca_system_score_gemma":0.0013337935,"threshold_uncertainty_score":0.014400363},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035739997,0.00032706006,0.9951852,0.000048949063,0.000034279637,0.000009989462,0.00003695363,0.00047981116,0.0003038524],"genre_scores_gemma":[0.18927357,0.0013945124,0.80542463,0.00009495389,0.00012921415,0.000093221446,0.00038784917,0.00021591224,0.002986055],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985707,0.000031093008,0.000010510561,0.000031794407,0.00005375203,0.00001576491],"domain_scores_gemma":[0.99947876,0.00037628916,0.000030145406,0.00004306841,0.000060745402,0.000011004896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040203828,0.00066791195,0.00072919304,0.000553306,0.0003041542,0.0006042646,0.0006731495,0.000629954,0.0016731683],"category_scores_gemma":[0.001417973,0.0003628155,0.0007398673,0.0008202498,0.00032080067,0.0010552221,0.0004051848,0.0013870167,0.0006759713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018538113,0.00012110424,0.0008713729,0.00023866326,0.0001410359,0.0001332167,0.00013125222,0.26565596,0.051005665,0.03341909,0.0039037997,0.6441934],"study_design_scores_gemma":[0.000004003582,0.00001737676,0.00026421744,0.000007472461,0.000014211323,0.000038198606,0.000005063261,0.98451984,0.0048654736,0.009496465,0.00075674226,0.000010900564],"about_ca_topic_score_codex":0.002271306,"about_ca_topic_score_gemma":0.0029994468,"teacher_disagreement_score":0.002271306,"about_ca_system_score_codex":0.00032546624,"about_ca_system_score_gemma":0.00045376428,"threshold_uncertainty_score":0.0055972934},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021879442,0.00023145984,0.97428507,0.00007683596,0.00007978803,0.00008695784,0.00016391563,0.0024961873,0.0007004356],"genre_scores_gemma":[0.2558469,0.00036064215,0.737817,0.0001165454,0.000154654,0.00015608355,0.00089785235,0.00026028714,0.0043900805],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990544,0.00008112073,0.00006184932,0.0003153839,0.0004009788,0.00008634879],"domain_scores_gemma":[0.99889046,0.00023147458,0.00016538009,0.00014575811,0.000456623,0.00011032408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079889514,0.0013208695,0.0013076102,0.0022916389,0.000546244,0.0009121117,0.0020579991,0.0009660269,0.001856269],"category_scores_gemma":[0.00190474,0.0004001689,0.0007631727,0.0017582731,0.00046900357,0.0013950432,0.0010954072,0.0013367904,0.0013168254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003827597,0.00030017726,0.0028370204,0.00007552383,0.00007956446,0.00013167359,0.00005801718,0.019672075,0.046758164,0.0013713088,0.00268377,0.92565],"study_design_scores_gemma":[0.000016968177,0.000100191195,0.001414665,0.000006954209,0.000036525056,0.00021089736,0.000027575972,0.9765836,0.018523458,0.0011957302,0.0018673978,0.000016072197],"about_ca_topic_score_codex":0.0056199776,"about_ca_topic_score_gemma":0.008249215,"teacher_disagreement_score":0.0056199776,"about_ca_system_score_codex":0.00056103896,"about_ca_system_score_gemma":0.0011232225,"threshold_uncertainty_score":0.0111745},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15814681,0.0003850013,0.83909416,0.000373823,0.000087111875,0.0000769921,0.00015211558,0.00045386178,0.001230068],"genre_scores_gemma":[0.94020563,0.000117758806,0.05876262,0.000106919906,0.00010585127,0.000051704967,0.00027382723,0.000040129624,0.0003355942],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99710244,0.00075692474,0.0002976692,0.000852735,0.0007811547,0.00020908541],"domain_scores_gemma":[0.9785073,0.01428902,0.0024823088,0.0012442862,0.0028700863,0.0006070131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048252246,0.0011422373,0.0018398917,0.0014587909,0.0004676012,0.002014715,0.0024711299,0.0015081564,0.000824712],"category_scores_gemma":[0.029111423,0.00059726165,0.00094236946,0.0010387334,0.00069504365,0.0038359347,0.0015544092,0.0023012112,0.00027469525],"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":[0.0012755598,0.00043694486,0.043273307,0.00028912205,0.00055662484,0.00027566613,0.0006774192,0.5852297,0.008444547,0.007910492,0.002153416,0.34947726],"study_design_scores_gemma":[0.000013802148,0.00011684449,0.0016357275,0.00000841818,0.000025773857,0.000043601467,0.000033682,0.9930472,0.001390668,0.0035115215,0.00015640025,0.000016496228],"about_ca_topic_score_codex":0.0016341133,"about_ca_topic_score_gemma":0.0013285828,"teacher_disagreement_score":0.0048252246,"about_ca_system_score_codex":0.0011164278,"about_ca_system_score_gemma":0.0005197704,"threshold_uncertainty_score":0.025518477},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021150032,0.0001250232,0.9989655,0.000040676234,0.000010669499,0.000025327725,0.000052253934,0.00039018437,0.00017881679],"genre_scores_gemma":[0.020256488,0.00047568846,0.9776962,0.000048002024,0.000072882576,0.0002803237,0.00034865446,0.00029595016,0.0005258215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958762,0.0017314348,0.00020506475,0.00053713284,0.001494855,0.00015526144],"domain_scores_gemma":[0.9937251,0.0031350162,0.0006781687,0.0009870902,0.001324417,0.0001501807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040461863,0.0019239098,0.0017803591,0.0047106766,0.0010044881,0.0031552329,0.00315768,0.001755723,0.0036112522],"category_scores_gemma":[0.01844155,0.0012389336,0.0020991785,0.005171964,0.001430842,0.0030338978,0.003300014,0.0037815683,0.002406699],"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":[0.00010811778,0.00010712009,0.0014666911,0.0006635342,0.000245612,0.0001679101,0.00063950394,0.32972276,0.008275618,0.32504082,0.014506575,0.31905577],"study_design_scores_gemma":[0.000012996437,0.000028183737,0.00049562904,0.000067131194,0.00002847878,0.000092598384,0.00006836481,0.89071566,0.0028910765,0.09389562,0.011654139,0.00005008999],"about_ca_topic_score_codex":0.00835002,"about_ca_topic_score_gemma":0.0057263873,"teacher_disagreement_score":0.00835002,"about_ca_system_score_codex":0.0022598065,"about_ca_system_score_gemma":0.0025791556,"threshold_uncertainty_score":0.021398544},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017259577,0.0002758071,0.98090863,0.0000784729,0.000046487796,0.00007379105,0.000096709526,0.0006164415,0.0006439771],"genre_scores_gemma":[0.56860393,0.00073860266,0.42755476,0.00006743032,0.00013841584,0.00028546745,0.0009168202,0.00016824294,0.0015263172],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971738,0.0003937988,0.00026372838,0.0006027783,0.0013455291,0.00022030351],"domain_scores_gemma":[0.99772555,0.0006195293,0.0003137915,0.00023403933,0.0010223627,0.00008474565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014694745,0.0011512883,0.0021419474,0.0035106193,0.000958705,0.0018731786,0.001535823,0.0007016992,0.0011771439],"category_scores_gemma":[0.0080217095,0.00040716535,0.0019658557,0.0028951713,0.001009861,0.0025631273,0.0012187893,0.0010410293,0.0004941174],"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":[0.00048683255,0.00012159479,0.009756571,0.0004941691,0.00034687514,0.00027510803,0.0004717211,0.51111156,0.01282555,0.025048735,0.0048945094,0.4341668],"study_design_scores_gemma":[0.000019431283,0.00007741717,0.0020993443,0.000024262226,0.000050551003,0.00013276239,0.00006985979,0.97904813,0.0057106605,0.010963353,0.001746678,0.000057532045],"about_ca_topic_score_codex":0.005827629,"about_ca_topic_score_gemma":0.0026742623,"teacher_disagreement_score":0.005827629,"about_ca_system_score_codex":0.0012086303,"about_ca_system_score_gemma":0.0015117222,"threshold_uncertainty_score":0.011587441},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019678252,0.00034197923,0.9785993,0.000076719974,0.000028555005,0.000025009585,0.00015392443,0.0007679754,0.00032832855],"genre_scores_gemma":[0.7765718,0.0006911369,0.21958384,0.0000500101,0.00006889196,0.00009849458,0.0012546577,0.00012566953,0.0015555461],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989197,0.00026379916,0.000068143396,0.0003057268,0.00032335316,0.000119361335],"domain_scores_gemma":[0.9965436,0.0014800406,0.00045273782,0.00046095919,0.00097479677,0.00008786819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014066389,0.0007914384,0.0011017502,0.0019600368,0.00046540133,0.0007542727,0.001635124,0.0008914554,0.0009807098],"category_scores_gemma":[0.009052077,0.00048641674,0.0008354329,0.0026016876,0.0004718335,0.0022410268,0.0008796415,0.0016242331,0.0006889839],"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":[0.00017697026,0.00011976611,0.010108053,0.00011020153,0.00013443071,0.00010915129,0.00012129315,0.7789416,0.002967316,0.009617386,0.002535397,0.19505842],"study_design_scores_gemma":[0.0000020396478,0.000009260035,0.0005637733,0.0000027086319,0.0000044830435,0.000018501492,0.0000069918683,0.9969227,0.00056354166,0.001595181,0.00030441658,0.0000064321875],"about_ca_topic_score_codex":0.018028568,"about_ca_topic_score_gemma":0.0068011354,"teacher_disagreement_score":0.018028568,"about_ca_system_score_codex":0.00095296314,"about_ca_system_score_gemma":0.0011278163,"threshold_uncertainty_score":0.035847247},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014902385,0.0002123305,0.9839553,0.00010425743,0.00001718566,0.000015298383,0.00004213739,0.00037790288,0.0003732474],"genre_scores_gemma":[0.6249168,0.0004159592,0.37052116,0.00021038366,0.0001222001,0.00020777484,0.00076208817,0.00032681163,0.0025168261],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932337,0.00034338897,0.000027063663,0.000129131,0.00011979339,0.00005727187],"domain_scores_gemma":[0.9977483,0.001389366,0.0001600219,0.00034163342,0.00024579515,0.00011489575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021911552,0.0010483639,0.0015850691,0.001033177,0.0004883352,0.00082909013,0.0017299931,0.001384734,0.0012613547],"category_scores_gemma":[0.005946783,0.0007100242,0.0010100116,0.0009038708,0.00084265554,0.0022096185,0.001691804,0.0022153566,0.00045910105],"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":[0.00005387469,0.00004787384,0.0009017913,0.000036175275,0.000078752564,0.000044147717,0.000052013962,0.9029683,0.0015784731,0.016527504,0.0014332549,0.07627789],"study_design_scores_gemma":[0.0000011616015,0.000004805434,0.000026699185,0.0000015379625,0.0000015906159,0.0000027796605,0.0000015622146,0.9943737,0.00011020029,0.0054060416,0.00006804528,0.0000019121808],"about_ca_topic_score_codex":0.0021803705,"about_ca_topic_score_gemma":0.0028788107,"teacher_disagreement_score":0.0021911552,"about_ca_system_score_codex":0.00067088864,"about_ca_system_score_gemma":0.0006548523,"threshold_uncertainty_score":0.011588097},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030188728,0.13051702,0.8495137,0.004986956,0.0013483985,0.00035191653,0.00039316976,0.0011147285,0.008755272],"genre_scores_gemma":[0.087807484,0.085995086,0.81176215,0.0044075428,0.0034546722,0.0012561599,0.0017339585,0.00037431487,0.0032086954],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9800821,0.007884935,0.0020806522,0.0032128538,0.0063755903,0.0003639179],"domain_scores_gemma":[0.9808646,0.012581799,0.0009349067,0.0030641083,0.0022732592,0.00028117796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016732052,0.0012881898,0.0033951164,0.0064426,0.001315773,0.0069091693,0.0044321404,0.0032988365,0.0025167812],"category_scores_gemma":[0.030099222,0.0013337898,0.00179343,0.013493465,0.0031801956,0.011731302,0.007879409,0.0060158265,0.002120399],"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":[0.00011573204,0.00016145063,0.0021497835,0.0038103806,0.0004794403,0.00028096186,0.00058563816,0.008465769,0.0016900507,0.26161456,0.013095921,0.70755035],"study_design_scores_gemma":[0.00004891121,0.00017842333,0.0016914754,0.002637547,0.00023917452,0.00092588505,0.00040277705,0.058100775,0.0054907408,0.6692408,0.26088428,0.00015920839],"about_ca_topic_score_codex":0.0008960126,"about_ca_topic_score_gemma":0.00049539475,"teacher_disagreement_score":0.016732052,"about_ca_system_score_codex":0.0017264221,"about_ca_system_score_gemma":0.0022019185,"threshold_uncertainty_score":0.08848858},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040397912,0.00033779445,0.95731264,0.000110593406,0.00005832216,0.000036936555,0.00016135056,0.0011371376,0.00044735093],"genre_scores_gemma":[0.6633077,0.0002600269,0.33249888,0.00008474308,0.00024389275,0.00012701246,0.0013930281,0.00023025814,0.0018544503],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99835217,0.00044833284,0.000100857906,0.000336499,0.00055507466,0.0002071976],"domain_scores_gemma":[0.9970282,0.0011990711,0.00028834227,0.00055238174,0.00079802866,0.00013411399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019663929,0.0009427776,0.0018433557,0.0029235873,0.0008418314,0.0011601218,0.0017117254,0.00090207567,0.0011672109],"category_scores_gemma":[0.0060849176,0.0002911862,0.001319558,0.0027528428,0.0005981455,0.0014960449,0.0011426036,0.0011429503,0.0007644268],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005774291,0.00060240336,0.009908732,0.00016013057,0.000343729,0.0002077824,0.00015719188,0.13994096,0.024316521,0.009469042,0.009348407,0.80496764],"study_design_scores_gemma":[0.000014326866,0.0000721864,0.0025269499,0.000005820968,0.000036862977,0.000102831866,0.000050111754,0.98466337,0.0031028334,0.008530624,0.00087295746,0.00002111439],"about_ca_topic_score_codex":0.0033987744,"about_ca_topic_score_gemma":0.0056895744,"teacher_disagreement_score":0.0033987744,"about_ca_system_score_codex":0.00042200997,"about_ca_system_score_gemma":0.0011923721,"threshold_uncertainty_score":0.010399401},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012636345,0.0008153994,0.9820051,0.000514734,0.00012265127,0.00011171839,0.000103579136,0.002184981,0.0015053474],"genre_scores_gemma":[0.2287399,0.0006812912,0.7581652,0.00034828656,0.00039789535,0.0004980311,0.00054114155,0.00025936897,0.010368923],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99135053,0.002972103,0.0005919164,0.0021223794,0.002342755,0.0006202206],"domain_scores_gemma":[0.991884,0.005238093,0.00037111278,0.00082563114,0.0014055298,0.0002755267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065995967,0.0023533886,0.0025431544,0.0029076845,0.0014061715,0.006584595,0.002679809,0.00445981,0.005020778],"category_scores_gemma":[0.014042961,0.0014979601,0.0022575979,0.0020769038,0.0018439578,0.005474519,0.0026936978,0.0057321354,0.0038177313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014145339,0.0005463999,0.0056347577,0.0003714111,0.0006126372,0.0004099311,0.00044124376,0.29267684,0.012668469,0.050289374,0.006576516,0.6283579],"study_design_scores_gemma":[0.00003975073,0.000099473116,0.00071569707,0.000028218768,0.00003184392,0.000091226924,0.00003896995,0.9803402,0.0038708453,0.011330493,0.003375427,0.000037905138],"about_ca_topic_score_codex":0.012639258,"about_ca_topic_score_gemma":0.008392747,"teacher_disagreement_score":0.012639258,"about_ca_system_score_codex":0.0027459292,"about_ca_system_score_gemma":0.002082494,"threshold_uncertainty_score":0.034902394},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012258898,0.00007333258,0.987294,0.00002411382,0.0000054663465,0.000013060316,0.000034921264,0.00007251743,0.00022358126],"genre_scores_gemma":[0.5923439,0.0008768918,0.40331185,0.000033816585,0.000038662205,0.00019009273,0.00061814126,0.00010171224,0.0024848867],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991027,0.00045444965,0.00005303816,0.000080683014,0.00026936462,0.000039822513],"domain_scores_gemma":[0.9965959,0.0025451356,0.0002871514,0.00024933208,0.00029167975,0.000030852723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002999307,0.00038725566,0.00060226745,0.0009708942,0.00009894305,0.00061821,0.00069300586,0.00046087513,0.0009200471],"category_scores_gemma":[0.011723539,0.00026025466,0.0006086453,0.0007773304,0.00030797612,0.00088945264,0.0006864743,0.000870136,0.00029724828],"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":[0.00018288476,0.00010206287,0.006077262,0.00022235762,0.000097923374,0.0002462945,0.00015709265,0.5620935,0.013935913,0.03649455,0.0011927034,0.37919742],"study_design_scores_gemma":[0.0000037936552,0.000030092808,0.0017858007,0.000008979116,0.000007429218,0.00006892249,0.000011949643,0.99140465,0.0020525176,0.0042324336,0.00038218236,0.000011178506],"about_ca_topic_score_codex":0.00052588375,"about_ca_topic_score_gemma":0.00031638594,"teacher_disagreement_score":0.002999307,"about_ca_system_score_codex":0.00023362695,"about_ca_system_score_gemma":0.0003650135,"threshold_uncertainty_score":0.015862048},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017163629,0.00020448217,0.9811613,0.0001016664,0.000016526428,0.000030067384,0.0001853208,0.00095687946,0.00018014212],"genre_scores_gemma":[0.62612975,0.00033951487,0.37021482,0.00015762122,0.000100296646,0.00014392997,0.002050001,0.00025084158,0.0006132698],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9913447,0.0025119018,0.0006210001,0.002057165,0.003147704,0.00031755047],"domain_scores_gemma":[0.98200893,0.008292438,0.0038299551,0.0036806597,0.0018428046,0.00034519433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005252661,0.001267286,0.001966144,0.004271103,0.00059914397,0.002244545,0.0035221884,0.0013568747,0.00045594716],"category_scores_gemma":[0.029871302,0.0004810475,0.0015725887,0.0046305247,0.0013732217,0.004106137,0.0029440834,0.0020945768,0.00045406786],"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":[0.00031049695,0.0002831138,0.041214265,0.00032486627,0.000676305,0.0004765425,0.00045997088,0.6150219,0.011335545,0.022797652,0.0033026156,0.30379674],"study_design_scores_gemma":[0.0000059042704,0.00003212863,0.0014451124,0.000007663981,0.000016730064,0.00017239938,0.000038255494,0.9806502,0.0022832237,0.014558855,0.000772947,0.000016696355],"about_ca_topic_score_codex":0.0038999282,"about_ca_topic_score_gemma":0.0032892975,"teacher_disagreement_score":0.005252661,"about_ca_system_score_codex":0.0012390374,"about_ca_system_score_gemma":0.0010169167,"threshold_uncertainty_score":0.027779043},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.076313116,0.00008515522,0.87958294,0.00031542763,0.00025232197,0.001002079,0.00856142,0.030767916,0.003119565],"genre_scores_gemma":[0.28595066,0.0001021838,0.6886281,0.00009692132,0.000042924406,0.0016010432,0.01950089,0.0025026058,0.0015747336],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99788874,0.00073065766,0.00020388245,0.00030320394,0.000793891,0.0000796371],"domain_scores_gemma":[0.9867855,0.0060239057,0.00056636165,0.0031236773,0.003155261,0.00034524058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035584117,0.0009123929,0.0005614373,0.0020713275,0.0004489267,0.000899863,0.0015967687,0.0006058586,0.0043121097],"category_scores_gemma":[0.015990185,0.0003120587,0.00058979203,0.0020219646,0.00050681224,0.0008092503,0.0011612097,0.0009118824,0.0011464538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017049914,0.0012672168,0.02843755,0.001061161,0.00028252232,0.0015068619,0.0012848405,0.3647825,0.04536338,0.02841318,0.07271301,0.45318267],"study_design_scores_gemma":[0.0001520172,0.00032332283,0.003349463,0.000040721323,0.000030939056,0.00038767615,0.00013182215,0.92639923,0.040780745,0.007299682,0.021033969,0.00007050018],"about_ca_topic_score_codex":0.0009468758,"about_ca_topic_score_gemma":0.00083892856,"teacher_disagreement_score":0.0043121097,"about_ca_system_score_codex":0.00055780035,"about_ca_system_score_gemma":0.00075881893,"threshold_uncertainty_score":0.018818915},"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":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79780865,0.0054050526,0.18678454,0.0009972693,0.00030568254,0.00045342091,0.0034126097,0.0016842735,0.0031483765],"genre_scores_gemma":[0.9221168,0.0006867843,0.070075,0.00014896003,0.00011617448,0.00017005183,0.0060582496,0.00017339454,0.00045468102],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.94542366,0.02698848,0.006062921,0.0050605163,0.015502291,0.00096212997],"domain_scores_gemma":[0.658196,0.27460694,0.017886227,0.022111516,0.025251886,0.0019475559],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03498443,0.0017102316,0.0016511352,0.00700916,0.0014417019,0.0030512244,0.0028106503,0.002473269,0.0006181667],"category_scores_gemma":[0.18735564,0.00036788103,0.0013873323,0.005792591,0.0022156802,0.005608084,0.0025984652,0.0017590482,0.00024108909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006365941,0.004265401,0.3466473,0.002784813,0.003256323,0.00041744698,0.0016084318,0.18952799,0.009163713,0.011655854,0.015000794,0.40930596],"study_design_scores_gemma":[0.00038321325,0.0043150834,0.075095475,0.00039356895,0.00088655687,0.0015282915,0.0014021716,0.8831693,0.012946387,0.015224966,0.0044422937,0.00021276023],"about_ca_topic_score_codex":0.004075689,"about_ca_topic_score_gemma":0.0049331607,"teacher_disagreement_score":0.9650156,"about_ca_system_score_codex":0.0021098466,"about_ca_system_score_gemma":0.0018210539,"threshold_uncertainty_score":0.18501753},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10718953,0.00043524016,0.8904106,0.00020979425,0.000036407124,0.000047970592,0.000081491846,0.000834634,0.00075428793],"genre_scores_gemma":[0.78769016,0.00026029258,0.21037735,0.00008072436,0.00009178429,0.00005573969,0.0002082793,0.00008097626,0.0011547004],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991291,0.00019875547,0.00004441263,0.0002501108,0.00028349564,0.0000941802],"domain_scores_gemma":[0.99554425,0.0024758363,0.0007324217,0.0005258052,0.0005671419,0.00015461726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016947694,0.0005631834,0.0011302808,0.0027345677,0.00034085443,0.0012738998,0.0016825172,0.0011326812,0.00075644656],"category_scores_gemma":[0.009243264,0.0003815119,0.0007433737,0.0016014422,0.0009692407,0.002415435,0.001998712,0.0013507516,0.00040421746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006857896,0.00034906424,0.037295394,0.0002761061,0.00038663027,0.00068838673,0.00046631045,0.1299897,0.058006085,0.014416826,0.0028433404,0.7545965],"study_design_scores_gemma":[0.000014019948,0.00007405788,0.004673796,0.0000073973565,0.00003962778,0.00041165345,0.000073509036,0.97607565,0.007232444,0.010907066,0.0004665161,0.000024303092],"about_ca_topic_score_codex":0.0010787772,"about_ca_topic_score_gemma":0.0010873299,"teacher_disagreement_score":0.0027345677,"about_ca_system_score_codex":0.0004296686,"about_ca_system_score_gemma":0.00033126917,"threshold_uncertainty_score":0.008962929},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018994045,0.00020459857,0.99151844,0.00015217763,0.000044655055,0.000046345034,0.0001592964,0.004651602,0.0013234585],"genre_scores_gemma":[0.12738922,0.00039600141,0.8662441,0.0002459779,0.000058729154,0.0001945372,0.0009041365,0.00036483936,0.004202402],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996424,0.00004694675,0.000024548104,0.00007588011,0.00015595075,0.000054231863],"domain_scores_gemma":[0.9996886,0.00008781382,0.000019964273,0.000058297188,0.00011074674,0.000034583416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070564495,0.0006968845,0.00060709234,0.00059659773,0.00053931534,0.0011084687,0.0024513635,0.00084915943,0.0029955304],"category_scores_gemma":[0.0011502409,0.00046804914,0.0009905074,0.00068256335,0.0006892013,0.0017006254,0.0013176887,0.0020008162,0.0009788298],"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":[0.00023832821,0.00033837304,0.0010916775,0.00023061373,0.00017914781,0.00028733155,0.00014746448,0.44786024,0.016240964,0.103416026,0.031114722,0.39885512],"study_design_scores_gemma":[0.000010570212,0.000013803205,0.0000675608,0.0000055776,0.000007322952,0.000024008403,0.0000059758618,0.9756659,0.0020084954,0.01740024,0.004782179,0.0000083603945],"about_ca_topic_score_codex":0.016414715,"about_ca_topic_score_gemma":0.023863997,"teacher_disagreement_score":0.016414715,"about_ca_system_score_codex":0.0012432004,"about_ca_system_score_gemma":0.0020753597,"threshold_uncertainty_score":0.03263831},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045854235,0.004616489,0.98496664,0.0020846848,0.00013397752,0.00030513026,0.0005845121,0.0012009885,0.0015220857],"genre_scores_gemma":[0.11176647,0.009211121,0.8704754,0.00064558774,0.0010735263,0.000851547,0.0032602034,0.000289194,0.0024269843],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9928208,0.0016562388,0.0006321127,0.0016076388,0.0028944246,0.00038870255],"domain_scores_gemma":[0.991771,0.0039013745,0.0010685602,0.0009729133,0.0019340133,0.00035227582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005597896,0.0018755283,0.0025471007,0.007941461,0.0014159441,0.0070990827,0.0041503687,0.0032561044,0.00076541625],"category_scores_gemma":[0.016361501,0.0009485359,0.0020923016,0.0140907755,0.0032811735,0.009185622,0.0040033343,0.004344362,0.0011912097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025586138,0.0005373029,0.018647412,0.0016674292,0.00033763977,0.0007291169,0.0009944703,0.114602976,0.0050738202,0.24565665,0.02650437,0.584993],"study_design_scores_gemma":[0.00003244346,0.00012672473,0.0031406698,0.00035846763,0.00007892363,0.00079553307,0.00069157354,0.71056795,0.0046316087,0.24954182,0.029925989,0.000108249194],"about_ca_topic_score_codex":0.006205387,"about_ca_topic_score_gemma":0.003349317,"teacher_disagreement_score":0.007941461,"about_ca_system_score_codex":0.0028357545,"about_ca_system_score_gemma":0.0035316483,"threshold_uncertainty_score":0.029604852},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016127931,0.00012356322,0.98223245,0.00019380878,0.00001953375,0.000037734822,0.00017612899,0.00054165773,0.0005471194],"genre_scores_gemma":[0.42738265,0.00028253577,0.5697024,0.00011240351,0.000099872166,0.00009594844,0.0009974848,0.00015168377,0.001175006],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975062,0.0006991929,0.0001446358,0.00069224794,0.0007353455,0.00022239724],"domain_scores_gemma":[0.992938,0.0041063745,0.0010723592,0.00077702466,0.00084116944,0.00026514326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002331504,0.0011494467,0.001312537,0.0038008885,0.0008498419,0.0015222077,0.0015491637,0.0013876898,0.002697265],"category_scores_gemma":[0.015060825,0.0008651344,0.0015251065,0.0017201458,0.0018493984,0.0031734128,0.0017392833,0.0017578681,0.0009855683],"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":[0.0007963201,0.00016637398,0.015279397,0.000291233,0.00017646764,0.0007142378,0.000702206,0.4200847,0.017687624,0.12997256,0.007917361,0.4062115],"study_design_scores_gemma":[0.000015700463,0.000028852333,0.001024254,0.000022008155,0.000013595889,0.00014621043,0.00003463931,0.921034,0.0017766849,0.074682206,0.0011984637,0.000023480727],"about_ca_topic_score_codex":0.0103130005,"about_ca_topic_score_gemma":0.008439468,"teacher_disagreement_score":0.0103130005,"about_ca_system_score_codex":0.0013787827,"about_ca_system_score_gemma":0.0017189549,"threshold_uncertainty_score":0.020505965},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29731545,0.0017165408,0.48198918,0.002329507,0.0018951278,0.005909961,0.01762971,0.17799611,0.013218408],"genre_scores_gemma":[0.6416342,0.000314787,0.2959093,0.0006384777,0.00019493418,0.0028007003,0.051376544,0.0023057153,0.004825282],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9932921,0.0021470631,0.0005175281,0.0012222045,0.0023242624,0.00049675757],"domain_scores_gemma":[0.98957354,0.002057142,0.0008440575,0.0021812492,0.004061265,0.001282838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012287382,0.0019704022,0.0015627317,0.002132278,0.0010485407,0.0017097115,0.0037167051,0.0015305344,0.0039555538],"category_scores_gemma":[0.0132865785,0.0005929105,0.0008262698,0.0015330169,0.0009301357,0.0029304954,0.0020822561,0.002200825,0.00386396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.007596415,0.008860501,0.025718292,0.0011271068,0.0012089853,0.001145275,0.00044604985,0.1564177,0.07110926,0.00882808,0.27963772,0.43790463],"study_design_scores_gemma":[0.0005416521,0.001154145,0.012455189,0.00005871517,0.00006411751,0.00017714694,0.00014269957,0.9339478,0.03344374,0.0035247656,0.014380425,0.00010948995],"about_ca_topic_score_codex":0.010271194,"about_ca_topic_score_gemma":0.009296687,"teacher_disagreement_score":0.012287382,"about_ca_system_score_codex":0.0022218388,"about_ca_system_score_gemma":0.0019708711,"threshold_uncertainty_score":0.06498265},"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":"commentary","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013160257,0.028385656,0.0011150064,0.82126176,0.14397816,0.000031450887,0.00058765366,0.00014504889,0.00436361],"genre_scores_gemma":[0.0049309703,0.04234944,0.002132593,0.62022716,0.28547835,0.00016537005,0.00090469,0.0005031638,0.04330833],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9807196,0.0051726606,0.0019466223,0.0018431277,0.009722498,0.00059548713],"domain_scores_gemma":[0.74693847,0.09778564,0.009598569,0.016527234,0.1196551,0.009495079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019835534,0.0008212262,0.0026670469,0.006733895,0.0028663191,0.008476999,0.002888798,0.009638863,0.021745559],"category_scores_gemma":[0.19986138,0.0008719799,0.0011966405,0.0063279383,0.005865831,0.008030448,0.0029957984,0.018231329,0.020163026],"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":[0.000004945828,0.000001952658,0.000111689136,0.00004237657,0.0000065825398,0.000015025395,0.000010330152,0.00000799591,0.0000034263942,0.00061491685,0.99068576,0.008494953],"study_design_scores_gemma":[0.00001975666,0.000008961662,0.00031137918,0.0004934008,0.000026185706,0.00022425361,0.00009553519,0.000124611,0.000037986018,0.0050529777,0.9935853,0.000019599174],"about_ca_topic_score_codex":0.011965709,"about_ca_topic_score_gemma":0.020763459,"teacher_disagreement_score":0.021745559,"about_ca_system_score_codex":0.006153846,"about_ca_system_score_gemma":0.013491172,"threshold_uncertainty_score":0.10490155},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0443184,0.00015087365,0.953326,0.00017608966,0.00003095822,0.00006240602,0.00009761986,0.00088358624,0.0009540871],"genre_scores_gemma":[0.65315,0.00014075032,0.3437066,0.00010621301,0.00010843521,0.00012819166,0.00056619203,0.0001345173,0.0019591022],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99741,0.0010496184,0.0001488752,0.0003762473,0.0008198736,0.0001955023],"domain_scores_gemma":[0.99469334,0.0023908361,0.0008596661,0.0008136792,0.0010417006,0.0002007374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002453348,0.0008135762,0.0013110664,0.003563061,0.00072184653,0.0014400101,0.0012906736,0.0009169106,0.0011375183],"category_scores_gemma":[0.008142428,0.00029736845,0.001022867,0.0021323257,0.00084018137,0.0019095669,0.0012351713,0.0014261268,0.00083297864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027553327,0.0006549983,0.011584215,0.00017580176,0.00020924474,0.00022787027,0.00024653223,0.2763279,0.02178056,0.030950837,0.0059094485,0.651657],"study_design_scores_gemma":[0.0000061197184,0.000043632488,0.00091236655,0.00000503792,0.0000066828597,0.00007256366,0.00002635928,0.9836721,0.002127835,0.012550538,0.00055952393,0.000017225913],"about_ca_topic_score_codex":0.0015007756,"about_ca_topic_score_gemma":0.0020984935,"teacher_disagreement_score":0.003563061,"about_ca_system_score_codex":0.00074758334,"about_ca_system_score_gemma":0.0009966091,"threshold_uncertainty_score":0.0129746795},"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":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00092605193,0.054316986,0.015532063,0.04059241,0.8335558,0.00035970012,0.0011012598,0.0009819695,0.052633792],"genre_scores_gemma":[0.003962085,0.027301703,0.004045509,0.0075456034,0.8594637,0.00021831937,0.0012555111,0.0009791906,0.09522844],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9947172,0.000858572,0.0005039988,0.001061248,0.002455932,0.0004031279],"domain_scores_gemma":[0.97644675,0.0110510485,0.0013080426,0.002243496,0.0055225138,0.00342816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005550959,0.0029435123,0.0042211344,0.0061497884,0.0020894003,0.008624916,0.00285926,0.004251824,0.09618542],"category_scores_gemma":[0.015676262,0.00089032814,0.0024801772,0.0038848622,0.0019184421,0.007330487,0.0030574037,0.0067496942,0.03946493],"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":[0.000054857373,0.000078612466,0.0002087813,0.00057578844,0.000052303454,0.00011962406,0.000028049899,0.00028224842,0.00030277745,0.00434196,0.94857764,0.04537739],"study_design_scores_gemma":[0.00002077886,0.00007707527,0.0005357114,0.00039565316,0.000026834765,0.00038233685,0.000035296514,0.0011539998,0.00020206888,0.006909075,0.9902374,0.000023859617],"about_ca_topic_score_codex":0.00066187535,"about_ca_topic_score_gemma":0.0013312661,"teacher_disagreement_score":0.09618542,"about_ca_system_score_codex":0.0025533668,"about_ca_system_score_gemma":0.0025668864,"threshold_uncertainty_score":0.32177228},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2517471,0.008748006,0.70934653,0.0036310984,0.0006982075,0.0010075592,0.0013567861,0.0012513928,0.02221338],"genre_scores_gemma":[0.48101,0.005990983,0.5040432,0.00041157892,0.00031005862,0.0002976469,0.00085554516,0.00007595402,0.0070049902],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998442,0.00044845228,0.00011484643,0.00022520433,0.0006906773,0.00007883364],"domain_scores_gemma":[0.9977604,0.00080963026,0.00035778427,0.00032231602,0.0006677816,0.00008214649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023820333,0.0005580824,0.00066546287,0.00611067,0.0007291065,0.0016823066,0.0010125773,0.0010503498,0.0024687133],"category_scores_gemma":[0.005297581,0.00037341917,0.00037258584,0.0015457004,0.000928196,0.0019367186,0.0012785469,0.00085528597,0.0013646276],"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":[0.0002895036,0.00034243116,0.049191527,0.0004622056,0.0001496506,0.000706863,0.00090048654,0.0027642178,0.030624093,0.006445653,0.007771053,0.90035236],"study_design_scores_gemma":[0.0002937167,0.0020856645,0.28009504,0.0018933808,0.00083156064,0.034095157,0.008712706,0.32703397,0.14073838,0.07177635,0.13185208,0.00059197895],"about_ca_topic_score_codex":0.0009989304,"about_ca_topic_score_gemma":0.0029038452,"teacher_disagreement_score":0.00611067,"about_ca_system_score_codex":0.0003263247,"about_ca_system_score_gemma":0.0006267877,"threshold_uncertainty_score":0.012597561},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07581459,0.006341588,0.9103637,0.00048454307,0.00020529723,0.000104020924,0.00009216868,0.0007150975,0.00587901],"genre_scores_gemma":[0.7617894,0.008050592,0.22557907,0.00017136107,0.000283081,0.00011309717,0.00021871726,0.00008582877,0.0037089167],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991671,0.00015422801,0.00004443834,0.00020236612,0.0003774695,0.00005449219],"domain_scores_gemma":[0.9985322,0.00057041843,0.00017232727,0.00008726694,0.00058721687,0.000050571343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068056927,0.00077547267,0.0006001035,0.00270785,0.0003126751,0.00087614363,0.0007282059,0.00058903947,0.00083528686],"category_scores_gemma":[0.002530371,0.00025955265,0.0005875466,0.0015957375,0.0006627685,0.002619888,0.00034013882,0.00050116755,0.00024054215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002631305,0.0001445348,0.014012804,0.0006185717,0.00015113023,0.00022493335,0.00034216332,0.037158035,0.038078725,0.015980514,0.0022698361,0.8907557],"study_design_scores_gemma":[0.000044096178,0.0006263993,0.024641693,0.00018603305,0.00029535638,0.0013607836,0.00051493285,0.8431308,0.0883549,0.017954448,0.022688553,0.00020201287],"about_ca_topic_score_codex":0.0021267605,"about_ca_topic_score_gemma":0.00096070155,"teacher_disagreement_score":0.00270785,"about_ca_system_score_codex":0.0005851265,"about_ca_system_score_gemma":0.00046755993,"threshold_uncertainty_score":0.0042454004},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09721616,0.00026182854,0.9009521,0.0000974113,0.000055770277,0.00003314575,0.0001134656,0.0007771596,0.0004931014],"genre_scores_gemma":[0.87108546,0.0004205191,0.12696227,0.000024236571,0.00006584785,0.00006374017,0.00044329258,0.00010017089,0.00083451986],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982578,0.00027571138,0.00013290308,0.00036335533,0.00085640146,0.000113764676],"domain_scores_gemma":[0.9977186,0.0007050873,0.00040077275,0.00033823424,0.00076174206,0.00007558011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012654304,0.0007396674,0.0010215961,0.0023173047,0.0004160391,0.00081536634,0.00080894766,0.00047276047,0.0006507452],"category_scores_gemma":[0.0064943456,0.00022283877,0.0009016232,0.0031511893,0.0004889071,0.0015532374,0.00061628455,0.0008165319,0.00017353852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000503731,0.00019758285,0.037448645,0.0005537127,0.0004479666,0.0009499419,0.0006367599,0.27891207,0.06846118,0.018328361,0.0022763768,0.59128356],"study_design_scores_gemma":[0.000007420798,0.0000764208,0.00609816,0.0000072176326,0.000030914216,0.00024690756,0.00006566132,0.9762447,0.010926009,0.0052066376,0.0010644746,0.000025493395],"about_ca_topic_score_codex":0.0023110756,"about_ca_topic_score_gemma":0.0014080885,"teacher_disagreement_score":0.0023173047,"about_ca_system_score_codex":0.00038175817,"about_ca_system_score_gemma":0.0005415236,"threshold_uncertainty_score":0.00669235},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20324507,0.0012477258,0.7826641,0.00024505868,0.0001157785,0.0003798346,0.0018236372,0.0052318713,0.005046848],"genre_scores_gemma":[0.7797271,0.00083220383,0.21432774,0.00007435866,0.00003600162,0.00022409722,0.0016479945,0.00016616337,0.0029644305],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994305,0.00006120394,0.000028601626,0.00016295542,0.0002422325,0.0000745071],"domain_scores_gemma":[0.9992949,0.00020679325,0.00012874858,0.00006412109,0.00026143558,0.000043968652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038966516,0.000900611,0.0005437495,0.0024287745,0.0003319001,0.0010947239,0.0007839925,0.000596154,0.00223553],"category_scores_gemma":[0.0019445866,0.00038487554,0.00055436126,0.0011647525,0.00027356096,0.0009243116,0.00058813015,0.00052089273,0.00074203603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083160715,0.00031325468,0.02497201,0.0006539142,0.00013821537,0.0005151354,0.0010072169,0.052878883,0.1738546,0.004016112,0.004542139,0.73627687],"study_design_scores_gemma":[0.00003228667,0.00037984786,0.041959617,0.00013239401,0.00010530375,0.0005311612,0.00089756714,0.8818032,0.060044274,0.0049002073,0.009137,0.00007726106],"about_ca_topic_score_codex":0.011495789,"about_ca_topic_score_gemma":0.0077475593,"teacher_disagreement_score":0.011495789,"about_ca_system_score_codex":0.00059370586,"about_ca_system_score_gemma":0.0009935725,"threshold_uncertainty_score":0.022857785},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056249198,0.00016438338,0.94161165,0.000056814577,0.000052036343,0.000045155146,0.00008070953,0.0005188751,0.0012212441],"genre_scores_gemma":[0.68715,0.00046290964,0.3079008,0.00004179309,0.000039476745,0.0001121176,0.0004931945,0.000055547145,0.0037442227],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952686,0.00006427693,0.000019148918,0.00014673424,0.0001794078,0.000063497435],"domain_scores_gemma":[0.9996675,0.00009030027,0.000057997175,0.000042769083,0.00011682146,0.000024614947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064049335,0.0007093732,0.0007722493,0.0017280821,0.0005458468,0.0008622914,0.0007580749,0.0007305163,0.00057223946],"category_scores_gemma":[0.0010894316,0.00048626884,0.00092568435,0.0019765773,0.00028948692,0.0007773012,0.00066328194,0.00071008445,0.00030068803],"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":[0.00022809203,0.00017656542,0.016127236,0.00012801749,0.00017957302,0.0002794269,0.00029583808,0.55699587,0.026637547,0.0034672266,0.003089613,0.39239505],"study_design_scores_gemma":[0.0000072468474,0.000026667558,0.0034106134,0.0000048183297,0.000018872837,0.000035388497,0.000025287809,0.9926645,0.002601943,0.00045033408,0.000742172,0.000012081391],"about_ca_topic_score_codex":0.021508884,"about_ca_topic_score_gemma":0.014989634,"teacher_disagreement_score":0.021508884,"about_ca_system_score_codex":0.00055167853,"about_ca_system_score_gemma":0.0009128876,"threshold_uncertainty_score":0.042767346},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29180723,0.010022275,0.6610679,0.0073887217,0.00093604706,0.00015615846,0.0019643032,0.00491129,0.021746038],"genre_scores_gemma":[0.67566234,0.009984564,0.20114644,0.00041059754,0.00028211533,0.00007308626,0.0040933257,0.00032603898,0.10802149],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966383,0.00005170625,0.000017150049,0.00007614285,0.00014774298,0.00004339842],"domain_scores_gemma":[0.9986111,0.00062921987,0.000036385278,0.00009198441,0.00055240834,0.00007885263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015417167,0.0006346593,0.00061215047,0.0014093408,0.00079158804,0.0012394678,0.00092458556,0.0007002345,0.002090765],"category_scores_gemma":[0.0036949096,0.00046684916,0.00037289746,0.0010820192,0.0005918025,0.00080842094,0.0003681728,0.00082005165,0.000650239],"study_design_candidate":"simulation_or_modeling","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":[0.0006283021,0.00028488023,0.0147634465,0.0001483309,0.00013626495,0.00026231637,0.000277906,0.43484193,0.0064754006,0.0079021985,0.03249653,0.50178254],"study_design_scores_gemma":[0.000018021266,0.000034132205,0.0039965454,0.000010819097,0.000030503354,0.00003746631,0.00009141948,0.9856188,0.0022430865,0.00227826,0.0056273537,0.000013454573],"about_ca_topic_score_codex":0.57223517,"about_ca_topic_score_gemma":0.6311201,"teacher_disagreement_score":0.57223517,"about_ca_system_score_codex":0.0028052174,"about_ca_system_score_gemma":0.0043487777,"threshold_uncertainty_score":0.86056805},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006749674,0.0030907593,0.97804356,0.00060884986,0.0002992824,0.0004849552,0.0017551045,0.004415708,0.0045522125],"genre_scores_gemma":[0.1535747,0.004093643,0.82896954,0.00036030842,0.0005773231,0.00075118645,0.0053057964,0.0004518345,0.005915643],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872833,0.0002893001,0.00015391642,0.00025852173,0.0004736079,0.00009630953],"domain_scores_gemma":[0.99756086,0.0011694123,0.00019615775,0.00040761477,0.0006039077,0.00006204436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018616405,0.0011259232,0.0011981226,0.0044171936,0.0008108609,0.003015973,0.0013375839,0.00094670214,0.00923115],"category_scores_gemma":[0.00584693,0.0003783195,0.0011699292,0.0035909386,0.0006533555,0.0028122545,0.0012053405,0.0009955894,0.006005272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035698627,0.00017933387,0.001168945,0.00074801384,0.000106379055,0.0002788334,0.00026241931,0.021594984,0.026207708,0.024954539,0.02415304,0.89998895],"study_design_scores_gemma":[0.00006952519,0.00023971291,0.0045459876,0.00016016976,0.00014687213,0.00072532706,0.00045069982,0.7909947,0.036064245,0.077589594,0.08889389,0.00011924222],"about_ca_topic_score_codex":0.004094736,"about_ca_topic_score_gemma":0.004223791,"teacher_disagreement_score":0.00923115,"about_ca_system_score_codex":0.00119085,"about_ca_system_score_gemma":0.0010843958,"threshold_uncertainty_score":0.030881286},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20539355,0.00014821082,0.7894205,0.00026449192,0.000027802264,0.000087885055,0.00008587254,0.004101349,0.0004703195],"genre_scores_gemma":[0.83658063,0.000057320707,0.16284183,0.000033315166,0.000018271334,0.00003972834,0.00009884957,0.00005586476,0.00027421472],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975042,0.00075011654,0.00014920681,0.0005254123,0.00083797035,0.00023306628],"domain_scores_gemma":[0.99370307,0.0030031733,0.0012623266,0.0009641596,0.00088541256,0.00018195566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017933855,0.0006442972,0.0007036542,0.0019225349,0.0007102143,0.0010331061,0.0014315792,0.0007350396,0.00038075235],"category_scores_gemma":[0.00790163,0.00034459506,0.00051508326,0.0009869693,0.0008850654,0.0011908539,0.0011392073,0.0008534465,0.00012181769],"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":[0.0003715519,0.00049457786,0.04011419,0.00014133108,0.00018679231,0.00071390806,0.0005617473,0.5451955,0.06718854,0.010250623,0.0023034578,0.33247778],"study_design_scores_gemma":[0.0000070051,0.00003105136,0.0020499786,0.0000031465704,0.000009132366,0.00007760498,0.000034682475,0.9871116,0.007736066,0.0026692287,0.00026134393,0.000009051245],"about_ca_topic_score_codex":0.0040132827,"about_ca_topic_score_gemma":0.0040912163,"teacher_disagreement_score":0.0040132827,"about_ca_system_score_codex":0.0010509738,"about_ca_system_score_gemma":0.0015200487,"threshold_uncertainty_score":0.00948441},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035193702,0.00021780124,0.0015917934,0.00015825155,0.00015720268,0.00009255475,0.98897296,0.003694836,0.0015952508],"genre_scores_gemma":[0.003949141,0.00008235761,0.0013795851,0.000036293604,0.000016977863,0.00010578438,0.9936918,0.000078729165,0.0006594176],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988908,0.00013690873,0.00018684955,0.00023904395,0.00039926334,0.00014710675],"domain_scores_gemma":[0.9983175,0.0002760367,0.00014878133,0.00055949995,0.00056466955,0.00013356541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081002834,0.0026045586,0.0011361295,0.0024445795,0.0005442908,0.001029617,0.0020826298,0.001663294,0.010985777],"category_scores_gemma":[0.0033129451,0.00037614163,0.0011806255,0.0029404757,0.00030741523,0.0008119336,0.0011945878,0.001378102,0.018693747],"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":[0.0003763609,0.0002222718,0.0036761183,0.0010447941,0.000107237785,0.00013414942,0.000037858616,0.005185691,0.0013991962,0.00057717727,0.9669216,0.020317663],"study_design_scores_gemma":[0.00075134303,0.00029763742,0.031737074,0.00041244162,0.00015129373,0.00085547793,0.00024476615,0.027539352,0.008719702,0.0037917807,0.9253113,0.0001878175],"about_ca_topic_score_codex":0.01388604,"about_ca_topic_score_gemma":0.02227112,"teacher_disagreement_score":0.01388604,"about_ca_system_score_codex":0.0011686778,"about_ca_system_score_gemma":0.0013817481,"threshold_uncertainty_score":0.036751032},"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":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002881352,0.14475864,0.84394914,0.0016669667,0.00055391144,0.00007728802,0.00013029978,0.0009503694,0.0050319675],"genre_scores_gemma":[0.1050683,0.25598904,0.6209898,0.0017895242,0.0050969347,0.00034974035,0.0010136339,0.0009377058,0.008765204],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970824,0.00095404615,0.00026466613,0.00075019995,0.00082536443,0.00012329253],"domain_scores_gemma":[0.99044925,0.0069816313,0.00030929508,0.00094941846,0.0011453523,0.00016489616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050992114,0.0015010278,0.003274834,0.0018155574,0.00057940127,0.0037560968,0.0045382185,0.0020287482,0.0030198281],"category_scores_gemma":[0.011268772,0.00094170845,0.0017027258,0.004900811,0.0017809186,0.005791874,0.002963498,0.0032915918,0.0018646945],"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":[0.000051522027,0.00016717736,0.0013960128,0.0016280251,0.00022854989,0.00004845196,0.0001717391,0.028469617,0.00088239426,0.029047037,0.009603984,0.92830545],"study_design_scores_gemma":[0.000040227547,0.0001928861,0.0033712243,0.0011979558,0.00028235084,0.00065333414,0.0003457817,0.6413129,0.0034497473,0.24024257,0.108712204,0.00019883121],"about_ca_topic_score_codex":0.003775636,"about_ca_topic_score_gemma":0.004098339,"teacher_disagreement_score":0.0050992114,"about_ca_system_score_codex":0.001032501,"about_ca_system_score_gemma":0.0015735144,"threshold_uncertainty_score":0.026967525},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062596337,0.00030103864,0.99212015,0.00007279912,0.000029787509,0.00003571458,0.00011394992,0.0007756791,0.00029139346],"genre_scores_gemma":[0.30503696,0.0006961656,0.68942654,0.00020125051,0.00023383752,0.0003246884,0.0019078729,0.0003017146,0.0018709445],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99681014,0.0011348816,0.00022946368,0.00065793947,0.0009046047,0.0002629302],"domain_scores_gemma":[0.99383575,0.0028975436,0.0007266991,0.0011787488,0.0011465118,0.00021468854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030361833,0.0013991998,0.0022690077,0.0034469983,0.001063716,0.0018284625,0.0021756608,0.0014224165,0.0013658453],"category_scores_gemma":[0.01064933,0.0006162047,0.0016681277,0.0035546417,0.0010929066,0.0022812074,0.0028518357,0.002260028,0.0010922478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002972281,0.00026240162,0.007435964,0.00026877483,0.00040585938,0.00020661078,0.0003358276,0.38671243,0.0068765488,0.02667286,0.0058154026,0.5647101],"study_design_scores_gemma":[0.0000077077975,0.00004485788,0.0007738988,0.000020580586,0.000017695258,0.00008171879,0.000050417704,0.9751561,0.0015440219,0.020622067,0.0016593934,0.000021517977],"about_ca_topic_score_codex":0.0039941138,"about_ca_topic_score_gemma":0.0042459457,"teacher_disagreement_score":0.0039941138,"about_ca_system_score_codex":0.0006964314,"about_ca_system_score_gemma":0.0012793483,"threshold_uncertainty_score":0.016057074},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07537708,0.002012621,0.89522624,0.00020989508,0.00042065757,0.00041655847,0.005242075,0.0123743545,0.008720643],"genre_scores_gemma":[0.2873642,0.001503736,0.6850118,0.00017542086,0.00023964843,0.00024332404,0.017579038,0.0004608032,0.0074219233],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99927455,0.000060732353,0.000030803647,0.00021997973,0.00030856553,0.000105311745],"domain_scores_gemma":[0.99956137,0.00004679566,0.000027680706,0.00009877275,0.00023256634,0.00003286908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005838127,0.0009851881,0.0015406954,0.0054652505,0.000506015,0.0011912391,0.0011513808,0.00081267825,0.0027440614],"category_scores_gemma":[0.0007126266,0.0005531792,0.00094537897,0.0033379553,0.00027356617,0.00091326045,0.0010544895,0.000717897,0.004843228],"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":[0.00023822305,0.0002344298,0.005352163,0.00016738674,0.000076919176,0.00013338006,0.000044275985,0.0041982955,0.051783666,0.00082809397,0.015450256,0.92149293],"study_design_scores_gemma":[0.000055097462,0.0004431417,0.043679874,0.000116009884,0.00025139636,0.0020469183,0.0002457492,0.71452004,0.18062301,0.009305523,0.048581604,0.00013167926],"about_ca_topic_score_codex":0.0032444734,"about_ca_topic_score_gemma":0.0063441647,"teacher_disagreement_score":0.0054652505,"about_ca_system_score_codex":0.00033052117,"about_ca_system_score_gemma":0.00076924823,"threshold_uncertainty_score":0.009179771},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013003086,0.00012894317,0.98524207,0.00008930626,0.000019398602,0.000030461813,0.000051962485,0.0010172888,0.00041755044],"genre_scores_gemma":[0.65817475,0.00039789427,0.33959466,0.00007032218,0.00007843885,0.00012160507,0.0003450605,0.00027693756,0.0009402833],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966634,0.000810225,0.00029421283,0.0005551272,0.0014600173,0.00021697744],"domain_scores_gemma":[0.9862504,0.007711208,0.0018713161,0.0017602254,0.0020900562,0.0003167543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002836515,0.0016055095,0.0011688542,0.005477402,0.0007328071,0.0021874895,0.0018813377,0.0014372222,0.0010425508],"category_scores_gemma":[0.021162715,0.0004275018,0.001199272,0.0032307827,0.0012366836,0.0026604459,0.0011845925,0.0025290078,0.00060250744],"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":[0.0003299668,0.00026296522,0.019009223,0.00026709115,0.00021460468,0.00057457516,0.0008411032,0.644932,0.023283424,0.0548257,0.0019064876,0.25355288],"study_design_scores_gemma":[0.0000041262097,0.000032933094,0.0009429139,0.000010580941,0.000010490043,0.00016502736,0.000041884687,0.9799095,0.0032576553,0.014383296,0.0012212527,0.000020370511],"about_ca_topic_score_codex":0.0034871409,"about_ca_topic_score_gemma":0.0017505076,"teacher_disagreement_score":0.005477402,"about_ca_system_score_codex":0.0014438198,"about_ca_system_score_gemma":0.0010311749,"threshold_uncertainty_score":0.015001059},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9676926,0.0016578538,0.011488274,0.0005075142,0.0002797332,0.00014428781,0.0007391698,0.00016021226,0.017330298],"genre_scores_gemma":[0.99136883,0.00019649138,0.0064380006,0.00014389005,0.000098428245,0.00006581957,0.00041415656,0.000044314595,0.0012299748],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99329364,0.0026803082,0.0010722819,0.0007932052,0.0018303845,0.00033018622],"domain_scores_gemma":[0.9807272,0.010747744,0.0026642086,0.0017645016,0.0030498875,0.0010464969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012139504,0.00070249435,0.00070304924,0.0038438325,0.00043231528,0.0014403043,0.0010481582,0.0010266331,0.003229576],"category_scores_gemma":[0.031837977,0.00032067072,0.00083395594,0.0013837387,0.0013585421,0.0012141151,0.0015561916,0.0014101114,0.0008724208],"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":[0.007899755,0.00017634283,0.94160813,0.00018300224,0.00041548026,0.00058075605,0.0008329219,0.000833271,0.017655045,0.002440884,0.0012368029,0.026137676],"study_design_scores_gemma":[0.00023798531,0.004153414,0.9534227,0.0001053842,0.0005376442,0.004493276,0.0012478234,0.014711389,0.012188577,0.0039650924,0.004847352,0.0000893103],"about_ca_topic_score_codex":0.0010201591,"about_ca_topic_score_gemma":0.0018590001,"teacher_disagreement_score":0.012139504,"about_ca_system_score_codex":0.00036013074,"about_ca_system_score_gemma":0.00050858513,"threshold_uncertainty_score":0.06420058},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39413437,0.00034243442,0.59914994,0.00016025413,0.0001409232,0.00008003325,0.0003315793,0.001218363,0.004442102],"genre_scores_gemma":[0.853084,0.00019153647,0.14258775,0.00004549164,0.00003172535,0.000044063636,0.00019221168,0.000022249566,0.0038009658],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997794,0.000017886425,0.000008749514,0.000057482943,0.00012279025,0.00001381215],"domain_scores_gemma":[0.99977094,0.000056042652,0.000029525025,0.000017352511,0.00011768825,0.000008459021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017888924,0.00021751954,0.00023631373,0.00061589497,0.00011998729,0.00027190952,0.00035824327,0.00031942857,0.001218567],"category_scores_gemma":[0.00042386676,0.00016337277,0.00015326764,0.00029831092,0.00011101377,0.00039044768,0.00018016004,0.00019394538,0.00042476555],"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":[0.00020131916,0.0001279078,0.011670352,0.00013585882,0.000041406314,0.00011526999,0.00009179089,0.016813628,0.5778986,0.0008035597,0.0019663244,0.390134],"study_design_scores_gemma":[0.00002065246,0.00035121187,0.018576264,0.000018658013,0.000040736955,0.000354763,0.000099543715,0.7045486,0.27057055,0.00073055946,0.0046369187,0.000051457406],"about_ca_topic_score_codex":0.00074846647,"about_ca_topic_score_gemma":0.0011604693,"teacher_disagreement_score":0.001218567,"about_ca_system_score_codex":0.00016835889,"about_ca_system_score_gemma":0.00017554242,"threshold_uncertainty_score":0.004076481},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18198325,0.026618464,0.54953504,0.036450904,0.01566776,0.0010071719,0.08178383,0.052074198,0.05487931],"genre_scores_gemma":[0.44666004,0.008971649,0.3493386,0.0034603106,0.0051041315,0.0008984981,0.134442,0.0033544097,0.047770396],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969009,0.00073882047,0.00023129924,0.00058329495,0.0012980709,0.00024758195],"domain_scores_gemma":[0.9916487,0.0038663824,0.0004664839,0.0015564329,0.0018867158,0.0005753922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060554524,0.002954156,0.002440346,0.0019913996,0.0008155692,0.0022956412,0.003918924,0.0035398467,0.005515192],"category_scores_gemma":[0.014957186,0.00055320875,0.0014757793,0.0019486572,0.0006559808,0.0025983094,0.0020401184,0.004148323,0.005858189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005075125,0.0006352491,0.0074849604,0.00096141105,0.00020284273,0.00022684856,0.00008552382,0.064953305,0.002954049,0.0044043,0.49620944,0.42137465],"study_design_scores_gemma":[0.00023571367,0.00075466366,0.0084682675,0.0002463854,0.00013381889,0.0006180055,0.0002302088,0.82124525,0.012557497,0.050195925,0.10519087,0.000123399],"about_ca_topic_score_codex":0.0038151972,"about_ca_topic_score_gemma":0.005350704,"teacher_disagreement_score":0.0060554524,"about_ca_system_score_codex":0.0010280164,"about_ca_system_score_gemma":0.0023682534,"threshold_uncertainty_score":0.03202474},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017836312,0.98953044,0.0027640848,0.0014786214,0.0005917657,0.00006700517,0.00019894465,0.00004069848,0.0035447124],"genre_scores_gemma":[0.008172324,0.98449904,0.0045900485,0.0007920016,0.000592715,0.00008562517,0.00029386496,0.000012692622,0.0009615687],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99847955,0.00033079964,0.00031149812,0.00027821367,0.0005305193,0.00006940315],"domain_scores_gemma":[0.9910444,0.005277988,0.0009325705,0.00014288604,0.002475531,0.00012657167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016815532,0.0007687862,0.0010424521,0.018270366,0.0007862494,0.00250146,0.0012502595,0.001453658,0.0029357502],"category_scores_gemma":[0.006258653,0.00057944685,0.0007743613,0.017871823,0.00073663774,0.0034553458,0.0008183206,0.000940252,0.0010796814],"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":[0.000115939,0.000120538854,0.0021674093,0.06475203,0.00021832119,0.0008812889,0.0008325919,0.0015802138,0.0023492186,0.008534148,0.05002643,0.86842185],"study_design_scores_gemma":[0.000025705733,0.00027330554,0.012551056,0.07674696,0.0005379158,0.0038196964,0.002389585,0.0021806648,0.0028918914,0.008843513,0.8896193,0.00012044675],"about_ca_topic_score_codex":0.0041993856,"about_ca_topic_score_gemma":0.006662197,"teacher_disagreement_score":0.018270366,"about_ca_system_score_codex":0.0018085532,"about_ca_system_score_gemma":0.003031414,"threshold_uncertainty_score":0.013122022},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3523285,0.0012708942,0.6375557,0.00016031234,0.00016937767,0.00012991084,0.0012247245,0.004125931,0.0030346494],"genre_scores_gemma":[0.8871418,0.0005035283,0.107995205,0.00007491435,0.000071149894,0.000085261076,0.0011803095,0.00012563728,0.0028222275],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997867,0.000022545226,0.000010276049,0.000059164253,0.00007663692,0.00004480371],"domain_scores_gemma":[0.99974626,0.000039566505,0.000048746457,0.000034606954,0.000101117774,0.000029738865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000223782,0.00063466385,0.0006913237,0.0012838066,0.00017494122,0.000298231,0.00044361455,0.0004823846,0.0014230047],"category_scores_gemma":[0.0005569434,0.00022384836,0.0004093631,0.00094184215,0.00016455141,0.0003761954,0.00043201473,0.00040074953,0.001169645],"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":[0.00041378778,0.00025377082,0.01801185,0.00014265295,0.00011059546,0.00019450355,0.000063541185,0.008328935,0.27691445,0.00032327295,0.0038227534,0.6914199],"study_design_scores_gemma":[0.000036852183,0.00045725168,0.12652601,0.0000433638,0.00020203607,0.0012662588,0.00013313357,0.79306734,0.07285865,0.0013944465,0.0039496133,0.000065007385],"about_ca_topic_score_codex":0.0018350895,"about_ca_topic_score_gemma":0.0048274007,"teacher_disagreement_score":0.0018350895,"about_ca_system_score_codex":0.0001263785,"about_ca_system_score_gemma":0.00038139898,"threshold_uncertainty_score":0.0047603846},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2751617,0.0021880374,0.6839229,0.0010928679,0.0005576292,0.00038664657,0.0023346387,0.027062103,0.007293542],"genre_scores_gemma":[0.7538899,0.0008050269,0.2388257,0.0001924609,0.000116982075,0.000098435725,0.0017225781,0.00039809637,0.00395068],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972934,0.000037429327,0.000010546758,0.00007184878,0.000093254166,0.000057685833],"domain_scores_gemma":[0.99964166,0.00011235713,0.00003743172,0.000059586553,0.00010574299,0.000043285156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004949008,0.0013128145,0.00055179134,0.001389694,0.00027968598,0.00047677374,0.00091800065,0.0007653269,0.0018708329],"category_scores_gemma":[0.0011656043,0.00031150642,0.00039102972,0.0009857034,0.00034290212,0.0007843807,0.0007096985,0.00064530043,0.00062765565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083315093,0.00034188078,0.012514124,0.0002762171,0.00022295053,0.0014010395,0.00017938907,0.12046275,0.09667959,0.0025940612,0.018722435,0.7457724],"study_design_scores_gemma":[0.000014383075,0.0000608878,0.0031597703,0.000012194146,0.000028282344,0.0002778844,0.000038014092,0.9661487,0.025614582,0.0014066256,0.0032209205,0.000017861506],"about_ca_topic_score_codex":0.0111745745,"about_ca_topic_score_gemma":0.013935116,"teacher_disagreement_score":0.0111745745,"about_ca_system_score_codex":0.0006675953,"about_ca_system_score_gemma":0.00043084493,"threshold_uncertainty_score":0.022219062},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009365275,0.00030776436,0.9888813,0.00013355465,0.00006503065,0.000022436685,0.00008205355,0.00072781194,0.00041475883],"genre_scores_gemma":[0.7428879,0.00034461456,0.25283068,0.00015637826,0.00016566181,0.00015806475,0.0006696695,0.00009623239,0.0026906948],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991266,0.00023706122,0.00004394859,0.00026646166,0.00023488441,0.000091071066],"domain_scores_gemma":[0.9985348,0.00053660193,0.00016198421,0.00032454063,0.00035985163,0.000082215425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009486453,0.0007426229,0.0011391412,0.0006535321,0.00031373848,0.00081366394,0.0016790768,0.0009301472,0.0019104396],"category_scores_gemma":[0.00290796,0.00029525,0.0006593494,0.0010508212,0.00031975977,0.0016348899,0.0008128204,0.001738784,0.0011603832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023585392,0.00032512072,0.002281634,0.0000887797,0.00011153027,0.00010534801,0.000061114304,0.42298073,0.007281008,0.006834228,0.0053705815,0.55432403],"study_design_scores_gemma":[0.0000048405914,0.00002256803,0.00015331236,0.0000022187348,0.000003108028,0.000016026635,0.0000056897898,0.99633163,0.00064147793,0.0024405562,0.0003752863,0.0000033127474],"about_ca_topic_score_codex":0.0019038102,"about_ca_topic_score_gemma":0.002149653,"teacher_disagreement_score":0.0019104396,"about_ca_system_score_codex":0.00054394285,"about_ca_system_score_gemma":0.00049600686,"threshold_uncertainty_score":0.006391108},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28947657,0.00050687575,0.6984514,0.0006407079,0.00007502054,0.00012426186,0.0008197248,0.0034993591,0.006406092],"genre_scores_gemma":[0.7143119,0.00047011636,0.2809817,0.00009660969,0.00008689182,0.00007069384,0.0015435278,0.00020303662,0.0022355777],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914265,0.00013873095,0.000058768434,0.00021466834,0.00037920877,0.000066017616],"domain_scores_gemma":[0.9966539,0.0014068614,0.00042075652,0.0010835109,0.00028315984,0.00015184376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008255363,0.0005254343,0.0008456623,0.0013721465,0.00068282953,0.0014701099,0.0010918145,0.000472448,0.0016164775],"category_scores_gemma":[0.005017299,0.00036121934,0.00059123005,0.0021302265,0.0007618092,0.00402489,0.002159169,0.0008214766,0.00046244063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001038417,0.0002836007,0.038686644,0.0004620359,0.00031495013,0.0024220427,0.002028583,0.102127105,0.095992394,0.04102841,0.008620643,0.70699525],"study_design_scores_gemma":[0.000065826556,0.00019620887,0.021608701,0.00004565799,0.00009913764,0.002256588,0.0007785615,0.8020102,0.043639164,0.110406905,0.018804062,0.0000889965],"about_ca_topic_score_codex":0.00092543557,"about_ca_topic_score_gemma":0.0011606766,"teacher_disagreement_score":0.0016164775,"about_ca_system_score_codex":0.00036299147,"about_ca_system_score_gemma":0.00037505655,"threshold_uncertainty_score":0.0054076314},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1416417,0.00026107032,0.85539377,0.00010933662,0.000034805264,0.000059488502,0.00003780306,0.001642774,0.0008192364],"genre_scores_gemma":[0.86321914,0.00009175205,0.13585192,0.000048262264,0.00001657208,0.00007786287,0.00013628798,0.00012379246,0.00043445654],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977465,0.0010753769,0.00013490922,0.00038727577,0.00055791385,0.000097981145],"domain_scores_gemma":[0.99118316,0.0049836724,0.000919295,0.0010312108,0.0017883873,0.00009419417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004817071,0.00069548225,0.00066267245,0.0012468084,0.00028170377,0.0007196612,0.0010568307,0.0008833072,0.0005683117],"category_scores_gemma":[0.022168119,0.0005061759,0.00052599126,0.00074812316,0.00047841083,0.0011759457,0.0007183908,0.0012321703,0.00036766508],"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":[0.00014425069,0.00013626924,0.009874372,0.000064092106,0.00010990027,0.00006030895,0.00016039285,0.74518377,0.009289575,0.0020225577,0.00060404674,0.23235044],"study_design_scores_gemma":[0.0000050606604,0.00003502816,0.0016355048,0.000007355722,0.0000065234553,0.000038219096,0.000012456938,0.99219257,0.0045482726,0.0012422262,0.0002643294,0.000012434908],"about_ca_topic_score_codex":0.0012824257,"about_ca_topic_score_gemma":0.00068679755,"teacher_disagreement_score":0.004817071,"about_ca_system_score_codex":0.0006262449,"about_ca_system_score_gemma":0.00042081502,"threshold_uncertainty_score":0.025475383},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.076546654,0.0034133443,0.90912086,0.001188617,0.0003214769,0.00021083753,0.0023037568,0.0033911462,0.003503315],"genre_scores_gemma":[0.858283,0.002259654,0.12926607,0.0002781921,0.00076386664,0.00037125373,0.0042673685,0.00023961977,0.0042709936],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786276,0.0007068995,0.00013411354,0.0008517238,0.00029176963,0.0001527763],"domain_scores_gemma":[0.9949521,0.00352623,0.00047213968,0.00042347139,0.0004926404,0.00013333965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028459516,0.0013798369,0.001632816,0.0036055706,0.0006177073,0.0025216148,0.002179877,0.0016104564,0.0012073738],"category_scores_gemma":[0.008690618,0.000707873,0.0019380104,0.0031895617,0.0005692339,0.003275698,0.0010640951,0.0021244152,0.0011517819],"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":[0.00064794597,0.00058446475,0.04944011,0.00050051225,0.00095833756,0.00037496432,0.0011177623,0.45879552,0.005330936,0.026260076,0.019016597,0.43697286],"study_design_scores_gemma":[0.000011183542,0.00002336394,0.0022515443,0.000015701253,0.000044713695,0.00008324472,0.00003890369,0.98699945,0.00036342288,0.008484651,0.0016659895,0.000017785658],"about_ca_topic_score_codex":0.010380444,"about_ca_topic_score_gemma":0.011236532,"teacher_disagreement_score":0.010380444,"about_ca_system_score_codex":0.0009923002,"about_ca_system_score_gemma":0.0007592063,"threshold_uncertainty_score":0.020640075},"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":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009999252,0.03803466,0.93768084,0.0005582701,0.0003447696,0.00021732913,0.0008690496,0.0011568276,0.011139],"genre_scores_gemma":[0.22455335,0.05282726,0.71522945,0.00033602654,0.0007197093,0.00062542636,0.0013972969,0.00040485617,0.003906659],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99549294,0.001015853,0.00049265596,0.00086668134,0.001957807,0.00017409584],"domain_scores_gemma":[0.9922863,0.0035415757,0.0014960243,0.00074102875,0.001775532,0.00015949886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042251768,0.0018947509,0.0015311743,0.012150946,0.0013423439,0.003963695,0.002501863,0.0019702148,0.0020701606],"category_scores_gemma":[0.010951,0.0010198079,0.0014784435,0.010848576,0.0020138293,0.005818776,0.0019176351,0.0023381184,0.0008758424],"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":[0.00022058323,0.00016891748,0.0197478,0.0038253851,0.0003984339,0.00033538067,0.0008879057,0.0748943,0.022098396,0.17438757,0.009479145,0.69355613],"study_design_scores_gemma":[0.000042676023,0.0012424141,0.034582123,0.0037836276,0.00072949106,0.0043088584,0.0013848259,0.40501535,0.05844714,0.19494314,0.29457358,0.0009468063],"about_ca_topic_score_codex":0.0032917918,"about_ca_topic_score_gemma":0.0022656366,"teacher_disagreement_score":0.012150946,"about_ca_system_score_codex":0.0024361473,"about_ca_system_score_gemma":0.0013373424,"threshold_uncertainty_score":0.022345126},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38471672,0.02134246,0.57180655,0.0015201876,0.001579553,0.0014690296,0.0024473649,0.0039249565,0.011193106],"genre_scores_gemma":[0.7582022,0.0020997894,0.23052448,0.0005137197,0.0005331754,0.00057561917,0.005369105,0.0005022105,0.001679688],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.95262444,0.020514455,0.0037322703,0.007264178,0.014815785,0.0010487606],"domain_scores_gemma":[0.8144079,0.1378654,0.012209519,0.016033681,0.017687187,0.0017962942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.031046838,0.002554409,0.0024909237,0.006524746,0.0015689812,0.0032137076,0.0030272189,0.0034486381,0.0015940814],"category_scores_gemma":[0.12576889,0.00036287762,0.0015849265,0.0053682583,0.0021866236,0.005639512,0.0030611297,0.0021867151,0.00069801015],"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":[0.0042292355,0.0020834324,0.050414998,0.0035602122,0.0024302397,0.00035321742,0.0007677502,0.23234394,0.013239605,0.012831699,0.012215124,0.66553056],"study_design_scores_gemma":[0.00034863764,0.0044184825,0.041643135,0.00054336706,0.00047839488,0.0012130588,0.0008300285,0.902669,0.019781379,0.019907236,0.007882511,0.00028472094],"about_ca_topic_score_codex":0.0034314657,"about_ca_topic_score_gemma":0.0028139025,"teacher_disagreement_score":0.031046838,"about_ca_system_score_codex":0.0019134779,"about_ca_system_score_gemma":0.0018166958,"threshold_uncertainty_score":0.16419333},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6624478,0.001277695,0.30797282,0.0005108212,0.00024337097,0.0006468957,0.0030534393,0.009172255,0.014674941],"genre_scores_gemma":[0.91268677,0.00025324445,0.08204323,0.00009475173,0.00007968706,0.00010897228,0.0027043158,0.000055910186,0.0019732227],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908197,0.00010105724,0.000046451245,0.00031398537,0.00030773118,0.00014879377],"domain_scores_gemma":[0.99913883,0.00015402537,0.00019738583,0.00011321043,0.00030814693,0.00008840472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005051447,0.000830931,0.00081263954,0.0040175333,0.00053296157,0.00078249123,0.0011197095,0.00069155864,0.0008134131],"category_scores_gemma":[0.0016542414,0.00027428908,0.00043992617,0.001142029,0.00045352452,0.0006498924,0.0009121064,0.00059284637,0.0006473233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006497265,0.001032779,0.07152368,0.00057363976,0.00034011673,0.0013661596,0.0006369063,0.065994024,0.13250622,0.0023997608,0.019245746,0.7037313],"study_design_scores_gemma":[0.000044301938,0.0002445136,0.08002265,0.000053534026,0.000113822854,0.0009696835,0.00048723212,0.8575414,0.049654424,0.0027987768,0.007999877,0.0000697981],"about_ca_topic_score_codex":0.016403925,"about_ca_topic_score_gemma":0.021224344,"teacher_disagreement_score":0.016403925,"about_ca_system_score_codex":0.0006655107,"about_ca_system_score_gemma":0.0008245788,"threshold_uncertainty_score":0.032616854},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009850091,0.00049454527,0.98483825,0.000958798,0.00006673211,0.000089035595,0.0009323037,0.002056578,0.00071373757],"genre_scores_gemma":[0.36383817,0.0006650345,0.6255647,0.00077279523,0.00035819842,0.0003344447,0.0060693645,0.00034139736,0.0020558953],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9929471,0.0024967813,0.00069490436,0.0013767651,0.0019918696,0.0004924789],"domain_scores_gemma":[0.978332,0.014445348,0.0014210981,0.003191022,0.0022313825,0.00037915327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005989671,0.00078497967,0.0014391498,0.002980813,0.0013859755,0.003133507,0.0038532396,0.0013297678,0.0034314608],"category_scores_gemma":[0.030895593,0.00058935874,0.0019440933,0.0035471388,0.0019044616,0.0056924266,0.0034122276,0.0037729267,0.0011849744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00082057493,0.0005819483,0.012121154,0.0012709871,0.000682681,0.00067254,0.0009919521,0.13088983,0.009684294,0.15008144,0.028520407,0.66368216],"study_design_scores_gemma":[0.00003453771,0.00006738978,0.0010388541,0.00007839817,0.000090199,0.00016526906,0.00019556198,0.80818725,0.0041378425,0.18129389,0.0046834787,0.00002725901],"about_ca_topic_score_codex":0.005781905,"about_ca_topic_score_gemma":0.009080128,"teacher_disagreement_score":0.005989671,"about_ca_system_score_codex":0.0020598862,"about_ca_system_score_gemma":0.0035862543,"threshold_uncertainty_score":0.03167677},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46186846,0.00037163036,0.52693254,0.00010382543,0.0000324659,0.000039537295,0.0014089473,0.0017384841,0.007504037],"genre_scores_gemma":[0.9568525,0.00012462343,0.03875581,0.000008716257,0.000016054262,0.00001573105,0.0010358188,0.00009518021,0.003095662],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998646,0.00002476089,0.000005248571,0.00002659693,0.000059340935,0.000019396088],"domain_scores_gemma":[0.99955755,0.00012813462,0.0000545796,0.0000471792,0.00018465819,0.000027906135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001362517,0.00033453747,0.00027471306,0.0013447258,0.00020121489,0.0005528885,0.00036217965,0.00037965956,0.0031925573],"category_scores_gemma":[0.0010531092,0.00012705673,0.00021113374,0.0009670454,0.00014827582,0.000611855,0.00028058255,0.0003147637,0.0008298134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000651339,0.00011578516,0.04539333,0.000121504156,0.00007887042,0.0003740536,0.00016044728,0.39310044,0.050754506,0.014516461,0.0042588585,0.49047434],"study_design_scores_gemma":[0.0000060236544,0.000039104914,0.006895267,0.000005590716,0.0000087757235,0.00005737704,0.000035600435,0.9818777,0.006047251,0.0038863167,0.0011345925,0.000006408846],"about_ca_topic_score_codex":0.0022630987,"about_ca_topic_score_gemma":0.0035097734,"teacher_disagreement_score":0.0031925573,"about_ca_system_score_codex":0.00023202263,"about_ca_system_score_gemma":0.00027150125,"threshold_uncertainty_score":0.010680139},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.104217604,0.00036592645,0.89314413,0.00022513652,0.00003973915,0.000031007967,0.00014906339,0.0012649301,0.00056245306],"genre_scores_gemma":[0.91197026,0.0001967955,0.08657565,0.00004555484,0.000041094067,0.000032086456,0.0003255953,0.00003982028,0.0007730609],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994887,0.00015360159,0.00003144835,0.00011749368,0.00014808521,0.00006061499],"domain_scores_gemma":[0.99782205,0.0016174298,0.00018812342,0.000119809214,0.00019326631,0.00005922457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088961795,0.00044632264,0.0005341144,0.0012123488,0.00037989757,0.0005714385,0.00077336904,0.00052413327,0.0005223719],"category_scores_gemma":[0.0034605577,0.00027274998,0.00049956195,0.000710453,0.0004472129,0.0010930622,0.00054064766,0.0009587142,0.00014508689],"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":[0.00023913555,0.00019590367,0.013887789,0.000114593066,0.00013365228,0.00019287343,0.00018544956,0.7826801,0.009190222,0.010084993,0.0014190715,0.18167618],"study_design_scores_gemma":[0.0000014190035,0.000006480197,0.0003627161,0.0000011576866,0.0000027447622,0.000009251523,0.0000034904806,0.99689937,0.0004233574,0.0022201526,0.00006754569,0.0000024427534],"about_ca_topic_score_codex":0.01030065,"about_ca_topic_score_gemma":0.010998248,"teacher_disagreement_score":0.01030065,"about_ca_system_score_codex":0.0008035187,"about_ca_system_score_gemma":0.0008018119,"threshold_uncertainty_score":0.020481348},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9838971,0.00006146521,0.014700727,0.00003266485,0.000017302933,0.000044171567,0.00021527575,0.0008069112,0.00022433371],"genre_scores_gemma":[0.9861648,0.000031739914,0.013181883,0.000014143133,0.000002412851,0.000023368575,0.00029783428,0.000053081003,0.00023063933],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827576,0.00047116203,0.00015695885,0.000299138,0.0006562982,0.00014071095],"domain_scores_gemma":[0.99237883,0.0043267733,0.0008847535,0.0010998048,0.0009719103,0.00033786483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012917885,0.0006003856,0.00040742048,0.0010020502,0.00015098449,0.00033802295,0.00093662227,0.0007581031,0.00067073584],"category_scores_gemma":[0.010201184,0.00026630922,0.0004480268,0.00047997618,0.00042447128,0.000628998,0.0005398994,0.0007054584,0.00021998568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0037983519,0.003124466,0.09106165,0.00076162134,0.0003933724,0.0009575182,0.0013698598,0.33090967,0.3956153,0.0017839243,0.0016034915,0.16862072],"study_design_scores_gemma":[0.000105274026,0.0034830875,0.027803935,0.00002392233,0.00007288628,0.0004472518,0.00019411593,0.81286347,0.15355515,0.0005428422,0.0008510886,0.000057007583],"about_ca_topic_score_codex":0.0017574121,"about_ca_topic_score_gemma":0.0022151172,"teacher_disagreement_score":0.0017574121,"about_ca_system_score_codex":0.00039934958,"about_ca_system_score_gemma":0.00040551327,"threshold_uncertainty_score":0.0068317056},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031886924,0.0004919717,0.9645176,0.00030923745,0.00008449318,0.00012619668,0.00029186046,0.0017946268,0.00049712666],"genre_scores_gemma":[0.50087607,0.0003752667,0.4951128,0.0002867203,0.00009509271,0.00025122665,0.0018105268,0.00014069024,0.0010515907],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800974,0.00032468568,0.00024747185,0.0005282915,0.0006688135,0.00022095781],"domain_scores_gemma":[0.99723107,0.0011851623,0.0003494864,0.00023767645,0.0008573199,0.00013925944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028460422,0.00081475783,0.0015381464,0.002047102,0.0006401574,0.0014715743,0.002688631,0.0011426662,0.00064087106],"category_scores_gemma":[0.007926617,0.00041182552,0.001404603,0.0022383342,0.0005675158,0.0015087044,0.0012946139,0.0012600026,0.0003374873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032658404,0.00026203506,0.017355546,0.00019679681,0.0001831387,0.00039615194,0.00021337924,0.44675738,0.008533294,0.004289899,0.004065784,0.51742],"study_design_scores_gemma":[0.000012878761,0.00004356754,0.0006179238,0.000007186029,0.000013557484,0.00008549509,0.00002106897,0.9954809,0.0012931541,0.0016762754,0.0007374592,0.000010483318],"about_ca_topic_score_codex":0.009288689,"about_ca_topic_score_gemma":0.0061684772,"teacher_disagreement_score":0.009288689,"about_ca_system_score_codex":0.0009167433,"about_ca_system_score_gemma":0.0021806797,"threshold_uncertainty_score":0.018469274},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0048068864,0.002482479,0.98797697,0.00058799353,0.00018911595,0.00002728988,0.00014596476,0.0015108967,0.0022723735],"genre_scores_gemma":[0.17265776,0.0031841525,0.8161731,0.0007290301,0.0003762257,0.00010472235,0.00086349563,0.00046751837,0.0054440126],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918276,0.00018302676,0.000028600236,0.00014473997,0.00036645308,0.00009438713],"domain_scores_gemma":[0.99865556,0.000521098,0.000079198835,0.00026345436,0.000376811,0.0001038772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019386192,0.00085561693,0.0011081198,0.0011102163,0.00042174832,0.0012186738,0.0017120285,0.0011635986,0.0024394498],"category_scores_gemma":[0.0038891437,0.00040662155,0.000526996,0.0017532244,0.0005469671,0.0032190243,0.0017791291,0.0023403617,0.0013954611],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002287565,0.00016216237,0.0022599744,0.0001899872,0.00017610198,0.00010581866,0.000072956645,0.16014637,0.004232446,0.07669681,0.015463376,0.74026525],"study_design_scores_gemma":[0.000015567082,0.00004072072,0.00023937371,0.000041422372,0.000021952646,0.00006433697,0.000016689197,0.90978193,0.0014844763,0.07515243,0.013127197,0.000013839578],"about_ca_topic_score_codex":0.00504886,"about_ca_topic_score_gemma":0.009639127,"teacher_disagreement_score":0.00504886,"about_ca_system_score_codex":0.00053347414,"about_ca_system_score_gemma":0.0010678275,"threshold_uncertainty_score":0.010252535},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025247307,0.00034576247,0.97322863,0.000095143194,0.000039017912,0.00002480217,0.00006654169,0.00047160088,0.00048113582],"genre_scores_gemma":[0.732061,0.000469289,0.26517797,0.00007750678,0.000098560486,0.000108649634,0.00047933598,0.000057392317,0.0014703898],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988493,0.00034339484,0.00007940283,0.00025559732,0.00035967297,0.00011255569],"domain_scores_gemma":[0.9986418,0.0005726215,0.00010756214,0.00013211304,0.0004927051,0.000053099757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022693812,0.00069418567,0.0010815632,0.0013693059,0.00041654115,0.00095847435,0.0012843081,0.000607635,0.0005805926],"category_scores_gemma":[0.0036044868,0.00029379522,0.0008749799,0.0013284499,0.00031891614,0.0012937395,0.0007719844,0.0011388569,0.00017703844],"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":[0.00013350531,0.00020001976,0.008768811,0.00006515806,0.00027914598,0.00010599451,0.00013633737,0.5589937,0.0037483058,0.008921837,0.0016117619,0.41703537],"study_design_scores_gemma":[0.0000014485374,0.000014644367,0.000349322,0.0000018255746,0.000008944659,0.000011167813,0.0000061575374,0.9975746,0.0002743017,0.0015532294,0.00020109379,0.0000033329818],"about_ca_topic_score_codex":0.010142785,"about_ca_topic_score_gemma":0.009713314,"teacher_disagreement_score":0.010142785,"about_ca_system_score_codex":0.00066944805,"about_ca_system_score_gemma":0.00078799884,"threshold_uncertainty_score":0.02016747},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041523527,0.004714368,0.8154823,0.0017536132,0.0020327067,0.00041651053,0.00072289445,0.0076780072,0.12567605],"genre_scores_gemma":[0.39318562,0.004304568,0.3882995,0.0009439918,0.00038351575,0.0002497163,0.0026170057,0.0006975231,0.20931858],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994087,0.00005320052,0.000039335195,0.00012210026,0.00029427075,0.0000823677],"domain_scores_gemma":[0.99952734,0.00006471724,0.000032793192,0.00015191558,0.00019288194,0.00003036472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005718529,0.001096449,0.0006929566,0.0016595774,0.0014197681,0.0014954973,0.0015888324,0.001245578,0.020934472],"category_scores_gemma":[0.0009821273,0.0003020669,0.00056214916,0.0008208393,0.00045862098,0.002233086,0.0029326936,0.0013188331,0.009655998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000320093,0.00009464187,0.0014538544,0.00025926493,0.00007794775,0.00031833554,0.00028195736,0.009972964,0.05701163,0.017727828,0.042418577,0.8700629],"study_design_scores_gemma":[0.00015772514,0.00073243474,0.005439431,0.0003001469,0.00020313237,0.0049737473,0.0010762265,0.2566767,0.14032628,0.035826772,0.5541391,0.00014834423],"about_ca_topic_score_codex":0.0013470055,"about_ca_topic_score_gemma":0.0026488751,"teacher_disagreement_score":0.020934472,"about_ca_system_score_codex":0.00042570214,"about_ca_system_score_gemma":0.00079874403,"threshold_uncertainty_score":0.070032775},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040883604,0.0012302139,0.99282527,0.00059533556,0.000062421364,0.000044051387,0.00011181448,0.00047711583,0.000565397],"genre_scores_gemma":[0.3658806,0.0029085935,0.62421787,0.00045121627,0.0008064142,0.00070265797,0.0012167264,0.00018291887,0.0036329413],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973793,0.001202614,0.00016860261,0.0005706593,0.0004971259,0.00018170562],"domain_scores_gemma":[0.98834103,0.008630301,0.0008843404,0.00089490984,0.0010542718,0.00019514283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041658427,0.0015486225,0.0023739068,0.0039416794,0.0008891819,0.0019947782,0.0032708272,0.0020746477,0.0016323709],"category_scores_gemma":[0.0145926,0.0008896688,0.0014084559,0.003587207,0.0019059052,0.002206132,0.0021838269,0.0034940704,0.00055631046],"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":[0.00009604952,0.00015434597,0.0039874096,0.00030339372,0.00050255796,0.00013446307,0.00012805156,0.6579397,0.0007389421,0.05372253,0.0036445216,0.27864796],"study_design_scores_gemma":[0.000007477391,0.000012717288,0.00017877224,0.000009404993,0.000013643944,0.000018107363,0.000013661783,0.93795615,0.00014264333,0.060896277,0.000744037,0.0000071123804],"about_ca_topic_score_codex":0.004605594,"about_ca_topic_score_gemma":0.0036216835,"teacher_disagreement_score":0.004605594,"about_ca_system_score_codex":0.0016853822,"about_ca_system_score_gemma":0.0013645971,"threshold_uncertainty_score":0.022031367},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6911922,0.0016927362,0.28350997,0.00034389843,0.0002712155,0.00022033617,0.00080936647,0.015344733,0.0066154646],"genre_scores_gemma":[0.9105145,0.00037141665,0.085504875,0.000063827894,0.000044569126,0.00003221016,0.0010499298,0.00042717313,0.001991557],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9954091,0.00076470984,0.00032098993,0.00071085803,0.0024324737,0.00036196943],"domain_scores_gemma":[0.9809301,0.008391502,0.0016050102,0.0023741256,0.006243911,0.00045536587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028737083,0.0012292292,0.00094456284,0.002166633,0.00050321495,0.0014779578,0.0016056601,0.0010021037,0.0018641399],"category_scores_gemma":[0.016049858,0.00029652592,0.00082408584,0.0012370357,0.00049343635,0.0018492109,0.0012741084,0.00087760616,0.0008420801],"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":[0.003023045,0.0010854659,0.03393785,0.0006220212,0.00041378522,0.0003887412,0.00024930073,0.059738234,0.10184599,0.0015824584,0.005339631,0.79177356],"study_design_scores_gemma":[0.000117872536,0.0018340193,0.015434097,0.000060324317,0.0002885211,0.00079645147,0.00019744488,0.79411834,0.18328041,0.0011135224,0.0026997905,0.00005921342],"about_ca_topic_score_codex":0.0021211344,"about_ca_topic_score_gemma":0.0018907018,"teacher_disagreement_score":0.0028737083,"about_ca_system_score_codex":0.00070964947,"about_ca_system_score_gemma":0.0010329915,"threshold_uncertainty_score":0.015197754},"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":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057620103,0.24047747,0.50666064,0.2140166,0.0035380963,0.00023858921,0.0002993855,0.0003293473,0.028677817],"genre_scores_gemma":[0.25335968,0.29519454,0.403595,0.018726455,0.019679582,0.0010579664,0.0005124053,0.00022061262,0.007653821],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9891209,0.0057505113,0.00054432056,0.0010520067,0.0032134196,0.00031879914],"domain_scores_gemma":[0.97686815,0.017764335,0.00070209004,0.0018458233,0.0020050167,0.00081457815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0150619075,0.0013374406,0.0018282884,0.0051808087,0.0023593912,0.01234158,0.0040983954,0.0056048436,0.0017237704],"category_scores_gemma":[0.02119963,0.0010781592,0.0010164954,0.0066336584,0.010573866,0.023834057,0.0076755695,0.01048082,0.0009135851],"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":[0.000025566713,0.00008956297,0.0016040324,0.0010258617,0.00007987073,0.00018014877,0.0005980208,0.009361601,0.00013395553,0.8664168,0.013293636,0.10719101],"study_design_scores_gemma":[0.0000054754883,0.000016342363,0.0001743237,0.00042248616,0.000009352483,0.00010107504,0.00047542597,0.018237833,0.00010769463,0.93762267,0.042802326,0.000025099653],"about_ca_topic_score_codex":0.0016343116,"about_ca_topic_score_gemma":0.001351644,"teacher_disagreement_score":0.0150619075,"about_ca_system_score_codex":0.0038789192,"about_ca_system_score_gemma":0.004039334,"threshold_uncertainty_score":0.079655886},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042552922,0.000685678,0.95196843,0.0006901338,0.00012675703,0.00003557157,0.0007024088,0.002147066,0.0010908615],"genre_scores_gemma":[0.71804214,0.00094006467,0.27371955,0.00031890298,0.00017437467,0.00012524439,0.0027161792,0.0003027591,0.003660787],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992847,0.00013377031,0.00005817368,0.00021325937,0.00021516455,0.0000950856],"domain_scores_gemma":[0.9981963,0.0007452823,0.00022529453,0.00030896423,0.00044124748,0.00008295279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001395157,0.0010730185,0.00070188876,0.00157924,0.0003937854,0.0008139252,0.0011962663,0.001045618,0.0018852046],"category_scores_gemma":[0.005390782,0.00045010034,0.0007517515,0.0019142192,0.0006204587,0.002108046,0.0011591298,0.0029765689,0.00078471756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023441388,0.00037709594,0.0088816555,0.00017043257,0.00019657459,0.0001951894,0.000117826195,0.45354426,0.02271328,0.01342908,0.010397822,0.48974243],"study_design_scores_gemma":[0.0000024719222,0.00001451961,0.00047255572,0.000005777683,0.000004980584,0.00001731528,0.0000075996927,0.99088037,0.0019152329,0.006057485,0.0006163059,0.0000053108015],"about_ca_topic_score_codex":0.0060149003,"about_ca_topic_score_gemma":0.0077434517,"teacher_disagreement_score":0.0060149003,"about_ca_system_score_codex":0.0009856288,"about_ca_system_score_gemma":0.0010448293,"threshold_uncertainty_score":0.011959791},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13677496,0.001307202,0.8590843,0.00045419377,0.00013289042,0.00014002573,0.00044935758,0.0010585772,0.0005985047],"genre_scores_gemma":[0.7285113,0.0007518241,0.2675431,0.00026527746,0.00018377596,0.00015106867,0.0013214416,0.00007078245,0.0012014102],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816966,0.0004161412,0.00025134886,0.00048280548,0.00053226284,0.00014783535],"domain_scores_gemma":[0.9922341,0.004962341,0.0008124067,0.0006740437,0.0011769974,0.00014016702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034990283,0.0013061111,0.0017953046,0.0023566184,0.00046350603,0.001544394,0.0012931704,0.0011733461,0.00036884012],"category_scores_gemma":[0.012315185,0.00029478845,0.0008538996,0.0020123406,0.00054301554,0.0018055489,0.00065336114,0.0012253813,0.00026510112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006237719,0.0006205409,0.047789574,0.00027723578,0.00030106018,0.00048302018,0.0003119987,0.28169248,0.007357909,0.0027929922,0.0024221614,0.65532726],"study_design_scores_gemma":[0.000011990991,0.00012881981,0.0036065602,0.000026268679,0.000040566625,0.00016845377,0.000043282973,0.9906024,0.0023456856,0.002428492,0.0005827653,0.000014857438],"about_ca_topic_score_codex":0.0052338815,"about_ca_topic_score_gemma":0.004039376,"teacher_disagreement_score":0.0052338815,"about_ca_system_score_codex":0.00047163272,"about_ca_system_score_gemma":0.00086381857,"threshold_uncertainty_score":0.018504858},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13333525,0.0006876229,0.850837,0.000703966,0.00029660258,0.00014318184,0.00012838813,0.0021596437,0.0117083555],"genre_scores_gemma":[0.9147582,0.00016165459,0.08108331,0.0002301788,0.00006390731,0.000117816366,0.00007687931,0.00010978897,0.003398377],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995797,0.00009062421,0.000024718733,0.0001201648,0.00011884697,0.00006596007],"domain_scores_gemma":[0.9991235,0.0002352951,0.00020993303,0.00019882488,0.00011619948,0.00011611142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005865484,0.00050093787,0.00027722574,0.00026257758,0.00036060982,0.0006700305,0.0009897768,0.00066401967,0.0021278176],"category_scores_gemma":[0.003248572,0.00017235335,0.00039352587,0.00013487543,0.0009636869,0.0010988059,0.0012795536,0.0006450179,0.00042801278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059719995,0.00033503748,0.009725167,0.0008241816,0.00029140586,0.000765585,0.0012718936,0.056509204,0.4628629,0.101754,0.007463865,0.35759956],"study_design_scores_gemma":[0.0001484995,0.0013579774,0.023112953,0.00009721436,0.0002848137,0.0019809452,0.00036306528,0.61732316,0.1549226,0.17120625,0.029001523,0.00020100203],"about_ca_topic_score_codex":0.00022105969,"about_ca_topic_score_gemma":0.00030465046,"teacher_disagreement_score":0.0021278176,"about_ca_system_score_codex":0.00030911804,"about_ca_system_score_gemma":0.0001898993,"threshold_uncertainty_score":0.0071182847},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056946203,0.00744021,0.92309517,0.0028637934,0.0013257532,0.00011423196,0.00046493686,0.001300984,0.006448742],"genre_scores_gemma":[0.8254571,0.0034473133,0.16639361,0.0006492093,0.00058317557,0.000078906545,0.000851143,0.000081902064,0.0024575822],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989108,0.00028341048,0.00008675045,0.00016271605,0.00047349322,0.00008278732],"domain_scores_gemma":[0.998735,0.00048637952,0.00011208899,0.00026234202,0.0003524903,0.000051657178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021406005,0.00054139795,0.0010664271,0.0015461876,0.0006230705,0.0017144065,0.00091921334,0.0010615294,0.0009734371],"category_scores_gemma":[0.002907187,0.00033124138,0.0008029179,0.0024784554,0.0007017092,0.0033951069,0.0015102474,0.001175584,0.0003220583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044985933,0.00040306753,0.012254012,0.0006751137,0.0006849776,0.0006194799,0.0003433661,0.1886422,0.019698093,0.070162915,0.019950055,0.68611693],"study_design_scores_gemma":[0.0000145688755,0.00010786389,0.004932427,0.00005744818,0.000100298006,0.00026348606,0.0002697792,0.82499444,0.010335812,0.14851591,0.010357151,0.000050804712],"about_ca_topic_score_codex":0.0017115412,"about_ca_topic_score_gemma":0.0018178948,"teacher_disagreement_score":0.0021406005,"about_ca_system_score_codex":0.0006782018,"about_ca_system_score_gemma":0.0005627281,"threshold_uncertainty_score":0.01132071},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1387122,0.0006264926,0.8554472,0.00023349102,0.00009948773,0.00006913879,0.00076593447,0.00273842,0.0013077011],"genre_scores_gemma":[0.8342552,0.00035122977,0.16187637,0.00007662859,0.000064353946,0.00004514103,0.0012584142,0.000121746954,0.0019508953],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997248,0.00004533362,0.000014547107,0.00008663768,0.00009796844,0.000030726904],"domain_scores_gemma":[0.9994831,0.00018653114,0.00010021439,0.000073231415,0.000124449,0.000032470776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020282308,0.00057401945,0.000666615,0.002270404,0.0002627053,0.00043054743,0.0007720727,0.00061324774,0.0010801232],"category_scores_gemma":[0.0010422241,0.00019872235,0.0005441868,0.0015928078,0.00033082574,0.0006620705,0.000433685,0.000517376,0.00040167436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006124331,0.00031103924,0.0100513,0.00023738413,0.00018618222,0.00029259277,0.00009147397,0.28560135,0.068579316,0.0053787185,0.0051910966,0.62346715],"study_design_scores_gemma":[0.0000060337215,0.000053369906,0.0031673638,0.0000035553971,0.000021337264,0.000071379785,0.000015236321,0.98891824,0.0038271707,0.0033016966,0.00060597586,0.000008636934],"about_ca_topic_score_codex":0.006385809,"about_ca_topic_score_gemma":0.00823893,"teacher_disagreement_score":0.006385809,"about_ca_system_score_codex":0.00032690854,"about_ca_system_score_gemma":0.00039570077,"threshold_uncertainty_score":0.012697279},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17879298,0.00039171593,0.6904335,0.0025069069,0.0005386896,0.0007173024,0.0031303866,0.05386306,0.06962558],"genre_scores_gemma":[0.4314258,0.00026218165,0.49947858,0.0003926841,0.00005007906,0.0003344281,0.0034611577,0.0013877996,0.06320727],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998141,0.000042413743,0.0000055009227,0.000051554725,0.00006278528,0.000023656026],"domain_scores_gemma":[0.99975723,0.000054211305,0.000021447604,0.000040713363,0.00006579184,0.00006064897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003061298,0.00069929153,0.00029766234,0.00037672961,0.00059566164,0.0006453721,0.00084442477,0.0010151922,0.022140495],"category_scores_gemma":[0.0010805174,0.0002285995,0.00022007778,0.00030440136,0.0006078502,0.0010435823,0.000838342,0.00063101127,0.0071270107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018761639,0.00031917315,0.008998632,0.0006931167,0.00007045045,0.0019467497,0.0023672034,0.020810075,0.19386359,0.020736799,0.1169204,0.6313976],"study_design_scores_gemma":[0.00024310571,0.0020220734,0.017375471,0.00021202963,0.00008711361,0.0029219864,0.0018386712,0.47403613,0.09521369,0.019933125,0.3858818,0.00023469162],"about_ca_topic_score_codex":0.0066370564,"about_ca_topic_score_gemma":0.01308624,"teacher_disagreement_score":0.022140495,"about_ca_system_score_codex":0.00036759867,"about_ca_system_score_gemma":0.00062403746,"threshold_uncertainty_score":0.074067354},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016847947,0.522681,0.44468182,0.0043015145,0.010615416,0.000097876764,0.0023183366,0.003416494,0.010202681],"genre_scores_gemma":[0.033354454,0.5251826,0.33395004,0.0022641434,0.01748136,0.00053509354,0.006863619,0.002762879,0.07760594],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990784,0.0001652351,0.00010389681,0.00023293796,0.00038868765,0.000030830426],"domain_scores_gemma":[0.9978963,0.0014506785,0.00015897471,0.00015583952,0.00027675278,0.00006157658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014328832,0.0024436344,0.0019931043,0.003006326,0.0002927548,0.0017229394,0.0010722398,0.0012405514,0.015333315],"category_scores_gemma":[0.0038206233,0.0012763768,0.001310497,0.004389458,0.0010522291,0.0027135978,0.0013219123,0.0030689163,0.010834846],"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":[0.000091947855,0.00006825716,0.0005986463,0.002995702,0.00023327713,0.0002711218,0.00025842167,0.0064563104,0.0049951845,0.017651472,0.3063782,0.66000146],"study_design_scores_gemma":[0.000030953717,0.00017046883,0.0044158855,0.0015595009,0.00015594244,0.0020052013,0.00022872374,0.05181942,0.0037488986,0.06906984,0.8666569,0.00013817262],"about_ca_topic_score_codex":0.0010879854,"about_ca_topic_score_gemma":0.0011928077,"teacher_disagreement_score":0.015333315,"about_ca_system_score_codex":0.00040593994,"about_ca_system_score_gemma":0.0004679151,"threshold_uncertainty_score":0.0512951},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05026241,0.0013862273,0.9354079,0.0017833035,0.000089992805,0.00006712015,0.00030251217,0.00046790214,0.010232621],"genre_scores_gemma":[0.77510864,0.0023731124,0.21318994,0.000983914,0.00048014097,0.00039631786,0.0008967049,0.0004697647,0.0061014844],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.995074,0.0013857172,0.00024454604,0.0010127398,0.0018264242,0.00045650935],"domain_scores_gemma":[0.97125906,0.01946398,0.0020896804,0.0032061387,0.0025797845,0.001401472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053608497,0.00093640375,0.0019123673,0.0030144262,0.0018982367,0.0043356232,0.0033436602,0.0031604862,0.0030938115],"category_scores_gemma":[0.05013538,0.0007922465,0.001060748,0.0027279037,0.006234838,0.010469738,0.005439783,0.0057219933,0.0010527826],"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":[0.00008808795,0.000039837636,0.00057608954,0.00014160905,0.000036896952,0.00010712326,0.0002526492,0.05817825,0.0018880793,0.92064834,0.0022727328,0.015770234],"study_design_scores_gemma":[0.000010358651,0.000024585532,0.00017506568,0.000024582485,0.0000061033575,0.00009311156,0.00005102754,0.19612515,0.0009177769,0.8012571,0.0012942224,0.00002097232],"about_ca_topic_score_codex":0.0012605751,"about_ca_topic_score_gemma":0.0008627651,"teacher_disagreement_score":0.0053608497,"about_ca_system_score_codex":0.0026460385,"about_ca_system_score_gemma":0.0013759382,"threshold_uncertainty_score":0.028351247},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.124074325,0.00077825534,0.8684614,0.00021854776,0.00010348858,0.0001519854,0.00042187457,0.0027123138,0.003077819],"genre_scores_gemma":[0.6994104,0.0010635497,0.29535526,0.00004932124,0.00011421888,0.0002098686,0.001252863,0.00030089938,0.0022436029],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99890685,0.00013826345,0.00005452862,0.00019980772,0.000637035,0.00006347475],"domain_scores_gemma":[0.9990415,0.00017660743,0.00013883325,0.00010490391,0.00050345145,0.000034739704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004400086,0.0008578312,0.00051695673,0.0036025485,0.000424084,0.0008358613,0.00042471735,0.00027676547,0.00095938944],"category_scores_gemma":[0.002209896,0.00025011806,0.0007399741,0.0031933815,0.0003710738,0.0012397604,0.00031845248,0.0005101541,0.0004607462],"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":[0.00012876582,0.0000789531,0.030256364,0.00040017857,0.000193624,0.00013682172,0.0002190132,0.037750434,0.042444155,0.0056057405,0.003984668,0.87880135],"study_design_scores_gemma":[0.000031235926,0.0002577726,0.081348754,0.000050032875,0.00019191088,0.00041948745,0.00025782007,0.8466792,0.048126806,0.009271208,0.013214204,0.00015161476],"about_ca_topic_score_codex":0.0045501594,"about_ca_topic_score_gemma":0.0031826377,"teacher_disagreement_score":0.0045501594,"about_ca_system_score_codex":0.00043649852,"about_ca_system_score_gemma":0.00085672276,"threshold_uncertainty_score":0.009047329},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00081382267,0.002058827,0.99339217,0.0006532331,0.0003021297,0.000039394075,0.00008127446,0.0008911288,0.0017681088],"genre_scores_gemma":[0.056148287,0.0043400982,0.93238413,0.00040785412,0.0008380531,0.000283485,0.0006008939,0.00045899232,0.0045381975],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990283,0.00024786813,0.00007662328,0.00015368893,0.00043337737,0.00006018757],"domain_scores_gemma":[0.99673295,0.0018877228,0.00020655913,0.00047251116,0.00062843936,0.00007194261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018274377,0.00142929,0.0012590254,0.0014591786,0.00063104974,0.001589023,0.0019884459,0.0012926826,0.00671091],"category_scores_gemma":[0.009187076,0.0006570723,0.00084707205,0.0020607118,0.00084316166,0.0033555178,0.001694988,0.0042118705,0.003950466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099156976,0.00009855182,0.0009596497,0.0006743632,0.00014044651,0.00016719717,0.000117027936,0.22386415,0.003659339,0.16806151,0.0411661,0.5609925],"study_design_scores_gemma":[0.00002085621,0.000025744503,0.00023874016,0.000048168855,0.000012190347,0.00011792478,0.000021330978,0.8582526,0.001679546,0.12172086,0.017840782,0.00002115517],"about_ca_topic_score_codex":0.0017097131,"about_ca_topic_score_gemma":0.002167762,"teacher_disagreement_score":0.00671091,"about_ca_system_score_codex":0.00079605874,"about_ca_system_score_gemma":0.00133083,"threshold_uncertainty_score":0.022450268},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010362651,0.00035620123,0.9856572,0.0006148196,0.000062167215,0.000039134484,0.00026263765,0.00030336203,0.0023419044],"genre_scores_gemma":[0.5200796,0.00146649,0.46176454,0.0011148549,0.00034439782,0.00074115954,0.0018116612,0.0003144855,0.012362762],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985775,0.0004598084,0.00007842185,0.00039535272,0.00035016224,0.00013870247],"domain_scores_gemma":[0.9963626,0.0022324517,0.00035780235,0.00049721537,0.00038611452,0.00016387524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022647153,0.0014278562,0.0013568043,0.0011738553,0.0004961939,0.00204388,0.0028155271,0.0018004399,0.0041381833],"category_scores_gemma":[0.009025199,0.0007739006,0.0013845637,0.0010611658,0.0016521298,0.004133913,0.0026733468,0.0035489714,0.0009767226],"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":[0.00009636526,0.00012123714,0.0014040638,0.0002234548,0.00012728877,0.000097245145,0.000121534875,0.6174368,0.001484753,0.26341292,0.0059090015,0.109565236],"study_design_scores_gemma":[0.0000072270054,0.000040525934,0.00010742622,0.00001979617,0.000010680597,0.000020713747,0.00001101287,0.85250396,0.00057314907,0.14441028,0.0022886237,0.000006752764],"about_ca_topic_score_codex":0.0019788586,"about_ca_topic_score_gemma":0.002049804,"teacher_disagreement_score":0.0041381833,"about_ca_system_score_codex":0.0018159162,"about_ca_system_score_gemma":0.0010304458,"threshold_uncertainty_score":0},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10516664,0.00040953234,0.8881314,0.0015150474,0.00007678589,0.00025818584,0.00040231278,0.0015993521,0.0024406938],"genre_scores_gemma":[0.8240249,0.00016250563,0.17393303,0.0001681546,0.00006574806,0.000099372184,0.00043923996,0.00006576769,0.0010413525],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956049,0.0015144682,0.00026577932,0.0008740745,0.0015139787,0.00022687069],"domain_scores_gemma":[0.9924125,0.0041093454,0.0009755772,0.00091628986,0.001370622,0.00021564524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003800327,0.0006283745,0.0007880744,0.0021695585,0.00063239236,0.0015271353,0.0015271963,0.0012962205,0.001220279],"category_scores_gemma":[0.012700146,0.0003757356,0.0009135942,0.0018350807,0.000904754,0.0025962908,0.0010670105,0.001308668,0.00033588367],"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":[0.000449564,0.00058746064,0.15339728,0.0004501054,0.0005009049,0.00084124133,0.0011829573,0.43780765,0.009778819,0.07741732,0.009587802,0.30799899],"study_design_scores_gemma":[0.000007970169,0.000037563495,0.0036395208,0.000008563129,0.000020388514,0.0001659793,0.00007407983,0.9775249,0.0010949997,0.015845971,0.00156708,0.000013001992],"about_ca_topic_score_codex":0.008799498,"about_ca_topic_score_gemma":0.0070210444,"teacher_disagreement_score":0.008799498,"about_ca_system_score_codex":0.0022115358,"about_ca_system_score_gemma":0.001701163,"threshold_uncertainty_score":0.020098329},"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":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03507841,0.0036869557,0.18596701,0.0016226296,0.002384432,0.001410995,0.069506995,0.19253704,0.50780547],"genre_scores_gemma":[0.28104636,0.0033901674,0.105185844,0.001710815,0.00038861972,0.0011337319,0.13983802,0.012452752,0.4548537],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993197,0.000036459845,0.000039321338,0.00015738288,0.0003535409,0.000093609706],"domain_scores_gemma":[0.99940276,0.000059423422,0.00003702642,0.00015667861,0.00027366256,0.0000703719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040659963,0.0013783606,0.0008494168,0.0013744098,0.0009461625,0.0022396552,0.0016401754,0.00118043,0.12421908],"category_scores_gemma":[0.0016508885,0.00052344706,0.00061454135,0.0012312001,0.00034178462,0.0024515898,0.0026772795,0.0009835932,0.09242789],"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":[0.001236821,0.00019373324,0.0057722568,0.0012357958,0.000076243356,0.0007559619,0.00055684784,0.0035930283,0.025136778,0.01058078,0.49139085,0.4594709],"study_design_scores_gemma":[0.0001339452,0.00019889089,0.0034839709,0.00017362517,0.000053946027,0.0005251506,0.00034217627,0.015594773,0.009158583,0.0049200044,0.96532625,0.00008881609],"about_ca_topic_score_codex":0.0053507066,"about_ca_topic_score_gemma":0.010280404,"teacher_disagreement_score":0.12421908,"about_ca_system_score_codex":0.00043671182,"about_ca_system_score_gemma":0.0009789006,"threshold_uncertainty_score":0},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012134432,0.0005288673,0.9860254,0.00014584351,0.000051319472,0.000030490104,0.000036865596,0.0006348054,0.00041198405],"genre_scores_gemma":[0.6581084,0.0012286624,0.33837858,0.00009799549,0.00014594436,0.00015342902,0.0003481625,0.00015547953,0.0013833313],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790996,0.0005061955,0.00014813275,0.00045259393,0.0008501774,0.00013296375],"domain_scores_gemma":[0.998292,0.00056112243,0.00028900692,0.00020277766,0.00060195406,0.000053029093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013410297,0.0010377864,0.0010948771,0.0015395845,0.00070840446,0.0010200829,0.0012778784,0.0006283362,0.00038126681],"category_scores_gemma":[0.0050267964,0.0004190848,0.000989181,0.0034735498,0.00077674916,0.0013902228,0.001004837,0.0012699062,0.00024717842],"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":[0.00018566867,0.00014759127,0.006862349,0.00025317018,0.00024054034,0.00033340094,0.0004037562,0.5361705,0.010822328,0.016581839,0.00410762,0.4238912],"study_design_scores_gemma":[0.000003036901,0.000021196123,0.000982645,0.0000048286433,0.000014384478,0.00004143463,0.000023347333,0.9934042,0.0016603934,0.003033166,0.00079935795,0.0000119417555],"about_ca_topic_score_codex":0.009125103,"about_ca_topic_score_gemma":0.00508768,"teacher_disagreement_score":0.009125103,"about_ca_system_score_codex":0.00072865986,"about_ca_system_score_gemma":0.0012188995,"threshold_uncertainty_score":0.018144011},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18348064,0.00059693056,0.80379254,0.00027888917,0.00025042202,0.00016389875,0.00016784744,0.0068867994,0.004382021],"genre_scores_gemma":[0.73274165,0.0003535983,0.26240414,0.00016319241,0.0000987163,0.00006123464,0.0002536501,0.00012772257,0.003796013],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991561,0.00011416008,0.000027684924,0.00011941157,0.00045721402,0.00012539694],"domain_scores_gemma":[0.99804676,0.00071214,0.00013508632,0.00033453098,0.0006848248,0.00008666103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048760674,0.0006506305,0.0006597029,0.0011520106,0.00028643486,0.0007523019,0.0011027576,0.0010273478,0.0011342862],"category_scores_gemma":[0.002735161,0.0002965395,0.00037493874,0.0004632377,0.00030838922,0.0014526062,0.0013908434,0.000565037,0.0007589379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003606286,0.000323338,0.008569807,0.00022574472,0.00006498627,0.0005892243,0.0002802831,0.037330035,0.39098915,0.003197527,0.0049777045,0.5530916],"study_design_scores_gemma":[0.000025189938,0.0004355327,0.0060255146,0.00001836999,0.000050694718,0.0012855227,0.00009910013,0.8711728,0.10803406,0.0029430427,0.009851164,0.000058992486],"about_ca_topic_score_codex":0.0017665438,"about_ca_topic_score_gemma":0.0022772483,"teacher_disagreement_score":0.0017665438,"about_ca_system_score_codex":0.0002709807,"about_ca_system_score_gemma":0.00043982387,"threshold_uncertainty_score":0.003794551},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005272072,0.015411945,0.9291342,0.0015354672,0.00051989756,0.00024817346,0.0017969518,0.0055876556,0.04049352],"genre_scores_gemma":[0.09636462,0.025864499,0.73099214,0.0007423937,0.00033879405,0.00022517385,0.0061587817,0.001013569,0.13829997],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938536,0.00004938241,0.000025171294,0.00015785907,0.00034292392,0.000039338327],"domain_scores_gemma":[0.999138,0.0002452373,0.00005475293,0.00009129933,0.000419647,0.000050987863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006750154,0.0012935621,0.00048157692,0.0016616762,0.00040354923,0.0013252009,0.0012583763,0.0008440321,0.0039977427],"category_scores_gemma":[0.0013365205,0.0004971197,0.00091785163,0.0016893165,0.00056010904,0.0008484878,0.00055780855,0.0010430292,0.002933784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051761683,0.00015919015,0.0035888825,0.0005653074,0.00007346092,0.00029016295,0.00036456753,0.033386998,0.011000784,0.023972465,0.06752087,0.85902554],"study_design_scores_gemma":[0.000017086442,0.00013832106,0.010717907,0.0004122689,0.0001461147,0.0018593699,0.00031154478,0.44105124,0.024726931,0.045177747,0.4753097,0.00013175185],"about_ca_topic_score_codex":0.07754373,"about_ca_topic_score_gemma":0.08558456,"teacher_disagreement_score":0.07754373,"about_ca_system_score_codex":0.0019097422,"about_ca_system_score_gemma":0.0023586585,"threshold_uncertainty_score":0.15418476},"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":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008911991,0.136894,0.8417724,0.003639457,0.00065252837,0.000100827965,0.00077784003,0.0011045841,0.006146409],"genre_scores_gemma":[0.19527471,0.23137628,0.5546761,0.002444059,0.0025962268,0.00032999678,0.003827082,0.0004241121,0.009051401],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988079,0.00024460373,0.00012396068,0.00023947404,0.0005037587,0.00008029627],"domain_scores_gemma":[0.99718446,0.0015950156,0.00013873371,0.00037156575,0.0006189807,0.000091285816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026140357,0.001377976,0.0018901099,0.0020178305,0.00043677364,0.0020558957,0.0019556205,0.0013115873,0.002642056],"category_scores_gemma":[0.0063413363,0.0006579452,0.00102757,0.0045195716,0.00063569815,0.0037659004,0.0019603083,0.0020392353,0.0011224711],"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":[0.00009840881,0.00017718029,0.0028310588,0.001436699,0.00018714467,0.000043695098,0.000051432697,0.03167177,0.0015744463,0.024096783,0.012640907,0.9251904],"study_design_scores_gemma":[0.000026999413,0.0002429333,0.0029857012,0.0009314913,0.00019092839,0.00026864457,0.00012691686,0.7440687,0.0076402137,0.14535214,0.09809111,0.00007425883],"about_ca_topic_score_codex":0.003770096,"about_ca_topic_score_gemma":0.004288717,"teacher_disagreement_score":0.003770096,"about_ca_system_score_codex":0.00103831,"about_ca_system_score_gemma":0.0016941152,"threshold_uncertainty_score":0.0138245225},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09814879,0.0006395705,0.89830893,0.00035215332,0.000057789915,0.000040073297,0.00012982632,0.0013706584,0.000952221],"genre_scores_gemma":[0.9097988,0.00019345226,0.088040635,0.00011914225,0.00004633614,0.00005104009,0.00037442023,0.000053929492,0.001322319],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992884,0.00023365201,0.000052418185,0.00014061654,0.00020425215,0.00008068624],"domain_scores_gemma":[0.9973654,0.0015098643,0.00028631827,0.00022780133,0.0005362685,0.00007444723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018750705,0.00077321805,0.00068329234,0.0006584662,0.00028173495,0.00061904726,0.001170821,0.0008708843,0.0006663485],"category_scores_gemma":[0.007434468,0.00039468493,0.00050321495,0.00064382766,0.0005469347,0.001308858,0.0009904866,0.001959356,0.0002037224],"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":[0.00033307966,0.00016984956,0.0044559785,0.000069183654,0.00008077573,0.00008366106,0.00008359104,0.78392446,0.0047723358,0.005500836,0.0014021479,0.19912408],"study_design_scores_gemma":[0.0000013982409,0.000007839177,0.00009834833,0.0000013909923,0.0000017762835,0.0000041267745,0.000001412073,0.99862826,0.00046812545,0.00074200024,0.000043967153,0.0000014183447],"about_ca_topic_score_codex":0.006412276,"about_ca_topic_score_gemma":0.006007686,"teacher_disagreement_score":0.006412276,"about_ca_system_score_codex":0.0010874579,"about_ca_system_score_gemma":0.000783422,"threshold_uncertainty_score":0.01274991},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16244107,0.0034461687,0.6670769,0.032500118,0.0032681823,0.00042640726,0.0042205546,0.005038674,0.12158193],"genre_scores_gemma":[0.7622222,0.0018288043,0.1727806,0.0016668708,0.0007851163,0.00037153315,0.0014777307,0.0013385001,0.05752859],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9901827,0.0025763437,0.00056285644,0.0033399053,0.0025765772,0.0007616337],"domain_scores_gemma":[0.9822796,0.007215428,0.0022051109,0.003220142,0.004357758,0.00072187284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007124423,0.00079689856,0.0012558624,0.004807767,0.001908899,0.0057480442,0.0015257333,0.0019189881,0.01340991],"category_scores_gemma":[0.05521876,0.0007458296,0.0013248572,0.0041486877,0.0023181844,0.004539188,0.002143079,0.0030499988,0.0071348497],"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":[0.0009840701,0.0001864809,0.058034215,0.00039628177,0.00031448063,0.00040778163,0.005180159,0.032545507,0.005541812,0.22027841,0.058848076,0.61728275],"study_design_scores_gemma":[0.000065449014,0.00049478974,0.08643452,0.00055325456,0.00024375491,0.0021508958,0.007655397,0.19049983,0.01252278,0.39252326,0.30607963,0.0007764976],"about_ca_topic_score_codex":0.023988022,"about_ca_topic_score_gemma":0.018161092,"teacher_disagreement_score":0.023988022,"about_ca_system_score_codex":0.0042351773,"about_ca_system_score_gemma":0.0030233948,"threshold_uncertainty_score":0.04769677},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058358077,0.00987493,0.8921274,0.02884148,0.00040776294,0.000108048334,0.0008303348,0.0010141798,0.00843766],"genre_scores_gemma":[0.80505776,0.006842464,0.18242013,0.001731628,0.0005106362,0.00011717943,0.00088942924,0.00008227794,0.0023484908],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922836,0.00030967742,0.00006660765,0.00012708032,0.00017569007,0.00009256857],"domain_scores_gemma":[0.9981481,0.001057352,0.00015318667,0.00021556107,0.0003206663,0.00010523367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002224809,0.00060163374,0.00077999494,0.0007706581,0.00034589728,0.0018707375,0.0012722017,0.0013106494,0.0014378026],"category_scores_gemma":[0.0053019426,0.00037231296,0.00053552934,0.0011397066,0.0009371695,0.0040406943,0.00210602,0.0027349333,0.0003678907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035283252,0.0002775739,0.01828064,0.00083287066,0.00025479123,0.00032478364,0.00044549158,0.33956698,0.0035359534,0.14087443,0.023533322,0.47172025],"study_design_scores_gemma":[0.000010762335,0.000022847571,0.0010831597,0.000076623735,0.000019900925,0.000036520694,0.00012394256,0.8947927,0.0011845594,0.0971956,0.0054420764,0.000011390926],"about_ca_topic_score_codex":0.003961585,"about_ca_topic_score_gemma":0.0057006613,"teacher_disagreement_score":0.003961585,"about_ca_system_score_codex":0.0015339092,"about_ca_system_score_gemma":0.0014778627,"threshold_uncertainty_score":0.0117660165},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90684116,0.00016309833,0.089525126,0.00014958638,0.00001699402,0.00014297529,0.00028662948,0.00065022055,0.0022242717],"genre_scores_gemma":[0.95325327,0.00005750395,0.045575526,0.000019028967,0.000009827189,0.000035209232,0.00033106704,0.000018216297,0.00070039724],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99750465,0.00082477817,0.00019420568,0.00046247293,0.0007872603,0.00022663757],"domain_scores_gemma":[0.9906114,0.0059230165,0.0013438133,0.00073170016,0.0010926938,0.00029736807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014874586,0.00044513374,0.00044287276,0.0053609186,0.0006387484,0.001432944,0.000677922,0.0010772547,0.0007106991],"category_scores_gemma":[0.00980651,0.00018930579,0.0003559086,0.0023636208,0.00061543554,0.0013279654,0.00073852076,0.00044935508,0.00028835094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00087060465,0.0010632573,0.44497046,0.00047294836,0.00019310723,0.005791356,0.0054721697,0.04617182,0.046120923,0.008695192,0.0037104834,0.43646768],"study_design_scores_gemma":[0.00004320937,0.00054097397,0.2256124,0.00010355588,0.00015179659,0.0057953466,0.00468795,0.67938495,0.058178242,0.01706938,0.008315507,0.00011663161],"about_ca_topic_score_codex":0.0021480864,"about_ca_topic_score_gemma":0.004341143,"teacher_disagreement_score":0.0053609186,"about_ca_system_score_codex":0.00049446675,"about_ca_system_score_gemma":0.00045874104,"threshold_uncertainty_score":0.007866561},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013350937,0.00063372956,0.98131186,0.00038837918,0.0000808564,0.000055672208,0.0008344642,0.001852576,0.0014915765],"genre_scores_gemma":[0.3979031,0.00051008305,0.5916106,0.00021969345,0.000213662,0.0001772111,0.005478259,0.00058950385,0.0032978936],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99793637,0.0004798566,0.0001323066,0.00060182274,0.00065444474,0.00019528338],"domain_scores_gemma":[0.9928416,0.004617696,0.00055145886,0.00082812604,0.0009555564,0.00020555149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029446923,0.00078989234,0.0015162699,0.0030072036,0.00091227726,0.0023477226,0.0023691922,0.003202153,0.004686705],"category_scores_gemma":[0.015577687,0.0010910223,0.0015560478,0.0027603684,0.0011839833,0.0036651813,0.0019670809,0.0020575654,0.0017323096],"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":[0.00040168714,0.00010419552,0.0028076468,0.0003389389,0.00017431586,0.00029637455,0.00029004697,0.56571746,0.005162113,0.09664396,0.015164124,0.31289914],"study_design_scores_gemma":[0.000008914954,0.000014492384,0.00032628493,0.00002612259,0.000011306816,0.00005243223,0.000019585601,0.93992347,0.0008800188,0.057117622,0.0016121204,0.000007745883],"about_ca_topic_score_codex":0.00639862,"about_ca_topic_score_gemma":0.009841499,"teacher_disagreement_score":0.00639862,"about_ca_system_score_codex":0.0018109116,"about_ca_system_score_gemma":0.0017626531,"threshold_uncertainty_score":0.015678644},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12805323,0.00025638903,0.866311,0.00039137035,0.00010796372,0.000085536674,0.0011233407,0.0029818653,0.000689395],"genre_scores_gemma":[0.8159109,0.00013879374,0.17856176,0.000120654644,0.00012530362,0.000120853874,0.0033552581,0.00016001379,0.0015065408],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99722385,0.0006378443,0.00022577892,0.0008636764,0.00075741915,0.00029153697],"domain_scores_gemma":[0.98595726,0.0061601168,0.0016936325,0.0031315368,0.0025645683,0.0004927552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003154243,0.0010162867,0.0017166442,0.0027256992,0.00066325476,0.0014145991,0.0027587083,0.0012477415,0.0010419239],"category_scores_gemma":[0.01773938,0.00044430286,0.0010129976,0.0024825078,0.00068285485,0.0018405723,0.0020180782,0.002178901,0.00065400347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011508476,0.00090207276,0.114573374,0.00030294308,0.00042300634,0.0010498998,0.0004894079,0.40913752,0.016918648,0.009395933,0.006666579,0.43898982],"study_design_scores_gemma":[0.000012035191,0.000075604206,0.0041166986,0.000010060632,0.000020724148,0.00014069767,0.000039066083,0.9867931,0.0022449014,0.0057824287,0.0007484935,0.000016147587],"about_ca_topic_score_codex":0.0046549686,"about_ca_topic_score_gemma":0.0050248867,"teacher_disagreement_score":0.0046549686,"about_ca_system_score_codex":0.00056630734,"about_ca_system_score_gemma":0.0013711107,"threshold_uncertainty_score":0.016681433},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011955592,0.00032822334,0.9722425,0.0002513605,0.00006304884,0.000196732,0.0021757046,0.01116853,0.0016184355],"genre_scores_gemma":[0.44487983,0.0007071286,0.5419973,0.00023037626,0.00019167115,0.00083090976,0.00802252,0.0012009789,0.0019393578],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983827,0.0003063485,0.00011306231,0.00027648133,0.00079994585,0.000121540856],"domain_scores_gemma":[0.99505436,0.0023329179,0.0007440347,0.0009821524,0.0006745763,0.0002119976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010778932,0.0024302746,0.0015241273,0.002295021,0.00047935324,0.0018880797,0.002448563,0.00096404087,0.0036080896],"category_scores_gemma":[0.008268204,0.00058138056,0.0012049054,0.002418713,0.0008133399,0.0019891881,0.0025651215,0.0020572727,0.0011423804],"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":[0.0005277976,0.00029732948,0.0051709055,0.0005711974,0.00029870207,0.000989247,0.00029012637,0.73984605,0.0109851565,0.020972198,0.016967798,0.2030836],"study_design_scores_gemma":[0.000008729292,0.000016487034,0.00031943477,0.000009807094,0.000005920374,0.000038032293,0.000018162376,0.9899462,0.0013884323,0.006948203,0.0012907705,0.0000097910115],"about_ca_topic_score_codex":0.007663147,"about_ca_topic_score_gemma":0.008547446,"teacher_disagreement_score":0.007663147,"about_ca_system_score_codex":0.0006792739,"about_ca_system_score_gemma":0.001617844,"threshold_uncertainty_score":0.015237093},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4443839,0.0028009764,0.54485506,0.0003454727,0.00038208664,0.0002191826,0.0011794928,0.0015993093,0.0042343987],"genre_scores_gemma":[0.87876517,0.001736125,0.116069965,0.00007572148,0.00026598756,0.000050821774,0.0011950274,0.00005464135,0.001786677],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997491,0.000021125552,0.000013662191,0.00006208139,0.00009712473,0.000056774694],"domain_scores_gemma":[0.999483,0.0001277489,0.00008852908,0.000050233288,0.0001903219,0.000060247472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003224949,0.00059197884,0.00070817495,0.002642354,0.00019911914,0.0007185723,0.00044702156,0.0005329621,0.00070169044],"category_scores_gemma":[0.0012575826,0.00018461075,0.00033700696,0.0012057079,0.00022320217,0.00075125333,0.00039985433,0.000683266,0.00037581063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001581493,0.00032707804,0.019515116,0.0003165409,0.00011474349,0.0011666683,0.00014528043,0.014426014,0.2626292,0.0023989053,0.0042453455,0.6931336],"study_design_scores_gemma":[0.00003339771,0.0006003857,0.06673652,0.000083572246,0.00017013084,0.0022708334,0.00034545248,0.8052294,0.11441312,0.0034906236,0.006571676,0.00005485764],"about_ca_topic_score_codex":0.0014495847,"about_ca_topic_score_gemma":0.0016383507,"teacher_disagreement_score":0.002642354,"about_ca_system_score_codex":0.00021171775,"about_ca_system_score_gemma":0.00035129406,"threshold_uncertainty_score":0.0028823018},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010646581,0.00026220837,0.98471177,0.00032794214,0.00006600986,0.000047986476,0.000011912618,0.00034833633,0.003577218],"genre_scores_gemma":[0.54697,0.00055906613,0.4486839,0.00026832471,0.000090919464,0.00013073262,0.00004886398,0.00007946588,0.0031687708],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999456,0.00014080627,0.000023056025,0.000119914446,0.0002021189,0.000058133402],"domain_scores_gemma":[0.99950373,0.00017201067,0.000056796278,0.00013081553,0.00009489588,0.000041811985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058766984,0.00060083176,0.00033866303,0.00066286826,0.00048179115,0.0010945915,0.00094763713,0.0006054545,0.0009940251],"category_scores_gemma":[0.0009211001,0.00031794983,0.0006175596,0.0004355498,0.0008794098,0.0019953412,0.0017301241,0.0011113933,0.00023571381],"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":[0.00023400206,0.00039013757,0.0040485626,0.0003416367,0.00023463782,0.0008597876,0.0013467276,0.25292715,0.16053763,0.1847064,0.0035961815,0.39077717],"study_design_scores_gemma":[0.000016095404,0.0002286854,0.00097056944,0.000049455837,0.000049792932,0.0005416926,0.00042722042,0.8788127,0.023298372,0.08201928,0.01352922,0.000057001052],"about_ca_topic_score_codex":0.0007339469,"about_ca_topic_score_gemma":0.00077698345,"teacher_disagreement_score":0.0010945915,"about_ca_system_score_codex":0.0004533058,"about_ca_system_score_gemma":0.00048716448,"threshold_uncertainty_score":0.0033253431},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11395478,0.00021895113,0.88155866,0.0002708181,0.00005174809,0.00011389734,0.00008958032,0.0025703916,0.001171209],"genre_scores_gemma":[0.43697986,0.00012588919,0.5614297,0.00010432759,0.000025235362,0.00023159546,0.00028444934,0.00013786656,0.00068118295],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990626,0.00027072203,0.000050095547,0.00016900705,0.00032357028,0.00012395953],"domain_scores_gemma":[0.9988105,0.00054577726,0.00009892378,0.000120779776,0.0003101439,0.00011385372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020798282,0.0006950162,0.0011413025,0.0009578824,0.00096631877,0.0009633816,0.0015348976,0.0007528917,0.0003889175],"category_scores_gemma":[0.003176096,0.00038877447,0.0010490213,0.0013450064,0.00083712384,0.00087445637,0.0011028706,0.0008618429,0.00014586744],"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":[0.00015060217,0.00015382009,0.0030406797,0.00005855426,0.00008839549,0.00015481774,0.00019294539,0.8976733,0.0047086705,0.00443694,0.0015959386,0.08774523],"study_design_scores_gemma":[0.000022791213,0.000020330437,0.00021625207,0.000002005639,0.000005085543,0.000017310636,0.00003560078,0.9963438,0.0009207315,0.002129462,0.0002804674,0.00000610491],"about_ca_topic_score_codex":0.013406541,"about_ca_topic_score_gemma":0.008941989,"teacher_disagreement_score":0.013406541,"about_ca_system_score_codex":0.0010189576,"about_ca_system_score_gemma":0.0022822146,"threshold_uncertainty_score":0.026657045},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054737605,0.00073948497,0.94088894,0.00013518415,0.000064949214,0.00010578515,0.00013206873,0.0013039851,0.0018920825],"genre_scores_gemma":[0.6148114,0.0003848088,0.38187897,0.00014977399,0.00009985541,0.0001632079,0.0005696363,0.0001499083,0.0017924514],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9943112,0.0010055142,0.0004418644,0.0010602535,0.0028412985,0.000339824],"domain_scores_gemma":[0.99130297,0.003818736,0.00095261366,0.0011234737,0.0025693306,0.00023287721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00401232,0.00148306,0.0021308812,0.005029249,0.0010890991,0.0015563308,0.0025939706,0.00094555324,0.0013515056],"category_scores_gemma":[0.012772487,0.00064618204,0.0015308246,0.0032580257,0.0006466028,0.0022243243,0.0022569054,0.0015378892,0.00051678554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042914605,0.00023176736,0.01610775,0.00028980593,0.00067489495,0.00037938447,0.00035037706,0.15655325,0.035360534,0.00676634,0.0026670373,0.78018975],"study_design_scores_gemma":[0.00002361035,0.00017723409,0.0039454806,0.000034791723,0.00022111835,0.00048727405,0.000096747055,0.96691364,0.015935948,0.009868105,0.002242689,0.000053410866],"about_ca_topic_score_codex":0.0025069118,"about_ca_topic_score_gemma":0.004159806,"teacher_disagreement_score":0.005029249,"about_ca_system_score_codex":0.00083793834,"about_ca_system_score_gemma":0.0010554363,"threshold_uncertainty_score":0.021219432},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05834993,0.018703323,0.8739269,0.023295557,0.0016318073,0.00028425668,0.0010828844,0.0007510867,0.021974323],"genre_scores_gemma":[0.6782565,0.01770038,0.29800814,0.0015318322,0.0006359539,0.00018062566,0.0008604719,0.00009050109,0.00273562],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991216,0.00030862365,0.000046628767,0.000097824115,0.00038556702,0.00003985389],"domain_scores_gemma":[0.99591136,0.0026519212,0.00018815843,0.00042601215,0.0006947898,0.00012779195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023371507,0.0006271552,0.0005078229,0.0017649913,0.00045563088,0.0022338554,0.0008945091,0.00087942474,0.0010792264],"category_scores_gemma":[0.0056518847,0.0003081218,0.00076695974,0.0023282599,0.0008910648,0.0038054232,0.0009950456,0.0016010985,0.00022341192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002564309,0.00029559297,0.019331453,0.0015119934,0.00041262896,0.0012037908,0.00058211293,0.14673556,0.011316999,0.26239783,0.01806845,0.5378872],"study_design_scores_gemma":[0.00002162452,0.00013976035,0.0039919848,0.00025495212,0.000062394334,0.0003539525,0.0007930167,0.7001377,0.006408454,0.2530819,0.03468446,0.00006978497],"about_ca_topic_score_codex":0.0013147008,"about_ca_topic_score_gemma":0.0015978143,"teacher_disagreement_score":0.0023371507,"about_ca_system_score_codex":0.0008482602,"about_ca_system_score_gemma":0.0010075335,"threshold_uncertainty_score":0.012360215},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05075369,0.0003538658,0.94485116,0.00015538475,0.00011109488,0.00007013813,0.00015147019,0.002209058,0.0013441636],"genre_scores_gemma":[0.75392044,0.00016661115,0.24284884,0.000111893285,0.00010078144,0.00012080147,0.0007265972,0.00014322386,0.001860851],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982492,0.00040374912,0.00010369976,0.00044335978,0.00056829414,0.00023170086],"domain_scores_gemma":[0.9943461,0.0021579666,0.00047786444,0.0011003,0.0016365866,0.00028114126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027510487,0.0008833846,0.0018187724,0.0022732064,0.0007521545,0.0010183719,0.0024811162,0.0011353478,0.0011603732],"category_scores_gemma":[0.007489802,0.0003955584,0.0010059965,0.0020325708,0.00042833746,0.0022168064,0.0020402323,0.0016505609,0.0005970172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055475935,0.0007953213,0.017288845,0.00010627564,0.000493528,0.00033625367,0.0003114504,0.25564066,0.018826727,0.004106817,0.0077807894,0.69375855],"study_design_scores_gemma":[0.000006340867,0.000044178767,0.00077592675,0.0000035021264,0.00003170279,0.00005945408,0.000028927065,0.9943212,0.0023166372,0.0019833616,0.00042113688,0.0000076165693],"about_ca_topic_score_codex":0.0044160276,"about_ca_topic_score_gemma":0.0054841307,"teacher_disagreement_score":0.0044160276,"about_ca_system_score_codex":0.000477255,"about_ca_system_score_gemma":0.0009480141,"threshold_uncertainty_score":0.014549136},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34885845,0.0017983797,0.6152181,0.0007709788,0.0003941992,0.00018297991,0.005155858,0.012063479,0.015557659],"genre_scores_gemma":[0.8781069,0.0002845256,0.10815426,0.00011881962,0.00003925863,0.00006921941,0.0042403,0.00013099184,0.008855726],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996655,0.000052150255,0.000016205686,0.00010701345,0.000089684734,0.00006934392],"domain_scores_gemma":[0.9995459,0.00014818551,0.000052441243,0.00007133695,0.00014395024,0.00003825281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055297755,0.0005222676,0.00025871856,0.0010981744,0.0003409643,0.00055083603,0.0009109375,0.0006582638,0.003162488],"category_scores_gemma":[0.0016519522,0.0002106863,0.00040216823,0.0005042232,0.00021056461,0.0007851379,0.00072025193,0.0009836447,0.0011041168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007750226,0.0004937648,0.033110335,0.00020903879,0.00023823945,0.00038221577,0.00025423948,0.035665534,0.052761514,0.008197671,0.027827386,0.840085],"study_design_scores_gemma":[0.000014178237,0.00006770732,0.015867164,0.00003068185,0.000053718402,0.00012288257,0.000083195686,0.9541219,0.017676346,0.0071911076,0.004752597,0.000018616158],"about_ca_topic_score_codex":0.014402629,"about_ca_topic_score_gemma":0.021958211,"teacher_disagreement_score":0.014402629,"about_ca_system_score_codex":0.0006799514,"about_ca_system_score_gemma":0.0007305556,"threshold_uncertainty_score":0.028637588},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37949973,0.0012306566,0.6142672,0.00042735372,0.00014235251,0.0000903817,0.00033391226,0.0015339289,0.0024745143],"genre_scores_gemma":[0.95499223,0.00014884883,0.043023176,0.000036312038,0.000044840366,0.000034446894,0.00027171147,0.000050829305,0.0013975009],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892825,0.0004025834,0.000038014747,0.00029836508,0.00020379666,0.00012909324],"domain_scores_gemma":[0.99785966,0.0009831707,0.0003366026,0.00029174285,0.00040457078,0.00012426497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010924288,0.00044215293,0.00074206793,0.001133488,0.0002931866,0.000614077,0.00080339436,0.00064850703,0.0011265439],"category_scores_gemma":[0.0067427857,0.00022667607,0.00044026432,0.0010129431,0.00031190758,0.0011519892,0.0009603506,0.0007746392,0.0008818196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011196875,0.00068674353,0.08784525,0.00023244678,0.0002988894,0.0004656632,0.00077446137,0.053688418,0.03505254,0.0038992774,0.0115324715,0.80440414],"study_design_scores_gemma":[0.000015325566,0.00013558322,0.025499143,0.000015825506,0.000046732603,0.00023841571,0.00021275035,0.9613265,0.0045372616,0.006335748,0.0016142272,0.000022445138],"about_ca_topic_score_codex":0.0034485937,"about_ca_topic_score_gemma":0.004375325,"teacher_disagreement_score":0.0034485937,"about_ca_system_score_codex":0.00030303694,"about_ca_system_score_gemma":0.0003864025,"threshold_uncertainty_score":0.0068570375},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15923236,0.00085317856,0.835517,0.00016387043,0.00013190498,0.00011031746,0.0004894804,0.0026414755,0.0008605273],"genre_scores_gemma":[0.8267761,0.0004475458,0.16964233,0.000056539306,0.000085765794,0.00014545894,0.0017608777,0.00015699747,0.00092839845],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99634796,0.0005664412,0.0003007542,0.00067331386,0.0018649214,0.00024665252],"domain_scores_gemma":[0.99246335,0.0029884,0.0013331503,0.00095238263,0.0020200182,0.00024269651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027359829,0.0011611212,0.0015308559,0.005098737,0.0006943346,0.0010912811,0.0013024284,0.00075437006,0.00050365546],"category_scores_gemma":[0.012669756,0.00030902552,0.0010427092,0.004037882,0.0007604727,0.001522244,0.0016186299,0.0010265665,0.0003741675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007101438,0.0003605462,0.08481147,0.0004800976,0.00047542198,0.0014954461,0.00066766347,0.31302288,0.03216297,0.007092666,0.0045503066,0.5541704],"study_design_scores_gemma":[0.000014181984,0.00013246013,0.0110728685,0.000020886386,0.000039672297,0.0004855579,0.00015670572,0.9722197,0.010155829,0.0032027273,0.0024468696,0.00005251788],"about_ca_topic_score_codex":0.004189837,"about_ca_topic_score_gemma":0.0031971177,"teacher_disagreement_score":0.005098737,"about_ca_system_score_codex":0.0007356012,"about_ca_system_score_gemma":0.0009712883,"threshold_uncertainty_score":0.014469445},"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":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043214704,0.3634821,0.2566213,0.016108803,0.012844012,0.00012897431,0.0016889739,0.0023683906,0.342436],"genre_scores_gemma":[0.055877462,0.30111867,0.098541066,0.0048547606,0.012602068,0.00020274801,0.0036204027,0.000847987,0.5223349],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99974984,0.000029021297,0.00001105614,0.000040562227,0.00015348035,0.000015961523],"domain_scores_gemma":[0.99969816,0.00014178049,0.000016703489,0.000058292735,0.00006377562,0.000021403837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030030412,0.0008760385,0.0006141622,0.001693056,0.00031954006,0.001616957,0.00066244806,0.00084058015,0.017056858],"category_scores_gemma":[0.0009719942,0.00036052766,0.0003025612,0.003036572,0.0009922718,0.0030242067,0.0012005526,0.0014263606,0.0061031333],"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":[0.000027949753,0.000032191023,0.00020884731,0.00070935156,0.000032285097,0.00005989759,0.000114877694,0.004305883,0.0017696689,0.21517377,0.26681063,0.51075464],"study_design_scores_gemma":[0.0000049239134,0.000021470045,0.00054277637,0.0002522924,0.000011194272,0.00015141856,0.000053581192,0.008099902,0.00093002594,0.1833294,0.80658597,0.000017080298],"about_ca_topic_score_codex":0.0010753102,"about_ca_topic_score_gemma":0.0015059542,"teacher_disagreement_score":0.017056858,"about_ca_system_score_codex":0.00059936446,"about_ca_system_score_gemma":0.00043425136,"threshold_uncertainty_score":0.057060838},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010004624,0.00058869115,0.9870657,0.00021033191,0.000056604502,0.00006598277,0.00039930467,0.0008866624,0.00072204066],"genre_scores_gemma":[0.2333785,0.0011809958,0.7591413,0.00016719887,0.00019213774,0.00025524682,0.0024292285,0.0002639489,0.0029914666],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99865806,0.0003598664,0.000107711196,0.00034131008,0.00039052565,0.00014254195],"domain_scores_gemma":[0.9966922,0.0011178235,0.0005635745,0.00066192506,0.00074834493,0.00021625588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017369573,0.0013930339,0.0013617702,0.0030397158,0.0008365314,0.0013179083,0.0019472111,0.0009982625,0.0021009704],"category_scores_gemma":[0.007924671,0.00057202944,0.0016976157,0.0037556065,0.00086105854,0.002290623,0.0016776371,0.0016500689,0.0011701537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005166124,0.00021096144,0.010438739,0.00061475305,0.0003266843,0.0003066463,0.00071689725,0.38327497,0.0110400235,0.051688224,0.013243926,0.52762157],"study_design_scores_gemma":[0.0000105309255,0.000033565666,0.0006201644,0.000015426376,0.000016829192,0.000061178966,0.00007074659,0.9778471,0.0009860375,0.017915783,0.002405258,0.000017293667],"about_ca_topic_score_codex":0.012210087,"about_ca_topic_score_gemma":0.012117966,"teacher_disagreement_score":0.012210087,"about_ca_system_score_codex":0.0009873662,"about_ca_system_score_gemma":0.0016496315,"threshold_uncertainty_score":0.024278045},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9935028,0.00039789858,0.0014705223,0.00012938624,0.00015706121,0.00016186616,0.00043181842,0.00009934628,0.0036492827],"genre_scores_gemma":[0.9961325,0.00014868905,0.001542633,0.000072790725,0.000030757168,0.00015756661,0.00044712747,0.000032601594,0.0014353165],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99591124,0.0015306475,0.0004449373,0.0007649169,0.0010846367,0.00026355553],"domain_scores_gemma":[0.96123683,0.017956339,0.003964871,0.005338681,0.010491029,0.0010123219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009203241,0.00047412567,0.0006860374,0.0010759281,0.00078181404,0.0012909406,0.00069578725,0.0010012062,0.0021681315],"category_scores_gemma":[0.0356633,0.0005964228,0.0010187674,0.00040277635,0.0013833967,0.0010433358,0.0014323422,0.001359393,0.0018137979],"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":[0.00075431395,0.0005068012,0.94977367,0.000119848926,0.00066832267,0.00010815765,0.0055290647,0.00044395516,0.0034511485,0.0003744338,0.0018221239,0.036448207],"study_design_scores_gemma":[0.000037806174,0.001133677,0.98946977,0.000052909985,0.00014053103,0.00045362065,0.0017725237,0.001073435,0.0026564149,0.00033679057,0.0028222091,0.000050320166],"about_ca_topic_score_codex":0.0020126577,"about_ca_topic_score_gemma":0.0027432262,"teacher_disagreement_score":0.009203241,"about_ca_system_score_codex":0.00056026527,"about_ca_system_score_gemma":0.00034525353,"threshold_uncertainty_score":0.04867196},"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":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0803921,0.001250287,0.037445486,0.0011109741,0.00085259764,0.0010660618,0.82087344,0.03151181,0.02549739],"genre_scores_gemma":[0.042396232,0.00018092981,0.034115665,0.00018119902,0.00004437882,0.00036667477,0.9187487,0.0008835383,0.0030827418],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986247,0.00020983403,0.000084754254,0.0004952334,0.00041442114,0.00017112229],"domain_scores_gemma":[0.99852556,0.00024158244,0.00008191242,0.00059547473,0.00041673603,0.00013877597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097068836,0.0021402685,0.00093485566,0.0026494889,0.0011133994,0.001457331,0.0032631645,0.002077904,0.007423473],"category_scores_gemma":[0.0025997062,0.0006005166,0.0015354318,0.0027235965,0.00086950033,0.0011885059,0.0019606424,0.0022769244,0.010661082],"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":[0.000456104,0.0009006686,0.007932558,0.0013809106,0.00026486238,0.00092841743,0.0002981885,0.0217548,0.0100820055,0.0035170766,0.867427,0.08505745],"study_design_scores_gemma":[0.00039132943,0.00046594226,0.0387909,0.00041723796,0.00019137337,0.0028424908,0.0012741293,0.17789894,0.03945946,0.008713664,0.7292798,0.00027469627],"about_ca_topic_score_codex":0.021126224,"about_ca_topic_score_gemma":0.057822805,"teacher_disagreement_score":0.021126224,"about_ca_system_score_codex":0.0011885971,"about_ca_system_score_gemma":0.001148895,"threshold_uncertainty_score":0.042006493},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031614095,0.046048373,0.75747585,0.12160631,0.0066288444,0.00009814174,0.00022047342,0.002877853,0.033430073],"genre_scores_gemma":[0.553628,0.017206393,0.38809776,0.00901581,0.0082647065,0.00012230173,0.00027519438,0.00095426943,0.02243559],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99549484,0.0020064497,0.00030349105,0.00067175674,0.001236086,0.0002874067],"domain_scores_gemma":[0.97954744,0.012813714,0.00066453917,0.0026541543,0.00332916,0.0009909928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008986546,0.0010070326,0.001242289,0.0025690638,0.0014678681,0.011262813,0.0026980347,0.00611303,0.008949421],"category_scores_gemma":[0.033200227,0.0013331001,0.0006250818,0.0020027412,0.00819891,0.032496426,0.005123182,0.009374515,0.0028192287],"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":[0.00039577595,0.0003454369,0.005403627,0.0005708622,0.00015454766,0.00021395004,0.0010567909,0.007386249,0.0045938296,0.2789982,0.03167491,0.66920584],"study_design_scores_gemma":[0.00003637711,0.00016779607,0.0025751768,0.0004544586,0.000103691425,0.00022643257,0.0009306744,0.07586671,0.0076299715,0.80918413,0.10271952,0.00010516534],"about_ca_topic_score_codex":0.002984517,"about_ca_topic_score_gemma":0.0044108755,"teacher_disagreement_score":0.011262813,"about_ca_system_score_codex":0.0014323198,"about_ca_system_score_gemma":0.0017663907,"threshold_uncertainty_score":0.047526002},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038952832,0.0002560186,0.99509454,0.00014509389,0.000025105757,0.000023450062,0.000043023305,0.00009679321,0.00042071284],"genre_scores_gemma":[0.4522786,0.0014496068,0.5388009,0.0003535829,0.0002320996,0.00032592818,0.0008636196,0.00029974457,0.005395827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976528,0.0011726215,0.00011436769,0.00049476913,0.00038651165,0.00017882159],"domain_scores_gemma":[0.99343675,0.005240825,0.00030442004,0.00027549258,0.00057666015,0.00016583833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00458275,0.0009490937,0.0019913143,0.0014548302,0.00070178945,0.0016682366,0.0034254247,0.0018718864,0.002225622],"category_scores_gemma":[0.01760994,0.0014409244,0.0017687752,0.0016644273,0.001993644,0.002468287,0.0026254249,0.003079981,0.00048580533],"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":[0.00004720793,0.00003371872,0.0013063082,0.00010299798,0.00007735985,0.00011163811,0.00023349315,0.85832494,0.0008332115,0.09719175,0.0011648401,0.040572535],"study_design_scores_gemma":[0.0000025002419,0.0000034884392,0.000081518825,0.000007437775,0.000003572991,0.000011045123,0.00000656054,0.97606885,0.00012543323,0.023400763,0.0002819926,0.000006752508],"about_ca_topic_score_codex":0.01882118,"about_ca_topic_score_gemma":0.016420105,"teacher_disagreement_score":0.01882118,"about_ca_system_score_codex":0.0022896403,"about_ca_system_score_gemma":0.0025133719,"threshold_uncertainty_score":0.037423253},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018628996,0.00029209832,0.97908187,0.00015207066,0.000062636245,0.000050616716,0.00015179187,0.0008103658,0.00076953764],"genre_scores_gemma":[0.63957614,0.0011045231,0.34923175,0.0001521203,0.00020891523,0.0003526102,0.0015224969,0.00023306932,0.007618433],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988637,0.00026378233,0.00006118836,0.00034975875,0.00034184023,0.00011970437],"domain_scores_gemma":[0.99924254,0.00023205556,0.000080586105,0.00006103453,0.00035439985,0.000029422168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010692221,0.0013644171,0.0010655802,0.001245501,0.00052932027,0.0011251416,0.0016374501,0.0007992457,0.0015133836],"category_scores_gemma":[0.002551194,0.00046679,0.001402692,0.0015789684,0.00040135763,0.0014041334,0.0006412929,0.0018179077,0.000852745],"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":[0.00032415905,0.00021599681,0.006345174,0.00015829195,0.0002933595,0.00018243339,0.00016970542,0.59861016,0.010398911,0.008551513,0.0058481605,0.36890206],"study_design_scores_gemma":[0.000002797576,0.000012779861,0.0005696641,0.0000016129571,0.0000095926825,0.0000114399845,0.0000043284394,0.99811757,0.00040190504,0.00052612665,0.00033562654,0.0000066238545],"about_ca_topic_score_codex":0.022856606,"about_ca_topic_score_gemma":0.014301797,"teacher_disagreement_score":0.022856606,"about_ca_system_score_codex":0.00064436696,"about_ca_system_score_gemma":0.0014274449,"threshold_uncertainty_score":0.04544711},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14022975,0.00039567403,0.812694,0.0002665854,0.00015867192,0.0003850688,0.0013615194,0.036589235,0.007919416],"genre_scores_gemma":[0.6208705,0.00021839072,0.36861247,0.000070182876,0.00012015517,0.00035636025,0.00318854,0.0026715747,0.0038918678],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9927858,0.00087804697,0.00051375205,0.0015867462,0.0033576586,0.0008779474],"domain_scores_gemma":[0.9820594,0.0041122795,0.003046007,0.004238727,0.0061006937,0.00044290995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024481567,0.001700818,0.0012730531,0.010015036,0.0013694051,0.0022055798,0.0025288851,0.00091873656,0.0033253094],"category_scores_gemma":[0.016112752,0.00057238835,0.0010183603,0.0059289723,0.00083508,0.0025338456,0.0017563383,0.0013859901,0.0021738664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070113054,0.0007137917,0.07618267,0.0005590486,0.00029032878,0.0007866335,0.0018636482,0.10699133,0.077299856,0.015695203,0.017720573,0.70119584],"study_design_scores_gemma":[0.000062902924,0.000389683,0.031361684,0.00007326411,0.00014268626,0.0005641414,0.00047056255,0.8707843,0.05567749,0.019174749,0.021100698,0.0001978497],"about_ca_topic_score_codex":0.006704324,"about_ca_topic_score_gemma":0.0040672836,"teacher_disagreement_score":0.010015036,"about_ca_system_score_codex":0.001181897,"about_ca_system_score_gemma":0.0021412666,"threshold_uncertainty_score":0.013330638},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019161277,0.00089025346,0.97097504,0.0011526363,0.00018104768,0.000063985506,0.00026680034,0.0020689147,0.005240077],"genre_scores_gemma":[0.52359354,0.0012987833,0.447487,0.0004972793,0.00028553416,0.0004015137,0.0010179045,0.00030653834,0.025111834],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997454,0.00007216596,0.00001641893,0.00006141216,0.00007314446,0.000031382988],"domain_scores_gemma":[0.99890065,0.00064801134,0.0000715814,0.00012285398,0.00021994188,0.000036938985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000619134,0.00060340564,0.00052302633,0.00076016807,0.00056961976,0.0011495135,0.0014850717,0.0011610438,0.006112399],"category_scores_gemma":[0.0037969693,0.00032310147,0.0005537162,0.00083489093,0.000545838,0.002233256,0.0008457325,0.0018145795,0.0009385894],"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":[0.0001399106,0.00009756748,0.00077242614,0.00020770157,0.00005612038,0.0000820279,0.00009500025,0.58738554,0.0026746546,0.10789131,0.008621796,0.291976],"study_design_scores_gemma":[0.0000066018943,0.000012754558,0.00006615618,0.000007023251,0.0000052049418,0.000008598561,0.0000055611713,0.95983166,0.00084499817,0.038010027,0.0011971297,0.0000042702623],"about_ca_topic_score_codex":0.005597166,"about_ca_topic_score_gemma":0.005197264,"teacher_disagreement_score":0.006112399,"about_ca_system_score_codex":0.0014814346,"about_ca_system_score_gemma":0.00089072774,"threshold_uncertainty_score":0.020448029},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039606895,0.00018312375,0.99487597,0.00007914568,0.00001539276,0.000036549172,0.0000364213,0.00049169676,0.00032110757],"genre_scores_gemma":[0.34698114,0.00052974565,0.64779377,0.00031665878,0.00017214257,0.00022783293,0.0008137587,0.00032488897,0.0028401075],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99623436,0.0014155974,0.00017584705,0.0010803078,0.00090179034,0.00019210645],"domain_scores_gemma":[0.99406224,0.0031192508,0.00060858787,0.001184993,0.00089491357,0.00012996803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043902476,0.0010859956,0.0021430005,0.0018072409,0.00059421203,0.00133096,0.003233943,0.0015637581,0.0017031091],"category_scores_gemma":[0.019813115,0.00083389593,0.0011991702,0.0017945127,0.0013557748,0.0021934207,0.0021137875,0.0019152939,0.0012427759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002563683,0.00022157117,0.007944833,0.00040505966,0.00026247767,0.00022840608,0.00032373238,0.22928086,0.012529147,0.046094835,0.008663119,0.6937896],"study_design_scores_gemma":[0.000018335364,0.000048710142,0.0012357396,0.000016702026,0.000025008565,0.00027940996,0.0000316553,0.9663411,0.0049471343,0.024138397,0.0028918474,0.000025942792],"about_ca_topic_score_codex":0.0025904053,"about_ca_topic_score_gemma":0.0022968058,"teacher_disagreement_score":0.0043902476,"about_ca_system_score_codex":0.0010337421,"about_ca_system_score_gemma":0.0011544945,"threshold_uncertainty_score":0.023218155},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024645591,0.00020156118,0.97270477,0.00012837081,0.000051026454,0.00001699472,0.00018201409,0.0007127119,0.0013569816],"genre_scores_gemma":[0.90356356,0.0002084648,0.091444194,0.00010136758,0.00003474751,0.00007109594,0.00047143677,0.000098843346,0.0040062913],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987745,0.000013884376,0.000006406746,0.00005025407,0.00003271717,0.000019342622],"domain_scores_gemma":[0.9998179,0.00006187523,0.000032341137,0.000026058258,0.000047789537,0.000014111951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027528175,0.0006076999,0.00054591236,0.00024553237,0.00018273354,0.0004062682,0.0009056117,0.0005304693,0.0015248763],"category_scores_gemma":[0.0007311166,0.0003789331,0.00044749264,0.00026986017,0.00024543336,0.0007083677,0.00055465143,0.0010740566,0.0004005181],"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":[0.000029385621,0.000017100887,0.000632891,0.000019800971,0.000027126556,0.000033401193,0.0000120175855,0.97069126,0.0028320772,0.0014441904,0.0004355192,0.02382522],"study_design_scores_gemma":[5.3889516e-7,0.0000037742104,0.00004324895,6.4530656e-7,9.829171e-7,0.0000022423517,4.8495497e-7,0.9995059,0.00019473715,0.0001738764,0.00007276338,8.2135585e-7],"about_ca_topic_score_codex":0.011383808,"about_ca_topic_score_gemma":0.014370363,"teacher_disagreement_score":0.011383808,"about_ca_system_score_codex":0.0004955719,"about_ca_system_score_gemma":0.00055847433,"threshold_uncertainty_score":0.022635102},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047299594,0.00081359857,0.93699354,0.00035647774,0.00030543338,0.00010200447,0.0017869483,0.010609111,0.0017331991],"genre_scores_gemma":[0.5508833,0.00056334847,0.4283945,0.00041652363,0.0001796652,0.00016933576,0.0038347386,0.00045557827,0.015102983],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973816,0.000026266076,0.0000105063145,0.00008860043,0.00008037381,0.00005610108],"domain_scores_gemma":[0.9997389,0.00008974771,0.00003065346,0.000042537176,0.0000687716,0.00002948322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004555412,0.0013097678,0.00096585345,0.0006391673,0.00027138586,0.00063366117,0.0015693293,0.0011575999,0.0030199743],"category_scores_gemma":[0.0010211203,0.00063929503,0.0007240795,0.00054474495,0.00024532128,0.00080850715,0.0012948643,0.0012759137,0.0012141082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006595997,0.00046371628,0.0065703695,0.0001682926,0.0003071418,0.00041470866,0.00006624055,0.13086638,0.024440847,0.0030637782,0.024853433,0.8081255],"study_design_scores_gemma":[0.000012651882,0.000041860872,0.0009914144,0.000009206529,0.000015715712,0.000099781704,0.000008376248,0.99250257,0.0036845251,0.0017808237,0.00084462686,0.00000846521],"about_ca_topic_score_codex":0.009835176,"about_ca_topic_score_gemma":0.021923993,"teacher_disagreement_score":0.009835176,"about_ca_system_score_codex":0.0004506587,"about_ca_system_score_gemma":0.0009840665,"threshold_uncertainty_score":0.019555867},"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":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002939752,0.0021452324,0.0016056778,0.9062258,0.08157465,0.000021460999,0.00011247569,0.0001696278,0.007851137],"genre_scores_gemma":[0.0057242783,0.0011559286,0.0008860936,0.9089275,0.07165866,0.00007328037,0.000040773324,0.00011566602,0.011417836],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949409,0.0013438336,0.00055661466,0.0007450301,0.0019638066,0.00044987726],"domain_scores_gemma":[0.98779404,0.007207273,0.0008005099,0.0010778435,0.0024700053,0.000650281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053886273,0.0007917858,0.000910138,0.00090002426,0.003158618,0.004989293,0.0032281072,0.028399756,0.009845128],"category_scores_gemma":[0.03914651,0.00048489572,0.0011137611,0.0006971429,0.0056116963,0.0055747684,0.0020340201,0.040937502,0.013880836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024858158,0.000012068254,0.00012350339,0.00003161833,0.000006093154,0.00035341503,0.00008162944,0.000048190544,0.00006447125,0.018993085,0.9719101,0.008350851],"study_design_scores_gemma":[0.000034705914,0.00003339367,0.00021437729,0.00014219778,0.00000976539,0.0010541384,0.000112944115,0.00036324203,0.00030959988,0.032993525,0.96470356,0.000028522341],"about_ca_topic_score_codex":0.0016174928,"about_ca_topic_score_gemma":0.002051459,"teacher_disagreement_score":0.028399756,"about_ca_system_score_codex":0.003456804,"about_ca_system_score_gemma":0.002449891,"threshold_uncertainty_score":0.03293526},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15360261,0.00039428857,0.83809626,0.00025561792,0.00022278534,0.000055529326,0.0005527622,0.0033346033,0.003485456],"genre_scores_gemma":[0.8699814,0.00026682095,0.12148191,0.00008076532,0.000074578835,0.000029291654,0.0009884118,0.00008966391,0.007007072],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999821,0.000011906473,0.0000067771334,0.000055251694,0.000059935006,0.00004515511],"domain_scores_gemma":[0.999777,0.000040388473,0.000021488851,0.000031587144,0.00011156348,0.00001794935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018477911,0.0005338404,0.00043315286,0.00064170716,0.00019070222,0.00039585886,0.0005396964,0.000511082,0.0012635401],"category_scores_gemma":[0.00048477118,0.00026881197,0.00042784758,0.00047674624,0.00013765988,0.00050160993,0.00047740334,0.00092545984,0.0007686825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029111523,0.0002579405,0.009341545,0.00007354845,0.00010434159,0.00029246305,0.00006347793,0.14599587,0.044832148,0.0016436345,0.0071368082,0.7899671],"study_design_scores_gemma":[0.000001903395,0.000021701484,0.002535505,0.000003889905,0.000011305299,0.000054468575,0.000007790816,0.9911179,0.0054103695,0.0003399953,0.0004896175,0.000005581551],"about_ca_topic_score_codex":0.009205968,"about_ca_topic_score_gemma":0.012989373,"teacher_disagreement_score":0.009205968,"about_ca_system_score_codex":0.0003486561,"about_ca_system_score_gemma":0.0005122498,"threshold_uncertainty_score":0.018304765},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019826747,0.00035205673,0.97813934,0.00037993563,0.000039219947,0.000022533444,0.000103876,0.0003708979,0.0007653987],"genre_scores_gemma":[0.9082804,0.000728837,0.086508915,0.00024667257,0.000055679007,0.00016454517,0.0004517977,0.00009317225,0.0034700097],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962294,0.00006742469,0.000028143268,0.00011441107,0.00010661945,0.00006036847],"domain_scores_gemma":[0.99940073,0.00026574236,0.000071461,0.00008698988,0.0001443147,0.00003080454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007724771,0.00075880846,0.0007464009,0.00047246605,0.00035035025,0.0009873911,0.0016237498,0.0008358263,0.0012944555],"category_scores_gemma":[0.0024832108,0.00038411227,0.00071028713,0.00075293577,0.0008348218,0.0020920357,0.0011347017,0.002377644,0.00020777292],"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":[0.00007362724,0.000048497244,0.0010704537,0.00007206833,0.000037047535,0.00008423424,0.00006311223,0.92491883,0.0020851607,0.02239718,0.0015138432,0.047636006],"study_design_scores_gemma":[0.0000012114966,0.0000050908966,0.000049269944,0.0000013178405,0.000002064114,0.000003625398,0.0000014445874,0.9961094,0.00017058582,0.003559937,0.000094565505,0.0000014712332],"about_ca_topic_score_codex":0.007790021,"about_ca_topic_score_gemma":0.007303756,"teacher_disagreement_score":0.007790021,"about_ca_system_score_codex":0.0012598904,"about_ca_system_score_gemma":0.0012157654,"threshold_uncertainty_score":0.015489399},"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":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015247919,0.00017987705,0.8065942,0.00021320509,0.00039991917,0.00030964895,0.009184076,0.1653798,0.0024913498],"genre_scores_gemma":[0.098825276,0.00010490923,0.8509668,0.0002768884,0.0001571407,0.0025524592,0.015486833,0.02517533,0.00645437],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99696547,0.0011609677,0.00034968398,0.00060601,0.00067394605,0.00024386864],"domain_scores_gemma":[0.96826696,0.025678804,0.0010801497,0.0030715144,0.0015818027,0.00032076627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048932843,0.0019922054,0.0015905692,0.0024165795,0.0006704601,0.0014363314,0.0023412905,0.0012990457,0.0450008],"category_scores_gemma":[0.041645307,0.00068299216,0.0018052574,0.0012408841,0.00074693706,0.0020172652,0.0019058035,0.0026830495,0.011070846],"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":[0.0039043177,0.0009989535,0.028493121,0.0021180534,0.0017098802,0.0010455728,0.00080851174,0.028767837,0.017837925,0.032145742,0.2754958,0.6066743],"study_design_scores_gemma":[0.0015456217,0.0014066565,0.017574625,0.00034916098,0.00052141113,0.001590506,0.0004920767,0.70966685,0.068460144,0.057144113,0.14097139,0.00027750336],"about_ca_topic_score_codex":0.0013893573,"about_ca_topic_score_gemma":0.0019208941,"teacher_disagreement_score":0.0450008,"about_ca_system_score_codex":0.0003618335,"about_ca_system_score_gemma":0.0016802441,"threshold_uncertainty_score":0.15054262},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006892823,0.00028565244,0.9914976,0.00014364027,0.000035527733,0.000027981514,0.000099809855,0.0003099477,0.000707124],"genre_scores_gemma":[0.5941342,0.0007070804,0.3991385,0.00017417748,0.00022683557,0.00014508188,0.00057509355,0.000115732815,0.0047832443],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993705,0.000111924914,0.000044424884,0.00019223195,0.00020615534,0.00007480613],"domain_scores_gemma":[0.9985145,0.000734498,0.000138803,0.00021038954,0.0003367737,0.00006512642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009163778,0.0005402919,0.0011252391,0.0015372656,0.00066211977,0.0014493716,0.0026334794,0.0009340086,0.0020650928],"category_scores_gemma":[0.0043962733,0.00039415306,0.00060381356,0.0020571747,0.0005793041,0.002601658,0.0014988919,0.0012094975,0.00035869115],"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":[0.00026159556,0.00012017219,0.0016439155,0.00013659877,0.00011015081,0.00020865687,0.00017083871,0.7032785,0.0044569913,0.044168733,0.003504696,0.24193919],"study_design_scores_gemma":[0.000002286693,0.000009333458,0.0000608028,0.0000024678357,0.0000073618585,0.000013624402,0.000008582107,0.9924787,0.00042236166,0.00665879,0.00033238556,0.000003356202],"about_ca_topic_score_codex":0.0127592245,"about_ca_topic_score_gemma":0.012317035,"teacher_disagreement_score":0.0127592245,"about_ca_system_score_codex":0.0011075584,"about_ca_system_score_gemma":0.0010424341,"threshold_uncertainty_score":0.025369942},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22360405,0.0002408129,0.7705686,0.00023549188,0.000097545635,0.000048628863,0.00039071462,0.0031869654,0.0016271985],"genre_scores_gemma":[0.8887192,0.00013118592,0.108266436,0.000048126858,0.000030987863,0.000026781725,0.0006788344,0.000065694345,0.0020328106],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996617,0.000046482633,0.00001548831,0.000113053946,0.0000941791,0.000069195674],"domain_scores_gemma":[0.9995474,0.00010756629,0.000060185543,0.000064190164,0.00018339344,0.00003726475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003243907,0.00064792414,0.0004671018,0.00086049957,0.0002069526,0.000357619,0.0006718833,0.0005141226,0.0006693648],"category_scores_gemma":[0.0008803529,0.00026051782,0.0003883335,0.00055343576,0.00027352,0.000612796,0.00047496744,0.0009426128,0.00031563695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048626857,0.0003429052,0.026448848,0.000097288495,0.00019893356,0.00061019266,0.00018193816,0.41804552,0.04332742,0.0026249955,0.004889779,0.502746],"study_design_scores_gemma":[0.0000023244056,0.00001905105,0.0021003273,0.0000027779781,0.0000062775894,0.00004838967,0.000009473175,0.9930101,0.004206357,0.000284664,0.00030534397,0.0000049043833],"about_ca_topic_score_codex":0.012435696,"about_ca_topic_score_gemma":0.014137186,"teacher_disagreement_score":0.012435696,"about_ca_system_score_codex":0.0005305068,"about_ca_system_score_gemma":0.00058156456,"threshold_uncertainty_score":0.02472663},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034214973,0.0005034585,0.95944095,0.00017816441,0.0000854759,0.00020814835,0.00022293627,0.0037323644,0.0014136591],"genre_scores_gemma":[0.5502748,0.00031681103,0.4419421,0.00036279167,0.0002844405,0.00039721318,0.0020173949,0.00070483,0.0036996228],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962872,0.0009282609,0.00017058795,0.001122155,0.0011260352,0.0003658031],"domain_scores_gemma":[0.99639255,0.0009065592,0.00034701408,0.0010398927,0.0010393806,0.00027444426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048734443,0.0027619135,0.0027585682,0.0021037892,0.0011751027,0.001875282,0.004352903,0.0019140106,0.0016812899],"category_scores_gemma":[0.008011922,0.0010235299,0.002003114,0.0022019478,0.0013680226,0.0033808576,0.0057640467,0.0027284368,0.0012021255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040595257,0.00043752344,0.012837134,0.00013953994,0.00073266396,0.0002618114,0.00027797054,0.43349913,0.01056653,0.004347633,0.008944556,0.52754956],"study_design_scores_gemma":[0.000018110728,0.0001528713,0.00095958,0.000008925977,0.00006872121,0.00012754252,0.000059957994,0.9886347,0.0035313265,0.0048176134,0.001590413,0.000030161713],"about_ca_topic_score_codex":0.005563144,"about_ca_topic_score_gemma":0.010224557,"teacher_disagreement_score":0.005563144,"about_ca_system_score_codex":0.0008600906,"about_ca_system_score_gemma":0.0020384737,"threshold_uncertainty_score":0.025773525},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033269577,0.0005653845,0.96129596,0.00062886503,0.000056653582,0.000026652551,0.0004767906,0.0004973176,0.0031827474],"genre_scores_gemma":[0.8949999,0.00052189565,0.09565826,0.00016280709,0.00006154453,0.0001313873,0.00071043125,0.000059366845,0.0076944316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967635,0.00009268472,0.000015788179,0.00008290602,0.000086181906,0.00004620629],"domain_scores_gemma":[0.99931633,0.00033820586,0.00007106569,0.0000349395,0.00021064345,0.000028767625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007810024,0.00056185696,0.0006242388,0.0006501563,0.00024982708,0.0007871839,0.0017739242,0.0010122871,0.002595866],"category_scores_gemma":[0.0027560412,0.00043754897,0.0005392519,0.0007052543,0.00046300786,0.0013360726,0.0008118833,0.0016970267,0.0005203337],"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":[0.00004456427,0.000021601025,0.00078020565,0.000023192402,0.000018190285,0.000027417835,0.000024005934,0.97279245,0.00038210468,0.0068116556,0.0007120512,0.01836264],"study_design_scores_gemma":[0.0000012473905,0.0000026846453,0.000047313068,0.0000013954173,0.0000016108509,0.0000019590661,9.156914e-7,0.9983601,0.000046051806,0.0014507036,0.000084888474,0.0000012045539],"about_ca_topic_score_codex":0.023794018,"about_ca_topic_score_gemma":0.022846585,"teacher_disagreement_score":0.023794018,"about_ca_system_score_codex":0.0012216822,"about_ca_system_score_gemma":0.0010222464,"threshold_uncertainty_score":0.047311068},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042637695,0.0006938482,0.95222175,0.0006755624,0.00007831999,0.00008496027,0.00008992045,0.0007672217,0.0027507367],"genre_scores_gemma":[0.8058107,0.00034335014,0.18827634,0.00061420747,0.00012106654,0.00019052764,0.00046990725,0.00028010743,0.0038937498],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997891,0.0012341281,0.00006751397,0.00037056347,0.0002734363,0.00016320916],"domain_scores_gemma":[0.99209106,0.006202854,0.0005027463,0.00061346387,0.00035211068,0.00023778377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006222489,0.0019231262,0.0015812884,0.0009401535,0.0005375069,0.0014391841,0.0024210496,0.0023628946,0.0018095082],"category_scores_gemma":[0.01199476,0.000788053,0.0011777579,0.0006513638,0.0024793937,0.0024042046,0.0030333067,0.003965614,0.0004809026],"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":[0.000066737215,0.000042869087,0.0008253243,0.000041081974,0.000054705357,0.00003498172,0.000040285417,0.96621746,0.0005275372,0.008456028,0.00071284117,0.022980224],"study_design_scores_gemma":[0.0000033965277,0.000012112662,0.000043399497,0.000004897427,0.0000023686093,0.000008936463,0.000002640591,0.9956298,0.0002712888,0.003918284,0.00010022487,0.0000025694633],"about_ca_topic_score_codex":0.0018601941,"about_ca_topic_score_gemma":0.0025446524,"teacher_disagreement_score":0.006222489,"about_ca_system_score_codex":0.0015032722,"about_ca_system_score_gemma":0.00090089685,"threshold_uncertainty_score":0.032908022},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.080873646,0.0012368846,0.90675706,0.0015580019,0.00015062635,0.00012265296,0.00041800452,0.0015447116,0.007338479],"genre_scores_gemma":[0.94384974,0.0005533843,0.045395605,0.00029062966,0.0001974794,0.00016803824,0.0003115338,0.0001202507,0.00911336],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832565,0.00033256417,0.00009348342,0.0005370921,0.00037344953,0.00033767568],"domain_scores_gemma":[0.9952427,0.0027565989,0.00051240105,0.00023824975,0.0009983754,0.00025166754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027683803,0.0009914185,0.0016665792,0.0021631895,0.00074156985,0.0017621691,0.0044603185,0.0021136256,0.0045090565],"category_scores_gemma":[0.0071980874,0.00060222123,0.0012643472,0.0013378685,0.001595015,0.0036873799,0.0018054087,0.0026137051,0.00071546726],"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":[0.00041324075,0.000255214,0.005452851,0.00027183833,0.00018327427,0.0004925791,0.00063103787,0.72642833,0.0061973315,0.18836069,0.004131673,0.06718196],"study_design_scores_gemma":[0.0000066977,0.000022367032,0.0002881956,0.000004186316,0.000014397897,0.000029749122,0.000008649714,0.9800034,0.00016743611,0.01918235,0.00026508354,0.0000073900446],"about_ca_topic_score_codex":0.021917123,"about_ca_topic_score_gemma":0.011344149,"teacher_disagreement_score":0.021917123,"about_ca_system_score_codex":0.0037215159,"about_ca_system_score_gemma":0.0014991752,"threshold_uncertainty_score":0.0435791},"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":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01582358,0.19722252,0.47238958,0.23301946,0.0037200754,0.00009953947,0.00019747969,0.0008272611,0.07670051],"genre_scores_gemma":[0.47416303,0.20761275,0.24186921,0.037583105,0.004954117,0.0003796168,0.00029513089,0.00084020745,0.0323028],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99654835,0.0015339383,0.00017234086,0.0003160253,0.0011862704,0.0002429603],"domain_scores_gemma":[0.9891805,0.008641397,0.0003170386,0.00089066394,0.00071916473,0.0002510997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007656388,0.00071734417,0.00061778,0.0019364071,0.001731139,0.007878376,0.0017839719,0.0037719498,0.0041526947],"category_scores_gemma":[0.016054459,0.0007205891,0.00044050382,0.0017193607,0.011207822,0.023732364,0.005541728,0.012305567,0.0012878597],"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":[0.00005575138,0.00004180016,0.00068694924,0.00048126926,0.000034697965,0.00014579619,0.0015091777,0.0022893352,0.00069657306,0.7627539,0.017076164,0.21422859],"study_design_scores_gemma":[0.000008566489,0.000059155464,0.0003446277,0.00103585,0.000011788148,0.00024138018,0.0007397957,0.012923515,0.0015824115,0.8221208,0.16089118,0.000040872445],"about_ca_topic_score_codex":0.0023116914,"about_ca_topic_score_gemma":0.003007288,"teacher_disagreement_score":0.007878376,"about_ca_system_score_codex":0.0021913864,"about_ca_system_score_gemma":0.00213591,"threshold_uncertainty_score":0.040491283},"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":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013780093,0.00029633666,0.5608955,0.00018828915,0.00016862211,0.00020937176,0.013822401,0.40277824,0.0078613125],"genre_scores_gemma":[0.22757669,0.0011553168,0.67572874,0.00046584272,0.00018305462,0.0010366895,0.035807006,0.03132611,0.02672059],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99980325,0.000016377544,0.000014687361,0.00004462654,0.00010359361,0.000017552973],"domain_scores_gemma":[0.99946254,0.00024101966,0.000043795557,0.00009830254,0.00010755305,0.000046656245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050710025,0.0013672535,0.00091264327,0.003045005,0.000400162,0.0009322944,0.001663781,0.00082728616,0.037651945],"category_scores_gemma":[0.001820365,0.00048165914,0.00081783533,0.00142186,0.00025792056,0.001285974,0.001511497,0.0008126005,0.007583092],"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":[0.0009463842,0.00048298977,0.013094237,0.0017076834,0.0006002129,0.0011842615,0.0005185436,0.06047067,0.04436817,0.011173507,0.17879544,0.68665785],"study_design_scores_gemma":[0.00023479835,0.00009609419,0.0064830314,0.00011578775,0.00010822546,0.0008952126,0.000110618814,0.81953126,0.03812248,0.02522074,0.108925104,0.00015671716],"about_ca_topic_score_codex":0.0033360224,"about_ca_topic_score_gemma":0.0060927,"teacher_disagreement_score":0.037651945,"about_ca_system_score_codex":0.00037413932,"about_ca_system_score_gemma":0.0005231869,"threshold_uncertainty_score":0.12595832},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055077124,0.0001698991,0.9436564,0.00011297511,0.000022315304,0.000021421485,0.00003422758,0.00019989684,0.00070580567],"genre_scores_gemma":[0.77306294,0.00018815591,0.22452681,0.000075323296,0.000026630814,0.0000812481,0.00014847796,0.000036791094,0.0018535971],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966705,0.00012011617,0.000013376248,0.00008649556,0.00008472015,0.000028299104],"domain_scores_gemma":[0.9989976,0.0005736959,0.00009549442,0.00017455287,0.000121313606,0.000037300197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010056932,0.00037549247,0.00044464564,0.00031364034,0.00018092473,0.0006283949,0.0009278304,0.00060626434,0.00071433774],"category_scores_gemma":[0.0027018622,0.00029746487,0.0005002107,0.0003466035,0.0007104791,0.0017503276,0.0009820763,0.0011650504,0.00016192549],"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":[0.000095327494,0.00014608583,0.0028489388,0.00015483955,0.00014593406,0.0001383428,0.0002469699,0.77454907,0.015777906,0.06544435,0.00084732077,0.13960499],"study_design_scores_gemma":[0.0000014310392,0.00001748631,0.000098484925,0.0000019740264,0.000002360339,0.000012441204,0.000005279414,0.9888681,0.0012043231,0.009583834,0.00020164864,0.000002579605],"about_ca_topic_score_codex":0.0007071677,"about_ca_topic_score_gemma":0.0007899292,"teacher_disagreement_score":0.0010056932,"about_ca_system_score_codex":0.00046885133,"about_ca_system_score_gemma":0.00045097258,"threshold_uncertainty_score":0.0053187013},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041076103,0.00038552686,0.9931773,0.00023585132,0.000050495422,0.000031343367,0.00009927149,0.00039983413,0.0015128719],"genre_scores_gemma":[0.3081888,0.0009309408,0.6743444,0.0005717998,0.00024485926,0.00022012307,0.0014233034,0.0005265025,0.01354924],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985071,0.00034118316,0.00007960669,0.0004075983,0.00053503213,0.0001294416],"domain_scores_gemma":[0.9982425,0.00045029033,0.00018444232,0.00031129472,0.0006597944,0.00015156079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020240142,0.0010180188,0.0020235598,0.0029012717,0.0010421472,0.0017559464,0.003927783,0.0019803143,0.0026574868],"category_scores_gemma":[0.0048124716,0.00058630726,0.0011674373,0.0029629064,0.0016852531,0.002682965,0.0030357433,0.003497502,0.0011242492],"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":[0.00013192596,0.00014798134,0.0017814949,0.00025824874,0.00017482135,0.00008042008,0.00024259623,0.601542,0.004531687,0.15809233,0.010492793,0.2225238],"study_design_scores_gemma":[0.000005040705,0.000015488898,0.00016107205,0.000011305669,0.0000059806785,0.00002900084,0.000016212394,0.94830376,0.0008726519,0.04868755,0.001878776,0.000013107203],"about_ca_topic_score_codex":0.006201803,"about_ca_topic_score_gemma":0.008618844,"teacher_disagreement_score":0.006201803,"about_ca_system_score_codex":0.003140799,"about_ca_system_score_gemma":0.0019929374,"threshold_uncertainty_score":0.022788167},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010736678,0.123226814,0.73152363,0.018584643,0.002492029,0.00011455776,0.0013710482,0.0014908385,0.11045981],"genre_scores_gemma":[0.45769793,0.12502262,0.3313517,0.0042209434,0.0098261135,0.00047329484,0.002373097,0.00072916073,0.06830519],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9969896,0.00071607035,0.0002487538,0.00045135836,0.0014673381,0.0001268822],"domain_scores_gemma":[0.98616624,0.010578787,0.00058778125,0.0014001529,0.00097094686,0.00029609667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043899785,0.0015977701,0.0011385822,0.0042915014,0.00058345776,0.008840321,0.0015240359,0.0013638429,0.0054842816],"category_scores_gemma":[0.016279584,0.0006975839,0.0009284384,0.005476773,0.0048587522,0.009971926,0.0019602168,0.0043703155,0.0014458211],"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":[0.000033323227,0.00005555516,0.0015910355,0.0003049563,0.00003224849,0.00005573261,0.00028977453,0.0040575042,0.00034390087,0.7666255,0.017970158,0.20864025],"study_design_scores_gemma":[0.0000044167095,0.000018451461,0.000704567,0.0001706518,0.00001564731,0.00013986361,0.00015672499,0.023725457,0.0002842281,0.9379314,0.036823995,0.000024581932],"about_ca_topic_score_codex":0.0014111599,"about_ca_topic_score_gemma":0.0013746755,"teacher_disagreement_score":0.008840321,"about_ca_system_score_codex":0.0015533025,"about_ca_system_score_gemma":0.0016168877,"threshold_uncertainty_score":0.023216665},"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":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025168562,0.0005042623,0.38197353,0.000582832,0.00047374264,0.00017792571,0.023549331,0.5794246,0.010796929],"genre_scores_gemma":[0.12353964,0.0010814724,0.46616244,0.0015839009,0.00058319105,0.0015539924,0.07437461,0.29455775,0.036563013],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99756634,0.00052521867,0.00020693304,0.0006204941,0.0007899391,0.00029103056],"domain_scores_gemma":[0.9904101,0.0055839815,0.00039042038,0.002345142,0.00087426754,0.00039607743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051204194,0.0028579591,0.0021290448,0.0029781673,0.0009453781,0.0038867744,0.005625227,0.0028486713,0.18437454],"category_scores_gemma":[0.028182188,0.0030935232,0.0029919213,0.001812933,0.0011898051,0.006119471,0.004260779,0.004060043,0.08287009],"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":[0.0018403843,0.00039569632,0.0052785627,0.002430079,0.0006825437,0.00049695565,0.00045464083,0.026801797,0.0071217394,0.053983994,0.6573954,0.2431182],"study_design_scores_gemma":[0.0013037557,0.0003435772,0.0031640213,0.0005724597,0.00034198194,0.0014926314,0.00011042392,0.37596163,0.03810974,0.2180931,0.36006245,0.00044413967],"about_ca_topic_score_codex":0.0033728278,"about_ca_topic_score_gemma":0.0057366644,"teacher_disagreement_score":0.18437454,"about_ca_system_score_codex":0.0014104156,"about_ca_system_score_gemma":0.0031097948,"threshold_uncertainty_score":0.6167943},"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":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005783954,0.0003376306,0.11595325,0.0002834123,0.00035971744,0.00020411766,0.026235742,0.8473302,0.008717575],"genre_scores_gemma":[0.028502027,0.0014093143,0.17228997,0.0012985279,0.00036865394,0.001266818,0.11799568,0.6276877,0.049181234],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978527,0.00030514286,0.0001371711,0.00044007585,0.0009082392,0.00035680906],"domain_scores_gemma":[0.99533504,0.001739009,0.0002348735,0.001312146,0.0009725297,0.0004064947],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0049530594,0.0032706375,0.00244536,0.0028690365,0.00085204124,0.0053493693,0.009649219,0.0033254658,0.29969826],"category_scores_gemma":[0.013448586,0.004928711,0.0030438413,0.0019723582,0.0014563043,0.005365502,0.0048018163,0.0069227144,0.27332377],"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":[0.00075779506,0.0001594669,0.001128966,0.0016341358,0.00038742792,0.0001836239,0.00024035583,0.0068181236,0.0029665425,0.01575129,0.8942623,0.07571],"study_design_scores_gemma":[0.0010864335,0.0002378887,0.0022123216,0.00071302877,0.00018977578,0.0009053774,0.00007434193,0.07178102,0.022884045,0.058477055,0.8409485,0.00049012125],"about_ca_topic_score_codex":0.0068780133,"about_ca_topic_score_gemma":0.0066333916,"teacher_disagreement_score":0.29969826,"about_ca_system_score_codex":0.0025915217,"about_ca_system_score_gemma":0.0031226953,"threshold_uncertainty_score":0.9988954},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70145273,0.0055628717,0.0037114753,0.002437392,0.0003574153,0.00020757265,0.22891723,0.0017779472,0.055575423],"genre_scores_gemma":[0.8625152,0.0048766853,0.0049893223,0.00030524548,0.00035867235,0.0003228887,0.10929604,0.00023354896,0.01710245],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9967901,0.0005024045,0.000489663,0.0003406215,0.0015570291,0.0003201476],"domain_scores_gemma":[0.98074865,0.0048827757,0.0054932274,0.001171622,0.006772572,0.00093107944],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0029438888,0.00070810615,0.00054116786,0.032639597,0.00059483416,0.0022503831,0.00062200427,0.0005620273,0.0030175233],"category_scores_gemma":[0.016248869,0.00015888433,0.00076082064,0.06281377,0.00044965203,0.0024572005,0.0014693268,0.00065857096,0.0023755787],"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":[0.00027703017,0.00022742049,0.60214144,0.0019096461,0.00036647895,0.0003963225,0.0025916183,0.006843638,0.001776367,0.004386995,0.07816977,0.3009133],"study_design_scores_gemma":[0.000019267763,0.00023514593,0.86111766,0.00036038985,0.00013566,0.00060264044,0.0027005367,0.0073673376,0.0046973987,0.002047402,0.12063953,0.00007713312],"about_ca_topic_score_codex":0.0067729354,"about_ca_topic_score_gemma":0.008111228,"teacher_disagreement_score":0.9673604,"about_ca_system_score_codex":0.0019740371,"about_ca_system_score_gemma":0.00184664,"threshold_uncertainty_score":0.015568972},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050595235,0.00012864555,0.94798434,0.0001096901,0.000015913392,0.000048008384,0.00011073739,0.0004897056,0.0005177099],"genre_scores_gemma":[0.840885,0.00012900142,0.15670265,0.000059392645,0.000060911025,0.00012925755,0.0004905731,0.00008462265,0.001458578],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988373,0.00043629075,0.00007754598,0.00029500813,0.00025127362,0.000102627724],"domain_scores_gemma":[0.9968183,0.001660353,0.0005139925,0.0003665617,0.0005589571,0.00008188193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017896437,0.0007821188,0.00097349,0.0014127507,0.0003811478,0.0008828179,0.001607863,0.00072971993,0.0004972976],"category_scores_gemma":[0.0036661355,0.00034969155,0.0009083763,0.00070380274,0.0006493949,0.0011602184,0.0007369276,0.0009715682,0.0002423042],"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":[0.00009319144,0.00020154937,0.007550168,0.000061371386,0.000118285054,0.000090420515,0.00020061989,0.9029145,0.0025628712,0.0027536047,0.0006741688,0.082779236],"study_design_scores_gemma":[0.0000011144141,0.000012670398,0.0005490241,0.0000025828501,0.000003271886,0.000016261683,0.000012272084,0.9975656,0.00029006938,0.0014461579,0.00009681293,0.000004254041],"about_ca_topic_score_codex":0.006334952,"about_ca_topic_score_gemma":0.0060960944,"teacher_disagreement_score":0.006334952,"about_ca_system_score_codex":0.0006579295,"about_ca_system_score_gemma":0.00095639005,"threshold_uncertainty_score":0.01259619},"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":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011616563,0.021230109,0.7927224,0.016684165,0.0060398933,0.0027397538,0.005143507,0.0071054115,0.13671818],"genre_scores_gemma":[0.07469292,0.0062918696,0.8835603,0.001966562,0.001065988,0.0059021395,0.012086393,0.0015341818,0.012899678],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.847952,0.06380273,0.028357064,0.004585154,0.05282353,0.0024794708],"domain_scores_gemma":[0.6902821,0.10395467,0.014778126,0.05332105,0.12986034,0.0078037474],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.15351379,0.0031599905,0.0025692778,0.018921042,0.0034524011,0.016306708,0.010987824,0.0064671286,0.007383289],"category_scores_gemma":[0.25519082,0.001435915,0.0024094689,0.01421361,0.0064644516,0.013568562,0.010691936,0.0096959965,0.0076111183],"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":[0.00041870648,0.0006680426,0.01424407,0.002257936,0.0002975606,0.00015396178,0.0016545538,0.008702926,0.003077587,0.3034811,0.15096444,0.51407915],"study_design_scores_gemma":[0.00021063072,0.0010256334,0.025379248,0.009375817,0.0003686538,0.0007321568,0.004076351,0.038723145,0.014503292,0.41181883,0.49351266,0.00027347472],"about_ca_topic_score_codex":0.0057915444,"about_ca_topic_score_gemma":0.0063070804,"teacher_disagreement_score":0.8464862,"about_ca_system_score_codex":0.006381877,"about_ca_system_score_gemma":0.015453892,"threshold_uncertainty_score":0.8118682},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55835694,0.071225874,0.21144594,0.00497109,0.0020778766,0.00089220016,0.011348929,0.06937566,0.07030547],"genre_scores_gemma":[0.7144647,0.019571094,0.20749424,0.0016172938,0.00030825217,0.0005271282,0.040274195,0.0060813962,0.009661706],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9885117,0.0017890774,0.0008708451,0.0014657867,0.0061077625,0.0012548304],"domain_scores_gemma":[0.97953117,0.00867454,0.0007892378,0.003457424,0.006650363,0.00089724344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011908977,0.0022204628,0.0011693677,0.009178851,0.0013821345,0.003413684,0.006384251,0.002262611,0.0030357358],"category_scores_gemma":[0.034240298,0.00091741874,0.0016474812,0.009627894,0.0013092615,0.00936112,0.0047947573,0.0030087223,0.001477955],"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":[0.0021645545,0.001811312,0.027154854,0.004268187,0.00088623,0.00043236685,0.00068652164,0.107003056,0.0060852473,0.04286075,0.083715,0.722932],"study_design_scores_gemma":[0.00055592635,0.0017500115,0.024976293,0.0019999428,0.0005211037,0.0011410953,0.0016343184,0.69763815,0.041547526,0.032242227,0.19558164,0.00041169024],"about_ca_topic_score_codex":0.021514079,"about_ca_topic_score_gemma":0.020521218,"teacher_disagreement_score":0.021514079,"about_ca_system_score_codex":0.0042588213,"about_ca_system_score_gemma":0.00379471,"threshold_uncertainty_score":0.06298143},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008928048,0.006713703,0.9748824,0.0048798374,0.00035060183,0.0002275445,0.0003534588,0.0011878025,0.0024766063],"genre_scores_gemma":[0.2710494,0.011372467,0.70968205,0.0008535156,0.0012685325,0.0005284987,0.0014858423,0.00021624239,0.0035434233],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9938964,0.0018173637,0.00044220625,0.0008371269,0.002866251,0.00014055376],"domain_scores_gemma":[0.9798809,0.010937591,0.0013818394,0.002412963,0.0050377683,0.00034874043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0077388845,0.0014545913,0.0015428608,0.0058450736,0.00065962813,0.0033253834,0.0025622621,0.0016146463,0.0018398041],"category_scores_gemma":[0.02696922,0.0007036502,0.0010345973,0.0050454,0.0010675417,0.0065282956,0.0014248963,0.003410339,0.0012824251],"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":[0.0001232957,0.00037675307,0.017860806,0.0007054268,0.0003427096,0.00008739041,0.00031482018,0.039408404,0.002176195,0.037599105,0.018031532,0.88297355],"study_design_scores_gemma":[0.000036084584,0.0001276728,0.0032811237,0.00035053663,0.00008739555,0.00018451508,0.00035513457,0.8815985,0.003109637,0.08962652,0.021175519,0.000067467736],"about_ca_topic_score_codex":0.0028837672,"about_ca_topic_score_gemma":0.003262398,"teacher_disagreement_score":0.0077388845,"about_ca_system_score_codex":0.0012401426,"about_ca_system_score_gemma":0.0010682493,"threshold_uncertainty_score":0.04092765},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049847137,0.0002118473,0.9458168,0.00040712484,0.00012368073,0.00004643494,0.00045514727,0.0011662515,0.0019255779],"genre_scores_gemma":[0.8721494,0.00018690234,0.12015717,0.00027081213,0.00013082901,0.00006044474,0.0015448898,0.00011259358,0.0053869374],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993068,0.0001154287,0.000027546293,0.00024972938,0.00019377089,0.00010667441],"domain_scores_gemma":[0.99843913,0.00055326347,0.00025654028,0.00032403148,0.0003326166,0.00009443875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008284433,0.0008408068,0.00089667906,0.0008586559,0.00026905249,0.0006232588,0.0014240084,0.0010688045,0.0011075236],"category_scores_gemma":[0.0030027437,0.00025575538,0.0005327767,0.0006374369,0.0006995828,0.0011631575,0.00087501906,0.0019663274,0.0005735475],"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":[0.00025240838,0.00020566609,0.012468393,0.00009130374,0.00010847727,0.00044178267,0.00008093183,0.7047862,0.011605424,0.014659093,0.009643371,0.24565698],"study_design_scores_gemma":[0.000001694919,0.000015242899,0.00038423255,0.0000027616056,0.000003885155,0.000050930317,0.0000032675061,0.99562234,0.0016018327,0.0018856678,0.0004244084,0.0000037659154],"about_ca_topic_score_codex":0.0034138092,"about_ca_topic_score_gemma":0.003560749,"teacher_disagreement_score":0.0034138092,"about_ca_system_score_codex":0.0006993878,"about_ca_system_score_gemma":0.00059009454,"threshold_uncertainty_score":0.0067878366},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037680183,0.00024467875,0.9569879,0.00017825833,0.000033900782,0.00010241768,0.0030642843,0.00094476884,0.0007635686],"genre_scores_gemma":[0.49274847,0.00073891645,0.4912572,0.000060202958,0.000082598795,0.0002710969,0.012858895,0.00014535552,0.001837295],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933916,0.0001319666,0.000046914785,0.00024619128,0.00018017272,0.00005561427],"domain_scores_gemma":[0.99878925,0.00041632485,0.00025601158,0.00026146186,0.00020689331,0.000070050104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007643528,0.0009180707,0.0005219904,0.0034155224,0.00044487286,0.0010176,0.0009765713,0.00063678174,0.00092943193],"category_scores_gemma":[0.0028286104,0.00030297224,0.0008480024,0.0036881575,0.00038623036,0.0021584816,0.0010793472,0.0010878677,0.0003883968],"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":[0.0004650792,0.00024413347,0.038000304,0.00046669558,0.00041133258,0.00072870124,0.00074484874,0.63838357,0.0218814,0.024507461,0.00783376,0.26633275],"study_design_scores_gemma":[0.0000044116355,0.000042853608,0.0055646403,0.000021394653,0.000041583095,0.00011378408,0.00006293374,0.97976035,0.0017186473,0.008086704,0.0045662066,0.000016505819],"about_ca_topic_score_codex":0.0097143,"about_ca_topic_score_gemma":0.016055528,"teacher_disagreement_score":0.0097143,"about_ca_system_score_codex":0.00085987675,"about_ca_system_score_gemma":0.0007687001,"threshold_uncertainty_score":0.01931554},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21307191,0.00025359436,0.7479024,0.0005897545,0.00015122023,0.00032920396,0.00006951843,0.016212605,0.021419732],"genre_scores_gemma":[0.7796334,0.00012498608,0.20627496,0.00019087513,0.00004061688,0.00012008176,0.00012789392,0.00022331381,0.013264],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953055,0.00008114276,0.000019905227,0.0000678065,0.00022871798,0.000071852446],"domain_scores_gemma":[0.99894613,0.00022182756,0.00010572577,0.00031729124,0.00031518855,0.000093864124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004465245,0.0003810081,0.00028402224,0.00057384244,0.00043877677,0.0015756111,0.0018565914,0.00084882986,0.0046548406],"category_scores_gemma":[0.0011926832,0.00027100832,0.00025766375,0.00023928544,0.00047219748,0.0014125253,0.0007061861,0.0006415628,0.0014209821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008841749,0.00085994025,0.006913261,0.00036103482,0.00016432295,0.00088277203,0.00072700076,0.04040997,0.5931535,0.050751176,0.0053609633,0.29953185],"study_design_scores_gemma":[0.00010743077,0.0016933705,0.0030052355,0.000040959618,0.00015674945,0.0010599196,0.00015882147,0.6936254,0.2588802,0.0056923223,0.03551856,0.000060955834],"about_ca_topic_score_codex":0.0010013764,"about_ca_topic_score_gemma":0.0013234629,"teacher_disagreement_score":0.0046548406,"about_ca_system_score_codex":0.0005541022,"about_ca_system_score_gemma":0.0007931499,"threshold_uncertainty_score":0.0155720115},"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":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02088764,0.20088126,0.7598234,0.010214009,0.0008381371,0.000117279254,0.000301133,0.0006616598,0.006275487],"genre_scores_gemma":[0.62163216,0.15129875,0.21955593,0.0014378711,0.0032787886,0.00016075405,0.0006829627,0.00012031348,0.0018325427],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99591476,0.0014311065,0.0003211061,0.00067400926,0.0014175259,0.00024147112],"domain_scores_gemma":[0.9872027,0.009004557,0.0007104856,0.00093345443,0.0018881977,0.0002605657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009269029,0.001109707,0.0028876308,0.0031413618,0.0006767716,0.0056366064,0.002486117,0.002843134,0.001320716],"category_scores_gemma":[0.011424395,0.0005329438,0.0014511136,0.0049508885,0.0025048936,0.008096146,0.0027448835,0.002561051,0.00052655383],"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":[0.00030753826,0.00034987112,0.0068466435,0.0025300628,0.000574174,0.00011737748,0.00050042913,0.05967806,0.004374605,0.09626028,0.006478437,0.82198256],"study_design_scores_gemma":[0.000028048424,0.00039477082,0.005286982,0.0010292111,0.0002585537,0.0004851494,0.0011254192,0.6326311,0.012354676,0.3024672,0.04372389,0.0002149109],"about_ca_topic_score_codex":0.0022425372,"about_ca_topic_score_gemma":0.00095828995,"teacher_disagreement_score":0.009269029,"about_ca_system_score_codex":0.0014250076,"about_ca_system_score_gemma":0.0014084291,"threshold_uncertainty_score":0.049019873},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75544775,0.01241837,0.16755229,0.0020260988,0.001592269,0.00046006244,0.008251088,0.037689056,0.014562995],"genre_scores_gemma":[0.8605224,0.0012656254,0.118648745,0.00028357655,0.00020580307,0.00011396121,0.01596501,0.0006749979,0.002319882],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99226123,0.0016448571,0.0007332722,0.0018350347,0.0028314737,0.00069421344],"domain_scores_gemma":[0.98605275,0.008200423,0.0010253134,0.0015563358,0.0027448204,0.0004203562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005264105,0.0022721628,0.0014293581,0.0043302434,0.0008603906,0.0022035134,0.0027780358,0.0021430994,0.0010392863],"category_scores_gemma":[0.02161497,0.00035150143,0.00130988,0.0035849682,0.00071976887,0.0027040436,0.0016806953,0.0016570361,0.0012759647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002237063,0.0012489758,0.07827467,0.0014502741,0.0010935073,0.000378949,0.000504473,0.27022958,0.016361965,0.0033785373,0.0372643,0.5875777],"study_design_scores_gemma":[0.00006715017,0.00053223834,0.016176743,0.00006368348,0.00010158023,0.0003389559,0.00028702765,0.9593819,0.015699938,0.0017639236,0.0055267103,0.000060233167],"about_ca_topic_score_codex":0.015436867,"about_ca_topic_score_gemma":0.012705153,"teacher_disagreement_score":0.015436867,"about_ca_system_score_codex":0.0018053837,"about_ca_system_score_gemma":0.0011509263,"threshold_uncertainty_score":0.030694008},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5615201,0.008544216,0.35380945,0.0027412348,0.0031432237,0.00027038975,0.0025971492,0.021880016,0.045494325],"genre_scores_gemma":[0.8138116,0.003156939,0.14040896,0.0010217242,0.0009726204,0.000073441144,0.0040188506,0.00084855786,0.035687245],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955255,0.000040555038,0.000027045768,0.000082892126,0.0002333116,0.00006359918],"domain_scores_gemma":[0.99852836,0.0005158442,0.00009349528,0.00039734787,0.00041567546,0.000049286355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037034156,0.000977362,0.0006092582,0.0013279319,0.00028044617,0.0010805498,0.00047444456,0.0010692023,0.0121602],"category_scores_gemma":[0.0023842156,0.00020387047,0.00038768747,0.001354399,0.00025085747,0.001019597,0.00048268226,0.00073813694,0.005344997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000806584,0.00021797832,0.001971116,0.0001421101,0.00004685927,0.00023093907,0.000025765192,0.011198055,0.13474604,0.0007713946,0.014191231,0.8356518],"study_design_scores_gemma":[0.00011848492,0.0007233661,0.016292114,0.00010293127,0.00021913233,0.0020610103,0.00012941168,0.539351,0.41256353,0.005323215,0.02304558,0.000070218746],"about_ca_topic_score_codex":0.0019307799,"about_ca_topic_score_gemma":0.0020477439,"teacher_disagreement_score":0.0121602,"about_ca_system_score_codex":0.00025487517,"about_ca_system_score_gemma":0.00028992747,"threshold_uncertainty_score":0.04067993},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18629313,0.00060392526,0.8108608,0.00035123262,0.00006357917,0.00003073972,0.00008306218,0.0007810806,0.00093244924],"genre_scores_gemma":[0.9398389,0.00014726435,0.05957798,0.000025029152,0.00003439012,0.000014037109,0.00006721549,0.000014639053,0.0002804877],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890196,0.00030226767,0.00008645013,0.0002015062,0.0004300724,0.00007769801],"domain_scores_gemma":[0.9985563,0.0006234593,0.00028998667,0.00014099301,0.00032693436,0.00006237599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011331339,0.00032553825,0.00048707693,0.0016233092,0.00039184972,0.0007843344,0.000566506,0.00057714235,0.00029276428],"category_scores_gemma":[0.0027711394,0.00016791624,0.00036344453,0.001350906,0.0007357106,0.0010575933,0.0005417521,0.0005615298,0.00009266806],"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":[0.00036381613,0.00022991616,0.02414237,0.00013876667,0.00019185334,0.0004994912,0.00041939886,0.5806876,0.041431103,0.020558843,0.0014034302,0.32993346],"study_design_scores_gemma":[0.0000024941585,0.000030115852,0.0020548962,0.000003390422,0.0000077088525,0.00006669029,0.000041858002,0.9896897,0.0026869844,0.004992896,0.0004150682,0.000008120194],"about_ca_topic_score_codex":0.004167083,"about_ca_topic_score_gemma":0.0023191597,"teacher_disagreement_score":0.004167083,"about_ca_system_score_codex":0.0007220313,"about_ca_system_score_gemma":0.0005496963,"threshold_uncertainty_score":0.008285642},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0051515787,0.0002247,0.9923638,0.00011898317,0.00005803501,0.00003715126,0.0000527967,0.0013917262,0.00060126185],"genre_scores_gemma":[0.16850324,0.00048101487,0.8248517,0.00020954487,0.0001862288,0.00024580795,0.00088341243,0.0004024526,0.004236574],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973628,0.000294612,0.00015210864,0.00052829005,0.0014244901,0.0002378151],"domain_scores_gemma":[0.9976724,0.0004738649,0.00020145846,0.00030008008,0.0012617509,0.00009044508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015260188,0.0011889929,0.0022752795,0.0035291775,0.0012642413,0.0017587895,0.0027158817,0.0014200602,0.0025564302],"category_scores_gemma":[0.006541744,0.00054111215,0.0014537285,0.0032699285,0.0010530705,0.0035155904,0.0017130784,0.0016721531,0.0016526247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002945182,0.00012476573,0.004363384,0.00014792549,0.00012217868,0.0002270248,0.00024198627,0.12612654,0.0132622775,0.013629883,0.008566229,0.8328933],"study_design_scores_gemma":[0.000043508953,0.00006836568,0.001058722,0.000014230348,0.000028050106,0.0002971602,0.00004839455,0.97851115,0.0079405485,0.0057607,0.006172245,0.0000569063],"about_ca_topic_score_codex":0.0140500795,"about_ca_topic_score_gemma":0.0066202204,"teacher_disagreement_score":0.0140500795,"about_ca_system_score_codex":0.0014594696,"about_ca_system_score_gemma":0.001673009,"threshold_uncertainty_score":0.027936578},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.091934636,0.0055289315,0.8716259,0.0012001616,0.00060714857,0.00037296716,0.005197104,0.014133879,0.009399313],"genre_scores_gemma":[0.49674192,0.0040498795,0.48170838,0.0006931157,0.0005871927,0.00022044583,0.00845924,0.0007504041,0.006789459],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853575,0.00013508124,0.0000725779,0.00046506416,0.000610316,0.00018122834],"domain_scores_gemma":[0.99875605,0.00024705537,0.0002160479,0.00028029425,0.00042988596,0.00007058518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076704455,0.0011230385,0.0010409416,0.0047603543,0.0003362401,0.0017799245,0.0009785048,0.0015127632,0.002923782],"category_scores_gemma":[0.0037796735,0.00037522407,0.0007783169,0.002702672,0.0007646315,0.0024537572,0.0015636196,0.0012707865,0.0028358325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059593463,0.0001689016,0.006374326,0.000691595,0.00019825152,0.0005933283,0.0001757036,0.01795442,0.10354741,0.0068432824,0.027044104,0.83581287],"study_design_scores_gemma":[0.0000525507,0.00049506576,0.037614945,0.0003324802,0.00018924342,0.0048952564,0.00055727153,0.6291002,0.17938302,0.057401758,0.08983599,0.00014222327],"about_ca_topic_score_codex":0.0021317108,"about_ca_topic_score_gemma":0.0020270632,"teacher_disagreement_score":0.0047603543,"about_ca_system_score_codex":0.0006754092,"about_ca_system_score_gemma":0.000572384,"threshold_uncertainty_score":0.009781063},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14244354,0.0021340176,0.84655213,0.0005213633,0.00029503417,0.00015221421,0.0008217174,0.005380802,0.001699075],"genre_scores_gemma":[0.77823824,0.0016131166,0.21571174,0.000083928324,0.00020713093,0.00012106119,0.0019172739,0.00026143432,0.0018460966],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99769753,0.00025388057,0.00020178502,0.0005433667,0.0010553781,0.00024792494],"domain_scores_gemma":[0.99450713,0.002064502,0.00086460105,0.0006171546,0.0016781979,0.0002684462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016787747,0.00084611203,0.0012200157,0.0020635407,0.0004760351,0.001488303,0.0012738294,0.00073328806,0.0010204896],"category_scores_gemma":[0.009044779,0.00031426892,0.0008979967,0.0026904165,0.00045849927,0.0018018058,0.00087411236,0.0010201101,0.00068368966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016204802,0.00035728852,0.039731674,0.000977964,0.00037959783,0.0010491363,0.00049741025,0.17557946,0.070364445,0.006764976,0.007563895,0.69511366],"study_design_scores_gemma":[0.00003342795,0.00034088216,0.011363049,0.000052803684,0.00007166564,0.0005670882,0.00020390579,0.93751115,0.036023915,0.008513002,0.005268641,0.0000505177],"about_ca_topic_score_codex":0.0019045033,"about_ca_topic_score_gemma":0.0014115731,"teacher_disagreement_score":0.0020635407,"about_ca_system_score_codex":0.0005024186,"about_ca_system_score_gemma":0.0008593766,"threshold_uncertainty_score":0.00887835},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01101742,0.00074984896,0.9838244,0.0003676251,0.00013284938,0.00007977804,0.00004319558,0.0014906591,0.0022942587],"genre_scores_gemma":[0.6934146,0.0010211277,0.3012709,0.0005794096,0.00013123128,0.00022909373,0.0002147333,0.00012040369,0.0030184202],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947304,0.00009742208,0.000028529723,0.00013351586,0.00023254623,0.000035045836],"domain_scores_gemma":[0.9994673,0.00017826435,0.00008466793,0.000121547986,0.00012541411,0.000022671726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006326706,0.0005915369,0.00047134914,0.0007021557,0.00040812968,0.0006409987,0.0018025341,0.00054824713,0.0011512296],"category_scores_gemma":[0.0013896789,0.0002418085,0.0005521754,0.00095315883,0.0004576262,0.0020649817,0.00072889356,0.0010681612,0.0003693968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004366536,0.0004050379,0.0049799117,0.0006795702,0.0001518798,0.00063422427,0.00033072184,0.14624918,0.10788798,0.06526263,0.013581256,0.65940094],"study_design_scores_gemma":[0.000029580855,0.00023600327,0.00080430653,0.000034000237,0.000054454806,0.0005360412,0.000053739714,0.9304716,0.021948328,0.030347683,0.015451,0.000033201857],"about_ca_topic_score_codex":0.00086523045,"about_ca_topic_score_gemma":0.0011334907,"teacher_disagreement_score":0.0018025341,"about_ca_system_score_codex":0.00080847566,"about_ca_system_score_gemma":0.0004944222,"threshold_uncertainty_score":0.0058659315},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01664078,0.00043350956,0.974499,0.00096062245,0.000106322135,0.000060965827,0.0006013985,0.0006439456,0.0060533984],"genre_scores_gemma":[0.54494464,0.0014306436,0.42508087,0.0014488043,0.00040032575,0.0006869689,0.00336208,0.0004705587,0.022175143],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985102,0.00039360384,0.00008494602,0.0004655757,0.0003836407,0.00016201728],"domain_scores_gemma":[0.99709797,0.0015128376,0.0003242811,0.00057096995,0.0003419784,0.00015199576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017691002,0.0012611751,0.0011046308,0.0013449488,0.0005685892,0.0022794248,0.0032132005,0.001715164,0.006868652],"category_scores_gemma":[0.007753053,0.00068308075,0.0014189855,0.0011940184,0.0016231107,0.0050761364,0.0032492834,0.003786733,0.001671382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012890145,0.0001403554,0.0020485632,0.00024647548,0.00012470095,0.00013851254,0.00016198083,0.30732453,0.0019160369,0.51879495,0.011590856,0.15738419],"study_design_scores_gemma":[0.000009252271,0.000054931796,0.00019871758,0.000029831002,0.000014674269,0.0000510477,0.000022286822,0.6486384,0.0011202032,0.34356698,0.0062833773,0.000010311924],"about_ca_topic_score_codex":0.0019153293,"about_ca_topic_score_gemma":0.0023976192,"teacher_disagreement_score":0.006868652,"about_ca_system_score_codex":0.0018686632,"about_ca_system_score_gemma":0.00097485754,"threshold_uncertainty_score":0},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22345006,0.00022850509,0.7709809,0.00024771583,0.00009688006,0.00004562437,0.0003581151,0.0028836522,0.0017086247],"genre_scores_gemma":[0.8892803,0.00012398728,0.10770794,0.00005154382,0.000032738382,0.000024767769,0.0006120665,0.000061488645,0.0021051872],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996877,0.000041741663,0.000013667176,0.00010663113,0.00008651789,0.00006369459],"domain_scores_gemma":[0.9995665,0.00010202914,0.00006254834,0.00006108595,0.0001710132,0.00003681448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000324477,0.00063598057,0.00045742645,0.00083344313,0.00020926823,0.00035708313,0.00070054486,0.00051471294,0.0006425008],"category_scores_gemma":[0.00087814324,0.00025397594,0.00037542268,0.0005465096,0.00028977988,0.0006221273,0.00048753936,0.0009602358,0.00029468475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042411976,0.00033182758,0.02781077,0.00009278459,0.00019238004,0.0005431936,0.00017762127,0.45422444,0.040300217,0.0031692379,0.0048953393,0.46783796],"study_design_scores_gemma":[0.0000019916604,0.000016663093,0.0017668616,0.0000025041961,0.000005407862,0.0000406514,0.0000089235255,0.99397224,0.0035570245,0.00033980113,0.0002834781,0.0000044921],"about_ca_topic_score_codex":0.011750113,"about_ca_topic_score_gemma":0.01373172,"teacher_disagreement_score":0.011750113,"about_ca_system_score_codex":0.00053944025,"about_ca_system_score_gemma":0.00057731976,"threshold_uncertainty_score":0.023363411},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8630356,0.005221188,0.056501016,0.0014407249,0.0006554213,0.0010589829,0.035048787,0.0137252165,0.023312917],"genre_scores_gemma":[0.83721673,0.0022627483,0.08590442,0.00019934199,0.0007363007,0.00030216336,0.060937155,0.00023517858,0.012206053],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994037,0.00006804637,0.000058227568,0.000106467545,0.00025197514,0.000111542875],"domain_scores_gemma":[0.9979274,0.00067765824,0.00033695818,0.00013693576,0.00079434743,0.00012670808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005090815,0.0007387752,0.0005774023,0.008766039,0.00042549704,0.0013512283,0.00045093714,0.00068897667,0.0017011111],"category_scores_gemma":[0.0025989818,0.00023114584,0.00040145466,0.0032161938,0.00019817911,0.00093981036,0.0006765301,0.00047170327,0.0021379967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006869895,0.00035905716,0.12316509,0.0014829743,0.00017763315,0.0024146647,0.001420284,0.0062158518,0.053406596,0.0007809049,0.07538222,0.7345077],"study_design_scores_gemma":[0.000078074,0.00039273858,0.5762442,0.00030421393,0.00027330796,0.002251517,0.004409798,0.27331862,0.052123748,0.0014924592,0.088945135,0.00016625394],"about_ca_topic_score_codex":0.011116377,"about_ca_topic_score_gemma":0.01204892,"teacher_disagreement_score":0.011116377,"about_ca_system_score_codex":0.00047904556,"about_ca_system_score_gemma":0.00048912317,"threshold_uncertainty_score":0.02210337},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039878716,0.00020132457,0.99493307,0.00012874173,0.000018783408,0.000009216393,0.00003405345,0.00039363344,0.00029337368],"genre_scores_gemma":[0.6030001,0.000928124,0.38970798,0.00024699784,0.00016922072,0.0002201221,0.0005909592,0.00018227144,0.004954151],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987929,0.0004137686,0.000060396746,0.00031641583,0.00032248034,0.00009414214],"domain_scores_gemma":[0.9972761,0.0018514735,0.00036998314,0.00014319664,0.0003049421,0.000054264714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017448995,0.0008816982,0.0010691086,0.0009960543,0.00028701604,0.0010708608,0.001420825,0.0012801485,0.00096313254],"category_scores_gemma":[0.0069593424,0.0009580699,0.00080288725,0.0010737915,0.00070998026,0.0016156136,0.0010546548,0.002360123,0.00047598407],"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":[0.000103593076,0.00007006624,0.0011248563,0.0000812878,0.000090370995,0.00006145869,0.0000835654,0.861478,0.002346556,0.012649989,0.0013442702,0.120566055],"study_design_scores_gemma":[0.0000034269503,0.000008201891,0.00011931093,0.0000029223722,0.0000029388423,0.000009114208,0.0000026225896,0.9960061,0.00024257188,0.0033209268,0.00027640036,0.000005495546],"about_ca_topic_score_codex":0.01066063,"about_ca_topic_score_gemma":0.0077231806,"teacher_disagreement_score":0.01066063,"about_ca_system_score_codex":0.0009775152,"about_ca_system_score_gemma":0.0011523267,"threshold_uncertainty_score":0.02119714},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030957926,0.00033992314,0.9665257,0.0002446897,0.000033965855,0.000026728127,0.00008482249,0.0010718096,0.0007145298],"genre_scores_gemma":[0.6802837,0.000626177,0.3139203,0.00027131193,0.00013204933,0.00009726785,0.0010428205,0.00031927548,0.0033070755],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908376,0.00019792913,0.000036860238,0.00025261426,0.0003150168,0.00011383785],"domain_scores_gemma":[0.99739635,0.001229644,0.0004098315,0.00032388387,0.00050008734,0.00014023349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00198081,0.0011831259,0.0010220931,0.0020903004,0.0004450158,0.0013545874,0.0023677836,0.001163878,0.0010532185],"category_scores_gemma":[0.0055327294,0.00049011846,0.0007049866,0.0012970663,0.001424869,0.0031543719,0.0023591344,0.0017844917,0.00046363883],"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":[0.00038988536,0.00017891546,0.010577433,0.00018180301,0.00020235822,0.0003327172,0.00020629272,0.56041086,0.01608545,0.032845624,0.0042982935,0.37429035],"study_design_scores_gemma":[0.0000023883836,0.000021572918,0.0002871724,0.000004285512,0.000006045311,0.000036134556,0.000010615731,0.99179524,0.0020455318,0.0053891144,0.00039678765,0.000005194479],"about_ca_topic_score_codex":0.003996877,"about_ca_topic_score_gemma":0.003793232,"teacher_disagreement_score":0.003996877,"about_ca_system_score_codex":0.0011127775,"about_ca_system_score_gemma":0.0008462669,"threshold_uncertainty_score":0.0104756355},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30580416,0.0017383405,0.65638,0.00093354244,0.00048029723,0.0003572832,0.0021617354,0.02563805,0.006506626],"genre_scores_gemma":[0.7629717,0.00065017305,0.23038165,0.0001580846,0.00009009261,0.000090385125,0.0015792917,0.00032556755,0.00375305],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997564,0.00003256436,0.000009939945,0.00006469455,0.000083949715,0.000052534037],"domain_scores_gemma":[0.9996965,0.00009489463,0.000031942098,0.000045517747,0.00009467507,0.0000366058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048504554,0.0011583373,0.0005154934,0.0013999103,0.0002741895,0.00042693128,0.00083075167,0.00067247526,0.0018764829],"category_scores_gemma":[0.0010083296,0.0002751524,0.0003517946,0.0010116821,0.00028530188,0.00070268684,0.0006169816,0.00053040864,0.0005664374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00076542376,0.00030784568,0.012147783,0.0002156169,0.00018549195,0.0012136303,0.00015689696,0.10332755,0.08856679,0.002035707,0.014988738,0.77608854],"study_design_scores_gemma":[0.000013536281,0.00006709421,0.0035506869,0.000010535091,0.00002864756,0.00025750225,0.000038721577,0.9662388,0.025456913,0.0011413594,0.0031779562,0.00001830041],"about_ca_topic_score_codex":0.011735633,"about_ca_topic_score_gemma":0.014147392,"teacher_disagreement_score":0.011735633,"about_ca_system_score_codex":0.00067904324,"about_ca_system_score_gemma":0.00043082004,"threshold_uncertainty_score":0.023334622},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008573149,0.0015148427,0.92534727,0.00094246294,0.00080313516,0.00021713073,0.0010214706,0.005762032,0.055818453],"genre_scores_gemma":[0.37304276,0.0030379654,0.41031846,0.0013483281,0.00091049913,0.0005037813,0.0055102487,0.0013215577,0.2040064],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965906,0.000053970558,0.000012149903,0.000087432854,0.00014626853,0.000041149186],"domain_scores_gemma":[0.9995383,0.00015816867,0.000019495224,0.0001726041,0.00009468284,0.000016622467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005083646,0.0009232272,0.00060835417,0.0007806408,0.00038176018,0.00083189557,0.0009121626,0.0009625605,0.024895139],"category_scores_gemma":[0.001807542,0.00027934992,0.0004424565,0.0007104879,0.00045059342,0.0011101506,0.0012513727,0.0017162381,0.010825222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017561983,0.00012533329,0.00029626224,0.00010656148,0.000053473843,0.0001431837,0.000028428307,0.19360216,0.010385348,0.04603859,0.071589194,0.67745596],"study_design_scores_gemma":[0.000014422823,0.00005236167,0.00021390482,0.00003669821,0.000018036284,0.00020065792,0.00001298181,0.9316594,0.012748642,0.029177148,0.025851373,0.000014335444],"about_ca_topic_score_codex":0.0013207746,"about_ca_topic_score_gemma":0.0019360727,"teacher_disagreement_score":0.024895139,"about_ca_system_score_codex":0.0004354213,"about_ca_system_score_gemma":0.00045791396,"threshold_uncertainty_score":0.08328253},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035897875,0.00011427087,0.99480635,0.0003485176,0.000038114,0.00001738087,0.00003499436,0.00011167724,0.0009388973],"genre_scores_gemma":[0.48071113,0.0007037412,0.50649613,0.00047810582,0.00036531396,0.00026162495,0.00024564526,0.00020027005,0.010538104],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99710196,0.001079879,0.0001505583,0.00056097005,0.00090305606,0.00020359157],"domain_scores_gemma":[0.9938771,0.0040333057,0.00042746705,0.000541268,0.0008941861,0.00022663838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032312388,0.000742268,0.001975177,0.0015796459,0.0009823224,0.0032859098,0.0034464563,0.0021794299,0.0036639748],"category_scores_gemma":[0.013418657,0.00083566614,0.001446744,0.002349992,0.0023343985,0.004513003,0.0026995789,0.0037980753,0.001033557],"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":[0.00016443011,0.000121738354,0.0007259454,0.00010131751,0.00010144408,0.000099004865,0.00014199168,0.58517206,0.0015727844,0.33962515,0.00220411,0.06996999],"study_design_scores_gemma":[0.0000037968493,0.000008474522,0.000044448527,0.0000054551556,0.000008234903,0.00001714611,0.000003992345,0.94714546,0.00021137286,0.052254155,0.00028968358,0.000007801603],"about_ca_topic_score_codex":0.005510123,"about_ca_topic_score_gemma":0.0048817024,"teacher_disagreement_score":0.005510123,"about_ca_system_score_codex":0.0016911501,"about_ca_system_score_gemma":0.0020751741,"threshold_uncertainty_score":0.017088652},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0658917,0.0027677305,0.91932863,0.0025710217,0.00030573815,0.00006882412,0.0001854233,0.0031882778,0.005692649],"genre_scores_gemma":[0.8029591,0.0017067245,0.18864626,0.0013768945,0.00039689543,0.00019981465,0.0005353052,0.0007356327,0.003443481],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953661,0.001680136,0.00021161571,0.0011002467,0.0013465931,0.00029535202],"domain_scores_gemma":[0.9818045,0.008971954,0.0015339975,0.004284243,0.0031153152,0.00029002535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009801947,0.0015469254,0.0011995884,0.0015289183,0.0010772122,0.0024237738,0.0031246287,0.0024550194,0.0025557214],"category_scores_gemma":[0.06634749,0.0009977521,0.00082413416,0.0013825584,0.0031317854,0.005964175,0.0037580675,0.006323694,0.000991137],"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":[0.00027007662,0.000072591734,0.0051115546,0.00013357533,0.000153817,0.00009772449,0.0002767117,0.7474628,0.0046913084,0.04942009,0.003786335,0.1885234],"study_design_scores_gemma":[0.000012279525,0.00004498034,0.0008077416,0.000054835615,0.000015932632,0.000055520795,0.000029581395,0.946363,0.003522083,0.04742347,0.0016450112,0.000025519344],"about_ca_topic_score_codex":0.0043114545,"about_ca_topic_score_gemma":0.0027311968,"teacher_disagreement_score":0.009801947,"about_ca_system_score_codex":0.0027789276,"about_ca_system_score_gemma":0.001476236,"threshold_uncertainty_score":0.05183822},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05557826,0.0007796672,0.9361975,0.00060795236,0.00014749845,0.00016193604,0.0002511669,0.0019114706,0.004364398],"genre_scores_gemma":[0.7790325,0.0006407281,0.21287149,0.00025082685,0.00021965624,0.00013275238,0.0006618255,0.00015044982,0.0060397754],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994167,0.00008430728,0.000022306675,0.00020063893,0.00018291184,0.000093248476],"domain_scores_gemma":[0.99935144,0.00017261578,0.00006508325,0.000058357164,0.00028132528,0.000071314644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007442747,0.0013577221,0.0009867201,0.0019415322,0.000390156,0.0014443534,0.0019848032,0.0011411547,0.0019716525],"category_scores_gemma":[0.0017627798,0.00052040815,0.0008606661,0.0009266311,0.00039109812,0.0018953425,0.0012575933,0.0012174961,0.0005416777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055186026,0.0006693766,0.00815702,0.0001825642,0.0002416192,0.0006684496,0.00027959837,0.38703644,0.02312307,0.008824184,0.0057971603,0.5644686],"study_design_scores_gemma":[0.0000050405215,0.000044394998,0.00072470604,0.000006435156,0.000020869304,0.000047438534,0.000025283498,0.99438065,0.0020832813,0.0020764384,0.00057477545,0.00001062133],"about_ca_topic_score_codex":0.016668579,"about_ca_topic_score_gemma":0.015531489,"teacher_disagreement_score":0.016668579,"about_ca_system_score_codex":0.0014139614,"about_ca_system_score_gemma":0.0009967767,"threshold_uncertainty_score":0.033143103},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29958662,0.0004275238,0.6798324,0.00013086038,0.00015902783,0.00017043178,0.0006083267,0.015193292,0.0038914792],"genre_scores_gemma":[0.8927988,0.0000840845,0.10518701,0.000053092557,0.00001837791,0.00007759373,0.00031169134,0.00016339177,0.0013060357],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996793,0.000046692632,0.000024986019,0.00008338942,0.00012355154,0.000042007643],"domain_scores_gemma":[0.99930763,0.00025317597,0.0001364665,0.00014970754,0.00013122302,0.000021627702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026039043,0.00057615223,0.00031312887,0.00074259454,0.00017013468,0.0004601541,0.0009641364,0.00022629925,0.0030699275],"category_scores_gemma":[0.001283973,0.00017679065,0.00018762506,0.0005732955,0.00018011848,0.00045488751,0.00017184038,0.0002948497,0.00051401334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022203359,0.00032714245,0.015555879,0.00062425894,0.00026158142,0.0015763093,0.00031493042,0.14209487,0.24801034,0.006095154,0.008362882,0.57455635],"study_design_scores_gemma":[0.00015205266,0.0010869827,0.013264791,0.000052467018,0.000116703835,0.0016059417,0.00006671168,0.6779922,0.29330832,0.0017336993,0.010538247,0.00008183388],"about_ca_topic_score_codex":0.001011154,"about_ca_topic_score_gemma":0.0014069224,"teacher_disagreement_score":0.0030699275,"about_ca_system_score_codex":0.0004306825,"about_ca_system_score_gemma":0.0003578141,"threshold_uncertainty_score":0.01026994},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30622828,0.00093371805,0.6885604,0.00020255677,0.00008640403,0.00008489749,0.0004210793,0.0025982773,0.0008843237],"genre_scores_gemma":[0.8816969,0.00037134235,0.115369685,0.000060540595,0.00008085242,0.000041386094,0.0011860493,0.000056701112,0.0011366577],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935824,0.0000977969,0.000034835928,0.00022125996,0.0002059455,0.00008194633],"domain_scores_gemma":[0.9991049,0.00024423748,0.0002421349,0.00011060938,0.00023218009,0.000065989065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006459173,0.00076707784,0.0009402305,0.0020575977,0.00021760106,0.0005376096,0.0008904108,0.0008312303,0.00047976233],"category_scores_gemma":[0.0024531763,0.00022147951,0.00049767457,0.0014382785,0.00037429805,0.00091717934,0.0006278998,0.00076246023,0.00031674598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008927613,0.00038821498,0.023151817,0.0002308292,0.00018181861,0.00078308216,0.00015605806,0.16443521,0.07749923,0.0035956756,0.003450767,0.72523457],"study_design_scores_gemma":[0.000006251148,0.00007569305,0.0044269003,0.0000067325004,0.000015378359,0.00027221278,0.000023964081,0.981531,0.010741063,0.0023801927,0.0005123669,0.000008147593],"about_ca_topic_score_codex":0.004005487,"about_ca_topic_score_gemma":0.0034064048,"teacher_disagreement_score":0.004005487,"about_ca_system_score_codex":0.00060347235,"about_ca_system_score_gemma":0.0005222482,"threshold_uncertainty_score":0.007964373},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21466887,0.001390559,0.7731963,0.00044057367,0.0004868251,0.00012532534,0.00075548893,0.0059318095,0.0030043023],"genre_scores_gemma":[0.83702797,0.0005140759,0.15796012,0.00010851071,0.00021354856,0.00007193726,0.0010097475,0.00019920144,0.0028948353],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99846184,0.00015361876,0.00011699791,0.00037383244,0.0007554029,0.00013827252],"domain_scores_gemma":[0.9944641,0.0027600508,0.00091264234,0.0006063944,0.0010360151,0.00022085545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011872483,0.0010954186,0.001247792,0.0028092633,0.00034332063,0.0012032711,0.0015638259,0.001188883,0.0012223242],"category_scores_gemma":[0.0057295947,0.0003175121,0.00060738,0.0015244227,0.00045226672,0.0013897602,0.00074259494,0.0015644804,0.0008169782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009033993,0.0006008666,0.081908904,0.00039787934,0.00030752725,0.0011957607,0.00021088251,0.07300875,0.09177107,0.004943113,0.008242023,0.73650986],"study_design_scores_gemma":[0.000027743543,0.00044814232,0.019760903,0.000039558872,0.00009305919,0.0012167364,0.000081457074,0.9240497,0.04007488,0.010456234,0.0036929124,0.00005857193],"about_ca_topic_score_codex":0.0009097154,"about_ca_topic_score_gemma":0.0010316462,"teacher_disagreement_score":0.0028092633,"about_ca_system_score_codex":0.00037386405,"about_ca_system_score_gemma":0.0004396214,"threshold_uncertainty_score":0.0062788725},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6211252,0.0009844069,0.36542356,0.00018205061,0.00004708496,0.0002414453,0.00333435,0.0071971808,0.0014647472],"genre_scores_gemma":[0.9198592,0.00019000123,0.07483307,0.000030539584,0.000023659397,0.00012348674,0.004150942,0.000109081884,0.000680003],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992428,0.0001102212,0.00009010417,0.00021994588,0.00023090965,0.0001061724],"domain_scores_gemma":[0.99790466,0.00068008155,0.0004597679,0.00034167359,0.00046673705,0.00014718606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006969695,0.0008799814,0.00062061066,0.004618687,0.00042537396,0.0008296612,0.000848715,0.0005004377,0.0005478348],"category_scores_gemma":[0.0037633358,0.00025410551,0.00057857734,0.0023013547,0.00018771901,0.00082074,0.0005647401,0.0005159751,0.0003574368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008306415,0.001271202,0.16779532,0.00055298203,0.00033389675,0.0010187603,0.0009364039,0.09543083,0.090058684,0.0021582462,0.0064149573,0.633198],"study_design_scores_gemma":[0.000020884761,0.00021545199,0.041656822,0.000038536982,0.000056945817,0.00048518513,0.00031165357,0.9304706,0.021638367,0.0025531508,0.0025185132,0.00003402907],"about_ca_topic_score_codex":0.003796374,"about_ca_topic_score_gemma":0.0046085985,"teacher_disagreement_score":0.004618687,"about_ca_system_score_codex":0.00035824074,"about_ca_system_score_gemma":0.00076174416,"threshold_uncertainty_score":0.0075485706},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36222044,0.0059649367,0.5902701,0.008344803,0.0012874639,0.0003014403,0.0056284117,0.009898148,0.016084224],"genre_scores_gemma":[0.8483358,0.0007998366,0.13903934,0.0010244271,0.00037401903,0.0001332513,0.007896182,0.0006714482,0.0017257289],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98864704,0.003326264,0.0007018614,0.0020626846,0.0044087777,0.0008534198],"domain_scores_gemma":[0.9570329,0.02250331,0.0028419823,0.00831295,0.008035816,0.0012729607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009735506,0.0012850607,0.0012018844,0.0054039005,0.0015376323,0.0034957328,0.0023922022,0.002256417,0.0018107668],"category_scores_gemma":[0.058977336,0.0002281092,0.0009684257,0.0046700384,0.0020458263,0.0050617633,0.004203529,0.0024241344,0.0010643192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011593133,0.000666362,0.12882695,0.0010250484,0.00035849412,0.001113481,0.0013161335,0.060917635,0.016912501,0.05904476,0.05415917,0.6745002],"study_design_scores_gemma":[0.00005935907,0.00037056496,0.041399844,0.00023424323,0.00013540819,0.0030411445,0.0017780028,0.61994874,0.046219353,0.23211949,0.054548096,0.0001458718],"about_ca_topic_score_codex":0.002793816,"about_ca_topic_score_gemma":0.0035639175,"teacher_disagreement_score":0.009735506,"about_ca_system_score_codex":0.0019242238,"about_ca_system_score_gemma":0.0016217383,"threshold_uncertainty_score":0.05148691},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5759568,0.016338129,0.353699,0.0037963134,0.003064165,0.0014317138,0.009144226,0.01125334,0.025316393],"genre_scores_gemma":[0.8011522,0.0011883134,0.1703445,0.0015526393,0.0004461118,0.0003753562,0.018872526,0.001081742,0.0049865902],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9717889,0.0135290185,0.0018644237,0.0039550746,0.00784359,0.0010190296],"domain_scores_gemma":[0.94652146,0.02607887,0.0029624694,0.013860584,0.009101044,0.0014754803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022676185,0.0024886641,0.00192262,0.0035672416,0.001364295,0.0034169154,0.0031097697,0.004145805,0.0029749335],"category_scores_gemma":[0.07276268,0.0004642115,0.0013087104,0.0022355746,0.002168669,0.0044988156,0.0036049695,0.002821609,0.0020334327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008084238,0.0033601674,0.086244926,0.0036900032,0.0026272833,0.00090326066,0.00074329745,0.17075555,0.03606186,0.019170769,0.08271823,0.58564043],"study_design_scores_gemma":[0.00090103265,0.0054594036,0.037721083,0.0007383371,0.0007068983,0.003465535,0.0011500422,0.7914667,0.08726365,0.02352966,0.04730358,0.00029405346],"about_ca_topic_score_codex":0.0035899726,"about_ca_topic_score_gemma":0.004886957,"teacher_disagreement_score":0.022676185,"about_ca_system_score_codex":0.0016338418,"about_ca_system_score_gemma":0.0013719186,"threshold_uncertainty_score":0.119924545},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024535464,0.00016473391,0.972969,0.0002586909,0.000049507347,0.000021810849,0.0001198403,0.0006724448,0.0012084971],"genre_scores_gemma":[0.8415571,0.0002339942,0.15471946,0.00016991602,0.00006308966,0.000103329425,0.0005420905,0.00028790205,0.00232316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987197,0.00046635643,0.000065351625,0.00035212334,0.00028514513,0.000111353176],"domain_scores_gemma":[0.9951663,0.0027025775,0.00053682574,0.00065940956,0.00075359526,0.00018128844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00296675,0.0009931381,0.0012071484,0.0010712587,0.00054351625,0.0017924588,0.0027265355,0.002161603,0.0023674571],"category_scores_gemma":[0.017770624,0.001550847,0.0009901951,0.0011781865,0.0011849435,0.0033070915,0.0026733046,0.0036047623,0.0008009635],"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":[0.00007497359,0.000032620057,0.0010288274,0.00003110583,0.00004900705,0.000023170405,0.00003314231,0.95495266,0.0009647411,0.011059804,0.0006016446,0.031148262],"study_design_scores_gemma":[0.0000033200745,0.0000041679614,0.00014225557,0.0000040825,0.0000027290087,0.000007639853,0.000002151156,0.9913885,0.00024798408,0.008071037,0.000120531586,0.000005574307],"about_ca_topic_score_codex":0.006944239,"about_ca_topic_score_gemma":0.006898861,"teacher_disagreement_score":0.006944239,"about_ca_system_score_codex":0.0016766199,"about_ca_system_score_gemma":0.0016594108,"threshold_uncertainty_score":0.01568985},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06376649,0.0009907972,0.9277057,0.00029712747,0.00017315977,0.00004663951,0.0008226257,0.0044495035,0.0017480138],"genre_scores_gemma":[0.8039213,0.0006587791,0.18794341,0.00017734765,0.00013422339,0.000058750593,0.0029149142,0.0002875163,0.0039037308],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995177,0.00005850798,0.000023878836,0.00021573358,0.000102120335,0.000082116974],"domain_scores_gemma":[0.99918693,0.00025630754,0.000117996664,0.0001527149,0.00024408729,0.000041914496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007693435,0.0010591631,0.0007858913,0.0013408631,0.00028514062,0.00060300465,0.0015068365,0.00080420275,0.0013474213],"category_scores_gemma":[0.002539288,0.00038665897,0.0006990037,0.0009503057,0.00034065175,0.0010388601,0.00071569404,0.0013112432,0.0007999163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032352112,0.00019221278,0.009001481,0.000115550436,0.00014978244,0.000291502,0.00013688441,0.26462805,0.023718333,0.005859627,0.010364278,0.6852187],"study_design_scores_gemma":[0.0000023676446,0.000014643679,0.00061269593,0.000007413571,0.000010799981,0.000036006495,0.000006158622,0.99358,0.0029787403,0.0021620532,0.00058305723,0.0000060587254],"about_ca_topic_score_codex":0.014410752,"about_ca_topic_score_gemma":0.020807525,"teacher_disagreement_score":0.014410752,"about_ca_system_score_codex":0.00085460284,"about_ca_system_score_gemma":0.000798016,"threshold_uncertainty_score":0.02865374},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28507197,0.00080775353,0.70698893,0.00018040596,0.00015069703,0.000072386494,0.0002471015,0.0027923374,0.0036884353],"genre_scores_gemma":[0.9552771,0.00019974474,0.042835116,0.0000410343,0.000020405756,0.000022924369,0.00016739142,0.000042109838,0.0013943481],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999671,0.00002447545,0.000019501678,0.000084899555,0.00017201337,0.00002819389],"domain_scores_gemma":[0.99945074,0.00015176131,0.00012703179,0.00007189291,0.00017725867,0.000021302516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028868928,0.0004974951,0.00033153233,0.0013237238,0.00027292402,0.00048824883,0.00038789227,0.00040114,0.0009510068],"category_scores_gemma":[0.001162308,0.00014486721,0.00023107798,0.0005140057,0.00019880914,0.0006065068,0.00039758417,0.00034504195,0.00041613256],"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":[0.00052360044,0.0001907925,0.031384442,0.0002856788,0.00009516258,0.00049531006,0.00030242692,0.05766984,0.17752308,0.0013641445,0.0020216717,0.7281439],"study_design_scores_gemma":[0.000016021579,0.00033718356,0.03812952,0.000048154965,0.000055292458,0.00042661332,0.00018526193,0.89859444,0.05675099,0.0020691315,0.0033390538,0.00004834524],"about_ca_topic_score_codex":0.0012733071,"about_ca_topic_score_gemma":0.0019324853,"teacher_disagreement_score":0.0013237238,"about_ca_system_score_codex":0.0002743419,"about_ca_system_score_gemma":0.00021118065,"threshold_uncertainty_score":0.003181398},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03688632,0.0002410842,0.960303,0.00014555712,0.00014414822,0.00008263908,0.00008240377,0.0013120859,0.000802701],"genre_scores_gemma":[0.6997224,0.00018474617,0.29688635,0.000118546894,0.00010067797,0.000081079605,0.0002343661,0.0000603802,0.0026114266],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886346,0.00017767341,0.000086195476,0.00022698827,0.0004988166,0.00014693798],"domain_scores_gemma":[0.9982179,0.00035121365,0.00017540265,0.00046253885,0.00069974683,0.00009320273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010301429,0.00066938717,0.000951824,0.0010826534,0.00071326806,0.00074568996,0.0013803185,0.00075552333,0.0012012544],"category_scores_gemma":[0.0025216304,0.00025722143,0.00064173376,0.0009372601,0.0004818722,0.0017916501,0.0014174675,0.0014324329,0.00042898636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012085093,0.00038103215,0.0058339685,0.0001532054,0.00013569208,0.00037275508,0.00016506521,0.059002332,0.21810575,0.01731563,0.0041627837,0.69316316],"study_design_scores_gemma":[0.000017473358,0.00023829385,0.0017003033,0.000008290442,0.00004117397,0.00033027364,0.000021949421,0.94947803,0.04018565,0.00534023,0.0026055868,0.000032744992],"about_ca_topic_score_codex":0.0012402702,"about_ca_topic_score_gemma":0.0018104289,"teacher_disagreement_score":0.0013803185,"about_ca_system_score_codex":0.00050500437,"about_ca_system_score_gemma":0.00088793825,"threshold_uncertainty_score":0.0054479837},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48070696,0.001470261,0.50369084,0.0011132538,0.0003086501,0.00017738235,0.00078415376,0.0022916757,0.009456779],"genre_scores_gemma":[0.9567883,0.00041602895,0.040298387,0.000062829604,0.00005372677,0.000062809064,0.00048354326,0.000015172044,0.0018192761],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996637,0.000084479114,0.000030204226,0.00008244759,0.00008928698,0.000049940267],"domain_scores_gemma":[0.99938786,0.00029130347,0.00008463408,0.000033895318,0.00017820582,0.000024110981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000664365,0.0005961478,0.00053836446,0.0016384138,0.00034704112,0.00090365554,0.0006113942,0.00069049536,0.0010389818],"category_scores_gemma":[0.0019894852,0.0001886776,0.00044099704,0.0010343776,0.00018966226,0.0009030096,0.00038926565,0.00066880655,0.00031213276],"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":[0.0004067403,0.00037785596,0.029722106,0.00012559113,0.0001939603,0.00025413657,0.00007887148,0.71289676,0.0031093804,0.0019256012,0.003211151,0.24769782],"study_design_scores_gemma":[0.000002226413,0.000017091785,0.0008232063,0.0000052462733,0.000008142661,0.000011636691,0.000008744935,0.99813336,0.00043549042,0.00040817985,0.0001433528,0.0000033113306],"about_ca_topic_score_codex":0.0074285488,"about_ca_topic_score_gemma":0.004749796,"teacher_disagreement_score":0.0074285488,"about_ca_system_score_codex":0.00066204026,"about_ca_system_score_gemma":0.0005053149,"threshold_uncertainty_score":0.014770627},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007567825,0.00021776021,0.98903286,0.00025293606,0.000032470398,0.000024786184,0.00019660936,0.0014318232,0.0012428642],"genre_scores_gemma":[0.6130246,0.00056458387,0.37870434,0.0003678294,0.000082553706,0.00013704246,0.0016258829,0.00044788214,0.0050452715],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943,0.00015014311,0.000020003888,0.00021691178,0.00012717058,0.000055833392],"domain_scores_gemma":[0.99888676,0.0005178798,0.00012604911,0.00023386501,0.00015624432,0.000079158875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006662666,0.0010097486,0.00081429916,0.0012516285,0.00059427205,0.0009390979,0.0017286506,0.0011905171,0.0028964148],"category_scores_gemma":[0.0036255557,0.00048884284,0.00086959364,0.0010619239,0.0009276898,0.0025490108,0.0018651055,0.0017001057,0.0008224146],"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":[0.00009666761,0.000084531406,0.0013555778,0.00010002907,0.00007666286,0.00011336996,0.00015254096,0.81726986,0.0026072343,0.056359354,0.0053472696,0.116436936],"study_design_scores_gemma":[0.000004278001,0.000011563565,0.00009203417,0.000005043256,0.000006388687,0.000012597293,0.000012414746,0.9536878,0.0005714709,0.04452921,0.0010616424,0.000005674461],"about_ca_topic_score_codex":0.017082883,"about_ca_topic_score_gemma":0.019069484,"teacher_disagreement_score":0.017082883,"about_ca_system_score_codex":0.0015682568,"about_ca_system_score_gemma":0.001437691,"threshold_uncertainty_score":0.0339669},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14611009,0.00035322038,0.8505699,0.00053015485,0.000047345307,0.00003052264,0.000053585038,0.00056554755,0.0017396366],"genre_scores_gemma":[0.92325103,0.00007663287,0.07555834,0.000087925655,0.000037393605,0.000026688243,0.00005035834,0.00002522954,0.0008863136],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981876,0.0006773887,0.00009985492,0.00037881592,0.00044451273,0.00021172859],"domain_scores_gemma":[0.9927672,0.004133814,0.00066579663,0.001145598,0.0010862795,0.00020120325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00621869,0.0008513137,0.0012994814,0.0005971472,0.0005758807,0.0015147661,0.0017968863,0.0019562738,0.0010989086],"category_scores_gemma":[0.014092033,0.0004548023,0.00049097557,0.0005283817,0.0009522539,0.0030293984,0.002470005,0.0022626463,0.00040891927],"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":[0.0004310893,0.00023037718,0.004248872,0.000094818664,0.00011636908,0.00012508033,0.00022588941,0.7896066,0.010809096,0.016060527,0.00080408686,0.17724723],"study_design_scores_gemma":[0.000002694025,0.000033074954,0.00015993677,0.0000037280895,0.000006411004,0.000010976552,0.0000053220815,0.9943362,0.0018335406,0.003497515,0.000105706196,0.0000049263936],"about_ca_topic_score_codex":0.0017855921,"about_ca_topic_score_gemma":0.0022256973,"teacher_disagreement_score":0.00621869,"about_ca_system_score_codex":0.001000097,"about_ca_system_score_gemma":0.0009606818,"threshold_uncertainty_score":0.032887936},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21481429,0.001551354,0.7743457,0.0006689947,0.00022787755,0.00010308761,0.0004943564,0.005379631,0.0024146927],"genre_scores_gemma":[0.82454413,0.00048318357,0.16973804,0.0003119138,0.000098580495,0.00008173217,0.0010268346,0.00026864838,0.0034468863],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99829584,0.0002753968,0.000102803795,0.0004677336,0.00061168824,0.0002465434],"domain_scores_gemma":[0.9957956,0.0018901193,0.0005663138,0.0006071206,0.00096664886,0.0001741164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024747516,0.0018179502,0.0013891615,0.0018010444,0.0005968687,0.0014803087,0.0018584365,0.0018024619,0.0010700332],"category_scores_gemma":[0.007369752,0.00047457666,0.0007092887,0.0012179473,0.0010180809,0.0025173414,0.0019723424,0.0028938267,0.00082543254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00094501965,0.0006590154,0.04041666,0.0002779003,0.00025198935,0.0004096394,0.00021146603,0.13756499,0.04175455,0.004048988,0.0072233323,0.7662366],"study_design_scores_gemma":[0.000013572048,0.00009653259,0.0038202745,0.00002666788,0.000028746554,0.00013864787,0.000042887095,0.96706396,0.023475455,0.0041798376,0.0010942917,0.00001905744],"about_ca_topic_score_codex":0.0033447915,"about_ca_topic_score_gemma":0.0053819483,"teacher_disagreement_score":0.0033447915,"about_ca_system_score_codex":0.0013246994,"about_ca_system_score_gemma":0.0008156558,"threshold_uncertainty_score":0.013087928},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013521058,0.000049234764,0.9982406,0.000023311413,0.000011187659,0.00000701078,0.00001865701,0.000112413,0.00018544927],"genre_scores_gemma":[0.054421313,0.00022747937,0.9424152,0.000092416936,0.000058708498,0.0000954248,0.0003533913,0.0001445613,0.0021914407],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995425,0.00009833197,0.000025003717,0.00009128343,0.00020866746,0.000034311655],"domain_scores_gemma":[0.99950683,0.00015378541,0.00005213716,0.000116240626,0.000143457,0.000027450256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005019686,0.0005680713,0.00067552057,0.00059818,0.00025584074,0.00046836422,0.001080851,0.00072429795,0.0016031701],"category_scores_gemma":[0.001672747,0.00033627392,0.00055011513,0.00076770084,0.00056175416,0.0009249031,0.0011506397,0.0012336158,0.001108454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014449633,0.00009769925,0.00082924304,0.0002516269,0.00009578947,0.00013344928,0.00017717776,0.18004729,0.09619542,0.029094052,0.0047208695,0.68821293],"study_design_scores_gemma":[0.000014322573,0.00007561324,0.0002486378,0.000014031946,0.000011477886,0.00013503655,0.000017011622,0.97259766,0.014459095,0.004771631,0.0076332046,0.000022249764],"about_ca_topic_score_codex":0.0015344416,"about_ca_topic_score_gemma":0.0020546105,"teacher_disagreement_score":0.0016031701,"about_ca_system_score_codex":0.0002359445,"about_ca_system_score_gemma":0.0007376549,"threshold_uncertainty_score":0.0053631067},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0074824323,0.00021672763,0.9906461,0.00012374586,0.000034900193,0.000037154183,0.000121119534,0.000794593,0.00054320955],"genre_scores_gemma":[0.49739835,0.0006216622,0.49622473,0.00023168643,0.0002975727,0.0002494281,0.00090485736,0.0004355541,0.0036361695],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989882,0.0002768979,0.000049705985,0.00027167174,0.00032428914,0.00008931127],"domain_scores_gemma":[0.99585944,0.0020894061,0.0004392282,0.0005488866,0.0008481591,0.00021495241],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022003434,0.00076940696,0.0011559933,0.0015589136,0.0005226409,0.0015555959,0.0019450985,0.0012245949,0.0039919973],"category_scores_gemma":[0.013805378,0.00038146405,0.00083055854,0.001635606,0.0009824295,0.002516299,0.0021084535,0.0022697127,0.0014559978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003719377,0.00014993399,0.00622072,0.00031023225,0.00011478835,0.00023758538,0.00025224808,0.3219366,0.009474137,0.0811592,0.007870682,0.571902],"study_design_scores_gemma":[0.0000071563995,0.000030747622,0.00072102447,0.000009011783,0.000006950596,0.000058048354,0.0000143180205,0.9682892,0.0016616927,0.027995182,0.0011923551,0.000014280602],"about_ca_topic_score_codex":0.0018996955,"about_ca_topic_score_gemma":0.0016547982,"teacher_disagreement_score":0.0039919973,"about_ca_system_score_codex":0.0009144499,"about_ca_system_score_gemma":0.0007343961,"threshold_uncertainty_score":0.01335454},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040302355,0.0005562611,0.9542471,0.00025965233,0.0001314804,0.000050208128,0.00063152344,0.003001685,0.0008197003],"genre_scores_gemma":[0.60592914,0.00063893147,0.3819191,0.0002313946,0.00015170165,0.00015211118,0.0039756373,0.0003703431,0.0066317418],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995327,0.00010302348,0.000043579603,0.00014500234,0.000115245115,0.0000604567],"domain_scores_gemma":[0.9988696,0.00047639647,0.000116658324,0.00022918358,0.0002665405,0.000041561074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004490915,0.0010804088,0.00061747717,0.0013723451,0.0003106816,0.0006205358,0.0009352984,0.00080538343,0.0018911698],"category_scores_gemma":[0.002855878,0.00041055356,0.0006237532,0.0013472454,0.00046209278,0.0020418065,0.0009815267,0.00159465,0.0010213602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030686878,0.0002385993,0.0037529855,0.00017781343,0.00013749351,0.00022677991,0.00019219781,0.30410355,0.011042179,0.008298126,0.005896262,0.66562706],"study_design_scores_gemma":[0.0000051499524,0.000037688118,0.00029309533,0.0000062674285,0.000007868999,0.00003725174,0.000019637626,0.9921604,0.0020521837,0.004590403,0.0007813536,0.000008699994],"about_ca_topic_score_codex":0.0077179926,"about_ca_topic_score_gemma":0.010703403,"teacher_disagreement_score":0.0077179926,"about_ca_system_score_codex":0.00048856495,"about_ca_system_score_gemma":0.0007410633,"threshold_uncertainty_score":0.0153461695},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.070025116,0.0011560931,0.92066765,0.0002967479,0.00019902145,0.00005288391,0.00087356323,0.004888673,0.001840272],"genre_scores_gemma":[0.8128962,0.0007025622,0.17896362,0.00018990955,0.00013055815,0.00006135224,0.0028735208,0.00027074886,0.0039115916],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999582,0.000045288813,0.000021089614,0.00018519726,0.00009006577,0.000076384626],"domain_scores_gemma":[0.99929464,0.00021656204,0.00010340089,0.000117731855,0.00023019296,0.00003747254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070003234,0.0010910924,0.00079220725,0.0012527591,0.00027026623,0.0005565091,0.0014394863,0.00075170887,0.0013023999],"category_scores_gemma":[0.0022234821,0.0003656092,0.00067685323,0.0008876235,0.00030792505,0.00096172316,0.00061968213,0.0012589553,0.0007443243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000324349,0.00019131682,0.008348589,0.000119837365,0.00015041785,0.0002795494,0.0001227609,0.26190394,0.025530372,0.004555642,0.009733692,0.6887394],"study_design_scores_gemma":[0.000002607317,0.000016921773,0.00066002726,0.000007940793,0.000012045102,0.000037670343,0.0000060733046,0.9937715,0.0032706768,0.0016350264,0.0005728811,0.0000065926606],"about_ca_topic_score_codex":0.0154861165,"about_ca_topic_score_gemma":0.02273468,"teacher_disagreement_score":0.0154861165,"about_ca_system_score_codex":0.00081158045,"about_ca_system_score_gemma":0.0008112735,"threshold_uncertainty_score":0.030791938},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004790745,0.00009540744,0.993284,0.00012694846,0.000021361147,0.000031163683,0.0000425534,0.0010363678,0.0005714534],"genre_scores_gemma":[0.6288752,0.0004323129,0.3661299,0.00014024535,0.000096095144,0.0001762503,0.00035041862,0.00018678766,0.0036128003],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996734,0.0012135132,0.0001868238,0.0006011911,0.0010522986,0.0002123026],"domain_scores_gemma":[0.99529195,0.0023744504,0.00043656526,0.0009683665,0.00082942646,0.00009930182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002175614,0.00091974204,0.00095286325,0.0015413138,0.0005843588,0.0014848716,0.0013852431,0.0010548612,0.0018531671],"category_scores_gemma":[0.009332694,0.0004423125,0.001430611,0.0012205051,0.000895036,0.0028115257,0.0015283207,0.0021842394,0.0010842351],"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":[0.000339842,0.00027520952,0.008862151,0.0001621419,0.0002447944,0.00034316487,0.00070580305,0.5079045,0.013149352,0.086909495,0.0047288653,0.37637466],"study_design_scores_gemma":[0.000002712606,0.00001855886,0.0002801918,0.000005198594,0.000008913263,0.000056157776,0.000017906958,0.9846444,0.0014414074,0.012500923,0.0010127347,0.000010856603],"about_ca_topic_score_codex":0.004180834,"about_ca_topic_score_gemma":0.0028238841,"teacher_disagreement_score":0.004180834,"about_ca_system_score_codex":0.0010784166,"about_ca_system_score_gemma":0.000899075,"threshold_uncertainty_score":0.011505902},"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":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033987702,0.29921332,0.6869498,0.0022672345,0.00078308856,0.000099276185,0.00028643233,0.0006344076,0.0063677034],"genre_scores_gemma":[0.08622466,0.37858313,0.52302253,0.0016563869,0.004176804,0.00032020127,0.0014145572,0.00026294516,0.004338763],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99660695,0.00089049316,0.00038287765,0.00053355633,0.001487218,0.00009890077],"domain_scores_gemma":[0.99531037,0.0029021087,0.00020492576,0.00048067328,0.0010389783,0.00006289591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004280842,0.0011906486,0.002780778,0.0040754215,0.00069982524,0.0030819452,0.0020359987,0.0017485897,0.002172937],"category_scores_gemma":[0.0091462005,0.00066745793,0.0016044229,0.008700451,0.0008443459,0.004314606,0.0018938052,0.002108305,0.001565586],"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":[0.00007520426,0.000113627095,0.0013211448,0.0025269473,0.00021120482,0.00006024396,0.00008955597,0.0131972125,0.0015671815,0.035093114,0.009830024,0.9359146],"study_design_scores_gemma":[0.000043843804,0.00040399842,0.004045739,0.0022159347,0.00039503365,0.0010666372,0.00028586862,0.37080604,0.011630583,0.25929657,0.3495995,0.00021026107],"about_ca_topic_score_codex":0.0015832243,"about_ca_topic_score_gemma":0.0011729326,"teacher_disagreement_score":0.004280842,"about_ca_system_score_codex":0.00096855895,"about_ca_system_score_gemma":0.0013364995,"threshold_uncertainty_score":0.022639513},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12210953,0.00031505068,0.87433636,0.00048300286,0.000095732066,0.00006210968,0.00012997416,0.0013509124,0.00111725],"genre_scores_gemma":[0.92419595,0.00014785264,0.072028436,0.0002139293,0.000076180986,0.000070621456,0.00036980028,0.000073391704,0.0028237905],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999501,0.00017252342,0.0000177538,0.00013583763,0.00011188274,0.000061007544],"domain_scores_gemma":[0.9982589,0.0011206735,0.00017509112,0.00018843942,0.0002001261,0.000056790228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014208502,0.00079890504,0.00071245176,0.00056512456,0.00029249422,0.000536622,0.0009860806,0.0007843725,0.00081819476],"category_scores_gemma":[0.003270359,0.0003272135,0.0005120119,0.00043766454,0.00079691183,0.0010880854,0.00092017854,0.0017343285,0.0003606425],"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":[0.0002466944,0.00012531108,0.0034717482,0.00003066005,0.000054065094,0.000113343085,0.00008603581,0.8937425,0.0053314483,0.0057293903,0.0015455493,0.08952324],"study_design_scores_gemma":[0.0000010223652,0.000009994114,0.000097285716,9.159678e-7,0.0000017065595,0.000008056978,0.0000019914175,0.9984824,0.0004425356,0.00088759646,0.00006482702,0.0000016158875],"about_ca_topic_score_codex":0.003204327,"about_ca_topic_score_gemma":0.0035534427,"teacher_disagreement_score":0.003204327,"about_ca_system_score_codex":0.00078128086,"about_ca_system_score_gemma":0.00046457048,"threshold_uncertainty_score":0.007514298},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006926436,0.00028302835,0.9918399,0.00012630256,0.000031713498,0.000014062315,0.0000462557,0.0005228734,0.00020946465],"genre_scores_gemma":[0.2847049,0.00055119663,0.7082675,0.00019563164,0.000098798206,0.00015626504,0.0005833454,0.00034355908,0.005098837],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931026,0.00022004088,0.00004612079,0.00017076454,0.00019087859,0.00006201887],"domain_scores_gemma":[0.9984224,0.0006336805,0.00015876452,0.00021828477,0.0005141474,0.00005266576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001456061,0.0006565959,0.0012430033,0.0005910768,0.00037121147,0.0006885736,0.0017775851,0.0013782476,0.0011178949],"category_scores_gemma":[0.0033297308,0.00042837526,0.0010789622,0.0009601452,0.00061346573,0.0009821426,0.0013467586,0.0019442627,0.00063023384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026434817,0.00022090961,0.0017280732,0.00021698668,0.00019377169,0.00010527089,0.00017378844,0.3833468,0.015895579,0.014590548,0.006599066,0.57666487],"study_design_scores_gemma":[0.0000022151362,0.000008877208,0.00013864058,0.0000027487797,0.0000044337785,0.000012292664,0.0000041906965,0.9971072,0.0006841353,0.0017888366,0.0002423912,0.0000041736384],"about_ca_topic_score_codex":0.0053960024,"about_ca_topic_score_gemma":0.005662944,"teacher_disagreement_score":0.0053960024,"about_ca_system_score_codex":0.00059312984,"about_ca_system_score_gemma":0.0015648915,"threshold_uncertainty_score":0.010729194},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021874472,0.00021574601,0.94530636,0.0010025179,0.00011217933,0.00016538761,0.0003094731,0.028338276,0.002675591],"genre_scores_gemma":[0.42155445,0.0002781039,0.5728768,0.00042429887,0.00009965083,0.00017710228,0.0011796548,0.00068263814,0.002727194],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983949,0.00037433466,0.00013889496,0.00042284248,0.00051048433,0.00015850356],"domain_scores_gemma":[0.99717677,0.0007699439,0.00020205973,0.0007010142,0.00089566637,0.00025446704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021929627,0.000996116,0.00091439195,0.0020728991,0.0011907563,0.003038751,0.0021221864,0.0013703092,0.0029843424],"category_scores_gemma":[0.0056679384,0.0004677592,0.00087859394,0.0018007854,0.0010354661,0.0032919906,0.0026842465,0.0015238733,0.0011702619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012965354,0.00083397777,0.020049335,0.00042969722,0.00035137753,0.0013053379,0.0018589402,0.27148935,0.027927527,0.08513191,0.023707498,0.5656185],"study_design_scores_gemma":[0.000022726374,0.00009443161,0.0006812073,0.000030509027,0.000047099616,0.00015206843,0.0001419632,0.9496184,0.0075198216,0.029584492,0.012056226,0.000051045765],"about_ca_topic_score_codex":0.017617952,"about_ca_topic_score_gemma":0.013416713,"teacher_disagreement_score":0.017617952,"about_ca_system_score_codex":0.001643958,"about_ca_system_score_gemma":0.0019687,"threshold_uncertainty_score":0.035030782},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007844717,0.00011795851,0.99061245,0.000085179105,0.000020501595,0.000015444572,0.00004297559,0.00074711256,0.00051360606],"genre_scores_gemma":[0.28879362,0.00027501758,0.70330524,0.00022603566,0.000054761444,0.000094843155,0.00064241234,0.0004935971,0.006114431],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939096,0.00012673112,0.000030294288,0.00023391635,0.00014927945,0.000068818845],"domain_scores_gemma":[0.99899787,0.0002636128,0.00011774594,0.0002799309,0.0002699916,0.00007083589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009320643,0.0009439488,0.0012201112,0.00093080016,0.00054791046,0.0011111944,0.0023383484,0.0016706351,0.0020005628],"category_scores_gemma":[0.002419804,0.00090682483,0.0009874324,0.0010814097,0.0010135904,0.0020562704,0.0020946346,0.0022037935,0.0012942244],"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":[0.000114473245,0.00007597564,0.0010651372,0.000080613296,0.000095691765,0.00006646441,0.0001726598,0.6397639,0.011460634,0.017773932,0.0034773173,0.32585314],"study_design_scores_gemma":[0.0000023621708,0.000008461268,0.00007223112,0.00000428229,0.0000031872542,0.000014564179,0.000008554869,0.9939173,0.0014841324,0.0040397495,0.00044017762,0.000004940924],"about_ca_topic_score_codex":0.008305058,"about_ca_topic_score_gemma":0.009863133,"teacher_disagreement_score":0.008305058,"about_ca_system_score_codex":0.0012427014,"about_ca_system_score_gemma":0.0011643032,"threshold_uncertainty_score":0.016513467},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37574884,0.007575028,0.5718232,0.0079051275,0.0015778261,0.00036017597,0.00628618,0.011467759,0.017255936],"genre_scores_gemma":[0.8395464,0.0009064635,0.14591722,0.0010854143,0.00038442673,0.00014525412,0.009346137,0.0007542554,0.0019144717],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98906446,0.0030524696,0.0007146394,0.0021702414,0.0041436297,0.0008544978],"domain_scores_gemma":[0.95955616,0.021444052,0.0024941918,0.0076728985,0.007576111,0.0012565276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009525801,0.0015254493,0.0012594975,0.0051630535,0.0015048233,0.0035434444,0.0024605913,0.002392497,0.0019564675],"category_scores_gemma":[0.058565296,0.00023119147,0.0010223945,0.0041577006,0.0020206624,0.005030965,0.004297296,0.0023788163,0.0011966246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012898672,0.00071118993,0.106443614,0.0012057297,0.00038500148,0.0010518829,0.0010682279,0.064611174,0.017806042,0.04206183,0.055338785,0.70802665],"study_design_scores_gemma":[0.00006870034,0.0004845301,0.040122002,0.00028663274,0.00015719561,0.0030898973,0.0017305516,0.6600865,0.051869508,0.18693435,0.055009577,0.00016049333],"about_ca_topic_score_codex":0.0030439065,"about_ca_topic_score_gemma":0.004083475,"teacher_disagreement_score":0.009525801,"about_ca_system_score_codex":0.0018724415,"about_ca_system_score_gemma":0.001649597,"threshold_uncertainty_score":0.050377905},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013514984,0.0002641844,0.98158914,0.00020892748,0.00008025765,0.00012001861,0.00007447237,0.0014983426,0.002649632],"genre_scores_gemma":[0.7405849,0.0003187519,0.25135982,0.0006820629,0.00010483585,0.00030601624,0.0004042717,0.00036265684,0.005876716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878925,0.000387753,0.00004293469,0.00029665395,0.00033401736,0.00014940958],"domain_scores_gemma":[0.9983651,0.0008257117,0.00014886989,0.00041936597,0.00015516572,0.000085689986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015051936,0.0015837792,0.0009924354,0.00054172624,0.00044314598,0.00072682725,0.0017184354,0.0012415556,0.0021690526],"category_scores_gemma":[0.004285979,0.00036999927,0.00065526686,0.00037614684,0.001527157,0.0016293151,0.002495226,0.002743182,0.00065178704],"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":[0.0002883146,0.00017003198,0.0015129212,0.00013910465,0.00012842621,0.00021720579,0.00010024045,0.7283475,0.01983527,0.031867616,0.009949785,0.20744368],"study_design_scores_gemma":[0.000009213117,0.00005503216,0.00015671612,0.000007353334,0.000008646582,0.000091037444,0.000009465444,0.983933,0.005265139,0.009200565,0.001254607,0.000009227062],"about_ca_topic_score_codex":0.001142493,"about_ca_topic_score_gemma":0.0013451338,"teacher_disagreement_score":0.0021690526,"about_ca_system_score_codex":0.00086445664,"about_ca_system_score_gemma":0.0006908473,"threshold_uncertainty_score":0.0079603195},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27960277,0.0006961843,0.71492237,0.00027803972,0.00010052813,0.000051532283,0.00015282292,0.0026362697,0.0015595126],"genre_scores_gemma":[0.92874885,0.00025132307,0.06927964,0.000054679607,0.00002961167,0.000020543215,0.00020844537,0.000034878634,0.0013720865],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947697,0.00006607007,0.000034544024,0.00014439179,0.00019069064,0.000087460874],"domain_scores_gemma":[0.9993235,0.00017056448,0.00012112621,0.000051942196,0.0002821269,0.000050795847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005446938,0.0005425096,0.00070409756,0.0015836639,0.00028691153,0.00028404174,0.00067413686,0.0005168523,0.000698435],"category_scores_gemma":[0.001449317,0.0002744149,0.00045252524,0.00056567043,0.00031564297,0.0006413693,0.0006585157,0.00058128306,0.00022284302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033810193,0.00036648972,0.026605027,0.00010184019,0.00010292063,0.0004045983,0.00012240803,0.14399591,0.035244714,0.0015662087,0.0026124574,0.7885393],"study_design_scores_gemma":[0.000004958114,0.000064738655,0.005602846,0.0000079931315,0.000021358297,0.0001141794,0.00001589771,0.98405725,0.008765546,0.0009716593,0.0003635746,0.000009993568],"about_ca_topic_score_codex":0.0042086537,"about_ca_topic_score_gemma":0.0046804673,"teacher_disagreement_score":0.0042086537,"about_ca_system_score_codex":0.0004461986,"about_ca_system_score_gemma":0.0006675371,"threshold_uncertainty_score":0.008368313},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026867665,0.0007049102,0.9597506,0.000634232,0.00014915687,0.000088725166,0.00078631745,0.007798684,0.003219678],"genre_scores_gemma":[0.29565546,0.0010815893,0.68960893,0.0004702858,0.0001781321,0.0002300161,0.0035854105,0.000498956,0.0086911535],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993734,0.00009443845,0.000044721422,0.00020272126,0.00020343454,0.000081337384],"domain_scores_gemma":[0.99898845,0.00023108613,0.00010150665,0.00020431785,0.00040955545,0.000065088345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010232184,0.0015328432,0.001016919,0.0022693824,0.00056411524,0.0016466295,0.0014748753,0.001011207,0.0018961331],"category_scores_gemma":[0.003068693,0.00079864776,0.000752037,0.0020228184,0.00055394345,0.0025602581,0.0027478011,0.0018858262,0.0020344956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002552252,0.00033305466,0.009611162,0.00026279065,0.00018258307,0.00017226976,0.00017560717,0.15108117,0.037612334,0.00874912,0.018229023,0.77333564],"study_design_scores_gemma":[0.0000101715295,0.000041927826,0.0016369408,0.000033893815,0.000014628995,0.000031485193,0.00005553634,0.96897304,0.0070629874,0.017588185,0.0045337426,0.000017515446],"about_ca_topic_score_codex":0.0055003436,"about_ca_topic_score_gemma":0.009980983,"teacher_disagreement_score":0.0055003436,"about_ca_system_score_codex":0.000653077,"about_ca_system_score_gemma":0.0012976694,"threshold_uncertainty_score":0.010936677},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10109151,0.00051251554,0.8918868,0.00022259293,0.00007948052,0.000065109525,0.00023373213,0.004757629,0.0011507453],"genre_scores_gemma":[0.804106,0.00027453146,0.19015183,0.000193792,0.00009446136,0.00011046959,0.001407538,0.00018032832,0.0034811858],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991604,0.00017461546,0.000056207748,0.00031022474,0.00019242427,0.00010600361],"domain_scores_gemma":[0.9967777,0.0016739297,0.000289349,0.00042678489,0.000746518,0.00008575633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012128829,0.0011560203,0.0011219821,0.00085404643,0.0003089286,0.00072661805,0.0014487671,0.001069942,0.00082775805],"category_scores_gemma":[0.0032189633,0.00065247377,0.0008578439,0.0005453263,0.00060760323,0.0009860623,0.0009099182,0.001629291,0.00057278323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003498199,0.00034684624,0.004115644,0.0001582247,0.00027537148,0.00024995793,0.0001398663,0.4654917,0.02739578,0.0019813552,0.00361645,0.4958789],"study_design_scores_gemma":[0.0000035105563,0.000021636524,0.00046074265,0.000004867979,0.000006926273,0.000027150343,0.000005046969,0.9961463,0.00239887,0.0007569523,0.0001636019,0.0000044130275],"about_ca_topic_score_codex":0.0064424197,"about_ca_topic_score_gemma":0.008653651,"teacher_disagreement_score":0.0064424197,"about_ca_system_score_codex":0.0006684503,"about_ca_system_score_gemma":0.00091430213,"threshold_uncertainty_score":0.012809873},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04492487,0.0015159837,0.9378233,0.0003991817,0.000744372,0.00026735006,0.0012774314,0.010317083,0.0027304573],"genre_scores_gemma":[0.21096048,0.0008016787,0.7628498,0.00027537646,0.0006709643,0.00028192738,0.0064913314,0.0014652162,0.016203236],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99760675,0.00043518347,0.00022620405,0.0006466948,0.00084314495,0.0002420295],"domain_scores_gemma":[0.9950106,0.001178366,0.0002701666,0.0007662555,0.0026003646,0.00017418069],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016303741,0.001624854,0.0019016793,0.005627775,0.0011170002,0.0018383267,0.0018898103,0.0016185506,0.005779833],"category_scores_gemma":[0.005276912,0.00047046418,0.0017004577,0.004767381,0.0004988472,0.0020158053,0.0016083461,0.0012017373,0.00891247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007091426,0.00029384272,0.0018630272,0.00028101818,0.00015650998,0.00012337916,0.00014577084,0.009862997,0.08500658,0.0013592212,0.014447407,0.8857511],"study_design_scores_gemma":[0.0000672474,0.00035526272,0.005444705,0.0000394145,0.00022999979,0.0004366994,0.00020787312,0.8962077,0.07678062,0.005611341,0.014536538,0.00008258534],"about_ca_topic_score_codex":0.004705004,"about_ca_topic_score_gemma":0.0075626504,"teacher_disagreement_score":0.005779833,"about_ca_system_score_codex":0.0005069009,"about_ca_system_score_gemma":0.0013906758,"threshold_uncertainty_score":0.019335449},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2793468,0.002339739,0.7085576,0.0007784153,0.0003617044,0.00019424985,0.0013668064,0.004714131,0.0023405533],"genre_scores_gemma":[0.82337886,0.0006501797,0.17020148,0.00013819119,0.00008582663,0.00014496254,0.0033697297,0.0003477905,0.0016829192],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99670273,0.0018199733,0.00020030448,0.0005165301,0.00057737716,0.00018311039],"domain_scores_gemma":[0.976534,0.019783635,0.0005475166,0.0012766536,0.0015226565,0.00033554187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006758574,0.0010202583,0.001208807,0.0012583003,0.0006238504,0.0011914427,0.0017090995,0.0017068564,0.0018395459],"category_scores_gemma":[0.024678579,0.0007188562,0.00093555293,0.0008413811,0.0005497917,0.0022365851,0.0010891752,0.0020935396,0.00063094357],"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":[0.000996586,0.00042359348,0.0077576456,0.00024171897,0.00029362526,0.0000799072,0.0001263611,0.7697497,0.0026294396,0.006474173,0.0046007396,0.20662652],"study_design_scores_gemma":[0.000010870435,0.000031578867,0.0002314884,0.0000062571944,0.000009471384,0.000007587578,0.000006485662,0.9976382,0.00052924897,0.0014206041,0.00010441874,0.0000038972044],"about_ca_topic_score_codex":0.016493535,"about_ca_topic_score_gemma":0.0153313065,"teacher_disagreement_score":0.016493535,"about_ca_system_score_codex":0.0013934446,"about_ca_system_score_gemma":0.0017212188,"threshold_uncertainty_score":0.035743177},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00707631,0.0005785033,0.96509504,0.0010655094,0.00015293284,0.00029066482,0.007166158,0.011688256,0.00688666],"genre_scores_gemma":[0.15619634,0.00061912555,0.83114547,0.00031271487,0.0001193448,0.0009712473,0.0060543017,0.0015685539,0.0030128758],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953949,0.002505623,0.00033412097,0.0006408177,0.0009316287,0.00019293341],"domain_scores_gemma":[0.981354,0.012177475,0.0011979458,0.0015956091,0.0031466007,0.00052840554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00841786,0.0017681085,0.001167067,0.005944796,0.0013966464,0.0063933195,0.0023577048,0.0016240337,0.017695108],"category_scores_gemma":[0.051806044,0.0009122485,0.0013810805,0.004438316,0.0009617877,0.0061610923,0.0034312473,0.0027993931,0.0036430485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014199069,0.00045862896,0.019011501,0.0014193748,0.00040444697,0.00033518317,0.007952857,0.07203226,0.004809426,0.116562754,0.10980305,0.6657907],"study_design_scores_gemma":[0.00013482418,0.00012500132,0.0047099255,0.00056445476,0.000085637126,0.00018711222,0.0022433517,0.6369975,0.004150694,0.26405782,0.086524405,0.00021920272],"about_ca_topic_score_codex":0.019754708,"about_ca_topic_score_gemma":0.031495444,"teacher_disagreement_score":0.019754708,"about_ca_system_score_codex":0.0017947725,"about_ca_system_score_gemma":0.0022528535,"threshold_uncertainty_score":0.059195995},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015923629,0.00053248554,0.9788891,0.00011453367,0.00006484936,0.00012316355,0.00043268315,0.0018686965,0.0020508084],"genre_scores_gemma":[0.30676225,0.00056005915,0.6839237,0.00022853941,0.00016954931,0.00032316527,0.0033706285,0.00030483812,0.004357326],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873465,0.00019858511,0.0000781372,0.0003398123,0.00046859297,0.00018021284],"domain_scores_gemma":[0.99860746,0.0005620306,0.000118151576,0.00013380645,0.0005114817,0.00006697162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009899295,0.0010530063,0.0013152123,0.002615068,0.0011658598,0.0010251445,0.0016931873,0.0011134907,0.002930125],"category_scores_gemma":[0.0034954606,0.0004429929,0.0014017678,0.0022070305,0.00051098777,0.0016180804,0.0011699799,0.0011202995,0.0017905113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006197171,0.00026882713,0.004995089,0.00034230054,0.00017563172,0.00018583625,0.00017810207,0.37109613,0.008432924,0.008600291,0.011372867,0.5937323],"study_design_scores_gemma":[0.000018633224,0.000035740202,0.0005853268,0.000015580581,0.000022698754,0.00007503769,0.000029610233,0.98971224,0.0018729125,0.005537948,0.0020817434,0.000012660486],"about_ca_topic_score_codex":0.008515827,"about_ca_topic_score_gemma":0.012411529,"teacher_disagreement_score":0.008515827,"about_ca_system_score_codex":0.0005040555,"about_ca_system_score_gemma":0.0015610028,"threshold_uncertainty_score":0.016932547},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3162001,0.0009934277,0.67862,0.0007248832,0.00011773772,0.00006730139,0.00076937577,0.001430529,0.0010767062],"genre_scores_gemma":[0.8822523,0.00040198362,0.115259685,0.00010792654,0.00009409527,0.0000498298,0.0009853602,0.00010867587,0.0007401868],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866176,0.00017528025,0.00010135865,0.00049234193,0.00041878267,0.00015052514],"domain_scores_gemma":[0.9961235,0.0016889421,0.0008876479,0.0006326279,0.00052546227,0.00014187097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014878481,0.0007101876,0.0008218349,0.0024938257,0.000495225,0.0012312563,0.0010273247,0.0010879501,0.00054197543],"category_scores_gemma":[0.0085279215,0.00039938878,0.0005446598,0.002453231,0.0012741861,0.002157657,0.0013923728,0.0012745326,0.00023054605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006118606,0.0002889347,0.087282516,0.00044601067,0.00027770194,0.0017263548,0.0009310512,0.42004633,0.042671,0.015869398,0.0055433656,0.4243054],"study_design_scores_gemma":[0.000006816363,0.000054515178,0.013234467,0.00001890232,0.000017765944,0.00041490074,0.00017637198,0.9611832,0.0070135966,0.016027397,0.0018288811,0.000023153201],"about_ca_topic_score_codex":0.0037690608,"about_ca_topic_score_gemma":0.003592672,"teacher_disagreement_score":0.0037690608,"about_ca_system_score_codex":0.00085659215,"about_ca_system_score_gemma":0.0006447425,"threshold_uncertainty_score":0.007868588},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019415205,0.00062384916,0.9736994,0.00039723155,0.00016611365,0.00007517831,0.00019250711,0.002920205,0.002510389],"genre_scores_gemma":[0.730193,0.00058680674,0.2503086,0.0007655961,0.000109081426,0.0002564745,0.0010524614,0.0003461744,0.016381716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997669,0.000046485446,0.000009172069,0.00007537988,0.00006226631,0.000039845796],"domain_scores_gemma":[0.9997607,0.00010279551,0.000026889564,0.0000392683,0.00005014193,0.000020136704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006427869,0.0010335594,0.00060323626,0.00026261463,0.00025635987,0.00044018796,0.0015743348,0.0009645536,0.002472108],"category_scores_gemma":[0.0012765294,0.00036120694,0.0005717512,0.00023831506,0.00057282194,0.0008449958,0.0012920038,0.0020755553,0.00065306417],"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":[0.00025562503,0.00012258884,0.0008569072,0.00009655578,0.00012451125,0.00019093609,0.00006899707,0.7231067,0.011930895,0.010603551,0.009459998,0.24318278],"study_design_scores_gemma":[0.0000026845719,0.000013797232,0.000039915125,0.0000029600303,0.0000027244382,0.000014091434,0.0000014724853,0.9973537,0.0011734064,0.00096720067,0.0004253423,0.0000027840053],"about_ca_topic_score_codex":0.0053877095,"about_ca_topic_score_gemma":0.0055923616,"teacher_disagreement_score":0.0053877095,"about_ca_system_score_codex":0.0007724311,"about_ca_system_score_gemma":0.0006783346,"threshold_uncertainty_score":0.010712743},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028620379,0.00010565311,0.9960496,0.00010177464,0.000028445074,0.000049383554,0.000025497755,0.00056802726,0.0002095456],"genre_scores_gemma":[0.07136811,0.00020327947,0.9270345,0.000102512204,0.00008361761,0.00018509848,0.0002119905,0.00012020696,0.0006907456],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977508,0.00042584803,0.0003232561,0.0006292008,0.0007214129,0.00014948015],"domain_scores_gemma":[0.9955776,0.0014330772,0.00033176894,0.000732943,0.0017384626,0.00018620778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002897945,0.0011603019,0.0014924662,0.0026066343,0.00097393803,0.001488515,0.0028105166,0.001255329,0.0014090261],"category_scores_gemma":[0.00947878,0.0006675877,0.0012602822,0.0023383496,0.0008788585,0.0030715275,0.0018606625,0.0015737212,0.00085145084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043101766,0.00022890589,0.0058130873,0.00031859605,0.0001758703,0.00035215277,0.00034406452,0.2436751,0.024295578,0.03377332,0.005123573,0.6854687],"study_design_scores_gemma":[0.00001907442,0.0000884111,0.00025591557,0.000012292536,0.000013893182,0.00013562298,0.00003147078,0.9842433,0.0044136234,0.007770864,0.003000862,0.000014673145],"about_ca_topic_score_codex":0.002877062,"about_ca_topic_score_gemma":0.0021611708,"teacher_disagreement_score":0.002897945,"about_ca_system_score_codex":0.00086584326,"about_ca_system_score_gemma":0.001771947,"threshold_uncertainty_score":0.015326023},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20949899,0.00048602678,0.775093,0.00059635175,0.0002476298,0.00015650528,0.00091612985,0.00943229,0.0035730724],"genre_scores_gemma":[0.86258817,0.00026824768,0.1297213,0.00013644798,0.000043758126,0.000089512854,0.00052880705,0.00018575905,0.0064378586],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986935,0.000021888662,0.0000052187884,0.000037388756,0.00004725656,0.000018922816],"domain_scores_gemma":[0.9997795,0.00010652445,0.0000146635275,0.000024687391,0.00005790916,0.000016683563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003534981,0.00076972856,0.00045879174,0.00045982876,0.00017410783,0.00033691924,0.0006322586,0.00059856236,0.003843234],"category_scores_gemma":[0.00080306135,0.0002026381,0.00031112647,0.00041200576,0.00019154286,0.0002071676,0.00041520968,0.00052857463,0.00084721326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00076208485,0.0003129094,0.0037518486,0.00023235651,0.00009887015,0.00055420975,0.00012347105,0.23662502,0.06841087,0.00089349726,0.0078047724,0.68043],"study_design_scores_gemma":[0.000012842742,0.00009028293,0.004100525,0.000010337182,0.000012933968,0.00013601045,0.000021656766,0.98172116,0.012373098,0.0004376012,0.0010715968,0.0000119102615],"about_ca_topic_score_codex":0.006550135,"about_ca_topic_score_gemma":0.008941905,"teacher_disagreement_score":0.006550135,"about_ca_system_score_codex":0.0002813535,"about_ca_system_score_gemma":0.00038968222,"threshold_uncertainty_score":0.013024032},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1713998,0.008328369,0.80537623,0.0034594175,0.0005509812,0.00014331758,0.00040547494,0.0010953997,0.009241044],"genre_scores_gemma":[0.8536865,0.0041982923,0.12996383,0.0008332581,0.0005961195,0.00030434816,0.000872103,0.0010086279,0.00853699],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9938134,0.0026937195,0.000404504,0.00098168,0.0015326982,0.0005740893],"domain_scores_gemma":[0.7971372,0.17463453,0.0059067355,0.006163409,0.014233826,0.0019243504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022906173,0.0016723934,0.0023245057,0.003609132,0.0012765068,0.003201427,0.0031742244,0.0032364356,0.002905149],"category_scores_gemma":[0.1653451,0.0011908436,0.0014144442,0.0019548528,0.0043585775,0.008368328,0.004182399,0.006203317,0.0007289693],"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":[0.0016759613,0.00017741641,0.022288509,0.00080175203,0.00040598042,0.00038010866,0.0008168902,0.5660072,0.008965959,0.23754352,0.007191044,0.1537457],"study_design_scores_gemma":[0.000017737913,0.00008137525,0.0013408145,0.00008146906,0.00004120642,0.0001179371,0.000048672035,0.9593884,0.00242047,0.035840962,0.0005917181,0.00002931812],"about_ca_topic_score_codex":0.004237382,"about_ca_topic_score_gemma":0.002533866,"teacher_disagreement_score":0.022906173,"about_ca_system_score_codex":0.0032922518,"about_ca_system_score_gemma":0.0018858021,"threshold_uncertainty_score":0.12114084},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.101785734,0.0077511286,0.8812471,0.0021474808,0.00036240896,0.0004802169,0.0018346079,0.0011026013,0.0032886327],"genre_scores_gemma":[0.53078485,0.007075976,0.45470327,0.000273905,0.00042208802,0.00083345163,0.004415005,0.00014251797,0.0013489644],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947437,0.0018516261,0.0004228807,0.0004543481,0.002288364,0.00023909901],"domain_scores_gemma":[0.99150956,0.0045251325,0.0008683702,0.001222787,0.0017616826,0.00011236023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042962683,0.0010910607,0.0012126518,0.0038073072,0.00082372787,0.0019591732,0.0006421868,0.00050930044,0.00065885915],"category_scores_gemma":[0.014121001,0.00029956235,0.0013577234,0.0041925577,0.0006480387,0.0017865543,0.0011405086,0.0011109293,0.00032749935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002537384,0.00036254778,0.02261127,0.0014960541,0.0008651815,0.0006088464,0.0007843522,0.17597109,0.009369897,0.027350202,0.012268292,0.74805856],"study_design_scores_gemma":[0.000029443856,0.00023181082,0.02311154,0.00023556061,0.00011994621,0.0004060159,0.00070994656,0.9037092,0.0075891637,0.051542617,0.012229469,0.00008521649],"about_ca_topic_score_codex":0.0016850117,"about_ca_topic_score_gemma":0.0012846636,"teacher_disagreement_score":0.0042962683,"about_ca_system_score_codex":0.0007505825,"about_ca_system_score_gemma":0.0010287317,"threshold_uncertainty_score":0.022721112},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012484164,0.0001197098,0.9858234,0.00003982288,0.000023236014,0.000055874847,0.00007583594,0.0010938188,0.00028414204],"genre_scores_gemma":[0.33541358,0.0001908427,0.6622437,0.00006165682,0.00007955734,0.00018065814,0.0005945075,0.00009907986,0.0011364059],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990312,0.000105931686,0.00007372436,0.00025066605,0.00043860736,0.000099844234],"domain_scores_gemma":[0.9987913,0.0003611389,0.00024081484,0.00014394597,0.00040142256,0.000061431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006396381,0.0008171183,0.0011492656,0.0031122717,0.00037490408,0.0007822817,0.0014631818,0.00064008206,0.00080198765],"category_scores_gemma":[0.0026708154,0.00033754937,0.00064606476,0.0015183858,0.00039610008,0.0015741782,0.00090816733,0.00093417725,0.00044903712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002109577,0.00024321595,0.004020677,0.0000946705,0.00007725013,0.0001814948,0.00009044203,0.04851191,0.035888996,0.0031593586,0.0021012253,0.9054198],"study_design_scores_gemma":[0.00001353847,0.00012490047,0.0021402922,0.000009209544,0.000020768937,0.00028979505,0.000037548845,0.9739821,0.018765306,0.002710752,0.0018841452,0.000021592676],"about_ca_topic_score_codex":0.0029784786,"about_ca_topic_score_gemma":0.0023094115,"teacher_disagreement_score":0.0031122717,"about_ca_system_score_codex":0.00063742325,"about_ca_system_score_gemma":0.00076649984,"threshold_uncertainty_score":0.005922258},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00747119,0.00056848605,0.98529804,0.0005327606,0.000091411515,0.00003689096,0.00019284057,0.0010663494,0.0047419397],"genre_scores_gemma":[0.49248713,0.001984261,0.47900975,0.0006111604,0.00033026843,0.00009288778,0.00095841935,0.00034950796,0.024176562],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901676,0.00014614333,0.000044172328,0.00020472212,0.00045090396,0.00013734566],"domain_scores_gemma":[0.99817324,0.00071062194,0.00015649018,0.00055887207,0.00034168555,0.00005903296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012053536,0.0007532014,0.0008919711,0.0009934916,0.0004151331,0.0018140415,0.0021262576,0.0013135888,0.0030616145],"category_scores_gemma":[0.0034268673,0.000446293,0.0008953157,0.0011659265,0.0009274508,0.0026445014,0.001861869,0.002695192,0.00080628874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017259496,0.00022027621,0.002091348,0.00015834336,0.0001063842,0.00021577743,0.00008842509,0.10302536,0.011990324,0.0762901,0.015633522,0.7900076],"study_design_scores_gemma":[0.0000060169937,0.000044460074,0.00044550712,0.000021847387,0.000031935797,0.00025497147,0.00001869091,0.91590726,0.008574376,0.06996291,0.0047171907,0.000014737636],"about_ca_topic_score_codex":0.0020442468,"about_ca_topic_score_gemma":0.0025217107,"teacher_disagreement_score":0.0030616145,"about_ca_system_score_codex":0.0010300026,"about_ca_system_score_gemma":0.0011151657,"threshold_uncertainty_score":0.010242164},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0444159,0.00056056806,0.9427238,0.00095350784,0.0002864636,0.00019570626,0.00009152889,0.0025616344,0.0082109915],"genre_scores_gemma":[0.6239777,0.00026541654,0.35822293,0.0003990127,0.00023632494,0.0002052052,0.00025109513,0.00033940814,0.01610283],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984263,0.00036576364,0.00012502044,0.00037657036,0.00053479604,0.0001716793],"domain_scores_gemma":[0.993983,0.0024788573,0.00041573824,0.0013957788,0.0013557582,0.00037082026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033871732,0.0009801026,0.00087303866,0.0013660892,0.0012978666,0.0017288876,0.003529058,0.0017010188,0.013801542],"category_scores_gemma":[0.015291297,0.00051114755,0.0004990227,0.0006372043,0.0015533314,0.005189555,0.0031186026,0.0024455169,0.002974932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060125557,0.0005189399,0.003339509,0.00016925245,0.000067798705,0.00016514011,0.00038588615,0.069604345,0.009097721,0.07575268,0.011710092,0.8285874],"study_design_scores_gemma":[0.00006193389,0.0002383572,0.0005397113,0.0000484586,0.000031391115,0.00022149977,0.00013792228,0.88724166,0.0063912068,0.09907129,0.005981551,0.00003502974],"about_ca_topic_score_codex":0.0015403719,"about_ca_topic_score_gemma":0.0027679682,"teacher_disagreement_score":0.013801542,"about_ca_system_score_codex":0.0007677337,"about_ca_system_score_gemma":0.0011478978,"threshold_uncertainty_score":0.04617077},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02883401,0.0000738188,0.96954566,0.00014804034,0.000017451393,0.00003060263,0.000060676997,0.0009828644,0.00030686872],"genre_scores_gemma":[0.47670737,0.00011284414,0.51988995,0.00014657741,0.000045595592,0.00016035818,0.0006056366,0.00017706834,0.0021546546],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995571,0.00011106627,0.000025934829,0.00012021594,0.00013275941,0.000052871084],"domain_scores_gemma":[0.99919564,0.00032549154,0.000092228045,0.000104279774,0.00024007863,0.00004231715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006948771,0.0006679921,0.00080375077,0.0010077279,0.00029327217,0.00064868724,0.0011567087,0.0009546967,0.0010206982],"category_scores_gemma":[0.001739938,0.00026443973,0.00090647675,0.00096302014,0.0005725307,0.00097420206,0.000749414,0.0010150399,0.0006310052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024199489,0.00022192196,0.0015235384,0.00010255486,0.00010321786,0.00008370109,0.000114092356,0.46275857,0.036030613,0.0070191696,0.004045028,0.4877556],"study_design_scores_gemma":[0.0000031416764,0.00001718763,0.0001370969,0.0000010245417,0.0000028803454,0.000008758871,0.0000039560887,0.9973623,0.0011540417,0.0011637392,0.0001426534,0.000003271965],"about_ca_topic_score_codex":0.0027416993,"about_ca_topic_score_gemma":0.0034145373,"teacher_disagreement_score":0.0027416993,"about_ca_system_score_codex":0.00061944,"about_ca_system_score_gemma":0.0006898275,"threshold_uncertainty_score":0.0054515004},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07845452,0.00088574074,0.9162716,0.00029993802,0.00017454033,0.00009752077,0.00007215626,0.0011717061,0.0025722196],"genre_scores_gemma":[0.8561256,0.00041675445,0.13755482,0.00028083054,0.00023656602,0.000094712406,0.0003391722,0.00013865046,0.004812887],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99762124,0.00061451306,0.00016011143,0.0005917576,0.0007366439,0.00027576002],"domain_scores_gemma":[0.9952512,0.0019510826,0.00040033943,0.00085307326,0.001360812,0.00018351998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003252785,0.0014037624,0.0021185533,0.0018067859,0.00046477027,0.0022267608,0.0016486243,0.0018946672,0.0021096226],"category_scores_gemma":[0.006402271,0.000378309,0.0014926312,0.0012373974,0.00079864246,0.0025477018,0.002045137,0.0021456624,0.0015405532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007132274,0.00041929874,0.010913363,0.00019287622,0.00043661296,0.00031546457,0.00019994994,0.17473732,0.021034293,0.0062659318,0.0036366063,0.7811351],"study_design_scores_gemma":[0.0000105688505,0.00015281836,0.0011827152,0.000025714084,0.000076489494,0.000105107676,0.000041213192,0.9864044,0.0074156104,0.0034181087,0.0011512426,0.00001599925],"about_ca_topic_score_codex":0.0009563609,"about_ca_topic_score_gemma":0.0009459133,"teacher_disagreement_score":0.003252785,"about_ca_system_score_codex":0.0005088773,"about_ca_system_score_gemma":0.0007988015,"threshold_uncertainty_score":0.017202616},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02457113,0.0002623556,0.97421515,0.0000997509,0.000034752677,0.000044519515,0.00006120698,0.00039167854,0.00031944131],"genre_scores_gemma":[0.37477693,0.00039095184,0.62317884,0.00006187025,0.00007920702,0.000084435,0.00022178395,0.000062245286,0.0011437818],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983341,0.0003001139,0.00014283817,0.00045402363,0.000638247,0.0001306759],"domain_scores_gemma":[0.9975503,0.0008759295,0.0004028751,0.00030872572,0.0007357161,0.00012637085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012460586,0.0004951004,0.00094759604,0.004071578,0.0004300548,0.0014930811,0.0013203991,0.0010990252,0.001002109],"category_scores_gemma":[0.004595685,0.00033706252,0.00091294514,0.0024991967,0.0008291771,0.0018421401,0.0011487752,0.0010415948,0.00048030526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004383661,0.00033375606,0.015734827,0.00041086652,0.00026159515,0.0006625446,0.0005367425,0.086785056,0.07258969,0.024271866,0.0022258353,0.7957489],"study_design_scores_gemma":[0.000011295416,0.00008833545,0.005591986,0.000013646771,0.000027132777,0.00043240312,0.00006002221,0.9773072,0.008808301,0.00577166,0.0018547046,0.00003330794],"about_ca_topic_score_codex":0.001524599,"about_ca_topic_score_gemma":0.0010156884,"teacher_disagreement_score":0.004071578,"about_ca_system_score_codex":0.0005885939,"about_ca_system_score_gemma":0.0005581463,"threshold_uncertainty_score":0.0065898895},"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":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01395233,0.037521604,0.9214829,0.019195832,0.00069400005,0.00002435133,0.0003129438,0.00049680105,0.0063191676],"genre_scores_gemma":[0.54202664,0.07133618,0.35814393,0.0034002862,0.005057177,0.00011180055,0.00079878955,0.0003951822,0.018729972],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990158,0.00027218985,0.000054901626,0.00021630431,0.00036672622,0.00007404948],"domain_scores_gemma":[0.9931179,0.005257698,0.00026520595,0.00043718482,0.0007411806,0.00018071114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002773444,0.000812511,0.0012141655,0.000903976,0.00043461745,0.0028133178,0.0020258836,0.0021109318,0.0017340282],"category_scores_gemma":[0.010029599,0.000614953,0.00054611015,0.001746935,0.0021468347,0.005985551,0.0025558323,0.004770162,0.0006628228],"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":[0.00008726913,0.00011736572,0.0028248546,0.00053760986,0.00009331973,0.000116978306,0.00014327616,0.09743954,0.0013302736,0.12945113,0.022834646,0.74502385],"study_design_scores_gemma":[0.000005089036,0.000027255152,0.0007324876,0.000099969206,0.0000147894825,0.000121207086,0.00008637639,0.52058494,0.0010331934,0.4676,0.009673051,0.000021613074],"about_ca_topic_score_codex":0.0034080774,"about_ca_topic_score_gemma":0.0031623603,"teacher_disagreement_score":0.0034080774,"about_ca_system_score_codex":0.0010826844,"about_ca_system_score_gemma":0.0009385867,"threshold_uncertainty_score":0.014667571},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12695546,0.00093294226,0.86321425,0.00030611272,0.00016688564,0.00010354292,0.0007750926,0.006764315,0.0007814567],"genre_scores_gemma":[0.7436865,0.00055560935,0.25240973,0.000088198445,0.000087073444,0.00012761308,0.0019721673,0.0002312163,0.00084185397],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800867,0.00018574829,0.00021586505,0.00056939316,0.00086160854,0.00015876624],"domain_scores_gemma":[0.99604625,0.001141126,0.00079180347,0.00054771884,0.0012474471,0.0002257139],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013753742,0.0011184744,0.0013650105,0.0021272972,0.00058227114,0.0012676108,0.0018415835,0.0008246684,0.0004996958],"category_scores_gemma":[0.007803803,0.00037621678,0.0007436037,0.0024511628,0.0006222812,0.0017099639,0.0011161455,0.0011972146,0.00036150852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010252197,0.00028523747,0.04447588,0.00053654646,0.00038999735,0.001817189,0.0006796592,0.33416763,0.06656682,0.0049656997,0.0075818026,0.53750837],"study_design_scores_gemma":[0.000018131823,0.00009993143,0.0044252714,0.000016774122,0.000023136745,0.0003403102,0.00013762382,0.9721316,0.0169893,0.0033573622,0.0024318541,0.000028707662],"about_ca_topic_score_codex":0.0039060572,"about_ca_topic_score_gemma":0.0030585492,"teacher_disagreement_score":0.0039060572,"about_ca_system_score_codex":0.00063185615,"about_ca_system_score_gemma":0.0009148111,"threshold_uncertainty_score":0.007766664},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028751684,0.00028402396,0.95818305,0.00024922012,0.00018231248,0.000087344524,0.0012140338,0.008537141,0.0025111276],"genre_scores_gemma":[0.4940461,0.00026946526,0.49282765,0.00023518916,0.00009273448,0.0001694293,0.005485258,0.0006862595,0.0061879084],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986222,0.0001939024,0.000096864744,0.00046234595,0.00049637025,0.00012829948],"domain_scores_gemma":[0.9981369,0.00047063126,0.00016429221,0.0005313391,0.0006379372,0.000058950693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013194858,0.00083999,0.0008371945,0.0019305199,0.0003557544,0.0012706897,0.0013231825,0.0010150756,0.0039550127],"category_scores_gemma":[0.006518774,0.00033516347,0.00078900537,0.0014920549,0.00050795457,0.0017941105,0.0022066832,0.0017505802,0.002353588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044664327,0.00023487063,0.01657949,0.00022713371,0.0002270659,0.00028288443,0.0001598201,0.13582951,0.026001541,0.013143099,0.020623777,0.78624415],"study_design_scores_gemma":[0.0000137682755,0.000057715068,0.0025478385,0.000020517135,0.000014424956,0.00017691338,0.00006307752,0.9479794,0.022664791,0.01466065,0.011776334,0.0000245366],"about_ca_topic_score_codex":0.0018004898,"about_ca_topic_score_gemma":0.0023232277,"teacher_disagreement_score":0.0039550127,"about_ca_system_score_codex":0.00038718164,"about_ca_system_score_gemma":0.0007959112,"threshold_uncertainty_score":0.013230801},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03861491,0.00044036467,0.95867145,0.0003746499,0.00004765314,0.000038320104,0.00011107744,0.000820683,0.0008809011],"genre_scores_gemma":[0.8191438,0.00034900746,0.17438614,0.00040410392,0.000121077275,0.00011187252,0.0006223499,0.00018115614,0.0046804934],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926966,0.00022963205,0.00003098809,0.0002087385,0.0001648037,0.00009629724],"domain_scores_gemma":[0.9979773,0.0013176566,0.00021835775,0.00020468823,0.00018186156,0.00010019423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001570268,0.0011417195,0.0011260922,0.0006135181,0.00034900228,0.0006137313,0.0016139792,0.0012415012,0.0011572229],"category_scores_gemma":[0.0043779216,0.0005519581,0.00090777717,0.0005349006,0.0010914687,0.0009482671,0.0015013389,0.0023036955,0.00036124224],"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":[0.00017935089,0.00007452844,0.0012576286,0.000032582484,0.000052848372,0.000082217106,0.00003780622,0.9378501,0.0027127718,0.003276957,0.0012609112,0.053182144],"study_design_scores_gemma":[0.0000017181555,0.000008740113,0.00007611868,0.0000019627269,0.000002118618,0.000010701445,0.000001720725,0.9983758,0.00036849597,0.0010927941,0.000057896552,0.0000019731692],"about_ca_topic_score_codex":0.0049712495,"about_ca_topic_score_gemma":0.0042991084,"teacher_disagreement_score":0.0049712495,"about_ca_system_score_codex":0.0010500196,"about_ca_system_score_gemma":0.00085214956,"threshold_uncertainty_score":0.009884596},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004685267,0.00012779175,0.9939031,0.00006260665,0.000024884068,0.000045778626,0.000058984933,0.0006237069,0.00046786712],"genre_scores_gemma":[0.26692784,0.00042850824,0.72732186,0.00018237844,0.000069439,0.000200192,0.0005051101,0.00009247462,0.0042722537],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995747,0.000053905518,0.000026953312,0.00013955943,0.00015027689,0.00005466498],"domain_scores_gemma":[0.99969625,0.000060877985,0.000048697602,0.000040409017,0.00013198127,0.000021873999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054539857,0.0007081456,0.000584556,0.0012560927,0.00030250265,0.000566449,0.001190292,0.0008255057,0.0011037787],"category_scores_gemma":[0.0010063804,0.0003453689,0.000663286,0.00081913284,0.0004979643,0.0010047039,0.00097410835,0.0011982505,0.00040245894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011138628,0.0001552298,0.0024668414,0.0001585634,0.00012300246,0.00018418516,0.000099803176,0.1911732,0.045731187,0.015772495,0.003152099,0.740872],"study_design_scores_gemma":[0.0000040482123,0.00005289993,0.00058338116,0.000010117641,0.000011095916,0.00010045653,0.000010164973,0.98298633,0.011040949,0.00337667,0.0018137848,0.0000100529405],"about_ca_topic_score_codex":0.004735949,"about_ca_topic_score_gemma":0.0046099746,"teacher_disagreement_score":0.004735949,"about_ca_system_score_codex":0.0008762601,"about_ca_system_score_gemma":0.00086591794,"threshold_uncertainty_score":0.009416759},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5270867,0.0029759624,0.45406553,0.0032486368,0.00061899074,0.00015354936,0.0016661405,0.00536925,0.004815311],"genre_scores_gemma":[0.9654606,0.00044538823,0.030781623,0.0002796547,0.0003573526,0.000045578683,0.0014854582,0.00015681553,0.0009875806],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942684,0.00017465586,0.000027566239,0.00019416674,0.00009725189,0.00007968124],"domain_scores_gemma":[0.9976915,0.0010636653,0.00032314874,0.0004750056,0.0002953838,0.00015130243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002166364,0.001149628,0.00081674627,0.00096700166,0.00037290298,0.0013231605,0.0014234675,0.0011699832,0.0005796054],"category_scores_gemma":[0.008091284,0.0004695636,0.0005458864,0.0011175994,0.00071880926,0.0019423908,0.0015068385,0.002416152,0.000612559],"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":[0.0004316118,0.00019110346,0.025379367,0.00008151578,0.00014684655,0.00012450742,0.00018763842,0.7805556,0.004585416,0.0015051299,0.006833961,0.17997733],"study_design_scores_gemma":[0.000006511678,0.00001762686,0.0025328815,0.000007950467,0.000007794298,0.00001448941,0.000023213088,0.99338186,0.0008422813,0.0026787973,0.00047826365,0.0000083675895],"about_ca_topic_score_codex":0.006545463,"about_ca_topic_score_gemma":0.0045680213,"teacher_disagreement_score":0.006545463,"about_ca_system_score_codex":0.00054969447,"about_ca_system_score_gemma":0.00060333346,"threshold_uncertainty_score":0.013014734},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14438042,0.002067304,0.7894208,0.00030281677,0.0012794433,0.00087811326,0.00922862,0.0305622,0.021880295],"genre_scores_gemma":[0.5501021,0.00071330357,0.40637165,0.00024208505,0.00024005023,0.00025593527,0.025198769,0.0016538872,0.015222142],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981883,0.00015313092,0.000060349477,0.0006006526,0.0006459619,0.00035155058],"domain_scores_gemma":[0.99854773,0.00018506122,0.00008198067,0.00038463177,0.0006227857,0.00017768063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008462847,0.0017879282,0.001970164,0.0036659755,0.0008720887,0.0014210389,0.002298233,0.0013376685,0.005405134],"category_scores_gemma":[0.0029493875,0.00060185225,0.00072791474,0.0021307352,0.0004370627,0.0013728321,0.002421052,0.00095677003,0.0048928084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011532286,0.00069331896,0.0052025565,0.00082737854,0.00040569223,0.00080499466,0.00024371,0.027090823,0.074803516,0.0023092667,0.058443785,0.82802165],"study_design_scores_gemma":[0.000046047775,0.00034862745,0.017833408,0.00010218611,0.00014331444,0.0019785138,0.00051168806,0.88060886,0.06592501,0.009257216,0.023142003,0.000103028025],"about_ca_topic_score_codex":0.0070644873,"about_ca_topic_score_gemma":0.01808166,"teacher_disagreement_score":0.0070644873,"about_ca_system_score_codex":0.00047170577,"about_ca_system_score_gemma":0.0012476833,"threshold_uncertainty_score":0.018081963},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07003676,0.0014965524,0.92224777,0.0011792312,0.00023516842,0.00007272204,0.00058413047,0.0021350596,0.0020126572],"genre_scores_gemma":[0.762062,0.0005707313,0.22777359,0.00042551302,0.00027783992,0.00013178165,0.002651971,0.000139558,0.0059670717],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994006,0.00020321865,0.000038502476,0.0001339421,0.00011547323,0.000108345856],"domain_scores_gemma":[0.99879587,0.0005330129,0.00012581535,0.00020806745,0.0002622552,0.00007485358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016035753,0.000712015,0.0009002266,0.0010272309,0.00043092892,0.00097353035,0.0014347866,0.0010436117,0.001325617],"category_scores_gemma":[0.0030622869,0.00039564588,0.0007613422,0.000977053,0.00047202376,0.0019501825,0.0014826928,0.0023174975,0.00072048744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039878293,0.0005732581,0.005729018,0.000113107504,0.00023989522,0.00014247466,0.00014969471,0.24741352,0.010813782,0.010375094,0.02098344,0.70306796],"study_design_scores_gemma":[0.000005200965,0.00002988659,0.0004705084,0.0000048602847,0.000010423982,0.000013166557,0.000014694129,0.99461466,0.00087112654,0.0034416583,0.00051968335,0.0000041797466],"about_ca_topic_score_codex":0.004510139,"about_ca_topic_score_gemma":0.007361992,"teacher_disagreement_score":0.004510139,"about_ca_system_score_codex":0.0006020757,"about_ca_system_score_gemma":0.0010385752,"threshold_uncertainty_score":0.008967757},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044064615,0.0030946215,0.94240016,0.0005056665,0.00027564124,0.00009324445,0.00091997033,0.0054690945,0.0031769834],"genre_scores_gemma":[0.74833715,0.0012084194,0.23529871,0.0005614107,0.00019691925,0.00016199713,0.003620375,0.000222694,0.010392406],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999315,0.000105366045,0.00003497843,0.00023395749,0.00020338832,0.00010729624],"domain_scores_gemma":[0.99935204,0.00020229474,0.00009013929,0.00012171991,0.00019231658,0.00004150753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010017086,0.0012856758,0.0008659492,0.0013246615,0.0002476566,0.0007487669,0.0017870596,0.0011030991,0.0019700713],"category_scores_gemma":[0.0016980465,0.0003681361,0.0006890344,0.000981135,0.00039372663,0.0017577183,0.0011725326,0.0013224971,0.0009044631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047000384,0.0003480758,0.006315576,0.0002406741,0.0004031665,0.0002399064,0.00005782544,0.2206157,0.016628703,0.008195074,0.012482472,0.73400277],"study_design_scores_gemma":[0.0000054332886,0.000054274857,0.0005727043,0.000008932863,0.000023425238,0.00007152478,0.0000074540553,0.9905246,0.003600042,0.003702366,0.0014218767,0.0000073918163],"about_ca_topic_score_codex":0.005032044,"about_ca_topic_score_gemma":0.0068166354,"teacher_disagreement_score":0.005032044,"about_ca_system_score_codex":0.0010575831,"about_ca_system_score_gemma":0.00063204445,"threshold_uncertainty_score":0.010005474},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9761391,0.00045923403,0.001852781,0.0010750009,0.00009814166,0.000018229572,0.01378358,0.00022654758,0.0063474094],"genre_scores_gemma":[0.99181986,0.00021901225,0.0007272509,0.000028732731,0.000009927863,0.000004828267,0.0039831856,0.000020740872,0.003186386],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998306,0.000011667111,0.000009571447,0.000032046897,0.00005755177,0.000058491256],"domain_scores_gemma":[0.9990607,0.00008702649,0.00006915421,0.000020378373,0.0006582622,0.000104489765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032629515,0.00034950356,0.00026017876,0.0010763331,0.0008335515,0.0012545681,0.00071278523,0.00044807358,0.0026446206],"category_scores_gemma":[0.0018553239,0.00015819514,0.00031609708,0.002325948,0.0002488481,0.00053911906,0.0003526512,0.0008641865,0.00039487382],"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":[0.0006807647,0.00021388837,0.5405151,0.00020882765,0.00033535954,0.0009753215,0.00083827815,0.3250659,0.0034173771,0.012276044,0.053739313,0.06173375],"study_design_scores_gemma":[0.000034438548,0.000033678723,0.30219728,0.00007110318,0.00008219664,0.000063074236,0.0014761789,0.6774906,0.0020233,0.0011034822,0.015353526,0.00007116105],"about_ca_topic_score_codex":0.9886313,"about_ca_topic_score_gemma":0.9862895,"teacher_disagreement_score":0.016070338,"about_ca_system_score_codex":0.016070338,"about_ca_system_score_gemma":0.01144203,"threshold_uncertainty_score":0.11659902},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010496705,0.40569824,0.16794889,0.17557861,0.062641926,0.00017253027,0.0037642808,0.0016831212,0.17201573],"genre_scores_gemma":[0.1829882,0.2553468,0.104735605,0.03878782,0.032441694,0.00041378112,0.0049677654,0.0013698261,0.37894854],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99724823,0.00057519384,0.00018847319,0.0005351483,0.0011871487,0.00026581567],"domain_scores_gemma":[0.9971131,0.00085011614,0.00016418868,0.00023667082,0.0012453195,0.00039053164],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0051727663,0.0016882496,0.0007082256,0.0020916346,0.0015248618,0.0049803387,0.0011735421,0.0031937852,0.007912624],"category_scores_gemma":[0.012650856,0.00082898274,0.00048446242,0.0023324091,0.00414218,0.008443855,0.004298937,0.0064572,0.0055627353],"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":[0.00023920694,0.00005262714,0.0005293951,0.00042898592,0.00003455482,0.00009013214,0.00031719936,0.0025713162,0.0007212941,0.2953972,0.44534695,0.25427106],"study_design_scores_gemma":[0.000022762883,0.000046783618,0.00042757875,0.00037848196,0.000014223418,0.000084733416,0.00006461375,0.0035006197,0.0013333483,0.050186656,0.9439113,0.000028878927],"about_ca_topic_score_codex":0.009998819,"about_ca_topic_score_gemma":0.006188991,"teacher_disagreement_score":0.99847513,"about_ca_system_score_codex":0.0082943905,"about_ca_system_score_gemma":0.0042379093,"threshold_uncertainty_score":0.060180306},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08826652,0.00047534335,0.9035182,0.00031038595,0.00016835117,0.00004764453,0.00034413143,0.0031222098,0.0037472823],"genre_scores_gemma":[0.9300343,0.00017403733,0.06628172,0.000090097994,0.000057325724,0.00004204644,0.00028095444,0.00003830614,0.003001179],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998896,0.000017197626,0.0000076064634,0.00003766516,0.000028866323,0.000019080893],"domain_scores_gemma":[0.99984026,0.000046286594,0.000027006792,0.000014424957,0.000061576626,0.000010334646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022906315,0.0004667335,0.000264668,0.00028085316,0.00014219302,0.00028397568,0.00057682587,0.00039433723,0.0014265113],"category_scores_gemma":[0.00061406125,0.00016373239,0.00022363079,0.00029452407,0.00014061494,0.0004850314,0.00032028515,0.00063747185,0.0003775679],"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":[0.00026025766,0.00024935364,0.0038801914,0.00013748162,0.00013261409,0.00021379358,0.00019142342,0.52758354,0.04105935,0.0034282776,0.0051221265,0.41774154],"study_design_scores_gemma":[0.0000015524496,0.000022981352,0.00040178487,0.0000028339357,0.000007035825,0.000012757608,0.0000058396276,0.9968072,0.0018577371,0.00060848694,0.00026871602,0.0000031228014],"about_ca_topic_score_codex":0.0057646967,"about_ca_topic_score_gemma":0.007485348,"teacher_disagreement_score":0.0057646967,"about_ca_system_score_codex":0.00034589565,"about_ca_system_score_gemma":0.00049663184,"threshold_uncertainty_score":0.011462271},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019812629,0.00020594154,0.97843564,0.00010344198,0.000088799585,0.000053413423,0.000027759374,0.0006383776,0.00063393003],"genre_scores_gemma":[0.60076094,0.0004213675,0.3961932,0.00018953864,0.00018539622,0.00014015852,0.00018809304,0.00011440561,0.0018068828],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991875,0.00012494024,0.00004804442,0.00027473108,0.00029777468,0.0000670609],"domain_scores_gemma":[0.9992455,0.00023520245,0.00010404985,0.00009645567,0.00027876,0.000040152223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052269566,0.0005768434,0.0007922677,0.0011748318,0.00058286445,0.0005165179,0.0011322567,0.0006159052,0.00042595586],"category_scores_gemma":[0.0023966853,0.0002352632,0.0006594418,0.0009775483,0.00045505862,0.00090855977,0.0007624334,0.00070957944,0.00013687584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019631498,0.00022058547,0.009153645,0.00018089455,0.00017725283,0.0005338446,0.00027110387,0.2276947,0.04049002,0.013373366,0.003622096,0.70408624],"study_design_scores_gemma":[0.0000063848543,0.000038931856,0.0010210858,0.000004752411,0.000024966384,0.00017762797,0.00002583975,0.9907375,0.0041907714,0.002268201,0.0014887175,0.00001516681],"about_ca_topic_score_codex":0.0033152164,"about_ca_topic_score_gemma":0.0040484215,"teacher_disagreement_score":0.0033152164,"about_ca_system_score_codex":0.0003749595,"about_ca_system_score_gemma":0.00063277344,"threshold_uncertainty_score":0.0065918565},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014442955,0.00019770824,0.982077,0.00008402431,0.00006479541,0.00008060712,0.000084003244,0.0020576431,0.00091122504],"genre_scores_gemma":[0.2497054,0.0002713065,0.74460745,0.00008785608,0.00008350794,0.00013823823,0.00080112286,0.0006276689,0.0036774888],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99783933,0.0002942528,0.0001116057,0.0005938407,0.00097317726,0.00018784861],"domain_scores_gemma":[0.99723,0.0007580482,0.00019523098,0.00055430084,0.0011336984,0.00012865974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012659222,0.001288207,0.002415034,0.0028364477,0.0008229873,0.0026808851,0.0029123751,0.0015464629,0.004229611],"category_scores_gemma":[0.00557425,0.0008097112,0.0016443954,0.002361563,0.0009585877,0.0021448284,0.0021424734,0.0018143894,0.002266867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005147471,0.00016059515,0.0029279075,0.00020993751,0.00010997311,0.0003093686,0.00013520088,0.050253898,0.05834098,0.009849929,0.008039351,0.8691481],"study_design_scores_gemma":[0.000016011418,0.00010250284,0.0009231522,0.000012259276,0.000024640502,0.000409651,0.000044330536,0.9721468,0.018527903,0.005562016,0.0022020133,0.000028698858],"about_ca_topic_score_codex":0.0025899052,"about_ca_topic_score_gemma":0.002136085,"teacher_disagreement_score":0.004229611,"about_ca_system_score_codex":0.0008059715,"about_ca_system_score_gemma":0.00088520034,"threshold_uncertainty_score":0.014149427},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021045728,0.0004011154,0.97595143,0.00016698684,0.00005352149,0.000021212043,0.000047131536,0.0009840692,0.0013288395],"genre_scores_gemma":[0.5153552,0.00078470504,0.47507468,0.00024543417,0.00014990976,0.000073214884,0.0004637095,0.0003735341,0.007479593],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971277,0.000058764126,0.00001221272,0.00007257169,0.00010208798,0.000041557065],"domain_scores_gemma":[0.9990466,0.0005470612,0.00008099642,0.000105231,0.00016137474,0.00005869328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008148394,0.0008226493,0.0007819389,0.0009220879,0.00032440588,0.001196007,0.0017262561,0.00081215415,0.0019830537],"category_scores_gemma":[0.002157923,0.00045652475,0.00058758975,0.00075124693,0.00054607424,0.0020836736,0.0013990779,0.0015040352,0.0004983876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042568517,0.00022107658,0.0033865673,0.00015129693,0.00017401467,0.0001505891,0.000087867935,0.35598493,0.01752766,0.045674242,0.00466591,0.57155013],"study_design_scores_gemma":[0.0000029598825,0.000019125942,0.00010776207,0.0000034473335,0.00000705752,0.00001621196,0.00000462468,0.9917365,0.0010587628,0.006670679,0.00037004674,0.0000027716935],"about_ca_topic_score_codex":0.0028746254,"about_ca_topic_score_gemma":0.0035446486,"teacher_disagreement_score":0.0028746254,"about_ca_system_score_codex":0.00057508977,"about_ca_system_score_gemma":0.0006070064,"threshold_uncertainty_score":0.0066339374},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00726254,0.7854934,0.10319272,0.068565376,0.0048864475,0.000061982035,0.00028244674,0.0003671545,0.029887952],"genre_scores_gemma":[0.11045672,0.76061857,0.09999832,0.00993393,0.011976185,0.00014285308,0.0004469213,0.00018853373,0.006238028],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9968924,0.0011351366,0.0002506378,0.00040569785,0.0011810753,0.00013517258],"domain_scores_gemma":[0.9895064,0.008088173,0.00041565995,0.0007581245,0.000975351,0.00025618053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004480793,0.0005876292,0.0009215912,0.004876068,0.00090695015,0.007951031,0.001007178,0.0020680241,0.0021483789],"category_scores_gemma":[0.009907069,0.00035098108,0.00051338924,0.008203006,0.00504349,0.012405078,0.0025228313,0.0039772894,0.0009578628],"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":[0.00005316772,0.00005446771,0.0022699384,0.0030625092,0.00008306475,0.00013386802,0.0010188158,0.0021576982,0.0016898667,0.43896502,0.037675973,0.5128357],"study_design_scores_gemma":[0.000006984515,0.000043829303,0.0015939402,0.001597243,0.000046093934,0.0004921448,0.00078531605,0.0062425286,0.0009013048,0.43040743,0.55782294,0.000060259605],"about_ca_topic_score_codex":0.0007842847,"about_ca_topic_score_gemma":0.0009833782,"teacher_disagreement_score":0.007951031,"about_ca_system_score_codex":0.0015520842,"about_ca_system_score_gemma":0.0025507414,"threshold_uncertainty_score":0.023696959},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010272275,0.00008363773,0.99785787,0.000058937534,0.000012742745,0.00001297384,0.000040849878,0.0005552439,0.00035059417],"genre_scores_gemma":[0.19006857,0.00037607705,0.80330884,0.00016722093,0.00006658882,0.00028001753,0.00047875848,0.0006327011,0.0046211663],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996488,0.00012987133,0.000025206327,0.0000595182,0.00010291465,0.000033614124],"domain_scores_gemma":[0.99938047,0.00035633252,0.00003505369,0.00007607497,0.0001230136,0.00002898494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013805875,0.000910987,0.000768484,0.0005779179,0.00029475498,0.0008762401,0.0015234395,0.0012467229,0.0040607047],"category_scores_gemma":[0.003234178,0.0007245557,0.00070527085,0.0004573855,0.00052975916,0.0010550417,0.0019427477,0.0021685595,0.0011274499],"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":[0.0000799661,0.00006219291,0.00074235146,0.00013704256,0.00010408451,0.0000969391,0.00005613509,0.7043552,0.0044477354,0.04525599,0.004053606,0.24060887],"study_design_scores_gemma":[0.0000021615851,0.0000043492746,0.00002392311,0.000003175322,0.0000016429941,0.0000049718565,0.0000017162215,0.99529946,0.0004264046,0.0037030585,0.0005272371,0.000001779201],"about_ca_topic_score_codex":0.005701889,"about_ca_topic_score_gemma":0.0071946457,"teacher_disagreement_score":0.005701889,"about_ca_system_score_codex":0.000767593,"about_ca_system_score_gemma":0.0010855374,"threshold_uncertainty_score":0.013584435},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09685275,0.0013760362,0.89049596,0.00020781599,0.00022735463,0.00014040616,0.00028230107,0.003562977,0.0068544503],"genre_scores_gemma":[0.5579483,0.0014484301,0.4308885,0.00012748926,0.0001475454,0.000089519235,0.0011254285,0.00015842088,0.008066461],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996308,0.000030950283,0.000015180204,0.00005936509,0.0002298978,0.0000338652],"domain_scores_gemma":[0.99962664,0.00008631721,0.000031074844,0.00006859647,0.00016887135,0.00001843643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026029462,0.00061820296,0.0004386122,0.0010797004,0.0002749646,0.00036012096,0.0005982227,0.00030312827,0.0017287254],"category_scores_gemma":[0.0009719997,0.00013441643,0.0002580325,0.0010455791,0.00023648707,0.00068616914,0.0004195433,0.00044414256,0.00074076996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057413307,0.00011773196,0.0014433463,0.00006880656,0.00003097022,0.00019142231,0.00008789372,0.037340272,0.10130411,0.005780743,0.0056234496,0.847437],"study_design_scores_gemma":[0.00003532412,0.00021399443,0.0019842722,0.000018637684,0.000025046496,0.0004137955,0.000030784166,0.88901275,0.10074037,0.0016034644,0.005888555,0.00003293644],"about_ca_topic_score_codex":0.008607504,"about_ca_topic_score_gemma":0.005265466,"teacher_disagreement_score":0.008607504,"about_ca_system_score_codex":0.00034726717,"about_ca_system_score_gemma":0.0006216221,"threshold_uncertainty_score":0.017114818},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07870688,0.0004560872,0.9185747,0.0005863459,0.000039307986,0.000027909153,0.00006137048,0.0007312589,0.0008161284],"genre_scores_gemma":[0.8616854,0.00028008365,0.13506472,0.00046229368,0.000082761864,0.000104081315,0.00020504397,0.0001727419,0.0019428888],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985904,0.00047157804,0.00007785799,0.00028733257,0.00039292025,0.00017982801],"domain_scores_gemma":[0.99492186,0.0031791208,0.0005400875,0.00061073416,0.00050522434,0.0002428656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046718144,0.0013217045,0.0014320558,0.0008371945,0.0008120415,0.001238092,0.002754312,0.0025023774,0.0008390796],"category_scores_gemma":[0.014019866,0.00083635445,0.00085428165,0.00071573193,0.002175018,0.0033604344,0.0044415114,0.004337169,0.00020837961],"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":[0.00012949597,0.000052715266,0.0030720204,0.000044467484,0.000074197014,0.00026855408,0.00016646556,0.94537497,0.0018433492,0.011330237,0.0008275235,0.036816094],"study_design_scores_gemma":[0.000002766787,0.000016214794,0.00008183359,0.00000628055,0.0000037807654,0.000017764294,0.000007410876,0.99005264,0.00055672345,0.009139101,0.00011198902,0.000003439187],"about_ca_topic_score_codex":0.0028292574,"about_ca_topic_score_gemma":0.0037848407,"teacher_disagreement_score":0.0046718144,"about_ca_system_score_codex":0.0014147958,"about_ca_system_score_gemma":0.0010398226,"threshold_uncertainty_score":0.024707198},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037266284,0.00075929594,0.94212973,0.00031829075,0.00020807506,0.0001665631,0.00034185656,0.012233888,0.0065760394],"genre_scores_gemma":[0.5466963,0.00043028264,0.43971932,0.00089908106,0.00013683153,0.00041145465,0.0021322567,0.0020939168,0.007480537],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948275,0.00015334353,0.000024176868,0.000118542615,0.0001520823,0.00006906812],"domain_scores_gemma":[0.9988772,0.0005926773,0.00004747483,0.0002743523,0.00015232973,0.00005594809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001381637,0.0015031742,0.00094729994,0.00050948083,0.00030760054,0.00063758274,0.0018506452,0.0007750058,0.0060561323],"category_scores_gemma":[0.0043176534,0.0006318267,0.00071713293,0.00035503492,0.00053835625,0.0013570691,0.0013099294,0.002498246,0.0020130512],"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":[0.00048323654,0.00026041843,0.0030031104,0.00018112264,0.00016747751,0.00021937337,0.00013500734,0.6476471,0.01754746,0.013180918,0.026701258,0.29047352],"study_design_scores_gemma":[0.00001571091,0.000022433804,0.00013657239,0.0000067263873,0.000005720055,0.000027554159,0.000005003792,0.993432,0.0021379231,0.0030038236,0.0012020261,0.000004583132],"about_ca_topic_score_codex":0.0034174738,"about_ca_topic_score_gemma":0.008100949,"teacher_disagreement_score":0.0060561323,"about_ca_system_score_codex":0.0006593968,"about_ca_system_score_gemma":0.0007319772,"threshold_uncertainty_score":0.020259798},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005703247,0.070029,0.9000458,0.0032377178,0.0019255747,0.0003735591,0.0012193095,0.0040558022,0.013409951],"genre_scores_gemma":[0.26660758,0.16026059,0.5287434,0.004096377,0.0047771577,0.00094791583,0.009939859,0.0011705644,0.023456544],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99138755,0.0013617798,0.0006920686,0.00221897,0.003920572,0.00041905706],"domain_scores_gemma":[0.9874746,0.004549724,0.0012755005,0.0015671062,0.0049202857,0.00021273196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00469326,0.0023048336,0.0033428909,0.006418854,0.0010687567,0.0038823683,0.0050687417,0.0023469555,0.0031068516],"category_scores_gemma":[0.019440278,0.00061792467,0.001947465,0.0073944377,0.0014270418,0.0043702945,0.0025988175,0.0025125865,0.0041801017],"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":[0.00008670292,0.00009410707,0.006376189,0.0019143294,0.00030784597,0.00020262113,0.00018424707,0.01321801,0.0027404039,0.025229659,0.038584244,0.9110617],"study_design_scores_gemma":[0.000086011336,0.00047469608,0.009961073,0.002729289,0.00062712585,0.005038718,0.0011745861,0.27195472,0.0240354,0.16000296,0.52356625,0.00034915915],"about_ca_topic_score_codex":0.0024473043,"about_ca_topic_score_gemma":0.001838997,"teacher_disagreement_score":0.006418854,"about_ca_system_score_codex":0.001325016,"about_ca_system_score_gemma":0.0019708243,"threshold_uncertainty_score":0.024820626},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.066461176,0.0026172127,0.9102543,0.00036951777,0.00042842864,0.00019628403,0.0042036073,0.008976765,0.0064926213],"genre_scores_gemma":[0.570028,0.0019853727,0.39191413,0.00042376938,0.0002194109,0.00033170872,0.011271045,0.00039555063,0.023430936],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978167,0.000026318723,0.0000079939855,0.00006825061,0.000053361353,0.00006237718],"domain_scores_gemma":[0.99983513,0.000051367206,0.000014064712,0.000024724697,0.000058258705,0.00001637093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003382497,0.0013479141,0.0010058776,0.0007975712,0.0002990955,0.0005411333,0.0014018688,0.0010413278,0.0049550384],"category_scores_gemma":[0.00074721436,0.0004530127,0.0007704125,0.0007684441,0.00019173362,0.000625943,0.00094233046,0.00095799373,0.0032230404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003531408,0.00022612866,0.00182309,0.00015253254,0.00011403455,0.00023415156,0.000048506947,0.07132418,0.024250964,0.00165819,0.017458709,0.8823564],"study_design_scores_gemma":[0.000008121002,0.00006239443,0.0012931002,0.000023659137,0.000025398798,0.00007603796,0.000022253318,0.9849519,0.010205486,0.0015946346,0.0017237014,0.000013275105],"about_ca_topic_score_codex":0.011787632,"about_ca_topic_score_gemma":0.02823053,"teacher_disagreement_score":0.011787632,"about_ca_system_score_codex":0.00051407045,"about_ca_system_score_gemma":0.0007025827,"threshold_uncertainty_score":0.023438036},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01981022,0.00030982413,0.97764516,0.00012610227,0.000069864465,0.00002448342,0.00014423653,0.0012851654,0.0005849335],"genre_scores_gemma":[0.31689063,0.0007422546,0.6768985,0.00012482725,0.00013334455,0.000061019724,0.0011103175,0.00022418155,0.003814952],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995945,0.000044511824,0.000018451812,0.00008813282,0.00020103474,0.00005332883],"domain_scores_gemma":[0.9993199,0.00021856415,0.00008339085,0.00011598558,0.0002334391,0.000028697086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036999927,0.0006334816,0.0008530616,0.0011321229,0.00021528949,0.00060833007,0.000836606,0.00072773686,0.0011439993],"category_scores_gemma":[0.0014281784,0.00029523004,0.00059607736,0.0011502266,0.000341957,0.0009795802,0.00073197234,0.0011859196,0.00069026346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027185693,0.00013956185,0.0017611029,0.00011733247,0.00006992238,0.0001872191,0.000057670928,0.05591419,0.099673375,0.005903278,0.005400239,0.83050424],"study_design_scores_gemma":[0.0000048328056,0.000051489103,0.0007664235,0.0000062690506,0.000016081756,0.0002265458,0.000011595057,0.9766092,0.018318009,0.0029738047,0.0010062966,0.000009462418],"about_ca_topic_score_codex":0.0022484814,"about_ca_topic_score_gemma":0.0032815086,"teacher_disagreement_score":0.0022484814,"about_ca_system_score_codex":0.0003944498,"about_ca_system_score_gemma":0.0004915726,"threshold_uncertainty_score":0.0044707656},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22348912,0.0018340844,0.7572403,0.00060202956,0.0004099645,0.0005633871,0.0017460694,0.00428494,0.009830108],"genre_scores_gemma":[0.72637564,0.00035313668,0.2666107,0.00021044165,0.00015444211,0.0003283015,0.003687615,0.00060446805,0.0016751882],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9721696,0.008948387,0.003028749,0.003417876,0.011514845,0.00092058204],"domain_scores_gemma":[0.92990345,0.030285979,0.009880595,0.008466583,0.01983213,0.0016312662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025752824,0.002216955,0.0022693833,0.0069388095,0.0013422513,0.0043837577,0.0022822905,0.002177985,0.0013203135],"category_scores_gemma":[0.09601629,0.00038174455,0.0014362391,0.0040000654,0.0019678562,0.0037940391,0.003600796,0.0016943484,0.00080510677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018104624,0.0009420441,0.116428226,0.0015851918,0.0015303219,0.00027381652,0.0011277588,0.22662845,0.023388946,0.02658168,0.0135216545,0.5861814],"study_design_scores_gemma":[0.00011745789,0.0015819336,0.031221693,0.00029469692,0.00028877382,0.00047772806,0.0009715053,0.88071394,0.05042863,0.026392743,0.007284181,0.0002267736],"about_ca_topic_score_codex":0.001395493,"about_ca_topic_score_gemma":0.001960982,"teacher_disagreement_score":0.025752824,"about_ca_system_score_codex":0.0015169097,"about_ca_system_score_gemma":0.0021615648,"threshold_uncertainty_score":0.1361956},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32639465,0.00074697705,0.65922236,0.0004787268,0.00019994672,0.0002064348,0.0012963562,0.008724423,0.002730129],"genre_scores_gemma":[0.933204,0.00019050251,0.060617257,0.000100663674,0.00009095012,0.00008103517,0.0021607215,0.00011925459,0.0034355181],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940705,0.000065819186,0.000027661947,0.00017618293,0.00022232224,0.0001009821],"domain_scores_gemma":[0.99890816,0.0003224336,0.00018955907,0.0001528633,0.0003734382,0.000053574062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004813071,0.00084130495,0.0007620993,0.0014646859,0.0002978596,0.0005235391,0.0012269743,0.00069432,0.0006910148],"category_scores_gemma":[0.0020511758,0.00020226672,0.0005059798,0.0008077426,0.0004098936,0.0011818525,0.0007260159,0.0010242269,0.00054167723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068959565,0.0006430577,0.067329526,0.00015773918,0.0001511859,0.0005830762,0.00022419429,0.14989614,0.037238453,0.0030537986,0.011861394,0.7281718],"study_design_scores_gemma":[0.000005941223,0.00005010267,0.006474536,0.0000061462842,0.000012809468,0.00015082314,0.00003237186,0.9823877,0.007815258,0.0017035435,0.0013496549,0.000011095305],"about_ca_topic_score_codex":0.0043895706,"about_ca_topic_score_gemma":0.0061055287,"teacher_disagreement_score":0.0043895706,"about_ca_system_score_codex":0.0004686186,"about_ca_system_score_gemma":0.00067980954,"threshold_uncertainty_score":0.008728087},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10219877,0.0007337888,0.894968,0.00020867446,0.00011558417,0.000053704698,0.00022358424,0.00054174167,0.0009561439],"genre_scores_gemma":[0.7726672,0.0010877518,0.22231738,0.00012680364,0.00020832964,0.000075040094,0.0010814862,0.00006771863,0.002368202],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995328,0.00006854053,0.000031550167,0.00012085444,0.0001739972,0.00007229775],"domain_scores_gemma":[0.9991027,0.0003061525,0.00014090867,0.000105825675,0.00027475518,0.000069633745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043727751,0.00051976694,0.0009479261,0.0012310857,0.00024381843,0.00061651313,0.0006496143,0.00056003884,0.00078251393],"category_scores_gemma":[0.0022280272,0.0001958537,0.00040470617,0.0010522075,0.00039700756,0.001033975,0.00062816346,0.0008547088,0.00035627605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009200459,0.0003679385,0.009079562,0.00024101778,0.00011378241,0.00039524256,0.00016633977,0.09663319,0.09055837,0.007133226,0.005850103,0.78854114],"study_design_scores_gemma":[0.000008549271,0.000099034485,0.002174831,0.0000072020093,0.000015039148,0.00016814505,0.000027558486,0.9900939,0.0053970846,0.0014292704,0.0005681917,0.000011199684],"about_ca_topic_score_codex":0.0023411848,"about_ca_topic_score_gemma":0.0028902928,"teacher_disagreement_score":0.0023411848,"about_ca_system_score_codex":0.00023889974,"about_ca_system_score_gemma":0.0005337714,"threshold_uncertainty_score":0.0046551824},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032470261,0.00013761327,0.99547136,0.000036165045,0.000016854065,0.000024829433,0.000034375407,0.00076638954,0.00026531293],"genre_scores_gemma":[0.5261366,0.0004286936,0.4695444,0.00016328468,0.000086459964,0.0002023264,0.0004964768,0.00014990964,0.0027918848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936575,0.00008937936,0.00003861144,0.00018332232,0.00022533681,0.00009771169],"domain_scores_gemma":[0.9994586,0.00011614106,0.00010520385,0.00007851979,0.0002104702,0.000030996463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010365291,0.001103497,0.0010744691,0.0009732145,0.0004175175,0.0006427529,0.001852458,0.0009255167,0.0012093167],"category_scores_gemma":[0.0018900656,0.0003551585,0.00089411455,0.000795482,0.00061842834,0.0011120357,0.0014701288,0.0011235602,0.00031645017],"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":[0.00007615146,0.000058040256,0.0010558986,0.0001018819,0.00004350133,0.00010426265,0.00004819634,0.76278824,0.0059437365,0.0077114836,0.0014964899,0.2205722],"study_design_scores_gemma":[0.0000020364084,0.00001926925,0.00011780534,0.000002413042,0.000003846613,0.0000199303,0.0000031156821,0.9976163,0.00072718656,0.0011118003,0.0003725779,0.0000038017224],"about_ca_topic_score_codex":0.013769038,"about_ca_topic_score_gemma":0.012664949,"teacher_disagreement_score":0.013769038,"about_ca_system_score_codex":0.0010842351,"about_ca_system_score_gemma":0.0017784034,"threshold_uncertainty_score":0.027377784},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055646315,0.0004253122,0.9407909,0.00020224237,0.00010864017,0.00007483489,0.00019547214,0.0018141083,0.0007422778],"genre_scores_gemma":[0.62369466,0.00035088958,0.3721901,0.00024095988,0.00021119989,0.00007997681,0.0009479063,0.00023209734,0.0020521574],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874395,0.00016391482,0.000057735197,0.00055635953,0.00033413884,0.00014395357],"domain_scores_gemma":[0.9977654,0.00060583756,0.0004069494,0.00047931387,0.0005384093,0.00020402996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087058387,0.0011633781,0.0020205595,0.0022105542,0.0005221813,0.00076697837,0.0032519673,0.0013923303,0.0006780864],"category_scores_gemma":[0.0031287544,0.0005241268,0.0009961592,0.0015043304,0.000914469,0.0021183568,0.0013598036,0.0017760027,0.00043411765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050221186,0.00063072174,0.018821768,0.00039436598,0.00039400585,0.00084010186,0.00031683635,0.1542135,0.09383117,0.005811132,0.006645141,0.71759903],"study_design_scores_gemma":[0.000010101611,0.00015300176,0.0023264717,0.000008425053,0.000032917447,0.00040377883,0.000038304865,0.97824436,0.012642783,0.004926406,0.0011903521,0.00002307581],"about_ca_topic_score_codex":0.0026190907,"about_ca_topic_score_gemma":0.0036653501,"teacher_disagreement_score":0.0032519673,"about_ca_system_score_codex":0.00050582405,"about_ca_system_score_gemma":0.000682272,"threshold_uncertainty_score":0.005207658},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0079133,0.0060416716,0.98066586,0.0017589009,0.00021214462,0.000018287059,0.00007602884,0.0002254433,0.003088422],"genre_scores_gemma":[0.44259632,0.010349011,0.5298942,0.0009546097,0.0008932366,0.0001267847,0.0003370707,0.00022980219,0.014618955],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994796,0.00021504075,0.000033436565,0.00009454167,0.00014148628,0.000035936166],"domain_scores_gemma":[0.997378,0.0019136289,0.00011073201,0.0001966205,0.00035846562,0.00004265014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013709924,0.0005323706,0.00069696223,0.00070503796,0.00026522472,0.0014168493,0.000949142,0.0011348828,0.002026437],"category_scores_gemma":[0.006091323,0.00036160162,0.00040531906,0.001322451,0.000980991,0.002340158,0.0011279102,0.0026131931,0.0005018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010728373,0.00013553479,0.0020915433,0.0005071174,0.00012775834,0.00009605302,0.00013098352,0.25225884,0.0039797304,0.25708562,0.011201152,0.4722784],"study_design_scores_gemma":[0.000004422363,0.000016378564,0.0003231535,0.000038949373,0.000012654407,0.000027107413,0.0000147372175,0.8624445,0.0013385427,0.13138635,0.0043852474,0.000007954394],"about_ca_topic_score_codex":0.003192636,"about_ca_topic_score_gemma":0.003109496,"teacher_disagreement_score":0.003192636,"about_ca_system_score_codex":0.00080612587,"about_ca_system_score_gemma":0.00065678864,"threshold_uncertainty_score":0.007250607},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01706886,0.00021770007,0.98151153,0.00013203699,0.00001916624,0.000012755213,0.00017873004,0.00054930063,0.00030981636],"genre_scores_gemma":[0.7705818,0.00051885966,0.22536989,0.00010162049,0.00010953217,0.00008455293,0.0012994275,0.00009281701,0.0018415764],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996797,0.00007244504,0.000022344357,0.00010004596,0.00008752468,0.000038042464],"domain_scores_gemma":[0.9989221,0.00059715146,0.00014742356,0.00011840632,0.00018448343,0.000030464407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005184979,0.00046738976,0.0006481706,0.0006139246,0.00020280085,0.00043256616,0.0006473048,0.0006014096,0.00079986517],"category_scores_gemma":[0.0028859451,0.00026455673,0.00043981714,0.0010599197,0.0003532661,0.00096272427,0.0006405321,0.0010200585,0.00026460213],"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":[0.00013913495,0.00009710611,0.0018964419,0.00008749744,0.0000628423,0.00010434969,0.00006964484,0.7025016,0.013434492,0.0089495545,0.0028321168,0.2698252],"study_design_scores_gemma":[0.0000013387037,0.000008956166,0.00020449817,0.0000013408875,0.0000022195143,0.000010456719,0.0000027254189,0.9969337,0.00062679796,0.0020115704,0.0001936436,0.0000027517895],"about_ca_topic_score_codex":0.006668675,"about_ca_topic_score_gemma":0.0045413366,"teacher_disagreement_score":0.006668675,"about_ca_system_score_codex":0.0005252323,"about_ca_system_score_gemma":0.0006087152,"threshold_uncertainty_score":0.0132597685},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30527166,0.0016360215,0.6786049,0.0021495884,0.00022690183,0.00012284523,0.00051086076,0.004151481,0.007325745],"genre_scores_gemma":[0.96102184,0.0004517836,0.035744675,0.00021189902,0.00006026263,0.00006589697,0.00058461406,0.00029936805,0.0015594868],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99771357,0.00065814675,0.00015762837,0.0004926846,0.0006886323,0.00028933765],"domain_scores_gemma":[0.9926766,0.0039577363,0.0007294829,0.0020271281,0.00046015988,0.00014889323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029455912,0.0016702017,0.00091807154,0.0008155544,0.00042052893,0.0015867121,0.0013372857,0.0015895761,0.0031551698],"category_scores_gemma":[0.020454954,0.0004902065,0.0008083031,0.0004523898,0.0014517183,0.004416975,0.0024424305,0.0028666952,0.0012480862],"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":[0.00038647006,0.000085612824,0.002363527,0.00010918805,0.000109047665,0.0000954398,0.000047567803,0.9269199,0.01152602,0.0061551295,0.0011191143,0.051083036],"study_design_scores_gemma":[0.000010031466,0.00015629485,0.00092976564,0.000022064018,0.000030003426,0.00012355688,0.000026579231,0.98000103,0.011506284,0.006400945,0.0007734799,0.000019965712],"about_ca_topic_score_codex":0.0019467024,"about_ca_topic_score_gemma":0.0012082309,"teacher_disagreement_score":0.0031551698,"about_ca_system_score_codex":0.0010606303,"about_ca_system_score_gemma":0.000647826,"threshold_uncertainty_score":0.015577912},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96316195,0.011558494,0.0038044213,0.0015005344,0.000188124,0.00013476286,0.0055824895,0.00012323256,0.013945987],"genre_scores_gemma":[0.9852997,0.003630871,0.0033570062,0.00012416973,0.00010949521,0.000068162015,0.0035846932,0.000038378414,0.003787523],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9962637,0.00042296678,0.0008072825,0.0003918685,0.0017603234,0.00035389187],"domain_scores_gemma":[0.98668575,0.004154246,0.0028117036,0.00046721252,0.005209998,0.0006711231],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.001899324,0.0002785834,0.000381503,0.016493086,0.0012388286,0.0015909827,0.00036524373,0.0002934492,0.0017955842],"category_scores_gemma":[0.0083689615,0.000116829404,0.0003710717,0.03171243,0.0007445783,0.0019944771,0.001066096,0.00035406317,0.0004996741],"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":[0.00030510433,0.00011347169,0.6226406,0.0025888328,0.00019212873,0.0026911926,0.014560639,0.0009161567,0.009128391,0.007976573,0.016224451,0.32266253],"study_design_scores_gemma":[0.000013008633,0.000116086965,0.9036382,0.0003871655,0.00018724833,0.0014622303,0.010508053,0.005023324,0.0050383294,0.0021238027,0.07142929,0.00007316962],"about_ca_topic_score_codex":0.021087643,"about_ca_topic_score_gemma":0.02214094,"teacher_disagreement_score":0.9835069,"about_ca_system_score_codex":0.0023742602,"about_ca_system_score_gemma":0.0040622903,"threshold_uncertainty_score":0},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96093136,0.00039910024,0.02966065,0.002568836,0.000081017046,0.00048618458,0.0007267895,0.00036311743,0.004782952],"genre_scores_gemma":[0.9651318,0.00063398515,0.03044821,0.0004516726,0.00005636573,0.00021547663,0.00074773014,0.000108407265,0.0022063795],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99551433,0.0016177439,0.00027123015,0.00033336467,0.0019136944,0.0003496065],"domain_scores_gemma":[0.98043114,0.012727372,0.001921194,0.00165331,0.002332096,0.00093486754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003908627,0.00069147424,0.0004831996,0.0014792278,0.001985615,0.0015456105,0.0015247894,0.002562116,0.000608302],"category_scores_gemma":[0.013026449,0.00036944926,0.0005235141,0.002756909,0.0025484336,0.0036832234,0.0024305347,0.0018011556,0.00028936632],"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":[0.0019568063,0.0039558765,0.3325212,0.004610745,0.0006659108,0.09324926,0.09110111,0.09011103,0.04031826,0.018429125,0.03748601,0.28559464],"study_design_scores_gemma":[0.00033328653,0.0059412625,0.29085454,0.0012224866,0.00039334188,0.034366783,0.23761572,0.20263113,0.064907864,0.029507522,0.13156296,0.0006631469],"about_ca_topic_score_codex":0.0048573115,"about_ca_topic_score_gemma":0.011185107,"teacher_disagreement_score":0.0048573115,"about_ca_system_score_codex":0.0008185371,"about_ca_system_score_gemma":0.0009952665,"threshold_uncertainty_score":0.02067107},"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":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019181322,0.2661678,0.69088197,0.0017859332,0.0011475792,0.000316563,0.0011689126,0.0046776417,0.014672243],"genre_scores_gemma":[0.12848435,0.24216372,0.6139997,0.0007910052,0.0013317124,0.00047034957,0.004171844,0.0015334226,0.007053891],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99563724,0.0006872825,0.00052259176,0.0008672286,0.0021209812,0.00016472898],"domain_scores_gemma":[0.9942123,0.0021429127,0.00031788767,0.0004324975,0.0027521788,0.00014222132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030823406,0.0022484388,0.0031588315,0.00577372,0.0015677274,0.0033831482,0.0028573137,0.0026789985,0.0026096362],"category_scores_gemma":[0.011583017,0.0011506464,0.00206271,0.01144695,0.0010249536,0.0039231027,0.0014001795,0.0019073861,0.0036078342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031077367,0.00012434286,0.0033431575,0.0030879,0.00032420733,0.00007672231,0.00021343422,0.040479116,0.002551206,0.006102369,0.019712096,0.92367476],"study_design_scores_gemma":[0.00009905621,0.0011047117,0.013751214,0.002564521,0.0008560379,0.0023742043,0.0011972237,0.5639298,0.032912243,0.034990866,0.34556696,0.0006531665],"about_ca_topic_score_codex":0.009061317,"about_ca_topic_score_gemma":0.0055865413,"teacher_disagreement_score":0.009061317,"about_ca_system_score_codex":0.0015947106,"about_ca_system_score_gemma":0.0028954162,"threshold_uncertainty_score":0.018017113},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33899185,0.0003487376,0.65455425,0.0005485401,0.00016867822,0.00009523506,0.00035219995,0.0037896272,0.001150882],"genre_scores_gemma":[0.9484114,0.00008851418,0.04965319,0.000077983306,0.000076673605,0.00003776054,0.00057515193,0.0000591814,0.0010200837],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934465,0.0001295948,0.00003100274,0.00018476733,0.00023334827,0.00007664403],"domain_scores_gemma":[0.99808645,0.0007794903,0.000283003,0.0003892691,0.00039182114,0.00007000936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011236635,0.00069331867,0.00083242124,0.0009999298,0.0003247902,0.000700812,0.000837358,0.0005881828,0.0003970526],"category_scores_gemma":[0.004297253,0.00021470136,0.0003836142,0.00070172624,0.00049577927,0.0013588837,0.0007577217,0.0010640145,0.00031378694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077911344,0.00042025486,0.042573623,0.000076303586,0.00011535406,0.0003902673,0.00022928916,0.44532138,0.026334934,0.003825716,0.0045572277,0.47537658],"study_design_scores_gemma":[0.000004028071,0.000039454084,0.0018620259,0.0000018176174,0.000004912324,0.00005575098,0.000018161889,0.993487,0.002547097,0.001635292,0.0003397809,0.0000045704355],"about_ca_topic_score_codex":0.0037704972,"about_ca_topic_score_gemma":0.0037674622,"teacher_disagreement_score":0.0037704972,"about_ca_system_score_codex":0.00057649805,"about_ca_system_score_gemma":0.000530769,"threshold_uncertainty_score":0.007497132},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007931649,0.0001795111,0.98947597,0.000059874335,0.000076890436,0.000041523446,0.00006499756,0.0018593562,0.00031027122],"genre_scores_gemma":[0.21657278,0.0004550467,0.77726114,0.00012493467,0.00028796215,0.000211663,0.0010958518,0.00050335756,0.0034872913],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981719,0.00018982949,0.000145362,0.00033781034,0.0010096076,0.00014540083],"domain_scores_gemma":[0.9961392,0.0014411714,0.00038366124,0.00069864816,0.0011844656,0.00015275436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015524662,0.001266424,0.0023014927,0.0017538329,0.00066626247,0.0020125168,0.0024835402,0.0011333314,0.0019524326],"category_scores_gemma":[0.00804949,0.0005232692,0.0011369946,0.002465329,0.00055691664,0.0024030781,0.0021053096,0.0025571599,0.0017953858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047724458,0.00026039992,0.002796497,0.00013243969,0.00014414053,0.00015711786,0.00009759979,0.082395926,0.02747318,0.008036267,0.006326674,0.87170255],"study_design_scores_gemma":[0.000011124789,0.000058469952,0.000404625,0.0000068602712,0.000015259462,0.00010056609,0.000013755688,0.98764426,0.005436565,0.004998711,0.0012988503,0.000010868494],"about_ca_topic_score_codex":0.0017499054,"about_ca_topic_score_gemma":0.0020880308,"teacher_disagreement_score":0.0024835402,"about_ca_system_score_codex":0.0006442171,"about_ca_system_score_gemma":0.001118913,"threshold_uncertainty_score":0.008210301},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23935205,0.00066413,0.752166,0.00033173806,0.000047033256,0.00005400485,0.0005076553,0.0054434906,0.0014338603],"genre_scores_gemma":[0.934703,0.00014629558,0.062476236,0.00010564857,0.000025187144,0.0000581446,0.00069734245,0.0000946374,0.001693501],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953353,0.00009656024,0.00003161169,0.00016626772,0.000093250484,0.000078774625],"domain_scores_gemma":[0.99849427,0.0009120062,0.00019436165,0.00016895746,0.00018400986,0.00004632159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009227694,0.0012956552,0.0006452912,0.0009768775,0.0002572045,0.0006991599,0.0010771393,0.0009084477,0.001082094],"category_scores_gemma":[0.0032451178,0.0005904484,0.0007037947,0.0005320208,0.00048925023,0.0015831558,0.00065476494,0.0015877238,0.0003028036],"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":[0.00010352174,0.00006639623,0.0018854977,0.000035210385,0.000057308243,0.000050813367,0.000044917622,0.94737077,0.0022071037,0.0011449227,0.00040969794,0.04662377],"study_design_scores_gemma":[7.1976257e-7,0.000004023904,0.00009071844,9.976116e-7,0.0000015284003,0.0000017635571,0.0000010964418,0.9991172,0.00028723644,0.00046936542,0.000024151113,0.0000012573597],"about_ca_topic_score_codex":0.018436503,"about_ca_topic_score_gemma":0.020835243,"teacher_disagreement_score":0.018436503,"about_ca_system_score_codex":0.001382407,"about_ca_system_score_gemma":0.0008270993,"threshold_uncertainty_score":0.036658347},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20843409,0.00038970148,0.7867051,0.0005479289,0.00005186519,0.000058334128,0.00051403866,0.0012945974,0.0020044222],"genre_scores_gemma":[0.8576788,0.00036333076,0.14007157,0.00004907534,0.000030730193,0.000074297284,0.0005125975,0.00010588735,0.0011136194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998491,0.000047458703,0.000007940666,0.000037806498,0.00003286177,0.000024722563],"domain_scores_gemma":[0.9993787,0.00041207953,0.00007227061,0.0000399257,0.000060383252,0.000036594163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006668224,0.0006008255,0.00033070525,0.0015765257,0.00028300995,0.0012027136,0.00050939195,0.00056940864,0.0016756855],"category_scores_gemma":[0.002410263,0.0003350152,0.0005010386,0.0010689619,0.0005123382,0.001513261,0.0006230141,0.0007335232,0.00019641193],"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":[0.00028967205,0.00014128498,0.0079547195,0.00014685445,0.00007485342,0.00020428112,0.00051518076,0.78535104,0.017427437,0.02297768,0.0023448872,0.16257203],"study_design_scores_gemma":[0.000004824015,0.000016930955,0.00082555145,0.000009732759,0.0000039679367,0.000012770652,0.0000388277,0.98806816,0.001181721,0.009500083,0.000328994,0.000008457936],"about_ca_topic_score_codex":0.004657753,"about_ca_topic_score_gemma":0.004444755,"teacher_disagreement_score":0.004657753,"about_ca_system_score_codex":0.0005965829,"about_ca_system_score_gemma":0.0005231779,"threshold_uncertainty_score":0.0092612505},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23288678,0.000655726,0.7618372,0.00026863226,0.000113724134,0.000069409325,0.00026983878,0.0021886819,0.0017100702],"genre_scores_gemma":[0.95066404,0.00010695779,0.048293937,0.000019147017,0.000034850378,0.000023156008,0.00020027008,0.000052907366,0.0006047137],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989524,0.00022886031,0.00006205629,0.0002094354,0.00043876586,0.000108539316],"domain_scores_gemma":[0.9982185,0.00075031054,0.00021981116,0.00016047212,0.0005901821,0.000060674418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082407607,0.0005080422,0.0008045416,0.0017347679,0.00058803766,0.00083046284,0.0009160361,0.00042626046,0.00055511395],"category_scores_gemma":[0.0028098563,0.00023312095,0.00043774274,0.0016925402,0.00042439075,0.0008145024,0.0005173447,0.00063596235,0.0001982999],"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":[0.00093056756,0.00035146307,0.034507968,0.00022530691,0.00032413052,0.00023518225,0.00036037364,0.4892492,0.028978907,0.0101306345,0.0046091704,0.43009707],"study_design_scores_gemma":[0.0000041545018,0.00003681392,0.0040045544,0.000003808115,0.000014897635,0.000054465745,0.000033245164,0.9899659,0.0032983443,0.0021103583,0.0004658298,0.0000075722505],"about_ca_topic_score_codex":0.008434956,"about_ca_topic_score_gemma":0.00604862,"teacher_disagreement_score":0.008434956,"about_ca_system_score_codex":0.0008249525,"about_ca_system_score_gemma":0.0006889343,"threshold_uncertainty_score":0.016771734},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016837073,0.00027582305,0.9772816,0.0002016814,0.000097702046,0.00018366925,0.0007158789,0.004001998,0.00040459237],"genre_scores_gemma":[0.34929627,0.00035823483,0.6436521,0.00029260505,0.00016638284,0.00046819553,0.00376472,0.0002804787,0.0017209946],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99505794,0.00056922255,0.0006981041,0.00080929266,0.0025112063,0.00035430412],"domain_scores_gemma":[0.99055964,0.0025361816,0.0014944931,0.0022578626,0.002733622,0.00041814387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037781352,0.0011238465,0.002012877,0.004490767,0.0013267872,0.0019478338,0.0030500684,0.0013020304,0.0013466836],"category_scores_gemma":[0.016864097,0.0003691595,0.0014873325,0.00469326,0.0010085073,0.0040960507,0.0034849457,0.002032684,0.0009904192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013635854,0.00036758496,0.022218578,0.00039907973,0.00030080063,0.00065854687,0.00060033926,0.09127229,0.030105423,0.02057654,0.011773346,0.820364],"study_design_scores_gemma":[0.000087926885,0.0003131805,0.0038654637,0.000038395418,0.00006273815,0.0007266327,0.00014119073,0.942124,0.017675247,0.02578995,0.009052397,0.00012286931],"about_ca_topic_score_codex":0.0030723596,"about_ca_topic_score_gemma":0.0028840015,"teacher_disagreement_score":0.004490767,"about_ca_system_score_codex":0.0013342617,"about_ca_system_score_gemma":0.0020617915,"threshold_uncertainty_score":0.019980907},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019132115,0.011416847,0.90780926,0.03980681,0.0017412663,0.001254668,0.008394149,0.0037638068,0.006681067],"genre_scores_gemma":[0.1118938,0.009625044,0.8412076,0.008286663,0.0019942669,0.0037110525,0.016339885,0.0018474734,0.005094176],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.93783313,0.02391522,0.005539044,0.005116389,0.026424298,0.0011718151],"domain_scores_gemma":[0.7931804,0.10005246,0.009855264,0.039908245,0.053160097,0.0038434968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.057101943,0.0011265101,0.0020323908,0.0041692522,0.0028305373,0.009194133,0.0050856415,0.00294739,0.003655462],"category_scores_gemma":[0.20775719,0.00137614,0.0015453898,0.007496398,0.0040999604,0.009662429,0.008774134,0.008299758,0.002638957],"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":[0.0004928268,0.00043010004,0.029330969,0.0037405018,0.0004032459,0.00043819757,0.0020491257,0.020050593,0.0077145207,0.08695638,0.11575163,0.73264194],"study_design_scores_gemma":[0.00016101878,0.0004631849,0.021110073,0.003583948,0.00019362797,0.0010663788,0.0030581516,0.102932036,0.021288935,0.39958695,0.44629467,0.00026106334],"about_ca_topic_score_codex":0.0054403045,"about_ca_topic_score_gemma":0.006050148,"teacher_disagreement_score":0.057101943,"about_ca_system_score_codex":0.0038927752,"about_ca_system_score_gemma":0.008029495,"threshold_uncertainty_score":0.3019876},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007252494,0.00035736448,0.99140257,0.00024795686,0.000041625015,0.000018089851,0.00002469434,0.000120502955,0.0005346565],"genre_scores_gemma":[0.6336772,0.0008695641,0.36248875,0.0003707264,0.00041893023,0.00018877337,0.00026299697,0.00006732025,0.0016558002],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996518,0.001333068,0.00015497852,0.000493429,0.00128924,0.00021112464],"domain_scores_gemma":[0.9915452,0.006062277,0.00045528627,0.00063778396,0.0011090675,0.00019037363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053560664,0.0007000016,0.0018022765,0.00257479,0.00060539995,0.0017838045,0.0021087686,0.002134381,0.0010249795],"category_scores_gemma":[0.019588588,0.0004111477,0.000991206,0.0019153301,0.0026532137,0.0022381095,0.0021526755,0.003472936,0.0005188079],"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":[0.00024015804,0.00013409811,0.0064408514,0.00015943844,0.00014333874,0.00040829804,0.00012632529,0.5913108,0.0037805287,0.12925388,0.00392215,0.26408005],"study_design_scores_gemma":[0.0000059584268,0.000025615573,0.00030145483,0.000008510798,0.0000046161417,0.00008508229,0.000005376084,0.96611816,0.00049733504,0.03251466,0.00042411964,0.00000907142],"about_ca_topic_score_codex":0.0008522645,"about_ca_topic_score_gemma":0.0003324182,"teacher_disagreement_score":0.0053560664,"about_ca_system_score_codex":0.0012541201,"about_ca_system_score_gemma":0.0006699373,"threshold_uncertainty_score":0.028325975},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7831351,0.0013980463,0.20636201,0.0016823877,0.00018732973,0.00011101608,0.00037744406,0.0013368824,0.005409677],"genre_scores_gemma":[0.98727554,0.00016468223,0.011360798,0.00014229705,0.000024085422,0.00002346895,0.00024096188,0.000028669438,0.0007394542],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987179,0.0003962082,0.000067793444,0.00018328492,0.0004232761,0.00021158505],"domain_scores_gemma":[0.99472505,0.00339333,0.00066484796,0.00051210076,0.00051289744,0.00019171866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029454401,0.0009002841,0.00065636437,0.00090190984,0.00037349266,0.0006038826,0.0007641969,0.0008305348,0.00055058254],"category_scores_gemma":[0.009592023,0.00028922732,0.00052255794,0.00043541673,0.0010772209,0.0014058261,0.0012029766,0.0013297087,0.00014567771],"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":[0.000269166,0.00012064015,0.0054424177,0.0000498528,0.00005919647,0.000100593024,0.00004286054,0.95167947,0.0022314899,0.0025678256,0.0012555687,0.036181],"study_design_scores_gemma":[0.0000014474069,0.00002139087,0.00038930398,0.0000034722152,0.0000026385912,0.000010361396,0.0000058337623,0.9978271,0.0008496684,0.0008189147,0.00006770907,0.0000021734868],"about_ca_topic_score_codex":0.0045966837,"about_ca_topic_score_gemma":0.004173747,"teacher_disagreement_score":0.0045966837,"about_ca_system_score_codex":0.0016932035,"about_ca_system_score_gemma":0.0006557798,"threshold_uncertainty_score":0.0155771375},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03217824,0.0002894014,0.96494514,0.00015669863,0.00006159954,0.00011572285,0.00014617764,0.0012817838,0.00082520687],"genre_scores_gemma":[0.5051774,0.00025795327,0.4922564,0.00011488218,0.000057995745,0.00021095415,0.00054943527,0.000112180176,0.0012628144],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998692,0.00022364804,0.00009837938,0.00034604533,0.00051864,0.00012139661],"domain_scores_gemma":[0.99784696,0.0007627288,0.00029658727,0.00023697334,0.0007704518,0.00008627807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012976236,0.0008944878,0.0012159084,0.002754553,0.000825724,0.0013491823,0.0017034253,0.0010612523,0.00057384465],"category_scores_gemma":[0.004297802,0.00040847025,0.0012517757,0.0027313344,0.0006495091,0.0014563751,0.0014262674,0.0010231341,0.00027778084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035810846,0.00034963276,0.016974699,0.00038269063,0.0004821802,0.0003409354,0.00053313957,0.31238455,0.0142148575,0.008775257,0.005183968,0.6400199],"study_design_scores_gemma":[0.000009780036,0.000058976497,0.0021392582,0.000012270352,0.000022528613,0.00011548238,0.00007779932,0.98545396,0.005422268,0.0051710443,0.0014879652,0.000028772138],"about_ca_topic_score_codex":0.004433018,"about_ca_topic_score_gemma":0.004362078,"teacher_disagreement_score":0.004433018,"about_ca_system_score_codex":0.00083780696,"about_ca_system_score_gemma":0.0009793633,"threshold_uncertainty_score":0.0088143945},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065361178,0.00018931381,0.9889936,0.000244557,0.00005354323,0.00016203435,0.00010240099,0.0017188865,0.0019994979],"genre_scores_gemma":[0.4191298,0.00036259822,0.57321995,0.0007298578,0.00007410012,0.0007648888,0.0007243957,0.00033896288,0.0046554916],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923646,0.00023801795,0.000031333344,0.0001989162,0.00022656882,0.00006875836],"domain_scores_gemma":[0.9988187,0.0005824326,0.000095076706,0.00013584139,0.00029011237,0.00007769331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018959985,0.00100563,0.0006596159,0.0005221947,0.0002861608,0.00070084335,0.0022677612,0.000974287,0.0025047162],"category_scores_gemma":[0.0037352198,0.00050814636,0.0006802392,0.00034231678,0.0008930529,0.0010829835,0.0013630989,0.002897978,0.0008621307],"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":[0.00007858335,0.00009883688,0.0010334882,0.00007509626,0.00006676117,0.00008806577,0.00003651699,0.8908518,0.0041234028,0.014129733,0.003262031,0.086155675],"study_design_scores_gemma":[0.0000034636369,0.000016141663,0.000038754155,0.0000029782625,0.0000025058591,0.000013042629,0.0000014876332,0.99699605,0.00081859797,0.0016774328,0.00042691073,0.0000027349004],"about_ca_topic_score_codex":0.002825954,"about_ca_topic_score_gemma":0.0032414247,"teacher_disagreement_score":0.002825954,"about_ca_system_score_codex":0.0011510641,"about_ca_system_score_gemma":0.001153843,"threshold_uncertainty_score":0.010027111},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048299424,0.0002894743,0.94904715,0.00041247727,0.000075498974,0.00006256603,0.00019280388,0.0005637589,0.0010568378],"genre_scores_gemma":[0.9202821,0.00034114483,0.074118406,0.00009347529,0.00008254155,0.0001552106,0.0004612908,0.000069629285,0.0043962705],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993917,0.00011087857,0.000042585147,0.00020261672,0.00013538061,0.00011689521],"domain_scores_gemma":[0.99901116,0.0003459759,0.0001551495,0.000068059475,0.00035439568,0.00006519922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013147853,0.0007649088,0.0011763866,0.0010614131,0.0005168809,0.0013255639,0.0021701048,0.0012189634,0.0017691213],"category_scores_gemma":[0.0025331455,0.00047036505,0.0009414519,0.0010240566,0.0006679326,0.0014770229,0.0010323225,0.0017950345,0.00041968748],"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":[0.00018010699,0.000076269076,0.003303398,0.000048106383,0.00006306446,0.00008883918,0.000066733395,0.93504435,0.0013721647,0.009239382,0.0010302032,0.04948737],"study_design_scores_gemma":[0.0000014093748,0.00000678697,0.000087274464,0.0000010913762,0.0000030150304,0.0000053313065,0.0000023848193,0.99897456,0.0000832639,0.0007382829,0.00009486934,0.000001777525],"about_ca_topic_score_codex":0.015897885,"about_ca_topic_score_gemma":0.008414028,"teacher_disagreement_score":0.015897885,"about_ca_system_score_codex":0.0012514866,"about_ca_system_score_gemma":0.0014020937,"threshold_uncertainty_score":0.031610668},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32470587,0.00019566459,0.6706603,0.00024094875,0.00006505359,0.00006872391,0.0005636046,0.0019306376,0.0015691087],"genre_scores_gemma":[0.95200723,0.00008122673,0.045683574,0.00003512313,0.000022980375,0.0000393411,0.0010754012,0.00004386327,0.001011274],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980074,0.000023901617,0.000011470965,0.00007716419,0.00004874712,0.000037987615],"domain_scores_gemma":[0.9994634,0.00019551639,0.000084928484,0.00008074178,0.00013960779,0.000035737357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000372761,0.0006911946,0.00035276063,0.0004789965,0.00020543799,0.0004269376,0.00077233283,0.00041141943,0.0005260123],"category_scores_gemma":[0.0017609869,0.00021807151,0.00031151206,0.0003983587,0.00025820427,0.0009376388,0.00057987525,0.0010123738,0.0003152574],"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":[0.0002804224,0.00023295474,0.01358541,0.0000956281,0.000061387334,0.0002966136,0.00016256016,0.6672023,0.027845532,0.0034324552,0.0023136262,0.28449103],"study_design_scores_gemma":[0.00000137552,0.000013033344,0.0008191409,0.0000019977256,0.0000026529372,0.000015906957,0.000011129876,0.99474263,0.0033995963,0.00087419414,0.00011546127,0.0000029096107],"about_ca_topic_score_codex":0.0051087514,"about_ca_topic_score_gemma":0.0060864347,"teacher_disagreement_score":0.0051087514,"about_ca_system_score_codex":0.00051290775,"about_ca_system_score_gemma":0.0006115113,"threshold_uncertainty_score":0.010158002},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03593276,0.0011467556,0.938918,0.00051385316,0.00035050247,0.000035691788,0.00015980609,0.00063730153,0.022305286],"genre_scores_gemma":[0.88902926,0.001504175,0.08706464,0.00026236565,0.00047938825,0.0000858643,0.00047044936,0.0002028099,0.020901179],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952507,0.000098638695,0.000025006932,0.0001448521,0.00013504238,0.00007140578],"domain_scores_gemma":[0.99793845,0.00128443,0.0001692978,0.0002494621,0.00020484917,0.00015346358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007031172,0.00065658905,0.000772959,0.0008923656,0.00053365115,0.0014147034,0.0011266565,0.00059594837,0.0056302836],"category_scores_gemma":[0.005209121,0.00031992563,0.00041256775,0.0012557774,0.00073593814,0.002030623,0.0024345755,0.0013593944,0.0008431334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053182425,0.00011896653,0.0024844888,0.0001379049,0.000057334346,0.00047067364,0.0002550576,0.16096279,0.009851476,0.6012246,0.007207662,0.2166972],"study_design_scores_gemma":[0.000016703802,0.00006624324,0.0007571936,0.00002378386,0.0000244615,0.00015264182,0.000052053856,0.72752595,0.0016493369,0.26470688,0.005006926,0.000017848826],"about_ca_topic_score_codex":0.0012269574,"about_ca_topic_score_gemma":0.0010335353,"teacher_disagreement_score":0.0056302836,"about_ca_system_score_codex":0.00044979024,"about_ca_system_score_gemma":0.00040882768,"threshold_uncertainty_score":0.018835187},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12108166,0.0008403594,0.8740495,0.00036620567,0.00007424231,0.0001778577,0.0011030836,0.0010593698,0.0012476965],"genre_scores_gemma":[0.80606985,0.0003916933,0.19016582,0.00008471587,0.00015069738,0.00014132427,0.0013763543,0.00006141285,0.0015581077],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990208,0.00018842219,0.00008995641,0.00025544432,0.00030063582,0.00014486439],"domain_scores_gemma":[0.99665254,0.0017390101,0.0004282775,0.00027889712,0.0006863188,0.00021485255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017996193,0.0008029038,0.0012227241,0.0032099888,0.00065894204,0.0010816277,0.0015264526,0.00085949746,0.0014112795],"category_scores_gemma":[0.005187618,0.00045225333,0.0011058463,0.0018556479,0.00044788333,0.0011946799,0.0010199876,0.0015869893,0.00036775324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007235152,0.0013790217,0.10549939,0.00034914643,0.00058316486,0.00048744216,0.0005248742,0.34763202,0.017945725,0.015137468,0.007403258,0.5023351],"study_design_scores_gemma":[0.000009140793,0.00009679305,0.007612641,0.000015583706,0.000052446252,0.000051597854,0.00003192045,0.98420435,0.0010141628,0.006480502,0.00041078043,0.00002010124],"about_ca_topic_score_codex":0.013994366,"about_ca_topic_score_gemma":0.018154504,"teacher_disagreement_score":0.013994366,"about_ca_system_score_codex":0.00081746915,"about_ca_system_score_gemma":0.0014939713,"threshold_uncertainty_score":0.027825832},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82597154,0.00018377445,0.16784334,0.00071045785,0.00009005768,0.00006998472,0.0001306755,0.003690814,0.0013093591],"genre_scores_gemma":[0.9623062,0.000035104,0.037016023,0.000053162694,0.0000125790975,0.000011764063,0.000091351176,0.00004864699,0.0004251371],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998845,0.00014786598,0.00005164011,0.00031829978,0.00044672913,0.00019034793],"domain_scores_gemma":[0.99766195,0.00070999895,0.00040737548,0.00036728906,0.00060078176,0.00025263272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011084371,0.00057219056,0.0004314281,0.0009666387,0.000960024,0.0010384697,0.0012959513,0.0006416579,0.0004403115],"category_scores_gemma":[0.003678229,0.00035414926,0.0003397577,0.00097389816,0.0006509899,0.0015666218,0.001134705,0.0012459607,0.0001484221],"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":[0.00095185876,0.00083034905,0.12126463,0.00015217827,0.00025014195,0.00358037,0.0009445513,0.43926728,0.111985244,0.0096190795,0.007484632,0.3036697],"study_design_scores_gemma":[0.0000065337204,0.000044032957,0.0043739947,0.0000022669435,0.000008480232,0.00009938418,0.000065414606,0.98809695,0.004917146,0.0019154117,0.00046186915,0.0000084369785],"about_ca_topic_score_codex":0.011963555,"about_ca_topic_score_gemma":0.013782149,"teacher_disagreement_score":0.011963555,"about_ca_system_score_codex":0.0011959312,"about_ca_system_score_gemma":0.0010819115,"threshold_uncertainty_score":0.023787796},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04083804,0.00054644176,0.95406055,0.00039869454,0.000108005945,0.000041574764,0.00028658973,0.0022913723,0.0014287145],"genre_scores_gemma":[0.80610734,0.0005335709,0.1820725,0.00025955532,0.00013397036,0.000063823994,0.0011136694,0.00031407332,0.009401507],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993886,0.00011829486,0.000026529638,0.00021742613,0.00014860695,0.00010061428],"domain_scores_gemma":[0.99793184,0.0008729665,0.0001631284,0.0005348267,0.0003900082,0.00010726254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012879365,0.00089222274,0.00089624175,0.001645299,0.0005975799,0.0009784558,0.0019832,0.0014244418,0.0029008824],"category_scores_gemma":[0.0043572923,0.0004011855,0.0007079486,0.0011800359,0.0011282929,0.0024084295,0.0012421819,0.001679088,0.00050762884],"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":[0.0005133159,0.0002064161,0.0046901503,0.0001519881,0.00019239528,0.00015915842,0.000089941495,0.50525224,0.014233763,0.080191925,0.009474097,0.38484466],"study_design_scores_gemma":[0.0000035145504,0.000016791608,0.00022130697,0.0000035231749,0.000009472867,0.00001212221,0.0000040084637,0.9713454,0.0015621663,0.026392039,0.00042580263,0.0000038761204],"about_ca_topic_score_codex":0.014304318,"about_ca_topic_score_gemma":0.017731033,"teacher_disagreement_score":0.014304318,"about_ca_system_score_codex":0.0016325286,"about_ca_system_score_gemma":0.0012394772,"threshold_uncertainty_score":0.028442085},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05035055,0.00021425202,0.9470971,0.000262946,0.00004766137,0.000027917613,0.00006603617,0.0014711921,0.00046221382],"genre_scores_gemma":[0.86703026,0.0001228521,0.13123313,0.00014270228,0.000051009407,0.000056260542,0.00020774388,0.00005612014,0.0010998732],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990263,0.00030310638,0.000060977523,0.00029313262,0.00018956393,0.00012694925],"domain_scores_gemma":[0.9980399,0.0007935094,0.0001915234,0.00039355396,0.0004769714,0.00010458349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019765322,0.0006541948,0.0010825554,0.0007618026,0.0005268634,0.0008633886,0.0018178298,0.0012323874,0.0007094655],"category_scores_gemma":[0.004911135,0.0003805549,0.0006551439,0.0008919894,0.0008443418,0.0021564614,0.0014496476,0.0016875883,0.00024102381],"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":[0.00022637493,0.00033313368,0.0037452069,0.000054462733,0.00008916973,0.00013262045,0.00013461043,0.7681336,0.004088098,0.004496192,0.0013397207,0.21722677],"study_design_scores_gemma":[0.0000039438764,0.00002229722,0.0001853485,0.0000019075178,0.0000046181285,0.000016014767,0.000007848527,0.99454427,0.00089382636,0.00417658,0.00013954368,0.0000037524107],"about_ca_topic_score_codex":0.0043106847,"about_ca_topic_score_gemma":0.004322945,"teacher_disagreement_score":0.0043106847,"about_ca_system_score_codex":0.0008800049,"about_ca_system_score_gemma":0.001140456,"threshold_uncertainty_score":0.010453045},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055221286,0.00028326947,0.9415086,0.00043301497,0.00004557668,0.000031231357,0.00009968799,0.001472712,0.0009046134],"genre_scores_gemma":[0.70392805,0.00020377302,0.29174042,0.0004081129,0.00007997899,0.00008553312,0.00047862026,0.00031927697,0.0027561823],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984426,0.00049337785,0.0000950778,0.00039936553,0.00040348436,0.00016612299],"domain_scores_gemma":[0.993284,0.00368451,0.0007451611,0.0011933015,0.000822912,0.00027008855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00407808,0.0008870866,0.0010862879,0.000951931,0.00045412587,0.0009812546,0.0017479224,0.0015479907,0.0010117934],"category_scores_gemma":[0.018593697,0.00053328223,0.00069062936,0.0006895534,0.002039832,0.0022377518,0.003019729,0.0033690212,0.0004372946],"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":[0.0007936054,0.00016831938,0.016733257,0.00015822129,0.00016754393,0.00035828914,0.0002496464,0.65359604,0.025464084,0.043550648,0.0041442434,0.25461602],"study_design_scores_gemma":[0.0000133917465,0.00004167105,0.00050312816,0.000010486911,0.0000061807023,0.00008515962,0.000008437165,0.98321575,0.005813674,0.00989737,0.00039172155,0.000012926114],"about_ca_topic_score_codex":0.0026603926,"about_ca_topic_score_gemma":0.0028493905,"teacher_disagreement_score":0.00407808,"about_ca_system_score_codex":0.0012981521,"about_ca_system_score_gemma":0.0013471916,"threshold_uncertainty_score":0.021567166},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6680104,0.00059736805,0.32680243,0.00034092853,0.00011118358,0.000060948794,0.00010547069,0.0008005027,0.0031708013],"genre_scores_gemma":[0.9856699,0.0000745598,0.01387248,0.00002453245,0.000008019558,0.000010183319,0.0000737716,0.000008380198,0.00025824658],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995214,0.00008591548,0.000026658765,0.00011468444,0.00018026262,0.00007108733],"domain_scores_gemma":[0.99879456,0.0005606382,0.00021887319,0.00008528196,0.0002802591,0.000060328803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070089946,0.0006592802,0.00047072972,0.00075059215,0.00031813633,0.00067496,0.00077322614,0.00087616284,0.00032310362],"category_scores_gemma":[0.003855957,0.00023950684,0.0004490018,0.00041006226,0.00060471945,0.0010021391,0.0005258472,0.0005794435,0.00009914583],"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":[0.00022644013,0.000111975576,0.02089243,0.000100187484,0.000059054222,0.00035858474,0.00008889436,0.9139976,0.00939072,0.0020230913,0.0006691117,0.052081782],"study_design_scores_gemma":[0.0000019194079,0.000013969644,0.0008230317,0.0000023116822,0.000002731676,0.000018664092,0.000008415847,0.99776363,0.001054887,0.00024332134,0.00006478715,0.0000023107632],"about_ca_topic_score_codex":0.008670356,"about_ca_topic_score_gemma":0.005007112,"teacher_disagreement_score":0.008670356,"about_ca_system_score_codex":0.00062263565,"about_ca_system_score_gemma":0.00050554087,"threshold_uncertainty_score":0.017239809},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009230683,0.00016297247,0.9881352,0.0000740398,0.000074667914,0.000016335629,0.000063258616,0.0013465782,0.0008962914],"genre_scores_gemma":[0.42526174,0.0003004498,0.5645329,0.00021586377,0.00015609666,0.00007747785,0.0008113653,0.0003122432,0.008331971],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999584,0.00007518527,0.000015468271,0.00014196061,0.00012820035,0.000055141543],"domain_scores_gemma":[0.9995167,0.00017146472,0.000035574645,0.00009924005,0.0001552363,0.000021669477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005561257,0.00075592875,0.00082443224,0.00046182214,0.00017547682,0.0004890721,0.0009975595,0.0007610875,0.0010823306],"category_scores_gemma":[0.0010609374,0.00043972075,0.0007255549,0.000488881,0.00032219835,0.00066186365,0.0008359823,0.0014777418,0.00076807075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022442626,0.00013648799,0.0017796017,0.000074216005,0.00016147176,0.0001907542,0.000048751997,0.24549092,0.044929836,0.005777612,0.0070983917,0.69408756],"study_design_scores_gemma":[0.0000019644172,0.000014517818,0.0002843732,0.0000021695212,0.0000069731364,0.00005402498,0.0000021616647,0.995097,0.0029433104,0.0011225704,0.0004667865,0.0000041645176],"about_ca_topic_score_codex":0.002270955,"about_ca_topic_score_gemma":0.0043614274,"teacher_disagreement_score":0.002270955,"about_ca_system_score_codex":0.00028123765,"about_ca_system_score_gemma":0.0004302393,"threshold_uncertainty_score":0.0045154095},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05674297,0.0013732142,0.9268703,0.00448774,0.00084689056,0.00052692613,0.0042933845,0.0010284089,0.003830175],"genre_scores_gemma":[0.88060373,0.0024941796,0.05647489,0.0014739159,0.0017653002,0.0017218386,0.0037647686,0.00029069913,0.05141083],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9899226,0.00522273,0.0004011756,0.0020105992,0.001119187,0.0013237154],"domain_scores_gemma":[0.9487724,0.039458662,0.0031137597,0.0056103696,0.0021368794,0.0009079758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02159803,0.0021704764,0.003698278,0.0017729248,0.00081340846,0.0030150984,0.007211079,0.0039448314,0.009960531],"category_scores_gemma":[0.042676657,0.0018548083,0.0029416378,0.0022704948,0.0035053769,0.0031991727,0.0029722347,0.005995423,0.002513317],"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":[0.00519454,0.00046778045,0.03502145,0.0012237196,0.0017006012,0.003604316,0.0009731102,0.46532825,0.0020307193,0.36803016,0.01634132,0.10008399],"study_design_scores_gemma":[0.00075953867,0.00060093455,0.0047578453,0.000100988764,0.0007247203,0.0011390218,0.00013219778,0.81359935,0.0008887456,0.17295122,0.0042395615,0.0001057906],"about_ca_topic_score_codex":0.005809953,"about_ca_topic_score_gemma":0.0027369095,"teacher_disagreement_score":0.02159803,"about_ca_system_score_codex":0.0015435985,"about_ca_system_score_gemma":0.0035461227,"threshold_uncertainty_score":0.114222646},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003537003,0.9779023,0.017185777,0.0012359937,0.00030651808,0.000016653697,0.00004943338,0.000044012897,0.0029056908],"genre_scores_gemma":[0.0053646076,0.9840497,0.0084788995,0.0003948213,0.0006006119,0.000029438686,0.000073089446,0.000017963375,0.0009908627],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991665,0.00018590846,0.00010897126,0.00012681168,0.00037144087,0.00004044789],"domain_scores_gemma":[0.9968143,0.0022640007,0.00014784315,0.00008999867,0.0006188648,0.00006498764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024309116,0.0011930821,0.0013042763,0.0028311643,0.00035628708,0.0016701237,0.001443004,0.0015398815,0.0026273795],"category_scores_gemma":[0.004138936,0.00071131135,0.0008122532,0.004361643,0.0010294435,0.0031723646,0.0010838293,0.0024605948,0.0011162737],"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":[0.000031256477,0.000060072583,0.00044430324,0.011140585,0.00013779645,0.00007981879,0.00007851443,0.007144458,0.00075443287,0.041920833,0.02016282,0.91804504],"study_design_scores_gemma":[0.000018692383,0.00019524871,0.0012957768,0.009690992,0.0002621642,0.0008010809,0.0001376734,0.020529721,0.0025800115,0.08030783,0.8840524,0.00012854104],"about_ca_topic_score_codex":0.0027452686,"about_ca_topic_score_gemma":0.0025802893,"teacher_disagreement_score":0.0028311643,"about_ca_system_score_codex":0.0013593956,"about_ca_system_score_gemma":0.002030076,"threshold_uncertainty_score":0.012856066},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.112729594,0.00028017882,0.8788316,0.00020761102,0.00005021059,0.00007001191,0.00065126165,0.0062680934,0.0009115018],"genre_scores_gemma":[0.7941181,0.00020106585,0.20247735,0.000062874315,0.00006044731,0.000104684514,0.0019453915,0.00024022792,0.0007897169],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980755,0.00040394746,0.00013490648,0.0005392175,0.0006743816,0.0001720311],"domain_scores_gemma":[0.99285054,0.0039902623,0.0014023182,0.0006854163,0.00086503767,0.00020638837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020195183,0.0010713056,0.0010254146,0.0038707256,0.0003714354,0.0014988019,0.001665472,0.0010113766,0.0006389091],"category_scores_gemma":[0.0075618876,0.0005201183,0.0012102633,0.002194242,0.00052347576,0.001511281,0.0011163232,0.0012422197,0.0006605255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067489553,0.0005556831,0.114254296,0.0003053482,0.00041844914,0.0013745172,0.00063278544,0.36142445,0.03742069,0.008332604,0.006841648,0.46776462],"study_design_scores_gemma":[0.000004744612,0.000023217402,0.00406277,0.000009235307,0.000012843604,0.00010860364,0.000036086447,0.98919475,0.0028458117,0.0031956665,0.0004936805,0.000012603981],"about_ca_topic_score_codex":0.0040206485,"about_ca_topic_score_gemma":0.0055276654,"teacher_disagreement_score":0.0040206485,"about_ca_system_score_codex":0.00061259663,"about_ca_system_score_gemma":0.0009542047,"threshold_uncertainty_score":0.0106803775},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030963734,0.00017806716,0.96753,0.00019085425,0.000050065228,0.00002957524,0.0000673122,0.0005667685,0.00042358862],"genre_scores_gemma":[0.87759924,0.0002571812,0.11904455,0.00017712542,0.00012103651,0.00007136872,0.00039968453,0.00011139627,0.0022184122],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914145,0.00017250744,0.000030771356,0.00029028807,0.0002348673,0.00013010595],"domain_scores_gemma":[0.9979685,0.0011460404,0.00024125385,0.00026900598,0.00026992723,0.00010522617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013912899,0.0009799341,0.0012698403,0.0009297614,0.0003214062,0.0007559399,0.001782086,0.0012361947,0.0008404363],"category_scores_gemma":[0.004711828,0.0004410225,0.0007649084,0.0010116088,0.0010170024,0.0012665096,0.0018236665,0.002076548,0.00028491343],"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":[0.00038067938,0.0001463198,0.0037204348,0.000087050335,0.00011709178,0.00023429202,0.000067084766,0.78607,0.013160604,0.008213748,0.002475671,0.18532705],"study_design_scores_gemma":[0.000002422799,0.000017277598,0.00016430121,0.0000018936995,0.0000040521318,0.000023134315,0.0000030704612,0.9966629,0.0010637417,0.0019702434,0.000084718544,0.000002266388],"about_ca_topic_score_codex":0.0031529735,"about_ca_topic_score_gemma":0.0029208115,"teacher_disagreement_score":0.0031529735,"about_ca_system_score_codex":0.00083369645,"about_ca_system_score_gemma":0.00069041643,"threshold_uncertainty_score":0.007357955},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09146163,0.0007742505,0.9013681,0.00049992325,0.00015208068,0.000107634,0.0003280306,0.0028399152,0.0024684246],"genre_scores_gemma":[0.8932126,0.00024061139,0.10048873,0.00048052828,0.00009505205,0.000093438925,0.00084377377,0.00045638473,0.004088824],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977616,0.0004146605,0.00015835818,0.0006095416,0.000746328,0.00030948184],"domain_scores_gemma":[0.9935987,0.003104165,0.00082996377,0.0012541679,0.00095796003,0.00025499344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003404832,0.0013834935,0.0014342573,0.0018157215,0.00059446436,0.0017680043,0.0024921123,0.001481563,0.0024034646],"category_scores_gemma":[0.0138637405,0.00037258692,0.0009292685,0.0012065179,0.0013956624,0.0023961791,0.0032422028,0.0020882743,0.0007220825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013039389,0.0004990537,0.035251476,0.00041716252,0.0003463457,0.0012929934,0.00033516355,0.21986152,0.027736252,0.026501866,0.012202005,0.6742523],"study_design_scores_gemma":[0.000016069767,0.00006985542,0.0043650568,0.000028334987,0.000027347429,0.00034541375,0.000049927374,0.9636107,0.012693909,0.017221225,0.0015428697,0.000029220193],"about_ca_topic_score_codex":0.0018734077,"about_ca_topic_score_gemma":0.0021754839,"teacher_disagreement_score":0.003404832,"about_ca_system_score_codex":0.0010915368,"about_ca_system_score_gemma":0.0010759532,"threshold_uncertainty_score":0.018006682},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052109033,0.001031317,0.9430218,0.00018148082,0.00012299718,0.00004746371,0.00022859644,0.0022989837,0.00095837045],"genre_scores_gemma":[0.71197546,0.00052342645,0.2828709,0.0002496842,0.00015846666,0.00012465418,0.001035635,0.00015313321,0.0029086082],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992681,0.00014325883,0.00005298626,0.00022890448,0.00020177374,0.00010491771],"domain_scores_gemma":[0.9976804,0.00090368517,0.00022403363,0.00048542087,0.0005829502,0.00012339484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020120123,0.0009695122,0.0012555777,0.0015536885,0.0006191567,0.00075184467,0.0020543144,0.0009182207,0.0015186114],"category_scores_gemma":[0.0059950086,0.0003748101,0.00086986454,0.0014991061,0.0004828488,0.0023222808,0.0014538227,0.0017104212,0.00058703753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002602711,0.00023755882,0.008075514,0.00009932038,0.00028018566,0.0001239211,0.00012192079,0.19790286,0.007248812,0.007078193,0.0049984497,0.7735729],"study_design_scores_gemma":[0.0000049928967,0.000051462255,0.0005256644,0.0000075228754,0.000025908183,0.000050362887,0.000012273597,0.9917672,0.0030853571,0.003972317,0.00048779766,0.000009172393],"about_ca_topic_score_codex":0.0056582876,"about_ca_topic_score_gemma":0.008667517,"teacher_disagreement_score":0.0056582876,"about_ca_system_score_codex":0.0007301463,"about_ca_system_score_gemma":0.0013254761,"threshold_uncertainty_score":0.011250734},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40553352,0.000098601784,0.59215355,0.00022010024,0.000044807002,0.000062999425,0.0001177294,0.000523848,0.0012448544],"genre_scores_gemma":[0.9474016,0.000031656167,0.052172247,0.000028591166,0.000014778314,0.000016358914,0.000076856035,0.000018081351,0.00023986897],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988568,0.00030639666,0.000065763496,0.00022209687,0.00045176962,0.00009728155],"domain_scores_gemma":[0.9935169,0.00413975,0.00088341744,0.0006282717,0.00071769254,0.00011388704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015337734,0.00046785266,0.0005108962,0.00081960397,0.00034585575,0.0007531712,0.000489564,0.0007550896,0.00072028115],"category_scores_gemma":[0.009993642,0.00022238052,0.00040761198,0.0006622299,0.00078533584,0.00075470906,0.000615081,0.0008494982,0.00012848867],"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":[0.00085414975,0.0005898525,0.0898713,0.00029417934,0.00044268605,0.00085442344,0.00042295657,0.52077895,0.12288331,0.025096204,0.0012950453,0.23661695],"study_design_scores_gemma":[0.0000148088475,0.00016828068,0.011698648,0.000010790869,0.00003155809,0.00014478368,0.00005197262,0.9592755,0.020866126,0.007238705,0.0004742539,0.00002464887],"about_ca_topic_score_codex":0.0019030346,"about_ca_topic_score_gemma":0.0015803048,"teacher_disagreement_score":0.0019030346,"about_ca_system_score_codex":0.00044089576,"about_ca_system_score_gemma":0.00080291025,"threshold_uncertainty_score":0.008111477},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029954512,0.0000831095,0.99651474,0.00003997732,0.000010274253,0.000010194552,0.000023355507,0.00017239766,0.00015049921],"genre_scores_gemma":[0.29067823,0.00056961615,0.7061049,0.0001026912,0.0002169495,0.00014976447,0.00037560533,0.00024008579,0.0015621117],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983858,0.0005850767,0.00009567433,0.00029097282,0.0005332966,0.00010913219],"domain_scores_gemma":[0.9937835,0.0036109132,0.0006653195,0.0008083436,0.0010026293,0.00012929106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034070718,0.00085224706,0.0010283593,0.003369668,0.00044348428,0.0011748726,0.0017114019,0.0010480189,0.001098911],"category_scores_gemma":[0.012748873,0.00040622946,0.0011162891,0.0025699572,0.0012037038,0.0029087937,0.0015336054,0.0017451165,0.0004888402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016993146,0.00022592905,0.010314668,0.0002593948,0.0003274025,0.00026240802,0.0004992597,0.31256196,0.013418893,0.13472004,0.0038190687,0.5234211],"study_design_scores_gemma":[0.000004998016,0.000023928756,0.0015773398,0.000013910427,0.000014568051,0.00011738306,0.000026354086,0.96270645,0.001963163,0.03210932,0.0014132598,0.000029373878],"about_ca_topic_score_codex":0.0025281576,"about_ca_topic_score_gemma":0.0014563417,"teacher_disagreement_score":0.0034070718,"about_ca_system_score_codex":0.0006638969,"about_ca_system_score_gemma":0.0007407406,"threshold_uncertainty_score":0.018018544},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04149828,0.00031213585,0.9545744,0.00058594666,0.000042509433,0.00012778073,0.000031763186,0.0007755876,0.002051534],"genre_scores_gemma":[0.8401686,0.00012854238,0.15704042,0.00040714326,0.00011348435,0.00017753607,0.00009023918,0.000067970424,0.0018060209],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966305,0.001561824,0.000139608,0.0007922362,0.0006237541,0.0002520685],"domain_scores_gemma":[0.9826015,0.012561911,0.00089079543,0.0018170023,0.0016157117,0.00051295524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068016085,0.0010313351,0.001191124,0.0011511573,0.00079274154,0.0018732634,0.0033436094,0.0024473593,0.001954238],"category_scores_gemma":[0.019230707,0.0005497348,0.0008392724,0.0008675225,0.0017137121,0.004495865,0.0028682018,0.0026987763,0.00043453087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007245856,0.0012940044,0.012060029,0.00028744887,0.0002524634,0.00024069233,0.0011053523,0.46694234,0.01075645,0.018414415,0.003145704,0.48477647],"study_design_scores_gemma":[0.000013593878,0.00009195382,0.00023756933,0.000008332746,0.000011004849,0.00003016443,0.00002593412,0.9921146,0.0018022348,0.0051828413,0.00047373737,0.000007938538],"about_ca_topic_score_codex":0.0025016586,"about_ca_topic_score_gemma":0.0023523644,"teacher_disagreement_score":0.0068016085,"about_ca_system_score_codex":0.0012962322,"about_ca_system_score_gemma":0.001218144,"threshold_uncertainty_score":0.035970747},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019041784,0.00027307795,0.9750231,0.0016232601,0.000059590533,0.000027374585,0.00021455916,0.0007667247,0.0029705751],"genre_scores_gemma":[0.85033804,0.0006474631,0.1438656,0.0012424149,0.00034200266,0.00018175346,0.0005719948,0.00056317623,0.002247499],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99429226,0.001697277,0.00035195507,0.0015760667,0.0015304999,0.0005520951],"domain_scores_gemma":[0.93993586,0.043499496,0.0041414243,0.009100218,0.002433078,0.0008899684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008715763,0.0014081005,0.002686158,0.0019009671,0.0012191708,0.0032048603,0.0033524781,0.0035442763,0.0033580512],"category_scores_gemma":[0.085031874,0.0017176545,0.0016131157,0.0011386757,0.008246117,0.013476933,0.0059568402,0.007871592,0.0012649008],"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":[0.00011623105,0.0000615103,0.0039011207,0.0003205512,0.00012584316,0.00021107266,0.0003430764,0.14233844,0.0020730596,0.80430335,0.00403613,0.042169593],"study_design_scores_gemma":[0.0000131364795,0.0000312032,0.0004726475,0.000038062524,0.000011135747,0.00017975784,0.000036377023,0.3127849,0.0010948831,0.68420947,0.0010980134,0.000030363863],"about_ca_topic_score_codex":0.0023001046,"about_ca_topic_score_gemma":0.0016442671,"teacher_disagreement_score":0.008715763,"about_ca_system_score_codex":0.0017619336,"about_ca_system_score_gemma":0.0017576732,"threshold_uncertainty_score":0.04609394},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004724649,0.98426074,0.0072714593,0.001578311,0.0018241559,0.00001694017,0.00009212948,0.000057448542,0.0044264593],"genre_scores_gemma":[0.0034979568,0.98648375,0.0045663635,0.00072004244,0.002412526,0.00001972508,0.00017182327,0.00001903822,0.002108717],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99933547,0.00013271706,0.000092765855,0.00011585551,0.00028289677,0.0000402137],"domain_scores_gemma":[0.9978619,0.0013134113,0.00012173689,0.00007704232,0.0005621948,0.00006376711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011802786,0.00087648054,0.0010663043,0.0028076903,0.0003909999,0.0012182369,0.0010966882,0.0009798706,0.0046154],"category_scores_gemma":[0.0028496427,0.00032769423,0.0006705205,0.005531032,0.0005597696,0.0022993376,0.0005179758,0.0014183013,0.002831171],"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":[0.00005326359,0.000077897304,0.00075890805,0.009649082,0.00014342477,0.00013995839,0.00007918531,0.0019861038,0.0014198207,0.013014403,0.09139943,0.8812784],"study_design_scores_gemma":[0.000008757288,0.00009727295,0.0019664853,0.0025698524,0.00011701979,0.00060813775,0.00006734303,0.0017683893,0.00069201534,0.011439846,0.98062265,0.000042185056],"about_ca_topic_score_codex":0.0014526221,"about_ca_topic_score_gemma":0.0017176102,"teacher_disagreement_score":0.0046154,"about_ca_system_score_codex":0.00064522785,"about_ca_system_score_gemma":0.0013631949,"threshold_uncertainty_score":0.015440047},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059052285,0.001087394,0.9331721,0.00046358848,0.00016673119,0.000089832094,0.00063944835,0.0022651288,0.0030634888],"genre_scores_gemma":[0.78728,0.0009089557,0.20181614,0.0002736284,0.00014128369,0.0001516976,0.0013281611,0.00008598079,0.0080141155],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981314,0.000021213882,0.0000102184085,0.00006604831,0.00005167261,0.000037603848],"domain_scores_gemma":[0.9998852,0.000023717346,0.000021872916,0.000008832639,0.00004587623,0.000014388481],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033969487,0.00091800507,0.00066046574,0.0011835006,0.00028429288,0.0005716139,0.0013200974,0.00083375495,0.0015914396],"category_scores_gemma":[0.00045516877,0.00026598366,0.0007276062,0.0007203982,0.000360031,0.00092871656,0.0005870003,0.0008589145,0.00053514534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040957934,0.00036430216,0.007667553,0.00018086884,0.0002230984,0.0004925108,0.00013438481,0.23806919,0.035687294,0.008861425,0.0069490806,0.70096076],"study_design_scores_gemma":[0.00000337875,0.000044432083,0.000982374,0.000009724329,0.000018619598,0.00006396719,0.000011577699,0.9935726,0.0026036291,0.0020426726,0.0006382734,0.000008784261],"about_ca_topic_score_codex":0.009663825,"about_ca_topic_score_gemma":0.011807572,"teacher_disagreement_score":0.009663825,"about_ca_system_score_codex":0.0008327045,"about_ca_system_score_gemma":0.00072346214,"threshold_uncertainty_score":0.019215167},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032219298,0.80720204,0.17834958,0.0028228213,0.00060059223,0.00020399678,0.00083521777,0.00045257146,0.006311196],"genre_scores_gemma":[0.06058977,0.68279344,0.2466449,0.001305524,0.0020578597,0.000786099,0.002550899,0.0004999471,0.0027715743],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98729604,0.0031630262,0.0018033194,0.002276533,0.0052243713,0.0002367722],"domain_scores_gemma":[0.9581084,0.027951537,0.003155826,0.0021830539,0.008118229,0.00048296797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014853082,0.0029847894,0.0035712826,0.016876744,0.0009453947,0.004680736,0.0036320002,0.0020024069,0.0018215933],"category_scores_gemma":[0.051657055,0.0012294836,0.0018008566,0.017183343,0.0042117983,0.010037442,0.0027813178,0.0035217863,0.0016295003],"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":[0.00007900742,0.00009002345,0.004697255,0.006481667,0.00038548518,0.000042770702,0.0002848949,0.005747597,0.0008734385,0.04620125,0.017749436,0.91736716],"study_design_scores_gemma":[0.00006025982,0.0006447987,0.019718744,0.0137601765,0.0010965811,0.0020195418,0.0010823921,0.054998793,0.007588262,0.30833727,0.5899936,0.0006994956],"about_ca_topic_score_codex":0.0044224453,"about_ca_topic_score_gemma":0.0027695587,"teacher_disagreement_score":0.016876744,"about_ca_system_score_codex":0.0031353484,"about_ca_system_score_gemma":0.0029797745,"threshold_uncertainty_score":0.07855153},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029097495,0.00008781251,0.99110454,0.000111407135,0.00001566433,0.00007713835,0.00031104786,0.0051730876,0.00020944215],"genre_scores_gemma":[0.1369113,0.0002291998,0.85893893,0.000121746125,0.000033063097,0.00025402012,0.001806611,0.0005904076,0.0011147128],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998395,0.00039748926,0.00010289602,0.00043426233,0.0005373815,0.0001328325],"domain_scores_gemma":[0.99750656,0.0011013435,0.000346667,0.0004305333,0.0004596991,0.0001552101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00220173,0.001849159,0.0011303622,0.0038764568,0.0008243423,0.0018792168,0.0031600553,0.0012682859,0.0016970559],"category_scores_gemma":[0.007189063,0.00081415405,0.002375791,0.0024186715,0.0010793359,0.0025526043,0.002021652,0.002469129,0.000698979],"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":[0.0002294994,0.00026797177,0.009798216,0.00038701354,0.00028489096,0.00039665558,0.0003784782,0.6500116,0.0056365184,0.070427045,0.008044434,0.25413758],"study_design_scores_gemma":[0.000009222003,0.000018069348,0.00021491315,0.0000090552685,0.000011381118,0.000052433992,0.000020471973,0.9728836,0.00080962275,0.02422496,0.0017345805,0.000011691529],"about_ca_topic_score_codex":0.021617556,"about_ca_topic_score_gemma":0.028618203,"teacher_disagreement_score":0.021617556,"about_ca_system_score_codex":0.0021872907,"about_ca_system_score_gemma":0.0034652574,"threshold_uncertainty_score":0.042983472},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013948294,0.00070207537,0.98233193,0.00036180692,0.00006414811,0.00006188308,0.00010970791,0.0010659622,0.0013542061],"genre_scores_gemma":[0.6931033,0.0010865486,0.3022581,0.0001345036,0.0001307162,0.00013658135,0.00025794332,0.00005630666,0.002835928],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982357,0.00072560355,0.000085560896,0.00029822843,0.0005593336,0.00009555892],"domain_scores_gemma":[0.9970937,0.0014056009,0.00049654813,0.0005153529,0.00042357377,0.00006519335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013590844,0.0005802159,0.0006167446,0.0018854799,0.0004747434,0.00095376297,0.00056267856,0.0007562869,0.0012931875],"category_scores_gemma":[0.0072373324,0.00016898774,0.0006781763,0.0019009572,0.0007114273,0.0013399936,0.0008209649,0.0013321191,0.0012000478],"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":[0.00020291783,0.00027101947,0.012582259,0.00021778543,0.0001470443,0.00025588667,0.0003638857,0.09806548,0.015130567,0.04056544,0.0049607367,0.8272371],"study_design_scores_gemma":[0.0000064301666,0.000085728774,0.0039035063,0.000028201468,0.000024438199,0.00020856076,0.000078401834,0.9568062,0.0061626937,0.02931961,0.003343692,0.00003253509],"about_ca_topic_score_codex":0.0015524242,"about_ca_topic_score_gemma":0.0011636928,"teacher_disagreement_score":0.0018854799,"about_ca_system_score_codex":0.00068336626,"about_ca_system_score_gemma":0.00064960506,"threshold_uncertainty_score":0.0071876645},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11075964,0.00059227354,0.88216496,0.00022512177,0.00009607513,0.00007884646,0.00023261414,0.004671427,0.0011789904],"genre_scores_gemma":[0.8229438,0.0002977611,0.17105922,0.00019297052,0.000104228326,0.00011451555,0.0013162716,0.00014979365,0.0038214612],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992085,0.00016012257,0.000054233973,0.00028075548,0.00018516938,0.00011117693],"domain_scores_gemma":[0.9971967,0.0014464554,0.00024735893,0.0003351575,0.00069417676,0.000080076876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011921613,0.0012567969,0.0011645949,0.0007943185,0.00033235893,0.0007142894,0.0014632466,0.0010342386,0.00090902654],"category_scores_gemma":[0.002704652,0.0006328986,0.00079057686,0.0005013391,0.00056464324,0.0009478597,0.000892431,0.0015734911,0.0005443354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037475946,0.00041439067,0.0040045157,0.00015521537,0.00026242214,0.00021488631,0.00012190689,0.432612,0.025161508,0.0015962089,0.0037856689,0.5312965],"study_design_scores_gemma":[0.000003817075,0.000022249022,0.0004392444,0.000004240448,0.000006801519,0.000024366678,0.0000047870635,0.9964629,0.0023372944,0.00053241797,0.00015739026,0.000004476338],"about_ca_topic_score_codex":0.0070372666,"about_ca_topic_score_gemma":0.010063103,"teacher_disagreement_score":0.0070372666,"about_ca_system_score_codex":0.00066070876,"about_ca_system_score_gemma":0.0010382066,"threshold_uncertainty_score":0.013992608},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003356112,0.000125099,0.9957683,0.000036618185,0.000024386061,0.000017834505,0.000024332538,0.0003787753,0.00026860854],"genre_scores_gemma":[0.14982997,0.000334665,0.8462111,0.000106468106,0.00012082956,0.00014897027,0.00041049696,0.00018348802,0.002654082],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822146,0.00029386708,0.00009289567,0.0002827076,0.0010015878,0.00010746878],"domain_scores_gemma":[0.99853694,0.0004016014,0.00013614437,0.00015540184,0.00068938534,0.00008056054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010540035,0.0009378216,0.0019408762,0.002046012,0.0008785529,0.001108009,0.002008789,0.00089010416,0.0016649449],"category_scores_gemma":[0.0029183102,0.00046967194,0.001078097,0.0024777257,0.000632189,0.0018324702,0.0017563233,0.0011325685,0.0010419472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024340024,0.000245346,0.002270667,0.00016904649,0.0002818819,0.00015597505,0.00015088814,0.105703145,0.029997887,0.010265791,0.0054836473,0.84503233],"study_design_scores_gemma":[0.000009327256,0.00004115233,0.0006054438,0.00000521131,0.000020800382,0.00013628553,0.000020251337,0.9906365,0.0048346985,0.0021832539,0.001477619,0.000029531011],"about_ca_topic_score_codex":0.00372736,"about_ca_topic_score_gemma":0.00406072,"teacher_disagreement_score":0.00372736,"about_ca_system_score_codex":0.000423629,"about_ca_system_score_gemma":0.0011120823,"threshold_uncertainty_score":0.007411301},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15664725,0.0014064078,0.8323479,0.0005803065,0.0004202125,0.00008279854,0.0003255015,0.0031724204,0.005017249],"genre_scores_gemma":[0.8346,0.00089274184,0.15902743,0.00015683021,0.00013345061,0.00004786094,0.00057020574,0.00018822226,0.004383341],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994326,0.00008045751,0.000024020026,0.00018884892,0.00020424774,0.00006975639],"domain_scores_gemma":[0.9995178,0.00013472472,0.00007944098,0.000090019275,0.00015878797,0.000019138683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066216366,0.0007633021,0.00048049755,0.0010346165,0.00025092086,0.000665696,0.00062619866,0.0006705917,0.0010171342],"category_scores_gemma":[0.0018786634,0.00023447674,0.0006889287,0.0010188707,0.0003245429,0.0011627985,0.00051885686,0.0010038672,0.0006863094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030226645,0.00021033146,0.011935059,0.00030213472,0.00026340148,0.0007538454,0.00024225077,0.11584922,0.11737274,0.0053056926,0.00878489,0.73867816],"study_design_scores_gemma":[0.000003913688,0.00006152819,0.008618288,0.000016676971,0.000030176616,0.00037240624,0.000070797105,0.9566325,0.028864533,0.0024954185,0.0028137292,0.000020042427],"about_ca_topic_score_codex":0.002327667,"about_ca_topic_score_gemma":0.002977918,"teacher_disagreement_score":0.002327667,"about_ca_system_score_codex":0.00041308507,"about_ca_system_score_gemma":0.00026070295,"threshold_uncertainty_score":0.004628241},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1530376,0.0003564687,0.83684725,0.00045641937,0.000042025495,0.000047842623,0.0004714498,0.0073119053,0.0014290125],"genre_scores_gemma":[0.9289967,0.00012938507,0.067521885,0.0001716141,0.00002693559,0.00005901351,0.0009687717,0.00016926246,0.0019563988],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959844,0.000076130615,0.000021953401,0.0001475103,0.000087467335,0.00006860232],"domain_scores_gemma":[0.9983417,0.0009510052,0.00022416138,0.00017986966,0.00023864815,0.00006465558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081336696,0.0013921652,0.00066522317,0.0008961743,0.00023680407,0.00069899234,0.0014343829,0.0009162076,0.0012851071],"category_scores_gemma":[0.003528654,0.0008327858,0.00091614306,0.00040518946,0.00056276406,0.0013620199,0.0006331393,0.0020244208,0.00043337012],"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":[0.00011923872,0.00010724164,0.0026971912,0.000049219005,0.00006137356,0.00007539148,0.000039378207,0.9279824,0.0034090215,0.0012551062,0.00069982506,0.06350451],"study_design_scores_gemma":[8.43671e-7,0.0000035281084,0.00008772966,0.0000012036008,0.0000012667514,0.0000019764677,0.0000011077203,0.99890673,0.00031038033,0.00065805594,0.000026210291,0.00000111139],"about_ca_topic_score_codex":0.019634482,"about_ca_topic_score_gemma":0.024817636,"teacher_disagreement_score":0.019634482,"about_ca_system_score_codex":0.0014818149,"about_ca_system_score_gemma":0.0011087566,"threshold_uncertainty_score":0.039040387},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01885235,0.0004411102,0.9792626,0.00008814139,0.000043879732,0.000021718437,0.00007756435,0.00055175176,0.0006610286],"genre_scores_gemma":[0.61988086,0.00048200935,0.37437508,0.00015514933,0.00013203379,0.00013476177,0.0005102096,0.00015319137,0.0041767084],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994861,0.00009306447,0.00004101229,0.00015981002,0.00013827201,0.000081659724],"domain_scores_gemma":[0.9976872,0.0012996622,0.00020673993,0.00030587593,0.00042159986,0.000078871766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012893829,0.00056087406,0.0010113958,0.00103038,0.00057773094,0.001020284,0.0022093547,0.001018911,0.0024676262],"category_scores_gemma":[0.004306804,0.0004064638,0.0005714369,0.0013725341,0.00063185743,0.0018142428,0.001306806,0.0019686953,0.00054388423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026549672,0.00021535877,0.002167192,0.00008192155,0.00007738836,0.000040752224,0.00007917349,0.422025,0.0048455293,0.012036064,0.002624,0.5555422],"study_design_scores_gemma":[0.000004424852,0.000015157597,0.00019245302,0.000003046076,0.0000054203974,0.00000700358,0.000004698444,0.99561137,0.0008017906,0.0031656045,0.00018521411,0.0000038536277],"about_ca_topic_score_codex":0.012578835,"about_ca_topic_score_gemma":0.013520205,"teacher_disagreement_score":0.012578835,"about_ca_system_score_codex":0.00089790986,"about_ca_system_score_gemma":0.0014019655,"threshold_uncertainty_score":0.025011241},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061877903,0.0007310578,0.77407867,0.0013607584,0.0009778399,0.00047850338,0.004589361,0.1284983,0.027407559],"genre_scores_gemma":[0.8584414,0.00033279505,0.11714575,0.00035927046,0.00021425294,0.00024245046,0.00491306,0.001608573,0.016742432],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993611,0.00012890235,0.00005507806,0.00009720711,0.00025214712,0.00010552698],"domain_scores_gemma":[0.9986644,0.00019098332,0.00008219654,0.0007529447,0.00021199645,0.00009757534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008542178,0.0008625786,0.0006179906,0.0007827224,0.0005443061,0.0014986335,0.0015328338,0.0006439841,0.015056355],"category_scores_gemma":[0.0022597979,0.00031854116,0.0004885156,0.0007287553,0.00074461626,0.0023694728,0.002347487,0.0010639223,0.0047515384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0037151615,0.00040607242,0.003780612,0.00043752868,0.0002979302,0.00041110913,0.0001981445,0.25375012,0.033625104,0.10329148,0.20687005,0.39321673],"study_design_scores_gemma":[0.0002040154,0.00022053477,0.00046150156,0.00002411948,0.000056286073,0.00010861547,0.000054453598,0.8804839,0.02420189,0.053415287,0.04073585,0.000033479937],"about_ca_topic_score_codex":0.0023735599,"about_ca_topic_score_gemma":0.0024235686,"teacher_disagreement_score":0.015056355,"about_ca_system_score_codex":0.00077734597,"about_ca_system_score_gemma":0.001291219,"threshold_uncertainty_score":0.050368488},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02797258,0.0007598929,0.9671581,0.00076293835,0.000090484944,0.00007612232,0.00033316403,0.000491064,0.0023556384],"genre_scores_gemma":[0.8317044,0.0012802589,0.15683551,0.0002657109,0.000117929645,0.00025026087,0.00076997385,0.000059700036,0.008716315],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996978,0.00003306918,0.000022467542,0.00010050594,0.0000949469,0.000051130795],"domain_scores_gemma":[0.99970335,0.00009039708,0.00005412909,0.00002862076,0.00010494198,0.000018538381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000488758,0.000652805,0.00053767214,0.0009575635,0.00033840985,0.0008345366,0.0013174941,0.00068754447,0.0012901004],"category_scores_gemma":[0.0012698664,0.00029911258,0.0006877014,0.0011278511,0.00044118942,0.0018835856,0.00097141037,0.0010515953,0.0002894386],"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":[0.00020046372,0.00017463377,0.0058602807,0.00025837714,0.0001697798,0.0004857206,0.00019661547,0.56510735,0.013469124,0.047291875,0.005980009,0.36080587],"study_design_scores_gemma":[0.0000023150487,0.000014297717,0.00033479434,0.0000057147163,0.000010772657,0.000021829292,0.000006913924,0.9929356,0.0006629055,0.00539116,0.0006086941,0.0000050545927],"about_ca_topic_score_codex":0.009378195,"about_ca_topic_score_gemma":0.007490444,"teacher_disagreement_score":0.009378195,"about_ca_system_score_codex":0.0010713756,"about_ca_system_score_gemma":0.0008177821,"threshold_uncertainty_score":0.018647194},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024050727,0.0006471477,0.97248554,0.0005101111,0.0000562519,0.000049321807,0.00022082082,0.00094534183,0.0010346728],"genre_scores_gemma":[0.7401311,0.0007097644,0.25249472,0.0007207416,0.00031484736,0.00022089045,0.00157304,0.00043457493,0.0034004455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973385,0.0006878061,0.00016282381,0.0005587946,0.0010506406,0.00020135894],"domain_scores_gemma":[0.98897386,0.007101002,0.0010448727,0.00130913,0.0012718774,0.00029921235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050685806,0.0010687124,0.001407268,0.0019268647,0.00082432193,0.0020212221,0.0026006114,0.0023497685,0.0017445666],"category_scores_gemma":[0.02849503,0.0009016319,0.0011104366,0.0012161412,0.0018816313,0.0042168773,0.003988884,0.004475738,0.00063495553],"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":[0.00033463392,0.000116631796,0.005597483,0.00018134157,0.00018257294,0.00016319289,0.0002715066,0.66517586,0.0052647544,0.033192065,0.0049224272,0.28459752],"study_design_scores_gemma":[0.0000039398055,0.000025414249,0.00044550744,0.000019817815,0.000008141875,0.000042167387,0.000018751341,0.9692394,0.0018894158,0.02765489,0.0006429688,0.000009655576],"about_ca_topic_score_codex":0.0044490197,"about_ca_topic_score_gemma":0.0050865216,"teacher_disagreement_score":0.0050685806,"about_ca_system_score_codex":0.0021370691,"about_ca_system_score_gemma":0.0011176054,"threshold_uncertainty_score":0.02680558},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39102352,0.00089879625,0.60114366,0.0005482471,0.00025660938,0.000120385725,0.0009408154,0.0023843334,0.0026836067],"genre_scores_gemma":[0.96091443,0.0001494721,0.037293743,0.000033851138,0.00006472596,0.00003338126,0.0007677863,0.000033187887,0.00070942356],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996166,0.00007890471,0.000022045526,0.00010476649,0.00010268184,0.0000749093],"domain_scores_gemma":[0.99905235,0.0003588906,0.00015116487,0.00011223254,0.00026245666,0.00006294685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000507821,0.0006026628,0.00062595785,0.0014098247,0.0002616115,0.00069202343,0.0008744152,0.0006878305,0.0006572574],"category_scores_gemma":[0.0025880556,0.00022630767,0.00044410827,0.0008937896,0.0002744429,0.0010620878,0.0006873901,0.00088615017,0.00034627458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001224142,0.0009691485,0.050268274,0.00017031784,0.00029130353,0.000653584,0.00022230588,0.21442172,0.03886791,0.004128514,0.011287595,0.6774952],"study_design_scores_gemma":[0.000011782021,0.000081219536,0.007062407,0.000006524888,0.000020265186,0.00017152593,0.000054686167,0.98501325,0.004172646,0.0029972563,0.00039646542,0.000012015775],"about_ca_topic_score_codex":0.0025416815,"about_ca_topic_score_gemma":0.003442083,"teacher_disagreement_score":0.0025416815,"about_ca_system_score_codex":0.0003380703,"about_ca_system_score_gemma":0.00046537587,"threshold_uncertainty_score":0.0050537586},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016976198,0.00014994168,0.9807382,0.00018438903,0.00004860898,0.000032254444,0.00012559224,0.0010165577,0.00072824664],"genre_scores_gemma":[0.81204355,0.0003462355,0.18343902,0.00028855432,0.00013538438,0.000088867804,0.0009748085,0.00014831098,0.0025351709],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989293,0.00023722788,0.000051136434,0.00034745858,0.00030377958,0.00013112994],"domain_scores_gemma":[0.9976192,0.0012223427,0.0003428434,0.0004218619,0.00028997436,0.00010380504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013442393,0.001318217,0.0011513036,0.0011059432,0.0003850598,0.00089759286,0.0020171143,0.00093032623,0.0010180853],"category_scores_gemma":[0.0045829043,0.00044593963,0.00087606726,0.0008345832,0.0010209375,0.001666922,0.0019857255,0.0023446644,0.0004380082],"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":[0.00019206073,0.00010598078,0.0056177136,0.000056636607,0.00007679312,0.00035147107,0.000085336724,0.84127325,0.005743022,0.008845859,0.0044962754,0.13315569],"study_design_scores_gemma":[0.000002237677,0.000011658471,0.00023213438,0.0000024432939,0.0000042203933,0.000047278674,0.000004190075,0.9955786,0.0011572219,0.0026706187,0.00028479914,0.0000045014754],"about_ca_topic_score_codex":0.003518699,"about_ca_topic_score_gemma":0.0025765772,"teacher_disagreement_score":0.003518699,"about_ca_system_score_codex":0.0006481234,"about_ca_system_score_gemma":0.0006757215,"threshold_uncertainty_score":0.007109046},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020888345,0.00023237901,0.9776188,0.000064185675,0.000044091288,0.000009159608,0.00006768164,0.00059112604,0.00048408462],"genre_scores_gemma":[0.70220244,0.00044468272,0.2902558,0.0000753711,0.00028848203,0.000056316938,0.0007172634,0.0003480189,0.005611605],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933535,0.0001685215,0.000045932626,0.00020269092,0.00019881829,0.00004859375],"domain_scores_gemma":[0.9974293,0.0014624358,0.0002547763,0.00046075988,0.0003299853,0.00006272351],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010502943,0.00058365223,0.0010713107,0.00043072068,0.00019066149,0.00071191834,0.0011383818,0.0006660643,0.0008805114],"category_scores_gemma":[0.0047382396,0.0003743926,0.00060063053,0.0006416206,0.00064948987,0.001291139,0.0008792572,0.0012690646,0.0005564889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000472591,0.00013210405,0.0026932845,0.0002405026,0.0001386037,0.00014474847,0.00010842184,0.29210642,0.039833244,0.016083015,0.004839367,0.64320767],"study_design_scores_gemma":[0.0000018311916,0.00002787151,0.00042582696,0.000003124825,0.000005456329,0.000059931906,0.0000032185558,0.99199283,0.0023221688,0.00480356,0.00034959725,0.0000045921574],"about_ca_topic_score_codex":0.0006153094,"about_ca_topic_score_gemma":0.00080075744,"teacher_disagreement_score":0.0011383818,"about_ca_system_score_codex":0.00029940705,"about_ca_system_score_gemma":0.00041036727,"threshold_uncertainty_score":0.005554557},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.088789776,0.0011375407,0.90446633,0.00023221291,0.00023219475,0.000081829145,0.0003663509,0.0028180366,0.0018756826],"genre_scores_gemma":[0.7189684,0.00069031684,0.27601472,0.00015401926,0.0003304429,0.00008215606,0.0012130855,0.00015445834,0.002392553],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916184,0.00008617748,0.000043275933,0.00025838835,0.00034457387,0.00010577573],"domain_scores_gemma":[0.99924266,0.00021687237,0.00014731611,0.00010089609,0.0002340271,0.000058123325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000525399,0.0010791955,0.0010408028,0.002442701,0.00030818259,0.0007347769,0.0011193127,0.00096129574,0.0006348757],"category_scores_gemma":[0.0020548073,0.00025017292,0.00073081534,0.0013829574,0.0004194865,0.0012307187,0.0011682871,0.0009162889,0.0005684919],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050680997,0.0002480764,0.007787831,0.0002029779,0.00012782526,0.0004833345,0.0001427635,0.039806888,0.08578555,0.001448716,0.0029794236,0.86047965],"study_design_scores_gemma":[0.000022184979,0.00017494493,0.008201933,0.00002623432,0.00006179596,0.00066022394,0.00011655834,0.9547281,0.027036479,0.004973153,0.0039608222,0.00003750174],"about_ca_topic_score_codex":0.0022452883,"about_ca_topic_score_gemma":0.0025599746,"teacher_disagreement_score":0.002442701,"about_ca_system_score_codex":0.00031532024,"about_ca_system_score_gemma":0.0004615381,"threshold_uncertainty_score":0.0044644475},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24669944,0.0009394394,0.74800324,0.00076530856,0.00008775335,0.00005428657,0.00021320514,0.00077585736,0.0024614264],"genre_scores_gemma":[0.96549183,0.0002687718,0.030990565,0.000119542456,0.00003377089,0.0000313675,0.00022941876,0.000026662157,0.002808155],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997429,0.00006341112,0.000011540239,0.000075083066,0.000065096385,0.000041942258],"domain_scores_gemma":[0.99948514,0.00025083998,0.00007971644,0.000054944077,0.000103990766,0.00002533312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007262626,0.000624113,0.0005292131,0.0004954162,0.0001686764,0.0004700856,0.0007698338,0.000746726,0.0005085837],"category_scores_gemma":[0.001325415,0.00021240681,0.00069368543,0.00027434557,0.0005115945,0.0005684006,0.00047463368,0.0009907198,0.00012617308],"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":[0.00012552967,0.00006041618,0.0037168444,0.000030816747,0.00007307139,0.00015732489,0.000035525307,0.9479346,0.004711996,0.003387149,0.00069206057,0.03907463],"study_design_scores_gemma":[8.7023125e-7,0.000014039812,0.00026789523,0.0000015949622,0.000004820482,0.000025671592,0.000001599915,0.99826115,0.00064322795,0.0006976868,0.000079159705,0.000002156297],"about_ca_topic_score_codex":0.004003743,"about_ca_topic_score_gemma":0.003527097,"teacher_disagreement_score":0.004003743,"about_ca_system_score_codex":0.00073621207,"about_ca_system_score_gemma":0.00052876887,"threshold_uncertainty_score":0.007960856},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025494134,0.00014927716,0.97306097,0.00020296154,0.000021275633,0.000024177918,0.00002959082,0.0003046651,0.0007129243],"genre_scores_gemma":[0.925406,0.00013470689,0.07304143,0.000066951616,0.00003735556,0.00009723655,0.000077520344,0.00003610818,0.0011026261],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875367,0.0005783002,0.000059350856,0.00025709512,0.00023167585,0.00011994246],"domain_scores_gemma":[0.9935753,0.0050457185,0.00057150755,0.00020056953,0.00047168334,0.00013526595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024957233,0.0009213959,0.0012380453,0.0009114654,0.0003802523,0.0012582858,0.0013350702,0.0010847119,0.0009196409],"category_scores_gemma":[0.0086741485,0.000518174,0.00074332696,0.00061729446,0.0014133266,0.0014910898,0.0010086442,0.0018707508,0.0001314855],"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":[0.000026764072,0.000029678404,0.00079810264,0.000015147205,0.000032513977,0.00002197326,0.000029983945,0.9800878,0.00020344931,0.005867801,0.00012548512,0.012761267],"study_design_scores_gemma":[0.0000017224639,0.000005093048,0.000036091755,0.0000010275223,0.0000014637417,0.000002117366,0.0000015055754,0.99756396,0.000047845468,0.0023118572,0.000025553793,0.0000017870915],"about_ca_topic_score_codex":0.015063988,"about_ca_topic_score_gemma":0.008927461,"teacher_disagreement_score":0.015063988,"about_ca_system_score_codex":0.0017547013,"about_ca_system_score_gemma":0.0014250863,"threshold_uncertainty_score":0.029952645},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032938108,0.0011643122,0.9538101,0.0013438521,0.00018117263,0.00041013153,0.0009891666,0.0044151847,0.0047479407],"genre_scores_gemma":[0.74945956,0.00068659155,0.23845688,0.0011365614,0.0003238605,0.00048352825,0.0018286327,0.0004546991,0.007169662],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9932286,0.0017263906,0.0004014803,0.0012974924,0.0023838007,0.0009622042],"domain_scores_gemma":[0.9895046,0.0030615523,0.0007256113,0.005511341,0.0007786128,0.00041836503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044710874,0.00096133153,0.0028323801,0.0012126981,0.0014287919,0.0028518087,0.005196034,0.0025208378,0.0040279627],"category_scores_gemma":[0.019031532,0.0005544241,0.0015947319,0.003010492,0.002114061,0.0052115577,0.0090225,0.0027198985,0.001912006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0042417953,0.0006926994,0.0067277625,0.0006317779,0.00047118985,0.0016259223,0.0011222999,0.25278407,0.033060305,0.19069351,0.035754908,0.4721938],"study_design_scores_gemma":[0.00029501333,0.0002895311,0.00095787994,0.000035561396,0.000081450075,0.0008920669,0.00020126085,0.8417982,0.009901797,0.13462193,0.0108530745,0.00007223734],"about_ca_topic_score_codex":0.002357612,"about_ca_topic_score_gemma":0.0025326984,"teacher_disagreement_score":0.005196034,"about_ca_system_score_codex":0.0017180174,"about_ca_system_score_gemma":0.0040427693,"threshold_uncertainty_score":0.023645699},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00084994826,0.98869365,0.00517357,0.0026357542,0.000791243,0.0000066019556,0.000022419212,0.00005440831,0.0017723695],"genre_scores_gemma":[0.014776621,0.9690261,0.0060368893,0.0036049085,0.0034291612,0.000018778599,0.00008935142,0.000040185525,0.002977955],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995403,0.00009745875,0.000062301784,0.0000921873,0.00017599417,0.000031686573],"domain_scores_gemma":[0.9982249,0.0012067576,0.0002028327,0.00008285556,0.00022612544,0.000056457706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014770587,0.0005569548,0.0009964928,0.001211123,0.00019584193,0.00091102725,0.0008189412,0.0009910956,0.0019730811],"category_scores_gemma":[0.003994774,0.00021755189,0.00041817594,0.0009046385,0.0011346699,0.0023314117,0.00058346835,0.0020912227,0.0010157243],"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":[0.000035873265,0.000023705014,0.0004888953,0.0024953592,0.00006626398,0.00007457859,0.000032620173,0.00021078641,0.0006344696,0.007177809,0.013023072,0.9757367],"study_design_scores_gemma":[0.00004619754,0.00029573374,0.0038987896,0.0046639293,0.00030927054,0.005278459,0.000111976726,0.0015648927,0.0036478932,0.03139923,0.9487225,0.00006110778],"about_ca_topic_score_codex":0.0005565823,"about_ca_topic_score_gemma":0.0011127759,"teacher_disagreement_score":0.0019730811,"about_ca_system_score_codex":0.00037577946,"about_ca_system_score_gemma":0.0008547882,"threshold_uncertainty_score":0.0078115463},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0455297,0.0008219066,0.9500145,0.0006481266,0.00010772261,0.00007375935,0.00056043227,0.001135033,0.0011087927],"genre_scores_gemma":[0.7389836,0.0007462273,0.2542125,0.00032356952,0.00050262804,0.00014450197,0.0030977034,0.00017698693,0.0018123545],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998841,0.00017883217,0.00009387144,0.00039250945,0.00033119123,0.00016273138],"domain_scores_gemma":[0.9961009,0.0013247693,0.00053917087,0.001270132,0.0005606608,0.0002043176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020542236,0.0009969501,0.0015992967,0.0030974066,0.00073870266,0.0017127556,0.00217334,0.0013032387,0.0015413066],"category_scores_gemma":[0.007609761,0.00046652777,0.00086677144,0.003944086,0.0009974849,0.0044765794,0.0022929038,0.002355047,0.00091418804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051232066,0.0005988619,0.017634815,0.0001835306,0.00014663472,0.00025460843,0.00027170873,0.24662241,0.013788449,0.011302918,0.01080991,0.6978738],"study_design_scores_gemma":[0.000012584423,0.000039000544,0.003071893,0.00002055588,0.000021037189,0.00008201021,0.00007480991,0.9731694,0.002144623,0.019181252,0.002166826,0.000016142083],"about_ca_topic_score_codex":0.004321593,"about_ca_topic_score_gemma":0.0055005336,"teacher_disagreement_score":0.004321593,"about_ca_system_score_codex":0.00095199037,"about_ca_system_score_gemma":0.0009893455,"threshold_uncertainty_score":0.01086396},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6638761,0.00040665126,0.32589903,0.00016679146,0.00007513344,0.00010977871,0.0017842267,0.0030702832,0.004611895],"genre_scores_gemma":[0.94039464,0.00028049274,0.057585616,0.000013588996,0.000021031077,0.000042369396,0.0009154714,0.00006182415,0.0006848376],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984634,0.000020500636,0.000009756397,0.000033025808,0.00006493403,0.000025397921],"domain_scores_gemma":[0.99965334,0.00006043396,0.000052943687,0.000030343941,0.00017109349,0.00003179751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021877105,0.0004037024,0.00022731975,0.002976201,0.00015490939,0.0005516396,0.00020198034,0.00018344457,0.00092295813],"category_scores_gemma":[0.0006944424,0.0000960262,0.000281876,0.0012605998,0.0001465939,0.0005062167,0.000295192,0.00023099229,0.00019645857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008374579,0.00021782506,0.06602132,0.00049853756,0.00013880256,0.0011457899,0.0013388539,0.04985672,0.1990811,0.0038988313,0.0060383403,0.67092645],"study_design_scores_gemma":[0.000021582626,0.0002772283,0.13420689,0.000060884693,0.000121473335,0.0010032222,0.0018231262,0.7690128,0.08405787,0.0017219433,0.007601727,0.00009118884],"about_ca_topic_score_codex":0.0037934952,"about_ca_topic_score_gemma":0.0043949313,"teacher_disagreement_score":0.0037934952,"about_ca_system_score_codex":0.00021015623,"about_ca_system_score_gemma":0.00023610766,"threshold_uncertainty_score":0.0075428486},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000022876744,0.0061841537,0.00031620482,0.023383003,0.96609634,0.000023954735,0.00017920109,0.0001407141,0.0036535463],"genre_scores_gemma":[0.0005100193,0.0045595244,0.00022446607,0.01372025,0.9503841,0.000042408483,0.00013563067,0.00010763531,0.030315956],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99661595,0.0004391324,0.00028122222,0.00048963726,0.0019076588,0.00026641026],"domain_scores_gemma":[0.9870358,0.0049985508,0.0009022214,0.00034796525,0.004559889,0.0021555275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00543772,0.003892119,0.004366686,0.0036229452,0.003047057,0.007616869,0.0037671118,0.014297078,0.06497123],"category_scores_gemma":[0.017683078,0.0012548747,0.0024335796,0.0014367522,0.0020973014,0.004405033,0.0019500435,0.014662738,0.048404593],"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":[0.000038439663,0.000005720377,0.0000075891753,0.00014385108,0.000011948048,0.000044491222,0.0000033403153,0.000030009645,0.00004933393,0.0002969352,0.9952186,0.0041497443],"study_design_scores_gemma":[0.00008468973,0.000030101337,0.00015141266,0.0002712887,0.000037336813,0.0001337232,0.000009984224,0.00020583853,0.00012401387,0.0016790811,0.99725455,0.00001789036],"about_ca_topic_score_codex":0.0014096993,"about_ca_topic_score_gemma":0.004283078,"teacher_disagreement_score":0.06497123,"about_ca_system_score_codex":0.002939999,"about_ca_system_score_gemma":0.0020813271,"threshold_uncertainty_score":0.21735042},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036595896,0.00092352426,0.8815652,0.0017214596,0.0004608941,0.0004822302,0.014556648,0.056638923,0.0070551303],"genre_scores_gemma":[0.18007024,0.000853462,0.794867,0.0005051611,0.00028469117,0.0010347012,0.012156581,0.007851679,0.002376524],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99797827,0.0004916274,0.00017816928,0.00031593957,0.0008732727,0.00016273416],"domain_scores_gemma":[0.9875382,0.006586379,0.0012918724,0.0015175153,0.0026132814,0.0004527904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049104663,0.001948062,0.0010920969,0.0074181096,0.00081389496,0.0050911307,0.0015734612,0.0011833276,0.011019878],"category_scores_gemma":[0.01979655,0.0005795018,0.0011865727,0.0038146044,0.00081675715,0.004260437,0.004638835,0.0032415728,0.0019843301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016253841,0.00053001277,0.019721877,0.0027576745,0.0004858782,0.0005407164,0.004191663,0.03783794,0.041638695,0.03837441,0.20341495,0.64888084],"study_design_scores_gemma":[0.00040608284,0.0005798798,0.030299297,0.0009087297,0.00020623994,0.0012040596,0.002555019,0.62849814,0.056765758,0.121504225,0.15654442,0.0005281218],"about_ca_topic_score_codex":0.0021554295,"about_ca_topic_score_gemma":0.002744569,"teacher_disagreement_score":0.011019878,"about_ca_system_score_codex":0.0007669174,"about_ca_system_score_gemma":0.0012991843,"threshold_uncertainty_score":0.036865175},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33648646,0.0012645664,0.65052426,0.0011611726,0.000111232184,0.000431123,0.0018870262,0.0032977306,0.0048365123],"genre_scores_gemma":[0.7734782,0.00047015422,0.22190998,0.00006553364,0.000021967815,0.00011249436,0.0009864027,0.000050264884,0.0029049555],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969435,0.000044824614,0.000018269422,0.00010090137,0.00007875741,0.000062887775],"domain_scores_gemma":[0.999647,0.00010415431,0.000033506527,0.000026761567,0.00013883538,0.000049786933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006184578,0.0007018961,0.0005403468,0.0008304608,0.00092460227,0.0012283238,0.0014037752,0.00078793283,0.0007741997],"category_scores_gemma":[0.0010329373,0.0002809094,0.00059132103,0.0009844642,0.000470727,0.0006549041,0.00060889305,0.0007592642,0.00017914767],"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":[0.00024024464,0.00033965852,0.015504988,0.00017060773,0.00013534166,0.0006070025,0.00043219407,0.8156441,0.0054767733,0.009088048,0.0041024303,0.14825872],"study_design_scores_gemma":[0.00000856592,0.00002664478,0.0016395701,0.0000072055263,0.000017682194,0.00003772669,0.00010090824,0.9952846,0.00073269487,0.0008827029,0.0012501815,0.0000114989925],"about_ca_topic_score_codex":0.44670773,"about_ca_topic_score_gemma":0.44939104,"teacher_disagreement_score":0.5532923,"about_ca_system_score_codex":0.0031369033,"about_ca_system_score_gemma":0.0043457625,"threshold_uncertainty_score":0.8882152},"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":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011715754,0.20276631,0.32433417,0.009506787,0.016638482,0.00020250063,0.0034925882,0.0046694446,0.43721813],"genre_scores_gemma":[0.012834554,0.122991845,0.12785256,0.0047813975,0.011852928,0.0003605232,0.0039540147,0.0017219861,0.7136503],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993104,0.00009273115,0.000044691064,0.00014170373,0.0003699693,0.000040492505],"domain_scores_gemma":[0.9989785,0.00054669846,0.00003545235,0.00013642854,0.00024427645,0.000058623384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068892544,0.0015568474,0.0013828612,0.0029699113,0.000576195,0.0025029953,0.0012275565,0.0012173065,0.09075305],"category_scores_gemma":[0.002276922,0.0005764848,0.00069486717,0.004326177,0.00085088273,0.0031779713,0.001366162,0.0025136527,0.06964779],"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":[0.000014270004,0.000044475546,0.0001374403,0.00060260063,0.000019183115,0.0000511639,0.00007104942,0.0017058237,0.0005747755,0.04851924,0.35527155,0.59298843],"study_design_scores_gemma":[0.000002806224,0.000017338898,0.00018562804,0.00024991707,0.0000065122645,0.00013003456,0.000021351,0.0014600527,0.00027399923,0.06092218,0.93671834,0.000011934251],"about_ca_topic_score_codex":0.0008187438,"about_ca_topic_score_gemma":0.0014633783,"teacher_disagreement_score":0.09075305,"about_ca_system_score_codex":0.00079488184,"about_ca_system_score_gemma":0.0012399364,"threshold_uncertainty_score":0.30359918},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5359381,0.00028569935,0.45892775,0.00015283126,0.00007770266,0.000076692224,0.0005069257,0.0015539984,0.0024803758],"genre_scores_gemma":[0.95920736,0.00017211346,0.03877655,0.000034271783,0.000023611394,0.00003453867,0.00044464567,0.00002419803,0.001282754],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982977,0.000018292663,0.000009896179,0.00005861249,0.00005098931,0.00003237871],"domain_scores_gemma":[0.999838,0.000020524645,0.000036188663,0.000015489637,0.00006859076,0.000021226026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016607568,0.00046703036,0.00036745047,0.0010841779,0.000121643214,0.00025967963,0.00030535215,0.0002714904,0.00072833593],"category_scores_gemma":[0.0005402909,0.00008857456,0.0003008744,0.0005019483,0.00012233372,0.00031546128,0.0003014173,0.00024515294,0.00029756926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034281844,0.00031458386,0.08135138,0.00007803669,0.00008313604,0.00037416356,0.0001735593,0.044675026,0.092448756,0.0008449361,0.0031918257,0.77612174],"study_design_scores_gemma":[0.000009511943,0.00021900077,0.07308593,0.000013802558,0.000039193477,0.000356223,0.00016648279,0.90015084,0.023948135,0.00066879456,0.0013186795,0.000023439323],"about_ca_topic_score_codex":0.0037926717,"about_ca_topic_score_gemma":0.0059095677,"teacher_disagreement_score":0.0037926717,"about_ca_system_score_codex":0.00025208335,"about_ca_system_score_gemma":0.00026499951,"threshold_uncertainty_score":0.0075411797},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49322408,0.0035005384,0.48930073,0.0006904736,0.00032604282,0.00024127423,0.0009173117,0.006631076,0.0051686233],"genre_scores_gemma":[0.8257593,0.0018861471,0.16897324,0.000089917325,0.00008118908,0.000080633974,0.0008907243,0.00022054288,0.002018253],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845004,0.0002495957,0.00012110752,0.00038070194,0.0006772257,0.00012123995],"domain_scores_gemma":[0.9951622,0.0015940452,0.0010221829,0.0006807219,0.0013956721,0.00014522918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015771601,0.0012139544,0.0008132275,0.0034074069,0.0006427097,0.0013430709,0.001429467,0.0012654436,0.0012990426],"category_scores_gemma":[0.009228848,0.0003457537,0.0007785373,0.0016007592,0.00070592505,0.0015692897,0.0010838205,0.00069583167,0.0007862606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004730446,0.0002252901,0.15512757,0.0008921169,0.00015953857,0.003646633,0.0012612471,0.047024734,0.07083675,0.004137115,0.0029349355,0.713281],"study_design_scores_gemma":[0.00006525736,0.00068916596,0.118935764,0.0005866361,0.00041606228,0.014429222,0.0030433745,0.65811795,0.15375204,0.014963477,0.034832742,0.000168293],"about_ca_topic_score_codex":0.0028195006,"about_ca_topic_score_gemma":0.0028502631,"teacher_disagreement_score":0.0034074069,"about_ca_system_score_codex":0.0006154707,"about_ca_system_score_gemma":0.0005954178,"threshold_uncertainty_score":0.008340895},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03848764,0.0014084354,0.9488241,0.0004921286,0.00021672531,0.00012296904,0.00029124168,0.0046580583,0.00549869],"genre_scores_gemma":[0.7948219,0.00055807055,0.18854964,0.00071060547,0.000096222335,0.0002074535,0.0008178087,0.00019300258,0.014045175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974495,0.000045891058,0.00001022473,0.00007773518,0.0000752572,0.00004592482],"domain_scores_gemma":[0.9997414,0.00009261396,0.000027492542,0.000056940342,0.000053874213,0.000027653577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053361093,0.0011904088,0.0008097755,0.00033237794,0.0003055589,0.0005223274,0.0018575283,0.0012811915,0.0026713642],"category_scores_gemma":[0.0010513329,0.00045290092,0.00058693485,0.00021824561,0.00065162766,0.0009770795,0.0014111263,0.0015528795,0.000803392],"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":[0.0005022637,0.00026342348,0.0013572675,0.00018337743,0.00018295296,0.0004482181,0.00009441189,0.60489523,0.029418435,0.013515178,0.009250426,0.3398888],"study_design_scores_gemma":[0.000012495328,0.000058049653,0.00007953136,0.0000059612807,0.000011658027,0.00005600295,0.0000034441318,0.9931814,0.0036978784,0.0021290763,0.0007568309,0.000007603834],"about_ca_topic_score_codex":0.003457254,"about_ca_topic_score_gemma":0.0055232765,"teacher_disagreement_score":0.003457254,"about_ca_system_score_codex":0.0007540054,"about_ca_system_score_gemma":0.0008590729,"threshold_uncertainty_score":0.008936584},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021254899,0.00018327516,0.9772561,0.00011681563,0.000031059604,0.000048682487,0.0001590501,0.00035794193,0.00059219525],"genre_scores_gemma":[0.9231177,0.0006045659,0.07052289,0.00007320395,0.00006226107,0.00037911808,0.0006329497,0.000102184466,0.0045050853],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804676,0.0005011845,0.00012260913,0.0006237952,0.0005162205,0.00018932877],"domain_scores_gemma":[0.9965641,0.0018851057,0.0006734548,0.00021137898,0.0005806437,0.0000852468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027219884,0.0010479445,0.001385371,0.0014155364,0.00038952086,0.0014767479,0.003118207,0.0013836029,0.0012694956],"category_scores_gemma":[0.008291018,0.00063240743,0.0015320379,0.0014452353,0.0012562153,0.0017385639,0.0013873734,0.0019416894,0.00031824247],"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":[0.00009646139,0.0000373337,0.0026427675,0.000093021954,0.00007624715,0.00014107446,0.00010165138,0.9644761,0.0013626849,0.018047089,0.00033098372,0.012594575],"study_design_scores_gemma":[0.000002443375,0.000015012776,0.00021200938,0.0000033971985,0.0000053846125,0.000021516975,0.0000046129735,0.9973908,0.00014801176,0.0020655172,0.00012596384,0.0000053702324],"about_ca_topic_score_codex":0.010003691,"about_ca_topic_score_gemma":0.0053820526,"teacher_disagreement_score":0.010003691,"about_ca_system_score_codex":0.0012020584,"about_ca_system_score_gemma":0.0011401719,"threshold_uncertainty_score":0.019890904},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20810561,0.00079900824,0.7726889,0.00053044746,0.00018959414,0.0001143055,0.00072024093,0.009640978,0.0072109355],"genre_scores_gemma":[0.8386311,0.00030178207,0.15352067,0.00023430213,0.00003302241,0.00011127233,0.0011075055,0.0002419117,0.005818541],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981993,0.000028363042,0.000008057913,0.000038209106,0.00006948545,0.000035914407],"domain_scores_gemma":[0.99968433,0.0001018075,0.000040268656,0.000051547988,0.00010253057,0.000019560164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036824722,0.0006620731,0.00023080427,0.00028834792,0.00015349974,0.00039306414,0.0010712148,0.00037175845,0.0020500654],"category_scores_gemma":[0.0011141786,0.00023062344,0.00022535509,0.00023517707,0.00026231134,0.0011545758,0.00054864737,0.0007620484,0.0005577615],"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":[0.0006450087,0.00027145757,0.006849702,0.00028981452,0.00016712367,0.00041842886,0.00013587876,0.49197814,0.077107236,0.015374931,0.020445671,0.38631654],"study_design_scores_gemma":[0.000016574731,0.00017230646,0.0007032048,0.000010878873,0.000016919894,0.000074251766,0.000020650534,0.97169703,0.019813074,0.0036415807,0.003823577,0.000010028941],"about_ca_topic_score_codex":0.002042238,"about_ca_topic_score_gemma":0.006339042,"teacher_disagreement_score":0.0020500654,"about_ca_system_score_codex":0.0004889295,"about_ca_system_score_gemma":0.00050990056,"threshold_uncertainty_score":0.00685817},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2961103,0.000804246,0.69664097,0.0011016353,0.00010303729,0.0002262148,0.00042157946,0.0019574496,0.0026345742],"genre_scores_gemma":[0.45292732,0.0006794118,0.5426415,0.00007061811,0.00007364925,0.00015664054,0.0009768491,0.00015032203,0.0023237453],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927086,0.00022259212,0.00003681693,0.0001479691,0.00027582515,0.00004600581],"domain_scores_gemma":[0.99831533,0.0007976549,0.00012501022,0.00022366882,0.00048809333,0.00005038895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013083727,0.00064319896,0.0005181368,0.0014420088,0.00052893267,0.000885948,0.00053657626,0.00058548746,0.0007927906],"category_scores_gemma":[0.0053999834,0.00022989741,0.0005330284,0.0013144667,0.00041788205,0.0009976375,0.0005728812,0.0009966884,0.0004413554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034163776,0.00040847214,0.01023152,0.00020335488,0.00012699593,0.00013560044,0.0005624471,0.19215427,0.020790357,0.008258592,0.006132707,0.76065403],"study_design_scores_gemma":[0.000019842295,0.00013573785,0.007984863,0.000019998435,0.00003239887,0.00008805927,0.00018385766,0.9728803,0.011086179,0.0047232336,0.00281511,0.000030360403],"about_ca_topic_score_codex":0.0043978053,"about_ca_topic_score_gemma":0.005530502,"teacher_disagreement_score":0.0043978053,"about_ca_system_score_codex":0.0005526161,"about_ca_system_score_gemma":0.0008988895,"threshold_uncertainty_score":0.008744419},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2949262,0.0008011711,0.6965279,0.0004749182,0.00011892192,0.0016620582,0.00036133575,0.0040893527,0.0010381925],"genre_scores_gemma":[0.4608264,0.00017571346,0.5373749,0.00007499796,0.000016135657,0.00077555847,0.00038286584,0.000057254227,0.00031621908],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9869265,0.0064017237,0.0015088053,0.0014035742,0.0033837853,0.00037567236],"domain_scores_gemma":[0.95373124,0.031845156,0.0017097353,0.002585007,0.00951464,0.0006142064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020354934,0.0008138321,0.0011026863,0.0024821963,0.00053296745,0.00166768,0.0019057865,0.0020327934,0.0013501927],"category_scores_gemma":[0.05926556,0.00039500842,0.0008712748,0.0018314187,0.0007045446,0.0016828955,0.0012259755,0.0012826798,0.0003943831],"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":[0.0023970234,0.0027119725,0.090801485,0.00092092715,0.00088826544,0.00047224708,0.00057993835,0.21157628,0.012225734,0.0040653367,0.003359675,0.67000103],"study_design_scores_gemma":[0.00019843763,0.000836116,0.006199326,0.00006596888,0.00012604024,0.00025851888,0.00010981825,0.9811056,0.008867502,0.0010407456,0.0011637823,0.00002805622],"about_ca_topic_score_codex":0.0035762088,"about_ca_topic_score_gemma":0.0019868086,"teacher_disagreement_score":0.020354934,"about_ca_system_score_codex":0.0015297572,"about_ca_system_score_gemma":0.002769651,"threshold_uncertainty_score":0.10764849},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29110888,0.0008255443,0.68839186,0.00019121957,0.00021798445,0.000228336,0.0020243411,0.012584387,0.0044274535],"genre_scores_gemma":[0.75890446,0.00074885023,0.2297381,0.00013400549,0.0002319131,0.00017672301,0.0046212873,0.0005189504,0.0049258675],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994943,0.00003858942,0.000035279143,0.00015238926,0.00021761739,0.000061829014],"domain_scores_gemma":[0.9993224,0.00014358382,0.000098221484,0.000064474094,0.00030625574,0.000064948916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002822697,0.0006666429,0.0010020541,0.0025402447,0.00027202777,0.00071310793,0.0007050474,0.0005352956,0.0014051788],"category_scores_gemma":[0.0017987227,0.00023715927,0.00031246923,0.0007606518,0.00014484733,0.0007757032,0.0006568995,0.0004563085,0.0016809361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006542049,0.00027144203,0.051078897,0.00036116227,0.0001575203,0.0005920745,0.00025320778,0.01688626,0.16140205,0.0009036172,0.008423626,0.759016],"study_design_scores_gemma":[0.000060875474,0.00040894796,0.10863326,0.0000976522,0.00010253655,0.0013221753,0.00030881228,0.77383465,0.10075313,0.002441971,0.011927493,0.00010855021],"about_ca_topic_score_codex":0.0026090478,"about_ca_topic_score_gemma":0.0042601456,"teacher_disagreement_score":0.0026090478,"about_ca_system_score_codex":0.00018642528,"about_ca_system_score_gemma":0.00034157856,"threshold_uncertainty_score":0.00518775},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016909502,0.00022780514,0.97525656,0.00008615255,0.000043588963,0.00005704581,0.00040008413,0.0063368888,0.00068237865],"genre_scores_gemma":[0.25625744,0.00028878002,0.7359718,0.00016481045,0.000083599974,0.0001416184,0.0026244796,0.0009329315,0.0035345159],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993808,0.000087256354,0.000028845967,0.00023073,0.00017721634,0.00009500845],"domain_scores_gemma":[0.9993105,0.0001641214,0.000120179175,0.00020136649,0.00014291135,0.000060814287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008101522,0.0011584708,0.0010714934,0.0017526854,0.00044391234,0.0010633888,0.0020671545,0.00091796485,0.0015384371],"category_scores_gemma":[0.0019180904,0.0004908543,0.001166664,0.0008178958,0.00080483564,0.0015860096,0.0020067901,0.0014774632,0.00077444833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056801876,0.00025845034,0.0137566775,0.0002503862,0.0003959704,0.0006103857,0.0003400183,0.13632572,0.06482119,0.023352364,0.015604667,0.7437163],"study_design_scores_gemma":[0.000011301518,0.00006265118,0.0018018486,0.000016399072,0.000022922717,0.00027340392,0.00003560981,0.9601364,0.017676037,0.014076702,0.0058603664,0.00002633542],"about_ca_topic_score_codex":0.005596672,"about_ca_topic_score_gemma":0.008441718,"teacher_disagreement_score":0.005596672,"about_ca_system_score_codex":0.0007259616,"about_ca_system_score_gemma":0.0010232237,"threshold_uncertainty_score":0.011128187},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35712317,0.0009521406,0.6384307,0.0004622864,0.00009970362,0.00018971735,0.0007749194,0.0008112075,0.0011561979],"genre_scores_gemma":[0.8929454,0.00036624476,0.10553117,0.000033943452,0.00006898849,0.000090303336,0.00072153576,0.000029162606,0.00021319109],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854577,0.00026128793,0.00022166276,0.00023837548,0.00059979566,0.00013311433],"domain_scores_gemma":[0.9952003,0.002598396,0.00088834425,0.0005377582,0.00063843286,0.00013684355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020198077,0.0005849244,0.0011135935,0.0048144544,0.00045738963,0.0024849547,0.0006153554,0.0006927971,0.0002728007],"category_scores_gemma":[0.0073132664,0.00022673186,0.0007516712,0.0031052243,0.000833569,0.001938028,0.0011687835,0.00076375,0.00008420158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001464501,0.00054561294,0.08287264,0.000971823,0.0006132316,0.002120601,0.0019780241,0.29859158,0.062293235,0.053037442,0.004359309,0.491152],"study_design_scores_gemma":[0.000017485334,0.00011510842,0.019720145,0.000067523506,0.00007506401,0.0002109137,0.0004072839,0.9331986,0.008057741,0.03669001,0.0014005246,0.00003955422],"about_ca_topic_score_codex":0.0014872412,"about_ca_topic_score_gemma":0.0010800575,"teacher_disagreement_score":0.0048144544,"about_ca_system_score_codex":0.0006440415,"about_ca_system_score_gemma":0.0004463843,"threshold_uncertainty_score":0.010681868},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021847442,0.00015342834,0.9739265,0.00022105439,0.000044238503,0.00006162319,0.00004567801,0.0011229783,0.0025771025],"genre_scores_gemma":[0.4194223,0.000215402,0.5755611,0.0001760194,0.000045514687,0.000188074,0.00018225628,0.0001726883,0.0040366603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991561,0.00016320925,0.000048248243,0.00020122482,0.00031396106,0.000117248215],"domain_scores_gemma":[0.99897975,0.0003332769,0.00013934699,0.00025686139,0.00024846976,0.000042288768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007092279,0.00089106057,0.00074552896,0.0005576111,0.00060273375,0.0010495551,0.001683623,0.0008985119,0.002864259],"category_scores_gemma":[0.0025975856,0.0003944393,0.0006905181,0.001047189,0.0007507298,0.0019243283,0.0010514406,0.0012207552,0.00065830897],"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":[0.00026708457,0.00013014242,0.0016017816,0.00022390427,0.00007681494,0.00022315029,0.00010992868,0.68900096,0.035002347,0.035490155,0.004984591,0.23288919],"study_design_scores_gemma":[0.000011832786,0.00006476365,0.00028939094,0.000006193445,0.000008465808,0.000051699662,0.000029909428,0.98465675,0.007235174,0.005624094,0.0020111192,0.000010540189],"about_ca_topic_score_codex":0.0029376855,"about_ca_topic_score_gemma":0.008565013,"teacher_disagreement_score":0.0029376855,"about_ca_system_score_codex":0.0010818853,"about_ca_system_score_gemma":0.0014083325,"threshold_uncertainty_score":0.0095819235},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17587695,0.0029631648,0.79922414,0.00057430007,0.00047523895,0.0005835107,0.0010315297,0.0062180706,0.013053089],"genre_scores_gemma":[0.88545936,0.00044484323,0.109603316,0.00010954021,0.00008680596,0.0004129463,0.0013981423,0.00033680256,0.0021483207],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99221426,0.0023308096,0.0008933717,0.0010760434,0.0029817764,0.0005037947],"domain_scores_gemma":[0.9749196,0.014100159,0.0028956726,0.0027139927,0.004466019,0.00090452796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0071804128,0.002059303,0.0015702216,0.003121054,0.0006718426,0.002016601,0.002228359,0.002347708,0.002065314],"category_scores_gemma":[0.039813984,0.0003214053,0.0008495034,0.0015525107,0.0015214527,0.0034077812,0.0018875495,0.0015865436,0.0008637852],"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":[0.0009363434,0.0005515306,0.013910162,0.00062373414,0.00055129454,0.00013342932,0.00015448577,0.7141324,0.010438979,0.007835326,0.0033056417,0.24742663],"study_design_scores_gemma":[0.00002880982,0.00087064196,0.0031250077,0.00003569269,0.00005355798,0.0001237125,0.000042426196,0.97980356,0.010936257,0.0042278427,0.00069075584,0.000061707084],"about_ca_topic_score_codex":0.004650779,"about_ca_topic_score_gemma":0.0029376948,"teacher_disagreement_score":0.0071804128,"about_ca_system_score_codex":0.0018388805,"about_ca_system_score_gemma":0.0013672776,"threshold_uncertainty_score":0.03797406},"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":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011415358,0.0004787059,0.45492232,0.00018340131,0.00034482096,0.00077343144,0.015741834,0.5047367,0.0114033995],"genre_scores_gemma":[0.11534552,0.0006756027,0.71706444,0.00034882675,0.00033038823,0.0014516932,0.063919656,0.0795377,0.021326095],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986589,0.00012192932,0.00013091291,0.0003946161,0.0005551594,0.00013851469],"domain_scores_gemma":[0.9950675,0.0015085607,0.00043908446,0.0007753491,0.0018505028,0.00035894118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014810615,0.0015868102,0.00081270613,0.00551361,0.00061662856,0.0017894265,0.0017693419,0.0010667833,0.026882945],"category_scores_gemma":[0.013168121,0.00076238834,0.0008311159,0.0018107495,0.00037549852,0.0024039815,0.0023286606,0.0010444287,0.015173864],"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":[0.0009075214,0.00019376552,0.009190604,0.0011394098,0.00017691142,0.00044109556,0.00056188885,0.0027397508,0.0317918,0.0043294206,0.22446221,0.7240656],"study_design_scores_gemma":[0.0002982337,0.00046352102,0.0508167,0.0008720535,0.00014568347,0.0029141784,0.0005714283,0.23932455,0.15217118,0.019527787,0.5323525,0.0005422258],"about_ca_topic_score_codex":0.0046531395,"about_ca_topic_score_gemma":0.004793775,"teacher_disagreement_score":0.026882945,"about_ca_system_score_codex":0.00077148393,"about_ca_system_score_gemma":0.0009777965,"threshold_uncertainty_score":0.08993244},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09982965,0.0004102077,0.8893738,0.0026084648,0.00022982755,0.0004977056,0.00022923108,0.0034008694,0.00342031],"genre_scores_gemma":[0.70936483,0.00040083137,0.28442842,0.0009829989,0.00010375632,0.0005341769,0.00029001606,0.00024293714,0.0036519875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989606,0.00042078143,0.000049312024,0.0002700508,0.00015819278,0.00014112772],"domain_scores_gemma":[0.99742645,0.0014431734,0.00019262011,0.0003246835,0.00043202937,0.00018105723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020035498,0.0006614446,0.00048843666,0.0003550373,0.0002489549,0.0010135527,0.0012049198,0.000976797,0.0025710207],"category_scores_gemma":[0.008763198,0.0005792947,0.000514289,0.00019332078,0.000773386,0.001957139,0.0011414135,0.0012606027,0.0015679245],"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":[0.00075440074,0.00056858355,0.03837697,0.0005770998,0.00017543828,0.0009105322,0.00060895976,0.49470687,0.046171624,0.012053981,0.0125195235,0.39257598],"study_design_scores_gemma":[0.000031567,0.00029424706,0.0028536215,0.00008036131,0.00003100318,0.00017243817,0.00015500037,0.9707581,0.01067454,0.009827354,0.0050964877,0.000025331703],"about_ca_topic_score_codex":0.0024808696,"about_ca_topic_score_gemma":0.002282661,"teacher_disagreement_score":0.0025710207,"about_ca_system_score_codex":0.00054073083,"about_ca_system_score_gemma":0.0006250937,"threshold_uncertainty_score":0.010595918},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057284143,0.00016287612,0.99218327,0.00009630683,0.00008369996,0.00008697843,0.00012192521,0.0009327571,0.0006037347],"genre_scores_gemma":[0.091413066,0.00016355018,0.90392965,0.00009863042,0.0000985631,0.00020188917,0.000700407,0.00027623755,0.0031180065],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960155,0.000774233,0.0003455586,0.0009171546,0.0016322106,0.00031538316],"domain_scores_gemma":[0.99556476,0.00097290357,0.00023651638,0.00074963225,0.0022715968,0.00020463047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026002713,0.0013685478,0.0020136742,0.0051839603,0.0025414592,0.0028893433,0.003751218,0.0024070651,0.00276678],"category_scores_gemma":[0.006489899,0.0006100066,0.0035038816,0.0047215386,0.00088274595,0.0020590376,0.002452384,0.0020469227,0.0023137806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051087246,0.00043557753,0.005013579,0.0003521811,0.00077702757,0.00023327729,0.0006337887,0.060261767,0.035989147,0.020165266,0.010143917,0.8654836],"study_design_scores_gemma":[0.000030841624,0.00015812393,0.0028040062,0.000030262463,0.0001444824,0.00040736987,0.00016237715,0.96122766,0.011233597,0.017897084,0.005786361,0.00011795538],"about_ca_topic_score_codex":0.008339366,"about_ca_topic_score_gemma":0.009668015,"teacher_disagreement_score":0.008339366,"about_ca_system_score_codex":0.0010689878,"about_ca_system_score_gemma":0.0018719702,"threshold_uncertainty_score":0.016581655},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049054854,0.0006614162,0.82521504,0.0008527505,0.00021694334,0.0014226387,0.014045661,0.10411906,0.004411654],"genre_scores_gemma":[0.14468919,0.0003394907,0.831199,0.0003660171,0.00007996451,0.0006154601,0.018080952,0.0006324329,0.003997589],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980533,0.00022613723,0.00026389956,0.000653779,0.0007233266,0.00007955815],"domain_scores_gemma":[0.9956891,0.0016410265,0.00045804874,0.0007566668,0.0012459572,0.0002092154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022223177,0.0011429072,0.0012506555,0.00423459,0.0007020035,0.0018714605,0.0024806962,0.0010531598,0.0029162767],"category_scores_gemma":[0.008499023,0.000529483,0.0012636123,0.0024459183,0.0003626357,0.0016847926,0.0012207278,0.0015330687,0.0029402715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054105493,0.0013772356,0.028585069,0.0007212036,0.0005352474,0.0011933028,0.00027903123,0.037823223,0.027068045,0.0047709383,0.0403365,0.85676926],"study_design_scores_gemma":[0.00012104533,0.00025992692,0.0063035996,0.00007931732,0.00015393112,0.00064050744,0.00010150317,0.94383913,0.01989544,0.007948846,0.020598935,0.00005778421],"about_ca_topic_score_codex":0.006553161,"about_ca_topic_score_gemma":0.010409508,"teacher_disagreement_score":0.006553161,"about_ca_system_score_codex":0.00067336153,"about_ca_system_score_gemma":0.0024426233,"threshold_uncertainty_score":0.013030052},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024163537,0.00032117945,0.9667573,0.00056696596,0.000096089454,0.000102273625,0.0006547928,0.005913045,0.0014248004],"genre_scores_gemma":[0.32511997,0.00044462827,0.6707553,0.00018431622,0.00013321113,0.00012371881,0.0012961071,0.0003091332,0.0016336577],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996018,0.000094927644,0.000038080845,0.00006473145,0.00015981328,0.00004070812],"domain_scores_gemma":[0.9982292,0.0007765438,0.00016368885,0.00016489025,0.0005523295,0.00011338417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00104126,0.0006034732,0.00053886446,0.0029866118,0.00038573839,0.0022738338,0.0009657477,0.0005943907,0.0024107655],"category_scores_gemma":[0.0039187507,0.00028803962,0.00084767607,0.0015472759,0.00041080196,0.0014010906,0.0012543101,0.0011239841,0.000520962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006568884,0.00050121464,0.0063248905,0.00038769373,0.00016462957,0.0005444158,0.0007587758,0.07562662,0.053650852,0.035024926,0.018838536,0.8075206],"study_design_scores_gemma":[0.000029808813,0.00009274217,0.0016725606,0.000042448395,0.000034800014,0.0001878951,0.00020411151,0.95504725,0.011173499,0.024570594,0.006910626,0.000033719745],"about_ca_topic_score_codex":0.004931677,"about_ca_topic_score_gemma":0.0041006147,"teacher_disagreement_score":0.004931677,"about_ca_system_score_codex":0.0004691967,"about_ca_system_score_gemma":0.0006693211,"threshold_uncertainty_score":0.009805977},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046782054,0.00055354123,0.94818866,0.0008084748,0.00010314823,0.00013585866,0.00016245301,0.001259595,0.002006319],"genre_scores_gemma":[0.92789644,0.00029991328,0.06761817,0.000540897,0.00006125538,0.00016286298,0.0003384597,0.000120477525,0.0029615022],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815327,0.00068063027,0.000089081186,0.00045116627,0.00041367323,0.00021207535],"domain_scores_gemma":[0.99250704,0.005327656,0.00057811005,0.000956699,0.00042571264,0.00020486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036743204,0.001362786,0.0010851114,0.0005674771,0.0005726656,0.00095154654,0.0021415886,0.0012318862,0.0015045954],"category_scores_gemma":[0.011328261,0.00047649886,0.00092215603,0.0003376778,0.0019171084,0.0027054404,0.0042104167,0.00302471,0.00030210117],"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":[0.00023172483,0.00007542436,0.0012042042,0.000076154356,0.00006982282,0.00013324615,0.00012010313,0.9495859,0.0036219058,0.01216464,0.0016396475,0.031077212],"study_design_scores_gemma":[0.00000661783,0.000030017942,0.00009430385,0.0000065140625,0.000006166214,0.00002074657,0.000008648836,0.99362904,0.0010270112,0.004936715,0.00022872006,0.0000055377527],"about_ca_topic_score_codex":0.0027424411,"about_ca_topic_score_gemma":0.0027007882,"teacher_disagreement_score":0.0036743204,"about_ca_system_score_codex":0.0013783324,"about_ca_system_score_gemma":0.0011559409,"threshold_uncertainty_score":0.01943183},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06377395,0.00026580272,0.9325395,0.00018382123,0.00004300886,0.000040869054,0.00007012375,0.002558651,0.0005243081],"genre_scores_gemma":[0.71821785,0.00017388223,0.2801421,0.00007290815,0.00005136744,0.00006504305,0.0001773525,0.0000909055,0.0010085605],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905926,0.00016801187,0.00006832889,0.00020410836,0.0004037116,0.00009649712],"domain_scores_gemma":[0.9980077,0.0009640617,0.00032897037,0.0002187504,0.0004167667,0.00006379735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080968067,0.00057519996,0.000761742,0.0015456134,0.00044624094,0.0006731735,0.00089631137,0.0005661414,0.00041084466],"category_scores_gemma":[0.0025788269,0.0002853745,0.0004252651,0.00092910475,0.00044508942,0.0011598903,0.00069149665,0.001152577,0.00025230818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003402606,0.00033164036,0.022645792,0.00015366818,0.00011896808,0.00037203805,0.00027251002,0.2434341,0.05928772,0.004624559,0.0025418454,0.6658769],"study_design_scores_gemma":[0.0000051818793,0.00004441709,0.0017555369,0.0000047489643,0.0000055260066,0.00009018636,0.000023330593,0.9859319,0.009120726,0.0023504877,0.0006588662,0.000009067885],"about_ca_topic_score_codex":0.0021473854,"about_ca_topic_score_gemma":0.0022759356,"teacher_disagreement_score":0.0021473854,"about_ca_system_score_codex":0.0006133114,"about_ca_system_score_gemma":0.0005653977,"threshold_uncertainty_score":0.0044499636},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017286262,0.0004280815,0.9790071,0.00015808622,0.00010921768,0.000045153545,0.00045769513,0.0019909497,0.0005173338],"genre_scores_gemma":[0.5257683,0.0006880765,0.46304035,0.00030185864,0.00010276895,0.00019312707,0.004976867,0.00027984774,0.0046486906],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992619,0.00010584466,0.000056429002,0.00016860427,0.00032798824,0.00007926204],"domain_scores_gemma":[0.9990994,0.0002514796,0.00011766563,0.00018917957,0.00028151218,0.00006071829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008499808,0.0008297375,0.0011373975,0.0013701238,0.00026051956,0.000999077,0.0015585226,0.00071474223,0.001310738],"category_scores_gemma":[0.002765095,0.00030049487,0.0008239795,0.0012515027,0.000541766,0.0013947138,0.0013812154,0.0016699368,0.00069775473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026246297,0.0001732153,0.005045607,0.0001504516,0.00013621237,0.00013495401,0.00006691235,0.17838292,0.013812472,0.009024957,0.013913703,0.77889615],"study_design_scores_gemma":[0.000007795094,0.000031388503,0.00042361228,0.0000063844273,0.0000058941546,0.00005654841,0.0000138405485,0.98922694,0.0050237793,0.003598226,0.0015936141,0.00001195051],"about_ca_topic_score_codex":0.003871246,"about_ca_topic_score_gemma":0.003907304,"teacher_disagreement_score":0.003871246,"about_ca_system_score_codex":0.0007342415,"about_ca_system_score_gemma":0.0011324806,"threshold_uncertainty_score":0.0076974034},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061209,0.00024098247,0.7379443,0.00048340647,0.00014378171,0.000733988,0.014312193,0.17745446,0.007477844],"genre_scores_gemma":[0.32424352,0.0002627069,0.65362746,0.00014874907,0.00005969289,0.0007632231,0.013426365,0.0036441337,0.0038241106],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917054,0.00015264077,0.000094216,0.00015267395,0.00036440208,0.00006565178],"domain_scores_gemma":[0.99572515,0.001987589,0.00047075446,0.000440953,0.0010603619,0.0003152263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015030324,0.001562011,0.000670967,0.0071169394,0.0004370629,0.0022731642,0.0012463109,0.00072595984,0.01171939],"category_scores_gemma":[0.008603082,0.00034238907,0.00070807507,0.0029323574,0.00031094736,0.0023770866,0.0019127531,0.000998634,0.0033315052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010302424,0.0007462489,0.021642892,0.0010481398,0.00017677849,0.00074290653,0.002894981,0.019331226,0.030203657,0.0076647974,0.06692234,0.8475959],"study_design_scores_gemma":[0.00021864491,0.0006170354,0.035455946,0.0005475921,0.00013022135,0.00093398517,0.0026443512,0.8029557,0.05462888,0.02474579,0.07674635,0.0003755115],"about_ca_topic_score_codex":0.0047668903,"about_ca_topic_score_gemma":0.005133819,"teacher_disagreement_score":0.01171939,"about_ca_system_score_codex":0.00046436267,"about_ca_system_score_gemma":0.0006869226,"threshold_uncertainty_score":0.039205194},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15976872,0.00095227844,0.833643,0.0010245728,0.00011767308,0.000065687345,0.00053368,0.0014201334,0.0024742968],"genre_scores_gemma":[0.9179282,0.0003831195,0.07541276,0.00030793148,0.00008908393,0.00009725868,0.001142045,0.00009369721,0.0045459587],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995819,0.00011241814,0.000024977151,0.00013396946,0.00006200013,0.00008479554],"domain_scores_gemma":[0.99781954,0.0015502807,0.00013827583,0.000105458486,0.0002976854,0.00008871302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014845507,0.0008594847,0.0010538192,0.00094868813,0.00036448197,0.0010114724,0.0020150573,0.001600936,0.0018585915],"category_scores_gemma":[0.003912909,0.0009974279,0.0010213227,0.0006994658,0.00074539974,0.0012252079,0.00097341894,0.0024675955,0.0003806647],"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":[0.00012509615,0.0000674892,0.0042772177,0.000043534055,0.00010341995,0.000071068374,0.00005507662,0.93691725,0.0007993409,0.0033837364,0.0010648547,0.053091887],"study_design_scores_gemma":[0.0000019411473,0.000004293873,0.00015017371,0.0000036645888,0.0000035060714,0.000003496118,0.0000023878385,0.998898,0.00008949836,0.000795489,0.000045503515,0.0000019160273],"about_ca_topic_score_codex":0.03946349,"about_ca_topic_score_gemma":0.033936094,"teacher_disagreement_score":0.03946349,"about_ca_system_score_codex":0.0014341869,"about_ca_system_score_gemma":0.0016020135,"threshold_uncertainty_score":0.07846755},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.070145994,0.0015076846,0.9011228,0.0020923153,0.0003537289,0.00042131037,0.0053826016,0.014484859,0.004488675],"genre_scores_gemma":[0.44445756,0.0011173396,0.5444454,0.0005558969,0.0002631587,0.00023733449,0.0074070473,0.0003487701,0.001167548],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99696416,0.00048216118,0.00038994657,0.000725213,0.0012158359,0.00022268335],"domain_scores_gemma":[0.9938846,0.0023087997,0.0007966365,0.001339015,0.0012863409,0.00038452537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028021897,0.0012514598,0.0011717019,0.005432938,0.0012484613,0.0033557587,0.0015025751,0.00096297235,0.001321938],"category_scores_gemma":[0.014666017,0.00042326417,0.0011918844,0.00702977,0.0006312636,0.00447551,0.003244569,0.001512208,0.0007424859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001068917,0.0008848176,0.0717315,0.0007584504,0.0005594519,0.0024041426,0.001487864,0.1076712,0.021558546,0.03549968,0.031165585,0.72520983],"study_design_scores_gemma":[0.000037401496,0.00013848396,0.0059597776,0.000078253965,0.00006449826,0.00076741894,0.0007343954,0.92158777,0.008968649,0.04143553,0.020168472,0.000059424583],"about_ca_topic_score_codex":0.0067372792,"about_ca_topic_score_gemma":0.007091662,"teacher_disagreement_score":0.0067372792,"about_ca_system_score_codex":0.0009520902,"about_ca_system_score_gemma":0.0012208575,"threshold_uncertainty_score":0.014819562},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018520929,0.00016669261,0.9789651,0.00009471168,0.0000462365,0.00003203534,0.00008734473,0.001595676,0.0004912324],"genre_scores_gemma":[0.5607498,0.00035703895,0.43436736,0.00016735787,0.00016716772,0.00007776216,0.0006885073,0.00035551877,0.0030694334],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993462,0.000094661846,0.000021701018,0.0002144714,0.0002431079,0.00007984139],"domain_scores_gemma":[0.9983901,0.0005511869,0.00022997693,0.0003410391,0.00037019548,0.00011761832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007846831,0.00091965706,0.0009618647,0.0012613666,0.00034643305,0.00055320136,0.0020186508,0.00091179844,0.0015559396],"category_scores_gemma":[0.0040965606,0.00036164175,0.0006254197,0.00085182977,0.00063802284,0.0012099515,0.0010825492,0.0018131273,0.00083422614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033896623,0.00029516764,0.011138133,0.00012288164,0.0001441823,0.00043047342,0.00012156207,0.3294599,0.04696872,0.00886278,0.008375007,0.5937422],"study_design_scores_gemma":[0.0000030133658,0.000022868022,0.00044804983,0.0000029740443,0.0000068944005,0.000085812884,0.0000051663474,0.9917951,0.004140019,0.0028500718,0.00063402724,0.0000060558928],"about_ca_topic_score_codex":0.00395592,"about_ca_topic_score_gemma":0.004730625,"teacher_disagreement_score":0.00395592,"about_ca_system_score_codex":0.0006440685,"about_ca_system_score_gemma":0.00051958486,"threshold_uncertainty_score":0.007865787},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057021757,0.00048503352,0.99059826,0.00021139187,0.000055747507,0.000048525442,0.00012135509,0.0019024616,0.0008749475],"genre_scores_gemma":[0.51245636,0.0008908446,0.47319865,0.0005058821,0.0002461571,0.00045664143,0.0012922852,0.00035756707,0.010595613],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926156,0.00014167411,0.000041337553,0.00022106245,0.00019374752,0.000140708],"domain_scores_gemma":[0.99931645,0.00020430426,0.000062565494,0.00007588461,0.00028446622,0.00005629464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001304516,0.0010797997,0.0014031971,0.0015380079,0.00051958807,0.0011470628,0.003907001,0.0017297791,0.0033399852],"category_scores_gemma":[0.002032211,0.0005527228,0.0013702589,0.0012852399,0.0006965655,0.0018202177,0.0017759216,0.002604991,0.0011610647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016445627,0.0002474668,0.0022748176,0.00012825469,0.00015995551,0.0002028569,0.00011204948,0.47384334,0.0039183707,0.024493424,0.0069474117,0.48750755],"study_design_scores_gemma":[0.0000024838382,0.0000137573625,0.00005617719,0.0000027557792,0.000004177436,0.000012725385,0.0000039897923,0.99584585,0.0002674704,0.0033832511,0.00040422048,0.0000032290054],"about_ca_topic_score_codex":0.017384786,"about_ca_topic_score_gemma":0.010419562,"teacher_disagreement_score":0.017384786,"about_ca_system_score_codex":0.0016653658,"about_ca_system_score_gemma":0.0017414289,"threshold_uncertainty_score":0.034567237},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001699712,0.9969382,0.00091141596,0.00026724313,0.00026094491,0.00000808872,0.000021501242,0.000016095672,0.0014065396],"genre_scores_gemma":[0.0013464723,0.9967899,0.0008004157,0.00017656366,0.0002650039,0.000009175411,0.00003877128,0.000004920817,0.00056883914],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996012,0.00006262649,0.00004803746,0.000087050495,0.00017119947,0.000029901754],"domain_scores_gemma":[0.9980165,0.0012346025,0.00015674818,0.000055442015,0.0004707139,0.0000660623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009461034,0.0010019739,0.001302201,0.004014329,0.0003629705,0.0012202587,0.0011613894,0.0015611454,0.0037361886],"category_scores_gemma":[0.00213527,0.00047088924,0.0007734656,0.0045735496,0.0005642911,0.0025867757,0.00073771673,0.0014263476,0.0020430281],"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":[0.000043035678,0.000089045796,0.0004139891,0.021151535,0.00013863723,0.00014174097,0.00007043707,0.0011252667,0.0012098968,0.010517687,0.030008137,0.9350906],"study_design_scores_gemma":[0.000011928677,0.00015586047,0.001455215,0.007508407,0.00025388683,0.0011993783,0.0001093569,0.00068418006,0.00087692006,0.0075042043,0.980185,0.000055733522],"about_ca_topic_score_codex":0.0013329503,"about_ca_topic_score_gemma":0.001652214,"teacher_disagreement_score":0.004014329,"about_ca_system_score_codex":0.0006458148,"about_ca_system_score_gemma":0.0013862468,"threshold_uncertainty_score":0.012498856},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21945226,0.0007650754,0.76081187,0.0005120981,0.0001905776,0.00012338809,0.0007136475,0.010457077,0.006973961],"genre_scores_gemma":[0.8340513,0.00026821042,0.15812853,0.0002407655,0.000030878207,0.00010899593,0.0010980383,0.00024169771,0.0058315233],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982774,0.000026945689,0.000008127644,0.0000381166,0.00006394528,0.000035087975],"domain_scores_gemma":[0.9996939,0.00009738063,0.00004089383,0.00004763687,0.00010066315,0.000019587103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037660627,0.00067182764,0.00023246554,0.0002902505,0.00015530916,0.0004014286,0.0010911012,0.00035702155,0.0021842218],"category_scores_gemma":[0.001069867,0.00022995837,0.00022833767,0.0002197228,0.00025913405,0.0011744904,0.0005505384,0.00077217683,0.00057809945],"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":[0.00067111215,0.0002837925,0.007723032,0.00029614964,0.00017730628,0.0004783979,0.00014287057,0.4648581,0.07887976,0.013974292,0.020866564,0.41164863],"study_design_scores_gemma":[0.000018275841,0.00020364847,0.0007869298,0.000012135826,0.000019115381,0.00008572453,0.00002275066,0.97059095,0.020803988,0.003427912,0.004017529,0.000011021275],"about_ca_topic_score_codex":0.0021186834,"about_ca_topic_score_gemma":0.0066448604,"teacher_disagreement_score":0.0021842218,"about_ca_system_score_codex":0.0004964174,"about_ca_system_score_gemma":0.0005293975,"threshold_uncertainty_score":0.0073069334},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19576128,0.0018240004,0.7962571,0.0008339235,0.00019712848,0.000118178265,0.00020890492,0.0008394512,0.0039600525],"genre_scores_gemma":[0.862252,0.00085471687,0.13076673,0.00025598356,0.000107004846,0.00007171521,0.000532537,0.00006507365,0.0050943303],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995159,0.000095722535,0.000022443113,0.00018180856,0.00013350669,0.000050717183],"domain_scores_gemma":[0.99848783,0.0008824557,0.00008558945,0.000102037586,0.0003447135,0.0000973982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001267542,0.00070213934,0.0008917778,0.0006198593,0.00037879025,0.0006970343,0.0010732122,0.0008669061,0.0015905517],"category_scores_gemma":[0.00565211,0.00033324127,0.00060875557,0.00053598,0.0004875382,0.0015517266,0.0007434956,0.0014353578,0.0003114764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054777437,0.0006004881,0.008965824,0.0003295261,0.00024686463,0.00023204059,0.00026692558,0.39134282,0.019769236,0.014067184,0.004657654,0.5589737],"study_design_scores_gemma":[0.0000025953505,0.000055422766,0.0009098001,0.0000070242945,0.000008986309,0.000032103133,0.000010944637,0.9956606,0.0013072584,0.0018050784,0.00019476246,0.0000052618616],"about_ca_topic_score_codex":0.005447102,"about_ca_topic_score_gemma":0.005417555,"teacher_disagreement_score":0.005447102,"about_ca_system_score_codex":0.0007188822,"about_ca_system_score_gemma":0.0009392685,"threshold_uncertainty_score":0.01083076},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14303066,0.00030446454,0.72791964,0.0004979012,0.00042527678,0.0006645492,0.014810748,0.10929597,0.003050794],"genre_scores_gemma":[0.5653643,0.0002068253,0.40396044,0.00015062695,0.00007993934,0.0007837736,0.025924448,0.002272857,0.0012567333],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920565,0.00019349904,0.000077277145,0.00018742018,0.00029210627,0.000044038286],"domain_scores_gemma":[0.9960621,0.002262916,0.00029773093,0.00069922325,0.0005522677,0.00012577673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016828505,0.001080972,0.00047301876,0.0019951817,0.00024106403,0.0005978964,0.0011179146,0.00049831136,0.0026501894],"category_scores_gemma":[0.011948313,0.00025833774,0.0006445779,0.0013708188,0.0003232577,0.0007137712,0.0006452746,0.00092224084,0.0008906615],"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":[0.0013718344,0.00065200916,0.030023897,0.00068740343,0.00047290928,0.00082751224,0.0005436038,0.47965875,0.020065568,0.010771139,0.064213246,0.39071202],"study_design_scores_gemma":[0.000051597046,0.000078398145,0.0017904445,0.00000902912,0.000013355135,0.00007576836,0.000027476177,0.98678476,0.005693319,0.0023111019,0.0031466766,0.000018055784],"about_ca_topic_score_codex":0.0035858867,"about_ca_topic_score_gemma":0.0031639931,"teacher_disagreement_score":0.0035858867,"about_ca_system_score_codex":0.00044017934,"about_ca_system_score_gemma":0.00066380773,"threshold_uncertainty_score":0.008899868},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18715453,0.0018000907,0.8066487,0.0004427733,0.00012757465,0.00005833479,0.00013634858,0.001551497,0.0020802529],"genre_scores_gemma":[0.94654644,0.0013005922,0.049760956,0.000051227886,0.00006823961,0.00004181401,0.0003747923,0.00003293263,0.0018228983],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928373,0.00010493956,0.00005971514,0.0001627979,0.00031133482,0.00007752353],"domain_scores_gemma":[0.99907327,0.00022237647,0.000110016634,0.00007121067,0.00048332088,0.000039711056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005404477,0.00067133986,0.0006557732,0.00092885556,0.00019017527,0.00056597305,0.00066595327,0.00041811078,0.0008171269],"category_scores_gemma":[0.0020091017,0.00022904591,0.0007068147,0.00064998685,0.00029759167,0.0015518966,0.00025732323,0.0006805631,0.00022366317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003297549,0.00027034507,0.023062842,0.00041383412,0.000273607,0.0003821756,0.00024771263,0.36995414,0.040663783,0.0070743742,0.0027535227,0.5545739],"study_design_scores_gemma":[0.000007829856,0.00015950811,0.0041119033,0.000008545898,0.000045105433,0.00008155498,0.000027401382,0.9891088,0.00458745,0.0009486251,0.0008950517,0.00001825961],"about_ca_topic_score_codex":0.00773995,"about_ca_topic_score_gemma":0.0031662001,"teacher_disagreement_score":0.00773995,"about_ca_system_score_codex":0.00034493918,"about_ca_system_score_gemma":0.0005196141,"threshold_uncertainty_score":0.0153898},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5650132,0.011701728,0.3884529,0.0018199239,0.0029472846,0.0007946031,0.0027686423,0.0055621145,0.020939615],"genre_scores_gemma":[0.8933556,0.0012470059,0.0996178,0.00019434305,0.00026937202,0.00028804608,0.0025801342,0.00020876627,0.0022391363],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99194324,0.001928959,0.00081951753,0.0011409874,0.0035528825,0.00061448663],"domain_scores_gemma":[0.9754878,0.013424513,0.0011853979,0.0022236493,0.0071694003,0.00050912244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063599534,0.0010706598,0.0013968606,0.0043948456,0.0007369533,0.002073054,0.0011265739,0.0014935547,0.002455845],"category_scores_gemma":[0.03245476,0.0002004973,0.0008731884,0.0034792968,0.0004915637,0.0025367024,0.0010131308,0.0012289538,0.0012608531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025893585,0.00063746615,0.02972022,0.00086046767,0.0004938617,0.00029431432,0.00021996265,0.08455993,0.0143029,0.004501457,0.012229322,0.8495907],"study_design_scores_gemma":[0.00008249726,0.0010030044,0.032178406,0.0002019783,0.00027737423,0.00040513504,0.0004649902,0.9080518,0.039808415,0.0072445367,0.010179327,0.00010247815],"about_ca_topic_score_codex":0.0022234493,"about_ca_topic_score_gemma":0.0013391076,"teacher_disagreement_score":0.0063599534,"about_ca_system_score_codex":0.0012224067,"about_ca_system_score_gemma":0.0010739368,"threshold_uncertainty_score":0.03363502},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32658207,0.0009944661,0.49395618,0.017161418,0.0016028197,0.0040444564,0.0017435085,0.05998267,0.09393252],"genre_scores_gemma":[0.46405873,0.00041896178,0.4775705,0.0023402616,0.00020389534,0.000869149,0.0024549833,0.0011865966,0.050896887],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99812776,0.00066155766,0.00009924382,0.00029295616,0.000555897,0.00026254056],"domain_scores_gemma":[0.99357986,0.0026098758,0.00022783643,0.0005144607,0.0013464771,0.0017214596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046877703,0.0008027672,0.000403183,0.00139263,0.0020860943,0.0022422501,0.0020624215,0.0016256897,0.0034100167],"category_scores_gemma":[0.0127692465,0.00038351695,0.00038798398,0.0004301594,0.0014642124,0.0026969765,0.0031664213,0.0019427199,0.0009881939],"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":[0.0013990301,0.0016357981,0.03574597,0.0010004904,0.00018534687,0.002460624,0.030944707,0.018838402,0.09165504,0.04948026,0.10906017,0.6575942],"study_design_scores_gemma":[0.00024672426,0.0013553622,0.014600354,0.00040603423,0.00020217444,0.0013103434,0.011866552,0.26100427,0.033928342,0.009569537,0.66485995,0.0006503332],"about_ca_topic_score_codex":0.08134071,"about_ca_topic_score_gemma":0.1490536,"teacher_disagreement_score":0.08134071,"about_ca_system_score_codex":0.0031181404,"about_ca_system_score_gemma":0.008506761,"threshold_uncertainty_score":0.16173446},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08930944,0.0011477902,0.9039986,0.0008274499,0.00007305766,0.00004457674,0.00014747697,0.001592115,0.0028594297],"genre_scores_gemma":[0.90075135,0.0007037475,0.092836484,0.0003474741,0.00007920679,0.000067209745,0.00038638475,0.000054588738,0.004773545],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997658,0.00004994394,0.000014626064,0.000061467785,0.00005406009,0.00005397535],"domain_scores_gemma":[0.9994629,0.00024811574,0.00006769297,0.000048831596,0.00014167644,0.000030847703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007066204,0.0008986095,0.0006720694,0.0005659118,0.00019605063,0.0005978799,0.0012716352,0.0010671518,0.0008285875],"category_scores_gemma":[0.0017539698,0.00041297034,0.00041145625,0.00049888215,0.00042306667,0.0010148,0.00071937183,0.0015912903,0.00040075052],"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":[0.0001835231,0.00023229026,0.0035468559,0.000106401065,0.00011143581,0.00013386855,0.000091624126,0.72794956,0.0071446425,0.0040289965,0.0020332937,0.25443754],"study_design_scores_gemma":[0.0000014015903,0.0000134201855,0.00013362685,0.000003936376,0.000005084109,0.0000070771766,0.0000039651536,0.9984053,0.0006641995,0.00065745093,0.00010196845,0.000002570804],"about_ca_topic_score_codex":0.0058739497,"about_ca_topic_score_gemma":0.0052746274,"teacher_disagreement_score":0.0058739497,"about_ca_system_score_codex":0.0006201737,"about_ca_system_score_gemma":0.0007613117,"threshold_uncertainty_score":0.01167953},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07165521,0.00025980096,0.91660863,0.00034331164,0.00007940267,0.00008855263,0.001199056,0.008582665,0.0011832741],"genre_scores_gemma":[0.6058914,0.00038276857,0.38353094,0.00021296357,0.0001011099,0.00016830345,0.0050926493,0.00031310873,0.004306839],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968374,0.00005794261,0.00002016617,0.00011131513,0.000083174076,0.000043706772],"domain_scores_gemma":[0.9993075,0.0002627441,0.000088331646,0.000100783385,0.0001847892,0.00005591574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010512697,0.0010128876,0.0005330345,0.0016369293,0.0003418522,0.0008323067,0.0010972144,0.0007403176,0.0018892734],"category_scores_gemma":[0.0022619523,0.0004039218,0.0005056161,0.0008406843,0.00044247528,0.0017458127,0.0008655442,0.0010231959,0.00045782668],"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":[0.00034622604,0.0004219246,0.0123045845,0.00011004546,0.00020523819,0.00023747966,0.00012336901,0.48723024,0.014597473,0.008996583,0.013313429,0.46211335],"study_design_scores_gemma":[0.000004865886,0.0000108911545,0.00042345334,0.0000015831332,0.0000029429866,0.000015783973,0.0000033062927,0.9957113,0.0014917552,0.0019102636,0.000419586,0.000004285828],"about_ca_topic_score_codex":0.013999321,"about_ca_topic_score_gemma":0.011359991,"teacher_disagreement_score":0.013999321,"about_ca_system_score_codex":0.00086132524,"about_ca_system_score_gemma":0.0011016966,"threshold_uncertainty_score":0.027835667},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19120187,0.00017560399,0.8063296,0.00006790013,0.000035620902,0.000040971816,0.0001374517,0.00086071,0.0011502529],"genre_scores_gemma":[0.94274294,0.00010118572,0.056328274,0.000008601327,0.0000114343675,0.00002227615,0.0001573392,0.000026764488,0.0006010985],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997682,0.00003875562,0.000017235334,0.00007817906,0.00006426253,0.000033383632],"domain_scores_gemma":[0.99956125,0.0001507714,0.00008571039,0.000049051203,0.00012879212,0.000024393377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028918928,0.0003063222,0.00040257195,0.0010119294,0.0002339339,0.0004532451,0.00056572305,0.00024238508,0.00054587726],"category_scores_gemma":[0.0014278438,0.00013490424,0.0003199717,0.0011708623,0.00021313169,0.00063596084,0.00040754245,0.00025036096,0.00015624464],"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":[0.00025324323,0.00007565968,0.041961905,0.00010561723,0.00009276943,0.00060241274,0.00025880063,0.6565966,0.012604037,0.0049019153,0.0013434098,0.2812036],"study_design_scores_gemma":[0.0000023115524,0.000016095653,0.0024945608,0.0000018357662,0.0000068270765,0.00009497165,0.000028823686,0.9955195,0.0009845783,0.0005778245,0.0002682353,0.0000043130412],"about_ca_topic_score_codex":0.009170488,"about_ca_topic_score_gemma":0.006002444,"teacher_disagreement_score":0.009170488,"about_ca_system_score_codex":0.00033972404,"about_ca_system_score_gemma":0.00042739426,"threshold_uncertainty_score":0.018234253},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10384767,0.0011218844,0.8880773,0.00044973983,0.000087390195,0.00007215142,0.00032701532,0.0037671502,0.0022496376],"genre_scores_gemma":[0.9189165,0.00039638684,0.07683901,0.00023054917,0.00005307766,0.000048017795,0.00076041516,0.00011076977,0.0026451822],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995566,0.0001237838,0.000016191623,0.0001540432,0.000093550676,0.000055786386],"domain_scores_gemma":[0.99923015,0.00037717828,0.00010097569,0.00009239736,0.0001560557,0.000043171633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000925843,0.0010116406,0.0006459296,0.00101035,0.00022614523,0.00045908047,0.0011895054,0.00073994976,0.0010700903],"category_scores_gemma":[0.0023437096,0.00028982852,0.00062916736,0.00063989696,0.0005047493,0.0007443431,0.00078461546,0.0011981112,0.00039068225],"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":[0.00028635564,0.0001454254,0.0054282215,0.000064814485,0.000115358234,0.00019678268,0.00006757532,0.678767,0.009911544,0.0030059093,0.0032043043,0.29880673],"study_design_scores_gemma":[0.0000014766109,0.000013794429,0.0003230128,0.0000021625808,0.000006210187,0.000022425678,0.0000030605954,0.9975752,0.0011556484,0.00077465054,0.000119111704,0.0000031472566],"about_ca_topic_score_codex":0.007859447,"about_ca_topic_score_gemma":0.007767688,"teacher_disagreement_score":0.007859447,"about_ca_system_score_codex":0.0011120489,"about_ca_system_score_gemma":0.0004343437,"threshold_uncertainty_score":0.015627384},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84990394,0.009197551,0.12835678,0.0008777197,0.00022032592,0.0001368643,0.0006588164,0.002725655,0.0079223635],"genre_scores_gemma":[0.9680482,0.0011678408,0.028694527,0.0000806256,0.00004001394,0.000028346602,0.0006934213,0.00007738057,0.0011695584],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929833,0.00023518527,0.00004094257,0.00016778913,0.0001487766,0.000108919456],"domain_scores_gemma":[0.99784136,0.0013032665,0.00015032635,0.00018720266,0.00042023783,0.000097560034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019954322,0.0010301768,0.00065247226,0.001143082,0.0002831997,0.00089909334,0.0011299757,0.00090982503,0.0015508272],"category_scores_gemma":[0.005869605,0.00024478204,0.0006436304,0.0007988434,0.00041386276,0.001651192,0.0005820847,0.00077337574,0.00045590458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015097643,0.0005843726,0.015118814,0.00088295876,0.00060230825,0.00052494014,0.0002062406,0.47232488,0.016901152,0.004975838,0.0062914738,0.48007727],"study_design_scores_gemma":[0.000020167428,0.00044936003,0.0021030563,0.000038251066,0.00009794709,0.000109717825,0.000085484025,0.9884193,0.006034481,0.0017372278,0.00088754174,0.000017438448],"about_ca_topic_score_codex":0.009776376,"about_ca_topic_score_gemma":0.0090515725,"teacher_disagreement_score":0.009776376,"about_ca_system_score_codex":0.0007994084,"about_ca_system_score_gemma":0.0005676949,"threshold_uncertainty_score":0.019438982},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051020756,0.024679402,0.9043907,0.0021216536,0.0001386262,0.00018718543,0.00027444717,0.0018328892,0.015354302],"genre_scores_gemma":[0.72648656,0.013617062,0.2547708,0.00023616993,0.00033682116,0.00016313847,0.00078628847,0.00048582058,0.0031173527],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959539,0.0013469054,0.00024708908,0.0007740095,0.0013890533,0.0002889519],"domain_scores_gemma":[0.97244406,0.020080518,0.0019624855,0.0024094225,0.0026003344,0.00050323707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009969516,0.0017619268,0.0011581866,0.0061295745,0.0006392679,0.0038849912,0.002653216,0.001715009,0.0040942384],"category_scores_gemma":[0.029806348,0.00061907084,0.0022259017,0.0031273225,0.0025934537,0.0065732673,0.0035560082,0.0027026255,0.0005774268],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004212859,0.00027424996,0.018805303,0.0013273751,0.00087491487,0.00028238518,0.0010529812,0.17704114,0.0026818214,0.32121003,0.0037017285,0.47232684],"study_design_scores_gemma":[0.000032342818,0.0002361685,0.0067349146,0.00042623546,0.00026206893,0.00045687373,0.00038679066,0.8249844,0.003653899,0.15228215,0.010475558,0.00006853528],"about_ca_topic_score_codex":0.005815944,"about_ca_topic_score_gemma":0.0030441235,"teacher_disagreement_score":0.009969516,"about_ca_system_score_codex":0.0037980299,"about_ca_system_score_gemma":0.0015673611,"threshold_uncertainty_score":0.05272448},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18613231,0.0013717805,0.80340123,0.002047535,0.00024126489,0.00011102149,0.0014931292,0.0030101896,0.00219152],"genre_scores_gemma":[0.85157025,0.00046880016,0.14325252,0.00031752262,0.0002051639,0.000080205544,0.001975855,0.000114460214,0.0020152377],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995409,0.00013316887,0.000024100375,0.00016887813,0.000076923454,0.000055983077],"domain_scores_gemma":[0.99800986,0.0012339168,0.0001770573,0.00022795526,0.00023883175,0.00011248717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013165935,0.0012209086,0.000788955,0.0008742352,0.00042455402,0.0007776179,0.0013643088,0.00089000195,0.0011170135],"category_scores_gemma":[0.005216143,0.00034297598,0.00062404224,0.0012430908,0.0006737717,0.0016554806,0.001704049,0.0023078949,0.00031411438],"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":[0.00039804928,0.00037934724,0.017788086,0.00013733197,0.00013376129,0.00030547,0.00022856955,0.6946571,0.004714832,0.008118548,0.010660375,0.26247853],"study_design_scores_gemma":[0.0000041661115,0.000017059225,0.00050031254,0.000004297969,0.0000043690543,0.000013319394,0.000011710431,0.9958008,0.0005818142,0.0027784132,0.00027934948,0.0000043534656],"about_ca_topic_score_codex":0.0114047825,"about_ca_topic_score_gemma":0.01470726,"teacher_disagreement_score":0.0114047825,"about_ca_system_score_codex":0.0007896458,"about_ca_system_score_gemma":0.00097597926,"threshold_uncertainty_score":0.022676826},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037694254,0.0009991594,0.99263144,0.0007055056,0.00006781679,0.000016073865,0.00014945051,0.00007496741,0.0015862453],"genre_scores_gemma":[0.4560528,0.008782365,0.5224042,0.00078222604,0.0013263391,0.0002726646,0.0019205001,0.00021740502,0.008241407],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989754,0.0002893783,0.000053746033,0.00031989528,0.00029429467,0.00006737298],"domain_scores_gemma":[0.9966403,0.0019805827,0.0004329298,0.00034293355,0.00045951942,0.00014372026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001707778,0.0011451107,0.0011801015,0.002632614,0.00065290707,0.002595823,0.0023693622,0.0019862396,0.0017720921],"category_scores_gemma":[0.007866927,0.0007563503,0.00078993407,0.002768346,0.0020668858,0.003964868,0.0027652096,0.002477886,0.00049393444],"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":[0.00010930021,0.000074544514,0.0028320742,0.00044683585,0.00016211242,0.00036458776,0.00033888957,0.46214914,0.0071509113,0.40458775,0.0046021827,0.11718161],"study_design_scores_gemma":[0.0000070018896,0.000041432453,0.00048723136,0.000054388544,0.000023694634,0.00012615975,0.00004586785,0.84604806,0.0011466941,0.14679986,0.005192435,0.000027137627],"about_ca_topic_score_codex":0.004269534,"about_ca_topic_score_gemma":0.0028533752,"teacher_disagreement_score":0.004269534,"about_ca_system_score_codex":0.0012703205,"about_ca_system_score_gemma":0.00083299016,"threshold_uncertainty_score":0.009216905},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15384863,0.0014740615,0.8243844,0.00082351005,0.0002959351,0.00022111206,0.0016778116,0.010994478,0.0062800176],"genre_scores_gemma":[0.7682936,0.0006645961,0.21761596,0.00055558106,0.00022099995,0.00019624937,0.004872968,0.0005365269,0.00704344],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993806,0.00008056005,0.000046004494,0.00022855938,0.00016313417,0.000101214],"domain_scores_gemma":[0.9991399,0.00023849003,0.00013393116,0.00019355994,0.0002102643,0.00008390003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011283917,0.0014947434,0.0009462977,0.0012430714,0.00044554152,0.00083427544,0.0018955864,0.0011020189,0.0029523463],"category_scores_gemma":[0.0032087106,0.0004820854,0.00064466946,0.0009704502,0.00092826405,0.0025259831,0.0018265907,0.0012867729,0.0007982711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00082581636,0.00032712094,0.013572293,0.00030393215,0.00026121488,0.00033751677,0.00013861043,0.24221177,0.044979937,0.0151946815,0.018762775,0.6630843],"study_design_scores_gemma":[0.000022195958,0.0001238552,0.0017494384,0.000013583174,0.000026792184,0.000110551024,0.000019478422,0.9803242,0.008562329,0.0064583546,0.002573652,0.000015464186],"about_ca_topic_score_codex":0.009394706,"about_ca_topic_score_gemma":0.014081821,"teacher_disagreement_score":0.009394706,"about_ca_system_score_codex":0.0009852435,"about_ca_system_score_gemma":0.0016036921,"threshold_uncertainty_score":0.018680036},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12363613,0.0006004651,0.87175786,0.00044001994,0.00016403454,0.000080265214,0.00011345774,0.0008487966,0.0023589167],"genre_scores_gemma":[0.9667199,0.000074241805,0.03253936,0.000039254533,0.000026408237,0.000018488177,0.000083986946,0.000015909483,0.00048239372],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881953,0.00032646712,0.00008263006,0.0001732259,0.00045962606,0.00013857396],"domain_scores_gemma":[0.99856085,0.00041477403,0.00016823808,0.00025614447,0.0005227615,0.00007725821],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019758833,0.00052657543,0.0007617217,0.00073668093,0.00046265448,0.0013258846,0.000757481,0.00079215143,0.0007494264],"category_scores_gemma":[0.0045758183,0.00018365846,0.0004529251,0.0007403042,0.00045163708,0.0023150116,0.0014316993,0.0010037601,0.00019587575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00091524946,0.00048709696,0.009990531,0.00024584436,0.0003740829,0.00048815954,0.00038521885,0.44426322,0.030266006,0.026867317,0.0054536173,0.48026362],"study_design_scores_gemma":[0.000004892395,0.00007617234,0.0008080886,0.000006653459,0.000018010744,0.0000359385,0.000042070653,0.98540133,0.0034713026,0.009685728,0.00043973202,0.000010095849],"about_ca_topic_score_codex":0.0023237728,"about_ca_topic_score_gemma":0.002536397,"teacher_disagreement_score":0.0023237728,"about_ca_system_score_codex":0.00075539626,"about_ca_system_score_gemma":0.0005701997,"threshold_uncertainty_score":0.010449588},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016343562,0.0006506072,0.9799385,0.00031943925,0.00010343994,0.00005080974,0.00014103792,0.00084405544,0.0016086191],"genre_scores_gemma":[0.59759134,0.00070346636,0.38748196,0.0007488286,0.00017430223,0.00023262348,0.0009221783,0.00023628092,0.011909033],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985598,0.00027944043,0.00007600089,0.00041255786,0.00052315096,0.00014907581],"domain_scores_gemma":[0.99844044,0.0004169633,0.00017720263,0.0004145016,0.00044553238,0.00010532383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018844643,0.0009685645,0.0016876262,0.0009203877,0.00069225335,0.0013865683,0.0033592721,0.0021889554,0.002931591],"category_scores_gemma":[0.0047133067,0.00056071853,0.0010085294,0.0010732946,0.000932067,0.0026469084,0.00220719,0.0018604208,0.0012971368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024332345,0.00034893185,0.0030785142,0.00016374956,0.00018137132,0.0001548265,0.00014962885,0.4067023,0.007421895,0.02898853,0.0124338595,0.5401331],"study_design_scores_gemma":[0.000007440948,0.00003494392,0.00017722344,0.000007874054,0.000007677625,0.000033456323,0.0000110216615,0.990699,0.0008756905,0.00727205,0.00086498423,0.000008624674],"about_ca_topic_score_codex":0.005975819,"about_ca_topic_score_gemma":0.008830182,"teacher_disagreement_score":0.005975819,"about_ca_system_score_codex":0.0012788343,"about_ca_system_score_gemma":0.0010437687,"threshold_uncertainty_score":0.011882067},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07739575,0.0025764972,0.7242469,0.009002676,0.020047147,0.008084344,0.03715644,0.08775993,0.033730336],"genre_scores_gemma":[0.26452145,0.0005898307,0.523729,0.00439282,0.004358219,0.012052792,0.117242865,0.041961983,0.031150933],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.78273505,0.12506288,0.016460357,0.017980276,0.05077783,0.0069837063],"domain_scores_gemma":[0.46562335,0.18815435,0.010367546,0.14318344,0.1757529,0.016918434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.13758515,0.006336552,0.005233755,0.005370777,0.0051157526,0.013428432,0.0077634957,0.009264222,0.020945037],"category_scores_gemma":[0.30070662,0.0020262548,0.0039369254,0.0034827208,0.0041983346,0.008087642,0.02195185,0.009208239,0.027357768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0050139595,0.002564531,0.009630659,0.0026129843,0.0011039276,0.0009543876,0.0022524623,0.01618075,0.0247633,0.007133025,0.67749494,0.25029498],"study_design_scores_gemma":[0.0033802479,0.0059993826,0.03264365,0.002131367,0.0009644645,0.0024413532,0.0032838667,0.31972343,0.1371138,0.074976146,0.41552275,0.0018195618],"about_ca_topic_score_codex":0.0048256665,"about_ca_topic_score_gemma":0.005860172,"teacher_disagreement_score":0.13758515,"about_ca_system_score_codex":0.0036372324,"about_ca_system_score_gemma":0.010221797,"threshold_uncertainty_score":0.72762847},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34012127,0.00034898423,0.6529172,0.0005726352,0.00017428116,0.00009778656,0.0003640557,0.004227281,0.0011765276],"genre_scores_gemma":[0.9448333,0.000091964605,0.05308836,0.00008373855,0.000083284904,0.00003835615,0.000627136,0.00006725464,0.0010865405],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992974,0.00013663476,0.000033378736,0.00019981968,0.00025174083,0.000081106344],"domain_scores_gemma":[0.99789953,0.00084351184,0.00031228593,0.0004458428,0.0004225442,0.000076347154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011643266,0.00072406017,0.00084057293,0.0010675702,0.0003425856,0.0007299981,0.00085422635,0.0006092756,0.00041840842],"category_scores_gemma":[0.0046084328,0.00022098403,0.00038360653,0.00074960646,0.00052617706,0.0014204625,0.0007919559,0.0011144282,0.00034842914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007858203,0.00043834845,0.046007965,0.000077624056,0.00012056704,0.00039687855,0.00023506372,0.4120481,0.027910119,0.0038016334,0.005050136,0.50312775],"study_design_scores_gemma":[0.000004309519,0.00004169457,0.0020564331,0.0000019759566,0.000005052846,0.00006342783,0.000019738727,0.9927799,0.002901455,0.0017391951,0.00038178204,0.000005039943],"about_ca_topic_score_codex":0.003795368,"about_ca_topic_score_gemma":0.0038953351,"teacher_disagreement_score":0.003795368,"about_ca_system_score_codex":0.000596671,"about_ca_system_score_gemma":0.00054395525,"threshold_uncertainty_score":0.007546544},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00049735385,0.9916882,0.0053693424,0.00052531203,0.0002906273,0.000012122977,0.000074965414,0.000043674176,0.0014984192],"genre_scores_gemma":[0.0032304425,0.9921787,0.00296452,0.00023673268,0.00029926727,0.000012614001,0.000108735905,0.000011466662,0.00095748727],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998105,0.000022333243,0.000021211723,0.000040590618,0.000088432265,0.000016890666],"domain_scores_gemma":[0.99943024,0.00031491995,0.00005018017,0.000019142937,0.00015703715,0.000028476854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006793724,0.000809612,0.0010296239,0.0013245991,0.00014874044,0.00083995005,0.0010267455,0.0009495997,0.0028254031],"category_scores_gemma":[0.0013545054,0.00032064077,0.000557321,0.00206867,0.00035281698,0.001529157,0.0007784709,0.0012157868,0.0011968891],"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":[0.000028930468,0.000059232585,0.0002160442,0.006094106,0.000081104234,0.000033721204,0.000016785063,0.0022924703,0.0007531958,0.004072456,0.018070413,0.9682814],"study_design_scores_gemma":[0.000043232343,0.00029338282,0.001794644,0.0071320175,0.00044861482,0.0006147979,0.00007291775,0.008876511,0.0028993397,0.015033223,0.96271837,0.000072907744],"about_ca_topic_score_codex":0.002305815,"about_ca_topic_score_gemma":0.0028971226,"teacher_disagreement_score":0.0028254031,"about_ca_system_score_codex":0.00049015746,"about_ca_system_score_gemma":0.0014175255,"threshold_uncertainty_score":0.009451926},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021050416,0.00021932655,0.97596437,0.00026993325,0.000042318516,0.00007440885,0.0002549909,0.0016642798,0.00045983805],"genre_scores_gemma":[0.35695526,0.00033586152,0.64039934,0.00008531196,0.00007526657,0.00013845702,0.0008584706,0.00015743868,0.0009946171],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833137,0.0004231889,0.00011879152,0.00036998792,0.0006450141,0.000111688845],"domain_scores_gemma":[0.99710006,0.0013418744,0.00030338787,0.0004226462,0.0006671545,0.0001648804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002007906,0.0009285741,0.001197631,0.003829832,0.0007224886,0.0013191578,0.0015126509,0.0010632632,0.0009731264],"category_scores_gemma":[0.005749985,0.0002803975,0.00096885103,0.005290972,0.00063919445,0.0017625358,0.0016289257,0.0012977627,0.00042886715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043855864,0.00042482832,0.024028597,0.00032358698,0.0003602361,0.000650289,0.00075832126,0.3066696,0.016207468,0.025343426,0.0061189923,0.61867607],"study_design_scores_gemma":[0.000009080362,0.00004875602,0.001017635,0.00000796362,0.000014626611,0.00012559643,0.00009767022,0.98122853,0.003294654,0.011713984,0.0024270574,0.000014492606],"about_ca_topic_score_codex":0.005545371,"about_ca_topic_score_gemma":0.004231264,"teacher_disagreement_score":0.005545371,"about_ca_system_score_codex":0.0008868774,"about_ca_system_score_gemma":0.0009819363,"threshold_uncertainty_score":0.011026204},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049682934,0.00068743766,0.9454601,0.00027049042,0.00009559535,0.00003068054,0.000078927005,0.0020779283,0.0016159317],"genre_scores_gemma":[0.73132396,0.0006760837,0.26281542,0.0003962798,0.00015896698,0.00005369116,0.00040635458,0.0002623167,0.003907106],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995814,0.000090970556,0.000021398968,0.00014029298,0.00010968446,0.000056293135],"domain_scores_gemma":[0.99864954,0.0007434057,0.00012127365,0.00017817321,0.00026270212,0.000044823908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008575504,0.0009381111,0.00058565906,0.00068030175,0.0002460463,0.000617298,0.0010408865,0.00075418607,0.001301227],"category_scores_gemma":[0.003817135,0.00033902083,0.00058223703,0.00046563763,0.0006619062,0.0013296126,0.0012729598,0.0017028457,0.00054231286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001645385,0.00018963206,0.0022617942,0.00014576539,0.00012327856,0.00016336318,0.00019141469,0.3284514,0.054378998,0.007718707,0.004372211,0.6018389],"study_design_scores_gemma":[0.0000027559934,0.000021528613,0.0005454804,0.000008649458,0.0000126925215,0.00004156339,0.000010972442,0.9883124,0.006998032,0.003472538,0.0005671674,0.0000062355525],"about_ca_topic_score_codex":0.0032434529,"about_ca_topic_score_gemma":0.0040619755,"teacher_disagreement_score":0.0032434529,"about_ca_system_score_codex":0.00047815728,"about_ca_system_score_gemma":0.00045940728,"threshold_uncertainty_score":0.006449163},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.122373536,0.00049143884,0.8653715,0.0008591629,0.00024270912,0.00014787607,0.00063829176,0.0074980906,0.0023773126],"genre_scores_gemma":[0.89798754,0.000112628346,0.10001305,0.000068206944,0.00008222936,0.00005424796,0.0005739338,0.00009896833,0.0010091198],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960769,0.00094928854,0.00037486592,0.0006425585,0.0015742076,0.00038227008],"domain_scores_gemma":[0.9858564,0.0048286305,0.0024192233,0.0024787968,0.0039838385,0.00043315376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038077885,0.0006421271,0.00097306666,0.0031015938,0.0009930388,0.0018520763,0.0012551484,0.0009837708,0.0013958963],"category_scores_gemma":[0.020454757,0.00025474906,0.00045306803,0.0019397212,0.000639426,0.0019352556,0.001462195,0.0016152606,0.00053940533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008810764,0.0004806705,0.06390839,0.00031369942,0.00022149138,0.00060102326,0.0005619431,0.078759775,0.046645526,0.028753374,0.010710317,0.76816255],"study_design_scores_gemma":[0.00001862172,0.00017558565,0.010118919,0.000033856333,0.0000462917,0.00040381367,0.00017052039,0.9432078,0.024360156,0.01621763,0.005214994,0.0000318422],"about_ca_topic_score_codex":0.0028417704,"about_ca_topic_score_gemma":0.0027148628,"teacher_disagreement_score":0.0038077885,"about_ca_system_score_codex":0.0009501106,"about_ca_system_score_gemma":0.002158195,"threshold_uncertainty_score":0.020137727},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6940474,0.0013399083,0.2933757,0.0014195222,0.00030716616,0.000095158764,0.0008875454,0.0018050256,0.0067225345],"genre_scores_gemma":[0.98809576,0.00014034806,0.009886135,0.00009782021,0.000032529442,0.000044101194,0.00031054302,0.000014398184,0.0013783538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999031,0.000016669148,0.00000787509,0.000036229903,0.000012774148,0.00002343814],"domain_scores_gemma":[0.9997193,0.00012653577,0.00002851004,0.00000841483,0.00010228491,0.0000150156575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045800392,0.0006845056,0.00045907043,0.00044708332,0.00027604078,0.00048365127,0.00086181174,0.00073988386,0.0014801064],"category_scores_gemma":[0.0010947937,0.00022699097,0.00049849146,0.0004085059,0.00017315158,0.0005972437,0.00036289232,0.00084905507,0.00024576962],"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":[0.00019109165,0.000119031734,0.006199736,0.000053212058,0.00006291296,0.00014528519,0.000079225414,0.92004484,0.0025243291,0.0010059433,0.0019396072,0.06763485],"study_design_scores_gemma":[0.000001758265,0.000008297922,0.00020400959,0.0000014801077,0.0000041972253,0.0000034788122,0.00000266252,0.999479,0.00010285558,0.00015211596,0.000038538998,0.000001593923],"about_ca_topic_score_codex":0.02323982,"about_ca_topic_score_gemma":0.018193496,"teacher_disagreement_score":0.02323982,"about_ca_system_score_codex":0.0007108082,"about_ca_system_score_gemma":0.00075381965,"threshold_uncertainty_score":0.046209097},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028847635,0.00016675965,0.96872276,0.00016139749,0.000046646073,0.00006216952,0.0000723244,0.0011222615,0.0007981101],"genre_scores_gemma":[0.5425819,0.00014820148,0.45309192,0.0003319757,0.00017425355,0.0001861801,0.0006673425,0.0002256073,0.0025926686],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986821,0.00028015138,0.000060591316,0.00047585188,0.00035449862,0.00014672661],"domain_scores_gemma":[0.99792045,0.0007851182,0.00030757725,0.00033845374,0.00050965353,0.00013874398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015361834,0.0009574485,0.0012568029,0.0017741269,0.00043307134,0.001061215,0.0023852524,0.0012635347,0.0010086788],"category_scores_gemma":[0.0042539546,0.00041367323,0.0010015478,0.000944818,0.0012969058,0.0014694849,0.002011257,0.0020656628,0.00067791936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039765146,0.0005611556,0.010665368,0.00015283911,0.00021550646,0.00028189213,0.00022918303,0.20448893,0.049652696,0.011090001,0.0055838204,0.7166809],"study_design_scores_gemma":[0.000011485251,0.00006374196,0.0005302867,0.0000039462784,0.000008068562,0.000058497713,0.000010594804,0.9899137,0.0050422074,0.0037895332,0.00055805413,0.000009916137],"about_ca_topic_score_codex":0.0014744595,"about_ca_topic_score_gemma":0.002137554,"teacher_disagreement_score":0.0023852524,"about_ca_system_score_codex":0.00057852775,"about_ca_system_score_gemma":0.0008303219,"threshold_uncertainty_score":0.008124232},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10126221,0.00012420188,0.89003664,0.00012420992,0.00003609947,0.00009637787,0.00003969424,0.0058371774,0.002443413],"genre_scores_gemma":[0.8806824,0.00005728282,0.11764503,0.00006559634,0.000012673004,0.00007865812,0.00007670262,0.00009156878,0.0012901068],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995161,0.000104810715,0.000026604577,0.0001731567,0.00011084512,0.00006856842],"domain_scores_gemma":[0.9986652,0.0006180747,0.0002167028,0.00024814147,0.00016641393,0.00008544422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067462114,0.00075834227,0.0005384496,0.00030715333,0.0003182152,0.00048064484,0.0011215968,0.0006733724,0.0010348907],"category_scores_gemma":[0.003037116,0.00032533254,0.000393185,0.00021442334,0.000715211,0.0008706818,0.0011966291,0.00094056875,0.0003468554],"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":[0.00017385658,0.00038050167,0.0058370666,0.00013892526,0.000082671024,0.0003485528,0.00027446594,0.6365682,0.060257707,0.0034358914,0.001794268,0.2907079],"study_design_scores_gemma":[0.0000057874827,0.00004476485,0.0005253526,0.0000030130223,0.000005400364,0.000026900683,0.000013763313,0.9919809,0.006051857,0.00093343045,0.00040097974,0.000007728593],"about_ca_topic_score_codex":0.0036538804,"about_ca_topic_score_gemma":0.003494534,"teacher_disagreement_score":0.0036538804,"about_ca_system_score_codex":0.00040788777,"about_ca_system_score_gemma":0.00090297393,"threshold_uncertainty_score":0.00726521},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012031235,0.9901799,0.0060350946,0.0007670808,0.00017713902,0.00007336655,0.00015617309,0.000049998438,0.001358104],"genre_scores_gemma":[0.013090239,0.9809871,0.004590181,0.0004217368,0.00022519779,0.000100329875,0.00021443731,0.000020148525,0.00035060037],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9985404,0.00048003028,0.00028704485,0.00024565504,0.00039947662,0.000047455018],"domain_scores_gemma":[0.9815031,0.016342806,0.0007399156,0.00028530383,0.0010440928,0.00008470684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003170902,0.0014882195,0.0016801539,0.005167182,0.00036240416,0.0015081699,0.0012246711,0.0012167523,0.003260645],"category_scores_gemma":[0.016065883,0.0006584307,0.0024569517,0.0043854066,0.0006018921,0.0020203826,0.0008140412,0.0011678471,0.00056541763],"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":[0.00012714838,0.00012729035,0.0037597183,0.11630174,0.0016639173,0.00034109227,0.00031318422,0.010406602,0.00069217087,0.009627274,0.012163766,0.84447616],"study_design_scores_gemma":[0.00012175192,0.0010206706,0.01610423,0.28253284,0.0124017,0.0047405786,0.0011341878,0.035433628,0.0035301973,0.04168103,0.6009371,0.00036217403],"about_ca_topic_score_codex":0.0031594948,"about_ca_topic_score_gemma":0.005535068,"teacher_disagreement_score":0.005167182,"about_ca_system_score_codex":0.0007905309,"about_ca_system_score_gemma":0.0032183682,"threshold_uncertainty_score":0.016769588},"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":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019458212,0.050539423,0.90589076,0.012688751,0.0017092266,0.00010458326,0.00017212961,0.00046073148,0.008976168],"genre_scores_gemma":[0.47845796,0.054535743,0.4367395,0.004037381,0.0064298376,0.00019539434,0.00076071185,0.0003548329,0.018488666],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99659425,0.0010530684,0.0002037178,0.00071230193,0.0013080015,0.00012860456],"domain_scores_gemma":[0.9792225,0.011472942,0.0007250819,0.0015216619,0.0068106367,0.00024725404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008091996,0.00075462065,0.0008359465,0.0017481222,0.00062668824,0.0020125303,0.0019744432,0.0016295027,0.0017787301],"category_scores_gemma":[0.02474044,0.0003938203,0.00056425313,0.0017782808,0.0017777678,0.004388018,0.0012846048,0.0031108372,0.0006993642],"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":[0.00016877118,0.00010823091,0.003699817,0.0007167828,0.00015893644,0.000096575575,0.00016308362,0.029623633,0.0043172925,0.08143568,0.017472737,0.8620385],"study_design_scores_gemma":[0.000019437064,0.00028505115,0.0052723777,0.00046745618,0.000092416434,0.0008135637,0.00027341195,0.8062244,0.014814795,0.10661134,0.06503708,0.00008865406],"about_ca_topic_score_codex":0.0030606308,"about_ca_topic_score_gemma":0.0030274475,"teacher_disagreement_score":0.008091996,"about_ca_system_score_codex":0.0014001376,"about_ca_system_score_gemma":0.0012447644,"threshold_uncertainty_score":0.04279512},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44218147,0.0022488474,0.55007654,0.0005236447,0.00021697348,0.00012988714,0.00008237882,0.00158925,0.0029510758],"genre_scores_gemma":[0.9545407,0.00021301553,0.044531338,0.00008229298,0.000036959722,0.000019897547,0.00013000036,0.000033064698,0.00041255268],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99703,0.001300204,0.00011690458,0.0004131401,0.00090926426,0.00023035966],"domain_scores_gemma":[0.9907856,0.00595064,0.00047916762,0.0010601103,0.0014814977,0.00024306108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0067143845,0.0006857641,0.00086604216,0.0008941091,0.00047307127,0.00075728714,0.0009934059,0.0007345435,0.0005839078],"category_scores_gemma":[0.014624628,0.00019661336,0.00046085558,0.0005631628,0.0008227163,0.001553485,0.0011856807,0.00086502114,0.00012254469],"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":[0.0020912276,0.00045738113,0.016019221,0.00016888633,0.00027764277,0.00017470843,0.00018026544,0.5977246,0.01334366,0.009102006,0.0018243988,0.35863602],"study_design_scores_gemma":[0.0000073741808,0.00016877407,0.000952522,0.000005468593,0.000020536805,0.00005417776,0.000019755194,0.99293,0.0043969234,0.0010907364,0.00034814535,0.000005675866],"about_ca_topic_score_codex":0.002894823,"about_ca_topic_score_gemma":0.0021123055,"teacher_disagreement_score":0.0067143845,"about_ca_system_score_codex":0.0009331529,"about_ca_system_score_gemma":0.0006884302,"threshold_uncertainty_score":0.035509527},"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":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8349592,0.0031973678,0.14538676,0.0028390067,0.0005408417,0.00033153215,0.00559718,0.003136521,0.0040115453],"genre_scores_gemma":[0.91739684,0.00040954983,0.0705792,0.00048083602,0.00018879921,0.00021617221,0.009716248,0.00023308478,0.00077917386],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.990436,0.0043883687,0.0006210693,0.0022780742,0.0017791752,0.000497297],"domain_scores_gemma":[0.9571288,0.029461212,0.0024368437,0.006924823,0.0028188927,0.0012294862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010900702,0.0017910457,0.0013340426,0.0023549192,0.0014537527,0.002346672,0.0036320915,0.0030773117,0.0007872127],"category_scores_gemma":[0.054120872,0.0005311101,0.0010475562,0.0020600185,0.0029011848,0.0035047624,0.0045059216,0.0045259413,0.00055659795],"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":[0.0015462962,0.0017498645,0.16728781,0.0015137845,0.0008145623,0.00069247466,0.0009593717,0.5683719,0.008206234,0.016182117,0.044532005,0.18814349],"study_design_scores_gemma":[0.000059670485,0.0003473194,0.011119135,0.00007645076,0.00004451651,0.00028387352,0.0002816279,0.9555627,0.008499816,0.020029174,0.0036387776,0.000056900244],"about_ca_topic_score_codex":0.004106137,"about_ca_topic_score_gemma":0.007451204,"teacher_disagreement_score":0.010900702,"about_ca_system_score_codex":0.0017471121,"about_ca_system_score_gemma":0.0016893196,"threshold_uncertainty_score":0.057649136},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86331534,0.0022169042,0.06355342,0.018101599,0.00013070936,0.00097329356,0.0004312632,0.000636214,0.050641295],"genre_scores_gemma":[0.96604276,0.0008451303,0.026535612,0.000905058,0.000095036914,0.00021516968,0.00020631973,0.00008211146,0.0050728233],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9895399,0.0071373316,0.000330797,0.00052385725,0.001535023,0.0009331319],"domain_scores_gemma":[0.9525964,0.03923452,0.0015481027,0.002460626,0.0021823724,0.0019779454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0079624355,0.0005941851,0.00046035263,0.0017224621,0.0037642994,0.003658051,0.0019400801,0.003643457,0.004543743],"category_scores_gemma":[0.022734687,0.00030839688,0.000726723,0.0019649093,0.0036350728,0.0039989008,0.0035622718,0.0027635281,0.0008732965],"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":[0.0012499704,0.00842842,0.18781559,0.0018867817,0.0002908152,0.027119055,0.13659613,0.07399486,0.0050349263,0.069575265,0.051921856,0.43608627],"study_design_scores_gemma":[0.0006134534,0.0036368466,0.09343934,0.0011449571,0.00025016724,0.019297117,0.13250211,0.31343183,0.020679057,0.08409305,0.33047146,0.0004406657],"about_ca_topic_score_codex":0.0072788782,"about_ca_topic_score_gemma":0.011528796,"teacher_disagreement_score":0.0079624355,"about_ca_system_score_codex":0.0027541337,"about_ca_system_score_gemma":0.0018612222,"threshold_uncertainty_score":0.042109907},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24118795,0.0009803615,0.7432427,0.0008544445,0.00016372596,0.00021484563,0.00022189801,0.003268607,0.009865539],"genre_scores_gemma":[0.94052255,0.00013263845,0.05829907,0.00014030137,0.000028518418,0.000022918863,0.00010223986,0.000043245444,0.00070844346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999597,0.00008160308,0.000017338396,0.000083366656,0.0001376282,0.000083035295],"domain_scores_gemma":[0.99940753,0.00019089135,0.00007907365,0.0001255895,0.0001298094,0.00006697761],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006993572,0.00035948842,0.00041266278,0.00050306384,0.00042842544,0.0011611278,0.0011906401,0.000521041,0.0006963325],"category_scores_gemma":[0.0011495465,0.00016436998,0.00021366701,0.00045502087,0.00049177086,0.001520486,0.001306073,0.00047596588,0.00024982681],"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":[0.0015370758,0.0005986339,0.016324589,0.00029580243,0.00016321044,0.0014574825,0.0005332513,0.47083715,0.09163207,0.045189448,0.009472145,0.36195922],"study_design_scores_gemma":[0.000014150178,0.000053608765,0.0013388006,0.000010442187,0.000011167097,0.00008626874,0.00007618019,0.9758253,0.0131195085,0.007053558,0.002400516,0.000010451144],"about_ca_topic_score_codex":0.0019285566,"about_ca_topic_score_gemma":0.0022075588,"teacher_disagreement_score":0.0019285566,"about_ca_system_score_codex":0.0004956035,"about_ca_system_score_gemma":0.0004505842,"threshold_uncertainty_score":0.0038346648},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02869182,0.00032710042,0.96155447,0.00023176652,0.00005282699,0.00011880689,0.00068057625,0.007101297,0.001241371],"genre_scores_gemma":[0.2897878,0.00030829836,0.7055699,0.00009564757,0.00006877624,0.00026904795,0.0016474514,0.00097009935,0.0012829605],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968875,0.0009873026,0.00027481216,0.0005817363,0.0010214495,0.0002472674],"domain_scores_gemma":[0.9917207,0.003639339,0.0012245991,0.0011433377,0.0019436167,0.000328562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004540237,0.0016950084,0.0013608056,0.00746756,0.0007099728,0.0034009444,0.001923722,0.001474265,0.002261766],"category_scores_gemma":[0.0167383,0.00039241774,0.0012138677,0.0030871066,0.0010913532,0.0026503003,0.0028435858,0.0016257416,0.0009294314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006058481,0.00027719102,0.025090767,0.0007161475,0.00028442047,0.00035061105,0.001971342,0.08395744,0.02053313,0.025429761,0.014653102,0.8261302],"study_design_scores_gemma":[0.000049743678,0.00019111467,0.011336422,0.00011570727,0.00006259524,0.0005473943,0.00082667975,0.91854155,0.020920146,0.034371555,0.012847785,0.00018930883],"about_ca_topic_score_codex":0.003503094,"about_ca_topic_score_gemma":0.0050334856,"teacher_disagreement_score":0.00746756,"about_ca_system_score_codex":0.0009153041,"about_ca_system_score_gemma":0.0013423369,"threshold_uncertainty_score":0.024011314},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.671491,0.0021347252,0.31299606,0.00029085804,0.00097069296,0.00015508485,0.00096140633,0.003500073,0.007500076],"genre_scores_gemma":[0.92329615,0.0004255083,0.07226283,0.000071609764,0.00004315861,0.00004532336,0.0005313333,0.0006636785,0.0026605458],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99545985,0.00092982233,0.00043333348,0.0008103828,0.0021176396,0.00024889183],"domain_scores_gemma":[0.97809887,0.008954685,0.0016874366,0.0027503138,0.0081461985,0.00036258227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048296163,0.00084565586,0.00079211936,0.0027321489,0.00087070797,0.0017418723,0.0008364373,0.0012355538,0.002218981],"category_scores_gemma":[0.017714797,0.00045538667,0.000583534,0.0012731531,0.00060546026,0.00088224944,0.0018432838,0.00086123217,0.0008436695],"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":[0.006896984,0.00031375035,0.11469377,0.0012701013,0.0010478706,0.0015931193,0.007467584,0.022477617,0.24724613,0.0030953013,0.005763904,0.58813393],"study_design_scores_gemma":[0.00015202767,0.001147033,0.31667298,0.00047288495,0.0017833161,0.0073239757,0.0052592577,0.35350806,0.28636193,0.0059706187,0.020761168,0.0005868408],"about_ca_topic_score_codex":0.0039341776,"about_ca_topic_score_gemma":0.0063362042,"teacher_disagreement_score":0.0048296163,"about_ca_system_score_codex":0.00056488765,"about_ca_system_score_gemma":0.0005452766,"threshold_uncertainty_score":0.025541723},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014905382,0.0002623261,0.9752814,0.00021856943,0.0000896961,0.00006905736,0.0010429319,0.003616984,0.0045136386],"genre_scores_gemma":[0.5459311,0.00028436712,0.43148404,0.0010615784,0.000101437705,0.00045889977,0.0065732105,0.0010971572,0.013008185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948335,0.00016979389,0.000019067393,0.00015748088,0.0001171699,0.000053198557],"domain_scores_gemma":[0.9990727,0.00042834572,0.000053385997,0.00024641945,0.0001553502,0.00004387084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010780985,0.00091023557,0.0006983112,0.00048448783,0.0002109305,0.00094402925,0.0016717146,0.00085777725,0.005518114],"category_scores_gemma":[0.0029321343,0.00032384315,0.0006048837,0.0007129017,0.0005966398,0.0011734158,0.0012945202,0.0018621819,0.0020399827],"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":[0.00040283124,0.00016288168,0.0029080752,0.0001981649,0.00010633325,0.00020213194,0.00012913467,0.583597,0.026927317,0.051833034,0.024267113,0.309266],"study_design_scores_gemma":[0.000010638183,0.000027658894,0.00025935506,0.000010332585,0.0000071345407,0.000051632633,0.000006703866,0.9818602,0.003590784,0.01210742,0.0020590348,0.00000908782],"about_ca_topic_score_codex":0.0010586925,"about_ca_topic_score_gemma":0.002706856,"teacher_disagreement_score":0.005518114,"about_ca_system_score_codex":0.00073182373,"about_ca_system_score_gemma":0.0005510098,"threshold_uncertainty_score":0.018459916},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6130143,0.0020758235,0.3722289,0.0013853554,0.0003776138,0.00014722832,0.0026904494,0.0035477693,0.0045325733],"genre_scores_gemma":[0.9487462,0.00044701088,0.046697423,0.00006878152,0.00009848936,0.000036563466,0.0022427347,0.00004015068,0.0016226163],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981933,0.0003970059,0.00011778325,0.0004274982,0.00064872165,0.00021557497],"domain_scores_gemma":[0.9943956,0.0019570875,0.0012510902,0.0014334931,0.00081370835,0.0001490157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012189657,0.00085856515,0.00095585984,0.0026041053,0.0005514409,0.001109929,0.0011991531,0.0015418665,0.00081619265],"category_scores_gemma":[0.009329661,0.00029486377,0.00054903026,0.002009642,0.00061412505,0.0021356875,0.0012688193,0.0012619494,0.0010266786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011548134,0.0004577001,0.12884668,0.00063652755,0.00027952617,0.0015566694,0.00059536955,0.10111016,0.021002727,0.0076982984,0.014520103,0.7221414],"study_design_scores_gemma":[0.000022474562,0.00015896907,0.033172186,0.000068975976,0.000053117823,0.0011090217,0.00038081472,0.93030554,0.021938285,0.0064360527,0.0063109905,0.000043530654],"about_ca_topic_score_codex":0.0026464753,"about_ca_topic_score_gemma":0.002882036,"teacher_disagreement_score":0.0026464753,"about_ca_system_score_codex":0.0008543444,"about_ca_system_score_gemma":0.00047429334,"threshold_uncertainty_score":0.0064465404},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8766088,0.0009541367,0.09806003,0.0009435839,0.00043627084,0.00040840957,0.003705428,0.0056400793,0.013243309],"genre_scores_gemma":[0.96867484,0.00017299711,0.025786418,0.00018440578,0.000064596046,0.00010051013,0.002448085,0.0000588675,0.002509427],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995672,0.000102889964,0.000021137508,0.00009302605,0.00013598947,0.00007979707],"domain_scores_gemma":[0.9996431,0.00010781733,0.00009335583,0.000035503457,0.000071162736,0.00004910329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004176523,0.00093650364,0.00042291204,0.001140871,0.0004333939,0.0004426018,0.00065625465,0.00063128164,0.0010015678],"category_scores_gemma":[0.001231763,0.00017563133,0.00025851562,0.00047377165,0.00031841337,0.0010175811,0.0012550624,0.00049133156,0.000312665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026680345,0.0013583616,0.17986894,0.00081234565,0.0005340276,0.0049657575,0.0015177744,0.1657527,0.05248279,0.008276424,0.06626093,0.51550204],"study_design_scores_gemma":[0.000042310265,0.00041094644,0.035023555,0.000060016293,0.000043817083,0.0007834684,0.0008899604,0.93336517,0.015221283,0.0046503423,0.009466796,0.000042395488],"about_ca_topic_score_codex":0.004477017,"about_ca_topic_score_gemma":0.009867725,"teacher_disagreement_score":0.004477017,"about_ca_system_score_codex":0.0005675482,"about_ca_system_score_gemma":0.00025715743,"threshold_uncertainty_score":0.008901954},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29030606,0.00034862163,0.70681626,0.00026113656,0.00014551192,0.000026781163,0.00019318974,0.0012394278,0.0006629726],"genre_scores_gemma":[0.9620279,0.00007887979,0.037068665,0.000032075008,0.000067964465,0.000014976785,0.00026204798,0.00003211357,0.0004153931],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871564,0.00020528575,0.000078588135,0.00041105712,0.000464187,0.00012523032],"domain_scores_gemma":[0.9975211,0.0011143773,0.0004762799,0.0002857884,0.00048842886,0.00011406113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013273812,0.0005700334,0.00078727916,0.0019125356,0.00036333455,0.00072197133,0.0009151358,0.0006704463,0.00053326774],"category_scores_gemma":[0.0044558444,0.00017416314,0.0005476484,0.0016746442,0.0004456549,0.0010284187,0.0005884352,0.0010674665,0.00020271946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009179087,0.00054651167,0.051032856,0.00015566628,0.00036511608,0.0006477548,0.00019739587,0.35842875,0.03725265,0.007942187,0.0038199376,0.53869325],"study_design_scores_gemma":[0.0000020077682,0.000020436353,0.0024200536,0.0000017973952,0.0000054833536,0.00004451225,0.000010246061,0.99379164,0.00198941,0.0015364117,0.00017410108,0.000003904887],"about_ca_topic_score_codex":0.002919183,"about_ca_topic_score_gemma":0.001732905,"teacher_disagreement_score":0.002919183,"about_ca_system_score_codex":0.0006087355,"about_ca_system_score_gemma":0.00035794938,"threshold_uncertainty_score":0.007019937},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19301228,0.0011197323,0.7940672,0.0006422596,0.0002264329,0.0006336155,0.0032083332,0.0045611225,0.0025289862],"genre_scores_gemma":[0.6449643,0.00034182958,0.34164497,0.00019070276,0.0002279877,0.00050387153,0.00945855,0.00019640574,0.0024714821],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99865735,0.0002125158,0.00009196543,0.0003650963,0.00036917368,0.00030391908],"domain_scores_gemma":[0.9988574,0.0003257365,0.000110557085,0.00011162446,0.00049114204,0.00010361995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001268565,0.001228559,0.0010634657,0.004596865,0.0012865143,0.0010033944,0.0011957444,0.0009965503,0.0018127062],"category_scores_gemma":[0.0034951482,0.00024313523,0.0021616719,0.0021926288,0.00030451224,0.0006509074,0.00096335966,0.00092086877,0.0010453898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012536203,0.0008388571,0.11122305,0.00027486956,0.00041949874,0.000648988,0.0006577869,0.064759,0.016836723,0.0021403157,0.023051983,0.77789533],"study_design_scores_gemma":[0.000104177496,0.00024119578,0.04807238,0.000059286525,0.0002385586,0.0005958192,0.00041256158,0.9314113,0.007721048,0.0052478816,0.0057959184,0.00009982305],"about_ca_topic_score_codex":0.016085584,"about_ca_topic_score_gemma":0.015143524,"teacher_disagreement_score":0.016085584,"about_ca_system_score_codex":0.00052488694,"about_ca_system_score_gemma":0.0015505635,"threshold_uncertainty_score":0.031983912},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017138544,0.0035433203,0.9349404,0.0019879788,0.0011291894,0.0009001026,0.0038225423,0.003832772,0.032705106],"genre_scores_gemma":[0.37307006,0.00647085,0.56511855,0.0017772433,0.0018959396,0.0014573123,0.014722382,0.0019691652,0.033518482],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99599755,0.0006454583,0.00024981648,0.0011819749,0.0014202717,0.0005049344],"domain_scores_gemma":[0.9976464,0.00069111324,0.00023609027,0.0003540569,0.00091472844,0.000157717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025711507,0.0024203553,0.0023642022,0.008691551,0.0019657481,0.005090858,0.0027076001,0.002510126,0.009844877],"category_scores_gemma":[0.008386637,0.00068431353,0.0022559662,0.0030791496,0.0016309796,0.00390398,0.0059999656,0.0015543249,0.004707416],"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":[0.00045012284,0.00014835536,0.005727871,0.001389205,0.0002617508,0.0007748633,0.0019550207,0.08120951,0.015284029,0.07709915,0.10696107,0.708739],"study_design_scores_gemma":[0.000066955334,0.00012200957,0.0082059065,0.00059195067,0.00016754112,0.000887485,0.0036280504,0.55139244,0.018485675,0.18788816,0.2283103,0.00025347434],"about_ca_topic_score_codex":0.009552729,"about_ca_topic_score_gemma":0.0055898665,"teacher_disagreement_score":0.009844877,"about_ca_system_score_codex":0.001801678,"about_ca_system_score_gemma":0.002670334,"threshold_uncertainty_score":0.032934368},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5459166,0.0013278314,0.4467406,0.00084266183,0.00019728889,0.00013357893,0.00052034593,0.0014570323,0.0028640325],"genre_scores_gemma":[0.92782104,0.00041398706,0.07051919,0.00009302769,0.000058147103,0.000048595073,0.0004819755,0.00003489097,0.0005291626],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915683,0.00015458616,0.00007776588,0.0001335615,0.00037742493,0.00009973516],"domain_scores_gemma":[0.99853384,0.00065665046,0.000176117,0.00014067783,0.0004376757,0.00005500445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013533862,0.0005479951,0.0006908104,0.0018049137,0.00043204916,0.000880524,0.0005219402,0.0005949014,0.00046583207],"category_scores_gemma":[0.0035778705,0.00015463194,0.00078897836,0.0012776088,0.00025225672,0.0011693643,0.0003826806,0.00045299137,0.00018658336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013837843,0.0008560032,0.13496318,0.00032404528,0.00036590573,0.0013225348,0.0006302172,0.21467224,0.04692213,0.0050289636,0.0045862985,0.58894473],"study_design_scores_gemma":[0.000013608431,0.00035068003,0.03620019,0.000028524946,0.00006938529,0.00035391815,0.00026449948,0.9397264,0.01848973,0.002612227,0.0018581985,0.00003269294],"about_ca_topic_score_codex":0.003010073,"about_ca_topic_score_gemma":0.0021634889,"teacher_disagreement_score":0.003010073,"about_ca_system_score_codex":0.00048154232,"about_ca_system_score_gemma":0.0003885999,"threshold_uncertainty_score":0.007157445},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024630297,0.5648732,0.39450836,0.00377665,0.0012228425,0.00021286782,0.0010090413,0.0016904308,0.008076254],"genre_scores_gemma":[0.23346977,0.5993568,0.15463999,0.0012249745,0.0037845827,0.00023257306,0.0029890342,0.00026106497,0.004041256],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978522,0.00030448486,0.00028266825,0.00051736814,0.000963017,0.00008028862],"domain_scores_gemma":[0.9948961,0.0032096666,0.00035875087,0.00036967933,0.0010541157,0.00011164908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021344735,0.0010453823,0.0017575225,0.004323838,0.0003605897,0.00183773,0.0015743312,0.0010068379,0.0011789292],"category_scores_gemma":[0.0066517284,0.00049489515,0.0012650864,0.007111594,0.000584959,0.0030612256,0.00078166067,0.0012670549,0.0006791652],"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":[0.000075187134,0.00013661037,0.0070671057,0.0021553733,0.00016743407,0.00015876486,0.00014702532,0.013198906,0.0020685527,0.008424805,0.010237979,0.9561622],"study_design_scores_gemma":[0.000052329186,0.0006159843,0.024571136,0.002355804,0.00049163547,0.0041017653,0.0008725799,0.4919231,0.010447888,0.061703388,0.40258652,0.00027787144],"about_ca_topic_score_codex":0.0023652136,"about_ca_topic_score_gemma":0.0010849667,"teacher_disagreement_score":0.004323838,"about_ca_system_score_codex":0.00059169,"about_ca_system_score_gemma":0.001016049,"threshold_uncertainty_score":0.011288285},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30491158,0.00054164906,0.68944585,0.00026669307,0.00007264352,0.00012392779,0.0003517657,0.0021363227,0.0021495472],"genre_scores_gemma":[0.8914004,0.00017546762,0.105898306,0.000059544054,0.000030057072,0.000046729445,0.00044500464,0.000058568377,0.0018858502],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998203,0.000024328134,0.000008236065,0.0000643218,0.000051153747,0.00003170555],"domain_scores_gemma":[0.99968696,0.0001059796,0.000054510558,0.00002418716,0.00010732235,0.000021117594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000284365,0.00064393325,0.00063604384,0.00126005,0.00025272084,0.00058893143,0.0009350735,0.0005455288,0.00044087306],"category_scores_gemma":[0.00091535033,0.0002464043,0.0006306923,0.0006125873,0.00029296978,0.00059360714,0.00037725846,0.00044900525,0.00015933461],"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":[0.00021846191,0.00021953123,0.008573558,0.00008588609,0.0001567386,0.00022116,0.00010897453,0.70039785,0.014206399,0.0015851543,0.002765029,0.27146134],"study_design_scores_gemma":[0.0000019129866,0.000014871774,0.0006311325,0.0000018359627,0.000003963351,0.000012599803,0.000008647701,0.9978295,0.0011500301,0.0002477032,0.00009550992,0.000002345696],"about_ca_topic_score_codex":0.010903474,"about_ca_topic_score_gemma":0.010676479,"teacher_disagreement_score":0.010903474,"about_ca_system_score_codex":0.0006122575,"about_ca_system_score_gemma":0.0005266904,"threshold_uncertainty_score":0.021679997},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012774624,0.00016224926,0.98572046,0.00013766791,0.000019670722,0.000027931375,0.000051625662,0.0007523126,0.00035359332],"genre_scores_gemma":[0.48594746,0.0003001719,0.50903857,0.00031846444,0.00005777179,0.00023319064,0.00085534755,0.00030270038,0.0029462923],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992204,0.00027928868,0.000051656756,0.00013432212,0.00025656432,0.000057690784],"domain_scores_gemma":[0.99775827,0.0012560454,0.00021148802,0.0002766918,0.00044516515,0.00005223196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018482474,0.0009398646,0.0009973072,0.0006128644,0.00023314741,0.0006089938,0.0013470376,0.0012459055,0.0011686522],"category_scores_gemma":[0.0050870986,0.00048393762,0.0007584239,0.0007128552,0.0006409279,0.0014449346,0.0008667896,0.002361254,0.0005263195],"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":[0.0001622844,0.00017542912,0.0012949249,0.00012403373,0.000095142044,0.00009630487,0.0000658963,0.77564466,0.012522979,0.0136590535,0.0031289028,0.19303031],"study_design_scores_gemma":[0.0000019257318,0.000009410923,0.00003380092,0.0000016928559,0.0000014336232,0.000007889434,0.0000013870566,0.9980274,0.0008132814,0.0009571819,0.00014203222,0.0000024262922],"about_ca_topic_score_codex":0.0029577173,"about_ca_topic_score_gemma":0.0027778563,"teacher_disagreement_score":0.0029577173,"about_ca_system_score_codex":0.0006762317,"about_ca_system_score_gemma":0.0008305012,"threshold_uncertainty_score":0.009774625},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15862633,0.0016611954,0.79236364,0.00055582164,0.0003782163,0.00036319258,0.005519853,0.036296487,0.004235283],"genre_scores_gemma":[0.627506,0.0007151939,0.35296062,0.00038252506,0.00007714466,0.00028928352,0.00965972,0.00018682874,0.0082226405],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966824,0.000034772573,0.00002715839,0.00012952059,0.00008807941,0.000052296276],"domain_scores_gemma":[0.99973863,0.000053744745,0.000035009543,0.00005621904,0.00009212455,0.00002419404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066412886,0.00084627775,0.00055833673,0.0010549779,0.00032587763,0.00043836934,0.0011196022,0.0006821566,0.0021230835],"category_scores_gemma":[0.0011914796,0.00030979014,0.0004039372,0.00070773007,0.00018268944,0.0008778952,0.00081518886,0.0010542846,0.0011968059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057289016,0.00049139425,0.004796668,0.00018689185,0.000173598,0.00027863405,0.00008535058,0.03601162,0.05649264,0.0017176832,0.020130854,0.8790619],"study_design_scores_gemma":[0.000014794489,0.00014072486,0.002816361,0.000017235354,0.00002480445,0.00010996516,0.000018248764,0.971006,0.021106543,0.0011350081,0.003589571,0.00002074932],"about_ca_topic_score_codex":0.010053688,"about_ca_topic_score_gemma":0.016703786,"teacher_disagreement_score":0.010053688,"about_ca_system_score_codex":0.00087931205,"about_ca_system_score_gemma":0.0006676367,"threshold_uncertainty_score":0.019990325},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004839488,0.00023009785,0.9930744,0.00012684932,0.00005696924,0.000041041974,0.00009835912,0.0007982825,0.0007344224],"genre_scores_gemma":[0.1878639,0.00044087376,0.8049541,0.00046303085,0.00023167433,0.0002937484,0.001533229,0.0003430645,0.0038763925],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976336,0.0008998382,0.00010704779,0.00039390693,0.0007480652,0.00021754562],"domain_scores_gemma":[0.9969223,0.0011292676,0.0002000313,0.0006931999,0.00091491704,0.00014018288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033734816,0.0011750936,0.0024275882,0.0019126977,0.00076714356,0.0013736979,0.0024900956,0.0015862392,0.0031752686],"category_scores_gemma":[0.0070478837,0.0005022326,0.0017342352,0.0018711603,0.0007721231,0.0025895974,0.0022174635,0.0023038755,0.002002787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017882584,0.00016615452,0.0041633626,0.00012242043,0.000398684,0.00012456156,0.00014597065,0.32757992,0.0052472213,0.023415208,0.012276211,0.6261815],"study_design_scores_gemma":[0.000009296267,0.000039669234,0.00029237615,0.00000859215,0.000013626902,0.00006417893,0.0000145878885,0.9869179,0.001149134,0.00878132,0.0026947993,0.000014522759],"about_ca_topic_score_codex":0.0025591808,"about_ca_topic_score_gemma":0.004480525,"teacher_disagreement_score":0.0033734816,"about_ca_system_score_codex":0.00042308262,"about_ca_system_score_gemma":0.0012437535,"threshold_uncertainty_score":0.017840922},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.099483036,0.0005188424,0.89461213,0.0007090146,0.00013791792,0.00006564086,0.0004699288,0.002181707,0.0018218287],"genre_scores_gemma":[0.8834305,0.00027318278,0.11150653,0.00023547358,0.00007954851,0.0001308546,0.00047863048,0.00006387094,0.0038014767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997212,0.000053949836,0.0000233973,0.00011350254,0.00004399832,0.000043947864],"domain_scores_gemma":[0.9995372,0.0002401584,0.000045260018,0.00003163901,0.000114820316,0.00003084514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058003503,0.0006295266,0.0005868943,0.00062365126,0.00028262194,0.0006990123,0.000893089,0.0012483323,0.0012490685],"category_scores_gemma":[0.0014647446,0.00038793276,0.0006552575,0.000603347,0.00023539513,0.0009890595,0.00059221726,0.0012103595,0.0005521054],"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":[0.00038626653,0.0004381139,0.011818827,0.00013669331,0.00028346726,0.00023805069,0.00023371569,0.6471035,0.014356938,0.0024056681,0.0024286392,0.32017007],"study_design_scores_gemma":[0.0000026582973,0.000026265194,0.00059348583,0.0000041132325,0.000010847285,0.000015773587,0.000010067599,0.9976654,0.00074370584,0.0007582577,0.00016415698,0.0000052375012],"about_ca_topic_score_codex":0.0105323065,"about_ca_topic_score_gemma":0.010523603,"teacher_disagreement_score":0.0105323065,"about_ca_system_score_codex":0.0005107552,"about_ca_system_score_gemma":0.000701756,"threshold_uncertainty_score":0.020942032},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4425933,0.0044501293,0.21852057,0.009629634,0.0011665494,0.0019977046,0.26111767,0.044459224,0.016065247],"genre_scores_gemma":[0.5879945,0.0014426392,0.21727203,0.0009912938,0.00023829882,0.00040226948,0.18958275,0.00040969328,0.0016665785],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99697804,0.00053854135,0.00041621365,0.0007476464,0.001087203,0.00023234676],"domain_scores_gemma":[0.99321413,0.0021534294,0.00085980387,0.0017982818,0.001457763,0.00051660725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026582603,0.001152762,0.0008528027,0.0052264836,0.0009187169,0.0021208772,0.0011632312,0.001041192,0.0012307592],"category_scores_gemma":[0.013029748,0.00033160346,0.0008847509,0.0059519974,0.0006182121,0.0030859592,0.002436932,0.001402869,0.0012125716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016870515,0.0011514039,0.33578512,0.0019872526,0.00075682136,0.0027179287,0.002316145,0.06337444,0.02979668,0.016761646,0.121206455,0.422459],"study_design_scores_gemma":[0.00012477564,0.00057556166,0.1379024,0.00049718,0.00020088829,0.0015292527,0.003529977,0.6663657,0.030188233,0.03310519,0.12578179,0.00019908634],"about_ca_topic_score_codex":0.0076618204,"about_ca_topic_score_gemma":0.008884769,"teacher_disagreement_score":0.0076618204,"about_ca_system_score_codex":0.0009166367,"about_ca_system_score_gemma":0.0013722648,"threshold_uncertainty_score":0.01523447},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16822483,0.018596476,0.7879375,0.005872846,0.00048525154,0.00022745125,0.0012558207,0.0052310373,0.01216878],"genre_scores_gemma":[0.9038879,0.0078028752,0.084217146,0.00026213218,0.00011392025,0.000060219947,0.0012318897,0.000059203543,0.0023647535],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957126,0.00009967895,0.00003058525,0.000077951205,0.0001893025,0.000031103333],"domain_scores_gemma":[0.99869186,0.00047973168,0.00013163008,0.00016366917,0.0004961673,0.000036890255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008951144,0.0005509151,0.0003751168,0.0012555209,0.00015155424,0.0009707203,0.0006144409,0.00044785923,0.0009982431],"category_scores_gemma":[0.0031223197,0.00015669328,0.00035202559,0.0012297245,0.0002718092,0.0019230866,0.0005892038,0.0009954097,0.0003683731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025277425,0.00019496736,0.009370602,0.0005212945,0.00013866428,0.00013398966,0.00016115187,0.075723626,0.019318944,0.007874529,0.0111714825,0.87513804],"study_design_scores_gemma":[0.00001781181,0.00019455701,0.009227046,0.00014569396,0.00007555144,0.00018561237,0.00019807991,0.92022395,0.032396544,0.018607683,0.01870059,0.000026854384],"about_ca_topic_score_codex":0.0021784357,"about_ca_topic_score_gemma":0.0026141242,"teacher_disagreement_score":0.0021784357,"about_ca_system_score_codex":0.0006253651,"about_ca_system_score_gemma":0.0006569232,"threshold_uncertainty_score":0.0047338605},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044321045,0.0019474246,0.94984347,0.00032981677,0.00019868097,0.00010792243,0.000072219205,0.00073728175,0.0024421192],"genre_scores_gemma":[0.6050472,0.0014247466,0.39021525,0.00017318736,0.00011851558,0.00015495662,0.000277784,0.00019649402,0.0023919013],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839956,0.0004405804,0.00016168109,0.0003600193,0.0005014356,0.00013678476],"domain_scores_gemma":[0.99768746,0.0006726074,0.00019393736,0.00029164358,0.0010925725,0.00006181927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018531184,0.000731738,0.00089673296,0.0007705854,0.00049347256,0.00087736413,0.00092515786,0.00090647326,0.0006724233],"category_scores_gemma":[0.0061939377,0.00022161768,0.00068275916,0.0012584212,0.00051330216,0.0014207291,0.00043365714,0.001280543,0.00084271736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004946113,0.00038361992,0.006646135,0.0007284633,0.00022882517,0.00037616523,0.0001607004,0.050965562,0.09101164,0.020796932,0.005210106,0.8229973],"study_design_scores_gemma":[0.00008115208,0.0005660902,0.012163727,0.0002062551,0.00022162579,0.0026670047,0.00015451964,0.76809525,0.1675266,0.023170698,0.02494363,0.00020344314],"about_ca_topic_score_codex":0.00082595774,"about_ca_topic_score_gemma":0.00059794204,"teacher_disagreement_score":0.0018531184,"about_ca_system_score_codex":0.00034698952,"about_ca_system_score_gemma":0.00047044177,"threshold_uncertainty_score":0.0098003745},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013634844,0.0007695125,0.98334724,0.00018852831,0.00009325427,0.000014021536,0.00006244165,0.00057213375,0.0013179453],"genre_scores_gemma":[0.7644867,0.0021544618,0.21938325,0.00022276519,0.00022784251,0.000086709326,0.0005847271,0.00030546405,0.012548117],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997843,0.00006301447,0.000010775684,0.000051803552,0.00007029698,0.00001983126],"domain_scores_gemma":[0.99909496,0.00068616075,0.000054607695,0.000072552924,0.00007812939,0.000013654775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056933047,0.0006851935,0.00053859485,0.00036702788,0.00014094127,0.0005066526,0.00076952536,0.0006675788,0.0015620057],"category_scores_gemma":[0.002106965,0.0003481833,0.0006708826,0.000530984,0.0004247716,0.0009451524,0.0009091414,0.0015820874,0.0004917547],"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":[0.000039526207,0.000033697088,0.00038972776,0.000051498675,0.00005324461,0.00004942317,0.00003839563,0.86818427,0.0030815103,0.012151023,0.001533561,0.11439419],"study_design_scores_gemma":[4.859571e-7,0.00000731157,0.000052672534,0.000002245235,0.0000033400474,0.0000106111775,0.0000012710451,0.9961415,0.0004274472,0.0031684807,0.00018269569,0.0000018693847],"about_ca_topic_score_codex":0.0011122905,"about_ca_topic_score_gemma":0.001548809,"teacher_disagreement_score":0.0015620057,"about_ca_system_score_codex":0.00042368638,"about_ca_system_score_gemma":0.00020319474,"threshold_uncertainty_score":0.00522542},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36609533,0.00040970798,0.6256865,0.00023549201,0.000030753028,0.00009373748,0.00033975978,0.006047344,0.0010613131],"genre_scores_gemma":[0.95509523,0.00012053859,0.04403002,0.000026474143,0.000007738103,0.000033572487,0.00026974874,0.00006415557,0.00035248214],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994822,0.000074283125,0.00004881326,0.00010988925,0.00023468406,0.00005009814],"domain_scores_gemma":[0.9986784,0.0004638701,0.00029082474,0.000252177,0.00026151337,0.000053166717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048064947,0.00085568987,0.00054451625,0.0016265104,0.00027595446,0.00076794124,0.0007509041,0.0003365438,0.00040060305],"category_scores_gemma":[0.0022261664,0.0001789411,0.00035993973,0.0011657058,0.00028462402,0.0010537349,0.0004953407,0.0004917453,0.00015605496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00093043124,0.0006827373,0.106454775,0.00035001285,0.00026965098,0.0013095844,0.0008404315,0.31647003,0.10731412,0.006556623,0.0022767528,0.4565449],"study_design_scores_gemma":[0.000004321755,0.00004868113,0.0042719166,0.000007791254,0.000012304398,0.000111607915,0.00004476166,0.98158616,0.011605941,0.0017842023,0.00051198,0.00001020921],"about_ca_topic_score_codex":0.0036951276,"about_ca_topic_score_gemma":0.0032084482,"teacher_disagreement_score":0.0036951276,"about_ca_system_score_codex":0.00058087433,"about_ca_system_score_gemma":0.000566611,"threshold_uncertainty_score":0.007347226},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050016157,0.0012756352,0.93892443,0.0006374141,0.000227727,0.00012375387,0.00018705889,0.0033912994,0.005216557],"genre_scores_gemma":[0.8382403,0.00045669897,0.14953169,0.0005112447,0.0001049029,0.00018600079,0.0004134825,0.00011139456,0.010444182],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997093,0.00005527556,0.000011259992,0.00008859584,0.000083138286,0.000052371543],"domain_scores_gemma":[0.99956137,0.00018707986,0.00005107348,0.00007708689,0.000095540476,0.000027729558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005796321,0.001169728,0.00071969035,0.00029024688,0.00026573855,0.00054072135,0.0016325449,0.0012305516,0.003501463],"category_scores_gemma":[0.0013466617,0.00037009027,0.00052891154,0.0002423733,0.0007269969,0.00091852515,0.001094239,0.0019432136,0.00081757084],"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":[0.00042751653,0.00015828254,0.0010481585,0.00013089136,0.00009645601,0.0004353592,0.000089345755,0.7111001,0.017757472,0.011600935,0.0053453436,0.25181022],"study_design_scores_gemma":[0.000007933478,0.000039304607,0.00006751815,0.0000053521107,0.000008390042,0.00004683179,0.000002770999,0.9950799,0.002523691,0.0017239006,0.0004885656,0.0000058346864],"about_ca_topic_score_codex":0.0033570796,"about_ca_topic_score_gemma":0.0042454093,"teacher_disagreement_score":0.003501463,"about_ca_system_score_codex":0.00070764334,"about_ca_system_score_gemma":0.0008311448,"threshold_uncertainty_score":0.011713624},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57034296,0.0074686776,0.4008233,0.0029616326,0.002084229,0.00033293673,0.001773475,0.006654256,0.007558547],"genre_scores_gemma":[0.95244414,0.0005677172,0.042252317,0.00034777969,0.00017158891,0.00006242075,0.0015510478,0.00012423916,0.0024787611],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954047,0.0019412492,0.00028210133,0.00093421043,0.0010547747,0.0003830096],"domain_scores_gemma":[0.9778657,0.016582616,0.0008376863,0.002133071,0.001863735,0.00071725494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011363911,0.001682547,0.0018729024,0.0015777511,0.0006761189,0.0014451975,0.0024740233,0.002761277,0.001311877],"category_scores_gemma":[0.025862928,0.0004915225,0.0008843245,0.00085680117,0.001439396,0.0030335921,0.0021663164,0.0025609487,0.0005424576],"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":[0.0009724028,0.0006173398,0.012127536,0.000191895,0.00034188485,0.00011258484,0.000034216177,0.89560735,0.0014590669,0.0037757405,0.0054269778,0.07933293],"study_design_scores_gemma":[0.000014936652,0.00010412226,0.00042363946,0.000010345834,0.000022369984,0.000040030503,0.000010211453,0.99644345,0.00086780713,0.0018725785,0.00018371407,0.0000067099777],"about_ca_topic_score_codex":0.0067303935,"about_ca_topic_score_gemma":0.0058275023,"teacher_disagreement_score":0.011363911,"about_ca_system_score_codex":0.0016900597,"about_ca_system_score_gemma":0.0014581388,"threshold_uncertainty_score":0.060098827},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.069203414,0.0058532245,0.9081553,0.0026475063,0.00046825258,0.00009098169,0.00011458708,0.00080549484,0.012661214],"genre_scores_gemma":[0.84465957,0.002256354,0.14568898,0.00024279521,0.00020200989,0.00006866908,0.00016240885,0.000049996375,0.0066692806],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956065,0.00014066625,0.000033018765,0.00006898273,0.00016196161,0.00003475474],"domain_scores_gemma":[0.99934894,0.0003480024,0.000059160913,0.00006983701,0.00015608958,0.000017912073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005963785,0.00024806612,0.00045404275,0.00048619098,0.00036067583,0.0011903362,0.00050476013,0.00056338153,0.0014041014],"category_scores_gemma":[0.001971088,0.00014075311,0.0003093598,0.00039869474,0.0005495533,0.0012366882,0.00053925265,0.0009249613,0.00023554105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020822715,0.00020930565,0.0035763714,0.00032430477,0.00016950718,0.00023637571,0.0001917408,0.40464807,0.01489525,0.09379114,0.006142523,0.47560722],"study_design_scores_gemma":[0.000006823433,0.00005049269,0.00062357436,0.00001848268,0.00001444266,0.000053348915,0.00003658751,0.966051,0.0030143147,0.026588025,0.0035370386,0.0000058934306],"about_ca_topic_score_codex":0.0023374932,"about_ca_topic_score_gemma":0.002042587,"teacher_disagreement_score":0.0023374932,"about_ca_system_score_codex":0.0006088574,"about_ca_system_score_gemma":0.00056066643,"threshold_uncertainty_score":0.0046972036},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12981421,0.001067477,0.86407465,0.00017432442,0.00011383854,0.00010691999,0.00017719719,0.0019869031,0.0024844948],"genre_scores_gemma":[0.67970437,0.00096648943,0.31218225,0.00018619002,0.00008476029,0.0000978274,0.00061137805,0.00011814176,0.006048647],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997861,0.00002383928,0.000016349035,0.000060038405,0.000084683874,0.000029045412],"domain_scores_gemma":[0.99975055,0.00003848893,0.000031284533,0.00003763881,0.00012904989,0.000012968532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003543823,0.0005497545,0.00041886355,0.00086307275,0.00016982561,0.00037669894,0.0006342406,0.00038796422,0.0010184946],"category_scores_gemma":[0.00081862014,0.0001782735,0.00054603233,0.00039167792,0.00019318222,0.0008097659,0.00043660525,0.00053881976,0.0002722034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038468032,0.0001513236,0.0033403202,0.00014754906,0.00010653009,0.00020323646,0.0001268045,0.040692054,0.12963293,0.002113055,0.0024621477,0.8206393],"study_design_scores_gemma":[0.000016872955,0.00015615957,0.003775109,0.000018381634,0.00007798338,0.00029829866,0.00004184329,0.9074228,0.08507605,0.00063595764,0.0024604069,0.000020290247],"about_ca_topic_score_codex":0.00409243,"about_ca_topic_score_gemma":0.004395217,"teacher_disagreement_score":0.00409243,"about_ca_system_score_codex":0.00041333243,"about_ca_system_score_gemma":0.0004916108,"threshold_uncertainty_score":0.008137226},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0182793,0.0013674082,0.974825,0.00024905562,0.0001212606,0.00007584403,0.00047997566,0.0034571697,0.0011449623],"genre_scores_gemma":[0.51631165,0.0014761874,0.4708995,0.0005654014,0.000253445,0.00019374529,0.0035942355,0.00025426593,0.0064515998],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898845,0.00019930181,0.000059572263,0.0002662084,0.00038555037,0.000100847814],"domain_scores_gemma":[0.99884665,0.0003469815,0.00011813976,0.00021196176,0.00042019613,0.000056083238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012816632,0.0013771941,0.0012589175,0.0015263262,0.00024317975,0.00068626466,0.0018004631,0.0008308704,0.0012582707],"category_scores_gemma":[0.0032375338,0.0003913827,0.00073726557,0.0011308364,0.00048366943,0.0015502521,0.0011111139,0.0020867505,0.0008457655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040103105,0.0003515667,0.006237748,0.00013989613,0.00015536064,0.00021203228,0.000076100114,0.115885876,0.020149399,0.007270265,0.008893654,0.84022707],"study_design_scores_gemma":[0.000009456132,0.000064630614,0.0010185941,0.000008869569,0.000025836629,0.00013200949,0.0000108522145,0.9876068,0.0063972804,0.003192014,0.0015199331,0.000013602955],"about_ca_topic_score_codex":0.007790629,"about_ca_topic_score_gemma":0.0085297525,"teacher_disagreement_score":0.007790629,"about_ca_system_score_codex":0.0006847577,"about_ca_system_score_gemma":0.0010968121,"threshold_uncertainty_score":0.015490532},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003843848,0.9264748,0.0444205,0.003256956,0.0016733713,0.000101174686,0.0014072679,0.0002987555,0.018523283],"genre_scores_gemma":[0.01613505,0.9473755,0.028752593,0.00061618263,0.0012907455,0.000085247964,0.0013438332,0.00004020236,0.0043606493],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99938095,0.0001485177,0.00007359939,0.00013665244,0.00022602253,0.00003419603],"domain_scores_gemma":[0.99853146,0.0007602426,0.00013732896,0.000065881286,0.00047446293,0.00003055472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094945816,0.0009837296,0.00074207777,0.003688983,0.00036438403,0.0011676174,0.0014366311,0.00050684845,0.0045871483],"category_scores_gemma":[0.0033005206,0.00034351903,0.0007652562,0.005227071,0.000372074,0.0015125781,0.00036282968,0.0006471903,0.002323218],"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":[0.000047852347,0.00007335775,0.0035177434,0.007972417,0.00012571922,0.0001049051,0.000098092394,0.002690427,0.00046793558,0.012457617,0.048709128,0.92373484],"study_design_scores_gemma":[0.00001582831,0.00022240166,0.012294662,0.009494365,0.00051060726,0.0012670585,0.00048454473,0.009363849,0.0024558587,0.022529738,0.94125736,0.00010365032],"about_ca_topic_score_codex":0.004052775,"about_ca_topic_score_gemma":0.0052316873,"teacher_disagreement_score":0.0045871483,"about_ca_system_score_codex":0.00044807268,"about_ca_system_score_gemma":0.0015094739,"threshold_uncertainty_score":0.015345514},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08953944,0.0006262379,0.9028312,0.0008425107,0.00014421476,0.00013112294,0.00010203368,0.0009430662,0.004840119],"genre_scores_gemma":[0.9752946,0.00013770629,0.022141935,0.00022719653,0.00004185151,0.000058194626,0.000077084915,0.000033401317,0.0019880992],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987208,0.0004163142,0.00004227035,0.00028974496,0.00032713357,0.00020364147],"domain_scores_gemma":[0.9966186,0.0022145165,0.00035819513,0.00027836894,0.00037818804,0.00015208591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020323647,0.0009551752,0.0010282261,0.000566729,0.00048445154,0.0008971525,0.0013585169,0.0010714929,0.001030483],"category_scores_gemma":[0.0059533264,0.00044880327,0.00071743986,0.00032603738,0.0014193017,0.0013387473,0.0016920762,0.0017502585,0.00025802242],"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":[0.00011202404,0.000031382206,0.0020106344,0.000041185293,0.00002922564,0.00012164642,0.00006279618,0.9675432,0.0018945831,0.0068845097,0.0008414621,0.020427333],"study_design_scores_gemma":[0.0000018890547,0.000010518823,0.00014055398,0.0000024553476,0.0000023313814,0.000018356981,0.0000041612807,0.9976814,0.00040129994,0.0016125962,0.00012092949,0.0000035076034],"about_ca_topic_score_codex":0.0052396595,"about_ca_topic_score_gemma":0.003549676,"teacher_disagreement_score":0.0052396595,"about_ca_system_score_codex":0.0015000977,"about_ca_system_score_gemma":0.00085811975,"threshold_uncertainty_score":0.010884047},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5694157,0.0008164545,0.42440522,0.0006295674,0.00028619493,0.00009878633,0.0017268286,0.0010939203,0.0015272927],"genre_scores_gemma":[0.97390205,0.00023424198,0.021339634,0.000034424,0.000061409104,0.0000796779,0.0014916615,0.00004489,0.0028120044],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992962,0.00020372441,0.000062073974,0.00017998894,0.00012332729,0.00013470011],"domain_scores_gemma":[0.9958073,0.0026710322,0.0004568715,0.00027990603,0.0006548846,0.0001299279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026693058,0.00081017305,0.0010811507,0.0016459455,0.00049211644,0.0012701954,0.001521197,0.0010401104,0.001707539],"category_scores_gemma":[0.006237607,0.00037503795,0.001344119,0.0010689471,0.0003995213,0.0012321444,0.0007785373,0.0023093917,0.0007401391],"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":[0.00056047045,0.0004897259,0.058070716,0.00008688091,0.00023935866,0.00013177193,0.00016922563,0.83251196,0.0016230368,0.005790898,0.0023453226,0.0979806],"study_design_scores_gemma":[0.000002215908,0.000017049744,0.0018293677,0.000004186488,0.000007957403,0.0000080517775,0.00001089869,0.996923,0.00015110777,0.0009491169,0.000091899885,0.0000050687963],"about_ca_topic_score_codex":0.02597636,"about_ca_topic_score_gemma":0.021313366,"teacher_disagreement_score":0.02597636,"about_ca_system_score_codex":0.0011092615,"about_ca_system_score_gemma":0.0013906083,"threshold_uncertainty_score":0.051650345},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09493961,0.0013280957,0.89337564,0.0010501733,0.000109759276,0.00011945978,0.0021941892,0.004754352,0.0021286607],"genre_scores_gemma":[0.8331448,0.00065243087,0.15469088,0.0005565634,0.00022041413,0.00021786909,0.0066642095,0.00028189356,0.0035709646],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990852,0.00036032515,0.000045513778,0.00029763574,0.00012950914,0.00008190481],"domain_scores_gemma":[0.99724543,0.0014053188,0.0003139365,0.0005322845,0.00038843788,0.00011460765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011686154,0.00087958993,0.0010761325,0.0017835746,0.0005560637,0.00066267693,0.0022399737,0.001137235,0.0012738749],"category_scores_gemma":[0.00382661,0.0004987279,0.001116306,0.002130274,0.0007840355,0.002113719,0.0011187451,0.0017999138,0.0005658511],"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":[0.00024276298,0.00041821544,0.012865031,0.00024829464,0.00027232125,0.00018680443,0.0002122577,0.7234183,0.0019748625,0.007956758,0.017330278,0.23487402],"study_design_scores_gemma":[0.000004849157,0.00000956036,0.00045974075,0.0000058603714,0.000008512864,0.000013879882,0.000014437406,0.9941455,0.00030166897,0.0045836787,0.00044715576,0.0000051854054],"about_ca_topic_score_codex":0.010917038,"about_ca_topic_score_gemma":0.019255664,"teacher_disagreement_score":0.010917038,"about_ca_system_score_codex":0.0012040811,"about_ca_system_score_gemma":0.0012625031,"threshold_uncertainty_score":0.021706998},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036681466,0.00036484937,0.96093774,0.00013994178,0.000060341776,0.00002600148,0.00006564259,0.0007503338,0.00097361434],"genre_scores_gemma":[0.73186684,0.00043567325,0.26210925,0.00026622013,0.00007954011,0.0001000016,0.00048048672,0.00010517023,0.004556723],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997881,0.00003200632,0.000014288769,0.000076125165,0.000057960046,0.00003154766],"domain_scores_gemma":[0.9996902,0.00013508763,0.000035099714,0.00003116755,0.000092367634,0.000016083803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005490858,0.0005528669,0.00044457827,0.0003393301,0.00017656088,0.0003129274,0.00080275343,0.0005725891,0.00080535887],"category_scores_gemma":[0.0012399931,0.00027017994,0.00054262544,0.00037050454,0.0003297947,0.00064375985,0.00056555565,0.0009603899,0.0002898755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020879105,0.00017667204,0.002222972,0.00011298634,0.00014517768,0.00020340724,0.00013552996,0.4016849,0.06098436,0.004888937,0.0026947903,0.5265415],"study_design_scores_gemma":[0.0000027125855,0.0000230216,0.00039394485,0.000004413626,0.0000102260465,0.00002668923,0.000004339671,0.99499583,0.0036330805,0.0006068039,0.00029408635,0.000004788073],"about_ca_topic_score_codex":0.006097776,"about_ca_topic_score_gemma":0.008662082,"teacher_disagreement_score":0.006097776,"about_ca_system_score_codex":0.00038133317,"about_ca_system_score_gemma":0.0006076753,"threshold_uncertainty_score":0.012124538},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04964555,0.00017707527,0.9482064,0.00012842425,0.00003759386,0.000029110635,0.00001849975,0.0004153536,0.0013418606],"genre_scores_gemma":[0.9466628,0.00009431823,0.05194324,0.00007240677,0.000030048875,0.000049140086,0.000037786725,0.00002168887,0.001088701],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918157,0.00013996776,0.00003829386,0.00033246225,0.00021475779,0.00009296391],"domain_scores_gemma":[0.9988084,0.0004918942,0.00023295141,0.0001563583,0.00025662954,0.00005389713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012011491,0.0008287521,0.00079513714,0.0004419599,0.0004613184,0.0006994635,0.0015267477,0.001047214,0.00060040416],"category_scores_gemma":[0.0031333906,0.00041775857,0.00051030534,0.00047147626,0.0007679602,0.0017808345,0.0013387761,0.0012887303,0.00015655484],"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":[0.0002354782,0.0001984647,0.0036456033,0.00007061113,0.000092276525,0.00015626923,0.00014364543,0.78335595,0.013452122,0.003487776,0.0008992682,0.19426244],"study_design_scores_gemma":[0.000002544257,0.000018263705,0.00020032498,0.0000014784623,0.0000051012034,0.0000112011285,0.0000037444267,0.9982784,0.0008467237,0.0005578236,0.00007080081,0.0000036012996],"about_ca_topic_score_codex":0.004469982,"about_ca_topic_score_gemma":0.0039915578,"teacher_disagreement_score":0.004469982,"about_ca_system_score_codex":0.00060939963,"about_ca_system_score_gemma":0.00067117263,"threshold_uncertainty_score":0.008887947},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26142755,0.0031178973,0.72071934,0.0008377804,0.00020212954,0.0002163196,0.000790614,0.008321242,0.004367054],"genre_scores_gemma":[0.6665296,0.001625405,0.32433096,0.00020294324,0.0000928582,0.00008003573,0.0015976018,0.0002594209,0.0052811718],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994844,0.00010036391,0.00001952938,0.00010616001,0.00018610228,0.00010337408],"domain_scores_gemma":[0.99948704,0.00013718117,0.000094318675,0.000082628845,0.00015777626,0.000040959752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005164702,0.0010203472,0.0006517249,0.0019397349,0.0003432789,0.0009698572,0.00091941486,0.0010802164,0.0026596703],"category_scores_gemma":[0.0011543873,0.00041269936,0.00059025537,0.0010555881,0.0004475722,0.001042902,0.00079927966,0.0006495242,0.0014948929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073703146,0.0003908617,0.013463955,0.0005341228,0.00013800822,0.00055462075,0.0002292279,0.06457929,0.21263069,0.0025422445,0.00859097,0.6956089],"study_design_scores_gemma":[0.000032603366,0.0003161294,0.0307274,0.00008569133,0.00005384231,0.0008861596,0.0002551816,0.8605308,0.09524137,0.004123909,0.0076996465,0.0000472588],"about_ca_topic_score_codex":0.0025051283,"about_ca_topic_score_gemma":0.0031949738,"teacher_disagreement_score":0.0026596703,"about_ca_system_score_codex":0.0004683515,"about_ca_system_score_gemma":0.00039904396,"threshold_uncertainty_score":0.008897483},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030467851,0.00033022507,0.91958994,0.00029123173,0.00015512935,0.00030030624,0.0005657235,0.0466009,0.0016987154],"genre_scores_gemma":[0.5055635,0.00023846624,0.48807353,0.00032359696,0.0001514127,0.00030415753,0.00210239,0.0009822296,0.002260695],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981439,0.00021641707,0.00016415885,0.00044332715,0.000831208,0.00020092285],"domain_scores_gemma":[0.9971169,0.0007963583,0.0003392045,0.0008492325,0.00063739164,0.00026087125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001882235,0.0013905618,0.0014513466,0.0016679879,0.00061399775,0.0017836373,0.0030820344,0.0009239326,0.0014731825],"category_scores_gemma":[0.0066703344,0.00047285194,0.00077818683,0.001213248,0.0007410425,0.0033585746,0.002897523,0.0014515076,0.00080856425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018479528,0.0005954962,0.013256733,0.00040762578,0.0002980582,0.0010876969,0.00041614065,0.21116886,0.056214795,0.013451204,0.032313436,0.6689419],"study_design_scores_gemma":[0.000037011545,0.00007089737,0.0005792584,0.0000062408876,0.000015740905,0.000084702006,0.000028153032,0.98602074,0.008400801,0.0026834328,0.0020538901,0.00001913089],"about_ca_topic_score_codex":0.0042810063,"about_ca_topic_score_gemma":0.0036479395,"teacher_disagreement_score":0.0042810063,"about_ca_system_score_codex":0.00070431334,"about_ca_system_score_gemma":0.0016127818,"threshold_uncertainty_score":0.009954333},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011435122,0.00023797282,0.98535895,0.00014070421,0.000021807156,0.00004554304,0.0001806051,0.0015655229,0.0010137475],"genre_scores_gemma":[0.43734565,0.0005442022,0.5509437,0.00045693232,0.00021408104,0.00029704414,0.0024499646,0.0014575842,0.006290881],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988605,0.00020083653,0.000053399814,0.00046917007,0.00027040517,0.00014579753],"domain_scores_gemma":[0.9986675,0.0005495641,0.00019393615,0.00028488558,0.00024124424,0.00006299176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009712359,0.0013932899,0.0016769415,0.002212179,0.0005891038,0.0016018341,0.0026451112,0.0021343718,0.0023936841],"category_scores_gemma":[0.0034604182,0.000691165,0.0016054866,0.0017807896,0.0016491408,0.0016993127,0.0022545636,0.0013967223,0.0009806256],"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":[0.00025650312,0.00012645028,0.002926444,0.0003361144,0.00024751673,0.00037739993,0.00037409746,0.5407684,0.03311475,0.03649316,0.008577762,0.37640136],"study_design_scores_gemma":[0.000008361483,0.000017778695,0.00047688987,0.0000128969305,0.00001574494,0.00014412291,0.000020839052,0.9724524,0.004438243,0.020605402,0.0017927383,0.000014586429],"about_ca_topic_score_codex":0.004405566,"about_ca_topic_score_gemma":0.0069451663,"teacher_disagreement_score":0.004405566,"about_ca_system_score_codex":0.0010652874,"about_ca_system_score_gemma":0.0019636883,"threshold_uncertainty_score":0.008759856},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09834956,0.0010558541,0.8967147,0.0013037132,0.00010473721,0.000056264733,0.000109304354,0.00087190955,0.0014340086],"genre_scores_gemma":[0.89613914,0.00061710173,0.09936525,0.00041732937,0.00017093352,0.00007338499,0.00028739581,0.00007599502,0.0028534373],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992874,0.00017016484,0.00004891959,0.00016139778,0.00019347464,0.00013859144],"domain_scores_gemma":[0.9981103,0.00095782615,0.00024909707,0.00012914767,0.000432314,0.00012125754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021316358,0.00077117304,0.0011441034,0.0011011872,0.00040371143,0.0011539772,0.0020339864,0.001286815,0.00053772953],"category_scores_gemma":[0.0044725565,0.00041478558,0.00070934114,0.0011258449,0.0007222129,0.0018034325,0.001550734,0.0020387908,0.00014898644],"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":[0.00018386939,0.00027655685,0.007723128,0.00007910552,0.00013606925,0.00024764036,0.0001907055,0.68055767,0.00268917,0.0071020895,0.0026195378,0.29819444],"study_design_scores_gemma":[0.0000010960068,0.000007630946,0.00016309971,0.000001894178,0.0000028501495,0.000006856547,0.000005797749,0.99804866,0.00017594716,0.0015015805,0.00008275525,0.0000017895516],"about_ca_topic_score_codex":0.006708707,"about_ca_topic_score_gemma":0.0039405543,"teacher_disagreement_score":0.006708707,"about_ca_system_score_codex":0.0012683982,"about_ca_system_score_gemma":0.001096916,"threshold_uncertainty_score":0.013339341},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39281732,0.0059187966,0.5381151,0.0016158344,0.0015066734,0.00058863754,0.01181927,0.029110419,0.01850793],"genre_scores_gemma":[0.790715,0.0011914589,0.17878813,0.0006480035,0.00020073971,0.00022973103,0.01513532,0.00027950518,0.012812184],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994653,0.000051499203,0.000029288509,0.00021190703,0.000110415254,0.00013158911],"domain_scores_gemma":[0.9996706,0.00004548628,0.000039360548,0.00005946339,0.00015490553,0.000030235273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050746213,0.0020311736,0.00081399427,0.0011554813,0.00037350514,0.00063193357,0.0016276388,0.0010186062,0.0020901691],"category_scores_gemma":[0.0013122782,0.0004984335,0.000898297,0.00081333716,0.0002190624,0.0010347861,0.0006167911,0.0012489597,0.0018350126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007706236,0.0006913402,0.0122081805,0.00028414675,0.0003058359,0.00070672313,0.00009034124,0.1160586,0.03547287,0.0010361739,0.035209976,0.7971652],"study_design_scores_gemma":[0.000016601278,0.00011867913,0.0036868025,0.000028659771,0.00005785939,0.000157908,0.00003762409,0.9806323,0.0121413665,0.00057748065,0.0025221275,0.000022624532],"about_ca_topic_score_codex":0.030365845,"about_ca_topic_score_gemma":0.037892155,"teacher_disagreement_score":0.030365845,"about_ca_system_score_codex":0.0011666124,"about_ca_system_score_gemma":0.00094290246,"threshold_uncertainty_score":0.060378194},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38724816,0.0008054091,0.60226345,0.0003480894,0.00020039923,0.00013429811,0.00029084765,0.0057169087,0.002992378],"genre_scores_gemma":[0.94150645,0.00022834931,0.05529292,0.00014010353,0.000039940696,0.00008310269,0.0004514005,0.00013304516,0.0021246544],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952936,0.000065808345,0.00003161294,0.00019051522,0.000103398546,0.00007936731],"domain_scores_gemma":[0.9987338,0.00037924087,0.0002038598,0.0002883792,0.00034439802,0.000050295064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009057463,0.0011791397,0.0007662417,0.00065993186,0.00021580595,0.00062777655,0.00087348174,0.0006788067,0.000810514],"category_scores_gemma":[0.0039567603,0.0002616604,0.0004918365,0.00028503707,0.00035574337,0.0011616352,0.0005502955,0.0013442923,0.0006900063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038415013,0.0005461642,0.033780754,0.0001291297,0.00019796874,0.00030672763,0.00022826322,0.16191694,0.0996864,0.0009385959,0.0050723106,0.69681257],"study_design_scores_gemma":[0.0000059423164,0.00007668233,0.005743529,0.000010647317,0.000020202362,0.000116710136,0.00003112141,0.97959316,0.013249583,0.00052880694,0.0006053824,0.00001830123],"about_ca_topic_score_codex":0.0042054877,"about_ca_topic_score_gemma":0.0044688615,"teacher_disagreement_score":0.0042054877,"about_ca_system_score_codex":0.00046243824,"about_ca_system_score_gemma":0.0005373908,"threshold_uncertainty_score":0.008362055},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7065946,0.0008424232,0.2733199,0.0017551288,0.00008238174,0.00006696962,0.0038495266,0.00077445665,0.012714662],"genre_scores_gemma":[0.98902506,0.0002742365,0.008256432,0.000052692787,0.000039194118,0.000023679573,0.001291142,0.000044210417,0.0009933709],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992792,0.00019014685,0.000037964324,0.00024875553,0.00015492614,0.00008894063],"domain_scores_gemma":[0.9942426,0.0033649143,0.0008954209,0.0008376078,0.0004893476,0.00017006148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021037713,0.0002845391,0.00042271477,0.0015542783,0.00036162866,0.0016578962,0.00065647793,0.0008342627,0.003921227],"category_scores_gemma":[0.015076394,0.0003582773,0.00037390096,0.001084071,0.001373104,0.0032117835,0.0008553687,0.001040474,0.0006703154],"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":[0.00028255762,0.000083172934,0.12674186,0.00021286587,0.00022203343,0.0007320332,0.0005466465,0.67037785,0.00479258,0.10941893,0.011373084,0.07521651],"study_design_scores_gemma":[0.00001807086,0.00004143615,0.05818771,0.000040144405,0.000029301033,0.0006132705,0.0002858147,0.8298545,0.001542949,0.10608154,0.0032034602,0.00010178376],"about_ca_topic_score_codex":0.0023353405,"about_ca_topic_score_gemma":0.0015871456,"teacher_disagreement_score":0.003921227,"about_ca_system_score_codex":0.0005186232,"about_ca_system_score_gemma":0.00022154393,"threshold_uncertainty_score":0.01311785},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024628134,0.005785713,0.94510144,0.0041537294,0.00039880912,0.000033578035,0.00026490857,0.0014881801,0.018145615],"genre_scores_gemma":[0.7099138,0.006212549,0.2547968,0.00070375553,0.00044017684,0.0001269817,0.00061664224,0.00021971544,0.026969625],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998242,0.0000427711,0.0000110808405,0.0000494436,0.000050080173,0.000022395656],"domain_scores_gemma":[0.9990695,0.00050830207,0.000046709607,0.00018667012,0.00014982739,0.000038981696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005472278,0.00048200428,0.0004652248,0.00047477713,0.00035802205,0.0010498691,0.0007653707,0.00084630476,0.0041638967],"category_scores_gemma":[0.0027427084,0.0003782158,0.00031963116,0.000577677,0.0010123763,0.0026054073,0.001158505,0.0030099156,0.0008001904],"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":[0.000064194275,0.000056962865,0.0011023984,0.00022818503,0.00007015665,0.000042024618,0.00012322707,0.18314235,0.0028989387,0.4338581,0.0105373645,0.36787617],"study_design_scores_gemma":[0.00000403325,0.000013042431,0.00016857097,0.000023155433,0.000009227272,0.000012931214,0.000013446757,0.5581767,0.00085149886,0.43445063,0.0062705614,0.0000062551303],"about_ca_topic_score_codex":0.002748367,"about_ca_topic_score_gemma":0.0038835981,"teacher_disagreement_score":0.0041638967,"about_ca_system_score_codex":0.0008191667,"about_ca_system_score_gemma":0.00076742034,"threshold_uncertainty_score":0.0139296055},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99803084,0.00017745794,0.00037529247,0.000060078582,0.000022303631,0.000020097663,0.00022358462,0.000058566657,0.001031736],"genre_scores_gemma":[0.9984201,0.0001237091,0.00014326522,0.000035541547,0.000016071213,0.0000068476866,0.00020044534,0.00001745822,0.0010366095],"study_design_codex":"case_report","study_design_gemma":"observational","domain_scores_codex":[0.9997508,0.000017700817,0.000022061527,0.00007937247,0.00005825246,0.000071780465],"domain_scores_gemma":[0.9995914,0.00006614106,0.00013911966,0.000060625378,0.00003892325,0.00010384047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001782662,0.0005675669,0.00044175942,0.0014472441,0.00053072575,0.00031516273,0.0006440059,0.00078289356,0.0026831168],"category_scores_gemma":[0.0006994655,0.00025363592,0.00044328754,0.00037408082,0.001060055,0.00026296792,0.0004936897,0.0007475309,0.0005384645],"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":[0.001961941,0.0011036859,0.3496596,0.00017769408,0.0003136132,0.39835882,0.000793547,0.0007101157,0.2271104,0.00076737674,0.0016368469,0.017406387],"study_design_scores_gemma":[0.000032496504,0.0015235266,0.71454465,0.000035730412,0.00012473755,0.26324764,0.00052302674,0.0007317096,0.018017586,0.000184811,0.0009888632,0.000045152443],"about_ca_topic_score_codex":0.0036876549,"about_ca_topic_score_gemma":0.004709586,"teacher_disagreement_score":0.0036876549,"about_ca_system_score_codex":0.00036920124,"about_ca_system_score_gemma":0.0002595915,"threshold_uncertainty_score":0.008975863},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011534374,0.0002518168,0.979324,0.00023439495,0.00015899556,0.00035086754,0.00013499364,0.0064732376,0.0015374378],"genre_scores_gemma":[0.22747798,0.00031863115,0.76018417,0.00046118247,0.0002421832,0.00061933656,0.0010522659,0.00025498465,0.00938924],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974481,0.0002989775,0.00025032065,0.00067240687,0.0010735926,0.00025667212],"domain_scores_gemma":[0.99623317,0.00064524414,0.00018960919,0.00081884884,0.0018706814,0.00024242111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024689364,0.0014845125,0.002350446,0.002613817,0.00095196406,0.0021982484,0.0027439112,0.002673029,0.004173729],"category_scores_gemma":[0.0044239718,0.0010950227,0.0012852926,0.0018598267,0.0005540496,0.0038055752,0.0021717306,0.0018226764,0.004382726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064734335,0.00091839914,0.005420286,0.00029400192,0.00023310736,0.0002525419,0.00020083348,0.021506852,0.0691999,0.005409803,0.010644745,0.88527226],"study_design_scores_gemma":[0.00007184635,0.0005087466,0.0026131973,0.000033801276,0.00014408877,0.0004513426,0.000049101505,0.95434004,0.027870117,0.005791782,0.007988666,0.00013715783],"about_ca_topic_score_codex":0.0023827688,"about_ca_topic_score_gemma":0.0028500613,"teacher_disagreement_score":0.004173729,"about_ca_system_score_codex":0.00067283714,"about_ca_system_score_gemma":0.0015440967,"threshold_uncertainty_score":0.013962507},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2293725,0.0008859975,0.7674288,0.00020387078,0.00008233323,0.000066757115,0.00016276442,0.00093623507,0.00086066575],"genre_scores_gemma":[0.8178039,0.00045903845,0.18033883,0.00003618021,0.00004072337,0.00007129543,0.0002926705,0.000040658953,0.00091674394],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998898,0.00035178073,0.00011644824,0.0001615859,0.00036948107,0.00010264395],"domain_scores_gemma":[0.9969216,0.0018823026,0.0003507427,0.00020703215,0.0005801617,0.00005816699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022853394,0.00060259097,0.00065008126,0.0020734258,0.0002184159,0.0009238336,0.00048574235,0.0008913382,0.0008083512],"category_scores_gemma":[0.007878138,0.0001609368,0.0007673081,0.0012855105,0.0004165096,0.001386429,0.00041483622,0.0006148979,0.00036723368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063137215,0.00030485718,0.015727947,0.00035630265,0.00017476064,0.0003060953,0.00033598792,0.20585638,0.02740257,0.0056844205,0.0014992722,0.74171996],"study_design_scores_gemma":[0.0000065002228,0.00010753805,0.0059113167,0.000017734244,0.000015636988,0.00008499345,0.00005666068,0.9881301,0.004003642,0.0012803769,0.00036352602,0.00002194746],"about_ca_topic_score_codex":0.0022664603,"about_ca_topic_score_gemma":0.0009926907,"teacher_disagreement_score":0.0022853394,"about_ca_system_score_codex":0.00042687063,"about_ca_system_score_gemma":0.0003967257,"threshold_uncertainty_score":0.012086213},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2293725,0.0008859975,0.7674288,0.00020387078,0.00008233323,0.000066757115,0.00016276442,0.00093623507,0.00086066575],"genre_scores_gemma":[0.8178039,0.00045903845,0.18033883,0.00003618021,0.00004072337,0.00007129543,0.0002926705,0.000040658953,0.00091674394],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998898,0.00035178073,0.00011644824,0.0001615859,0.00036948107,0.00010264395],"domain_scores_gemma":[0.9969216,0.0018823026,0.0003507427,0.00020703215,0.0005801617,0.00005816699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022853394,0.00060259097,0.00065008126,0.0020734258,0.0002184159,0.0009238336,0.00048574235,0.0008913382,0.0008083512],"category_scores_gemma":[0.007878138,0.0001609368,0.0007673081,0.0012855105,0.0004165096,0.001386429,0.00041483622,0.0006148979,0.00036723368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063137215,0.00030485718,0.015727947,0.00035630265,0.00017476064,0.0003060953,0.00033598792,0.20585638,0.02740257,0.0056844205,0.0014992722,0.74171996],"study_design_scores_gemma":[0.0000065002228,0.00010753805,0.0059113167,0.000017734244,0.000015636988,0.00008499345,0.00005666068,0.9881301,0.004003642,0.0012803769,0.00036352602,0.00002194746],"about_ca_topic_score_codex":0.0022664603,"about_ca_topic_score_gemma":0.0009926907,"teacher_disagreement_score":0.0022853394,"about_ca_system_score_codex":0.00042687063,"about_ca_system_score_gemma":0.0003967257,"threshold_uncertainty_score":0.012086213},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028847635,0.00016675965,0.96872276,0.00016139749,0.000046646073,0.00006216952,0.0000723244,0.0011222615,0.0007981101],"genre_scores_gemma":[0.5425819,0.00014820148,0.45309192,0.0003319757,0.00017425355,0.0001861801,0.0006673425,0.0002256073,0.0025926686],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986821,0.00028015138,0.000060591316,0.00047585188,0.00035449862,0.00014672661],"domain_scores_gemma":[0.99792045,0.0007851182,0.00030757725,0.00033845374,0.00050965353,0.00013874398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015361834,0.0009574485,0.0012568029,0.0017741269,0.00043307134,0.001061215,0.0023852524,0.0012635347,0.0010086788],"category_scores_gemma":[0.0042539546,0.00041367323,0.0010015478,0.000944818,0.0012969058,0.0014694849,0.002011257,0.0020656628,0.00067791936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039765146,0.0005611556,0.010665368,0.00015283911,0.00021550646,0.00028189213,0.00022918303,0.20448893,0.049652696,0.011090001,0.0055838204,0.7166809],"study_design_scores_gemma":[0.000011485251,0.00006374196,0.0005302867,0.0000039462784,0.000008068562,0.000058497713,0.000010594804,0.9899137,0.0050422074,0.0037895332,0.00055805413,0.000009916137],"about_ca_topic_score_codex":0.0014744595,"about_ca_topic_score_gemma":0.002137554,"teacher_disagreement_score":0.0023852524,"about_ca_system_score_codex":0.00057852775,"about_ca_system_score_gemma":0.0008303219,"threshold_uncertainty_score":0.008124232},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44426674,0.000407739,0.5502026,0.0002063289,0.000116603456,0.0001733492,0.00014313475,0.0019249474,0.002558594],"genre_scores_gemma":[0.87574124,0.00017219574,0.1224055,0.000026398184,0.000022267946,0.00008778699,0.00009079938,0.000068879315,0.0013849624],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952185,0.00012321041,0.000032222255,0.00011661511,0.00015654955,0.000049641458],"domain_scores_gemma":[0.9982529,0.0007931567,0.00016711211,0.00014133526,0.000604984,0.00004049335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015269462,0.0007844386,0.00036285736,0.0013161045,0.00032355398,0.0011328734,0.0006475177,0.0007060409,0.001062575],"category_scores_gemma":[0.005319651,0.00036093447,0.0005585646,0.00087959785,0.000426972,0.0010859463,0.00037749016,0.00052970106,0.00030797874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047925505,0.00026607208,0.013650734,0.00025839228,0.00016880584,0.00031607147,0.00082603993,0.3616814,0.039917227,0.0028458643,0.0012385786,0.5783515],"study_design_scores_gemma":[0.000010600849,0.00007626747,0.004756824,0.000012804662,0.000028908855,0.00004613388,0.00011010137,0.9826262,0.010879594,0.00078388856,0.0006512174,0.000017413391],"about_ca_topic_score_codex":0.0055420063,"about_ca_topic_score_gemma":0.0058989855,"teacher_disagreement_score":0.0055420063,"about_ca_system_score_codex":0.0006402386,"about_ca_system_score_gemma":0.00035680717,"threshold_uncertainty_score":0.011019528},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03300986,0.00023631852,0.95179594,0.0002822098,0.00014713118,0.00014134025,0.0011264099,0.012445698,0.0008150723],"genre_scores_gemma":[0.36589324,0.0001862967,0.6233563,0.00026636722,0.0003125091,0.00032889986,0.005005126,0.0011134436,0.0035378519],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99666506,0.0010071922,0.00019668206,0.0005454327,0.0013592796,0.00022634275],"domain_scores_gemma":[0.9907268,0.0047315755,0.0006630217,0.002091417,0.0013620758,0.00042504593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023546494,0.0009833309,0.001872109,0.0044559794,0.0008381951,0.0014101337,0.0028031815,0.0017714007,0.0039852876],"category_scores_gemma":[0.011720484,0.0004991455,0.0010862764,0.0033805703,0.0010477918,0.0031184368,0.0025371185,0.0017026252,0.0015235251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016215029,0.00043344096,0.019488594,0.00032440078,0.00049220375,0.00046298318,0.00021452524,0.062273216,0.023981286,0.011066377,0.025487479,0.854154],"study_design_scores_gemma":[0.0001250522,0.00042542876,0.006802814,0.00002965591,0.00009708835,0.00058368285,0.00014848362,0.92407125,0.017501263,0.045200266,0.0049448805,0.00007010399],"about_ca_topic_score_codex":0.001603941,"about_ca_topic_score_gemma":0.0019824726,"teacher_disagreement_score":0.0044559794,"about_ca_system_score_codex":0.000580152,"about_ca_system_score_gemma":0.0009866721,"threshold_uncertainty_score":0.0133321285},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51019615,0.022730008,0.012197466,0.013639776,0.0045078457,0.0006552568,0.0049442756,0.0019192146,0.42921004],"genre_scores_gemma":[0.88977593,0.004553172,0.0064575737,0.0033872782,0.0015704315,0.00005924997,0.0015411577,0.00026484168,0.0923904],"study_design_codex":"case_report","study_design_gemma":"case_report","domain_scores_codex":[0.9997582,0.000009560426,0.000024494702,0.00005811827,0.00007250194,0.00007709001],"domain_scores_gemma":[0.9995541,0.000056702593,0.000070519,0.00007933763,0.0000635383,0.00017579009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013576355,0.00041289552,0.00033432638,0.0020447196,0.0011592574,0.00083995005,0.0004989171,0.0008610422,0.037861776],"category_scores_gemma":[0.001668227,0.00021139499,0.0005973718,0.00082002784,0.0006239247,0.0007045927,0.00079402566,0.0012842922,0.0071600927],"study_design_candidate":"case_report","study_design_consensus":"case_report","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006138714,0.00016892987,0.13251303,0.00050914194,0.0001319553,0.48188308,0.00044928814,0.00032250254,0.02666975,0.008345796,0.036245883,0.31214678],"study_design_scores_gemma":[0.000047042562,0.00010739679,0.047807936,0.0001350259,0.00004892063,0.84454525,0.00023856238,0.00025553425,0.0047233803,0.0014702064,0.10058804,0.00003266667],"about_ca_topic_score_codex":0.005323828,"about_ca_topic_score_gemma":0.008404898,"teacher_disagreement_score":0.037861776,"about_ca_system_score_codex":0.0009197215,"about_ca_system_score_gemma":0.0016123764,"threshold_uncertainty_score":0.12666023},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05182892,0.00027817299,0.9466284,0.00011316148,0.00003718202,0.00003617458,0.00006988738,0.0005321801,0.00047597752],"genre_scores_gemma":[0.7072269,0.00016041352,0.29096654,0.00008884698,0.00008930487,0.00009404861,0.00037904194,0.00009284677,0.000902126],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898106,0.0003193518,0.00006363045,0.0002667747,0.00029930734,0.00006990237],"domain_scores_gemma":[0.9975828,0.001367987,0.000271928,0.00022250216,0.00048493705,0.000069794485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016183216,0.0005199636,0.0011095379,0.0012778692,0.0003207646,0.00068372063,0.0010903676,0.0009833219,0.00066403457],"category_scores_gemma":[0.0055818143,0.0002803347,0.00052511186,0.00081822206,0.0005212732,0.0010334565,0.00067713513,0.00087488483,0.00028609208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055405416,0.00020669027,0.009748573,0.0001734897,0.00018790615,0.00016526834,0.00012990135,0.1557775,0.031930797,0.0057923896,0.0040291524,0.79130435],"study_design_scores_gemma":[0.000008445249,0.000035070574,0.0012085171,0.000003877003,0.000008949937,0.00008096201,0.000014255361,0.9907516,0.004521182,0.0029293094,0.0004277057,0.000010070384],"about_ca_topic_score_codex":0.0009177077,"about_ca_topic_score_gemma":0.0007825934,"teacher_disagreement_score":0.0016183216,"about_ca_system_score_codex":0.00038239415,"about_ca_system_score_gemma":0.00041140136,"threshold_uncertainty_score":0.008558631},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015246151,0.0035963077,0.9397761,0.00056337746,0.00036288248,0.000118136406,0.0014315674,0.010007967,0.028897626],"genre_scores_gemma":[0.17105971,0.0056786216,0.7287116,0.0005198827,0.00029035413,0.00012505276,0.009723611,0.0015050014,0.082386196],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974376,0.000014816305,0.000015332253,0.00008618789,0.00011394782,0.000025955756],"domain_scores_gemma":[0.9996929,0.000060779686,0.00002754443,0.000088027424,0.000112561196,0.000018184206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032318683,0.0009927044,0.0005557808,0.0013806322,0.00025436922,0.0011411612,0.0009393821,0.00051896984,0.0073342156],"category_scores_gemma":[0.0011229962,0.00045192777,0.00083758985,0.0014656781,0.00028464792,0.0020697215,0.00096934324,0.0016195248,0.0056562168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036812893,0.0000405977,0.00082420534,0.000118873264,0.00002729622,0.00005416313,0.00003216977,0.0054316944,0.010896536,0.010067633,0.019787222,0.95268285],"study_design_scores_gemma":[0.000017515395,0.000086072156,0.0033214851,0.00021493911,0.00014140915,0.00096650724,0.00007790045,0.61286753,0.07038852,0.11874173,0.19311468,0.000061632316],"about_ca_topic_score_codex":0.0014577911,"about_ca_topic_score_gemma":0.0037171782,"teacher_disagreement_score":0.0073342156,"about_ca_system_score_codex":0.00048744061,"about_ca_system_score_gemma":0.0005354727,"threshold_uncertainty_score":0.024535358},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062484697,0.00020427049,0.9358361,0.0001323635,0.000025168376,0.000040061273,0.00008239071,0.0007119997,0.00048292227],"genre_scores_gemma":[0.78277993,0.00020871617,0.2156816,0.000029929777,0.000031594904,0.00007131765,0.00029452992,0.00006592025,0.0008364587],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994863,0.00021217378,0.000029959572,0.000105441606,0.0001237129,0.00004245519],"domain_scores_gemma":[0.9977391,0.0014892041,0.0002288779,0.00019655742,0.00027653427,0.0000698162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009490131,0.0005371612,0.0006360957,0.0013267398,0.0004889351,0.00056641607,0.00072937546,0.0006828425,0.00049567956],"category_scores_gemma":[0.0047605103,0.0004600057,0.000584111,0.0012234622,0.00044592496,0.00068673113,0.0005847809,0.0006859021,0.00015921434],"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":[0.00006575013,0.0000335076,0.0017881724,0.000024137358,0.0000440589,0.000048047063,0.00009713608,0.942557,0.0013523635,0.0029910805,0.00027318712,0.050725587],"study_design_scores_gemma":[0.0000016027994,0.0000052272358,0.00017947801,0.0000016431421,0.0000020354132,0.0000075403386,0.0000059115637,0.9977356,0.00032274565,0.0016516338,0.00008326952,0.000003285568],"about_ca_topic_score_codex":0.016493658,"about_ca_topic_score_gemma":0.010670049,"teacher_disagreement_score":0.016493658,"about_ca_system_score_codex":0.0010192522,"about_ca_system_score_gemma":0.0009653672,"threshold_uncertainty_score":0.03279531},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047358545,0.0035258937,0.85168356,0.0008453332,0.001432732,0.00038943015,0.0020862166,0.017155072,0.118145816],"genre_scores_gemma":[0.10957135,0.0074108923,0.5515558,0.0014315868,0.0016295562,0.00033537764,0.010713969,0.0025510574,0.31480038],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992206,0.000049504357,0.000032924563,0.00026778088,0.0003719546,0.000057205667],"domain_scores_gemma":[0.99895597,0.00024256333,0.00007171327,0.00024711285,0.0004191954,0.000063514206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007896089,0.0017572975,0.00093675614,0.0027305903,0.0006739801,0.0025489538,0.0022965139,0.0011118762,0.03684516],"category_scores_gemma":[0.002544541,0.000545162,0.00080824335,0.0018762981,0.0004431008,0.0024376079,0.0015923193,0.0014279806,0.032054324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000111260204,0.00010391876,0.00082318415,0.00032546592,0.000038693124,0.00013515033,0.00008508304,0.0026498148,0.014975382,0.022343196,0.07958488,0.87882394],"study_design_scores_gemma":[0.000033294626,0.00020707192,0.0032591573,0.00032967623,0.00014625987,0.0024636677,0.00019241274,0.10516624,0.07640325,0.095224656,0.7164505,0.00012376814],"about_ca_topic_score_codex":0.0009579535,"about_ca_topic_score_gemma":0.0011625732,"teacher_disagreement_score":0.03684516,"about_ca_system_score_codex":0.0005878102,"about_ca_system_score_gemma":0.00069210294,"threshold_uncertainty_score":0.123259306},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039312057,0.005045687,0.8339128,0.0013324012,0.0016668196,0.0002616607,0.0011260279,0.014237043,0.13848633],"genre_scores_gemma":[0.07793104,0.010087108,0.52749926,0.0014251955,0.0017687732,0.00028265198,0.0061117928,0.0027745767,0.37211964],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99895954,0.00006333019,0.000039956336,0.00029239384,0.00058285485,0.00006193671],"domain_scores_gemma":[0.99868196,0.0002802299,0.00007488974,0.00031881136,0.00058413116,0.000059899165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076568,0.0016439604,0.000970221,0.0029417076,0.0007339463,0.0024858294,0.002499525,0.0012486136,0.029015746],"category_scores_gemma":[0.002864033,0.00056640635,0.00076001015,0.002271989,0.00064116396,0.0029887098,0.0018737002,0.0019705344,0.02874226],"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":[0.00004409311,0.00006519841,0.0005072094,0.00022923111,0.000024645227,0.000098444965,0.000084369945,0.0026865737,0.009035296,0.033099208,0.08591168,0.8682141],"study_design_scores_gemma":[0.000013844221,0.000113094844,0.0015082638,0.00025231444,0.00007628728,0.0022542672,0.00012582997,0.06795247,0.0382433,0.13143472,0.7579331,0.00009241208],"about_ca_topic_score_codex":0.00093739235,"about_ca_topic_score_gemma":0.0011460982,"teacher_disagreement_score":0.029015746,"about_ca_system_score_codex":0.00072635635,"about_ca_system_score_gemma":0.0008730162,"threshold_uncertainty_score":0.097067356},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006346546,0.0004181606,0.0009575572,0.00007751174,0.00019431332,0.0000721326,0.9925559,0.0017886019,0.0033012244],"genre_scores_gemma":[0.0009362424,0.0002638033,0.0017563354,0.00009218469,0.000027603575,0.00018896497,0.99464625,0.000187719,0.0019008288],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984036,0.00012568224,0.0003083416,0.0004958581,0.00053057604,0.00013585565],"domain_scores_gemma":[0.99793196,0.00043246045,0.00021147059,0.000670366,0.0006605907,0.00009312318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009139547,0.0025006533,0.0018208459,0.0056080897,0.0009718761,0.0022421295,0.0027347822,0.0010175272,0.083629355],"category_scores_gemma":[0.005648193,0.00059081207,0.0013882992,0.0063608997,0.0003235062,0.002099679,0.0014067615,0.0021837458,0.118662216],"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":[0.00011631016,0.0000570632,0.001200283,0.00080350816,0.000040729967,0.000029902047,0.000020468657,0.00042801996,0.0005484571,0.0005770395,0.9773232,0.018855132],"study_design_scores_gemma":[0.00014415797,0.000060939587,0.007548898,0.00033846172,0.000049443657,0.0003402852,0.00007908213,0.0012795475,0.0019419196,0.002677707,0.98544323,0.00009635787],"about_ca_topic_score_codex":0.01290113,"about_ca_topic_score_gemma":0.023393163,"teacher_disagreement_score":0.083629355,"about_ca_system_score_codex":0.0013698165,"about_ca_system_score_gemma":0.0015012975,"threshold_uncertainty_score":0.27976805},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1865476,0.0005097879,0.78474516,0.00017559249,0.00022201377,0.00023668677,0.0009707313,0.020917615,0.005674759],"genre_scores_gemma":[0.7613057,0.00015099504,0.23372033,0.000103330494,0.000048375372,0.0001933268,0.0007789997,0.00045235205,0.0032465537],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996592,0.000043137723,0.000021967197,0.00007925484,0.00015207128,0.00004442543],"domain_scores_gemma":[0.9994542,0.00019513216,0.00007615084,0.000121897894,0.00012759255,0.00002499543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022626776,0.00057333295,0.00035433198,0.0008146992,0.00017535506,0.00044932505,0.0010707786,0.00026696484,0.005706056],"category_scores_gemma":[0.0011602405,0.00021053731,0.00019253568,0.0006696069,0.00016280159,0.00047506412,0.00022945354,0.00032522433,0.0010233652],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001657321,0.00028420382,0.0088885175,0.00057626574,0.00018849516,0.0012870317,0.00019654792,0.08739268,0.2000953,0.004789294,0.013794687,0.6808496],"study_design_scores_gemma":[0.00024780154,0.0011167348,0.014762087,0.00006839858,0.0001370504,0.001955501,0.00007167986,0.60686624,0.3444997,0.0022139836,0.027942736,0.000118030875],"about_ca_topic_score_codex":0.0006846237,"about_ca_topic_score_gemma":0.0009096668,"teacher_disagreement_score":0.005706056,"about_ca_system_score_codex":0.00037731568,"about_ca_system_score_gemma":0.00027869153,"threshold_uncertainty_score":0.019088626},"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":"methods","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3573522,0.003892708,0.3598458,0.0089838365,0.0009490283,0.0019878217,0.22865058,0.019254562,0.01908348],"genre_scores_gemma":[0.37663287,0.0014851568,0.26769587,0.0010411763,0.00030285586,0.0010152521,0.3473228,0.000716158,0.003787867],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98062176,0.002527771,0.0026750679,0.0027089454,0.010589713,0.0008767049],"domain_scores_gemma":[0.9562855,0.010683543,0.0039790994,0.017594343,0.010715113,0.000742343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008672291,0.0011805838,0.0011034107,0.0044925627,0.0012037731,0.0062239375,0.0020511495,0.0013253082,0.0016958073],"category_scores_gemma":[0.034101762,0.0005817708,0.00091126864,0.010954854,0.00094554026,0.0044210493,0.0038303616,0.0023956732,0.0017686018],"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":[0.0019283978,0.0013688088,0.22238758,0.0036800085,0.000980387,0.0022392164,0.0020912646,0.041917026,0.050694656,0.024784798,0.2393539,0.40857393],"study_design_scores_gemma":[0.00039503622,0.00050255127,0.14254607,0.0007012398,0.00048044822,0.0025988757,0.0026569641,0.23054446,0.087620385,0.06745306,0.46419,0.0003109124],"about_ca_topic_score_codex":0.0053047123,"about_ca_topic_score_gemma":0.0059947413,"teacher_disagreement_score":0.008672291,"about_ca_system_score_codex":0.0014146118,"about_ca_system_score_gemma":0.0022284796,"threshold_uncertainty_score":0.045864046},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03757453,0.006326746,0.9420948,0.0018163722,0.00023846986,0.00041413124,0.0019238837,0.0065547675,0.0030561762],"genre_scores_gemma":[0.37336966,0.0048106895,0.61512065,0.00049229286,0.00044762495,0.00043693435,0.003670262,0.00033373557,0.0013181823],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9938214,0.0020936485,0.0006243244,0.0010672995,0.0021265354,0.00026674013],"domain_scores_gemma":[0.9798451,0.011217642,0.0021171,0.0021498569,0.004334342,0.00033592654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008360075,0.0021947997,0.0013653492,0.010896185,0.00061113614,0.0039821565,0.0017245782,0.0009003421,0.0016985419],"category_scores_gemma":[0.018102875,0.00055260665,0.0018478947,0.006209335,0.00078645884,0.003484166,0.0020095482,0.0015985522,0.0011972602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002925356,0.00042285284,0.039587222,0.0015897934,0.0010449243,0.00043467464,0.00060997286,0.02210246,0.011339994,0.007861702,0.012678752,0.902035],"study_design_scores_gemma":[0.00009780982,0.00048399362,0.04084371,0.0009223206,0.0007235084,0.0013031587,0.0010072144,0.834673,0.017737761,0.07124998,0.030689009,0.00026851273],"about_ca_topic_score_codex":0.0028479467,"about_ca_topic_score_gemma":0.0032135365,"teacher_disagreement_score":0.010896185,"about_ca_system_score_codex":0.00076569273,"about_ca_system_score_gemma":0.0014715638,"threshold_uncertainty_score":0.04421282},"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":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004536384,0.0013358347,0.9476442,0.00091017515,0.0005745113,0.00031073234,0.0032863885,0.014974165,0.026427653],"genre_scores_gemma":[0.14337061,0.0018428311,0.7591172,0.0020194268,0.0010582883,0.0009856892,0.019982269,0.0034659074,0.06815773],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974368,0.00029017555,0.00018415117,0.000586429,0.0011848717,0.0003176032],"domain_scores_gemma":[0.99630594,0.0009992251,0.00028004107,0.0011379672,0.0011458396,0.00013098714],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002449889,0.0014119705,0.0012120722,0.0046512317,0.0017640365,0.0034139974,0.0025705968,0.0024991776,0.060795642],"category_scores_gemma":[0.013050496,0.0004509423,0.0015354508,0.0038825031,0.0010666212,0.004647123,0.0024969855,0.0020372595,0.043184865],"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":[0.00034319892,0.00018875932,0.0038505362,0.00039426232,0.0000808166,0.0003186929,0.00019908801,0.014063823,0.00535064,0.062045004,0.11251703,0.8006482],"study_design_scores_gemma":[0.00013179916,0.00030814402,0.003509858,0.00023497577,0.0000879798,0.0021861012,0.00039361513,0.27684838,0.016327847,0.14779352,0.5519979,0.00017986674],"about_ca_topic_score_codex":0.00519168,"about_ca_topic_score_gemma":0.006434181,"teacher_disagreement_score":0.93920434,"about_ca_system_score_codex":0.0010985411,"about_ca_system_score_gemma":0.0016783862,"threshold_uncertainty_score":0},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001168772,0.00055529905,0.0012448783,0.00019637751,0.00033038977,0.00011410519,0.9860124,0.0033256563,0.007052097],"genre_scores_gemma":[0.0012312286,0.00019620806,0.0014588713,0.00012302556,0.00002946093,0.00014777687,0.9942299,0.00018151644,0.002401962],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998517,0.00016967826,0.00018424004,0.00045355537,0.00044703667,0.00022849564],"domain_scores_gemma":[0.9985927,0.00020386445,0.00011067746,0.00061613653,0.0003584206,0.00011816684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009231054,0.003441314,0.001810567,0.0033371574,0.0015652217,0.0029572449,0.0048716227,0.0018026317,0.068737574],"category_scores_gemma":[0.0039958083,0.00071896444,0.002249363,0.004207601,0.00047161948,0.002768824,0.0025235394,0.0026772437,0.1286652],"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":[0.00010630938,0.00004840646,0.0009143948,0.0004514283,0.00003235482,0.000031970405,0.000013776263,0.0004781113,0.00017914346,0.00060354173,0.9882541,0.008886353],"study_design_scores_gemma":[0.00019482944,0.00007395808,0.004950797,0.00034748833,0.000049764654,0.00036370617,0.00013390137,0.0029452152,0.0013695015,0.0037146779,0.98578215,0.000074088755],"about_ca_topic_score_codex":0.015082471,"about_ca_topic_score_gemma":0.04205089,"teacher_disagreement_score":0.068737574,"about_ca_system_score_codex":0.0013875331,"about_ca_system_score_gemma":0.0019881537,"threshold_uncertainty_score":0.22995007},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004094369,0.0034127715,0.8600315,0.00073075056,0.001194468,0.00034123028,0.0017500396,0.015125326,0.11331943],"genre_scores_gemma":[0.09471222,0.0073199226,0.55183065,0.0012505009,0.0013573782,0.00030290044,0.009081691,0.0023849586,0.33175972],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926907,0.00004651998,0.000030337367,0.0002413564,0.00036014544,0.000052538482],"domain_scores_gemma":[0.9990602,0.0002265993,0.000064696316,0.00021448855,0.0003793225,0.00005467385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007277906,0.0017678678,0.0009521306,0.002689911,0.0006544377,0.002452839,0.002311322,0.0010830386,0.036375463],"category_scores_gemma":[0.0023028716,0.0005552833,0.00078818103,0.0018802903,0.00044974263,0.0023712474,0.0015550805,0.0013756754,0.03115767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009525817,0.00008805085,0.00068997813,0.0002996681,0.000034404085,0.00012583507,0.0000858787,0.002648955,0.013686423,0.021298325,0.07551588,0.8854314],"study_design_scores_gemma":[0.00003123569,0.00018670538,0.00300882,0.0003430748,0.00013823877,0.002501664,0.00020138151,0.10179434,0.073790334,0.09649626,0.7213838,0.00012412731],"about_ca_topic_score_codex":0.0010045273,"about_ca_topic_score_gemma":0.0012627707,"teacher_disagreement_score":0.036375463,"about_ca_system_score_codex":0.00058011134,"about_ca_system_score_gemma":0.00067273807,"threshold_uncertainty_score":0.12168807},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9434992,0.011072436,0.024538964,0.0018794048,0.00012530792,0.00024289699,0.013054223,0.0003880256,0.005199586],"genre_scores_gemma":[0.981768,0.0023912229,0.009477889,0.00004507872,0.00010194507,0.00006738068,0.0055586467,0.000021968524,0.0005678667],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9971125,0.0008796645,0.0003724304,0.0004402051,0.0010166938,0.00017855779],"domain_scores_gemma":[0.9865862,0.006854684,0.0019893611,0.001155643,0.0030353176,0.0003787724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004209501,0.00042127102,0.00080564705,0.010972735,0.00046338674,0.0017289442,0.00096589583,0.00051872904,0.0013097704],"category_scores_gemma":[0.010775684,0.00015076222,0.00079441804,0.009122998,0.0005515952,0.0011281746,0.0006205932,0.00044116942,0.00032180664],"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":[0.0016381101,0.0003510605,0.7839804,0.0009766776,0.0009705931,0.00039836552,0.0007341318,0.008671204,0.0019736,0.0035161956,0.005840622,0.1909489],"study_design_scores_gemma":[0.000054486718,0.0007137478,0.93825895,0.0004900822,0.00097192614,0.0008355052,0.0030642275,0.03763241,0.0043017063,0.0041577965,0.009417501,0.00010164675],"about_ca_topic_score_codex":0.015345953,"about_ca_topic_score_gemma":0.012946478,"teacher_disagreement_score":0.015345953,"about_ca_system_score_codex":0.0009683514,"about_ca_system_score_gemma":0.0014830705,"threshold_uncertainty_score":0.030513287},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028847635,0.00016675965,0.96872276,0.00016139749,0.000046646073,0.00006216952,0.0000723244,0.0011222615,0.0007981101],"genre_scores_gemma":[0.5425819,0.00014820148,0.45309192,0.0003319757,0.00017425355,0.0001861801,0.0006673425,0.0002256073,0.0025926686],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986821,0.00028015138,0.000060591316,0.00047585188,0.00035449862,0.00014672661],"domain_scores_gemma":[0.99792045,0.0007851182,0.00030757725,0.00033845374,0.00050965353,0.00013874398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015361834,0.0009574485,0.0012568029,0.0017741269,0.00043307134,0.001061215,0.0023852524,0.0012635347,0.0010086788],"category_scores_gemma":[0.0042539546,0.00041367323,0.0010015478,0.000944818,0.0012969058,0.0014694849,0.002011257,0.0020656628,0.00067791936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039765146,0.0005611556,0.010665368,0.00015283911,0.00021550646,0.00028189213,0.00022918303,0.20448893,0.049652696,0.011090001,0.0055838204,0.7166809],"study_design_scores_gemma":[0.000011485251,0.00006374196,0.0005302867,0.0000039462784,0.000008068562,0.000058497713,0.000010594804,0.9899137,0.0050422074,0.0037895332,0.00055805413,0.000009916137],"about_ca_topic_score_codex":0.0014744595,"about_ca_topic_score_gemma":0.002137554,"teacher_disagreement_score":0.0023852524,"about_ca_system_score_codex":0.00057852775,"about_ca_system_score_gemma":0.0008303219,"threshold_uncertainty_score":0.008124232},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010910293,0.22708356,0.6988691,0.004364469,0.008509441,0.00025192223,0.005123405,0.0043983916,0.05030861],"genre_scores_gemma":[0.03224713,0.3556525,0.4797077,0.0074588475,0.021817986,0.001781082,0.019153314,0.003955143,0.07822639],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99881124,0.0004121251,0.0001179676,0.0002822898,0.00030975603,0.00006649583],"domain_scores_gemma":[0.99749124,0.0017941907,0.0001205109,0.00019917855,0.00032123708,0.00007361058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017341205,0.0025730564,0.0017207707,0.002306651,0.00045961817,0.0018748367,0.0023313577,0.0027169073,0.053539265],"category_scores_gemma":[0.005348875,0.0010196097,0.0016421651,0.0036570134,0.0007842298,0.004463142,0.0014064473,0.003896577,0.027180659],"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":[0.000078609504,0.00012063271,0.00080932764,0.00445814,0.0002567061,0.00051159225,0.00033801125,0.0248358,0.002003896,0.23735239,0.34116656,0.38806835],"study_design_scores_gemma":[0.0000126545965,0.00005415293,0.00041434198,0.0006524103,0.000053205382,0.00055104843,0.00003950658,0.02456653,0.0003791214,0.16151318,0.8117149,0.000048959922],"about_ca_topic_score_codex":0.0028152193,"about_ca_topic_score_gemma":0.002081538,"teacher_disagreement_score":0.053539265,"about_ca_system_score_codex":0.0012470952,"about_ca_system_score_gemma":0.0013152098,"threshold_uncertainty_score":0.17910665},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14330599,0.0030365565,0.8429135,0.00066404045,0.0003431464,0.00018957359,0.0015044773,0.003667729,0.004374942],"genre_scores_gemma":[0.7766497,0.0018677544,0.21550226,0.0001675173,0.00020827632,0.0000984465,0.0028018348,0.00009992845,0.0026041805],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923694,0.00009847774,0.00006273181,0.00025726392,0.00024622667,0.00009834373],"domain_scores_gemma":[0.9992092,0.00016340856,0.00018499406,0.00012552123,0.00026401025,0.00005298516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000622122,0.00094658945,0.0007625802,0.002957152,0.00038948446,0.000990649,0.0008112577,0.00083227496,0.00082877226],"category_scores_gemma":[0.0016004293,0.00023141883,0.00060164806,0.0014835205,0.00030164068,0.0016767451,0.0007054167,0.000706446,0.0005907653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043050366,0.00028028453,0.050490495,0.00043139345,0.0001544768,0.00069962256,0.00026895897,0.03811684,0.06496123,0.0066472855,0.013087691,0.8244313],"study_design_scores_gemma":[0.000017718405,0.00022609546,0.030275058,0.0000848476,0.00011986101,0.0010967092,0.00039762224,0.8997713,0.042473555,0.009159906,0.016320596,0.000056708956],"about_ca_topic_score_codex":0.0027135527,"about_ca_topic_score_gemma":0.002966096,"teacher_disagreement_score":0.002957152,"about_ca_system_score_codex":0.00053630804,"about_ca_system_score_gemma":0.000546928,"threshold_uncertainty_score":0.0053955317},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064982558,0.00057047466,0.9842515,0.0002219585,0.00014288713,0.00006301695,0.0006808016,0.003657204,0.003913954],"genre_scores_gemma":[0.36541465,0.000556113,0.60621685,0.0010752224,0.00017969574,0.00043925043,0.0038499834,0.00086258276,0.02140568],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996729,0.00007931764,0.000018393766,0.00009714159,0.00008075246,0.000051503856],"domain_scores_gemma":[0.99950874,0.00023571537,0.000020887475,0.00010618284,0.00010730885,0.000021137017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006287747,0.0008660186,0.00068816065,0.00040024272,0.00021666281,0.000711064,0.0010732189,0.00088601164,0.011890615],"category_scores_gemma":[0.0016495256,0.0003232275,0.00055516657,0.00064019096,0.0003310851,0.0008115349,0.0008963797,0.0015036523,0.0036949185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037955484,0.0001252875,0.00051166926,0.00021858593,0.00007950979,0.0001268887,0.000054698994,0.19517037,0.031847093,0.016627446,0.0203041,0.7345549],"study_design_scores_gemma":[0.00001399685,0.000045264933,0.00010895629,0.000015122776,0.000011301393,0.00003779492,0.000009194305,0.9823445,0.0059602573,0.008235602,0.0032092382,0.0000087492035],"about_ca_topic_score_codex":0.0021735746,"about_ca_topic_score_gemma":0.005606779,"teacher_disagreement_score":0.011890615,"about_ca_system_score_codex":0.00047842631,"about_ca_system_score_gemma":0.00081159826,"threshold_uncertainty_score":0.039778113},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31870803,0.0022073584,0.65897864,0.0005738689,0.00037374246,0.00028991987,0.002497713,0.00927955,0.0070912265],"genre_scores_gemma":[0.85699636,0.0010446068,0.12933044,0.00035866673,0.00013394064,0.0001885121,0.0048628557,0.00015355668,0.0069311247],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964213,0.00003672567,0.0000141431765,0.00012992756,0.00009789823,0.00007911582],"domain_scores_gemma":[0.9996592,0.000072047245,0.00005917183,0.000038630995,0.00013692978,0.00003400072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004202486,0.0012047585,0.0005032567,0.0014725028,0.0001979468,0.0006051366,0.00089246465,0.0005856806,0.0012595778],"category_scores_gemma":[0.0013011489,0.00031485103,0.0004653767,0.0007298813,0.00024617018,0.00073009485,0.00085540494,0.00077739893,0.0007071502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050329656,0.0004766761,0.017655479,0.00021071717,0.00020661888,0.00024526374,0.000073393334,0.076621935,0.044593025,0.0008324447,0.00975884,0.8488224],"study_design_scores_gemma":[0.000024354873,0.0001813246,0.011167527,0.000038788174,0.000044523782,0.000101611695,0.000044711982,0.96696144,0.017531956,0.0017154434,0.0021650419,0.000023359236],"about_ca_topic_score_codex":0.011233412,"about_ca_topic_score_gemma":0.016939353,"teacher_disagreement_score":0.011233412,"about_ca_system_score_codex":0.0006653731,"about_ca_system_score_gemma":0.00077673135,"threshold_uncertainty_score":0.022336066},"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":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0600152,0.0026426963,0.51374,0.03313583,0.2689577,0.00088587136,0.023904314,0.079615094,0.017103275],"genre_scores_gemma":[0.45018438,0.0018806203,0.37896276,0.005065436,0.0083514815,0.0004096704,0.01913171,0.0072435155,0.12877041],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99836177,0.00011938287,0.00015054453,0.00040352842,0.00077885354,0.00018597409],"domain_scores_gemma":[0.98933345,0.0013526819,0.0009067509,0.00209575,0.005885246,0.00042619795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001110187,0.002134038,0.0013954259,0.002111777,0.0012018736,0.0017054075,0.0015686279,0.003070679,0.049978014],"category_scores_gemma":[0.01736567,0.0005947806,0.0006038866,0.0017457944,0.00070446334,0.0012062496,0.0021546204,0.0034398574,0.01824795],"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":[0.001102112,0.00009287331,0.004033481,0.0007749173,0.000100575075,0.0019965714,0.0002446376,0.008754613,0.028681464,0.005152337,0.5989769,0.3500896],"study_design_scores_gemma":[0.00041897615,0.00034724837,0.013254892,0.00063652644,0.00017631691,0.005090432,0.00060799666,0.31896642,0.11254397,0.015084914,0.5325679,0.00030440115],"about_ca_topic_score_codex":0.011346471,"about_ca_topic_score_gemma":0.012932855,"teacher_disagreement_score":0.049978014,"about_ca_system_score_codex":0.0010907062,"about_ca_system_score_gemma":0.0035223274,"threshold_uncertainty_score":0.16719311},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032452021,0.000052415053,0.9961773,0.000032776457,0.000007973297,0.0000152316425,0.000015080269,0.00024217392,0.00021182394],"genre_scores_gemma":[0.23385057,0.00016986777,0.76377845,0.000072220726,0.000056836965,0.00018105659,0.00025135733,0.00017478474,0.0014648155],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989184,0.00035235737,0.000060205457,0.00019959215,0.00035632154,0.0001130766],"domain_scores_gemma":[0.9965442,0.0021836683,0.00030102924,0.00018142661,0.0007026922,0.00008696621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013044265,0.0010092013,0.0020945002,0.0017561787,0.0006756413,0.0014436686,0.0013463924,0.00090483075,0.0018246747],"category_scores_gemma":[0.0047976733,0.0006136026,0.0011447596,0.0020133578,0.00079582736,0.0015411642,0.0011250575,0.001664391,0.00054070714],"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":[0.00015385365,0.00016668766,0.00091083185,0.00012796572,0.000104098996,0.00004912831,0.000073271025,0.71396464,0.004431139,0.008994274,0.001856138,0.26916805],"study_design_scores_gemma":[0.0000037572966,0.000019303177,0.000055820343,0.0000025995673,0.000003928626,0.000010926397,0.000008809928,0.9969349,0.00049646676,0.002336149,0.00012289561,0.000004392436],"about_ca_topic_score_codex":0.005068381,"about_ca_topic_score_gemma":0.0046090013,"teacher_disagreement_score":0.005068381,"about_ca_system_score_codex":0.00061682425,"about_ca_system_score_gemma":0.0015904276,"threshold_uncertainty_score":0.0100777745},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016828036,0.00018476395,0.9788599,0.00010328865,0.000045840443,0.0001694521,0.00021938897,0.0028167204,0.0007726834],"genre_scores_gemma":[0.36612505,0.00028823514,0.6271704,0.00014544562,0.000093778144,0.00025360566,0.0017537165,0.00019906886,0.0039706333],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908686,0.00009443316,0.000044881446,0.00025613132,0.00036479835,0.00015284968],"domain_scores_gemma":[0.9993649,0.00008969155,0.000049806073,0.000059587896,0.00038017274,0.000055832203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010150602,0.0011275499,0.00091095525,0.0026448998,0.0005611978,0.001035881,0.0020141217,0.0008310335,0.0011698032],"category_scores_gemma":[0.0016935436,0.00031912632,0.0012556459,0.0010988107,0.00044365806,0.0013763503,0.0012139302,0.001306404,0.00060499733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032218182,0.00050079275,0.008891883,0.0001299533,0.00022388976,0.00036828284,0.00019140868,0.114329286,0.043373507,0.011930607,0.007970865,0.81176734],"study_design_scores_gemma":[0.0000057177613,0.00005913392,0.0009146045,0.0000046188907,0.000017903185,0.000087279666,0.000024242534,0.9900974,0.0047576316,0.0023123152,0.0017043691,0.000014721605],"about_ca_topic_score_codex":0.024685154,"about_ca_topic_score_gemma":0.02318918,"teacher_disagreement_score":0.024685154,"about_ca_system_score_codex":0.0010509474,"about_ca_system_score_gemma":0.0014731229,"threshold_uncertainty_score":0.049082935},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008731199,0.0001361627,0.98363113,0.00008685688,0.000081831306,0.00027500794,0.000052160292,0.004876346,0.0021291967],"genre_scores_gemma":[0.3494698,0.0002841791,0.6434646,0.00026036662,0.00008987104,0.00070232287,0.00030794076,0.00015090969,0.0052699493],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994785,0.00007699778,0.000029908239,0.00013920254,0.00020312636,0.00007226466],"domain_scores_gemma":[0.99950063,0.000055533146,0.00003866876,0.00005148001,0.00030646406,0.000047208825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000722825,0.0005459431,0.00061868393,0.00089295965,0.00041335434,0.0009244309,0.0017382627,0.0010105646,0.003199956],"category_scores_gemma":[0.0011395519,0.00033732515,0.00037167323,0.00040778957,0.00026204763,0.00085576245,0.0005311868,0.0006811948,0.0017736203],"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":[0.0007412167,0.00045595755,0.0031968537,0.00041750996,0.00014821373,0.00061430654,0.00035667236,0.04412662,0.20855099,0.00958105,0.011024219,0.72078645],"study_design_scores_gemma":[0.00011036087,0.0008196002,0.002650748,0.00005522926,0.00010526379,0.00057185045,0.00011113302,0.88133997,0.09127297,0.0018790746,0.021013184,0.00007061714],"about_ca_topic_score_codex":0.005008036,"about_ca_topic_score_gemma":0.0029341495,"teacher_disagreement_score":0.005008036,"about_ca_system_score_codex":0.0005929543,"about_ca_system_score_gemma":0.0010227652,"threshold_uncertainty_score":0.010704935},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020812517,0.0002451126,0.99338853,0.00010151367,0.00008534062,0.000036745907,0.00014847305,0.0029804066,0.00093265885],"genre_scores_gemma":[0.20181134,0.00059278915,0.7797219,0.00052338885,0.00017449065,0.0002777834,0.0016515834,0.0011870088,0.014059716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999278,0.00021780758,0.000024164543,0.00020188103,0.00021585327,0.00006222457],"domain_scores_gemma":[0.99890304,0.0006154951,0.000051239855,0.00022220673,0.00016811992,0.000039921928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012619513,0.0012957946,0.0010386924,0.0008060568,0.00040416542,0.00085316965,0.0025978435,0.0015588113,0.0051298337],"category_scores_gemma":[0.0030608315,0.00057743373,0.0008993779,0.0007520384,0.0007603354,0.0012481742,0.0022150562,0.003311283,0.0022545883],"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":[0.0002196287,0.00012096288,0.00075123506,0.0001313498,0.00014411955,0.0001590643,0.000052549367,0.52175355,0.009174362,0.02527459,0.02364733,0.41857126],"study_design_scores_gemma":[0.0000036563092,0.000012534037,0.000056764962,0.0000036829836,0.000006165461,0.00003642515,0.0000020107277,0.98917013,0.0016236647,0.0076975683,0.0013819628,0.0000053667595],"about_ca_topic_score_codex":0.0027718158,"about_ca_topic_score_gemma":0.004107385,"teacher_disagreement_score":0.0051298337,"about_ca_system_score_codex":0.0007410284,"about_ca_system_score_gemma":0.0006219563,"threshold_uncertainty_score":0.017161012},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014687613,0.0007286489,0.98018336,0.00030469103,0.00017739991,0.00009756488,0.00020250896,0.002171413,0.00144682],"genre_scores_gemma":[0.4235468,0.0004609757,0.57145673,0.00030895672,0.00033633344,0.00025944677,0.00063845236,0.0010967217,0.0018955979],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9943457,0.0012002696,0.0004138567,0.001776061,0.0019303109,0.00033378124],"domain_scores_gemma":[0.986827,0.005052122,0.0017937438,0.0032000826,0.002727563,0.0003993871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006414087,0.0014425664,0.0015962496,0.0022282095,0.0012850293,0.0028598215,0.0029841892,0.001884902,0.0030043086],"category_scores_gemma":[0.044034936,0.00047609842,0.0010338328,0.0025265336,0.0022069088,0.0052592554,0.0044533913,0.003694978,0.0013036806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072198693,0.00021849555,0.008487977,0.00034711373,0.00020596526,0.0002250974,0.00090653624,0.30610424,0.031033942,0.047422238,0.00872336,0.59560305],"study_design_scores_gemma":[0.00003766749,0.00014850289,0.002635149,0.00008203018,0.000059487527,0.00031725212,0.00015728202,0.90545267,0.025470614,0.057796624,0.007753163,0.00008955439],"about_ca_topic_score_codex":0.0035668805,"about_ca_topic_score_gemma":0.0035939456,"teacher_disagreement_score":0.006414087,"about_ca_system_score_codex":0.0022527387,"about_ca_system_score_gemma":0.0016019837,"threshold_uncertainty_score":0.03392136},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6152063,0.001802252,0.34927335,0.00083560904,0.0006085015,0.00019851088,0.01320901,0.015932795,0.0029336987],"genre_scores_gemma":[0.8753417,0.00046458392,0.10700803,0.00010703052,0.00012928531,0.00014123287,0.014008463,0.000097390905,0.002702264],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967253,0.000046014382,0.000028024619,0.00010489692,0.000091807124,0.0000568398],"domain_scores_gemma":[0.9994253,0.00013970208,0.00007080067,0.00006266093,0.00025255463,0.000049081358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003853835,0.0011137123,0.0008346348,0.0015283754,0.00020272624,0.00040564087,0.00062470953,0.0004863233,0.00078498834],"category_scores_gemma":[0.0015593509,0.00024362857,0.0005558853,0.00090586423,0.00009357995,0.0005494456,0.00045322478,0.0006398046,0.0013440882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011119545,0.0011505584,0.1168776,0.00029053245,0.0002857882,0.0005272603,0.00012422209,0.1342487,0.02815892,0.0006056102,0.023935674,0.6926832],"study_design_scores_gemma":[0.000015906704,0.00010621165,0.019775195,0.000012007216,0.00002158376,0.00007334382,0.000038953982,0.973464,0.0046170875,0.00043675452,0.0014250631,0.00001389322],"about_ca_topic_score_codex":0.009566842,"about_ca_topic_score_gemma":0.0092589855,"teacher_disagreement_score":0.009566842,"about_ca_system_score_codex":0.00030601514,"about_ca_system_score_gemma":0.00041778685,"threshold_uncertainty_score":0.019022346},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05082949,0.0024684712,0.92311466,0.002388052,0.0004231172,0.0006853371,0.0024140873,0.009040956,0.008635756],"genre_scores_gemma":[0.7628226,0.0009982336,0.21956335,0.0015685864,0.00046512127,0.0005355492,0.003481691,0.00057337806,0.009991417],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.993739,0.001267142,0.00040969823,0.0012678781,0.0023143736,0.0010019231],"domain_scores_gemma":[0.99084294,0.002349466,0.0007665455,0.0048749275,0.0007340619,0.00043196505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036285333,0.0009776684,0.0027495604,0.0012905949,0.0017146212,0.002827192,0.005091053,0.0025569338,0.005219447],"category_scores_gemma":[0.01735085,0.00056011067,0.0015422733,0.0028926875,0.0018927635,0.005398949,0.010551853,0.0028123227,0.002251332],"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":[0.0061271177,0.0008504271,0.009689171,0.0008741506,0.00055428245,0.0022076457,0.001201721,0.15403947,0.037099365,0.16939878,0.06458359,0.55337435],"study_design_scores_gemma":[0.00045993994,0.0004709508,0.001920761,0.00006692025,0.00012355915,0.0013594411,0.0003498658,0.82549334,0.013228759,0.13550885,0.02088798,0.00012973981],"about_ca_topic_score_codex":0.003911051,"about_ca_topic_score_gemma":0.005087579,"teacher_disagreement_score":0.005219447,"about_ca_system_score_codex":0.0019933432,"about_ca_system_score_gemma":0.0047827633,"threshold_uncertainty_score":0.019189775},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01830202,0.0007684834,0.9791607,0.00027385965,0.00005090793,0.000019039944,0.00006496182,0.00030884892,0.001051038],"genre_scores_gemma":[0.67092866,0.0011137931,0.31965816,0.00047829785,0.00017083129,0.00013101037,0.0005467245,0.0002737058,0.006698874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993474,0.00029376592,0.000036509082,0.00013393088,0.00012332502,0.000065120235],"domain_scores_gemma":[0.99655366,0.0026018475,0.00018431935,0.00026774095,0.00031710812,0.00007535132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023048269,0.00091079087,0.00090836815,0.0006291053,0.00039868598,0.00084531464,0.0015399143,0.0012063552,0.0012447502],"category_scores_gemma":[0.006613773,0.0007260601,0.00088844594,0.0005686678,0.0010409019,0.001272356,0.0012315635,0.0024091122,0.00033149755],"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":[0.000059595888,0.00003448711,0.0015875429,0.00006872842,0.000110367386,0.000065358596,0.00007732194,0.8972396,0.0025151204,0.0377343,0.00103656,0.059471026],"study_design_scores_gemma":[0.000001441303,0.000005759799,0.00006003594,0.0000037113866,0.0000032706175,0.000006136018,0.000001896513,0.9943718,0.00028095391,0.0050619887,0.00020031686,0.0000027203678],"about_ca_topic_score_codex":0.005123549,"about_ca_topic_score_gemma":0.005418557,"teacher_disagreement_score":0.005123549,"about_ca_system_score_codex":0.0009836307,"about_ca_system_score_gemma":0.000812966,"threshold_uncertainty_score":0.012189209},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029423926,0.00027910597,0.9629801,0.0005498331,0.00018341385,0.0001356976,0.00017076952,0.0045146057,0.0017625672],"genre_scores_gemma":[0.76140803,0.00020594159,0.2307419,0.00051033334,0.00011457523,0.00018737502,0.00064992026,0.0003071302,0.0058749127],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984255,0.0004445148,0.00005620005,0.0003334772,0.0005787431,0.00016151807],"domain_scores_gemma":[0.9942135,0.0038197949,0.00032293375,0.0011014362,0.00041183026,0.00013037815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024676356,0.0010511451,0.00089532003,0.00071405695,0.00048010517,0.00063418533,0.001938946,0.0011935589,0.0019325643],"category_scores_gemma":[0.009692582,0.00041493864,0.0005545436,0.00035764492,0.0013691728,0.0017231076,0.0035232576,0.0023899283,0.00045488164],"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":[0.00047191203,0.00014569951,0.0012946078,0.0000879765,0.00012028862,0.00014002193,0.000072980445,0.8582683,0.010877323,0.012938846,0.006732176,0.108849846],"study_design_scores_gemma":[0.000006391148,0.00002362747,0.00006974286,0.0000028853572,0.000004244701,0.000016299355,0.000004274118,0.9945328,0.001996178,0.0030137855,0.00032728075,0.0000024614728],"about_ca_topic_score_codex":0.0028713604,"about_ca_topic_score_gemma":0.0037232472,"teacher_disagreement_score":0.0028713604,"about_ca_system_score_codex":0.00090983586,"about_ca_system_score_gemma":0.0010371909,"threshold_uncertainty_score":0.013050258},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09293148,0.00029944116,0.90347004,0.00045155836,0.000054037213,0.00005370733,0.000107741565,0.0006720075,0.0019600552],"genre_scores_gemma":[0.91283214,0.0002467146,0.084435984,0.0001838926,0.00006371694,0.000059568494,0.00035386052,0.00007619697,0.0017478593],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786276,0.0007109547,0.00009728563,0.000448426,0.00069908245,0.0001815018],"domain_scores_gemma":[0.99361104,0.0032508064,0.000841092,0.0014258598,0.00058454135,0.00028663472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002165655,0.0007194836,0.00082018133,0.0010106436,0.00046986208,0.0011080513,0.00096730894,0.0011511149,0.0008874489],"category_scores_gemma":[0.012626863,0.0002926613,0.00063962414,0.0004841473,0.0019730895,0.002207663,0.0023989081,0.0020961193,0.00040565056],"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":[0.00035429894,0.0001948387,0.017364105,0.00014544796,0.0001594052,0.00035714655,0.00028579956,0.6621447,0.032035906,0.069645286,0.0033223785,0.2139907],"study_design_scores_gemma":[0.00000220971,0.00004665844,0.00085000915,0.000008633315,0.0000041816515,0.00011553825,0.000030512685,0.98171896,0.005061674,0.011525609,0.00062525214,0.0000107769565],"about_ca_topic_score_codex":0.0008269784,"about_ca_topic_score_gemma":0.00067260023,"teacher_disagreement_score":0.002165655,"about_ca_system_score_codex":0.0005967959,"about_ca_system_score_gemma":0.0006788662,"threshold_uncertainty_score":0.011453271},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052708205,0.0006420754,0.942097,0.00062495435,0.000054406733,0.000052051055,0.00012365078,0.0008255064,0.0028721138],"genre_scores_gemma":[0.8050798,0.000714739,0.18409742,0.00065214816,0.00007848985,0.00010891958,0.0005618237,0.00028495418,0.008421753],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918944,0.00024241625,0.000034520977,0.0002838802,0.00015818707,0.0000915623],"domain_scores_gemma":[0.9978588,0.0010966635,0.00024359903,0.00043893576,0.000246067,0.000115872026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022438928,0.001420746,0.001208879,0.00060604594,0.00059563236,0.0009923928,0.0020554448,0.0018475358,0.0015845689],"category_scores_gemma":[0.0045930175,0.0005912241,0.00073825335,0.00064445933,0.0019978143,0.002517229,0.0023876305,0.002936046,0.00054285204],"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":[0.00010089639,0.00004292036,0.00062931236,0.00004551653,0.00004059466,0.000047883135,0.000061294704,0.94944805,0.0026104192,0.0182432,0.0010327977,0.02769711],"study_design_scores_gemma":[0.00000386991,0.000020184789,0.000080828686,0.0000065078207,0.000004071446,0.000013844242,0.000005652427,0.99347734,0.00089312554,0.0051665427,0.0003226454,0.0000053944377],"about_ca_topic_score_codex":0.004415705,"about_ca_topic_score_gemma":0.00488258,"teacher_disagreement_score":0.004415705,"about_ca_system_score_codex":0.0014690736,"about_ca_system_score_gemma":0.0009996912,"threshold_uncertainty_score":0.011866987},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3321868,0.0009742061,0.661582,0.00028477845,0.000102418075,0.00008039078,0.00046772617,0.0031195888,0.0012021502],"genre_scores_gemma":[0.8957769,0.00034017835,0.10077554,0.00007853339,0.00006920555,0.00004035415,0.0012887055,0.000058756166,0.0015717504],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941266,0.00007723148,0.00003197185,0.0002159983,0.00018011156,0.0000820074],"domain_scores_gemma":[0.99925715,0.00021870618,0.00016986475,0.000097578246,0.00019045734,0.00006619554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006478297,0.00069633714,0.0008722337,0.0016938742,0.00023692806,0.00057984405,0.001053614,0.0009078628,0.0005373804],"category_scores_gemma":[0.0024616437,0.00022230315,0.00045279792,0.0012814886,0.00037677668,0.0010341879,0.00073136715,0.0008246943,0.00033481952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00081858726,0.000408318,0.023793448,0.00016025334,0.0001599007,0.000676568,0.0001466387,0.17930226,0.04631245,0.004133339,0.004221499,0.73986685],"study_design_scores_gemma":[0.0000048565307,0.00004775429,0.002817646,0.0000046598016,0.0000098202145,0.00016991525,0.000017710117,0.98733354,0.0070221955,0.0021041005,0.00046200224,0.0000058455266],"about_ca_topic_score_codex":0.005325765,"about_ca_topic_score_gemma":0.004629804,"teacher_disagreement_score":0.005325765,"about_ca_system_score_codex":0.0007326409,"about_ca_system_score_gemma":0.00062577595,"threshold_uncertainty_score":0.01058954},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03465178,0.044964105,0.83097565,0.031608388,0.003111882,0.0010867569,0.0021666791,0.00052863284,0.050906137],"genre_scores_gemma":[0.50671244,0.015839705,0.45565286,0.005039596,0.0069947047,0.0028777008,0.0014089867,0.00030998833,0.005164044],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8918447,0.044893466,0.020329809,0.010908871,0.030615814,0.0014073594],"domain_scores_gemma":[0.5963211,0.3209361,0.025052382,0.03416043,0.020076279,0.00345362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0808324,0.0032415495,0.004921116,0.021208718,0.002850151,0.015486408,0.005027486,0.009630077,0.006278182],"category_scores_gemma":[0.31753576,0.0022914165,0.0038850058,0.011449524,0.028550943,0.037329387,0.010432659,0.008309552,0.0017377887],"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":[0.00046623274,0.00017079631,0.011412278,0.0039605373,0.0011951447,0.0008813718,0.0013480224,0.0045329127,0.0023086239,0.85374516,0.0067777657,0.11320117],"study_design_scores_gemma":[0.00008685282,0.00019301163,0.0021213153,0.0012119106,0.00042335468,0.0019918927,0.0004251521,0.0058098994,0.0012658904,0.97076887,0.015541486,0.00016035474],"about_ca_topic_score_codex":0.0007134108,"about_ca_topic_score_gemma":0.00055901514,"teacher_disagreement_score":0.0808324,"about_ca_system_score_codex":0.003578136,"about_ca_system_score_gemma":0.0041281926,"threshold_uncertainty_score":0.42748767},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.072996095,0.000595194,0.923505,0.00026711286,0.000049174367,0.00006361415,0.00028739846,0.0012153669,0.0010210302],"genre_scores_gemma":[0.675493,0.00094630104,0.32025582,0.00021599552,0.000148753,0.00007961103,0.0009419257,0.0002537563,0.001664834],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930775,0.00011468333,0.000029649065,0.00021792689,0.000178189,0.0001517833],"domain_scores_gemma":[0.9988102,0.00032082177,0.00020734129,0.000355241,0.00021079271,0.00009571317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007202084,0.0010103303,0.0013286992,0.0019545448,0.00033958914,0.0014335581,0.0013036472,0.0014089107,0.00069267565],"category_scores_gemma":[0.0035867454,0.000664385,0.00080705783,0.0014011781,0.00093309063,0.0024724477,0.0023641584,0.0013061023,0.0011805986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008347849,0.00019702491,0.012713712,0.00053637126,0.0002287424,0.001614496,0.0007550199,0.12226209,0.31415933,0.011341803,0.009510867,0.52584577],"study_design_scores_gemma":[0.00002130645,0.00015333433,0.008201039,0.000043675074,0.00004207423,0.00088683836,0.00027872468,0.9430984,0.029401219,0.0134415915,0.004399811,0.000031943553],"about_ca_topic_score_codex":0.0024495556,"about_ca_topic_score_gemma":0.0027181958,"teacher_disagreement_score":0.0024495556,"about_ca_system_score_codex":0.0003821857,"about_ca_system_score_gemma":0.0006772543,"threshold_uncertainty_score":0.004870653},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008533163,0.00019466422,0.98963505,0.0001952145,0.000035491717,0.00002590673,0.00006540351,0.0007112855,0.00060377154],"genre_scores_gemma":[0.3896682,0.00056732254,0.6010439,0.00031124888,0.00012608255,0.00026571736,0.0008538311,0.00056698854,0.0065966495],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99778205,0.00092161313,0.00011298114,0.00045305022,0.0005485304,0.00018169371],"domain_scores_gemma":[0.9952732,0.0024873863,0.00024934928,0.0008354736,0.0010419142,0.00011259737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052818675,0.00126482,0.0012197475,0.0014808316,0.0005593949,0.0019502838,0.0018566003,0.0021399162,0.0025128222],"category_scores_gemma":[0.014709665,0.00073573407,0.0012532228,0.000796889,0.00094248354,0.0023111305,0.0018346763,0.0030312713,0.0017113783],"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":[0.00023284367,0.00024699903,0.004871823,0.00017858192,0.00024007846,0.00022558664,0.0002492981,0.3604066,0.008168201,0.052546266,0.008841314,0.56379247],"study_design_scores_gemma":[0.0000069825705,0.000035988523,0.0004488895,0.0000209494,0.000016729833,0.00006110835,0.000021748057,0.96572787,0.0022692385,0.02995952,0.001421341,0.00000953932],"about_ca_topic_score_codex":0.0037577727,"about_ca_topic_score_gemma":0.0035556701,"teacher_disagreement_score":0.0052818675,"about_ca_system_score_codex":0.0011825147,"about_ca_system_score_gemma":0.0011988162,"threshold_uncertainty_score":0.027933538},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0549587,0.00018845944,0.92564297,0.00045926566,0.00013802352,0.00023255349,0.0011721364,0.015561093,0.0016468941],"genre_scores_gemma":[0.6320236,0.00024270473,0.36088127,0.00014173452,0.000050572053,0.0002607394,0.0035654812,0.00060428656,0.0022296258],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980878,0.00040652227,0.00015066838,0.00042938758,0.0007927669,0.00013289286],"domain_scores_gemma":[0.9964335,0.0008642446,0.00030446486,0.0011162711,0.0011031242,0.00017836406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001974057,0.0007591307,0.0005100316,0.002106941,0.00046491847,0.0012183394,0.0015446771,0.00056596467,0.001153942],"category_scores_gemma":[0.008599662,0.00033085095,0.0006191558,0.0015239125,0.0004104264,0.0023407193,0.0012066764,0.0012502422,0.00091729895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008157316,0.00070270343,0.047853652,0.0003232274,0.00019320643,0.0006495915,0.000973102,0.110335045,0.034780234,0.019452494,0.019632218,0.76428884],"study_design_scores_gemma":[0.00002183074,0.00010492235,0.005740819,0.000023965267,0.000024789637,0.000309325,0.000110254,0.9415447,0.033791248,0.009010731,0.009269145,0.000048192593],"about_ca_topic_score_codex":0.0028605638,"about_ca_topic_score_gemma":0.0019915209,"teacher_disagreement_score":0.0028605638,"about_ca_system_score_codex":0.0008998197,"about_ca_system_score_gemma":0.0010632784,"threshold_uncertainty_score":0.010439932},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12380271,0.00038110776,0.8660369,0.002993557,0.00011003898,0.000091754955,0.0002794348,0.002061403,0.004243087],"genre_scores_gemma":[0.8903452,0.00015371873,0.10670649,0.00030180713,0.00012463432,0.000091373135,0.00043979398,0.0001389846,0.0016981047],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990119,0.00026917475,0.000052692638,0.0003395483,0.00022907002,0.000097720134],"domain_scores_gemma":[0.9950826,0.0030227506,0.00040598758,0.0008573031,0.00047652528,0.00015484239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013884181,0.00051136734,0.0005525155,0.00044574542,0.00041763636,0.0013546029,0.0011428262,0.0013137463,0.002596044],"category_scores_gemma":[0.00850231,0.00024043852,0.00047471307,0.00051941304,0.0011451511,0.0027275002,0.0014885496,0.0015832636,0.00039708533],"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":[0.00038214872,0.0003424847,0.016677355,0.00034067966,0.00017758158,0.00073302333,0.0005090459,0.5938756,0.015586078,0.09005645,0.011312449,0.270007],"study_design_scores_gemma":[0.000005861858,0.000016917196,0.0005490225,0.000007650796,0.000004681635,0.000032915756,0.000046557867,0.9589421,0.0020088155,0.037529867,0.0008517111,0.000003917286],"about_ca_topic_score_codex":0.0024124403,"about_ca_topic_score_gemma":0.00266384,"teacher_disagreement_score":0.002596044,"about_ca_system_score_codex":0.00078957283,"about_ca_system_score_gemma":0.0008526118,"threshold_uncertainty_score":0.008684635},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067723626,0.0003110968,0.9917788,0.00006890308,0.000021516858,0.00003493117,0.00005447429,0.0005726668,0.00038523576],"genre_scores_gemma":[0.3620187,0.00065840356,0.6331562,0.00015939445,0.00015549225,0.00034720206,0.00089295313,0.00015223355,0.002459403],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998123,0.0006557368,0.00015760466,0.0003648503,0.00059904275,0.0000996522],"domain_scores_gemma":[0.9961028,0.001898192,0.00047832492,0.00040485337,0.0010469289,0.00006889727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018016332,0.0011179349,0.0012677991,0.0016499661,0.00039966166,0.0009121174,0.00175815,0.0008997447,0.0009175739],"category_scores_gemma":[0.0057050954,0.0003842701,0.0008168069,0.0015059406,0.00063811196,0.0013626615,0.00083426357,0.0016353216,0.000688888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018275445,0.0003092415,0.0028821935,0.00023580165,0.0002541821,0.000094386116,0.00012577849,0.25078046,0.009406115,0.010757975,0.0032834478,0.7216877],"study_design_scores_gemma":[0.000007064891,0.000051516618,0.000535374,0.000009566111,0.000011365433,0.00004253091,0.000016459764,0.99038357,0.002999279,0.0050427127,0.0008857844,0.000014733881],"about_ca_topic_score_codex":0.0015327593,"about_ca_topic_score_gemma":0.0015060554,"teacher_disagreement_score":0.0018016332,"about_ca_system_score_codex":0.0005319838,"about_ca_system_score_gemma":0.00070993084,"threshold_uncertainty_score":0.0095281005},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0109906765,0.000115118695,0.98587,0.00013969485,0.000037119553,0.00004868778,0.00005356499,0.00066368724,0.0020814415],"genre_scores_gemma":[0.41143933,0.0001965175,0.58353496,0.000117489406,0.0000460578,0.00022652453,0.00031564856,0.00019485732,0.0039285626],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994879,0.00013708543,0.000034003973,0.00013534265,0.00014105832,0.00006458318],"domain_scores_gemma":[0.99947554,0.00021396122,0.000049310627,0.00004855359,0.00019037002,0.000022228181],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008621386,0.00072677317,0.0009515075,0.0008655583,0.0006070692,0.0011361917,0.0010532787,0.0008278563,0.0027518717],"category_scores_gemma":[0.0021601522,0.0003902274,0.00055896136,0.001047623,0.00039130976,0.0013255494,0.000803593,0.0008485556,0.0006166747],"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":[0.000103175415,0.00003445827,0.000810876,0.000038927763,0.000025836642,0.00003511237,0.000046560453,0.9030312,0.0014526877,0.0058469637,0.0017966265,0.086777546],"study_design_scores_gemma":[0.0000044150115,0.0000062271674,0.00005537055,0.0000011454152,0.0000017340259,0.0000043628634,0.0000035215419,0.9988482,0.00019764181,0.0006095639,0.00026596198,0.0000018637512],"about_ca_topic_score_codex":0.016345985,"about_ca_topic_score_gemma":0.008792659,"teacher_disagreement_score":0.016345985,"about_ca_system_score_codex":0.0011198663,"about_ca_system_score_gemma":0.0015562952,"threshold_uncertainty_score":0.032501638},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28389677,0.0010680823,0.70896035,0.00034093254,0.0001430385,0.00009861212,0.000361831,0.0036082927,0.0015221303],"genre_scores_gemma":[0.88292056,0.00032586115,0.11470505,0.000059719507,0.000060941402,0.000059164704,0.0006003298,0.000062622996,0.001205803],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999238,0.00016312179,0.0000662889,0.00023538692,0.00019337948,0.00010387764],"domain_scores_gemma":[0.9985146,0.0005922304,0.00027285644,0.00008890258,0.0004863318,0.00004511013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013158439,0.0009866111,0.00090713566,0.001527736,0.00022265463,0.00065589166,0.0008163965,0.00058611925,0.00049251324],"category_scores_gemma":[0.00398638,0.0002953508,0.0007169727,0.0009531653,0.00022295373,0.00095460156,0.00042699315,0.0008813664,0.00029950525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005764092,0.00042888397,0.018231876,0.00016004298,0.00030160203,0.00050677033,0.00015924551,0.38780174,0.031738605,0.0014563431,0.0030886377,0.5555499],"study_design_scores_gemma":[0.0000025377472,0.000037676604,0.0012758083,0.0000034616207,0.000011099191,0.000031019605,0.000008534249,0.9956624,0.002616315,0.00022701561,0.00011894523,0.000005196365],"about_ca_topic_score_codex":0.0071700797,"about_ca_topic_score_gemma":0.0052741114,"teacher_disagreement_score":0.0071700797,"about_ca_system_score_codex":0.00071290514,"about_ca_system_score_gemma":0.00037575385,"threshold_uncertainty_score":0.014256716},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0426858,0.0011300163,0.94859964,0.00020059092,0.000116866366,0.000110058245,0.00041545965,0.005978983,0.0007625617],"genre_scores_gemma":[0.5605019,0.00071038166,0.43360168,0.00018684924,0.0002371816,0.00014516682,0.0023272268,0.00028785292,0.0020018583],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852717,0.00017779358,0.00008121181,0.00047942894,0.00057605014,0.00015838281],"domain_scores_gemma":[0.9983632,0.00040616817,0.0002986659,0.00028382457,0.00056819327,0.00008007236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010584298,0.001310159,0.001994408,0.002725741,0.0003087228,0.000837973,0.0021886774,0.0008949984,0.0005913681],"category_scores_gemma":[0.0030968795,0.00036768627,0.0012180049,0.0018373206,0.00045586028,0.0016001778,0.0011574384,0.0013900608,0.0005760139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036164117,0.00038733808,0.011098116,0.0001694805,0.0003165609,0.00019146869,0.000076892764,0.042885847,0.037342206,0.0021790003,0.0056884866,0.89930296],"study_design_scores_gemma":[0.000019826939,0.0001185313,0.004896148,0.000012279613,0.00006178326,0.00029751263,0.000023519846,0.96935284,0.021279365,0.0021651203,0.0017434177,0.000029650948],"about_ca_topic_score_codex":0.005087007,"about_ca_topic_score_gemma":0.0054075154,"teacher_disagreement_score":0.005087007,"about_ca_system_score_codex":0.0007130176,"about_ca_system_score_gemma":0.00086046354,"threshold_uncertainty_score":0.010114789},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043825326,0.00015806522,0.9949238,0.000074173455,0.000015881322,0.000016322767,0.000011532199,0.00010888868,0.00030891935],"genre_scores_gemma":[0.63524395,0.0005332412,0.35916215,0.00029000104,0.0002158406,0.00031192118,0.00019056143,0.00008878323,0.003963506],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979851,0.00090460625,0.000112859605,0.00036458237,0.00050331507,0.00012938147],"domain_scores_gemma":[0.9929509,0.0051224595,0.00047101104,0.00041929583,0.0009092826,0.00012710526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047942568,0.000841537,0.001586357,0.001138969,0.0005555067,0.0013180538,0.002312271,0.00161041,0.0012793513],"category_scores_gemma":[0.009559805,0.00052884093,0.0009353845,0.00095831003,0.0011529531,0.0020683962,0.0012832101,0.002299968,0.00039994804],"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":[0.00025215372,0.00019952234,0.0020980604,0.0001406687,0.00014962014,0.00011504905,0.00016687163,0.674126,0.0044940785,0.04404599,0.0017617771,0.27245018],"study_design_scores_gemma":[0.0000026240969,0.000015696234,0.00006304689,0.0000027681172,0.0000039552233,0.000010990505,0.0000025445313,0.9960645,0.0003888085,0.0032776154,0.00016443526,0.000002990894],"about_ca_topic_score_codex":0.0013711642,"about_ca_topic_score_gemma":0.0013689068,"teacher_disagreement_score":0.0047942568,"about_ca_system_score_codex":0.0009500812,"about_ca_system_score_gemma":0.0008506694,"threshold_uncertainty_score":0.025354743},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031574585,0.00017678551,0.96635103,0.0002182102,0.000038235063,0.000038015784,0.00008119939,0.0010221034,0.00049991993],"genre_scores_gemma":[0.7887489,0.00022137996,0.20753047,0.00025461923,0.00008523008,0.00008022845,0.000514742,0.00016027069,0.002404274],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982461,0.00044274403,0.00007679759,0.00054875785,0.0005169601,0.00016878887],"domain_scores_gemma":[0.9960337,0.0021090184,0.0006200508,0.0006459152,0.0004285646,0.00016277682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017330701,0.0011420214,0.0010034048,0.00094030844,0.0003509215,0.00076155836,0.0014263564,0.0011085205,0.00058825343],"category_scores_gemma":[0.006970519,0.00043520404,0.00082402973,0.00068458845,0.001601697,0.0017567271,0.0018290702,0.0026064804,0.00025191327],"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":[0.0003213964,0.00012039338,0.0050767884,0.00006354373,0.00014538094,0.00029259553,0.0001410336,0.7814464,0.015990421,0.01416434,0.0032610777,0.17897663],"study_design_scores_gemma":[0.0000031182217,0.000019286117,0.00024315303,0.000002749017,0.0000038328494,0.000047225018,0.0000058088513,0.99315506,0.0027466223,0.0034937803,0.00027372583,0.0000054797943],"about_ca_topic_score_codex":0.0030252393,"about_ca_topic_score_gemma":0.0030307136,"teacher_disagreement_score":0.0030252393,"about_ca_system_score_codex":0.0011375314,"about_ca_system_score_gemma":0.00091706956,"threshold_uncertainty_score":0.009165406},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87216324,0.00016608839,0.12620866,0.000062840045,0.000034924655,0.000032157503,0.00017752804,0.0005839274,0.000570719],"genre_scores_gemma":[0.9777103,0.000052701926,0.021960326,0.000006912572,0.000007646472,0.000011406169,0.000101589685,0.000012522903,0.00013643737],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957186,0.00008557017,0.00003340657,0.000094912946,0.00016989443,0.00004433056],"domain_scores_gemma":[0.99907196,0.00037491892,0.00021008763,0.00007651591,0.00021250895,0.000054066382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048463096,0.0005389061,0.0003839951,0.0014020797,0.00014354853,0.0003274104,0.0003947325,0.0003593836,0.00039756653],"category_scores_gemma":[0.0020468924,0.00012230025,0.00018004139,0.001159344,0.0002068573,0.00047595162,0.00033572232,0.00028009078,0.00014739677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010776676,0.0004580375,0.13688546,0.00034102678,0.00023107055,0.0013307526,0.000538461,0.20015588,0.18511091,0.0011852083,0.0013628842,0.47132266],"study_design_scores_gemma":[0.000007220214,0.0001629017,0.036864597,0.000009350854,0.000020427744,0.00027991415,0.00011252621,0.92795956,0.033633616,0.00048792042,0.00044407768,0.000017843482],"about_ca_topic_score_codex":0.0011325042,"about_ca_topic_score_gemma":0.00097508845,"teacher_disagreement_score":0.0014020797,"about_ca_system_score_codex":0.00022170645,"about_ca_system_score_gemma":0.00011848118,"threshold_uncertainty_score":0.0025629997},"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 & 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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062741816,0.00012462272,0.93503743,0.00014650196,0.00003466828,0.00005150359,0.00006989781,0.0013675417,0.00042601133],"genre_scores_gemma":[0.70696896,0.00013480705,0.28981665,0.00020872553,0.0000978428,0.000110247296,0.0005530579,0.0002480796,0.0018616114],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889034,0.0002117282,0.000053522752,0.00040756562,0.00033147144,0.000105442865],"domain_scores_gemma":[0.9974679,0.000765526,0.00045895722,0.0005564502,0.0006323078,0.00011885528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010489323,0.000718384,0.0010439397,0.0011253405,0.00029759933,0.0007481702,0.0016499526,0.001108039,0.00047850155],"category_scores_gemma":[0.003945632,0.00037053495,0.0009360545,0.0006942906,0.00091129116,0.0011395058,0.001106682,0.0011650204,0.00034098836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044686053,0.0003343309,0.012801664,0.000158784,0.00018889012,0.00033395615,0.0002668718,0.45757365,0.055073872,0.0059358403,0.003976004,0.46290934],"study_design_scores_gemma":[0.0000060957177,0.000039460774,0.00065482856,0.0000034850939,0.0000068791187,0.00010568694,0.000012932219,0.99040484,0.006551865,0.0017864504,0.000419929,0.000007643812],"about_ca_topic_score_codex":0.0012876347,"about_ca_topic_score_gemma":0.002246855,"teacher_disagreement_score":0.0016499526,"about_ca_system_score_codex":0.00053384877,"about_ca_system_score_gemma":0.00058589195,"threshold_uncertainty_score":0.0055474043},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063476562,0.00013540362,0.991032,0.000058401827,0.0000705967,0.00008410425,0.0001406292,0.0014517034,0.0006796559],"genre_scores_gemma":[0.11270796,0.00019327416,0.87954974,0.00008249093,0.000070459515,0.0002751526,0.00033472962,0.00015323256,0.006632958],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990101,0.000111283414,0.000058658236,0.00032680636,0.0004363146,0.000056773428],"domain_scores_gemma":[0.9986284,0.00045235967,0.00015655512,0.0002625067,0.00042730596,0.00007302136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007839156,0.0009907811,0.000936145,0.0020965282,0.0004817106,0.0009270016,0.0012760218,0.0013563068,0.0045703636],"category_scores_gemma":[0.0021021343,0.0003590637,0.00066747156,0.0008051022,0.00052178843,0.0007911982,0.0011585547,0.0010687802,0.002836662],"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":[0.00072109693,0.00021668093,0.005212318,0.00041503887,0.00014336097,0.00016816746,0.0000926629,0.007226849,0.19738749,0.008035244,0.0034642362,0.776917],"study_design_scores_gemma":[0.0002325851,0.0006834384,0.010577108,0.00006976448,0.00033236115,0.0032220783,0.00012396082,0.72025615,0.22261265,0.016270688,0.025450911,0.0001683706],"about_ca_topic_score_codex":0.0011760416,"about_ca_topic_score_gemma":0.0015952832,"teacher_disagreement_score":0.0045703636,"about_ca_system_score_codex":0.00033310533,"about_ca_system_score_gemma":0.0010905644,"threshold_uncertainty_score":0.015289366},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005355458,0.00047990863,0.95699954,0.00011364389,0.000134511,0.00012115016,0.00078520697,0.034876186,0.0011343907],"genre_scores_gemma":[0.047794264,0.00043432662,0.9411015,0.00014607233,0.00008767404,0.00017260197,0.0027766277,0.0018440101,0.0056428527],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993123,0.00007069795,0.000031983705,0.00015049052,0.0003719242,0.00006274653],"domain_scores_gemma":[0.9994646,0.00018357244,0.000036645437,0.00012769003,0.00014788829,0.000039599367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072989863,0.001648457,0.0013964782,0.0018557718,0.0004388809,0.0013668687,0.002341461,0.0013653283,0.009102762],"category_scores_gemma":[0.0020116714,0.0006820061,0.001007883,0.0011925078,0.0004321202,0.0012752102,0.0019903982,0.001491589,0.0045636976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044698812,0.000102148166,0.00071754237,0.00023130246,0.00012550918,0.00020823644,0.00006510592,0.011118984,0.041481573,0.0029141672,0.039449904,0.90313864],"study_design_scores_gemma":[0.00006442371,0.00022725704,0.0017510175,0.000035014637,0.00006479315,0.0011999299,0.000050936524,0.8942079,0.063384205,0.012921196,0.026032684,0.000060644106],"about_ca_topic_score_codex":0.002246232,"about_ca_topic_score_gemma":0.004202286,"teacher_disagreement_score":0.009102762,"about_ca_system_score_codex":0.00038904752,"about_ca_system_score_gemma":0.0006706038,"threshold_uncertainty_score":0.030451775},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0501041,0.0004876009,0.9454442,0.000120374374,0.00007990356,0.00005104492,0.00032999838,0.0020824668,0.0013003502],"genre_scores_gemma":[0.45270687,0.0004021708,0.542816,0.000099469224,0.00009695583,0.000065643275,0.001383774,0.00018532726,0.002243703],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99878913,0.0002050015,0.00006371859,0.0001931767,0.00061476027,0.00013431755],"domain_scores_gemma":[0.99752015,0.0007743526,0.00020037968,0.00031500048,0.001106645,0.00008350821],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011780541,0.00049724337,0.0010974105,0.002494877,0.00040690752,0.0010077966,0.0011263582,0.000694235,0.0017146701],"category_scores_gemma":[0.0036580663,0.0002098791,0.0007443275,0.002863506,0.00028401654,0.0019528172,0.0009274499,0.0007745333,0.0009554537],"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":[0.0007663578,0.00030197084,0.00996675,0.00027601185,0.0002179859,0.00029884782,0.00025283697,0.13493091,0.045210306,0.010715275,0.007666342,0.7893964],"study_design_scores_gemma":[0.000011946902,0.00006620185,0.00221459,0.000008446106,0.000028564855,0.0002236084,0.000029693318,0.98351926,0.0081164045,0.0034462332,0.0023185057,0.000016569873],"about_ca_topic_score_codex":0.0026941153,"about_ca_topic_score_gemma":0.002124293,"teacher_disagreement_score":0.0026941153,"about_ca_system_score_codex":0.0003878505,"about_ca_system_score_gemma":0.00062657206,"threshold_uncertainty_score":0.006230235},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2386893,0.0044039395,0.6278946,0.0025949527,0.0012689751,0.0013025379,0.084647395,0.025946224,0.0132520795],"genre_scores_gemma":[0.4051655,0.0019125639,0.46709678,0.0006812813,0.00041973282,0.0010078311,0.114781454,0.001986805,0.006948032],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.991856,0.00056322076,0.0014577828,0.0017500351,0.0035301617,0.0008428861],"domain_scores_gemma":[0.9815088,0.0033514446,0.003642463,0.004932752,0.0058912057,0.00067339285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031160156,0.0012365516,0.0019668506,0.012145235,0.0019171641,0.0048908866,0.0037261562,0.002113515,0.0024108249],"category_scores_gemma":[0.023551662,0.0005592388,0.0017119144,0.013051541,0.0014877829,0.0056011667,0.0032823402,0.0022937253,0.0045217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016231912,0.0006128091,0.21103272,0.00150401,0.00021481488,0.0019921847,0.0034682213,0.014301128,0.04287183,0.03285671,0.086728804,0.6027935],"study_design_scores_gemma":[0.00017009818,0.0006688809,0.109075114,0.0009727864,0.0004146897,0.004195668,0.009084595,0.30296725,0.08040569,0.1480805,0.3433533,0.00061142445],"about_ca_topic_score_codex":0.007900131,"about_ca_topic_score_gemma":0.011106668,"teacher_disagreement_score":0.012145235,"about_ca_system_score_codex":0.0015239924,"about_ca_system_score_gemma":0.0022795398,"threshold_uncertainty_score":0.016479254},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07903954,0.000344095,0.91847456,0.00009422984,0.000058584574,0.000027930151,0.00008195174,0.0013348674,0.0005443097],"genre_scores_gemma":[0.8580768,0.00016577666,0.14055064,0.00003776432,0.000045730445,0.00003887994,0.0002330662,0.00007987832,0.00077143684],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908185,0.00020564147,0.000054725115,0.00022400498,0.00034028757,0.000093549774],"domain_scores_gemma":[0.99874276,0.0005287592,0.00020732966,0.000118085576,0.0003586126,0.000044523887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009150595,0.0007354271,0.00078075944,0.0013633657,0.00038938626,0.000687591,0.0009116782,0.0006155528,0.0005279885],"category_scores_gemma":[0.0031082295,0.0003370381,0.00063541555,0.0011533694,0.0004009639,0.0011361446,0.00075111917,0.001270176,0.0002456546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011558791,0.0005569878,0.020186841,0.00016885913,0.0003942835,0.000310305,0.00017735313,0.31795335,0.04432315,0.0038096968,0.0023620813,0.6086013],"study_design_scores_gemma":[0.000003972393,0.000027183713,0.0018630817,0.000002579463,0.00001283431,0.000041451593,0.000008406444,0.99516726,0.0019267069,0.00081239705,0.00012766967,0.000006463567],"about_ca_topic_score_codex":0.002813449,"about_ca_topic_score_gemma":0.0036769558,"teacher_disagreement_score":0.002813449,"about_ca_system_score_codex":0.00039311888,"about_ca_system_score_gemma":0.0005439695,"threshold_uncertainty_score":0.005594194},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056637775,0.0010352634,0.9329162,0.00049992505,0.00029585758,0.00030240318,0.0023323246,0.002907031,0.0030731596],"genre_scores_gemma":[0.41369882,0.0007280725,0.5765813,0.00017180292,0.00011844676,0.00044386348,0.004228702,0.00017608856,0.0038528536],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861693,0.0003602751,0.00018825226,0.0002846645,0.0004304919,0.00011946863],"domain_scores_gemma":[0.9983381,0.0007379561,0.0002360139,0.00019122413,0.00044598707,0.000050731676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009368196,0.0009189309,0.0006206932,0.0027114013,0.0005351635,0.0019845993,0.0012032762,0.0010021786,0.0033087533],"category_scores_gemma":[0.0057111173,0.00021101923,0.00079565716,0.0034599197,0.00045061854,0.0038721468,0.0010490647,0.0009077633,0.001514206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008185271,0.00031409823,0.0036632817,0.0005485336,0.00010402297,0.00041129484,0.0007978761,0.05266603,0.02801876,0.03604814,0.010598975,0.8660105],"study_design_scores_gemma":[0.000062166946,0.00067991286,0.0051589417,0.00016303473,0.000118876866,0.0007123218,0.000856936,0.9008471,0.014915215,0.045648217,0.030666493,0.0001707503],"about_ca_topic_score_codex":0.0052359723,"about_ca_topic_score_gemma":0.0037551192,"teacher_disagreement_score":0.0052359723,"about_ca_system_score_codex":0.0007245661,"about_ca_system_score_gemma":0.0010557299,"threshold_uncertainty_score":0.011068821},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39639232,0.00054332416,0.5942053,0.0003676063,0.00014429819,0.00015371408,0.00039060143,0.0059417384,0.0018610683],"genre_scores_gemma":[0.91357505,0.000086286345,0.0852017,0.000047902402,0.000019278938,0.00005932885,0.00029688372,0.00003652385,0.00067710335],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915874,0.00013166347,0.00006517199,0.0002414299,0.00028091716,0.00012220553],"domain_scores_gemma":[0.9975101,0.0010206652,0.0004007616,0.00031526596,0.00065665436,0.0000966145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011210846,0.00067020964,0.0007858325,0.0011141851,0.00079493754,0.00072119257,0.0010889133,0.00055619644,0.0005454414],"category_scores_gemma":[0.0034544426,0.00022951678,0.0004143491,0.00076723896,0.00051649497,0.001190622,0.0005262829,0.0006300173,0.00021447652],"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":[0.00045298826,0.000533759,0.02492391,0.00018794222,0.00011721171,0.00042895734,0.00023122909,0.57520336,0.015097416,0.0013480716,0.0032968142,0.3781783],"study_design_scores_gemma":[0.0000061806995,0.000043387878,0.0018801058,0.000004432951,0.0000059489166,0.000033861936,0.000050463663,0.99200565,0.0044220025,0.0012431055,0.00029664487,0.000008166409],"about_ca_topic_score_codex":0.0076095597,"about_ca_topic_score_gemma":0.005528347,"teacher_disagreement_score":0.0076095597,"about_ca_system_score_codex":0.00086272456,"about_ca_system_score_gemma":0.0008142634,"threshold_uncertainty_score":0.01513052},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026339987,0.00034284857,0.9695802,0.00014540718,0.00007232541,0.000026910453,0.00018836299,0.002375359,0.00092861074],"genre_scores_gemma":[0.5200587,0.000390852,0.46931964,0.00025040947,0.000170628,0.00015717131,0.0021011375,0.00046656202,0.007084793],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938095,0.0001329879,0.000034956527,0.00021311501,0.00013169224,0.000106422594],"domain_scores_gemma":[0.99808407,0.0011082473,0.000094030744,0.0002283025,0.0003733631,0.00011206764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001081264,0.00068157143,0.0013573808,0.0010099915,0.0006153,0.0010951097,0.0024951217,0.0014923249,0.0031241935],"category_scores_gemma":[0.0040057,0.0007471969,0.0009428733,0.0011069978,0.00059958186,0.001739746,0.0016421145,0.0022236598,0.0021975331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044183765,0.00022963173,0.004647157,0.00010602759,0.00012882636,0.00017101878,0.00020998169,0.21981695,0.016130377,0.007513709,0.006095371,0.74450916],"study_design_scores_gemma":[0.0000031751285,0.000013481581,0.0002618221,0.0000039707697,0.0000048914853,0.000023146999,0.000010964556,0.9946126,0.001415609,0.003348073,0.00029620077,0.000006073571],"about_ca_topic_score_codex":0.006728335,"about_ca_topic_score_gemma":0.012263376,"teacher_disagreement_score":0.006728335,"about_ca_system_score_codex":0.0008026845,"about_ca_system_score_gemma":0.0008741282,"threshold_uncertainty_score":0.013378322},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1438171,0.000776649,0.8512508,0.00040426018,0.0001407013,0.00007489891,0.00024425576,0.0021734037,0.0011179298],"genre_scores_gemma":[0.89957887,0.0002667571,0.09697376,0.00019592133,0.00010007761,0.00009573674,0.00068793783,0.00008198088,0.0020189262],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993262,0.00015765267,0.000030136207,0.00023670553,0.00012392757,0.00012543492],"domain_scores_gemma":[0.99957603,0.00013958929,0.00006982318,0.00006087624,0.00010745423,0.000046201294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081266585,0.0010262849,0.0012698628,0.0010339913,0.00034738606,0.00068909716,0.0012213052,0.0007928926,0.00052046345],"category_scores_gemma":[0.0018609184,0.0003748687,0.0011867491,0.0006268023,0.00060462,0.00089055125,0.001133102,0.0010076578,0.00034277266],"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":[0.00038876632,0.000257947,0.01153055,0.00009461927,0.00019692908,0.00033769695,0.00029063417,0.54655993,0.016921585,0.0033637239,0.0056540086,0.41440362],"study_design_scores_gemma":[0.0000033220297,0.000022063226,0.00072115153,0.0000036709077,0.0000067977694,0.000033297703,0.000019900002,0.99589086,0.0014339642,0.0016137183,0.00024405781,0.000007173073],"about_ca_topic_score_codex":0.0059798765,"about_ca_topic_score_gemma":0.004089833,"teacher_disagreement_score":0.0059798765,"about_ca_system_score_codex":0.00060169597,"about_ca_system_score_gemma":0.0006249022,"threshold_uncertainty_score":0.011890113},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047349697,0.0007124764,0.98919976,0.0004689259,0.00008112897,0.000106073,0.00043585885,0.0014866493,0.002774169],"genre_scores_gemma":[0.2805987,0.002557919,0.6872146,0.00042317287,0.0001675167,0.00033330976,0.0029408382,0.00042142562,0.025342548],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983677,0.0003506751,0.00008495075,0.00051376194,0.0005494238,0.0001334975],"domain_scores_gemma":[0.99785703,0.00070268614,0.00025387682,0.0004580331,0.00063685636,0.00009156517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015334885,0.0015360299,0.0009555312,0.0020787448,0.00070823607,0.0018212628,0.0024104696,0.0013463584,0.0028151872],"category_scores_gemma":[0.0040649916,0.0006804376,0.001847199,0.0013932184,0.0007679043,0.0015431731,0.0012243744,0.0017625717,0.001667745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022077079,0.000547886,0.018512178,0.000611957,0.00043214764,0.00042708978,0.0006455995,0.21334334,0.023458749,0.06714654,0.013891425,0.6607624],"study_design_scores_gemma":[0.000008652679,0.000066473454,0.0032425676,0.00006414999,0.000065665,0.00022261517,0.00015226884,0.94272727,0.0105764605,0.02375225,0.019082543,0.00003908052],"about_ca_topic_score_codex":0.044184126,"about_ca_topic_score_gemma":0.05710719,"teacher_disagreement_score":0.044184126,"about_ca_system_score_codex":0.0017858444,"about_ca_system_score_gemma":0.0025304721,"threshold_uncertainty_score":0.08785385},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31799835,0.0011468986,0.67652804,0.00032037887,0.0001293606,0.00006060206,0.000276575,0.0018190797,0.0017207466],"genre_scores_gemma":[0.8760748,0.00032130396,0.12096138,0.00011825761,0.000077726574,0.00003555603,0.0005622215,0.000085545566,0.0017633141],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994367,0.000096562304,0.000030738265,0.00017879879,0.0001826441,0.00007456464],"domain_scores_gemma":[0.99852437,0.0008232885,0.00017024291,0.00015744806,0.0002605883,0.00006410912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010881257,0.0007434683,0.00077437784,0.0011655777,0.00025927144,0.00086915906,0.0008424713,0.00083183096,0.0005263466],"category_scores_gemma":[0.0031451478,0.00040624183,0.0009989657,0.0006625794,0.00045150385,0.0011202866,0.0009152696,0.0012135215,0.00030455115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055362,0.00021780796,0.011814172,0.00015763218,0.00032996206,0.00043995524,0.00024358094,0.44198376,0.04696511,0.0025703977,0.002060358,0.49266362],"study_design_scores_gemma":[0.000005550792,0.000049086644,0.0035923112,0.000007908717,0.000016548418,0.000094022325,0.00001938167,0.98890615,0.005836683,0.0010636142,0.00039779642,0.0000109399025],"about_ca_topic_score_codex":0.003071663,"about_ca_topic_score_gemma":0.004011114,"teacher_disagreement_score":0.003071663,"about_ca_system_score_codex":0.00058595417,"about_ca_system_score_gemma":0.0003969383,"threshold_uncertainty_score":0.0061075687},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28502482,0.0004912266,0.708497,0.00038801864,0.00014632719,0.00010593423,0.0017427109,0.0024135388,0.0011903937],"genre_scores_gemma":[0.89770865,0.00019571756,0.09798247,0.00005384045,0.00007351745,0.000054302687,0.002808206,0.000054643635,0.0010686014],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992322,0.000120016404,0.000053150052,0.00023714144,0.0002330336,0.00012454276],"domain_scores_gemma":[0.9984509,0.0005001899,0.00025904458,0.00021451889,0.000471233,0.00010420553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007300009,0.00071633584,0.00074396125,0.0027158223,0.00039148066,0.0007502622,0.0009946869,0.0005134984,0.000525879],"category_scores_gemma":[0.003119405,0.00023004213,0.0009085464,0.0015387103,0.00033686336,0.0010307054,0.0007643772,0.00087764923,0.00028874047],"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":[0.00065574003,0.00045041778,0.16086432,0.0001829238,0.0003295619,0.0010530086,0.000366787,0.4379966,0.01539683,0.0077962717,0.00811166,0.36679587],"study_design_scores_gemma":[0.0000059406243,0.000028196338,0.005121522,0.0000055037976,0.000015739446,0.00009225336,0.0000284222,0.9913298,0.0012273011,0.0014587982,0.00067461794,0.000011824577],"about_ca_topic_score_codex":0.021056421,"about_ca_topic_score_gemma":0.018139308,"teacher_disagreement_score":0.021056421,"about_ca_system_score_codex":0.0006913606,"about_ca_system_score_gemma":0.0014255194,"threshold_uncertainty_score":0.041867733},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005755293,0.00013158095,0.99343467,0.00009497108,0.000029921875,0.000014092597,0.00003189392,0.00031158593,0.00019599653],"genre_scores_gemma":[0.39641172,0.0004169956,0.5987163,0.00033661333,0.00015306215,0.00015068341,0.000585434,0.00017437407,0.0030547883],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984243,0.0002976276,0.00011937885,0.00037276073,0.0006823537,0.00010360989],"domain_scores_gemma":[0.99755484,0.0012784161,0.0002406342,0.00018794967,0.00063787715,0.00010027922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027429732,0.0008471948,0.001384057,0.0009750814,0.00049731607,0.0014205362,0.001363993,0.0013713908,0.0010361297],"category_scores_gemma":[0.007077987,0.000341936,0.00079709635,0.0008443668,0.0009745572,0.0015820708,0.0011794397,0.0016331582,0.0005838044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038389993,0.00023243563,0.002782104,0.00028396165,0.00017198098,0.00021475759,0.00015073051,0.4209949,0.030178307,0.019621436,0.004066735,0.5209188],"study_design_scores_gemma":[0.000008092494,0.000051470008,0.00036964446,0.0000072019934,0.000007838825,0.00005643997,0.000007813478,0.9925478,0.0032237687,0.002930263,0.00077410927,0.000015645337],"about_ca_topic_score_codex":0.0042642197,"about_ca_topic_score_gemma":0.0030694443,"teacher_disagreement_score":0.0042642197,"about_ca_system_score_codex":0.0010086909,"about_ca_system_score_gemma":0.0018161373,"threshold_uncertainty_score":0.0145064},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06432481,0.00040389536,0.9326145,0.00045337487,0.00004693524,0.00007566916,0.000041912528,0.0012283238,0.0008105627],"genre_scores_gemma":[0.9237832,0.00014599477,0.07469841,0.0001986853,0.000048409987,0.00010853415,0.00013233566,0.000052740084,0.00083176955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986137,0.00058834953,0.000074266725,0.0003232273,0.00025565235,0.00014471935],"domain_scores_gemma":[0.99482334,0.0035101648,0.00048212806,0.00027504066,0.00069084286,0.00021857147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032609657,0.0014433882,0.0018858425,0.0010575847,0.0005163178,0.0009660982,0.001882228,0.0010438821,0.0007086806],"category_scores_gemma":[0.008330628,0.000593198,0.0008821818,0.0006045875,0.000920204,0.0011811734,0.0011669213,0.0021612328,0.00027819164],"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":[0.00009508551,0.00013036073,0.002615322,0.000035201207,0.0000895202,0.00010101376,0.00005823324,0.9273471,0.0017048415,0.002328303,0.0007702886,0.064724766],"study_design_scores_gemma":[0.0000033577264,0.0000131291545,0.000047104197,0.0000010855285,0.0000033567437,0.000006512879,0.0000015485961,0.9991085,0.00017430758,0.00060889474,0.000030049638,0.0000021262374],"about_ca_topic_score_codex":0.0067032953,"about_ca_topic_score_gemma":0.004609115,"teacher_disagreement_score":0.0067032953,"about_ca_system_score_codex":0.0010394873,"about_ca_system_score_gemma":0.0013971698,"threshold_uncertainty_score":0.017245889},"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":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0126961125,0.0026582477,0.95292217,0.022423888,0.0016222787,0.00022358041,0.00095139875,0.0038501401,0.0026522614],"genre_scores_gemma":[0.3655902,0.0035059247,0.603319,0.0107138315,0.0026465913,0.00060767314,0.0038429499,0.0028797835,0.006894075],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.91639346,0.032264475,0.008106238,0.010972134,0.029824883,0.0024387718],"domain_scores_gemma":[0.5475173,0.27553946,0.020243151,0.11501076,0.039113853,0.0025754631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07504271,0.0013401154,0.0018737047,0.0033574654,0.0020407934,0.0087179085,0.0053613456,0.0031046455,0.008250373],"category_scores_gemma":[0.34553275,0.0017177836,0.0023480644,0.004443671,0.005624774,0.013719326,0.008858597,0.0105229225,0.0026286207],"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":[0.0013492646,0.00030394158,0.03078489,0.0018367243,0.0006904826,0.0016490265,0.0036947012,0.039291628,0.010437591,0.22656302,0.06973614,0.6136626],"study_design_scores_gemma":[0.00019498251,0.00048332376,0.00794734,0.0020849274,0.00034174547,0.0029400983,0.0018725634,0.25392085,0.07065006,0.4926087,0.16657926,0.00037613517],"about_ca_topic_score_codex":0.0042857327,"about_ca_topic_score_gemma":0.002848395,"teacher_disagreement_score":0.07504271,"about_ca_system_score_codex":0.0037018147,"about_ca_system_score_gemma":0.0047124363,"threshold_uncertainty_score":0.39686853},"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":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000296201,0.011901811,0.00050401397,0.033460982,0.9520978,0.000018013949,0.000060464947,0.00007596976,0.001851448],"genre_scores_gemma":[0.00031755277,0.006348736,0.00025127296,0.010671689,0.976379,0.000020187932,0.00003664767,0.000055350054,0.0059194905],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9926547,0.0014609935,0.0007833063,0.00073095155,0.004057033,0.0003130682],"domain_scores_gemma":[0.9533703,0.018350368,0.0018365871,0.001393971,0.020410776,0.0046379967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01109205,0.0034108367,0.003932171,0.008279903,0.0036075844,0.01169112,0.0035837677,0.01565779,0.013888392],"category_scores_gemma":[0.030833151,0.0014449051,0.002871056,0.0036206963,0.0041403943,0.0054632816,0.0028740375,0.017019404,0.011211988],"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":[0.000015848227,0.000006145116,0.000017503804,0.00014921416,0.000017840985,0.000049801107,0.0000071416875,0.000018550556,0.000039727172,0.00030416588,0.9902586,0.009115618],"study_design_scores_gemma":[0.00004146221,0.000020735863,0.0002678507,0.00054245663,0.000061269,0.00025607826,0.000037101858,0.00024801734,0.000100457626,0.0032705746,0.99512887,0.000025059091],"about_ca_topic_score_codex":0.0018279465,"about_ca_topic_score_gemma":0.0069566607,"teacher_disagreement_score":0.01565779,"about_ca_system_score_codex":0.0029778755,"about_ca_system_score_gemma":0.0032590276,"threshold_uncertainty_score":0.058661103},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10514691,0.00044617805,0.8886476,0.0004339095,0.00026648835,0.000044766362,0.00021602177,0.0030975842,0.0017005402],"genre_scores_gemma":[0.8663211,0.000136365,0.13041058,0.00010111968,0.0000820472,0.00005402804,0.0003736458,0.00014861944,0.0023725156],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993305,0.000108546235,0.00003753727,0.00023679144,0.00017509898,0.00011150016],"domain_scores_gemma":[0.9977823,0.0009667742,0.00023757276,0.00029117576,0.0006148796,0.0001074467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010685017,0.000775398,0.0009183927,0.00094314024,0.0007051542,0.0012343028,0.001445627,0.001265672,0.0017039023],"category_scores_gemma":[0.0043070717,0.000384509,0.00056006684,0.0014247361,0.00073938654,0.001471577,0.0009881016,0.002344814,0.00054920616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005781321,0.0002527976,0.007989389,0.0001266793,0.00011479413,0.00034709054,0.00016649315,0.38943797,0.021187253,0.012262123,0.006668137,0.56086916],"study_design_scores_gemma":[0.0000019636495,0.0000099501385,0.0003784808,0.0000020858533,0.00000301785,0.000018901846,0.000005635952,0.99570376,0.0016063942,0.0021267359,0.00013916913,0.0000039402776],"about_ca_topic_score_codex":0.01035386,"about_ca_topic_score_gemma":0.008366619,"teacher_disagreement_score":0.01035386,"about_ca_system_score_codex":0.0012055472,"about_ca_system_score_gemma":0.0011692474,"threshold_uncertainty_score":0.020587206},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10508249,0.002469892,0.84895635,0.0015196854,0.0003456605,0.0002871119,0.020123702,0.017770372,0.0034447713],"genre_scores_gemma":[0.7909662,0.001002002,0.15809882,0.0005935338,0.00049235066,0.00077713793,0.03896024,0.0014746916,0.0076349583],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99729186,0.00096264516,0.00021941496,0.0007456953,0.00048281395,0.0002975614],"domain_scores_gemma":[0.98972845,0.006786855,0.0006983157,0.0012325128,0.0012517383,0.0003021428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031728216,0.0013425191,0.0011741247,0.0030739873,0.0006614027,0.002312677,0.0018954824,0.0016469134,0.0042231088],"category_scores_gemma":[0.018148066,0.0004504598,0.0018621525,0.001990363,0.0006731947,0.0031053498,0.0018016291,0.0025711812,0.003802693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002501299,0.0014026078,0.05466366,0.00154754,0.0010550498,0.001219456,0.0013357993,0.2533299,0.018853988,0.0700983,0.08047367,0.5135187],"study_design_scores_gemma":[0.000051249754,0.00009924257,0.0021824425,0.000044334738,0.0000504603,0.00020411354,0.00007202178,0.9511955,0.0017722881,0.039013214,0.0052739494,0.00004128048],"about_ca_topic_score_codex":0.0069805267,"about_ca_topic_score_gemma":0.008763897,"teacher_disagreement_score":0.0069805267,"about_ca_system_score_codex":0.0010879319,"about_ca_system_score_gemma":0.0013032393,"threshold_uncertainty_score":0.016779661},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007443881,0.8781285,0.06548814,0.0048353677,0.0019443299,0.00026561524,0.0015291779,0.0014042659,0.038960766],"genre_scores_gemma":[0.023998512,0.9279522,0.036596067,0.0016591946,0.0022203503,0.00014134243,0.0023992115,0.00021777143,0.004815251],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99885285,0.00015228796,0.00013317952,0.0002033594,0.000539266,0.000119069584],"domain_scores_gemma":[0.99789447,0.0011521424,0.00010559855,0.00013819685,0.0006104821,0.000099047895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012896655,0.0012793019,0.0008329647,0.005408085,0.0006868149,0.0034075803,0.001429254,0.0011514286,0.00519594],"category_scores_gemma":[0.0038291772,0.0006633827,0.00078058464,0.007238714,0.0006092269,0.0069515235,0.0015579335,0.0021678726,0.0030316128],"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":[0.00011377163,0.00012495142,0.0026171596,0.0058531314,0.00006684993,0.00024716943,0.00033960267,0.0021393017,0.0044902796,0.039677348,0.0756895,0.86864084],"study_design_scores_gemma":[0.000012244254,0.00014791316,0.0023421056,0.00343368,0.0000880583,0.0009912773,0.00042298206,0.010584344,0.0032520203,0.022203762,0.9564385,0.00008317831],"about_ca_topic_score_codex":0.0026481522,"about_ca_topic_score_gemma":0.0025017075,"teacher_disagreement_score":0.005408085,"about_ca_system_score_codex":0.00069629104,"about_ca_system_score_gemma":0.0013750676,"threshold_uncertainty_score":0.017382145},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040915307,0.0002740652,0.9573789,0.000120016484,0.00003284136,0.000029992418,0.000056218414,0.00059202657,0.00060055644],"genre_scores_gemma":[0.6425818,0.0007132603,0.3540819,0.00009696247,0.00005987127,0.000106604006,0.00042254498,0.00009606633,0.0018409115],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965334,0.00005417725,0.000034574838,0.00009292926,0.00012736155,0.00003755097],"domain_scores_gemma":[0.9995846,0.00016331772,0.0000703949,0.00007415569,0.00008866949,0.000018838788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049132365,0.0006798348,0.00055870146,0.00088910694,0.00028420237,0.000624019,0.0005516415,0.00048161825,0.000693583],"category_scores_gemma":[0.0012398803,0.00020593317,0.00059863995,0.001058514,0.00042835766,0.00090559624,0.0005647735,0.0010399603,0.0003524682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020512473,0.00025440304,0.0057814275,0.00030039134,0.000114358394,0.00034868147,0.00019519156,0.11524502,0.12773138,0.00947284,0.0015130816,0.7388381],"study_design_scores_gemma":[0.0000035776927,0.000063033774,0.0034073612,0.000011068582,0.000017299311,0.000089750945,0.00002957116,0.9658856,0.025719242,0.0036955564,0.0010630416,0.00001501008],"about_ca_topic_score_codex":0.0014458927,"about_ca_topic_score_gemma":0.0014865169,"teacher_disagreement_score":0.0014458927,"about_ca_system_score_codex":0.00030909388,"about_ca_system_score_gemma":0.00054954854,"threshold_uncertainty_score":0.0028749704},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059280902,0.00062724814,0.935422,0.0003590179,0.00010603499,0.0000612836,0.00031182572,0.0025785298,0.0012531786],"genre_scores_gemma":[0.821353,0.0004371386,0.17573035,0.00012829428,0.00006944462,0.00006322848,0.0006826436,0.00012060872,0.0014153164],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995221,0.00007547378,0.000028860777,0.0001264903,0.00015288958,0.00009422686],"domain_scores_gemma":[0.99955267,0.00009475132,0.00007802531,0.00007644308,0.00015474536,0.000043294553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007284866,0.001088345,0.00073864707,0.001715917,0.00032990164,0.0011001088,0.0008502059,0.00060670363,0.0013522885],"category_scores_gemma":[0.0019404729,0.00026895784,0.00095597643,0.0010036372,0.00041132548,0.0014084942,0.0012729391,0.0011726203,0.00029464628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033701182,0.00027052558,0.005117716,0.00013281663,0.00014215523,0.00028018883,0.00023037265,0.24108836,0.031604722,0.0057623,0.0071941703,0.70783955],"study_design_scores_gemma":[0.000004741045,0.000035676385,0.0016079404,0.000009503332,0.000016684036,0.00003633323,0.000037945996,0.98727536,0.007357982,0.002755031,0.0008507277,0.0000119977785],"about_ca_topic_score_codex":0.0056258193,"about_ca_topic_score_gemma":0.004454768,"teacher_disagreement_score":0.0056258193,"about_ca_system_score_codex":0.00073183316,"about_ca_system_score_gemma":0.000740633,"threshold_uncertainty_score":0.011186123},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03611106,0.00017291213,0.96077806,0.00023630075,0.000026293723,0.00007210553,0.00016448426,0.00040141787,0.0020373955],"genre_scores_gemma":[0.66227037,0.00024342995,0.33094028,0.00023275215,0.00006816457,0.00040268365,0.0011368749,0.00022324949,0.0044822027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845195,0.0009288173,0.000055071738,0.00023177998,0.00025078293,0.00008153945],"domain_scores_gemma":[0.99421304,0.004008453,0.00038903,0.0004888736,0.00072844,0.00017227429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003813641,0.0012354437,0.0010538704,0.0026384313,0.0005885322,0.0014267226,0.001401413,0.0009833243,0.0047257333],"category_scores_gemma":[0.014921411,0.00057023007,0.0011477536,0.0011826967,0.00089421764,0.0016704394,0.00286521,0.0015912668,0.00055231754],"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":[0.00016104979,0.000066463894,0.004693183,0.00008984199,0.00014117236,0.0001690118,0.000108660926,0.83277017,0.0013964113,0.04617408,0.002220122,0.11200978],"study_design_scores_gemma":[0.0000036842596,0.000011659792,0.00027265956,0.000008346851,0.0000051190405,0.000009531813,0.000006079487,0.9857303,0.00018592612,0.013438968,0.00032267655,0.0000050724657],"about_ca_topic_score_codex":0.0038191138,"about_ca_topic_score_gemma":0.0041660285,"teacher_disagreement_score":0.0047257333,"about_ca_system_score_codex":0.0012064725,"about_ca_system_score_gemma":0.0010416827,"threshold_uncertainty_score":0.020168722},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1940559,0.00087239826,0.7958531,0.00010527042,0.00015344942,0.00017097898,0.00056730426,0.0045289197,0.0036926162],"genre_scores_gemma":[0.74413717,0.00023287095,0.24821827,0.0000507711,0.0000926361,0.00010231777,0.002608561,0.0003997436,0.0041576237],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995572,0.0008138521,0.0003023027,0.0006891381,0.0022986587,0.0003241497],"domain_scores_gemma":[0.9923006,0.002215238,0.00072243565,0.00087374216,0.0036426622,0.00024525548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025981155,0.00094290206,0.0017415597,0.0033544367,0.0005963163,0.0018711702,0.001864036,0.0011371786,0.0020534543],"category_scores_gemma":[0.008869285,0.00025049035,0.00068551133,0.0029680734,0.0005638435,0.0016989326,0.0015181986,0.0006779504,0.0012909509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016363012,0.00054946705,0.015104554,0.00029368195,0.0003433213,0.00019457971,0.0001452696,0.06543631,0.071694545,0.0035151744,0.005733463,0.8353533],"study_design_scores_gemma":[0.000028025355,0.00043781791,0.0071569695,0.000015934229,0.00007131881,0.00038020036,0.00008645904,0.94413465,0.04389142,0.002168525,0.0015924,0.000036319285],"about_ca_topic_score_codex":0.0023020126,"about_ca_topic_score_gemma":0.0032536106,"teacher_disagreement_score":0.0033544367,"about_ca_system_score_codex":0.00080238056,"about_ca_system_score_gemma":0.000838891,"threshold_uncertainty_score":0.013740361},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1153667,0.0005623383,0.88156253,0.00016343954,0.000047823694,0.00003551001,0.00010051957,0.0016479061,0.0005131563],"genre_scores_gemma":[0.8703144,0.00016386887,0.12826906,0.000094486946,0.000037962374,0.000036152374,0.00020598047,0.00004770175,0.0008304015],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996145,0.00007078086,0.00002220631,0.00009686746,0.00012891498,0.00006679422],"domain_scores_gemma":[0.9993299,0.00029768367,0.00008355623,0.000083806655,0.0001700963,0.000034932185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004907556,0.0007038769,0.0008280014,0.00048186624,0.00025517255,0.00038480954,0.0006906626,0.00043828576,0.00041441288],"category_scores_gemma":[0.001965187,0.0002168939,0.0003794909,0.0004372379,0.00023104626,0.0006438589,0.00049289525,0.00060695695,0.00017817171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005794854,0.00026380894,0.007445903,0.000092425566,0.00007146987,0.0003630062,0.00010923814,0.34729573,0.046542227,0.0018305762,0.0024839842,0.59292215],"study_design_scores_gemma":[0.0000063488424,0.000032409323,0.00078681274,0.0000028399681,0.000010422924,0.000042100957,0.000008662904,0.99098045,0.0074086557,0.0005056856,0.00021014936,0.0000054100547],"about_ca_topic_score_codex":0.005950801,"about_ca_topic_score_gemma":0.007313592,"teacher_disagreement_score":0.005950801,"about_ca_system_score_codex":0.00036174039,"about_ca_system_score_gemma":0.0008075771,"threshold_uncertainty_score":0.011832356},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04491303,0.0009354431,0.95205426,0.0001851117,0.000056200715,0.00004465576,0.000081583516,0.00015312879,0.0015766112],"genre_scores_gemma":[0.8741773,0.0008452892,0.12337931,0.000065000386,0.00007963732,0.000104664614,0.00021490168,0.000056669043,0.0010773703],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998995,0.00040104712,0.00007182086,0.00017081457,0.0002755994,0.00008574144],"domain_scores_gemma":[0.99832004,0.0010761027,0.0001715723,0.000083781066,0.00029999262,0.000048402457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032483065,0.0006983931,0.0010763771,0.00221837,0.0006273916,0.0014015303,0.0008441113,0.00075618096,0.0009202264],"category_scores_gemma":[0.006140161,0.00024641884,0.0013346666,0.0021382358,0.0007246583,0.0013164587,0.0010971397,0.0010461475,0.00012248161],"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":[0.00013261917,0.00007076518,0.003460418,0.00018420833,0.00021446282,0.000164287,0.00024969183,0.90389377,0.003365607,0.037722573,0.0007288399,0.04981275],"study_design_scores_gemma":[0.0000014867763,0.0000105686295,0.000329602,0.000004843265,0.00001083147,0.00001141173,0.000020287307,0.99319804,0.00014405014,0.0060779597,0.00018451778,0.000006484244],"about_ca_topic_score_codex":0.009402547,"about_ca_topic_score_gemma":0.004044263,"teacher_disagreement_score":0.009402547,"about_ca_system_score_codex":0.0013943233,"about_ca_system_score_gemma":0.0011461764,"threshold_uncertainty_score":0.018695652},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009137778,0.00035569444,0.98872465,0.00007620288,0.000082921615,0.000037064827,0.000046865236,0.0008609616,0.0006777961],"genre_scores_gemma":[0.33420876,0.000777174,0.66181374,0.00008038513,0.00014967576,0.00011959794,0.00022907193,0.0001463335,0.0024752927],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847966,0.00032559191,0.00011394384,0.0003786934,0.00060809846,0.00009402649],"domain_scores_gemma":[0.998351,0.0006509664,0.00015061499,0.00015152406,0.0006447537,0.00005112555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012010615,0.0007857857,0.0010258358,0.0026786355,0.0005959924,0.0009753908,0.0010781279,0.0011467732,0.0014485499],"category_scores_gemma":[0.004086016,0.00028746243,0.00073510193,0.0022947383,0.00051666214,0.0011185814,0.0007221417,0.0011967334,0.0007353496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021688914,0.00014400652,0.0045474097,0.00031181826,0.00018419363,0.0004957928,0.00024980816,0.09850746,0.08743735,0.015087653,0.0028971373,0.7899205],"study_design_scores_gemma":[0.0000119370925,0.00011759317,0.00200241,0.000019722122,0.000036658123,0.00075412705,0.000049271628,0.95500356,0.025300782,0.009225476,0.0074376524,0.00004082168],"about_ca_topic_score_codex":0.0016381191,"about_ca_topic_score_gemma":0.00084613066,"teacher_disagreement_score":0.0026786355,"about_ca_system_score_codex":0.00043726832,"about_ca_system_score_gemma":0.0006353693,"threshold_uncertainty_score":0.006351888},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030328475,0.00025564368,0.9628963,0.0002390828,0.00006874319,0.000058949583,0.00019110543,0.0045762192,0.0013854872],"genre_scores_gemma":[0.58652717,0.0003084348,0.40388852,0.00027926455,0.00009823313,0.00016910765,0.0013605653,0.0005222762,0.006846467],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991887,0.00014818089,0.00003876331,0.00032096295,0.00020013569,0.00010326537],"domain_scores_gemma":[0.9985599,0.00047967446,0.00014311499,0.00046311493,0.00025827924,0.00009597268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007564483,0.0014948893,0.0011293617,0.0010603084,0.0004823651,0.001063013,0.0022464092,0.0013243253,0.0024912483],"category_scores_gemma":[0.0033556821,0.0004170994,0.001069681,0.00087740814,0.0011099544,0.0033206001,0.0026444646,0.0027526151,0.0014482829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036624927,0.00027952893,0.0019395467,0.0001674705,0.0000995012,0.00018061812,0.00021986327,0.092287846,0.031905614,0.018203894,0.0058851796,0.84846467],"study_design_scores_gemma":[0.000014197502,0.00008927321,0.00030138157,0.000011099687,0.00001845475,0.00009868422,0.000032349366,0.96504277,0.016571729,0.01595025,0.0018498299,0.00001991987],"about_ca_topic_score_codex":0.0032172196,"about_ca_topic_score_gemma":0.0039868345,"teacher_disagreement_score":0.0032172196,"about_ca_system_score_codex":0.0007189829,"about_ca_system_score_gemma":0.0013059009,"threshold_uncertainty_score":0.008334041},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7364754,0.0021678586,0.2506073,0.00013480117,0.00016269492,0.00008068328,0.0009949891,0.001214037,0.008162315],"genre_scores_gemma":[0.93757874,0.0007576934,0.05681792,0.000022529412,0.000091452246,0.00002801206,0.0017039495,0.00019425855,0.0028055026],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938774,0.00012859389,0.00003927413,0.00013475961,0.0002330432,0.00007651674],"domain_scores_gemma":[0.9973327,0.0015481394,0.00020593891,0.00021833868,0.0006036731,0.000091199574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011578022,0.0005942831,0.0006213502,0.0028958074,0.00041828296,0.0014487933,0.00066530023,0.00065276306,0.0018284502],"category_scores_gemma":[0.003292211,0.00019501033,0.0005374231,0.0032535533,0.00040704195,0.0012796518,0.00034363155,0.00027123297,0.0009135337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0034696022,0.00043510072,0.043943014,0.00079004647,0.00029835556,0.0008279502,0.0011245784,0.079904966,0.1068271,0.0051340074,0.0035378449,0.75370747],"study_design_scores_gemma":[0.000057532092,0.0013896488,0.21031672,0.00007537725,0.0005817022,0.002195716,0.0022025805,0.6966605,0.07392527,0.0039310707,0.008546439,0.00011739814],"about_ca_topic_score_codex":0.0032559945,"about_ca_topic_score_gemma":0.0030878552,"teacher_disagreement_score":0.0032559945,"about_ca_system_score_codex":0.0004591192,"about_ca_system_score_gemma":0.00038510788,"threshold_uncertainty_score":0.0064740777},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06687376,0.00045938962,0.9301701,0.00017042086,0.0000783789,0.00005481365,0.0001639015,0.0007615186,0.0012676873],"genre_scores_gemma":[0.76002526,0.00044390743,0.23748286,0.00013320168,0.000050337316,0.0000589722,0.00054367905,0.00006681215,0.0011949298],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942005,0.00006825885,0.000027078777,0.00014692762,0.00024517765,0.00009254148],"domain_scores_gemma":[0.99938893,0.00016910562,0.00009078518,0.00009367899,0.00021611969,0.00004136167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043153728,0.0007076184,0.0007707499,0.0007639348,0.0006795028,0.0008991898,0.0017546746,0.00061792077,0.00036630922],"category_scores_gemma":[0.0018017164,0.00023237799,0.00052710925,0.0010320039,0.00040125183,0.001600472,0.0011604114,0.0007613371,0.0001888176],"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":[0.0005956543,0.00017382568,0.01106674,0.00013033328,0.00006795242,0.000677695,0.00024124104,0.5991654,0.036922395,0.008384593,0.0050858064,0.33748844],"study_design_scores_gemma":[0.0000029318019,0.000016461161,0.0005443149,0.0000031928464,0.000004427062,0.00006743273,0.000030480956,0.99431956,0.003230164,0.0012883767,0.0004870486,0.00000556828],"about_ca_topic_score_codex":0.009266354,"about_ca_topic_score_gemma":0.00674924,"teacher_disagreement_score":0.009266354,"about_ca_system_score_codex":0.000697936,"about_ca_system_score_gemma":0.0007510884,"threshold_uncertainty_score":0.018424809},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1103594,0.00019098833,0.8863625,0.00017269485,0.000065158725,0.00006254826,0.00012389978,0.0019511057,0.00071171613],"genre_scores_gemma":[0.91229635,0.00005828169,0.08622491,0.00006936876,0.000017852619,0.00006266144,0.00020457387,0.000041005038,0.0010250112],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995266,0.00009738671,0.000043100466,0.00016323147,0.00009376241,0.00007586265],"domain_scores_gemma":[0.9982274,0.00091136125,0.00029002194,0.00022984364,0.00021851997,0.00012293964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010773441,0.00090183894,0.0009424408,0.001621482,0.0005912624,0.00064969575,0.0011320102,0.00070802856,0.0009766369],"category_scores_gemma":[0.003732197,0.00028042746,0.00050772756,0.0009296439,0.00069899304,0.0012724949,0.0011576412,0.0011042799,0.0002661648],"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":[0.0004062882,0.00017552434,0.008708377,0.00008233552,0.00008697832,0.00017035508,0.00018533003,0.68236494,0.0055906274,0.0056948694,0.0008194421,0.29571497],"study_design_scores_gemma":[0.0000072461758,0.000057961417,0.0004060912,0.0000031518741,0.0000058540236,0.000024148092,0.000018226163,0.99275994,0.001567896,0.00488181,0.0002622452,0.000005534703],"about_ca_topic_score_codex":0.0041714604,"about_ca_topic_score_gemma":0.004460513,"teacher_disagreement_score":0.0041714604,"about_ca_system_score_codex":0.0009388949,"about_ca_system_score_gemma":0.0009888734,"threshold_uncertainty_score":0.008294344},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0072846874,0.00023607946,0.9894561,0.00012579394,0.00006204531,0.000026529347,0.000083553685,0.0018028477,0.0009224116],"genre_scores_gemma":[0.49151322,0.0004180443,0.49334696,0.0006367437,0.00024217274,0.00016590922,0.0016007267,0.0011102147,0.010966075],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989874,0.0002843473,0.000036869336,0.0003461639,0.00022073848,0.00012440911],"domain_scores_gemma":[0.9982339,0.00085404265,0.00011990142,0.0004330439,0.0002877976,0.00007131688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015346806,0.0011390715,0.0019322876,0.0006353484,0.00028867193,0.001046472,0.002332782,0.0014790857,0.0018479862],"category_scores_gemma":[0.0035233584,0.00075136614,0.0010593977,0.0008495479,0.0007048683,0.0015605355,0.0018689957,0.0020502526,0.0014980457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043086286,0.00025807036,0.0024147811,0.00018914045,0.00027404173,0.00016293985,0.00010804014,0.23985727,0.0230844,0.014050953,0.016888604,0.70228094],"study_design_scores_gemma":[0.0000041999183,0.000013889934,0.0001791087,0.0000035828625,0.0000070230835,0.000050218026,0.000003585084,0.9941977,0.001409896,0.0037123729,0.0004135213,0.000004988069],"about_ca_topic_score_codex":0.0014930598,"about_ca_topic_score_gemma":0.0032756918,"teacher_disagreement_score":0.002332782,"about_ca_system_score_codex":0.00058672734,"about_ca_system_score_gemma":0.00058921974,"threshold_uncertainty_score":0.008116305},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.083309196,0.00037667018,0.9019676,0.0005357781,0.00043065107,0.0001513255,0.000902454,0.008529348,0.0037970608],"genre_scores_gemma":[0.8294423,0.00025807755,0.16245368,0.00045449272,0.000099504585,0.00024658433,0.0025731667,0.0006864029,0.0037859133],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981592,0.00031373816,0.00015486497,0.0005282641,0.0006140657,0.0002299176],"domain_scores_gemma":[0.9911452,0.0027832508,0.0007140339,0.0031847593,0.0019465277,0.00022616376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002829591,0.0021669115,0.0012448593,0.0008988334,0.00047772887,0.0010619492,0.0025419004,0.0013376978,0.0045459606],"category_scores_gemma":[0.020225279,0.0006602124,0.0009675924,0.0006104655,0.0011377551,0.0021685245,0.0028235875,0.0023990008,0.0016015216],"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":[0.0008424236,0.00024251631,0.010689072,0.00050918385,0.00017245843,0.0009591057,0.0003041465,0.6911683,0.027067602,0.011349078,0.01531504,0.24138111],"study_design_scores_gemma":[0.000023597973,0.00018205709,0.0013819868,0.00007548551,0.000046642028,0.00016453072,0.00006824784,0.9586036,0.022016603,0.01459838,0.0028095955,0.000029277378],"about_ca_topic_score_codex":0.0021312148,"about_ca_topic_score_gemma":0.003263653,"teacher_disagreement_score":0.0045459606,"about_ca_system_score_codex":0.0008164941,"about_ca_system_score_gemma":0.0015474366,"threshold_uncertainty_score":0.0152077675},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09828635,0.0003015289,0.8987833,0.00016973671,0.000055270593,0.000054240038,0.000070649985,0.00084585755,0.0014331469],"genre_scores_gemma":[0.7848541,0.00018734245,0.2119724,0.0000935321,0.00006785844,0.000057881654,0.00019958215,0.000089670495,0.0024776498],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953127,0.000056966554,0.000021250722,0.00012866047,0.00019438734,0.00006763314],"domain_scores_gemma":[0.9986922,0.00044272144,0.00015706987,0.00017650847,0.00043769524,0.000093925504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069761614,0.00042576078,0.00070263527,0.0011678321,0.00032633962,0.00079370715,0.0011405972,0.000650429,0.0011321316],"category_scores_gemma":[0.0022150814,0.00019307819,0.00038684794,0.00078626885,0.00052143994,0.0011685031,0.00080281345,0.0010176857,0.00040813102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038804443,0.00046899606,0.011892722,0.000100313846,0.000087584456,0.00022184313,0.00011399006,0.06522764,0.06187134,0.0068306606,0.0022004968,0.85059637],"study_design_scores_gemma":[0.0000050230024,0.00009325345,0.00205794,0.0000043314126,0.000010120671,0.00012085204,0.000010673107,0.98491734,0.010385977,0.0018781857,0.0005064577,0.000009837544],"about_ca_topic_score_codex":0.0021958034,"about_ca_topic_score_gemma":0.003082483,"teacher_disagreement_score":0.0021958034,"about_ca_system_score_codex":0.0005345237,"about_ca_system_score_gemma":0.0005909376,"threshold_uncertainty_score":0.0043659806},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02576041,0.001171333,0.96711427,0.0004998035,0.00020098343,0.000109763474,0.0003026637,0.00094558386,0.0038951826],"genre_scores_gemma":[0.8688113,0.0013793445,0.11866329,0.0003120092,0.00027071557,0.00039048414,0.00091861363,0.00014082856,0.009113329],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989122,0.00025956103,0.000054831788,0.00030578458,0.00029581066,0.00017193526],"domain_scores_gemma":[0.9991698,0.00029284024,0.0000797706,0.000046660556,0.000367981,0.000042953347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019593262,0.0010542048,0.0018989714,0.0010284499,0.00061631476,0.0011614674,0.002258507,0.0011300988,0.002391738],"category_scores_gemma":[0.0025705232,0.00051278574,0.0011175206,0.0010247212,0.0005601002,0.0012659982,0.0006513648,0.0011357584,0.0009633918],"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":[0.00012387536,0.00005202935,0.0028009638,0.000086848624,0.000082427934,0.00014780721,0.00004604611,0.9252752,0.0012908829,0.008483051,0.003181906,0.05842892],"study_design_scores_gemma":[0.0000058782703,0.00001354969,0.00017590282,0.000004299183,0.000009529561,0.000020702013,0.000002029766,0.99753654,0.00014150442,0.0017237116,0.00036046858,0.000005906547],"about_ca_topic_score_codex":0.015869368,"about_ca_topic_score_gemma":0.007602681,"teacher_disagreement_score":0.015869368,"about_ca_system_score_codex":0.00087534386,"about_ca_system_score_gemma":0.00149937,"threshold_uncertainty_score":0.031554043},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031481296,0.00056300435,0.9650039,0.00026227618,0.00009596258,0.000035759796,0.00018190607,0.0007975202,0.0015784279],"genre_scores_gemma":[0.7855009,0.0006611795,0.20220676,0.00017971445,0.00017863861,0.00013457525,0.00083870086,0.00012995009,0.010169674],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961543,0.0000914362,0.000018010936,0.00013567565,0.00008718583,0.00005226081],"domain_scores_gemma":[0.99907744,0.000357201,0.00008233388,0.00013625962,0.00029246876,0.000054273423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007797386,0.0006286135,0.00075745606,0.0009927186,0.0004586328,0.00083773234,0.0012127351,0.0009669657,0.0023081913],"category_scores_gemma":[0.002706837,0.0002700442,0.00042468763,0.0012404236,0.00048613234,0.0013532457,0.0010232233,0.001198166,0.0005707418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049664936,0.00026322546,0.004107583,0.00010149097,0.0001632362,0.00018571479,0.000089445195,0.4032089,0.013090017,0.026358005,0.008912713,0.54302305],"study_design_scores_gemma":[0.0000015655295,0.000011157313,0.00012784221,0.0000013536312,0.000004786168,0.0000133403555,0.0000039782017,0.99636203,0.0007391644,0.0025090817,0.00022323757,0.0000024415533],"about_ca_topic_score_codex":0.010232643,"about_ca_topic_score_gemma":0.009933647,"teacher_disagreement_score":0.010232643,"about_ca_system_score_codex":0.0007211053,"about_ca_system_score_gemma":0.0009331725,"threshold_uncertainty_score":0.020346165},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008249782,0.000062924104,0.99123526,0.000039940085,0.000009005304,0.000013797634,0.000016435803,0.00019451632,0.0001783626],"genre_scores_gemma":[0.34472454,0.00019779326,0.65349275,0.00009513123,0.00009047961,0.000087868306,0.00023047054,0.00016621935,0.0009147495],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9970489,0.0010406106,0.00016624398,0.00055382535,0.001029488,0.00016088459],"domain_scores_gemma":[0.9935363,0.0032227354,0.0008246407,0.00089368294,0.0013665325,0.00015599602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028378023,0.0005508347,0.0013569217,0.0015519719,0.00044274886,0.0011166157,0.0015017284,0.001123037,0.00078809954],"category_scores_gemma":[0.01505108,0.00041006538,0.0008534127,0.0012239313,0.0010469935,0.0015044945,0.0014982739,0.001540529,0.0006249981],"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":[0.0005508621,0.00030117293,0.018030828,0.0004496104,0.00042119774,0.0006791259,0.0003634319,0.28586105,0.082887776,0.08784901,0.003648761,0.5189572],"study_design_scores_gemma":[0.000021424961,0.0000880605,0.0019723787,0.0000140738985,0.000017770015,0.0002890252,0.00002219547,0.97491395,0.0094331615,0.012188084,0.0010028771,0.000037062036],"about_ca_topic_score_codex":0.00064684445,"about_ca_topic_score_gemma":0.0005669119,"teacher_disagreement_score":0.0028378023,"about_ca_system_score_codex":0.0003282214,"about_ca_system_score_gemma":0.0008675853,"threshold_uncertainty_score":0.015007913},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7792473,0.00041490069,0.21506755,0.00045229486,0.00008655424,0.000087776105,0.0012321194,0.0020053913,0.0014061064],"genre_scores_gemma":[0.9600585,0.000082161845,0.038042724,0.000020078827,0.000046484933,0.000022948067,0.0012750033,0.000021734337,0.0004302781],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955064,0.00007440265,0.000048121918,0.00009509821,0.00015328873,0.00007850688],"domain_scores_gemma":[0.9974564,0.001108449,0.00041655792,0.0002881763,0.00061817537,0.000112341404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001068551,0.0005558973,0.0006306457,0.002936078,0.00023334622,0.000983277,0.00049829786,0.00057720474,0.00079912465],"category_scores_gemma":[0.004232407,0.00012268289,0.0005631335,0.0015883582,0.00021682795,0.0008096856,0.0005874341,0.00078337267,0.00059547316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063489634,0.0008961013,0.19450317,0.00012408436,0.00013860126,0.0003586248,0.00017279039,0.0719553,0.029774163,0.0018444918,0.0054083276,0.6941895],"study_design_scores_gemma":[0.000009855784,0.00009907914,0.0345517,0.000012881634,0.000024921334,0.00011167199,0.00006965884,0.95689726,0.0058424813,0.0015538188,0.00080865074,0.000018047556],"about_ca_topic_score_codex":0.0039558657,"about_ca_topic_score_gemma":0.0024214436,"teacher_disagreement_score":0.0039558657,"about_ca_system_score_codex":0.00036145927,"about_ca_system_score_gemma":0.0005506523,"threshold_uncertainty_score":0.007865667},"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":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06826972,0.01806152,0.7637872,0.1113028,0.00094536593,0.00093565846,0.00034439616,0.0028946714,0.0334587],"genre_scores_gemma":[0.2351661,0.0062851133,0.7474171,0.0035568865,0.00047135242,0.0005885486,0.00046454094,0.0004965742,0.005553799],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9672538,0.016016468,0.0024877829,0.0030693752,0.010194414,0.000978185],"domain_scores_gemma":[0.79780495,0.15416133,0.0047121397,0.0108868005,0.02935821,0.0030766125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.044214062,0.0006311547,0.0009986374,0.002501968,0.0020794452,0.0075140833,0.0048541776,0.0034999975,0.0020582126],"category_scores_gemma":[0.11063812,0.0010201897,0.00082190614,0.0029060496,0.004069885,0.011773814,0.0046676407,0.0054985154,0.002313883],"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":[0.000092940245,0.0004609605,0.011183663,0.0018509066,0.000113061076,0.0006191273,0.0048185303,0.026891734,0.004157687,0.073231295,0.023967685,0.85261244],"study_design_scores_gemma":[0.00008017374,0.00030125154,0.0071888156,0.0012720522,0.000059860507,0.0021185984,0.00756769,0.2563591,0.011754997,0.37513703,0.337992,0.00016846079],"about_ca_topic_score_codex":0.0029970135,"about_ca_topic_score_gemma":0.002472382,"teacher_disagreement_score":0.044214062,"about_ca_system_score_codex":0.00265567,"about_ca_system_score_gemma":0.0062039346,"threshold_uncertainty_score":0.23382908},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015763935,0.0001426341,0.9827429,0.000053221574,0.000020787596,0.000036039277,0.000060892628,0.00075276423,0.00042677345],"genre_scores_gemma":[0.39132246,0.0003323338,0.6052099,0.0000968276,0.00007493469,0.000118146774,0.00053092506,0.00013699097,0.0021775078],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954706,0.000083844854,0.000022733033,0.00013761896,0.0001521713,0.000056565437],"domain_scores_gemma":[0.9993456,0.00018440718,0.00011228701,0.00011139109,0.00020613642,0.00004015234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071267923,0.0007453479,0.0006590522,0.0013167496,0.00035911662,0.00051500933,0.0012460283,0.0005941817,0.0010890254],"category_scores_gemma":[0.0020078975,0.00026327194,0.0005618993,0.00089992734,0.00046160232,0.0011899077,0.00079729385,0.0011333022,0.0003939504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004656683,0.00017275485,0.0033708427,0.00010142948,0.000066472334,0.00011172661,0.00013086777,0.13123067,0.06609703,0.009441311,0.0025992095,0.78621197],"study_design_scores_gemma":[0.000008238667,0.000074784315,0.00067293434,0.000006133509,0.000011674785,0.000062546875,0.000014464621,0.9775394,0.01743229,0.0029575953,0.001208656,0.000011235639],"about_ca_topic_score_codex":0.0045283632,"about_ca_topic_score_gemma":0.006430436,"teacher_disagreement_score":0.0045283632,"about_ca_system_score_codex":0.0005847134,"about_ca_system_score_gemma":0.0009195677,"threshold_uncertainty_score":0.009004056},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8008515,0.025606172,0.15106393,0.004243894,0.00093499804,0.0008947645,0.0053048264,0.0044057355,0.006694185],"genre_scores_gemma":[0.9212815,0.0036657332,0.060468487,0.00086068694,0.00022505075,0.00021568289,0.01138228,0.00021777269,0.001682821],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960383,0.001800163,0.0003165644,0.0006967968,0.0009361531,0.00021199323],"domain_scores_gemma":[0.9897155,0.006928145,0.00068715255,0.0011612948,0.001255507,0.00025243397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009859367,0.001816496,0.0012220575,0.0022262512,0.00045485108,0.0012560904,0.00158389,0.0013859009,0.0006822119],"category_scores_gemma":[0.017011251,0.00034116444,0.0013793783,0.0013402923,0.0010844753,0.0013585318,0.0011392029,0.0018376347,0.00040627358],"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":[0.0014348015,0.001503583,0.07225628,0.0021885906,0.0017728334,0.0010456522,0.00038077633,0.5073556,0.007809593,0.0037373926,0.026646335,0.37386858],"study_design_scores_gemma":[0.0000623856,0.00050050596,0.017799221,0.00022497414,0.00024941107,0.00078725204,0.00019806204,0.962405,0.008255707,0.0037517028,0.0056965537,0.00006926272],"about_ca_topic_score_codex":0.0068532517,"about_ca_topic_score_gemma":0.006109448,"teacher_disagreement_score":0.009859367,"about_ca_system_score_codex":0.0012368996,"about_ca_system_score_gemma":0.0008606796,"threshold_uncertainty_score":0.052141964},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4303736,0.0012245175,0.5626564,0.0001283825,0.0002558359,0.00019825305,0.0004284057,0.0016372077,0.003097344],"genre_scores_gemma":[0.8267755,0.0006079083,0.16943851,0.00009528821,0.000053827007,0.0001738895,0.00026831494,0.000052521176,0.0025342356],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946207,0.000086296815,0.000029540937,0.00017934761,0.00021211704,0.000030702016],"domain_scores_gemma":[0.9996592,0.00009638018,0.00008283253,0.000041755924,0.00010141742,0.000018450119],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028455295,0.00059292774,0.0005781041,0.00063842273,0.00020649728,0.0003946106,0.0004248755,0.00054245116,0.0010920495],"category_scores_gemma":[0.0010047508,0.00021364013,0.0003107675,0.00057923817,0.00018581869,0.00054905016,0.0004383434,0.00029781845,0.00062276126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00093345664,0.00030024967,0.020143772,0.0005444314,0.00019423345,0.0004887284,0.00034540863,0.0084156245,0.32619298,0.00048333016,0.0017313291,0.6402264],"study_design_scores_gemma":[0.00009856511,0.0020829146,0.111342296,0.00012090002,0.00027050468,0.0043402975,0.00046281007,0.587769,0.28400394,0.0014210251,0.007850577,0.00023724206],"about_ca_topic_score_codex":0.0007738261,"about_ca_topic_score_gemma":0.0011503049,"teacher_disagreement_score":0.0010920495,"about_ca_system_score_codex":0.00015360897,"about_ca_system_score_gemma":0.00015117045,"threshold_uncertainty_score":0.003653288},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073778997,0.751421,0.21967639,0.004226529,0.0004174395,0.00010034994,0.00061518536,0.0010608583,0.015104401],"genre_scores_gemma":[0.07335602,0.82248276,0.0956494,0.0011797057,0.00077409315,0.00015860678,0.0011289272,0.0002227484,0.0050476906],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99917644,0.00014248746,0.00010112927,0.00014026904,0.00038014745,0.00005939059],"domain_scores_gemma":[0.9988482,0.00065372343,0.00006149312,0.0001021457,0.0002809812,0.00005342085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016761394,0.0010363469,0.0008699782,0.0024772922,0.00026842498,0.0019422781,0.0014094469,0.0009292508,0.0023351237],"category_scores_gemma":[0.002763937,0.0007060267,0.0006533845,0.0033635148,0.00072794536,0.0030882163,0.0016451958,0.0021715786,0.00121295],"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":[0.000048886563,0.000082349994,0.001767445,0.0023909914,0.00008328909,0.00005147712,0.000080955455,0.012407341,0.0009882415,0.032699466,0.015192003,0.9342075],"study_design_scores_gemma":[0.0000339033,0.00024976625,0.0036403262,0.004483547,0.0002142399,0.0007797648,0.00024683026,0.17197892,0.007126305,0.16468348,0.64643025,0.00013255766],"about_ca_topic_score_codex":0.003254203,"about_ca_topic_score_gemma":0.0025103313,"teacher_disagreement_score":0.003254203,"about_ca_system_score_codex":0.001037743,"about_ca_system_score_gemma":0.0012443436,"threshold_uncertainty_score":0.008864403},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90613437,0.0037485273,0.03321963,0.003791108,0.0024446466,0.0007480372,0.024776975,0.010419291,0.014717404],"genre_scores_gemma":[0.88141704,0.0005615941,0.04144948,0.0010997455,0.00014999828,0.00042956398,0.07069168,0.00036922222,0.0038317123],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99589086,0.0010550002,0.00047532358,0.0011818865,0.0010729945,0.00032391166],"domain_scores_gemma":[0.9893059,0.0055824444,0.00039385862,0.0019536102,0.002484407,0.0002796834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004925045,0.0016016542,0.001224242,0.0010116614,0.0011614414,0.0014188422,0.0019158737,0.0023103412,0.002295687],"category_scores_gemma":[0.01720156,0.00039975444,0.0013635922,0.0010264311,0.0009589923,0.0021517486,0.001482546,0.0018186597,0.0016515055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0040814388,0.004142259,0.098370165,0.0016567612,0.00092758477,0.0008562324,0.00037970196,0.25304115,0.024841106,0.0013407652,0.090552606,0.5198102],"study_design_scores_gemma":[0.00030735479,0.0010346953,0.076288484,0.0003331108,0.0003444108,0.00059890194,0.00073844363,0.86303973,0.037984055,0.0013267024,0.017880848,0.00012323885],"about_ca_topic_score_codex":0.025620729,"about_ca_topic_score_gemma":0.024278926,"teacher_disagreement_score":0.025620729,"about_ca_system_score_codex":0.0015045797,"about_ca_system_score_gemma":0.0012798645,"threshold_uncertainty_score":0.050943196},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08171843,0.0010092916,0.9054255,0.0006612976,0.00024222281,0.00009047822,0.0014615305,0.007466943,0.0019242241],"genre_scores_gemma":[0.75337565,0.00054258324,0.23819308,0.0003981688,0.00008480386,0.00010032441,0.0018423848,0.00016999451,0.005293031],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998518,0.000024576628,0.000010422538,0.000047310838,0.000044132667,0.000021741109],"domain_scores_gemma":[0.99975115,0.00010042824,0.000031375268,0.00003345717,0.00007097305,0.000012618723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042262048,0.0005028649,0.00034523627,0.00042662717,0.00017048416,0.00036193596,0.00093660847,0.0006933516,0.0017115794],"category_scores_gemma":[0.0011370888,0.00024487308,0.00041239892,0.00032069642,0.00019005472,0.00063861266,0.0007393355,0.0009304579,0.00029013984],"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":[0.0003977831,0.00020926392,0.010632667,0.00019964963,0.00029241855,0.00040686593,0.00008834239,0.42207345,0.01609075,0.005114921,0.011467168,0.53302675],"study_design_scores_gemma":[0.000007871906,0.00003455539,0.0007253088,0.000008345609,0.000015169705,0.00004599928,0.0000046065197,0.9934615,0.0027909076,0.0018531145,0.0010457253,0.0000068418462],"about_ca_topic_score_codex":0.0048409486,"about_ca_topic_score_gemma":0.008236826,"teacher_disagreement_score":0.0048409486,"about_ca_system_score_codex":0.0004350098,"about_ca_system_score_gemma":0.00049351866,"threshold_uncertainty_score":0.009625554},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55410784,0.0021135893,0.42945123,0.0007655968,0.0010013983,0.00007578668,0.0049759033,0.0036738662,0.0038346543],"genre_scores_gemma":[0.9731406,0.00039798996,0.022616917,0.000083115585,0.00014050314,0.00002399232,0.001969179,0.000056810433,0.0015708477],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99980813,0.00001183069,0.000009777242,0.000068116504,0.000047372243,0.00005485083],"domain_scores_gemma":[0.9996232,0.000095125615,0.0000819096,0.000036997302,0.0001252374,0.000037533475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028714768,0.0008287963,0.0006893077,0.0018852144,0.00017208929,0.00057575636,0.00086416263,0.00075604476,0.0009942624],"category_scores_gemma":[0.0011633481,0.0004681984,0.00068454444,0.0014114516,0.00023703765,0.00070967857,0.0005810424,0.00090291584,0.00053550635],"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":[0.00056081684,0.00042720238,0.06724181,0.00013091198,0.00028626763,0.0004907514,0.00006326006,0.49372447,0.020292608,0.0018903958,0.011611166,0.40328035],"study_design_scores_gemma":[0.0000025833465,0.000015567795,0.0033508234,0.000004470609,0.000010289606,0.00002241352,0.0000073048263,0.994865,0.00088313635,0.000589558,0.00024414633,0.0000048135153],"about_ca_topic_score_codex":0.018263856,"about_ca_topic_score_gemma":0.025572503,"teacher_disagreement_score":0.018263856,"about_ca_system_score_codex":0.00057037035,"about_ca_system_score_gemma":0.00063022063,"threshold_uncertainty_score":0.036315084},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049255844,0.00058274635,0.946588,0.0003915939,0.000086436055,0.000041979827,0.00020362098,0.0019462947,0.0009035088],"genre_scores_gemma":[0.7777363,0.00042136866,0.2174842,0.00027481388,0.000091320064,0.00008382405,0.0006904042,0.00010476779,0.0031129138],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996271,0.00004898394,0.000028413986,0.000108465225,0.00012142016,0.00006559628],"domain_scores_gemma":[0.9993494,0.0002500942,0.000077045115,0.000064046064,0.0002110844,0.000048263006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007188983,0.0006735971,0.00056788424,0.0007565287,0.00029628546,0.00057723036,0.001374273,0.000734576,0.0007541512],"category_scores_gemma":[0.0019106416,0.0003024862,0.00045564238,0.0008454311,0.00034848895,0.0010549844,0.00089117,0.0016037272,0.00025879923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021608897,0.0003652476,0.0058394317,0.00010320443,0.0001140402,0.00020887233,0.000106303545,0.42257866,0.015233563,0.004588973,0.0044672806,0.54617834],"study_design_scores_gemma":[0.0000015422061,0.000011745433,0.0001695002,0.0000019835584,0.0000029615285,0.000010805757,0.000003576952,0.99790883,0.0008055403,0.00091501744,0.00016647561,0.000002079731],"about_ca_topic_score_codex":0.009389081,"about_ca_topic_score_gemma":0.0109091485,"teacher_disagreement_score":0.009389081,"about_ca_system_score_codex":0.00077807484,"about_ca_system_score_gemma":0.0011434953,"threshold_uncertainty_score":0.01866883},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07184545,0.00062068505,0.924145,0.00015661604,0.00007048958,0.000026044214,0.00010957591,0.0019332466,0.0010929944],"genre_scores_gemma":[0.86931497,0.0002889374,0.12778187,0.00007046467,0.000035935922,0.000030841038,0.0002772059,0.000050740495,0.0021489726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969006,0.000040478884,0.000018559045,0.00009645239,0.000107937936,0.000046451245],"domain_scores_gemma":[0.9994324,0.00024197508,0.00006801783,0.000060707895,0.00018119017,0.000015609867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005895863,0.00062723045,0.00046248676,0.00067192083,0.00020575261,0.00039136497,0.0007622064,0.0005532939,0.00045941296],"category_scores_gemma":[0.0015231547,0.0002702152,0.00042695692,0.00051994104,0.0003032829,0.00064928195,0.00040946266,0.0009752041,0.00017574507],"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":[0.0001619652,0.00012380227,0.00444138,0.00005590427,0.00010851318,0.00013092785,0.000064324006,0.55415195,0.022339255,0.0025786276,0.0017390588,0.41410428],"study_design_scores_gemma":[8.9216076e-7,0.000009618865,0.0004566774,0.0000018227836,0.000004143541,0.000014770173,0.0000018413596,0.9965739,0.002390599,0.0003577071,0.00018515924,0.0000028861934],"about_ca_topic_score_codex":0.010006596,"about_ca_topic_score_gemma":0.008821747,"teacher_disagreement_score":0.010006596,"about_ca_system_score_codex":0.00080520695,"about_ca_system_score_gemma":0.00057251524,"threshold_uncertainty_score":0.019896746},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013682921,0.00035019708,0.98220086,0.000094180985,0.000048281727,0.0002719241,0.00020060716,0.00249685,0.00065427984],"genre_scores_gemma":[0.32250825,0.00030039935,0.6730967,0.00013509445,0.00013729179,0.00029047907,0.0012961879,0.00016462104,0.0020709566],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868387,0.00022671037,0.00007790421,0.00037069485,0.00048717117,0.00015371581],"domain_scores_gemma":[0.9985483,0.0004885379,0.00020348304,0.00017411534,0.0004803473,0.00010521795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016332524,0.0012891348,0.0013771638,0.0027945999,0.0005391044,0.0009833575,0.0022622019,0.0012130175,0.0020160691],"category_scores_gemma":[0.0034464805,0.00040883353,0.0011262568,0.00087305694,0.0006987426,0.0013859445,0.0013698387,0.0010248906,0.0008445744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073038414,0.00040499502,0.0053052246,0.0003641816,0.00023151393,0.0009797626,0.00040189043,0.097109646,0.06545785,0.0095630335,0.008891072,0.8105604],"study_design_scores_gemma":[0.000029914527,0.0001898192,0.0018250869,0.000016946175,0.0000509582,0.00031996926,0.00008133334,0.9732536,0.017096613,0.004597874,0.0025039413,0.000033893142],"about_ca_topic_score_codex":0.00898531,"about_ca_topic_score_gemma":0.0077808346,"teacher_disagreement_score":0.00898531,"about_ca_system_score_codex":0.00089714996,"about_ca_system_score_gemma":0.001983858,"threshold_uncertainty_score":0.017866015},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2101845,0.0014180576,0.77463096,0.0013625995,0.00018316155,0.0004831434,0.004710568,0.003893796,0.0031331542],"genre_scores_gemma":[0.7540968,0.0003982612,0.23682441,0.0003180323,0.00018930256,0.00042529756,0.00602002,0.00019810944,0.0015297803],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99751484,0.00074772164,0.000139818,0.00079335447,0.00062009704,0.00018407285],"domain_scores_gemma":[0.9930092,0.0029509517,0.0015368551,0.0011193694,0.0011093426,0.00027428818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024422095,0.0010684619,0.0008149275,0.0047208555,0.0009681511,0.0018707231,0.0017313657,0.0012461073,0.00074312335],"category_scores_gemma":[0.0107704345,0.00022625459,0.0009600116,0.003460793,0.0009746784,0.003369694,0.0018152923,0.0014099074,0.00047198555],"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":[0.0012790944,0.00076273613,0.23247252,0.0007822758,0.00057079137,0.00072700385,0.0015289685,0.20486727,0.013909478,0.029671151,0.018343547,0.49508503],"study_design_scores_gemma":[0.000015566182,0.000090602596,0.009677554,0.00004195617,0.000039849194,0.00016877,0.00035014577,0.96423906,0.0029706045,0.018880885,0.0034967843,0.000028176528],"about_ca_topic_score_codex":0.0055917464,"about_ca_topic_score_gemma":0.009005241,"teacher_disagreement_score":0.0055917464,"about_ca_system_score_codex":0.0011076797,"about_ca_system_score_gemma":0.0010252637,"threshold_uncertainty_score":0.01291579},"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":"commentary","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007617602,0.0062560975,0.0016788159,0.5043738,0.45002836,0.00028360132,0.0009200354,0.0008980751,0.034799427],"genre_scores_gemma":[0.0126129165,0.008318156,0.0032370796,0.3117878,0.26734224,0.0010162636,0.0021705562,0.0010673086,0.39244762],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9949596,0.0008836007,0.00025456413,0.00068574725,0.0024175972,0.0007989258],"domain_scores_gemma":[0.9864624,0.001152621,0.0003661581,0.000466777,0.007124929,0.004427132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010102759,0.0024032167,0.0016753514,0.0014288009,0.0038769706,0.009449014,0.0022817652,0.020285642,0.05566532],"category_scores_gemma":[0.011642828,0.00076056365,0.0014251365,0.0014034543,0.001435599,0.006218613,0.0029810427,0.015965661,0.073338225],"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":[0.000022350592,0.000014926991,0.000049419785,0.000023615577,0.000001945177,0.000032934993,0.000007936349,0.000015374248,0.00007401587,0.00020686706,0.99565744,0.0038932422],"study_design_scores_gemma":[0.000018627823,0.000022939003,0.00043717193,0.00006125935,0.000006454521,0.000049057842,0.000101141,0.00017718207,0.00012155628,0.00036957068,0.9986141,0.000020875308],"about_ca_topic_score_codex":0.005527367,"about_ca_topic_score_gemma":0.0077481903,"teacher_disagreement_score":0.05566532,"about_ca_system_score_codex":0.0025278665,"about_ca_system_score_gemma":0.005580288,"threshold_uncertainty_score":0.18621904},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40828907,0.003376786,0.5726719,0.00075517414,0.00045481432,0.00020378875,0.00058425777,0.005863549,0.0078007123],"genre_scores_gemma":[0.95261115,0.0003827441,0.042277414,0.0002335918,0.000077040895,0.00006757445,0.0006789839,0.000053738662,0.0036177523],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995384,0.00006850799,0.000029444973,0.00017914693,0.00009028973,0.000094240444],"domain_scores_gemma":[0.9995442,0.00014700768,0.00005592344,0.00003718255,0.0001900477,0.00002561568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062336307,0.0008744281,0.0007844402,0.00089403236,0.0003282702,0.0004984653,0.0011536859,0.0006876398,0.00094981154],"category_scores_gemma":[0.0017637385,0.00027394673,0.0005870652,0.00074147334,0.0003042673,0.00074493565,0.0005879774,0.0009198862,0.00032879578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005444888,0.0005248531,0.012324248,0.0001256026,0.0001640892,0.00041759506,0.00015704083,0.1951418,0.021337144,0.0010758128,0.0075072194,0.7606801],"study_design_scores_gemma":[0.0000060746806,0.000055166864,0.0021962118,0.000007907123,0.000026518594,0.00004986335,0.000019213732,0.9941474,0.0026765508,0.00042383402,0.0003810985,0.000010137903],"about_ca_topic_score_codex":0.015922433,"about_ca_topic_score_gemma":0.0130121475,"teacher_disagreement_score":0.015922433,"about_ca_system_score_codex":0.00081510615,"about_ca_system_score_gemma":0.00066969055,"threshold_uncertainty_score":0.031659484},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025304329,0.0052769464,0.8149633,0.061611265,0.0004911141,0.0016634723,0.0001143886,0.0018144158,0.088760816],"genre_scores_gemma":[0.25450468,0.0037216758,0.73186064,0.001775193,0.00012091164,0.0016495925,0.00012530888,0.00010449779,0.00613749],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99248886,0.0049577393,0.00034180353,0.0005679446,0.0012442598,0.00039933302],"domain_scores_gemma":[0.9805454,0.01048378,0.0014862656,0.0019017477,0.0038705126,0.0017124015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014638639,0.0010650916,0.0004501231,0.001671051,0.0015545216,0.0053741587,0.0037426034,0.0040209214,0.008566288],"category_scores_gemma":[0.029760547,0.0006459299,0.0006467723,0.0007066827,0.003913539,0.008081447,0.0044738944,0.002688943,0.0029530888],"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":[0.00026181404,0.0013330395,0.007795282,0.0032810334,0.00014517894,0.0008029871,0.010673809,0.06264943,0.016044311,0.26428524,0.021675464,0.61105233],"study_design_scores_gemma":[0.00021834204,0.00206855,0.0058502983,0.0047190115,0.00010758293,0.001398012,0.030009976,0.121793926,0.012559824,0.2586464,0.56238496,0.0002430266],"about_ca_topic_score_codex":0.0020089154,"about_ca_topic_score_gemma":0.0035639887,"teacher_disagreement_score":0.014638639,"about_ca_system_score_codex":0.0017300682,"about_ca_system_score_gemma":0.004906751,"threshold_uncertainty_score":0.07741743},"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":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019542498,0.0012024419,0.003973441,0.011167549,0.0054054363,0.00058178045,0.005062573,0.0037875832,0.966865],"genre_scores_gemma":[0.012723328,0.0011579817,0.001942853,0.0051479843,0.00087020284,0.00025365176,0.004306498,0.00077090185,0.9728266],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99709415,0.00039675218,0.00024281951,0.0006333314,0.0012248924,0.00040798823],"domain_scores_gemma":[0.9899372,0.00089799525,0.0003827398,0.0013872656,0.0057735387,0.0016213737],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002130283,0.0009772428,0.0011059158,0.0026156104,0.0049523264,0.006592657,0.0022295858,0.002709013,0.7799283],"category_scores_gemma":[0.013746807,0.0004893202,0.000605582,0.0017538796,0.00071821146,0.00433754,0.004182389,0.0024210257,0.5758427],"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":[0.000041399406,0.000079833706,0.00058822945,0.00010154291,0.000003908536,0.00024383792,0.00018754794,0.00003865626,0.00017681114,0.0034254005,0.877123,0.11798984],"study_design_scores_gemma":[0.0000068172935,0.000017321774,0.0003996985,0.00006494944,0.0000022934353,0.00023054788,0.00019642364,0.00009307872,0.00009846048,0.0007809926,0.9981036,0.000005811504],"about_ca_topic_score_codex":0.0049611926,"about_ca_topic_score_gemma":0.007050357,"teacher_disagreement_score":0.7799283,"about_ca_system_score_codex":0.0018616635,"about_ca_system_score_gemma":0.0037702373,"threshold_uncertainty_score":0.31390548},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07004593,0.0001153874,0.92619896,0.00018543833,0.000059564307,0.000061047416,0.00016764528,0.0021786108,0.0009874635],"genre_scores_gemma":[0.6149126,0.00019780116,0.38051754,0.0002917079,0.00006684119,0.00021555908,0.0009577366,0.0005478952,0.002292247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990876,0.00013468777,0.000062321626,0.00029717982,0.0003219114,0.00009638617],"domain_scores_gemma":[0.99686974,0.001115373,0.00044305122,0.00093290536,0.00056215783,0.000076761135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015991491,0.0011952246,0.0008703135,0.0008069086,0.00028489987,0.0006870038,0.001612143,0.0009255652,0.0009144969],"category_scores_gemma":[0.00799865,0.0006750121,0.00081912184,0.0006502923,0.0007336421,0.0013658134,0.0014313639,0.0015351686,0.0004701465],"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":[0.00022946308,0.00015669079,0.0047201915,0.00015497964,0.00016300555,0.00025606918,0.00014962337,0.7161634,0.06877472,0.011249543,0.0031417222,0.19484065],"study_design_scores_gemma":[0.0000064073893,0.000041460193,0.0003957999,0.0000074172785,0.000016498869,0.000048428305,0.0000074273303,0.974794,0.020867841,0.0030346918,0.00077127945,0.000008726166],"about_ca_topic_score_codex":0.0022156106,"about_ca_topic_score_gemma":0.0029819256,"teacher_disagreement_score":0.0022156106,"about_ca_system_score_codex":0.0008001099,"about_ca_system_score_gemma":0.0007725246,"threshold_uncertainty_score":0.008457184},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031129692,0.00012223561,0.9932902,0.00007933167,0.00005680029,0.00003386513,0.00023710934,0.00238031,0.0006871368],"genre_scores_gemma":[0.1413067,0.00020410282,0.8521176,0.00016261403,0.00014207653,0.00025694323,0.0013580233,0.00080156507,0.0036503242],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997811,0.0005721048,0.00015740612,0.00045374202,0.00089543255,0.0001102916],"domain_scores_gemma":[0.9931496,0.0032650393,0.00045313788,0.0017781642,0.001209268,0.00014491503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018348353,0.00097376644,0.00097393384,0.0023239716,0.000629824,0.0019279125,0.0022481713,0.0013951183,0.004653175],"category_scores_gemma":[0.008672402,0.0005922017,0.0010537228,0.0014396323,0.0008870283,0.0035798298,0.0019624103,0.0023784179,0.0020574487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008465697,0.00027962256,0.0031916301,0.00046235157,0.00016157373,0.00035613458,0.0003344203,0.06996047,0.040645424,0.12710615,0.01354925,0.7431064],"study_design_scores_gemma":[0.000024245319,0.000077772886,0.0004276188,0.000043452368,0.000033886532,0.00017616461,0.000038746235,0.8690728,0.015157496,0.10891833,0.005995149,0.000034232],"about_ca_topic_score_codex":0.0011051559,"about_ca_topic_score_gemma":0.001680553,"teacher_disagreement_score":0.004653175,"about_ca_system_score_codex":0.0008997417,"about_ca_system_score_gemma":0.0010061321,"threshold_uncertainty_score":0.015566468},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019262558,0.4898424,0.45320895,0.017681539,0.0009279719,0.00009114303,0.0004642803,0.0010170087,0.017504096],"genre_scores_gemma":[0.28257588,0.5564152,0.14685237,0.0024414847,0.001471191,0.00014429721,0.0011730805,0.00030756803,0.008618986],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985928,0.00042065416,0.00012914395,0.00023416817,0.0005399602,0.000083259314],"domain_scores_gemma":[0.99686277,0.0017615583,0.00015049592,0.00024130943,0.0008936164,0.00009025076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037968557,0.0010686172,0.0008233539,0.0013208846,0.00027063186,0.0028495817,0.0022035127,0.0015023949,0.0027964637],"category_scores_gemma":[0.0061764773,0.0006551723,0.001058022,0.0015893702,0.001148647,0.004137725,0.0017913415,0.0036023003,0.0014902465],"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":[0.00009958517,0.000117388496,0.0029855492,0.0020538438,0.00015770351,0.0001264431,0.00019022421,0.07410452,0.0016560487,0.08967746,0.01438778,0.81444347],"study_design_scores_gemma":[0.000029635052,0.0001823131,0.0015976762,0.0024007326,0.0001388475,0.0003441041,0.00032690412,0.6527854,0.004802367,0.1514941,0.1857776,0.000120240904],"about_ca_topic_score_codex":0.0045806947,"about_ca_topic_score_gemma":0.0031340672,"teacher_disagreement_score":0.0045806947,"about_ca_system_score_codex":0.001255476,"about_ca_system_score_gemma":0.0013437766,"threshold_uncertainty_score":0.02007997},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11492256,0.001706547,0.8524734,0.0006249223,0.0004918466,0.00035021667,0.0015498936,0.023376547,0.004504148],"genre_scores_gemma":[0.34585178,0.0005328142,0.63859254,0.00062439014,0.00024448498,0.0003242333,0.006395835,0.0005773682,0.006856512],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929106,0.00005750919,0.00004758036,0.00025732396,0.00022198142,0.0001246133],"domain_scores_gemma":[0.9991725,0.00018936147,0.0000979413,0.00012395118,0.00034610718,0.00007015415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062803447,0.0011181112,0.0012803979,0.0031388649,0.0006652336,0.0012876905,0.0022701274,0.0010253247,0.003189674],"category_scores_gemma":[0.0021723516,0.0003330358,0.0006747311,0.0014852406,0.0004712896,0.0016890906,0.0012765366,0.0011775268,0.0017884056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00091197377,0.0005400126,0.007139549,0.00022811211,0.00014552649,0.00015391373,0.000089801484,0.037981816,0.047532946,0.0028030628,0.03128772,0.8711857],"study_design_scores_gemma":[0.000083637075,0.00020412843,0.002705907,0.000018551318,0.00003726997,0.00016627111,0.00006739934,0.9670176,0.019085,0.0014457015,0.009133281,0.000035262306],"about_ca_topic_score_codex":0.010161252,"about_ca_topic_score_gemma":0.011377537,"teacher_disagreement_score":0.010161252,"about_ca_system_score_codex":0.00090697437,"about_ca_system_score_gemma":0.0014044016,"threshold_uncertainty_score":0.020204186},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10749673,0.0005913002,0.8895006,0.0003382802,0.000109050205,0.000024910032,0.00020422749,0.00060989556,0.0011249997],"genre_scores_gemma":[0.97778887,0.00019248211,0.019432796,0.00005021105,0.000062279,0.000022776094,0.00031917926,0.00004575493,0.0020857006],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996922,0.000063882486,0.000014879827,0.00008983069,0.00008665667,0.000052602954],"domain_scores_gemma":[0.99776256,0.0014296812,0.00026796758,0.00015324962,0.00028668286,0.00009984686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000940644,0.0009080548,0.0009141345,0.00049014157,0.00022711979,0.0006247051,0.000908128,0.0008898812,0.0010093426],"category_scores_gemma":[0.0045826696,0.0003419694,0.00041007163,0.00042340925,0.00074451446,0.00110681,0.00087867724,0.0013984492,0.0003476584],"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":[0.000102698745,0.00002920175,0.0010846399,0.000031669402,0.000014904301,0.000062489555,0.000021479125,0.9755721,0.001239441,0.0015977726,0.000674902,0.01956871],"study_design_scores_gemma":[6.3733046e-7,0.000004184851,0.00008852661,0.0000010518793,9.504451e-7,0.0000041022727,0.0000011346182,0.9993293,0.00016098749,0.0003847666,0.000023134042,0.000001223423],"about_ca_topic_score_codex":0.0039081913,"about_ca_topic_score_gemma":0.0039559766,"teacher_disagreement_score":0.0039081913,"about_ca_system_score_codex":0.0005619834,"about_ca_system_score_gemma":0.0005617932,"threshold_uncertainty_score":0.007770896},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2126698,0.0013168243,0.77668756,0.0002690984,0.0001703656,0.00008431047,0.00073562044,0.0034476456,0.004618762],"genre_scores_gemma":[0.85145926,0.0005779021,0.1416021,0.00010947975,0.000050988227,0.0000417816,0.0009832063,0.000054838965,0.0051204194],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996636,0.000036303256,0.00001386397,0.00012237647,0.000106349995,0.000057555273],"domain_scores_gemma":[0.9998209,0.0000415103,0.00003556678,0.000037813872,0.000048615413,0.000015628866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032311995,0.0005960887,0.00036192132,0.0008275842,0.00014267761,0.0003565606,0.00047944434,0.00044775123,0.0011199537],"category_scores_gemma":[0.00068808754,0.0002771351,0.0004164562,0.0007610208,0.00026126704,0.00052135915,0.00044628626,0.000417939,0.00038406573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032627405,0.00013600009,0.006655549,0.00012337427,0.00016417139,0.00034127157,0.00008529674,0.11331295,0.12055919,0.002856824,0.0037730408,0.75166595],"study_design_scores_gemma":[0.0000049686805,0.0000654315,0.009727486,0.000012391029,0.000020650465,0.0001401386,0.000019975643,0.95877343,0.027663654,0.0017113928,0.0018451111,0.00001535095],"about_ca_topic_score_codex":0.010778119,"about_ca_topic_score_gemma":0.010991514,"teacher_disagreement_score":0.010778119,"about_ca_system_score_codex":0.0006153663,"about_ca_system_score_gemma":0.00031264592,"threshold_uncertainty_score":0.02143079},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9232812,0.0005604451,0.069443285,0.0006224357,0.00011407992,0.00015460292,0.0023945097,0.0015081983,0.0019213121],"genre_scores_gemma":[0.9800939,0.00024234198,0.01633903,0.000112689166,0.00005336885,0.00005708816,0.0023366802,0.000039191993,0.0007256146],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99955946,0.00010818836,0.000050579314,0.0001416588,0.00008394863,0.000056180455],"domain_scores_gemma":[0.9988397,0.0004883104,0.0002468933,0.00011162089,0.00021224347,0.000101432706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063095475,0.0005828717,0.0005685792,0.001292764,0.00016473728,0.000586212,0.00036340192,0.0005168414,0.00084994675],"category_scores_gemma":[0.0028927017,0.00014464444,0.00054935063,0.00046659916,0.00016058072,0.0003599536,0.00058255345,0.00062353123,0.0005190082],"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":[0.0011618196,0.0005345231,0.58654153,0.00017031758,0.00035724603,0.0007010047,0.0002566554,0.0124076065,0.020617774,0.00042785628,0.0045397216,0.37228397],"study_design_scores_gemma":[0.00007852892,0.0010625236,0.56572574,0.000117966505,0.00021829261,0.0016971356,0.00045339114,0.4065793,0.016373519,0.004349702,0.0032650155,0.000078865975],"about_ca_topic_score_codex":0.0019664003,"about_ca_topic_score_gemma":0.0047342563,"teacher_disagreement_score":0.0019664003,"about_ca_system_score_codex":0.00020001577,"about_ca_system_score_gemma":0.00025815208,"threshold_uncertainty_score":0.0039099455},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8221577,0.0007565894,0.14190954,0.0012530172,0.0008119676,0.0005556109,0.008720783,0.015048565,0.008786289],"genre_scores_gemma":[0.96363264,0.000077588826,0.031239834,0.00008870492,0.000054421147,0.00008712372,0.003510612,0.000077153774,0.0012319048],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928147,0.0001390492,0.000032650085,0.00019070717,0.0002724906,0.00008375289],"domain_scores_gemma":[0.99835604,0.0004923595,0.00010721167,0.00032047843,0.00046858736,0.0002553289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006782396,0.0007012011,0.0008500791,0.0010416693,0.0004290988,0.00056928,0.0014553409,0.00082442834,0.001244298],"category_scores_gemma":[0.0031972774,0.00025783552,0.00026912894,0.00088608393,0.0003446882,0.0007116124,0.0015967018,0.00062693586,0.00095173455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0036427963,0.0021043879,0.07742135,0.0010109064,0.0004252048,0.002123211,0.0024797213,0.22806144,0.07344183,0.003045834,0.05126975,0.5549736],"study_design_scores_gemma":[0.00011095227,0.0003249653,0.016116695,0.00004058371,0.00003359025,0.00016971758,0.00087179727,0.9597855,0.01181326,0.0032926567,0.0073971367,0.000043030985],"about_ca_topic_score_codex":0.010499203,"about_ca_topic_score_gemma":0.014943877,"teacher_disagreement_score":0.010499203,"about_ca_system_score_codex":0.00043716765,"about_ca_system_score_gemma":0.0006505973,"threshold_uncertainty_score":0.02087617},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2491418,0.0010391957,0.74052304,0.001014128,0.00011722145,0.00014587888,0.00089652557,0.004923678,0.002198488],"genre_scores_gemma":[0.9650795,0.00016408162,0.03206369,0.00012998753,0.000043447206,0.000106660846,0.0010071464,0.000079735946,0.0013258487],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941504,0.00017874684,0.00003388541,0.00020687532,0.00009968809,0.000065773966],"domain_scores_gemma":[0.99610645,0.0027561611,0.00024028892,0.00035135107,0.00041383723,0.00013188802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001633844,0.0010139158,0.0008807741,0.00091869105,0.00052263815,0.00057316496,0.0013366682,0.0012360987,0.0014565311],"category_scores_gemma":[0.007840131,0.0004131113,0.0006905892,0.0008108381,0.0008181889,0.001572609,0.0010945069,0.0022924305,0.0004896827],"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":[0.00005879204,0.00009412869,0.0033864009,0.000024897821,0.00003873629,0.00004780238,0.00003740626,0.94397324,0.0005321684,0.0009890201,0.0008676965,0.049949713],"study_design_scores_gemma":[0.000002178953,0.0000108656295,0.00018245919,0.000001669989,0.0000014703544,0.0000032104897,0.000004957825,0.99771476,0.00021057577,0.0018048944,0.000060511506,0.0000023325845],"about_ca_topic_score_codex":0.017243804,"about_ca_topic_score_gemma":0.0096001495,"teacher_disagreement_score":0.017243804,"about_ca_system_score_codex":0.0014520234,"about_ca_system_score_gemma":0.0012557727,"threshold_uncertainty_score":0.034286857},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3847287,0.041650396,0.5573995,0.0018812023,0.0010852795,0.00079747016,0.0015499339,0.0036121567,0.0072953235],"genre_scores_gemma":[0.7526343,0.0039135767,0.23608948,0.000557973,0.00054493634,0.00045660388,0.004129233,0.0006236225,0.0010502324],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.96188843,0.019015986,0.0028572264,0.004973297,0.010454839,0.0008102549],"domain_scores_gemma":[0.8621141,0.10232044,0.007322048,0.013413644,0.01349314,0.0013365969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04101722,0.003863413,0.0045732413,0.0087099355,0.0014220346,0.0033660044,0.0030987407,0.0038317775,0.00082396256],"category_scores_gemma":[0.09605254,0.0006378058,0.002631884,0.0062866188,0.0018796212,0.0058921915,0.003514807,0.003190109,0.00054357434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021565002,0.0010576445,0.039309025,0.0017446134,0.0033613092,0.00018046582,0.00073042815,0.46523464,0.0039658435,0.008263889,0.0085894605,0.46540627],"study_design_scores_gemma":[0.000111870264,0.0019956874,0.015893346,0.00029481566,0.00046530803,0.00042559943,0.00042498557,0.9605491,0.007019425,0.009382459,0.0032688263,0.000168507],"about_ca_topic_score_codex":0.0047843726,"about_ca_topic_score_gemma":0.003499206,"teacher_disagreement_score":0.04101722,"about_ca_system_score_codex":0.0021871575,"about_ca_system_score_gemma":0.0018539407,"threshold_uncertainty_score":0.2169224},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69405866,0.0006774261,0.30117026,0.00014194526,0.00014285787,0.000039574406,0.0004972259,0.0006931804,0.0025788692],"genre_scores_gemma":[0.97295535,0.00025570643,0.02584477,0.000016437323,0.00006396582,0.0000125702845,0.00033868378,0.000026027486,0.00048651654],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977404,0.00003072947,0.00001686951,0.000046133657,0.00009264173,0.00003961582],"domain_scores_gemma":[0.99957234,0.00014509761,0.00009649347,0.000036401376,0.00011955431,0.000030078614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029067116,0.00035465605,0.00032569718,0.0020853402,0.00017408509,0.00049595936,0.00028763432,0.00032296474,0.0006535084],"category_scores_gemma":[0.0008327188,0.00012720742,0.00039201858,0.0012879696,0.00018089316,0.0004896074,0.00027753864,0.00034140534,0.00026660087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010553187,0.00058680784,0.09204292,0.0002793523,0.00030734992,0.0006085367,0.00022206349,0.10726016,0.22762507,0.003183746,0.002485693,0.56434304],"study_design_scores_gemma":[0.00001543484,0.0001738675,0.08676879,0.000018870844,0.00009694642,0.00056977704,0.00023279914,0.8888597,0.019165982,0.0024124973,0.0016542407,0.0000311322],"about_ca_topic_score_codex":0.001523792,"about_ca_topic_score_gemma":0.0016948881,"teacher_disagreement_score":0.0020853402,"about_ca_system_score_codex":0.00015693686,"about_ca_system_score_gemma":0.00019361559,"threshold_uncertainty_score":0.0030298233},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18263054,0.00047148543,0.8133645,0.00015840714,0.000044193268,0.00007890152,0.00027564113,0.0022006112,0.000775666],"genre_scores_gemma":[0.7513144,0.0002691933,0.24603833,0.000063570435,0.000079756406,0.000059582653,0.0007462745,0.0001420478,0.0012868491],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907494,0.00024219551,0.00006912889,0.0002013413,0.00030554255,0.00010698164],"domain_scores_gemma":[0.9977335,0.00089956453,0.00023864745,0.00027899383,0.0007189865,0.00013039591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066881353,0.0007550389,0.0010610047,0.002293507,0.0003217999,0.0010048705,0.0011699136,0.0009317465,0.00067597104],"category_scores_gemma":[0.0032211829,0.00039671262,0.0007049402,0.0009948398,0.00038207442,0.0006886609,0.001176195,0.0005735013,0.0006443746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009286661,0.00035474016,0.043373536,0.00026455842,0.00019742512,0.00075529655,0.00011466631,0.046562716,0.25148952,0.001886768,0.0034253206,0.6506468],"study_design_scores_gemma":[0.000012939393,0.00008680501,0.0073305434,0.000009534924,0.000046317422,0.00066745706,0.00002635071,0.95919704,0.0304885,0.0013716093,0.0007450685,0.000017850045],"about_ca_topic_score_codex":0.001265508,"about_ca_topic_score_gemma":0.001968848,"teacher_disagreement_score":0.002293507,"about_ca_system_score_codex":0.00019859734,"about_ca_system_score_gemma":0.0007291006,"threshold_uncertainty_score":0.0035370588},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001507433,0.9411221,0.047835622,0.0011752273,0.00061345805,0.00004758316,0.00028296126,0.00024337623,0.0071722115],"genre_scores_gemma":[0.011287326,0.9567037,0.025828708,0.0005635652,0.00058437017,0.00006042335,0.0005824481,0.00005229819,0.004337139],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99962974,0.00004957613,0.00005513239,0.00008174112,0.0001560181,0.00002772979],"domain_scores_gemma":[0.99895537,0.0005587448,0.00006164758,0.000042602536,0.00035153498,0.00003005833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076597085,0.0010111872,0.0007728552,0.0024126996,0.00024244355,0.0008181448,0.0011148236,0.00092227285,0.0036755828],"category_scores_gemma":[0.002398421,0.0004726496,0.00066579715,0.0033695288,0.00033398648,0.0016607502,0.0006084168,0.0013192238,0.0021873624],"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":[0.00002777133,0.000052062955,0.00038068593,0.0042816713,0.0000659838,0.0000495128,0.000031150445,0.0027101208,0.0008862311,0.0048673344,0.017775236,0.9688722],"study_design_scores_gemma":[0.000019605872,0.00027322516,0.0028298956,0.006290997,0.0003157014,0.0011492842,0.00010170524,0.021737041,0.005168756,0.016744599,0.9452709,0.0000982986],"about_ca_topic_score_codex":0.0024654076,"about_ca_topic_score_gemma":0.0027093969,"teacher_disagreement_score":0.0036755828,"about_ca_system_score_codex":0.0006059544,"about_ca_system_score_gemma":0.0011475256,"threshold_uncertainty_score":0.012296021},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08018883,0.0015530975,0.9135491,0.00044025545,0.00018100746,0.000099576275,0.001385832,0.0015617076,0.0010407264],"genre_scores_gemma":[0.6644927,0.00055258063,0.32599595,0.00031398842,0.00026028196,0.00021379073,0.0054356507,0.0005016552,0.0022333923],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9954536,0.0016378658,0.00038669287,0.0008007936,0.0014742769,0.00024665333],"domain_scores_gemma":[0.9824326,0.010004878,0.0016449745,0.0030121047,0.0025026917,0.00040268432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011238058,0.0015819074,0.0015171841,0.0032010784,0.00047948663,0.0014716008,0.002116241,0.002096621,0.001340985],"category_scores_gemma":[0.031950116,0.00026263518,0.0010661356,0.0021042558,0.0015694167,0.002455058,0.002587203,0.0017374951,0.00026690835],"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":[0.0010503455,0.00031361752,0.07033607,0.00059840997,0.001592982,0.00035551793,0.0002604512,0.5006758,0.0068996684,0.018695772,0.00929668,0.38992462],"study_design_scores_gemma":[0.000032479766,0.00032836047,0.010307127,0.000073551004,0.00012835709,0.0002208765,0.00006699086,0.9669151,0.0058538984,0.013851843,0.0021707928,0.000050568662],"about_ca_topic_score_codex":0.0031914923,"about_ca_topic_score_gemma":0.0036148622,"teacher_disagreement_score":0.011238058,"about_ca_system_score_codex":0.0009485571,"about_ca_system_score_gemma":0.0014800449,"threshold_uncertainty_score":0.05943322},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4362455,0.0003122935,0.560816,0.00023195238,0.000046179695,0.000037096877,0.0001373566,0.0012842922,0.0008893068],"genre_scores_gemma":[0.9668588,0.0000709077,0.032313425,0.000022509084,0.000014724952,0.000010302806,0.00011217542,0.000044381748,0.0005527255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993649,0.000088056135,0.000033720826,0.00018688636,0.00023057807,0.00009592153],"domain_scores_gemma":[0.9987822,0.00047191183,0.00024334289,0.00016714954,0.00028763316,0.00004765538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060912356,0.00049750006,0.0005251975,0.0012907621,0.00033530346,0.0007507757,0.0007530295,0.00062900997,0.0004778468],"category_scores_gemma":[0.003163927,0.00031424317,0.000563177,0.0010169351,0.00061347015,0.00094441616,0.00064926676,0.0006963829,0.00016414365],"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":[0.00032729507,0.00011505683,0.032816116,0.00009847303,0.00011404077,0.0006118684,0.00026598157,0.7041439,0.037437107,0.008782563,0.0010686206,0.2142191],"study_design_scores_gemma":[0.0000015332968,0.000013223499,0.0021656642,0.0000018319614,0.0000048979196,0.000044049797,0.000011723887,0.9933994,0.002877683,0.0013155708,0.0001604517,0.0000039911424],"about_ca_topic_score_codex":0.007383076,"about_ca_topic_score_gemma":0.0051612216,"teacher_disagreement_score":0.007383076,"about_ca_system_score_codex":0.0011202071,"about_ca_system_score_gemma":0.0005719592,"threshold_uncertainty_score":0.014680207},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4362455,0.0003122935,0.560816,0.00023195238,0.000046179695,0.000037096877,0.0001373566,0.0012842922,0.0008893068],"genre_scores_gemma":[0.9668588,0.0000709077,0.032313425,0.000022509084,0.000014724952,0.000010302806,0.00011217542,0.000044381748,0.0005527255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993649,0.000088056135,0.000033720826,0.00018688636,0.00023057807,0.00009592153],"domain_scores_gemma":[0.9987822,0.00047191183,0.00024334289,0.00016714954,0.00028763316,0.00004765538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060912356,0.00049750006,0.0005251975,0.0012907621,0.00033530346,0.0007507757,0.0007530295,0.00062900997,0.0004778468],"category_scores_gemma":[0.003163927,0.00031424317,0.000563177,0.0010169351,0.00061347015,0.00094441616,0.00064926676,0.0006963829,0.00016414365],"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":[0.00032729507,0.00011505683,0.032816116,0.00009847303,0.00011404077,0.0006118684,0.00026598157,0.7041439,0.037437107,0.008782563,0.0010686206,0.2142191],"study_design_scores_gemma":[0.0000015332968,0.000013223499,0.0021656642,0.0000018319614,0.0000048979196,0.000044049797,0.000011723887,0.9933994,0.002877683,0.0013155708,0.0001604517,0.0000039911424],"about_ca_topic_score_codex":0.007383076,"about_ca_topic_score_gemma":0.0051612216,"teacher_disagreement_score":0.007383076,"about_ca_system_score_codex":0.0011202071,"about_ca_system_score_gemma":0.0005719592,"threshold_uncertainty_score":0.014680207},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028850554,0.00045850474,0.96777314,0.00042299158,0.000059708967,0.000087956534,0.00022375825,0.0011201567,0.0010031764],"genre_scores_gemma":[0.40840295,0.0006921726,0.5855447,0.00028438043,0.00021347356,0.00018981489,0.0015803162,0.00019362384,0.002898584],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975484,0.000766493,0.00018013582,0.0004355757,0.0008229326,0.00024644058],"domain_scores_gemma":[0.99546707,0.0014103429,0.00094344636,0.0008455805,0.0011483927,0.00018522012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028553242,0.0013355986,0.0017062691,0.0028086356,0.00072831794,0.001973347,0.0021092903,0.0016259714,0.0010097154],"category_scores_gemma":[0.010454041,0.00045259707,0.0013208626,0.0026429673,0.0011871527,0.0022706955,0.0018622518,0.002275811,0.0008445357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043473492,0.0005086193,0.012181511,0.00035034958,0.00032288814,0.0005066122,0.00024957716,0.3734246,0.015881566,0.021741977,0.013988853,0.56040865],"study_design_scores_gemma":[0.00001576423,0.000035446523,0.00089483,0.000010741826,0.000012682326,0.00010240293,0.0000246913,0.9882449,0.0033109945,0.0062649297,0.0010648831,0.00001774563],"about_ca_topic_score_codex":0.003134082,"about_ca_topic_score_gemma":0.0026720949,"teacher_disagreement_score":0.003134082,"about_ca_system_score_codex":0.00084779755,"about_ca_system_score_gemma":0.0017819988,"threshold_uncertainty_score":0.015100598},"labels":[],"label_agreement":null},{"id":"W4379798269","doi":"10.21203/rs.3.rs-3024402/v1","title":"SSIVD-Net: A Novel Salient Super Image Classification & 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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03728862,0.00082754676,0.9500118,0.00027797837,0.0003520117,0.00014943641,0.0008270686,0.0059800483,0.0042854818],"genre_scores_gemma":[0.22343615,0.000674706,0.75947595,0.0003792289,0.0001685837,0.00012197072,0.0020465688,0.00040674672,0.01329009],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996631,0.00003220985,0.000014079911,0.000069185684,0.00017460022,0.000046832083],"domain_scores_gemma":[0.9997181,0.000051600044,0.000031374646,0.000052280233,0.00010796917,0.000038686474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044270055,0.0007744993,0.00079923857,0.0016330502,0.0004726088,0.00075248897,0.001478292,0.00071589084,0.0037400066],"category_scores_gemma":[0.0006864272,0.0003948859,0.0005833658,0.0009115691,0.00033780016,0.0008617136,0.0011900238,0.00087087014,0.0015980958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043817647,0.00020655614,0.0021920132,0.00014144597,0.00006844004,0.0001643809,0.000040880434,0.010209867,0.09764141,0.0028558502,0.012539017,0.87350196],"study_design_scores_gemma":[0.000031562453,0.00018261487,0.0032137781,0.000021482914,0.000053397565,0.00047373018,0.000043761283,0.8810273,0.09687289,0.00321103,0.014833762,0.000034695164],"about_ca_topic_score_codex":0.0029805196,"about_ca_topic_score_gemma":0.0073598204,"teacher_disagreement_score":0.0037400066,"about_ca_system_score_codex":0.00047867422,"about_ca_system_score_gemma":0.00074748317,"threshold_uncertainty_score":0.012511611},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22149694,0.0038882974,0.75378704,0.0007514736,0.00068268983,0.0003108584,0.0016121564,0.00757127,0.00989936],"genre_scores_gemma":[0.8615359,0.00094902795,0.12968752,0.0003054488,0.00018594465,0.000159325,0.0030867865,0.00019135764,0.003898645],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988689,0.00017587122,0.0000570672,0.00029202178,0.0004310841,0.00017512745],"domain_scores_gemma":[0.9978521,0.0006863249,0.00021267112,0.00043959782,0.0006991456,0.0001101931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016223885,0.0013421797,0.0011429982,0.0014304223,0.0005360661,0.0008805576,0.0015110191,0.0010155138,0.0028065157],"category_scores_gemma":[0.004797801,0.00021215806,0.00073740183,0.0013471332,0.00041356104,0.0013338323,0.0012190435,0.0015895163,0.0016652192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007681858,0.00068051356,0.016358951,0.0002263486,0.00022489062,0.00014444649,0.000101762205,0.05917726,0.010230723,0.0018248004,0.014349588,0.8959125],"study_design_scores_gemma":[0.00001999163,0.00025132735,0.0073860134,0.000043764663,0.000052025778,0.00023676874,0.00006875188,0.97485954,0.010642874,0.0026770108,0.0037253872,0.000036445275],"about_ca_topic_score_codex":0.0066835,"about_ca_topic_score_gemma":0.009096403,"teacher_disagreement_score":0.0066835,"about_ca_system_score_codex":0.00063409796,"about_ca_system_score_gemma":0.0010487795,"threshold_uncertainty_score":0.013289213},"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":"commentary","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009778577,0.12608977,0.10220007,0.5928514,0.0149205495,0.00006470127,0.0005034821,0.00089371455,0.15269771],"genre_scores_gemma":[0.29109704,0.16160259,0.150738,0.27129766,0.033822723,0.00035107005,0.00043702792,0.00163879,0.08901508],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.992611,0.0027954134,0.0004840356,0.0006693069,0.003204329,0.00023587604],"domain_scores_gemma":[0.93262494,0.054468866,0.002031349,0.0029472315,0.0072469916,0.00068064895],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.012012205,0.00080317346,0.0005838995,0.0056570563,0.0025028416,0.0056747384,0.002474295,0.0046271607,0.0040948796],"category_scores_gemma":[0.05417029,0.00068391644,0.0005838071,0.005363757,0.02149188,0.016014565,0.0034243197,0.014988086,0.0022199526],"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":[0.000045166318,0.00004211305,0.00087674806,0.00048648776,0.000035042507,0.0002896558,0.0041363686,0.0010382286,0.00030085188,0.72207946,0.14834939,0.122320525],"study_design_scores_gemma":[0.000011809238,0.000017645807,0.00035150172,0.0012206422,0.000023776167,0.0009023339,0.001178151,0.002439566,0.0010354697,0.54980826,0.44295275,0.000058167756],"about_ca_topic_score_codex":0.005970021,"about_ca_topic_score_gemma":0.008497443,"teacher_disagreement_score":0.9879878,"about_ca_system_score_codex":0.0030855543,"about_ca_system_score_gemma":0.0024202657,"threshold_uncertainty_score":0.063527346},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046446517,0.00044673926,0.9515395,0.00015155376,0.00004780508,0.000023452754,0.0000735043,0.00055568287,0.00071519293],"genre_scores_gemma":[0.8261608,0.00038600602,0.1710058,0.00017285926,0.000101843114,0.00010417586,0.00053444406,0.00009506919,0.0014389877],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918157,0.00026749633,0.000042530093,0.0002046729,0.00021761279,0.00008609507],"domain_scores_gemma":[0.9981433,0.0009672946,0.00015188662,0.00019928519,0.0004788634,0.00005949349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020439366,0.00087112165,0.0012655805,0.0012137503,0.000513129,0.00060048915,0.0012388062,0.00076279964,0.0005979423],"category_scores_gemma":[0.0051181708,0.0003855463,0.0009480761,0.0012413167,0.00036858875,0.0009292663,0.0007928001,0.0014765589,0.00018630337],"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":[0.000037821508,0.000059028967,0.0032164396,0.00001672803,0.00015198492,0.000029700315,0.000044459604,0.8831872,0.0010686996,0.0020703042,0.0006643774,0.10945324],"study_design_scores_gemma":[0.0000012115779,0.000008646878,0.00016682888,0.0000013962235,0.000007287042,0.0000039680444,0.0000023366406,0.9988674,0.00014722582,0.00072178093,0.00006937412,0.0000025402994],"about_ca_topic_score_codex":0.012872658,"about_ca_topic_score_gemma":0.01210319,"teacher_disagreement_score":0.012872658,"about_ca_system_score_codex":0.00078248116,"about_ca_system_score_gemma":0.001011924,"threshold_uncertainty_score":0.025595486},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02561102,0.00044393644,0.97023714,0.00006608155,0.00005724125,0.000018608465,0.000045310455,0.0013374395,0.0021831486],"genre_scores_gemma":[0.56271803,0.0008780322,0.42508712,0.00009063235,0.0001719593,0.000074339565,0.00031108008,0.00021107469,0.010457714],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997714,0.000031687498,0.000013080695,0.00005860818,0.00010731453,0.000017915203],"domain_scores_gemma":[0.9996697,0.00014839621,0.000030787276,0.000039876435,0.00010296235,0.000008305847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025183306,0.00033050985,0.0004211879,0.0005133127,0.00019586117,0.00036621004,0.0005813648,0.00041305184,0.0009974687],"category_scores_gemma":[0.0006942105,0.00018643825,0.0002747235,0.0006722599,0.00023309341,0.0006897062,0.00028447292,0.0005022362,0.00047474317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000148855,0.000099513876,0.0032564003,0.00012805698,0.000045211214,0.00013607409,0.000117942254,0.052918613,0.1099764,0.005457845,0.0034677638,0.82424736],"study_design_scores_gemma":[0.0000036573122,0.000079449805,0.0026623632,0.000010938385,0.00002390975,0.00022619698,0.000018257306,0.96071225,0.02968195,0.0033989607,0.0031671468,0.000014955562],"about_ca_topic_score_codex":0.0007001681,"about_ca_topic_score_gemma":0.00084157987,"teacher_disagreement_score":0.0009974687,"about_ca_system_score_codex":0.00025149377,"about_ca_system_score_gemma":0.00018448108,"threshold_uncertainty_score":0.0033367872},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008692159,0.00024582542,0.9848593,0.00020520549,0.000102429265,0.000064330015,0.0003635865,0.004993361,0.0004737361],"genre_scores_gemma":[0.2597899,0.0002896128,0.7340816,0.00034224815,0.00010610111,0.0002840441,0.0019921972,0.00096808566,0.0021461067],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988738,0.00037100766,0.00009434983,0.00027947043,0.00027698738,0.00010448037],"domain_scores_gemma":[0.9979672,0.00078846293,0.00012524809,0.0006287628,0.00040040075,0.00008986497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036739768,0.0011164933,0.0014155693,0.0017612668,0.0006217174,0.0011476196,0.0022284607,0.0013015809,0.0029902107],"category_scores_gemma":[0.007630264,0.00058841414,0.0018057877,0.0010881237,0.0005981193,0.0018622626,0.0029075507,0.003258964,0.0010475243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042763673,0.00027580696,0.005487389,0.00021038731,0.00039418365,0.00021738558,0.00017627268,0.41226515,0.0069281077,0.01693834,0.017644571,0.5390348],"study_design_scores_gemma":[0.000016598671,0.000033406428,0.00023720614,0.000011698823,0.000018182638,0.000047856378,0.000017909895,0.9867764,0.0015110986,0.009690405,0.0016318012,0.0000074884097],"about_ca_topic_score_codex":0.0038166053,"about_ca_topic_score_gemma":0.0042672576,"teacher_disagreement_score":0.0038166053,"about_ca_system_score_codex":0.0008111943,"about_ca_system_score_gemma":0.0016012159,"threshold_uncertainty_score":0.019430041},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003716783,0.9809021,0.006453964,0.0015382934,0.0015086717,0.000010786438,0.000054484102,0.000048761653,0.009111354],"genre_scores_gemma":[0.004319292,0.9758632,0.0059355954,0.0011244777,0.002333124,0.000014171673,0.00012203687,0.000029489383,0.010258592],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996276,0.000076203,0.000040460225,0.000059963026,0.00016980834,0.000025951158],"domain_scores_gemma":[0.9987889,0.00078126055,0.00006837082,0.000044168904,0.00027279765,0.00004460278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086195493,0.00059768074,0.00073006627,0.0020722493,0.00021034689,0.0014198304,0.00085354655,0.0008230869,0.0070706382],"category_scores_gemma":[0.001954184,0.00028247002,0.0004151828,0.0035064162,0.00045717164,0.0019504265,0.0006558298,0.001221201,0.003704658],"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":[0.000029592438,0.000041250398,0.00016792909,0.0038092032,0.000046704055,0.00007118436,0.00003891263,0.001256442,0.000721192,0.0132139195,0.059781823,0.9208218],"study_design_scores_gemma":[0.000005500984,0.000078352285,0.0005620127,0.002401128,0.000043320986,0.0005507297,0.00004482202,0.0017750005,0.00066465564,0.009791497,0.9840598,0.000023191791],"about_ca_topic_score_codex":0.0009657121,"about_ca_topic_score_gemma":0.0019169014,"teacher_disagreement_score":0.0070706382,"about_ca_system_score_codex":0.0005688702,"about_ca_system_score_gemma":0.0007878007,"threshold_uncertainty_score":0.023653626},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033651154,0.00033783814,0.9629416,0.00037857782,0.000032813743,0.000035216093,0.00021460622,0.0015375138,0.0008706144],"genre_scores_gemma":[0.800835,0.0005378508,0.1928405,0.0004665078,0.0001551269,0.00024000279,0.0017408906,0.00047911512,0.0027050446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986935,0.00046301793,0.00005539039,0.00038307495,0.00030796468,0.00009702773],"domain_scores_gemma":[0.9923808,0.005387161,0.000543962,0.00080726424,0.00073154265,0.00014918807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033947472,0.0015171791,0.0012724246,0.0012608777,0.000501894,0.0015522017,0.002027809,0.0013432852,0.0018750171],"category_scores_gemma":[0.018774634,0.0010076646,0.0009552849,0.0010226844,0.0012578184,0.004061808,0.0018781412,0.0034121547,0.00068557076],"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":[0.00013098824,0.000057770772,0.0030990432,0.000055864977,0.000074317926,0.000084782485,0.000079812606,0.90551394,0.00096797786,0.016709307,0.0021635108,0.071062736],"study_design_scores_gemma":[0.0000040510517,0.000006222293,0.00011698481,0.0000058676887,0.000003060657,0.000008100662,0.0000054828033,0.9867142,0.00025002784,0.01269122,0.00019102101,0.0000037332672],"about_ca_topic_score_codex":0.0058898735,"about_ca_topic_score_gemma":0.008202603,"teacher_disagreement_score":0.0058898735,"about_ca_system_score_codex":0.0015959115,"about_ca_system_score_gemma":0.0011153856,"threshold_uncertainty_score":0.017953396},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.165525,0.00020417196,0.8301618,0.00040692667,0.000057908877,0.000074682954,0.000038900176,0.001898952,0.0016317354],"genre_scores_gemma":[0.93801486,0.000026085363,0.060855336,0.000078544734,0.0000125184115,0.000040472063,0.000031049894,0.000040896444,0.0009002095],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974936,0.000072411676,0.000011514765,0.00006120162,0.00005440998,0.000051043204],"domain_scores_gemma":[0.99890053,0.0005571041,0.00012717948,0.0000779553,0.00023538285,0.00010181522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009684667,0.0005284753,0.0005976523,0.00032465244,0.0002857328,0.00048708095,0.00076036365,0.00058869744,0.0008611345],"category_scores_gemma":[0.0028205037,0.00029054453,0.0003118532,0.00013267758,0.0005418458,0.000461132,0.0009241434,0.00088480196,0.00020206757],"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":[0.00018234624,0.00014348477,0.0037085724,0.00005039172,0.000043140775,0.00014114726,0.00012013631,0.8588034,0.0069625825,0.0032915547,0.0010611911,0.12549204],"study_design_scores_gemma":[0.0000036934753,0.000019842215,0.00007459031,0.0000010736641,0.0000010842655,0.0000036761367,0.0000021226738,0.99901533,0.00041296126,0.00040757834,0.000056813158,0.0000011583485],"about_ca_topic_score_codex":0.0064469907,"about_ca_topic_score_gemma":0.0038308545,"teacher_disagreement_score":0.0064469907,"about_ca_system_score_codex":0.0005282598,"about_ca_system_score_gemma":0.0008794098,"threshold_uncertainty_score":0.0128189325},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008157153,0.0002883676,0.9886507,0.00022163302,0.000049455746,0.000024891495,0.000019648533,0.00072604994,0.0018621468],"genre_scores_gemma":[0.3652853,0.0009735755,0.6287391,0.00039122647,0.00011316665,0.000059113474,0.00015820764,0.00009846784,0.0041817315],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993616,0.000114504925,0.00004189177,0.00013558794,0.00029381886,0.00005266023],"domain_scores_gemma":[0.99920577,0.00020359624,0.00010963017,0.0001553602,0.00029250243,0.000033156644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067877787,0.00052614196,0.00039746592,0.00089487305,0.0002355442,0.0011075337,0.0010354103,0.0009093873,0.0008736565],"category_scores_gemma":[0.0016909924,0.00027475262,0.00043796565,0.0008210286,0.00071044895,0.0018234869,0.0010338622,0.0012944778,0.0007395757],"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":[0.00012272535,0.00009853476,0.0021621487,0.00017750588,0.000046952497,0.0001576656,0.00016107484,0.13809764,0.15582603,0.027942752,0.003165242,0.6720417],"study_design_scores_gemma":[0.0000053529698,0.00007306577,0.0007576478,0.000022311317,0.000016302307,0.00012160412,0.000031804364,0.9346932,0.043614686,0.009353774,0.011284425,0.000025863816],"about_ca_topic_score_codex":0.001917345,"about_ca_topic_score_gemma":0.002380357,"teacher_disagreement_score":0.001917345,"about_ca_system_score_codex":0.0007512398,"about_ca_system_score_gemma":0.0008930341,"threshold_uncertainty_score":0.005450666},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056073613,0.00083852845,0.9315323,0.00028133564,0.00026271274,0.00020678695,0.000529791,0.0078963805,0.0023784647],"genre_scores_gemma":[0.25133365,0.0005916366,0.73886275,0.00033967572,0.00014024117,0.00020868331,0.002304256,0.00023978038,0.005979322],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993925,0.000027161803,0.00005145677,0.00023088172,0.00020315209,0.0000948258],"domain_scores_gemma":[0.99951947,0.000056656194,0.00005448178,0.000056881792,0.00028751793,0.000025002593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046009288,0.000793365,0.000978771,0.0020936748,0.0005425624,0.0010067679,0.0014133702,0.00094395695,0.00160618],"category_scores_gemma":[0.0010085771,0.0003557939,0.0007372614,0.0013449142,0.00034147178,0.0013866435,0.0011160668,0.00085347303,0.001071094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002582808,0.0001544476,0.006139071,0.00016963268,0.00010584994,0.00022312219,0.00011795167,0.027315233,0.05984707,0.002576148,0.011854208,0.891239],"study_design_scores_gemma":[0.00006298654,0.00020533559,0.006285579,0.000023524053,0.00006957667,0.00067777,0.00010360608,0.9312308,0.04227584,0.0017197162,0.017291011,0.000054318407],"about_ca_topic_score_codex":0.006351789,"about_ca_topic_score_gemma":0.006809392,"teacher_disagreement_score":0.006351789,"about_ca_system_score_codex":0.0006298726,"about_ca_system_score_gemma":0.00094709825,"threshold_uncertainty_score":0.012629628},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12644745,0.0019699272,0.8456106,0.010257402,0.00048629602,0.00015109424,0.00020157342,0.009366908,0.005508638],"genre_scores_gemma":[0.8408302,0.000423596,0.15084156,0.0017658976,0.00019225896,0.00009017091,0.00027039243,0.0005792202,0.005006689],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953577,0.0016287646,0.00019627482,0.0011227826,0.001152499,0.0005419968],"domain_scores_gemma":[0.98881114,0.004207419,0.0010485227,0.004118769,0.001131564,0.0006825737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006010209,0.0014076674,0.0018308485,0.0012406146,0.0024282974,0.0037200823,0.0027105503,0.004051622,0.0027493057],"category_scores_gemma":[0.029113382,0.0009369369,0.00085466396,0.00076163135,0.004462561,0.01607306,0.005943655,0.0050705117,0.0014183722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022190248,0.0006526447,0.026984144,0.00033093357,0.00042843164,0.001416078,0.002828775,0.17273933,0.010403094,0.176968,0.056459904,0.5485696],"study_design_scores_gemma":[0.00006993441,0.00020922428,0.0007613933,0.0000959042,0.000053153894,0.0006670121,0.00059969665,0.8294881,0.010141005,0.14744775,0.010397484,0.00006934055],"about_ca_topic_score_codex":0.0019457855,"about_ca_topic_score_gemma":0.0024542862,"teacher_disagreement_score":0.006010209,"about_ca_system_score_codex":0.0013175368,"about_ca_system_score_gemma":0.0011410905,"threshold_uncertainty_score":0.03178537},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03554139,0.00039877076,0.9604618,0.00026037885,0.000076864555,0.00006891067,0.0000854808,0.0023243616,0.00078201934],"genre_scores_gemma":[0.6308359,0.00027788107,0.36436638,0.0003440161,0.00012376493,0.0002553377,0.0007597183,0.00019307606,0.0028439173],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994747,0.00008180768,0.000040961902,0.0001822896,0.00013875296,0.00008137194],"domain_scores_gemma":[0.9984804,0.00077009876,0.00014713003,0.00013589601,0.00040052872,0.00006603385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010113833,0.0009872124,0.0013442829,0.0011227,0.00039995016,0.0008303884,0.0028850508,0.001267216,0.0014381018],"category_scores_gemma":[0.003858337,0.00044027355,0.00079806725,0.0009704373,0.0005963387,0.0011752783,0.001033783,0.0020915938,0.0006852154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036295265,0.00031075362,0.004317548,0.0001108635,0.00010836105,0.00017532297,0.00011779376,0.31419086,0.008002054,0.0035363093,0.0036853335,0.66508186],"study_design_scores_gemma":[0.0000063429607,0.000028626673,0.00017221537,0.000003165858,0.000005684091,0.00002504117,0.0000047775593,0.9971275,0.00127479,0.0011280902,0.00021977519,0.000003911748],"about_ca_topic_score_codex":0.0060577574,"about_ca_topic_score_gemma":0.0054567484,"teacher_disagreement_score":0.0060577574,"about_ca_system_score_codex":0.0008473344,"about_ca_system_score_gemma":0.0012372113,"threshold_uncertainty_score":0.012045026},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7159147,0.0015991087,0.27352074,0.0018098835,0.00013629546,0.0003797157,0.0016303825,0.0016501107,0.003359052],"genre_scores_gemma":[0.9735318,0.00018645027,0.024738058,0.00011154603,0.000034879253,0.00006989419,0.00059319386,0.000033692835,0.0007004579],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912435,0.000229575,0.00007781528,0.0002376413,0.0001855485,0.00014514202],"domain_scores_gemma":[0.9960593,0.002695611,0.00033541003,0.00016819555,0.000603787,0.00013771871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023762034,0.0013687523,0.0010538463,0.0014046691,0.00049675896,0.0014868072,0.00178036,0.001270064,0.00079083565],"category_scores_gemma":[0.0064941333,0.00026998218,0.0009457358,0.0010068964,0.00052034366,0.0008679359,0.0008387203,0.0015734866,0.00019666979],"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":[0.00030367606,0.00040771475,0.03425406,0.00017656398,0.00013771329,0.0005010813,0.00017393919,0.90666,0.0021124245,0.0027973522,0.0018736224,0.05060182],"study_design_scores_gemma":[0.0000045712754,0.000022145052,0.0014679559,0.0000050590097,0.0000072411626,0.00002363803,0.000023257311,0.99741983,0.00033766573,0.00055159995,0.00013065836,0.0000063451107],"about_ca_topic_score_codex":0.031641107,"about_ca_topic_score_gemma":0.02132421,"teacher_disagreement_score":0.031641107,"about_ca_system_score_codex":0.0015092804,"about_ca_system_score_gemma":0.00094100076,"threshold_uncertainty_score":0.062913835},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033697058,0.0003110939,0.95432943,0.00008556779,0.00016647749,0.00006964598,0.00016146513,0.00937743,0.0018019412],"genre_scores_gemma":[0.56254536,0.00027855914,0.4323468,0.00021069098,0.00014653619,0.00015781756,0.0005629379,0.0002986788,0.0034527034],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99894184,0.000089973146,0.00005972387,0.0002786259,0.00055513484,0.00007461114],"domain_scores_gemma":[0.9984648,0.0004039101,0.00029700875,0.00025836233,0.00048234386,0.00009363798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039855164,0.0011007997,0.0009589965,0.001688269,0.00040125544,0.0009003175,0.0015968673,0.0010247871,0.0015451993],"category_scores_gemma":[0.0020853092,0.0003241919,0.00045698782,0.0008825164,0.0003319919,0.0012591725,0.0011317158,0.00084621,0.0014644781],"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":[0.0005972636,0.00035710254,0.0075052306,0.00039540094,0.000090357884,0.00040242603,0.00017435467,0.027780075,0.21370733,0.0020815292,0.004155212,0.7427537],"study_design_scores_gemma":[0.000041102106,0.0004183151,0.007695842,0.000042005206,0.000087158165,0.0007406318,0.00009046461,0.83642894,0.14041348,0.0031977005,0.010767834,0.00007645993],"about_ca_topic_score_codex":0.0006793963,"about_ca_topic_score_gemma":0.0011644629,"teacher_disagreement_score":0.001688269,"about_ca_system_score_codex":0.00038672183,"about_ca_system_score_gemma":0.0004898924,"threshold_uncertainty_score":0.005169153},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36582723,0.00074505835,0.6290051,0.0009359525,0.0001045702,0.000020374851,0.00027516705,0.0010359096,0.0020506768],"genre_scores_gemma":[0.9772389,0.00015073238,0.02016649,0.00004976764,0.000074665986,0.000009117779,0.0002668817,0.000049688064,0.0019936601],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995252,0.00011228127,0.000023870203,0.00011997319,0.00011391194,0.00010481007],"domain_scores_gemma":[0.9974147,0.0015583956,0.0003619008,0.0002201238,0.00032472366,0.00012007491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012560568,0.000480143,0.0009071753,0.0010439428,0.00037702496,0.0014014422,0.0014579687,0.0010111597,0.0015678656],"category_scores_gemma":[0.005273594,0.0003988232,0.00048649998,0.0011559427,0.00059608475,0.0022260342,0.0011041678,0.0015613649,0.00030799344],"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":[0.0006677787,0.00038637972,0.038722206,0.0001483301,0.0002844784,0.00065324066,0.00020090034,0.5979116,0.009178527,0.054545153,0.006380719,0.29092073],"study_design_scores_gemma":[0.0000027792348,0.000007616668,0.0006751452,0.0000016332951,0.000003146917,0.000019467641,0.0000073081883,0.9910454,0.00028296295,0.007827875,0.00012455138,0.0000020118748],"about_ca_topic_score_codex":0.0040975874,"about_ca_topic_score_gemma":0.0041579455,"teacher_disagreement_score":0.0040975874,"about_ca_system_score_codex":0.00056727545,"about_ca_system_score_gemma":0.00064308866,"threshold_uncertainty_score":0.008147478},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023298005,0.00027010593,0.97296685,0.00012525241,0.000046616096,0.00003853635,0.0001801029,0.0024830748,0.0005914661],"genre_scores_gemma":[0.53367555,0.0005040052,0.45987776,0.00018638618,0.00012716252,0.00013389488,0.0017480722,0.00034043702,0.0034067347],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899536,0.00022192653,0.000045660974,0.00030114639,0.00032609672,0.00010983467],"domain_scores_gemma":[0.99853075,0.0004929308,0.00018572513,0.00027230196,0.00043895975,0.00007934072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011715501,0.0011717264,0.0011454413,0.0019223547,0.00046123902,0.00101824,0.0014234282,0.00071736355,0.001067283],"category_scores_gemma":[0.0037202945,0.00053603825,0.0012699527,0.0015887802,0.00065212813,0.0018966276,0.0016507014,0.0020166554,0.0007311719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030618237,0.0002569269,0.010041378,0.000094627794,0.00031816924,0.00029846837,0.0001481847,0.14856218,0.0263411,0.009167029,0.0067178123,0.7977479],"study_design_scores_gemma":[0.000005160236,0.00002551988,0.0013540838,0.0000052177284,0.000015966127,0.00012682806,0.00001262633,0.9882459,0.0044758576,0.0047747716,0.0009431035,0.000014887303],"about_ca_topic_score_codex":0.004413123,"about_ca_topic_score_gemma":0.0051386505,"teacher_disagreement_score":0.004413123,"about_ca_system_score_codex":0.00047633811,"about_ca_system_score_gemma":0.0010670623,"threshold_uncertainty_score":0.008774877},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14645347,0.00232701,0.83079165,0.0008099832,0.000438175,0.00024135219,0.00054618885,0.003949567,0.014442577],"genre_scores_gemma":[0.8546879,0.00067110517,0.13628733,0.00030475252,0.00015552179,0.00010070644,0.0007288155,0.000056738205,0.0070071523],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997137,0.000044857898,0.000013644559,0.00006055259,0.00010902367,0.000058236303],"domain_scores_gemma":[0.9997845,0.00006039931,0.00003414146,0.00002326634,0.000084351166,0.000013297723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035172838,0.0009054836,0.00044518977,0.0011275724,0.0003118255,0.0006353668,0.0006781743,0.0005345746,0.0013578358],"category_scores_gemma":[0.00085445266,0.00017249935,0.00052284886,0.00069156097,0.00022356423,0.0008084799,0.0004620791,0.0006794556,0.00052772707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025569432,0.00019767554,0.005807479,0.000113972295,0.00013739479,0.00033564997,0.00009620183,0.46075514,0.02206566,0.0060256566,0.0048556123,0.49935383],"study_design_scores_gemma":[0.000003738305,0.000034977096,0.0006533516,0.0000039941265,0.000011250271,0.000050197308,0.000015869147,0.99429,0.0027089391,0.0011771808,0.0010447854,0.0000057141897],"about_ca_topic_score_codex":0.010707145,"about_ca_topic_score_gemma":0.011954805,"teacher_disagreement_score":0.010707145,"about_ca_system_score_codex":0.00073978165,"about_ca_system_score_gemma":0.00054270506,"threshold_uncertainty_score":0.021289647},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007005851,0.4202778,0.07961562,0.074231006,0.01475826,0.0003036845,0.00030898637,0.00025475142,0.40324408],"genre_scores_gemma":[0.12963352,0.63381517,0.034573745,0.027451681,0.02314846,0.00053982483,0.0006214034,0.00014984177,0.15006632],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988256,0.00025810496,0.00013398264,0.00021629035,0.0004506821,0.00011533516],"domain_scores_gemma":[0.9988392,0.0005192512,0.000086816064,0.00007969456,0.00041008712,0.0000650378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014105929,0.0005618725,0.0003085801,0.0027621856,0.0013532393,0.003337537,0.0010405866,0.0023946716,0.010778139],"category_scores_gemma":[0.0020768088,0.00022927925,0.0006228514,0.0031838606,0.0018156222,0.0062610283,0.00096403953,0.0024617773,0.003995514],"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":[0.00004255061,0.00006493387,0.0015770011,0.0016507132,0.000020264239,0.00072523055,0.0009133303,0.0010651135,0.0027667887,0.6592318,0.0747229,0.25721946],"study_design_scores_gemma":[0.0000062471145,0.000035730227,0.0011852663,0.0006387471,0.00001293429,0.0011636149,0.00037141942,0.0011138465,0.001291271,0.091913454,0.90223825,0.000029292694],"about_ca_topic_score_codex":0.001408279,"about_ca_topic_score_gemma":0.0007407206,"teacher_disagreement_score":0.010778139,"about_ca_system_score_codex":0.0016640787,"about_ca_system_score_gemma":0.002022113,"threshold_uncertainty_score":0.03605646},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025778595,0.0005818766,0.9182789,0.00046070237,0.0003536409,0.00033780877,0.0005905344,0.04931115,0.0043067643],"genre_scores_gemma":[0.3906228,0.00044551515,0.5898576,0.00080074277,0.0003663333,0.00042174794,0.0015759225,0.00066895597,0.015240419],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918705,0.000089618996,0.00006122942,0.00029000355,0.00027360348,0.00009844795],"domain_scores_gemma":[0.9988556,0.00022484493,0.000079188714,0.00023330016,0.0005203369,0.00008671803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009448594,0.0008562526,0.0010484977,0.001542822,0.0007410386,0.0020112952,0.0031828664,0.0013596837,0.008723078],"category_scores_gemma":[0.0016498616,0.0006116798,0.00051202305,0.001104655,0.00039899183,0.0024976423,0.0013991392,0.0011968543,0.0039042854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010715844,0.0006625028,0.0071594943,0.00040326404,0.00036021302,0.00052535464,0.00032935085,0.02937263,0.10960183,0.010071314,0.04886907,0.79157335],"study_design_scores_gemma":[0.00008875007,0.00037373125,0.0025344212,0.000043764692,0.0002198109,0.00046356878,0.00006172952,0.9187856,0.050497636,0.0066217156,0.020225396,0.00008378155],"about_ca_topic_score_codex":0.0065745907,"about_ca_topic_score_gemma":0.009379746,"teacher_disagreement_score":0.008723078,"about_ca_system_score_codex":0.0010757959,"about_ca_system_score_gemma":0.0013592468,"threshold_uncertainty_score":0.0291816},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067377836,0.00011785469,0.9924987,0.00007109144,0.000024165878,0.000020568705,0.000009080415,0.0001503501,0.00037033012],"genre_scores_gemma":[0.40318736,0.00031907915,0.5932074,0.00014795056,0.0000803694,0.0001956454,0.00011282288,0.00009176084,0.002657639],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991967,0.00019642283,0.000052509233,0.00016497138,0.00031366618,0.00007578208],"domain_scores_gemma":[0.99944097,0.00021896575,0.00009780995,0.00004347602,0.00016490425,0.00003389948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015078628,0.00089280354,0.0013032345,0.0011747949,0.00042827928,0.0007649708,0.0014581131,0.0011285874,0.00068534445],"category_scores_gemma":[0.0018504154,0.00043762947,0.0012895095,0.0010943788,0.0005815751,0.0010360606,0.0011041688,0.0010127723,0.00018042514],"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":[0.00012100756,0.00009553006,0.0015608186,0.00011850748,0.00014155895,0.00013015413,0.0001382175,0.81420827,0.014321258,0.006569916,0.0010988067,0.16149597],"study_design_scores_gemma":[0.000003482421,0.000015249167,0.000111495254,0.000002293571,0.000005707786,0.000013231118,0.0000036121567,0.99847406,0.0006723001,0.00047578066,0.00021822758,0.0000044687845],"about_ca_topic_score_codex":0.0039286576,"about_ca_topic_score_gemma":0.0028319734,"teacher_disagreement_score":0.0039286576,"about_ca_system_score_codex":0.0007033609,"about_ca_system_score_gemma":0.0009567163,"threshold_uncertainty_score":0.007974446},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2381449,0.0011345197,0.7521265,0.00049774017,0.00041342303,0.00014672747,0.0010427338,0.0044935574,0.0019998078],"genre_scores_gemma":[0.8322468,0.00045446644,0.1639757,0.00012713576,0.00013818064,0.00008795006,0.0021352114,0.00016376682,0.00067085156],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957632,0.00089104444,0.00042476345,0.0008540214,0.0017969833,0.0002700553],"domain_scores_gemma":[0.99225825,0.0036431109,0.0009745529,0.0012858021,0.0016617505,0.00017646418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028525016,0.000954708,0.001110159,0.0027473816,0.00064792694,0.0015378249,0.0014196971,0.00085787947,0.00054860994],"category_scores_gemma":[0.013441085,0.00021503572,0.0007998739,0.003270863,0.0007391213,0.0020452978,0.0011110716,0.0010397448,0.00031701213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083217473,0.00046369233,0.057265703,0.00060260034,0.000519312,0.00080856803,0.0004581708,0.40312657,0.025219243,0.010818878,0.0058694347,0.4940157],"study_design_scores_gemma":[0.0000096908325,0.00011364778,0.0077024945,0.000018925914,0.000027562735,0.0003315662,0.00012996064,0.9720552,0.010896711,0.0066352594,0.0020487555,0.000030157118],"about_ca_topic_score_codex":0.0025323587,"about_ca_topic_score_gemma":0.002205943,"teacher_disagreement_score":0.0028525016,"about_ca_system_score_codex":0.0008370389,"about_ca_system_score_gemma":0.0007168453,"threshold_uncertainty_score":0.015085638},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052732654,0.00035912954,0.9437658,0.0004332967,0.00007663611,0.000036680292,0.00008212554,0.0012147662,0.0012988745],"genre_scores_gemma":[0.7975146,0.00031552388,0.19501601,0.0004836826,0.000080951046,0.00005608251,0.00037727124,0.00027416888,0.00588179],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947876,0.00012984923,0.000020824991,0.00017396672,0.00013494873,0.00006156129],"domain_scores_gemma":[0.99853075,0.0006276389,0.00016338483,0.00030706407,0.00026912853,0.00010201658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012885556,0.0008200543,0.00082401955,0.000424141,0.0004394884,0.00064486545,0.0014705517,0.00086387567,0.0014594704],"category_scores_gemma":[0.0033076152,0.0003999986,0.000489933,0.00047894698,0.0010931492,0.001604601,0.00173783,0.0020435222,0.00043407961],"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":[0.00017979207,0.000094426156,0.0013481358,0.00005164567,0.00007222215,0.00010484198,0.00009311624,0.86553943,0.009052255,0.012476479,0.0031797502,0.10780806],"study_design_scores_gemma":[0.0000023840007,0.000016300626,0.000097274286,0.0000028012946,0.0000034825857,0.000015288215,0.0000037294838,0.99508756,0.0014318146,0.00309637,0.00023909227,0.0000038906223],"about_ca_topic_score_codex":0.0045899143,"about_ca_topic_score_gemma":0.0049664117,"teacher_disagreement_score":0.0045899143,"about_ca_system_score_codex":0.00088814885,"about_ca_system_score_gemma":0.0006853532,"threshold_uncertainty_score":0.009126425},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16624416,0.0010261884,0.82399696,0.0005993941,0.00020033619,0.00019439761,0.0012189334,0.0039483276,0.0025712785],"genre_scores_gemma":[0.89768916,0.00023417584,0.09918786,0.00010611572,0.00013682472,0.0001079129,0.0010976569,0.000082574465,0.0013576403],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986327,0.0003554771,0.000096031785,0.00039214102,0.00039139992,0.00013218961],"domain_scores_gemma":[0.9970511,0.0011706693,0.00052012457,0.00047250514,0.00063107297,0.00015454767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015850359,0.00079876615,0.0010908529,0.0018396659,0.00047189984,0.0006947503,0.0014388012,0.0006233924,0.0011450883],"category_scores_gemma":[0.006959019,0.00023947716,0.0007959468,0.0013691554,0.0006064932,0.0014203314,0.0015463377,0.0010435852,0.00047513883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00089486997,0.0008239927,0.1153332,0.0003632395,0.00062974694,0.00059918227,0.00057258026,0.25884917,0.011482252,0.014209989,0.013129742,0.58311194],"study_design_scores_gemma":[0.000009653723,0.0001214076,0.00706319,0.0000114797795,0.000029870938,0.00016666568,0.00005364165,0.984082,0.0023764994,0.0045591053,0.0015028433,0.000023579567],"about_ca_topic_score_codex":0.005811832,"about_ca_topic_score_gemma":0.008371518,"teacher_disagreement_score":0.005811832,"about_ca_system_score_codex":0.0006938477,"about_ca_system_score_gemma":0.000986612,"threshold_uncertainty_score":0.011556029},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2632266,0.0013317895,0.7241748,0.0008043524,0.00036340294,0.00014257485,0.00092808285,0.0070187105,0.0020096472],"genre_scores_gemma":[0.8201909,0.00022717833,0.17641197,0.0001341465,0.00012517082,0.000072192066,0.0018127279,0.000120856435,0.00090485957],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9959466,0.00084290904,0.00035118012,0.0010769154,0.0014519285,0.0003305469],"domain_scores_gemma":[0.99043393,0.0045521017,0.0011289184,0.0014357836,0.002171508,0.00027766702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003526353,0.0012196242,0.0016648117,0.0059592044,0.0009166582,0.0016010077,0.0016158413,0.0014287322,0.00063205283],"category_scores_gemma":[0.013296215,0.00028141978,0.0009030066,0.0034754535,0.0006658422,0.0017751753,0.0014453117,0.001583523,0.00054914854],"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":[0.00046944842,0.0005628022,0.050399035,0.00023839499,0.0002498856,0.00061576086,0.0002895541,0.20345226,0.019139215,0.002927173,0.0066596298,0.7149969],"study_design_scores_gemma":[0.000011349045,0.000069619186,0.005455579,0.000012305486,0.000030173367,0.00021931538,0.00008733119,0.9783271,0.009633416,0.0043006726,0.0018347917,0.000018355888],"about_ca_topic_score_codex":0.0029803016,"about_ca_topic_score_gemma":0.0021516823,"teacher_disagreement_score":0.0059592044,"about_ca_system_score_codex":0.0009346837,"about_ca_system_score_gemma":0.00087084033,"threshold_uncertainty_score":0.0186494},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7431265,0.0009816301,0.24937123,0.0012116682,0.00026339787,0.00008829491,0.00046000665,0.0014705576,0.003026787],"genre_scores_gemma":[0.9922552,0.0001320062,0.006770219,0.00010552642,0.000024246645,0.00000954584,0.00031529105,0.000101762795,0.00028628405],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978415,0.0004894165,0.000118061624,0.00067110226,0.00052351545,0.00035636296],"domain_scores_gemma":[0.989179,0.005472382,0.001601023,0.002042315,0.0013803157,0.00032492777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031125129,0.0009873883,0.00064962456,0.0008801436,0.00063255214,0.0015030254,0.0009494106,0.0012065638,0.0008125211],"category_scores_gemma":[0.033424,0.0005034932,0.00061946304,0.0008032163,0.0017860221,0.0030045027,0.0014433655,0.0022755263,0.00034502483],"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":[0.00077500177,0.00029314676,0.09051428,0.0001949764,0.0003051374,0.00070503633,0.00070039544,0.73382664,0.02736563,0.0042609633,0.0038320243,0.13722676],"study_design_scores_gemma":[0.000014854944,0.00013004614,0.044544984,0.000059984406,0.000056961006,0.0004442383,0.00026824424,0.9276864,0.018237114,0.0070794304,0.0014132002,0.000064540785],"about_ca_topic_score_codex":0.007506758,"about_ca_topic_score_gemma":0.004748858,"teacher_disagreement_score":0.007506758,"about_ca_system_score_codex":0.0012131725,"about_ca_system_score_gemma":0.0008491077,"threshold_uncertainty_score":0.016460717},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32918972,0.0050881337,0.64035016,0.0033684291,0.00159233,0.00015128686,0.001817626,0.0036784522,0.014763834],"genre_scores_gemma":[0.9585007,0.0010818513,0.035120174,0.00031147958,0.00016034665,0.00008457575,0.0010356569,0.00012241112,0.003582832],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871707,0.00026766944,0.000083330924,0.00038194854,0.0004010151,0.00014906874],"domain_scores_gemma":[0.9943995,0.0026614717,0.0006075596,0.0010500011,0.0011515871,0.00012982578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016191887,0.0011411437,0.00095557945,0.0010492567,0.00057813607,0.0015091347,0.0019281546,0.0017785024,0.0038700176],"category_scores_gemma":[0.014188437,0.00041668888,0.0005078396,0.00089520146,0.0009459011,0.0022905532,0.00096332113,0.0012718684,0.0011187514],"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":[0.00063037995,0.00016622932,0.017155878,0.00055747305,0.00027170594,0.00081954873,0.00015950543,0.7756725,0.013603227,0.025096111,0.009291311,0.15657611],"study_design_scores_gemma":[0.000020719925,0.00014837174,0.0022149507,0.00003496099,0.00004558373,0.0003938092,0.00005105242,0.96145266,0.007387371,0.024800586,0.003431481,0.000018484074],"about_ca_topic_score_codex":0.0025984836,"about_ca_topic_score_gemma":0.0019828924,"teacher_disagreement_score":0.0038700176,"about_ca_system_score_codex":0.0011950311,"about_ca_system_score_gemma":0.0005447288,"threshold_uncertainty_score":0.0129464865},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076389057,0.0047747535,0.9377298,0.00072863314,0.0008237329,0.00016396976,0.0014742903,0.007398502,0.039267514],"genre_scores_gemma":[0.20028466,0.004993306,0.6662566,0.00070861954,0.0012548256,0.00041258585,0.008913992,0.0010203663,0.11615498],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99895525,0.00018430209,0.0000627751,0.00025176172,0.0004768644,0.000069121335],"domain_scores_gemma":[0.99878746,0.00042783775,0.00007539903,0.00025728386,0.00041050607,0.000041527903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069972017,0.0011923397,0.0012239313,0.0012201786,0.00040829778,0.002018939,0.0012885451,0.0010454606,0.016586736],"category_scores_gemma":[0.0037711633,0.00041313903,0.0006384736,0.0018483802,0.00035319474,0.0017252162,0.000957204,0.0014923022,0.01816757],"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":[0.00006240769,0.00007269513,0.00034212662,0.00013993445,0.00004759423,0.00003515542,0.00001977483,0.01939593,0.0018095295,0.013215578,0.033796325,0.93106294],"study_design_scores_gemma":[0.000033350167,0.000161542,0.0010811645,0.00012907042,0.000061814666,0.0002692761,0.000070078124,0.78791124,0.010692709,0.08048027,0.119060524,0.00004899152],"about_ca_topic_score_codex":0.0007748769,"about_ca_topic_score_gemma":0.00096013193,"teacher_disagreement_score":0.016586736,"about_ca_system_score_codex":0.00028811267,"about_ca_system_score_gemma":0.0006315338,"threshold_uncertainty_score":0.05548823},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.081191,0.00025714515,0.9155635,0.0002696781,0.000053170876,0.000026912507,0.00015537882,0.0016405366,0.0008426203],"genre_scores_gemma":[0.9306545,0.00012775366,0.06737433,0.00008638328,0.00003844621,0.00002898595,0.00031303708,0.00004553816,0.0013310162],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996666,0.000060186576,0.0000201216,0.000081923325,0.00009697742,0.000074209376],"domain_scores_gemma":[0.99935144,0.00024974716,0.00008394509,0.00006864217,0.0002100314,0.000036204114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065515476,0.0006028796,0.0004925963,0.00068756344,0.00030332746,0.00041472312,0.000771301,0.00058433483,0.000595696],"category_scores_gemma":[0.0018793135,0.00020308782,0.00030734373,0.00063265435,0.00034666003,0.0008835505,0.0006654391,0.0011324676,0.0001789237],"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":[0.00021504128,0.00021292527,0.009063532,0.000049876104,0.00008039456,0.00012563544,0.0000939387,0.5954583,0.016692474,0.005985703,0.00261844,0.3694036],"study_design_scores_gemma":[0.0000010024368,0.00001000967,0.00027607754,0.0000013495206,0.0000023717168,0.000008942251,0.0000031875122,0.9975286,0.0010715607,0.0009628874,0.00013183836,0.0000020581383],"about_ca_topic_score_codex":0.007112409,"about_ca_topic_score_gemma":0.007398653,"teacher_disagreement_score":0.007112409,"about_ca_system_score_codex":0.00090633985,"about_ca_system_score_gemma":0.0008235851,"threshold_uncertainty_score":0.014142036},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007844717,0.00011795851,0.99061245,0.000085179105,0.000020501595,0.000015444572,0.00004297559,0.00074711256,0.00051360606],"genre_scores_gemma":[0.28879362,0.00027501758,0.70330524,0.00022603566,0.000054761444,0.000094843155,0.00064241234,0.0004935971,0.006114431],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939096,0.00012673112,0.000030294288,0.00023391635,0.00014927945,0.000068818845],"domain_scores_gemma":[0.99899787,0.0002636128,0.00011774594,0.0002799309,0.0002699916,0.00007083589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009320643,0.0009439488,0.0012201112,0.00093080016,0.00054791046,0.0011111944,0.0023383484,0.0016706351,0.0020005628],"category_scores_gemma":[0.002419804,0.00090682483,0.0009874324,0.0010814097,0.0010135904,0.0020562704,0.0020946346,0.0022037935,0.0012942244],"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":[0.000114473245,0.00007597564,0.0010651372,0.000080613296,0.000095691765,0.00006646441,0.0001726598,0.6397639,0.011460634,0.017773932,0.0034773173,0.32585314],"study_design_scores_gemma":[0.0000023621708,0.000008461268,0.00007223112,0.00000428229,0.0000031872542,0.000014564179,0.000008554869,0.9939173,0.0014841324,0.0040397495,0.00044017762,0.000004940924],"about_ca_topic_score_codex":0.008305058,"about_ca_topic_score_gemma":0.009863133,"teacher_disagreement_score":0.008305058,"about_ca_system_score_codex":0.0012427014,"about_ca_system_score_gemma":0.0011643032,"threshold_uncertainty_score":0.016513467},"labels":[],"label_agreement":null},{"id":"W4385564986","doi":"10.21203/rs.3.rs-3024402/v2","title":"SSIVD-Net: A Novel Salient Super Image Classification & 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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03728868,0.00082754757,0.9500118,0.0002779789,0.00035201205,0.0001494367,0.0008270694,0.0059800544,0.004285484],"genre_scores_gemma":[0.2234362,0.0006747063,0.7594759,0.0003792287,0.00016858369,0.000121970836,0.0020465679,0.0004067463,0.01329007],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996631,0.000032209882,0.000014079911,0.00006918562,0.00017460022,0.000046832127],"domain_scores_gemma":[0.9997181,0.000051600044,0.000031374646,0.000052280335,0.00010796917,0.000038686474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044270055,0.00077449967,0.00079923926,0.0016330518,0.0004726088,0.00075248897,0.0014782915,0.00071589084,0.003740003],"category_scores_gemma":[0.0006864278,0.00039488627,0.000583366,0.0009115695,0.0003378003,0.000861714,0.001190025,0.0008708706,0.0015980958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043817668,0.00020655614,0.0021920153,0.00014144623,0.00006844004,0.0001643809,0.00004088051,0.010209875,0.09764141,0.0028558485,0.01253901,0.87350196],"study_design_scores_gemma":[0.000031562457,0.0001826149,0.0032137802,0.000021482916,0.00005339752,0.00047373024,0.00004376133,0.8810274,0.09687279,0.0032110275,0.014833736,0.00003469517],"about_ca_topic_score_codex":0.0029805212,"about_ca_topic_score_gemma":0.0073598204,"teacher_disagreement_score":0.003740003,"about_ca_system_score_codex":0.00047867445,"about_ca_system_score_gemma":0.0007474836,"threshold_uncertainty_score":0.012511611},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03941364,0.00050125306,0.95743424,0.00029137847,0.00009251934,0.00009156982,0.00009805353,0.0012442645,0.0008330442],"genre_scores_gemma":[0.8000391,0.00028381118,0.19547518,0.0005025464,0.00024680613,0.0001856214,0.00048460325,0.00013008807,0.0026522218],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998694,0.00024141175,0.00007515078,0.00048724675,0.0002779178,0.00022440116],"domain_scores_gemma":[0.9978017,0.0010129043,0.00024600438,0.000193328,0.0005541325,0.00019193166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017485889,0.0016715149,0.002028858,0.0013961385,0.0005257427,0.00092149904,0.0024218801,0.0017714275,0.0012017448],"category_scores_gemma":[0.00425668,0.0004386981,0.0012519164,0.0011606036,0.00064613525,0.0017762858,0.0018158868,0.002074248,0.0003914329],"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":[0.00060205965,0.0006804896,0.005861931,0.00022448384,0.00021663494,0.00031819261,0.00020962603,0.26458266,0.021361632,0.0036139877,0.004722219,0.697606],"study_design_scores_gemma":[0.000011098842,0.00005114229,0.00043067092,0.000003600182,0.0000140734155,0.00003203114,0.0000115009,0.99530256,0.0020293184,0.0018810377,0.000225232,0.000007730077],"about_ca_topic_score_codex":0.0065508387,"about_ca_topic_score_gemma":0.004602594,"teacher_disagreement_score":0.0065508387,"about_ca_system_score_codex":0.0010965571,"about_ca_system_score_gemma":0.0011778533,"threshold_uncertainty_score":0.013025463},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017967587,0.004684004,0.9594305,0.0020604264,0.00016933877,0.000048323123,0.00016546765,0.0015449403,0.013929381],"genre_scores_gemma":[0.83828247,0.0027548769,0.15296258,0.0002637371,0.00013766754,0.00010978189,0.00012742016,0.000053631036,0.0053077973],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984515,0.000036396767,0.000009772924,0.00003659847,0.00006027051,0.000011692911],"domain_scores_gemma":[0.99959415,0.00014614874,0.000054454686,0.00004492792,0.00013473371,0.000025598669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036518718,0.0004660086,0.00034433513,0.00036638568,0.0002824046,0.0009271725,0.0008209375,0.0006314436,0.0031555044],"category_scores_gemma":[0.0012789267,0.00016151454,0.00017945045,0.0006752647,0.0006006924,0.0007766822,0.00058206695,0.00070906535,0.00066509703],"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":[0.00010262583,0.00009617287,0.0017155537,0.00029312377,0.00009871943,0.00016375758,0.0001289707,0.5849559,0.012702788,0.06466919,0.0056067295,0.32946652],"study_design_scores_gemma":[0.0000037215923,0.000020183328,0.00026977915,0.00002070208,0.000010414485,0.00003339157,0.000018149523,0.97188056,0.0015021056,0.023105046,0.0031270094,0.000008994404],"about_ca_topic_score_codex":0.0050485292,"about_ca_topic_score_gemma":0.005737214,"teacher_disagreement_score":0.0050485292,"about_ca_system_score_codex":0.00077798293,"about_ca_system_score_gemma":0.00071130425,"threshold_uncertainty_score":0.010556221},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017967587,0.004684004,0.9594305,0.0020604264,0.00016933877,0.000048323123,0.00016546765,0.0015449403,0.013929381],"genre_scores_gemma":[0.83828247,0.0027548769,0.15296258,0.0002637371,0.00013766754,0.00010978189,0.00012742016,0.000053631036,0.0053077973],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984515,0.000036396767,0.000009772924,0.00003659847,0.00006027051,0.000011692911],"domain_scores_gemma":[0.99959415,0.00014614874,0.000054454686,0.00004492792,0.00013473371,0.000025598669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036518718,0.0004660086,0.00034433513,0.00036638568,0.0002824046,0.0009271725,0.0008209375,0.0006314436,0.0031555044],"category_scores_gemma":[0.0012789267,0.00016151454,0.00017945045,0.0006752647,0.0006006924,0.0007766822,0.00058206695,0.00070906535,0.00066509703],"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":[0.00010262583,0.00009617287,0.0017155537,0.00029312377,0.00009871943,0.00016375758,0.0001289707,0.5849559,0.012702788,0.06466919,0.0056067295,0.32946652],"study_design_scores_gemma":[0.0000037215923,0.000020183328,0.00026977915,0.00002070208,0.000010414485,0.00003339157,0.000018149523,0.97188056,0.0015021056,0.023105046,0.0031270094,0.000008994404],"about_ca_topic_score_codex":0.0050485292,"about_ca_topic_score_gemma":0.005737214,"teacher_disagreement_score":0.0050485292,"about_ca_system_score_codex":0.00077798293,"about_ca_system_score_gemma":0.00071130425,"threshold_uncertainty_score":0.010556221},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42747104,0.0010872463,0.5617295,0.0008240961,0.00050843065,0.000122032485,0.00051471853,0.004468399,0.0032744834],"genre_scores_gemma":[0.8870598,0.00023536582,0.10840332,0.0002798703,0.00010683901,0.00007450624,0.0012135912,0.00007349481,0.0025531997],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995974,0.000072279596,0.000029223516,0.00012775756,0.000089957786,0.000083467545],"domain_scores_gemma":[0.99930894,0.00025194552,0.00006848442,0.00005611198,0.00028095243,0.000033650223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076054956,0.0009734237,0.0007607503,0.00086278043,0.000376045,0.0005794024,0.00077066757,0.0008607907,0.0006833711],"category_scores_gemma":[0.0019536298,0.00023043246,0.00066860777,0.0005482085,0.00029103577,0.0009451871,0.0007007579,0.0012881487,0.00037287598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004945384,0.0005916446,0.011678239,0.00015141486,0.00019008965,0.00030301578,0.0001940913,0.2973141,0.038661435,0.0016813857,0.009196826,0.63954324],"study_design_scores_gemma":[0.000004098825,0.000041549374,0.000588553,0.0000037179445,0.000011031075,0.000023740296,0.000015234865,0.9948414,0.0038324604,0.00031316114,0.00032074106,0.0000043298287],"about_ca_topic_score_codex":0.0053950227,"about_ca_topic_score_gemma":0.006372338,"teacher_disagreement_score":0.0053950227,"about_ca_system_score_codex":0.00044209138,"about_ca_system_score_gemma":0.00065831176,"threshold_uncertainty_score":0.010727227},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026376804,0.00012842477,0.9706908,0.00012508687,0.00009070016,0.000050827166,0.0001966902,0.0018442038,0.0004965224],"genre_scores_gemma":[0.4783512,0.00011538187,0.5168654,0.00013926937,0.00010297185,0.00020586881,0.0016252693,0.00040364853,0.0021909825],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995448,0.00009666443,0.000023426392,0.0001475733,0.00013231779,0.0000551711],"domain_scores_gemma":[0.99856865,0.0006981107,0.0000703922,0.00027306488,0.0003350607,0.00005465195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008902488,0.0007966593,0.0006769361,0.0010467247,0.00035499112,0.0004969664,0.0011322211,0.0012283917,0.0028707995],"category_scores_gemma":[0.0053692795,0.00044613032,0.00057054893,0.0011188708,0.0004252803,0.0007919204,0.00090514024,0.0011946355,0.0011711445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005877659,0.00014463866,0.0033324,0.00014134,0.000086278866,0.0001106377,0.00008597446,0.34227917,0.035569567,0.0055578654,0.007643549,0.60446084],"study_design_scores_gemma":[0.000011290678,0.000024270857,0.00026918773,0.0000026323753,0.0000050603503,0.00002069412,0.00000569909,0.99351317,0.0040877783,0.0016740425,0.0003829298,0.0000033363726],"about_ca_topic_score_codex":0.0035654488,"about_ca_topic_score_gemma":0.003758703,"teacher_disagreement_score":0.0035654488,"about_ca_system_score_codex":0.00041225346,"about_ca_system_score_gemma":0.00083970913,"threshold_uncertainty_score":0.009603798},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01801062,0.00040877322,0.97335577,0.0005937052,0.00015901166,0.00008227065,0.00043590416,0.002127643,0.0048262984],"genre_scores_gemma":[0.55300415,0.0008314358,0.42576945,0.00073078973,0.00013435518,0.00067475165,0.0016383742,0.00045778582,0.016758937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99979883,0.000046070258,0.000008896103,0.00006107649,0.00006262508,0.000022472106],"domain_scores_gemma":[0.9996247,0.00014591926,0.000037863498,0.000050058192,0.000109716515,0.000031583833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007129319,0.000671882,0.0006003484,0.00063276495,0.00043315184,0.0009845443,0.0025719148,0.0015081894,0.0036568204],"category_scores_gemma":[0.001695089,0.0004249413,0.0010345116,0.00079680205,0.0006712772,0.0011611682,0.0011927725,0.0016050746,0.0012602096],"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":[0.000058725607,0.000038977618,0.00063980435,0.000039468527,0.000043239237,0.000060243252,0.000031381853,0.93903255,0.0020626152,0.008151689,0.0025311606,0.047310192],"study_design_scores_gemma":[0.0000022296167,0.000004876664,0.000038039983,0.0000019756324,0.0000015481603,0.000005975641,0.0000011680735,0.99725014,0.00023915619,0.0020408267,0.00041161207,0.0000024103465],"about_ca_topic_score_codex":0.012652464,"about_ca_topic_score_gemma":0.008700417,"teacher_disagreement_score":0.012652464,"about_ca_system_score_codex":0.0015055599,"about_ca_system_score_gemma":0.0010822898,"threshold_uncertainty_score":0.02515763},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01367972,0.00012529518,0.9823416,0.00016355621,0.000067095745,0.000053962325,0.0003446717,0.0024796713,0.00074443227],"genre_scores_gemma":[0.38137946,0.00013390937,0.61369026,0.0002194766,0.00014136541,0.00028218562,0.0013683005,0.0005127864,0.0022723423],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971885,0.0007982175,0.00022265149,0.0005665489,0.0010654676,0.00015866345],"domain_scores_gemma":[0.9897608,0.0050400584,0.00085878186,0.0022586046,0.0018289848,0.0002526924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022301832,0.0008119186,0.0009549514,0.0028071217,0.0008265292,0.0019397489,0.0020812894,0.0015598573,0.0026794358],"category_scores_gemma":[0.01133823,0.0005128151,0.00096479716,0.0014324697,0.0009961592,0.0037835357,0.0018887124,0.0022268826,0.000929311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017452888,0.00065867417,0.012177947,0.00051787053,0.0002703293,0.0007022803,0.0005856283,0.13068008,0.056403115,0.15239713,0.010254649,0.6336071],"study_design_scores_gemma":[0.000026921974,0.00007859065,0.0006066674,0.00003094675,0.000033199685,0.00011451888,0.000039761657,0.9154114,0.012342959,0.06887507,0.002411715,0.00002821568],"about_ca_topic_score_codex":0.0016773824,"about_ca_topic_score_gemma":0.0028603517,"teacher_disagreement_score":0.0028071217,"about_ca_system_score_codex":0.0010283089,"about_ca_system_score_gemma":0.0015545505,"threshold_uncertainty_score":0.011794448},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06742181,0.0008803094,0.8678468,0.00031288996,0.000276025,0.00033024556,0.0017982484,0.057026606,0.004107036],"genre_scores_gemma":[0.36325964,0.0003741267,0.6098137,0.0005011136,0.00021247957,0.0003446745,0.0138346255,0.0025506706,0.0091090575],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982198,0.00018772607,0.00006289714,0.0007388545,0.00052724144,0.0002634419],"domain_scores_gemma":[0.9981292,0.00040295112,0.00010784364,0.000728709,0.00044799212,0.00018342459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012571082,0.0018472449,0.0016893607,0.0018189212,0.00069646427,0.0014501308,0.0033451924,0.0016428791,0.003376869],"category_scores_gemma":[0.0030561294,0.0005507473,0.0011371055,0.0012789646,0.00088637776,0.0028084342,0.003312333,0.0020232797,0.0028702973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011792304,0.0004326879,0.0033156432,0.00028249001,0.00019590513,0.0003000798,0.00016367895,0.023239993,0.06650373,0.0030515261,0.030068891,0.8712661],"study_design_scores_gemma":[0.0000686271,0.00044257296,0.003154531,0.000020989648,0.000086756256,0.0005518762,0.00010554485,0.9157453,0.059080537,0.008385082,0.012286597,0.00007162912],"about_ca_topic_score_codex":0.0058689374,"about_ca_topic_score_gemma":0.010421195,"teacher_disagreement_score":0.0058689374,"about_ca_system_score_codex":0.00077922794,"about_ca_system_score_gemma":0.0015937968,"threshold_uncertainty_score":0.011669576},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8901767,0.0023314466,0.07126991,0.002244171,0.00087679905,0.0007876947,0.0095695555,0.014004002,0.008739753],"genre_scores_gemma":[0.769011,0.00090883847,0.20217475,0.0008398139,0.00020976354,0.0004955261,0.01716721,0.000429456,0.008763581],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981679,0.00037940897,0.0002876227,0.00046985457,0.0005620648,0.00013312945],"domain_scores_gemma":[0.9965088,0.0015969961,0.00015746416,0.00074010843,0.0007902606,0.00020640065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00201565,0.00092608074,0.000903022,0.0011247735,0.0006807843,0.0012248259,0.0016920175,0.0015365676,0.0022394133],"category_scores_gemma":[0.0060453326,0.0001960794,0.00055335404,0.0017631534,0.00043376442,0.0016197653,0.0010172606,0.0010352732,0.0023898375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019693046,0.0015810918,0.021137359,0.0015822644,0.00023546699,0.0067473496,0.00069598295,0.033407398,0.042138718,0.0016165354,0.066679455,0.8222091],"study_design_scores_gemma":[0.0006443915,0.002819137,0.06555939,0.00025066,0.00024057552,0.009003645,0.0022523992,0.5955333,0.21105608,0.0055078818,0.10678931,0.00034323655],"about_ca_topic_score_codex":0.0039962125,"about_ca_topic_score_gemma":0.006589132,"teacher_disagreement_score":0.0039962125,"about_ca_system_score_codex":0.0005832118,"about_ca_system_score_gemma":0.00061677874,"threshold_uncertainty_score":0.010659933},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047063865,0.0002889219,0.95019466,0.00035703267,0.000055079745,0.000030763418,0.0000898316,0.00042069092,0.0014992489],"genre_scores_gemma":[0.90814096,0.00032360628,0.08750012,0.00032370567,0.00006262506,0.000091086426,0.00037527175,0.00008306987,0.0030995386],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995511,0.00012074744,0.000017038408,0.00012921193,0.00011196841,0.00006987559],"domain_scores_gemma":[0.99926275,0.00041580707,0.00007944706,0.00010688375,0.00010046872,0.00003467427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012760729,0.00071611325,0.0006680249,0.00035488198,0.00030195864,0.00069180154,0.0010534864,0.0006263297,0.0009892258],"category_scores_gemma":[0.0022345209,0.000316433,0.0006902865,0.00046145188,0.00080974156,0.0012751616,0.0015694862,0.0016660874,0.0002460151],"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":[0.00014881439,0.000056593406,0.00234929,0.000040961095,0.000049954848,0.00009857884,0.00007964633,0.9073727,0.0040210807,0.01051974,0.0015206728,0.07374194],"study_design_scores_gemma":[0.0000018474243,0.000011143818,0.00014502495,0.0000027297797,0.000003192154,0.000009097166,0.0000052086166,0.9954739,0.0006216634,0.003497192,0.00022663132,0.0000022792965],"about_ca_topic_score_codex":0.0023583455,"about_ca_topic_score_gemma":0.0030237122,"teacher_disagreement_score":0.0023583455,"about_ca_system_score_codex":0.00077890203,"about_ca_system_score_gemma":0.00051195454,"threshold_uncertainty_score":0.0067486167},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024534022,0.00027643592,0.97270995,0.000053033444,0.00005442152,0.000039635343,0.0000840695,0.0012844783,0.0009639586],"genre_scores_gemma":[0.5548707,0.0005244597,0.43832737,0.00012317847,0.00005886559,0.000104208244,0.00051369803,0.00013852483,0.005338964],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977344,0.000030697294,0.000011314916,0.00006041719,0.000091786176,0.000032392512],"domain_scores_gemma":[0.99976486,0.000042205476,0.000022472545,0.000033061213,0.00011477169,0.000022598204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002624827,0.00060479325,0.0006567655,0.00090964994,0.00022320409,0.00045309565,0.0007726136,0.00051932293,0.0018132896],"category_scores_gemma":[0.00080898625,0.00025158585,0.00037045227,0.00059015385,0.00026245543,0.0010147,0.0008473816,0.00054636993,0.0012430974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046524612,0.00008493529,0.0030956762,0.00017199798,0.00008303085,0.00028901486,0.00008387406,0.046391554,0.22489497,0.0041344524,0.005005959,0.7152993],"study_design_scores_gemma":[0.000017840293,0.00013234065,0.002341505,0.000013668821,0.0000357432,0.00027261075,0.0000364276,0.9390894,0.05223702,0.0021224155,0.0036756664,0.000025365385],"about_ca_topic_score_codex":0.0018337815,"about_ca_topic_score_gemma":0.0018236188,"teacher_disagreement_score":0.0018337815,"about_ca_system_score_codex":0.00030343505,"about_ca_system_score_gemma":0.00053751277,"threshold_uncertainty_score":0.006066084},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8932146,0.0008038654,0.10210334,0.00019354819,0.00015861522,0.000056086985,0.00016317208,0.0015376938,0.0017690024],"genre_scores_gemma":[0.9684542,0.00015777141,0.02939668,0.00007928571,0.000017621154,0.000023728006,0.00040514165,0.000042051335,0.0014234273],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99956506,0.00008419701,0.00004205314,0.00014688323,0.00007492652,0.00008681621],"domain_scores_gemma":[0.9988356,0.0005429504,0.00009944385,0.000107230444,0.00036892315,0.00004586979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012337809,0.000846878,0.0004807372,0.00037615115,0.0002575618,0.00042656047,0.00064812024,0.0006961862,0.0005343581],"category_scores_gemma":[0.0042855255,0.0002949397,0.00048323945,0.00024812526,0.00042143086,0.00052749825,0.0004437727,0.0010045235,0.0003137983],"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":[0.0007023107,0.0003615641,0.020793641,0.00016422203,0.00022177876,0.00034645465,0.00031265558,0.53249604,0.040136077,0.00052107405,0.002206605,0.4017376],"study_design_scores_gemma":[0.000007456781,0.00014100179,0.0067838887,0.000022524637,0.000024641513,0.000052329116,0.000050397757,0.981218,0.011274872,0.00016393908,0.00024892195,0.000012099187],"about_ca_topic_score_codex":0.015151988,"about_ca_topic_score_gemma":0.016750515,"teacher_disagreement_score":0.015151988,"about_ca_system_score_codex":0.00060982956,"about_ca_system_score_gemma":0.00058601366,"threshold_uncertainty_score":0.030127585},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011192004,0.00016708384,0.9701386,0.00014374024,0.00004840228,0.00007416033,0.00052491494,0.014988488,0.0027225518],"genre_scores_gemma":[0.33380032,0.00026667854,0.65386593,0.00043901007,0.00006568671,0.00031259694,0.0029530337,0.0025376559,0.005759161],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994667,0.00007683922,0.00002392875,0.00019353724,0.00017666827,0.000062322986],"domain_scores_gemma":[0.9991353,0.0002692021,0.0000997646,0.00023129143,0.00020021357,0.00006415758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012692515,0.001289184,0.00074078864,0.0012605608,0.00061443064,0.0018646998,0.003158192,0.00145328,0.0060901167],"category_scores_gemma":[0.0039219405,0.000848662,0.0008708427,0.0007137991,0.00094032055,0.003815342,0.0029444993,0.0016234731,0.0018734701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063731853,0.00019044378,0.00480879,0.00045500603,0.0002726226,0.00040313604,0.00034150897,0.40580353,0.039405096,0.021578798,0.017916078,0.50818765],"study_design_scores_gemma":[0.000015627702,0.000053558044,0.00041467894,0.000025301755,0.000023449202,0.000084538086,0.000025779931,0.9659353,0.011407398,0.016522413,0.005467567,0.000024413255],"about_ca_topic_score_codex":0.0046196408,"about_ca_topic_score_gemma":0.00789511,"teacher_disagreement_score":0.0060901167,"about_ca_system_score_codex":0.0014575211,"about_ca_system_score_gemma":0.0011945434,"threshold_uncertainty_score":0.020373404},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007503067,0.000075839744,0.9886314,0.00006331163,0.00003188145,0.0000726645,0.000055781595,0.0031154845,0.00045063032],"genre_scores_gemma":[0.23308174,0.0001004732,0.76466393,0.00011523209,0.00006975183,0.00022925336,0.000324277,0.000555669,0.00085970794],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99731123,0.0006626752,0.00027412703,0.0007237199,0.0008493352,0.00017885142],"domain_scores_gemma":[0.99486285,0.0024171337,0.00045623624,0.0009808989,0.0010744983,0.0002083772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003480309,0.0009587566,0.0009874932,0.0023774495,0.0007507921,0.0014571957,0.0020811914,0.001117708,0.004167962],"category_scores_gemma":[0.014278181,0.00044196312,0.0010415035,0.001629353,0.0011283783,0.0023047854,0.0022023888,0.0021208155,0.0012223865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005413588,0.0004009941,0.005200034,0.00028160246,0.00016908512,0.00033785834,0.0007553172,0.06502089,0.076579124,0.023089679,0.008651166,0.81897295],"study_design_scores_gemma":[0.000047822243,0.00016271202,0.0023420737,0.000028885326,0.000046218658,0.00033151556,0.000093982446,0.9318406,0.042444587,0.016688244,0.0058977986,0.00007553013],"about_ca_topic_score_codex":0.0017523293,"about_ca_topic_score_gemma":0.0021765986,"teacher_disagreement_score":0.004167962,"about_ca_system_score_codex":0.0006513284,"about_ca_system_score_gemma":0.001268048,"threshold_uncertainty_score":0.018405914},"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":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89545023,0.000116950316,0.10157487,0.0003212613,0.000043859378,0.000048165235,0.0005047952,0.0010048128,0.0009349559],"genre_scores_gemma":[0.98626244,0.000049336933,0.013095954,0.000010252038,0.000010811407,0.000013297435,0.0003981276,0.000015643347,0.00014418483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994898,0.000121274104,0.00004229031,0.000119575794,0.00018233286,0.0000446938],"domain_scores_gemma":[0.9970866,0.0015348232,0.00050895097,0.00030247346,0.00048017138,0.00008691137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013292314,0.00048450558,0.00033973146,0.0014835917,0.00021471754,0.0006502576,0.0006006406,0.00050585275,0.0003408148],"category_scores_gemma":[0.00822213,0.000121692065,0.00032546624,0.0011032416,0.00035350228,0.00084814173,0.00030649954,0.0006639599,0.00012849296],"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":[0.0002446033,0.00024731507,0.13529554,0.00007143939,0.000104377206,0.00024707365,0.00022775869,0.75596875,0.0077872626,0.0037347914,0.0013248071,0.09474631],"study_design_scores_gemma":[0.0000017585742,0.000021503094,0.007211031,0.0000023375994,0.0000031943703,0.00003113327,0.000018102623,0.9905586,0.0013071562,0.0007015278,0.00013927079,0.0000044019466],"about_ca_topic_score_codex":0.006612181,"about_ca_topic_score_gemma":0.0036736485,"teacher_disagreement_score":0.006612181,"about_ca_system_score_codex":0.0009271305,"about_ca_system_score_gemma":0.0004323736,"threshold_uncertainty_score":0.013147414},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027602427,0.00032025116,0.9707308,0.0001084341,0.00006433391,0.000026679287,0.00011157941,0.0004823659,0.0005531729],"genre_scores_gemma":[0.6388321,0.00049566734,0.35667068,0.000112335874,0.00011535508,0.00010898604,0.000750057,0.00007441316,0.002840411],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995509,0.00009369586,0.000026221815,0.00015273076,0.00012239185,0.000054049135],"domain_scores_gemma":[0.99955076,0.00013126795,0.000055611672,0.000056316003,0.00018205016,0.000023896735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010155162,0.0007217815,0.0008202142,0.00084058574,0.0004271566,0.0005358528,0.001117261,0.0008769922,0.0007674328],"category_scores_gemma":[0.0016696985,0.00038853806,0.0007759087,0.0010564442,0.00036942514,0.0013893176,0.0011106663,0.00074312836,0.00033582796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004562539,0.00017234574,0.002979266,0.00008790009,0.0001795876,0.00017026735,0.00012275107,0.37941077,0.02490835,0.007918289,0.0037269644,0.5798672],"study_design_scores_gemma":[0.0000017503517,0.000017618446,0.00026627138,0.0000018647773,0.000011070816,0.000019907278,0.0000038699936,0.9973482,0.0012883799,0.000838913,0.00019790343,0.000004229145],"about_ca_topic_score_codex":0.0067051523,"about_ca_topic_score_gemma":0.006409391,"teacher_disagreement_score":0.0067051523,"about_ca_system_score_codex":0.00049063703,"about_ca_system_score_gemma":0.00071802875,"threshold_uncertainty_score":0.013332248},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051653124,0.0005155702,0.9452812,0.00012430226,0.000055689092,0.000026728201,0.00013511552,0.0009986472,0.0012095403],"genre_scores_gemma":[0.7375261,0.00075176224,0.25590843,0.00019409959,0.00010000361,0.00006784915,0.0010833928,0.00017323393,0.004195153],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998004,0.000027834127,0.000007001934,0.00007657814,0.000051961386,0.00003623043],"domain_scores_gemma":[0.9996568,0.00012454283,0.000043278174,0.000048417467,0.00009521521,0.00003179889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002951622,0.00070658466,0.00058213744,0.0004966077,0.00020616087,0.00031644688,0.00079311035,0.00049157074,0.0008688093],"category_scores_gemma":[0.0010086952,0.00034221087,0.00055342395,0.000394922,0.00029921898,0.0006793999,0.00057313323,0.0008597235,0.00043415697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003931746,0.00014378199,0.005720408,0.00014901141,0.0001043379,0.00029075466,0.00028045822,0.35542372,0.051417172,0.0040535387,0.0044218986,0.5776018],"study_design_scores_gemma":[0.0000022237282,0.000015190599,0.0004778164,0.000003558771,0.0000055865453,0.00004221237,0.000008869432,0.9953271,0.0031768903,0.000540931,0.000395141,0.000004413812],"about_ca_topic_score_codex":0.0087057,"about_ca_topic_score_gemma":0.010036102,"teacher_disagreement_score":0.0087057,"about_ca_system_score_codex":0.00034933284,"about_ca_system_score_gemma":0.00062854734,"threshold_uncertainty_score":0.017310083},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02515297,0.030996315,0.61180365,0.041329455,0.0029559617,0.00032176,0.00077941443,0.002963481,0.283697],"genre_scores_gemma":[0.4278731,0.038449567,0.42303973,0.005478877,0.0020719161,0.0004365687,0.00090004,0.0004436894,0.10130659],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986946,0.0002966779,0.000051708317,0.00015930958,0.00072713924,0.00007061208],"domain_scores_gemma":[0.99832875,0.0009173041,0.00017792209,0.00019605168,0.00028675585,0.00009314403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011868909,0.0009660306,0.0006559895,0.0017406091,0.00075469434,0.0044388515,0.002162535,0.001719396,0.0076073706],"category_scores_gemma":[0.002820347,0.00029631087,0.0004865753,0.0042754794,0.0015752744,0.004898488,0.0015172526,0.0017759604,0.002262201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008646178,0.00031537795,0.0026547017,0.00083236955,0.0000773223,0.00007750598,0.00035850506,0.0061792783,0.0031086123,0.26650864,0.04194576,0.6778554],"study_design_scores_gemma":[0.000045451638,0.00029039363,0.0053139166,0.0008128454,0.000081637896,0.0003092265,0.001065189,0.05662615,0.007358596,0.66552114,0.26246983,0.00010563803],"about_ca_topic_score_codex":0.0014830551,"about_ca_topic_score_gemma":0.002494711,"teacher_disagreement_score":0.0076073706,"about_ca_system_score_codex":0.0011913402,"about_ca_system_score_gemma":0.0018158089,"threshold_uncertainty_score":0.025449216},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6043874,0.0017247614,0.38732347,0.00019698952,0.00014565782,0.00022305419,0.00080372486,0.0019740288,0.0032208716],"genre_scores_gemma":[0.9527395,0.00048399923,0.044948902,0.00002508687,0.000051347142,0.00004775015,0.0009269356,0.00002980009,0.00074672437],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928206,0.00009523218,0.000057774516,0.0002087355,0.00023888433,0.00011742301],"domain_scores_gemma":[0.9992773,0.00015456132,0.00016512856,0.000071107934,0.0002532069,0.000078708705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006807613,0.000662681,0.00075965934,0.0039228234,0.00038465695,0.00071168906,0.00053869735,0.00037110213,0.0002608454],"category_scores_gemma":[0.0015555138,0.00018199053,0.00047568133,0.0012814038,0.00034498257,0.00086816837,0.00081835454,0.00039514177,0.00017856307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012981104,0.00035561505,0.09245498,0.00044917446,0.00029895286,0.0016283877,0.0010600584,0.07655993,0.109797,0.0035262043,0.00766044,0.7049112],"study_design_scores_gemma":[0.000021341551,0.0002992907,0.057054162,0.00005957948,0.00011398677,0.00093998935,0.0007186773,0.8953294,0.037712086,0.003227484,0.004461367,0.00006275838],"about_ca_topic_score_codex":0.005568792,"about_ca_topic_score_gemma":0.0049111233,"teacher_disagreement_score":0.005568792,"about_ca_system_score_codex":0.00044274676,"about_ca_system_score_gemma":0.00040408337,"threshold_uncertainty_score":0.011072755},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037308957,0.00052310165,0.9596979,0.0001629219,0.00008946015,0.000017603135,0.00015066276,0.0013060566,0.00074338267],"genre_scores_gemma":[0.7100321,0.00073412305,0.28555727,0.00006278189,0.00017597844,0.000040600335,0.00042736673,0.00022700209,0.0027427115],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998024,0.00005071029,0.000012443017,0.0000598529,0.00005939686,0.00001534838],"domain_scores_gemma":[0.99899584,0.0006365801,0.00010049296,0.00008283655,0.00014764648,0.000036537822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057484256,0.00057588867,0.0007712212,0.00068991655,0.00020561786,0.00047794927,0.0006577721,0.00048062674,0.0007869386],"category_scores_gemma":[0.0021146808,0.00027699803,0.0005969498,0.0012534173,0.00028979313,0.00066217344,0.0004701335,0.001030753,0.00019115533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017560473,0.00018087267,0.0024629529,0.000102554404,0.00008862808,0.00013326849,0.00007478694,0.48106897,0.0081528695,0.008756384,0.0049447487,0.4938583],"study_design_scores_gemma":[0.0000013454923,0.000009835062,0.000304036,9.1635576e-7,0.0000037217642,0.000014545526,0.0000025913903,0.99675757,0.00035362627,0.0023911982,0.00015834281,0.0000023476923],"about_ca_topic_score_codex":0.0042336415,"about_ca_topic_score_gemma":0.004455326,"teacher_disagreement_score":0.0042336415,"about_ca_system_score_codex":0.0003440747,"about_ca_system_score_gemma":0.000305491,"threshold_uncertainty_score":0.008417964},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3215071,0.000886057,0.664259,0.0009114756,0.00018274483,0.00013140228,0.0003984155,0.0041894703,0.00753443],"genre_scores_gemma":[0.95300096,0.00024168946,0.04412004,0.0001281718,0.00003654206,0.000041878342,0.000280633,0.000030879386,0.002119254],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951875,0.000117866446,0.000026295747,0.00012565954,0.00011295923,0.00009844476],"domain_scores_gemma":[0.9994111,0.0001673288,0.00013870934,0.000057558736,0.00016811113,0.000057287987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063759094,0.0007512845,0.00050440105,0.001326194,0.00038928402,0.0005773908,0.00062052964,0.0005906043,0.00048733148],"category_scores_gemma":[0.0015213998,0.00031995674,0.00040443425,0.0005184371,0.00044564152,0.00089163973,0.001152661,0.0006768155,0.00028575133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058831857,0.00051357626,0.033084743,0.00022787778,0.00016465526,0.0007457807,0.00092050264,0.23482212,0.046695422,0.0059093866,0.009237442,0.66709024],"study_design_scores_gemma":[0.0000040515624,0.000034406992,0.0030121093,0.000018733806,0.000013587604,0.000065474946,0.000086880464,0.98553884,0.0069412435,0.0031457366,0.0011270145,0.000011911213],"about_ca_topic_score_codex":0.0078395195,"about_ca_topic_score_gemma":0.008335152,"teacher_disagreement_score":0.0078395195,"about_ca_system_score_codex":0.0007228315,"about_ca_system_score_gemma":0.0008120593,"threshold_uncertainty_score":0.015587807},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11043518,0.00035640973,0.88620675,0.0002393536,0.000031503045,0.00011520631,0.00012487413,0.0013803976,0.0011103575],"genre_scores_gemma":[0.8589419,0.0001231691,0.13906416,0.00007549653,0.00003670971,0.000057955516,0.00031028898,0.000022524757,0.0013677895],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992417,0.00015464456,0.000047268124,0.00019607943,0.00022883338,0.00013147715],"domain_scores_gemma":[0.9993513,0.00016373929,0.00012083054,0.00008254123,0.00022279737,0.000058777907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009295138,0.0007832312,0.00082658365,0.0014512865,0.0003788921,0.0009191849,0.0010163912,0.00074434915,0.0005110218],"category_scores_gemma":[0.0013303028,0.00031870278,0.0010270053,0.00046584147,0.00042118187,0.00090107194,0.0010004,0.0007306168,0.0002323935],"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":[0.00031685666,0.00049069937,0.036984622,0.00009726676,0.00029317266,0.00034963913,0.00023973019,0.5900761,0.032125898,0.006752434,0.0017515541,0.33052203],"study_design_scores_gemma":[0.0000025769161,0.00008084335,0.001998701,0.0000030195285,0.000020944655,0.000051438743,0.0000158126,0.99309397,0.0029903117,0.0013793377,0.00035503937,0.000008036702],"about_ca_topic_score_codex":0.007356937,"about_ca_topic_score_gemma":0.008451691,"teacher_disagreement_score":0.007356937,"about_ca_system_score_codex":0.0006668497,"about_ca_system_score_gemma":0.001096234,"threshold_uncertainty_score":0.0146282315},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07140886,0.0005775538,0.89475316,0.00061110983,0.00011147302,0.00014695703,0.0047991644,0.026640076,0.0009516511],"genre_scores_gemma":[0.3261311,0.0003136359,0.6590366,0.000103008635,0.00014528292,0.00012806348,0.011332728,0.00093104347,0.0018784957],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999338,0.00009761639,0.000096796524,0.00018693427,0.0002118766,0.00006866757],"domain_scores_gemma":[0.9972807,0.0012764502,0.00031813065,0.00056367903,0.0004503701,0.00011066495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001304311,0.0012091134,0.0011929588,0.003717694,0.00078810024,0.0012004937,0.0014032432,0.0013469254,0.0027962215],"category_scores_gemma":[0.0072199153,0.0006308324,0.0011102457,0.002544835,0.00036147734,0.0012951129,0.0015559589,0.0015662161,0.0016699146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034879395,0.00023222137,0.01726865,0.00036121852,0.0002589725,0.00042903228,0.00037451313,0.11396858,0.014073894,0.0022509955,0.017431593,0.83300155],"study_design_scores_gemma":[0.000043068692,0.000051039602,0.008609735,0.000023238934,0.00005806153,0.00018922197,0.00018212196,0.9696735,0.008647189,0.006944127,0.005550882,0.000027789609],"about_ca_topic_score_codex":0.008341042,"about_ca_topic_score_gemma":0.010463934,"teacher_disagreement_score":0.008341042,"about_ca_system_score_codex":0.00039641646,"about_ca_system_score_gemma":0.0012425949,"threshold_uncertainty_score":0.016584992},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6176131,0.00071181956,0.37064496,0.000075952616,0.00016252641,0.00032385392,0.0031195458,0.0036382896,0.0037099898],"genre_scores_gemma":[0.90269864,0.00036617473,0.09331841,0.000044937413,0.0000331246,0.000119050106,0.0013168451,0.00006518408,0.0020376476],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.999884,0.000009413803,0.0000058597307,0.0000375902,0.000049064514,0.000014010189],"domain_scores_gemma":[0.999796,0.000041994233,0.00003644777,0.000023058481,0.00008417358,0.000018377687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000888611,0.0004632836,0.00039065004,0.0010137125,0.00009614861,0.00025053884,0.00031654857,0.00031807384,0.0019328191],"category_scores_gemma":[0.0006129716,0.000116419265,0.00020284456,0.00041351613,0.00010087413,0.00028554816,0.0002983331,0.00016456335,0.00080764096],"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":[0.00096425426,0.00023944445,0.030734064,0.00066748867,0.000102537,0.0011853231,0.00031689488,0.0084063215,0.29989976,0.0006562687,0.0058832094,0.65094435],"study_design_scores_gemma":[0.00011672495,0.0009273058,0.2932979,0.00018713316,0.00010546217,0.0033235725,0.00059944484,0.51623154,0.1742438,0.0015402706,0.009321087,0.00010583596],"about_ca_topic_score_codex":0.002009442,"about_ca_topic_score_gemma":0.005150406,"teacher_disagreement_score":0.002009442,"about_ca_system_score_codex":0.00010213065,"about_ca_system_score_gemma":0.0001384673,"threshold_uncertainty_score":0.0064659715},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11152386,0.0011953369,0.86649656,0.00072512537,0.00036052545,0.00048204543,0.00028482292,0.0053616874,0.013570005],"genre_scores_gemma":[0.6920027,0.0006220924,0.29479787,0.000263249,0.0000919324,0.00023943074,0.00038612413,0.00021103748,0.011385526],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997266,0.000060205686,0.000020627136,0.00006476497,0.000091552494,0.000036268102],"domain_scores_gemma":[0.99966323,0.00007181978,0.000021258595,0.000039465805,0.00017283058,0.000031477815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048558728,0.00027294457,0.0002903446,0.00040590216,0.00041719148,0.0008818015,0.0009395714,0.00047980328,0.002914885],"category_scores_gemma":[0.00066886167,0.00023733411,0.0002348235,0.00030015386,0.00017466537,0.00054602034,0.0003696171,0.0002965056,0.0006425581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070189,0.00032754795,0.011006913,0.0004784899,0.00017437164,0.0009328197,0.00054139044,0.08892292,0.24833417,0.027067147,0.0220817,0.59943074],"study_design_scores_gemma":[0.00010762073,0.00089179596,0.0049718,0.00005764216,0.00017589936,0.00069183356,0.00022596482,0.7857935,0.1381415,0.0055091097,0.063386574,0.000046777368],"about_ca_topic_score_codex":0.0016875836,"about_ca_topic_score_gemma":0.0010080818,"teacher_disagreement_score":0.002914885,"about_ca_system_score_codex":0.00039465196,"about_ca_system_score_gemma":0.0006811916,"threshold_uncertainty_score":0.00975126},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.116323434,0.0002658219,0.8811607,0.00013304784,0.000039591083,0.00002610465,0.00012786595,0.001050959,0.00087251986],"genre_scores_gemma":[0.9129209,0.00010910882,0.085751764,0.00003724034,0.000042698215,0.0000196718,0.00026573119,0.000040891973,0.000812116],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950886,0.00009028981,0.00003163286,0.00010946761,0.00018875871,0.000071035065],"domain_scores_gemma":[0.99869484,0.000569124,0.00021639242,0.00013072148,0.000340943,0.000047899157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050107756,0.00043610693,0.00043342277,0.0012047808,0.00040971898,0.00052287854,0.0008127975,0.00037384426,0.00027792124],"category_scores_gemma":[0.0028191805,0.00016140229,0.00027064505,0.0008214994,0.00036176437,0.00068850897,0.0005364916,0.00056876807,0.00012389636],"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":[0.00031314182,0.00011649185,0.026870744,0.0000738073,0.000113619164,0.00031403257,0.00018514921,0.5158924,0.025205389,0.007616694,0.002484534,0.420814],"study_design_scores_gemma":[0.0000029951111,0.000023973425,0.002240403,0.0000017739643,0.0000053616773,0.00008559413,0.000026457072,0.9918779,0.0026108595,0.0025781705,0.00054081244,0.000005671869],"about_ca_topic_score_codex":0.0057091652,"about_ca_topic_score_gemma":0.0066704256,"teacher_disagreement_score":0.0057091652,"about_ca_system_score_codex":0.0006261154,"about_ca_system_score_gemma":0.0005182034,"threshold_uncertainty_score":0.011351883},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01128162,0.00018550662,0.9855445,0.00006161681,0.000031821633,0.0000299695,0.000099931494,0.0014637791,0.0013011822],"genre_scores_gemma":[0.1736853,0.0003152976,0.81983185,0.00007313553,0.00006635402,0.00007505161,0.00056833297,0.00064057694,0.00474408],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978155,0.000026386877,0.000009558407,0.000063082785,0.00009452352,0.000024977353],"domain_scores_gemma":[0.99976486,0.000072336596,0.000025033618,0.00005742627,0.00006666401,0.0000137846055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028646545,0.0006187724,0.00056212855,0.0009632821,0.00019294549,0.0008241138,0.0007335705,0.00067120715,0.0020719445],"category_scores_gemma":[0.0005851438,0.00042675834,0.00065151625,0.00085551786,0.00033951245,0.0005502578,0.00079937105,0.0006125108,0.0013004972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023110108,0.000047466794,0.0010094426,0.00014123356,0.00006661273,0.00015102435,0.00006298976,0.0481745,0.21104753,0.005411893,0.0026692932,0.73098695],"study_design_scores_gemma":[0.000010818293,0.0000733729,0.0012456329,0.000012972582,0.00004055268,0.0006069746,0.000023224016,0.88560694,0.100746825,0.0059397095,0.0056718765,0.000021094991],"about_ca_topic_score_codex":0.00082010473,"about_ca_topic_score_gemma":0.0015497834,"teacher_disagreement_score":0.0020719445,"about_ca_system_score_codex":0.00027472433,"about_ca_system_score_gemma":0.00044369086,"threshold_uncertainty_score":0.0069313645},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12253332,0.0006066849,0.8711915,0.0003926226,0.00028668696,0.00005586104,0.00023680486,0.0028131923,0.0018833594],"genre_scores_gemma":[0.8566292,0.00014798554,0.14003037,0.00011189951,0.00009427881,0.000067687295,0.00047258154,0.00014136119,0.0023046792],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994844,0.00008198826,0.00002864384,0.00018166452,0.00013711162,0.00008611098],"domain_scores_gemma":[0.9981665,0.00084867154,0.00012678702,0.00021747506,0.0005512122,0.000089357025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093871815,0.00073916325,0.0010327302,0.0006160511,0.0004673864,0.0008251632,0.0017648353,0.0011217438,0.0020778577],"category_scores_gemma":[0.0038885083,0.00046758092,0.00052798016,0.00088088313,0.00056051597,0.0012807741,0.0010494356,0.0016690006,0.0006069087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045656398,0.00023180009,0.0050595137,0.00013754934,0.0001092122,0.00022239548,0.00012686585,0.36048594,0.016208712,0.0061263046,0.006536866,0.60429823],"study_design_scores_gemma":[0.0000023930154,0.000012597461,0.0002799413,0.0000020192704,0.0000038199164,0.00001549597,0.0000041313388,0.9973929,0.0012710373,0.00087029574,0.0001426488,0.0000027557417],"about_ca_topic_score_codex":0.010045718,"about_ca_topic_score_gemma":0.010247262,"teacher_disagreement_score":0.010045718,"about_ca_system_score_codex":0.00075356354,"about_ca_system_score_gemma":0.0011469678,"threshold_uncertainty_score":0.01997447},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024645543,0.000066989945,0.9619325,0.00007807725,0.00004074014,0.00010126695,0.000098588265,0.011030375,0.002006039],"genre_scores_gemma":[0.527827,0.00011695697,0.465631,0.00014357925,0.000036152436,0.00031039794,0.00045989416,0.00052556786,0.00494943],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994981,0.00006406714,0.000032658314,0.00024286006,0.00011953734,0.00004272743],"domain_scores_gemma":[0.99950516,0.00015208572,0.000042699503,0.00009664695,0.00015515619,0.000048346454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043391512,0.00063374545,0.0007983578,0.000581521,0.0006151024,0.000850695,0.002084224,0.00088919717,0.0058889077],"category_scores_gemma":[0.0014361639,0.00038960736,0.000556284,0.00035246683,0.00040003253,0.0015457644,0.0013312389,0.0005048626,0.0014057899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013113025,0.0005563971,0.0064945184,0.00058661023,0.00023885572,0.001978065,0.0017161289,0.038912818,0.3863079,0.016587893,0.010065451,0.535244],"study_design_scores_gemma":[0.000066920766,0.00033496655,0.0046426933,0.000032472683,0.00024483944,0.0009907982,0.00032643843,0.88179386,0.0858823,0.008922573,0.016660634,0.000101470796],"about_ca_topic_score_codex":0.0019836465,"about_ca_topic_score_gemma":0.0016869579,"teacher_disagreement_score":0.0058889077,"about_ca_system_score_codex":0.00028034538,"about_ca_system_score_gemma":0.0006589461,"threshold_uncertainty_score":0.019700348},"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":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024248188,0.0037220998,0.9567275,0.0014735826,0.0010174646,0.00025318243,0.0021976794,0.006210748,0.0041494854],"genre_scores_gemma":[0.25025752,0.0024562015,0.7306721,0.00048194372,0.0005679108,0.00030942686,0.007328385,0.00036503718,0.0075613805],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972289,0.00047818432,0.00020298583,0.00041573236,0.0015386229,0.00013542185],"domain_scores_gemma":[0.99727136,0.0009583751,0.0002127668,0.0004851478,0.00090614206,0.00016619799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026641695,0.00084190554,0.0010242532,0.002870596,0.0006265476,0.0014682855,0.0013309296,0.0010100049,0.0020100696],"category_scores_gemma":[0.0071552913,0.00034278704,0.0013437611,0.001562681,0.0006871789,0.0013763346,0.0022278235,0.0017888859,0.0011691566],"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":[0.00025086655,0.00017735541,0.008596549,0.0004059028,0.00032266724,0.00029687877,0.00023038183,0.022274867,0.017490664,0.012634035,0.03981871,0.8975011],"study_design_scores_gemma":[0.000071833965,0.00034215432,0.013025021,0.000114285685,0.000107040665,0.0014361958,0.00022889981,0.8403226,0.024401767,0.04591474,0.073933505,0.00010203037],"about_ca_topic_score_codex":0.004785185,"about_ca_topic_score_gemma":0.007075531,"teacher_disagreement_score":0.004785185,"about_ca_system_score_codex":0.0008479704,"about_ca_system_score_gemma":0.0010142095,"threshold_uncertainty_score":0.014089584},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2847373,0.000492741,0.71017,0.0007308055,0.00007205152,0.0001003212,0.0003078724,0.0010216001,0.0023672802],"genre_scores_gemma":[0.905362,0.00014207078,0.09289052,0.00008213852,0.00007339728,0.00005325069,0.00059007824,0.00003722414,0.0007693425],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9975383,0.0008885883,0.00015279962,0.0004949463,0.00070478383,0.00022053017],"domain_scores_gemma":[0.9875402,0.007863172,0.0011627107,0.0017461849,0.001463145,0.0002244333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032419406,0.0008219837,0.001007701,0.0017851628,0.00076265173,0.0017897101,0.001262755,0.0009520924,0.0005309666],"category_scores_gemma":[0.01719437,0.00026111162,0.00065193546,0.0015691111,0.0012073519,0.0022881506,0.001402509,0.0015702193,0.000234024],"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":[0.00029793696,0.0004226429,0.019530531,0.00008061712,0.000105773135,0.00018980967,0.0003215782,0.7004125,0.003957521,0.012139122,0.0014937564,0.26104823],"study_design_scores_gemma":[0.0000016754398,0.000024721201,0.0011326197,0.0000033975853,0.0000038671087,0.000019769644,0.00001770928,0.9946906,0.0007922695,0.0031615177,0.00014670893,0.000004964121],"about_ca_topic_score_codex":0.005302282,"about_ca_topic_score_gemma":0.0056985077,"teacher_disagreement_score":0.005302282,"about_ca_system_score_codex":0.0011663318,"about_ca_system_score_gemma":0.0009303754,"threshold_uncertainty_score":0.017145216},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005977236,0.117570244,0.72871935,0.046581764,0.0040775747,0.0002630944,0.00079284183,0.0019754244,0.094042405],"genre_scores_gemma":[0.23251784,0.10213931,0.60686487,0.012748853,0.0072513507,0.0007461671,0.0024007494,0.00063622085,0.0346946],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99349517,0.0023587968,0.0006321248,0.00095311,0.0023475054,0.00021345375],"domain_scores_gemma":[0.99006313,0.005385326,0.0003335394,0.0023046143,0.0015550874,0.00035833177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007175078,0.00084580865,0.0017458227,0.0036561326,0.0010581707,0.010681567,0.0022372473,0.002618071,0.0074874344],"category_scores_gemma":[0.012450596,0.00075216923,0.0009893851,0.0063497256,0.0048483745,0.013324258,0.0062079253,0.0053247223,0.0026251676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005379693,0.000091359325,0.00089827075,0.0008299442,0.00018426654,0.000116682095,0.000334783,0.0052350573,0.00070063124,0.75002867,0.020606179,0.22092037],"study_design_scores_gemma":[0.000014811038,0.000035709872,0.00034807305,0.0003573285,0.000043206972,0.00009838031,0.00011003235,0.014443748,0.0006404819,0.8391635,0.14471672,0.000028121294],"about_ca_topic_score_codex":0.0016505765,"about_ca_topic_score_gemma":0.0014004722,"teacher_disagreement_score":0.010681567,"about_ca_system_score_codex":0.0023461273,"about_ca_system_score_gemma":0.0034523013,"threshold_uncertainty_score":0.037945867},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40858862,0.0013464951,0.582968,0.0017756352,0.00027229887,0.00010958966,0.00036515502,0.0017817506,0.0027925689],"genre_scores_gemma":[0.95650303,0.0002450025,0.042285427,0.00012339125,0.000064336884,0.000039313465,0.0002770088,0.000045699417,0.00041682972],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999062,0.00043786902,0.000048556187,0.00016614016,0.00020095121,0.00008449545],"domain_scores_gemma":[0.9880029,0.009284241,0.0005262666,0.0011607981,0.00085264415,0.0001731781],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031249302,0.0009654983,0.00058905553,0.0004942573,0.0003062454,0.0006071163,0.00094763574,0.00084577355,0.00063992135],"category_scores_gemma":[0.016902713,0.00028265614,0.0005440164,0.0004790647,0.00096174877,0.002191101,0.0010117645,0.0016030967,0.00018154303],"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":[0.0005209544,0.00024211903,0.008221043,0.000121523306,0.000061834064,0.00016776375,0.00014345761,0.8875181,0.0071651214,0.005896457,0.001511542,0.08843016],"study_design_scores_gemma":[0.000004491721,0.00003791591,0.00047494427,0.0000055423898,0.000005904526,0.000023074866,0.000011306076,0.9954184,0.0019069236,0.0019041469,0.00020225089,0.0000050386852],"about_ca_topic_score_codex":0.002865338,"about_ca_topic_score_gemma":0.001865267,"teacher_disagreement_score":0.0031249302,"about_ca_system_score_codex":0.0004944017,"about_ca_system_score_gemma":0.00054049405,"threshold_uncertainty_score":0.01652646},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008390753,0.00023861165,0.9889053,0.0011397565,0.000029336008,0.00007959857,0.00012780746,0.000732973,0.00035586685],"genre_scores_gemma":[0.4984485,0.0005755917,0.49710476,0.0007786998,0.0002110677,0.00043954139,0.00096421014,0.00031929353,0.0011584107],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973109,0.0010388881,0.00025697186,0.0006180805,0.0006533058,0.00012193766],"domain_scores_gemma":[0.97505796,0.01890216,0.0018835596,0.0016276865,0.0021249107,0.0004036945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005013349,0.0015219265,0.0014208381,0.0017476386,0.000554197,0.002116931,0.0020225297,0.0017740426,0.0014495531],"category_scores_gemma":[0.023340419,0.00084440777,0.0017703351,0.0008049947,0.0014697026,0.0034938015,0.0028895098,0.004363917,0.0004274303],"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":[0.00022071371,0.00034412567,0.016817197,0.0005623733,0.00037096825,0.0003226694,0.0008447825,0.7294872,0.005140045,0.06541321,0.0035757294,0.17690101],"study_design_scores_gemma":[0.000008356228,0.000023823619,0.00032019548,0.0000299477,0.00001240594,0.000027722781,0.000018984718,0.9767005,0.00063724973,0.021272946,0.0009356135,0.00001232342],"about_ca_topic_score_codex":0.005656863,"about_ca_topic_score_gemma":0.005635302,"teacher_disagreement_score":0.005656863,"about_ca_system_score_codex":0.00161823,"about_ca_system_score_gemma":0.0023760307,"threshold_uncertainty_score":0.026513457},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0678585,0.0005750687,0.81875235,0.00030695696,0.00030718689,0.00021716,0.0010413943,0.10799524,0.0029460914],"genre_scores_gemma":[0.7214002,0.0002803636,0.2696016,0.00018032329,0.000121794845,0.00015996475,0.0018831843,0.0012634376,0.005109086],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993331,0.00011899716,0.000032038588,0.00012868109,0.00029971567,0.000087450615],"domain_scores_gemma":[0.99879956,0.00042559957,0.00010616196,0.00033367914,0.00025086786,0.000084074614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011062967,0.0008941385,0.0007624548,0.001461039,0.00036361886,0.0008862034,0.001485489,0.0006981535,0.0030540063],"category_scores_gemma":[0.0029475845,0.000370722,0.00036989103,0.00069158373,0.0003735826,0.0014452299,0.001494979,0.00097417337,0.0010808613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019842116,0.0003547264,0.012542994,0.0002575595,0.00027295284,0.00041598472,0.00017514554,0.15201163,0.0320336,0.009879763,0.05397918,0.7360923],"study_design_scores_gemma":[0.000059988513,0.0001461986,0.0012387786,0.000008863863,0.000026492453,0.00017153812,0.000019318293,0.9763767,0.012998325,0.0036501596,0.0052821264,0.000021533242],"about_ca_topic_score_codex":0.002609631,"about_ca_topic_score_gemma":0.002714318,"teacher_disagreement_score":0.0030540063,"about_ca_system_score_codex":0.00050205237,"about_ca_system_score_gemma":0.00069181836,"threshold_uncertainty_score":0.010216653},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3636716,0.00028956417,0.6321875,0.00011447609,0.00003389449,0.00006525352,0.00012827611,0.0021530683,0.0013563324],"genre_scores_gemma":[0.946713,0.00007751304,0.052690696,0.000015692229,0.000012266683,0.000020913067,0.0000692828,0.000017361499,0.00038325143],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976176,0.00005096071,0.000011715052,0.00006686462,0.00008976011,0.000018990413],"domain_scores_gemma":[0.9993716,0.00026984574,0.00014345934,0.00006025196,0.000130648,0.00002416662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029155597,0.0005277394,0.0004138014,0.00052446686,0.00016462957,0.00031140307,0.0005289256,0.00039057483,0.00034547233],"category_scores_gemma":[0.0012615396,0.00012551236,0.0001757072,0.00034865923,0.0002396599,0.00061731535,0.0003358891,0.00043302964,0.00014190993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051169936,0.00025533658,0.02824394,0.00015490867,0.000077834164,0.0003071631,0.00019622756,0.41227302,0.06477632,0.0015922491,0.00122641,0.49038494],"study_design_scores_gemma":[0.000008687723,0.000108922184,0.0036942426,0.0000056618637,0.000008068709,0.00015257504,0.000015199098,0.98533165,0.009528599,0.0007672082,0.00037112294,0.000008116885],"about_ca_topic_score_codex":0.0011782735,"about_ca_topic_score_gemma":0.0012553015,"teacher_disagreement_score":0.0011782735,"about_ca_system_score_codex":0.00031197807,"about_ca_system_score_gemma":0.0002213347,"threshold_uncertainty_score":0.0023428202},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4155445,0.0002299028,0.5818928,0.00011408965,0.00002802216,0.00007570394,0.000252304,0.0009292001,0.00093340204],"genre_scores_gemma":[0.9352138,0.0000725316,0.063726634,0.000016873859,0.000017376235,0.000034376182,0.00028244883,0.000023763501,0.00061215734],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956185,0.00008773714,0.000031179305,0.00014712235,0.00010650299,0.00006557373],"domain_scores_gemma":[0.99905854,0.0004504189,0.00015688567,0.000072812014,0.0002336406,0.00002772851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005156592,0.00045229992,0.00037969608,0.0016381213,0.00026648853,0.00048093242,0.0005866516,0.00042370698,0.00031520604],"category_scores_gemma":[0.0018945049,0.00020246235,0.00043870814,0.0009447379,0.00028367963,0.00053385284,0.00045195615,0.00049646356,0.00011858199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004206901,0.0004118761,0.066435404,0.00013871863,0.00022436195,0.00044031287,0.0005199035,0.40381855,0.038094703,0.002032858,0.0015404599,0.48592213],"study_design_scores_gemma":[0.0000029596545,0.000031230964,0.011027206,0.0000035960693,0.000011361637,0.000052417872,0.00006369112,0.9854774,0.0023558415,0.00075658556,0.00020869456,0.0000091456795],"about_ca_topic_score_codex":0.0068421992,"about_ca_topic_score_gemma":0.009362279,"teacher_disagreement_score":0.0068421992,"about_ca_system_score_codex":0.00040435794,"about_ca_system_score_gemma":0.00047069558,"threshold_uncertainty_score":0.01360476},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017256975,0.00018155333,0.99701107,0.00013235712,0.000019129997,0.000010835736,0.000020852058,0.00028124626,0.0006172586],"genre_scores_gemma":[0.22586752,0.00083904515,0.765106,0.000279716,0.00015550436,0.00018413298,0.00030267524,0.00048268895,0.0067826076],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995184,0.0001817563,0.000021259428,0.000103322105,0.00014020201,0.000035046625],"domain_scores_gemma":[0.9982224,0.001220771,0.000109527195,0.00023024969,0.00017199671,0.000045133114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010866869,0.0010613924,0.0010378798,0.0008021959,0.00037647688,0.0013985097,0.0019364828,0.0018074808,0.0034704085],"category_scores_gemma":[0.0047524795,0.000903668,0.0011673493,0.0008587186,0.0012532127,0.0022903483,0.0025216413,0.0030846407,0.00090929633],"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":[0.00009936803,0.00005306577,0.00030547232,0.00024981907,0.00014133124,0.00011469303,0.0001918925,0.5366704,0.0076842913,0.20182842,0.003974219,0.24868707],"study_design_scores_gemma":[0.0000037254952,0.000008817098,0.000045655972,0.000009819529,0.0000073323854,0.000013230695,0.0000071745285,0.91205573,0.0007594442,0.085976,0.0011077101,0.0000052578503],"about_ca_topic_score_codex":0.0024998102,"about_ca_topic_score_gemma":0.002080234,"teacher_disagreement_score":0.0034704085,"about_ca_system_score_codex":0.0007235632,"about_ca_system_score_gemma":0.0005859886,"threshold_uncertainty_score":0.011609733},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03728862,0.00082754676,0.9500118,0.00027797837,0.0003520117,0.00014943641,0.0008270686,0.0059800483,0.0042854818],"genre_scores_gemma":[0.22343615,0.000674706,0.75947595,0.0003792289,0.0001685837,0.00012197072,0.0020465688,0.00040674672,0.01329009],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996631,0.00003220985,0.000014079911,0.000069185684,0.00017460022,0.000046832083],"domain_scores_gemma":[0.9997181,0.000051600044,0.000031374646,0.000052280233,0.00010796917,0.000038686474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044270055,0.0007744993,0.00079923857,0.0016330502,0.0004726088,0.00075248897,0.001478292,0.00071589084,0.0037400066],"category_scores_gemma":[0.0006864272,0.0003948859,0.0005833658,0.0009115691,0.00033780016,0.0008617136,0.0011900238,0.00087087014,0.0015980958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043817647,0.00020655614,0.0021920132,0.00014144597,0.00006844004,0.0001643809,0.000040880434,0.010209867,0.09764141,0.0028558502,0.012539017,0.87350196],"study_design_scores_gemma":[0.000031562453,0.00018261487,0.0032137781,0.000021482914,0.000053397565,0.00047373018,0.000043761283,0.8810273,0.09687289,0.00321103,0.014833762,0.000034695164],"about_ca_topic_score_codex":0.0029805196,"about_ca_topic_score_gemma":0.0073598204,"teacher_disagreement_score":0.0037400066,"about_ca_system_score_codex":0.00047867422,"about_ca_system_score_gemma":0.00074748317,"threshold_uncertainty_score":0.012511611},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05112741,0.00033524403,0.9446323,0.00070087385,0.000057839166,0.000042510554,0.00020063059,0.0014260705,0.0014770805],"genre_scores_gemma":[0.8298466,0.0003170227,0.16472995,0.00065120205,0.0001447478,0.00011442438,0.0007825119,0.00045033346,0.0029633383],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983165,0.0004003469,0.00007360997,0.0005098872,0.000498164,0.00020137832],"domain_scores_gemma":[0.9940918,0.0027474319,0.0008583121,0.001076233,0.00090850994,0.00031768883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032150687,0.0010834226,0.0012885316,0.002229588,0.00095646706,0.00218997,0.0027532605,0.0017406879,0.0016991509],"category_scores_gemma":[0.02196265,0.00073220604,0.0009903867,0.00147635,0.0026888265,0.0033426208,0.004699576,0.00407493,0.00049303717],"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":[0.00049329334,0.00019901393,0.0404161,0.00021682221,0.00028232142,0.00093737035,0.00070836436,0.47815856,0.013608418,0.10672952,0.012817329,0.34543294],"study_design_scores_gemma":[0.0000071524933,0.000018888482,0.0014275381,0.000016032129,0.000010112764,0.000119498145,0.000043737313,0.9571165,0.00229464,0.037770636,0.0011575167,0.000017749098],"about_ca_topic_score_codex":0.008771584,"about_ca_topic_score_gemma":0.00966729,"teacher_disagreement_score":0.008771584,"about_ca_system_score_codex":0.0020040446,"about_ca_system_score_gemma":0.0015477594,"threshold_uncertainty_score":0.017441094},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029782608,0.00060534256,0.96749026,0.00033965448,0.000034544522,0.000025704041,0.000057271453,0.0009005731,0.0007639676],"genre_scores_gemma":[0.91998065,0.00033651423,0.07724711,0.00023126519,0.00007923485,0.000057941485,0.00021033634,0.00006915958,0.0017878613],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953115,0.000117350966,0.000025714427,0.00018628198,0.00009456746,0.000044882996],"domain_scores_gemma":[0.9983809,0.0011663752,0.00012401113,0.000118074095,0.00017643042,0.000034274362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012702807,0.00077210687,0.00077284046,0.0005739152,0.00032215597,0.00046971848,0.0012471623,0.0009434879,0.0008780596],"category_scores_gemma":[0.0039035678,0.00034975223,0.0006704083,0.0006860789,0.0006939354,0.0009947612,0.00090537145,0.0013901421,0.00017185397],"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":[0.00018332123,0.00008604967,0.001219336,0.0000877466,0.00007873141,0.0001251588,0.00011030876,0.81727326,0.0054595037,0.0049184808,0.0014603278,0.16899772],"study_design_scores_gemma":[0.0000031341606,0.000011528178,0.00015429065,0.0000018217835,0.0000068320505,0.000014056571,0.0000030953843,0.996816,0.0009652882,0.0018474981,0.00017333141,0.0000031325796],"about_ca_topic_score_codex":0.008561726,"about_ca_topic_score_gemma":0.003865838,"teacher_disagreement_score":0.008561726,"about_ca_system_score_codex":0.0009961814,"about_ca_system_score_gemma":0.0008818725,"threshold_uncertainty_score":0.017023802},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04151503,0.00029512672,0.9567066,0.00015712107,0.000026401794,0.000025807372,0.000072234856,0.0005594207,0.0006423112],"genre_scores_gemma":[0.8957848,0.00021619035,0.10219846,0.000067198365,0.000031122665,0.000053453026,0.00029477946,0.000072965675,0.0012809508],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997062,0.00007557837,0.0000150527085,0.0000753536,0.00008532106,0.000042465024],"domain_scores_gemma":[0.99949825,0.00024985484,0.00006900842,0.000047143658,0.0001093816,0.000026336618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005590671,0.0009121899,0.00065936596,0.00059693324,0.00032409982,0.000658823,0.00096849987,0.00060607574,0.00065589324],"category_scores_gemma":[0.0018560342,0.0005667771,0.00093713467,0.0004192818,0.0004907692,0.00091164815,0.0009007637,0.0014608664,0.00018236088],"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":[0.00008147419,0.000044102973,0.0024479583,0.000045644643,0.00007614311,0.000074699994,0.00005449768,0.9074909,0.004429796,0.0035983585,0.0008316092,0.08082486],"study_design_scores_gemma":[6.2352564e-7,0.000004475146,0.00013926937,0.0000010189542,0.0000017083698,0.000004454414,0.0000018119027,0.99898607,0.00024586645,0.0005618923,0.000051460178,0.0000014194903],"about_ca_topic_score_codex":0.010198286,"about_ca_topic_score_gemma":0.009836184,"teacher_disagreement_score":0.010198286,"about_ca_system_score_codex":0.00055147405,"about_ca_system_score_gemma":0.00093802885,"threshold_uncertainty_score":0.020277858},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35634103,0.00023924127,0.6419717,0.00018728795,0.000026726362,0.000025441892,0.0001097495,0.0006418868,0.00045693302],"genre_scores_gemma":[0.95743054,0.00007983488,0.041994225,0.000019183113,0.0000203145,0.000015687561,0.00017497042,0.000034893706,0.00023032639],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992525,0.00019591594,0.000045495013,0.00020557542,0.00023066803,0.00006982808],"domain_scores_gemma":[0.9928725,0.004740247,0.0009068065,0.00031960206,0.0010423736,0.000118452896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015629288,0.00041950532,0.00074494485,0.0016986171,0.00023526904,0.000701441,0.0007802366,0.0007008948,0.00052721554],"category_scores_gemma":[0.00871655,0.00019636157,0.00042853673,0.00089844165,0.00060934643,0.0010186029,0.00066953903,0.00082857686,0.00011281513],"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":[0.0006482196,0.000242176,0.0740939,0.00015035372,0.00017662051,0.00027391934,0.00025615963,0.6998597,0.02077333,0.007682419,0.0011903179,0.19465289],"study_design_scores_gemma":[0.0000019188633,0.000019172307,0.004207561,0.000004342209,0.00000464242,0.00004414012,0.000011434198,0.9932847,0.001603989,0.0007442018,0.00006685184,0.000007074162],"about_ca_topic_score_codex":0.004214167,"about_ca_topic_score_gemma":0.002622532,"teacher_disagreement_score":0.004214167,"about_ca_system_score_codex":0.00067732786,"about_ca_system_score_gemma":0.00054299226,"threshold_uncertainty_score":0.008379281},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09404932,0.00037389706,0.9025236,0.00041230686,0.0000645613,0.000030216826,0.00015005041,0.0015327067,0.00086336356],"genre_scores_gemma":[0.92607075,0.00015727922,0.071330175,0.00018358472,0.000054875294,0.00004833749,0.00045012805,0.00008972292,0.0016152167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996001,0.00007841439,0.000018474188,0.00011719341,0.00010399509,0.00008174766],"domain_scores_gemma":[0.99918896,0.00035113265,0.00011392043,0.00007888638,0.00021461913,0.000052451018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084890425,0.0007770365,0.0008678805,0.00069046026,0.00034750497,0.00055330945,0.0016236795,0.00079676183,0.0005154976],"category_scores_gemma":[0.002441421,0.00029953095,0.0004955853,0.00066823664,0.0005231341,0.001183897,0.0010111314,0.0016383732,0.00015356297],"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":[0.00019459821,0.00022046786,0.0057193823,0.000056432047,0.00007072004,0.000121816636,0.00006646841,0.81513953,0.0055200397,0.004514521,0.002831286,0.16554482],"study_design_scores_gemma":[0.0000010378664,0.000005478038,0.0001203129,8.7481504e-7,0.0000015054509,0.000005303469,0.000002760579,0.99826753,0.0004094717,0.0011215262,0.000062846855,0.0000012676502],"about_ca_topic_score_codex":0.0055100396,"about_ca_topic_score_gemma":0.005543331,"teacher_disagreement_score":0.0055100396,"about_ca_system_score_codex":0.0010182929,"about_ca_system_score_gemma":0.0007890343,"threshold_uncertainty_score":0.01095593},"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":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03730291,0.04216688,0.84142274,0.03236066,0.0027600091,0.00096155895,0.0018265301,0.00045775238,0.04074087],"genre_scores_gemma":[0.5420678,0.013825179,0.4239304,0.0051370077,0.0065982067,0.002748342,0.0011571442,0.00026988683,0.0042659766],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8799007,0.052600887,0.022605412,0.012860026,0.030571865,0.0014610944],"domain_scores_gemma":[0.5465623,0.36421743,0.028288689,0.036835805,0.020431176,0.0036646156],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08933943,0.003296097,0.0052983905,0.022746248,0.0030710935,0.017005457,0.0056163496,0.010047553,0.0058504436],"category_scores_gemma":[0.34565088,0.002465873,0.004009202,0.012464412,0.033431593,0.042056777,0.01152653,0.008993857,0.0014659964],"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":[0.00043882773,0.00014867332,0.010399615,0.0037450325,0.0012432354,0.00087980455,0.0016346247,0.004497062,0.0019280709,0.87644196,0.004976597,0.09366646],"study_design_scores_gemma":[0.00007921012,0.00015974019,0.0017967944,0.0010224225,0.0003752159,0.001567514,0.0004262886,0.0051539005,0.00085999694,0.97685426,0.011556697,0.00014792514],"about_ca_topic_score_codex":0.0007917544,"about_ca_topic_score_gemma":0.0005883222,"teacher_disagreement_score":0.91066056,"about_ca_system_score_codex":0.00404836,"about_ca_system_score_gemma":0.0044283904,"threshold_uncertainty_score":0.47247767},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060743622,0.0010382648,0.92944634,0.00021427829,0.00026833802,0.00009822227,0.001158921,0.0028665578,0.004165471],"genre_scores_gemma":[0.5849521,0.0012371028,0.40523675,0.000081713246,0.0002769585,0.00009216357,0.0024126535,0.00022168455,0.0054887203],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982846,0.000010882773,0.000007548521,0.00005654851,0.00007633849,0.000020309642],"domain_scores_gemma":[0.9995803,0.00010655047,0.000050368137,0.00006882992,0.00016634022,0.000027521923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003173726,0.00072506757,0.00059773686,0.0011978534,0.00027302577,0.00052344304,0.00074228115,0.00048114496,0.002422462],"category_scores_gemma":[0.001220401,0.00022518456,0.00031753967,0.0010193931,0.00013383603,0.00072879426,0.0003705948,0.00077185786,0.0015751468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003794263,0.00016976139,0.007771817,0.00010368155,0.000038620714,0.00023803575,0.000048293532,0.04074741,0.06549244,0.0024452927,0.008178855,0.8743863],"study_design_scores_gemma":[0.0000056342615,0.000075976626,0.003451198,0.000019262861,0.000029850959,0.00020210625,0.000021734002,0.97102326,0.019826869,0.0023585232,0.0029730995,0.000012614751],"about_ca_topic_score_codex":0.0039382344,"about_ca_topic_score_gemma":0.0050608558,"teacher_disagreement_score":0.0039382344,"about_ca_system_score_codex":0.00029721568,"about_ca_system_score_gemma":0.0003370561,"threshold_uncertainty_score":0.008103907},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13249737,0.0025319194,0.82775116,0.0016643454,0.00080505636,0.00032538897,0.0027191795,0.019551514,0.012154065],"genre_scores_gemma":[0.8438455,0.0009341808,0.13025951,0.0009600969,0.0001680506,0.00024781495,0.004112241,0.00030455025,0.019168027],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986064,0.000011768316,0.000006498808,0.00005879233,0.000027291422,0.000034952132],"domain_scores_gemma":[0.99987495,0.000026888638,0.000018944227,0.000014323246,0.00004774484,0.000017092778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030129752,0.0014900741,0.0006166056,0.0007902518,0.00032548394,0.00055505097,0.002119726,0.0010109267,0.0027366166],"category_scores_gemma":[0.0007640914,0.0004913824,0.00088903453,0.0005299544,0.00032178086,0.0011639505,0.0007799459,0.0015933046,0.0011850016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006064583,0.00054313545,0.006121736,0.00030583757,0.0004629735,0.0005504872,0.00014508517,0.32155022,0.031195516,0.0042486335,0.02814485,0.60612506],"study_design_scores_gemma":[0.000011185399,0.00006295998,0.0006983855,0.00001392315,0.00003901036,0.00007275915,0.000010423702,0.99329126,0.0034496577,0.0011397498,0.0011976539,0.000012994237],"about_ca_topic_score_codex":0.014140272,"about_ca_topic_score_gemma":0.021784749,"teacher_disagreement_score":0.014140272,"about_ca_system_score_codex":0.0009941999,"about_ca_system_score_gemma":0.0008867199,"threshold_uncertainty_score":0.028115928},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033038966,0.0005462858,0.96257335,0.00030313575,0.000054020904,0.00008406368,0.00016059073,0.0017215608,0.0015179701],"genre_scores_gemma":[0.37242982,0.0004066454,0.6218574,0.000399589,0.00008900162,0.00024357681,0.0008710115,0.00024508603,0.003457835],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989028,0.00027086778,0.00007356348,0.0003664886,0.00028363586,0.00010254434],"domain_scores_gemma":[0.9978738,0.00088243757,0.0001906624,0.0004404979,0.00048942625,0.00012321389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021516513,0.0010116512,0.001612381,0.002520405,0.0008208874,0.002541585,0.0026149387,0.001789389,0.0025912283],"category_scores_gemma":[0.0035047242,0.00048963376,0.0011918162,0.0023498423,0.00089258025,0.003697702,0.0029330882,0.0015722315,0.0010248257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022912245,0.0004360525,0.008522355,0.00020994263,0.00022656133,0.00021219262,0.0004296448,0.28965515,0.0070606633,0.014110193,0.004777705,0.6741305],"study_design_scores_gemma":[0.000004614415,0.00003148026,0.00026609938,0.000012350865,0.000008741577,0.000035883728,0.000051389306,0.99075246,0.00077889435,0.0071629514,0.0008869331,0.000008234058],"about_ca_topic_score_codex":0.0048215035,"about_ca_topic_score_gemma":0.0064597153,"teacher_disagreement_score":0.0048215035,"about_ca_system_score_codex":0.00074481504,"about_ca_system_score_gemma":0.0012411352,"threshold_uncertainty_score":0.011379182},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25958213,0.0007583819,0.7342073,0.00028934982,0.00030171697,0.000043704375,0.00016666048,0.0022578558,0.0023929258],"genre_scores_gemma":[0.9440271,0.000100047255,0.0542745,0.000044304976,0.000035289166,0.000023617447,0.00016492644,0.000048623177,0.0012814887],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997483,0.000029663126,0.0000129479895,0.00008954226,0.000073728894,0.00004577678],"domain_scores_gemma":[0.9993605,0.00023858227,0.00005658412,0.00006314733,0.00024632076,0.00003477546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047563371,0.0005608281,0.00065487507,0.00043901312,0.00034923328,0.0004795666,0.0009847474,0.0006822499,0.0011095749],"category_scores_gemma":[0.0015896244,0.00031186268,0.00039490583,0.00051942916,0.0003315434,0.0006915382,0.00053847046,0.00086860155,0.00029216005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000464383,0.00018357702,0.0054018376,0.00011612692,0.00008511809,0.000310567,0.00013043021,0.4163172,0.026952395,0.0027358504,0.003949121,0.5433534],"study_design_scores_gemma":[0.0000016969141,0.00001464503,0.00052827346,0.0000017991932,0.0000039544016,0.00001866813,0.0000046151463,0.9972498,0.0016902728,0.00034253445,0.000140925,0.0000027428143],"about_ca_topic_score_codex":0.008457875,"about_ca_topic_score_gemma":0.008193476,"teacher_disagreement_score":0.008457875,"about_ca_system_score_codex":0.0004689108,"about_ca_system_score_gemma":0.00074613246,"threshold_uncertainty_score":0.016817272},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3228698,0.0005314076,0.66805905,0.0005541829,0.000079876845,0.00013714726,0.0005827294,0.005664133,0.0015216961],"genre_scores_gemma":[0.8189675,0.0002319873,0.17755663,0.00009699469,0.000055815497,0.000107279106,0.0015452647,0.00034374325,0.0010946757],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99811506,0.0007129747,0.00011244754,0.00043766454,0.00043631054,0.00018555933],"domain_scores_gemma":[0.98731226,0.010100106,0.00050801993,0.0006761198,0.0012304612,0.00017316436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034560876,0.0011455242,0.00095920847,0.0025404342,0.0007827817,0.0014281566,0.0009851904,0.0009878027,0.00068592234],"category_scores_gemma":[0.014147564,0.00025055223,0.0013114143,0.0014678632,0.0008342412,0.0038058537,0.000866669,0.0016726777,0.00077489205],"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":[0.0010879012,0.0010574001,0.042828448,0.0003727391,0.00028925098,0.00031769736,0.00068521133,0.095732346,0.042965956,0.0031032385,0.0030269711,0.80853295],"study_design_scores_gemma":[0.000015295487,0.00017544415,0.0053974036,0.000019073701,0.00006217536,0.00015180605,0.00019509577,0.974164,0.014818866,0.0040525836,0.000911136,0.000037130354],"about_ca_topic_score_codex":0.0068048337,"about_ca_topic_score_gemma":0.008979639,"teacher_disagreement_score":0.0068048337,"about_ca_system_score_codex":0.0007528121,"about_ca_system_score_gemma":0.001506617,"threshold_uncertainty_score":0.018277764},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014607267,0.00015520274,0.98264796,0.000063117535,0.00007143088,0.000037431724,0.00015066582,0.0015904168,0.0006766178],"genre_scores_gemma":[0.35727933,0.0003007923,0.63547736,0.00012721313,0.0001343609,0.00016696929,0.0012016476,0.0003014225,0.0050108633],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994791,0.00008643337,0.000025834364,0.00014260515,0.00019121643,0.00007478601],"domain_scores_gemma":[0.99944085,0.000116121824,0.0000675259,0.00009106331,0.0002478466,0.000036614452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082003325,0.00091750926,0.0011277678,0.0019529641,0.00044495604,0.000944408,0.0014643805,0.0010307065,0.0021732976],"category_scores_gemma":[0.0014444786,0.00049174693,0.0008039009,0.0012469182,0.00041669048,0.00129968,0.0011309216,0.0010767884,0.0009826917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037367613,0.00034785035,0.003884328,0.00018775788,0.00011357775,0.00010201192,0.00008998512,0.14477837,0.041657567,0.004849491,0.0070675886,0.7965478],"study_design_scores_gemma":[0.00000448588,0.000020422902,0.0005117313,0.000004702884,0.000007507464,0.000030892497,0.0000062879826,0.99348986,0.0042071375,0.0009877005,0.00072204985,0.000007213828],"about_ca_topic_score_codex":0.0030740483,"about_ca_topic_score_gemma":0.0036521603,"teacher_disagreement_score":0.0030740483,"about_ca_system_score_codex":0.00038392973,"about_ca_system_score_gemma":0.0011741723,"threshold_uncertainty_score":0.0072704554},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13877423,0.00013945402,0.85905635,0.00009774867,0.000060209495,0.0000835412,0.00017646149,0.00105714,0.00055488304],"genre_scores_gemma":[0.764655,0.00008893868,0.23394111,0.000026086382,0.000033026412,0.00008286883,0.0003244882,0.000042249398,0.0008062424],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934274,0.00012545973,0.00006599458,0.0001483447,0.00025522828,0.00006223596],"domain_scores_gemma":[0.9987419,0.00042661547,0.00018250094,0.00007764216,0.00050447154,0.00006688866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009025245,0.000652712,0.0005493216,0.0019099177,0.00038761226,0.00044400795,0.0008814503,0.00051116385,0.00048084097],"category_scores_gemma":[0.003060453,0.00017083972,0.00042477893,0.0012975866,0.00027454548,0.0006514529,0.00049067754,0.0005800422,0.00018362729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027135538,0.00026166666,0.03034559,0.00014255471,0.00015497177,0.00036582685,0.0002569369,0.25963813,0.034493048,0.002704188,0.0023011225,0.6690646],"study_design_scores_gemma":[0.0000061735195,0.000053153657,0.004072612,0.0000040824243,0.000011976232,0.00010923286,0.00003385209,0.99023825,0.0043864506,0.00060678326,0.00046453392,0.000012945047],"about_ca_topic_score_codex":0.004504741,"about_ca_topic_score_gemma":0.0043259095,"teacher_disagreement_score":0.004504741,"about_ca_system_score_codex":0.00046982666,"about_ca_system_score_gemma":0.0006383166,"threshold_uncertainty_score":0.008957028},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13078472,0.00198164,0.8603167,0.00034186238,0.0003024432,0.00015026152,0.0005290951,0.002033242,0.0035600958],"genre_scores_gemma":[0.8750178,0.00073277956,0.11889265,0.00023392336,0.00013324241,0.0000958617,0.001218854,0.00006644827,0.0036084037],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952674,0.000046852183,0.000021995163,0.00015683963,0.00014886029,0.00009869438],"domain_scores_gemma":[0.9996617,0.000059754995,0.00004831733,0.00005689504,0.000139972,0.000033386634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069682475,0.0008780324,0.0010338494,0.0010432781,0.00030111172,0.0006976507,0.0013826316,0.00072328944,0.0009812391],"category_scores_gemma":[0.0012944764,0.0002574221,0.00070500944,0.0008774319,0.00038692408,0.0011486995,0.0010595997,0.0007807208,0.0004180187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005450542,0.00031677904,0.007426768,0.00016495031,0.00019361083,0.00032762546,0.00009114322,0.13460286,0.05442764,0.0039782077,0.006294727,0.7916306],"study_design_scores_gemma":[0.000006351791,0.00009420957,0.001465498,0.000010096484,0.000028913026,0.00008921829,0.000017182856,0.98844516,0.008236055,0.00085828506,0.00073508354,0.000013927474],"about_ca_topic_score_codex":0.00828329,"about_ca_topic_score_gemma":0.00750674,"teacher_disagreement_score":0.00828329,"about_ca_system_score_codex":0.0009000889,"about_ca_system_score_gemma":0.0008946983,"threshold_uncertainty_score":0.016470134},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.089738175,0.00022941982,0.90494084,0.00020742389,0.00009645048,0.00005006563,0.00020491566,0.002959875,0.001572752],"genre_scores_gemma":[0.8014949,0.00016118262,0.19473298,0.00012419821,0.000092837414,0.00004960815,0.0007012028,0.00029385646,0.00234919],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925333,0.00007059534,0.000034397188,0.00013931832,0.0003360158,0.00016621001],"domain_scores_gemma":[0.99828327,0.0005428029,0.00022995932,0.00032306332,0.0005352429,0.00008572989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077406917,0.0007835321,0.0011414689,0.0016645454,0.00062637683,0.0013212023,0.0010377849,0.0009045873,0.0013168148],"category_scores_gemma":[0.002763469,0.00030838276,0.0005187782,0.0013314633,0.00027649722,0.0013711681,0.0011902609,0.001417838,0.0011914927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005363675,0.000576042,0.022517033,0.000080718564,0.0001100587,0.0004041552,0.000117360294,0.03343821,0.12715377,0.0046164407,0.006494467,0.8039554],"study_design_scores_gemma":[0.000010874751,0.00007989437,0.0061618886,0.00000636045,0.000026821152,0.00027455614,0.000039040697,0.95626444,0.030120121,0.0046422463,0.0023556314,0.000018177458],"about_ca_topic_score_codex":0.0030436984,"about_ca_topic_score_gemma":0.005738832,"teacher_disagreement_score":0.0030436984,"about_ca_system_score_codex":0.00057000597,"about_ca_system_score_gemma":0.0008790654,"threshold_uncertainty_score":0.006052017},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07876066,0.000373372,0.91764855,0.00042143042,0.000051969753,0.00009376358,0.000054998734,0.0012289456,0.0013663418],"genre_scores_gemma":[0.85605675,0.00021085788,0.14087678,0.00042459977,0.000049693717,0.00014532295,0.00017050846,0.000114533694,0.0019509547],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874324,0.00040949945,0.00006946055,0.00030107386,0.00035065116,0.00012612755],"domain_scores_gemma":[0.99596393,0.002268717,0.00042753652,0.0007449897,0.00047063475,0.00012414939],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026994324,0.0013078892,0.00093202485,0.0004894443,0.00043261852,0.0008646729,0.0014283207,0.0012967971,0.0010683286],"category_scores_gemma":[0.011606707,0.0004635557,0.0005304855,0.00030067938,0.0018836717,0.0025287108,0.0021315932,0.0026786495,0.0004791442],"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":[0.0007134128,0.00052470237,0.009748218,0.0002305022,0.0001497451,0.0004870877,0.00037073722,0.560879,0.05134977,0.03393989,0.0034047004,0.33820224],"study_design_scores_gemma":[0.000014583558,0.00013660059,0.00028569688,0.000010288781,0.000014109308,0.00008461129,0.000013067158,0.98222566,0.011406757,0.0052985046,0.00049962936,0.000010422518],"about_ca_topic_score_codex":0.0012684284,"about_ca_topic_score_gemma":0.0013600269,"teacher_disagreement_score":0.0026994324,"about_ca_system_score_codex":0.0009857988,"about_ca_system_score_gemma":0.001042316,"threshold_uncertainty_score":0.014276147},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012944265,0.00034029636,0.9854812,0.000049713937,0.00003396649,0.00002447721,0.00007690144,0.00075608684,0.00029313733],"genre_scores_gemma":[0.4766748,0.00064527633,0.5187002,0.00013388574,0.00015554437,0.00009625878,0.00064181146,0.0002309433,0.0027212824],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939585,0.00009437305,0.000033702934,0.00014531722,0.00024151528,0.00008920774],"domain_scores_gemma":[0.99934834,0.00013263489,0.00009823714,0.00014147672,0.00023928324,0.00003990854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083273463,0.00061816117,0.0011189167,0.0011905823,0.00027652807,0.0007385253,0.001988058,0.00060750765,0.0014210106],"category_scores_gemma":[0.0019370393,0.0003018392,0.00076400576,0.0012955256,0.0003490512,0.0010865647,0.0011308925,0.0012781267,0.00055949995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000522685,0.00030324934,0.0033184218,0.00015297966,0.00017523087,0.00013374463,0.00006526727,0.0877957,0.06509367,0.009386042,0.0037817485,0.8292714],"study_design_scores_gemma":[0.000011502397,0.00008851697,0.0008402859,0.000005915085,0.000034304627,0.00009933589,0.000012023065,0.9853994,0.009446143,0.0028479993,0.0012017913,0.0000127617395],"about_ca_topic_score_codex":0.0065909396,"about_ca_topic_score_gemma":0.007035241,"teacher_disagreement_score":0.0065909396,"about_ca_system_score_codex":0.00042473033,"about_ca_system_score_gemma":0.0011323899,"threshold_uncertainty_score":0.013105154},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36250663,0.0011006208,0.6276855,0.0006596118,0.00014985475,0.00019416203,0.0015169467,0.003695154,0.0024914714],"genre_scores_gemma":[0.8533733,0.00041701074,0.14353521,0.000091152986,0.000074058036,0.00006341488,0.0016410965,0.00006189209,0.00074288604],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995029,0.000052752228,0.000043918455,0.00015319519,0.00015787779,0.000089292196],"domain_scores_gemma":[0.99854124,0.00051379146,0.00028576792,0.00012638351,0.0004519563,0.000080796715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010053713,0.0007242689,0.0008163599,0.0022799377,0.00029822162,0.00094268244,0.0010211822,0.00073471706,0.00050539296],"category_scores_gemma":[0.0033415027,0.00019276969,0.0005922788,0.0013906427,0.00044372494,0.0014916741,0.00075904856,0.0010975855,0.00023729092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055827724,0.0006025508,0.15778029,0.00036232953,0.00025648467,0.00056919915,0.00041368717,0.2316638,0.039603032,0.0049174563,0.0049945572,0.5582784],"study_design_scores_gemma":[0.0000063653865,0.00006865864,0.01922584,0.000022225031,0.000020613235,0.00013408344,0.00010537376,0.9658251,0.010291409,0.0030490693,0.0012331648,0.00001822275],"about_ca_topic_score_codex":0.0061666653,"about_ca_topic_score_gemma":0.008465372,"teacher_disagreement_score":0.0061666653,"about_ca_system_score_codex":0.00064072077,"about_ca_system_score_gemma":0.0008658086,"threshold_uncertainty_score":0.0122615695},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06337567,0.00030397912,0.919503,0.00035152287,0.00010689963,0.00013628072,0.00066841935,0.012524931,0.0030293097],"genre_scores_gemma":[0.65177995,0.00015136496,0.33883426,0.00034069174,0.00016884097,0.00018717318,0.0033670198,0.0012277359,0.0039429488],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99763596,0.00047461258,0.00009690169,0.0008833044,0.0006645652,0.0002446655],"domain_scores_gemma":[0.9942035,0.0018113913,0.0004515909,0.0024197558,0.0008806814,0.00023305687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021805272,0.0010651934,0.0011876842,0.0014660243,0.0006532397,0.0015599955,0.0021817219,0.0013120483,0.0019104832],"category_scores_gemma":[0.006969919,0.00041757067,0.001105562,0.0010220284,0.0015959988,0.0030856996,0.0026978643,0.0024699597,0.001652115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00086300535,0.0004536763,0.024015604,0.00022393718,0.00028927936,0.00020767645,0.00036364733,0.12873927,0.03941321,0.014583935,0.026203156,0.76464355],"study_design_scores_gemma":[0.000031465075,0.00013813995,0.004640439,0.00001933616,0.000031672203,0.00022582246,0.00008621047,0.94634056,0.017927248,0.02538641,0.005128038,0.000044638946],"about_ca_topic_score_codex":0.004049291,"about_ca_topic_score_gemma":0.0065221903,"teacher_disagreement_score":0.004049291,"about_ca_system_score_codex":0.0007274904,"about_ca_system_score_gemma":0.0011668529,"threshold_uncertainty_score":0.011531889},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041205264,0.00017374654,0.9941362,0.00014494352,0.000039350893,0.000012548237,0.000019182431,0.0004723542,0.00088103674],"genre_scores_gemma":[0.3535069,0.00049555337,0.6426612,0.0002076574,0.0001645173,0.00012514922,0.0002419392,0.00040492293,0.0021921943],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962853,0.0016939503,0.00017436923,0.00040497104,0.0012661834,0.00017516107],"domain_scores_gemma":[0.9798212,0.0144573515,0.0007612958,0.002284077,0.0024328185,0.0002432462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051973476,0.0012629956,0.0017747782,0.0028185463,0.0008593431,0.0016184774,0.0020451916,0.0017452578,0.0030201783],"category_scores_gemma":[0.031171557,0.0008458138,0.001137405,0.0020922816,0.0013105383,0.0040214984,0.0026673109,0.0019261258,0.001004389],"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":[0.00031856212,0.00017315397,0.0023509704,0.00017875512,0.00014092684,0.000071739225,0.000121909005,0.74481845,0.005294478,0.07410126,0.0019289007,0.17050081],"study_design_scores_gemma":[0.000007664163,0.000023594434,0.00015896843,0.000007804451,0.000012333035,0.00002874556,0.0000052094715,0.9863594,0.0010953016,0.011853243,0.000437827,0.000009948194],"about_ca_topic_score_codex":0.0045175017,"about_ca_topic_score_gemma":0.00491951,"teacher_disagreement_score":0.0051973476,"about_ca_system_score_codex":0.0016042457,"about_ca_system_score_gemma":0.002112399,"threshold_uncertainty_score":0.027486503},"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":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019198453,0.28666717,0.6825847,0.0030868098,0.0006109366,0.00012315394,0.00015185191,0.0009291054,0.0066479095],"genre_scores_gemma":[0.38237685,0.2603863,0.3433748,0.0012933377,0.0034311258,0.00020470549,0.00079580385,0.0002565241,0.007880658],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986504,0.00025957444,0.00009475195,0.00042576512,0.000506119,0.00006348436],"domain_scores_gemma":[0.9963953,0.0017058572,0.00031391333,0.00032878108,0.0011317673,0.00012436068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029053823,0.0009243231,0.0018570041,0.0021680747,0.00042747107,0.002424392,0.0024186245,0.001646628,0.0008884955],"category_scores_gemma":[0.004599023,0.00055690866,0.00079181104,0.00286577,0.0012204313,0.0039572525,0.0013016156,0.0015194801,0.0005814717],"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":[0.00008273844,0.000339674,0.004839997,0.0012964768,0.00018763465,0.00005559025,0.000103274426,0.024336465,0.0028953715,0.017753918,0.0059764897,0.94213235],"study_design_scores_gemma":[0.000025045932,0.00042468085,0.006031262,0.0007947581,0.00020691527,0.0008517367,0.00030898603,0.81360155,0.011538656,0.10885024,0.057225265,0.00014093558],"about_ca_topic_score_codex":0.0026848796,"about_ca_topic_score_gemma":0.0022360438,"teacher_disagreement_score":0.0029053823,"about_ca_system_score_codex":0.0010210692,"about_ca_system_score_gemma":0.0010832524,"threshold_uncertainty_score":0.015365303},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21362543,0.00085352594,0.77955866,0.00061981386,0.00017939962,0.000034375746,0.00034237173,0.0016553513,0.0031309987],"genre_scores_gemma":[0.94265395,0.00027959567,0.054360833,0.00010727268,0.000048009686,0.000028633409,0.0003349643,0.00003117306,0.0021555],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983513,0.000024979763,0.000012330586,0.00006011354,0.000034681598,0.000032828233],"domain_scores_gemma":[0.99965847,0.000100709905,0.000046943744,0.000047820253,0.000116686904,0.00002943941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065599295,0.0006459247,0.00046471797,0.0004422684,0.000289988,0.0005249627,0.0010323058,0.00065549853,0.0010491017],"category_scores_gemma":[0.0011809478,0.00034175836,0.00069735025,0.0004312996,0.0003149258,0.0010300797,0.0007148491,0.0008204049,0.00025991446],"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":[0.00018198586,0.00011993914,0.007270131,0.000054103926,0.00014117845,0.00020735271,0.00009855891,0.82582253,0.012265569,0.0055323485,0.0011384893,0.14716771],"study_design_scores_gemma":[0.000001406661,0.000012188744,0.00030402085,0.0000017302918,0.000007241385,0.000009023788,0.0000026092416,0.99822503,0.0006117616,0.00069330464,0.00012857444,0.0000031551356],"about_ca_topic_score_codex":0.018095747,"about_ca_topic_score_gemma":0.022552608,"teacher_disagreement_score":0.018095747,"about_ca_system_score_codex":0.0007354842,"about_ca_system_score_gemma":0.001128811,"threshold_uncertainty_score":0.03598082},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16001186,0.000733806,0.82927966,0.00026591055,0.0002748836,0.00014908693,0.00007651858,0.0026324631,0.0065757595],"genre_scores_gemma":[0.534499,0.00028578754,0.46193272,0.00018741815,0.000050091086,0.00010580888,0.0001163991,0.00013095018,0.0026918042],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99868435,0.0001884978,0.00007014816,0.0003568887,0.0006122287,0.000087904904],"domain_scores_gemma":[0.99873525,0.0004352289,0.00017479702,0.00018252026,0.00039830044,0.0000739651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010262335,0.00091476575,0.0008335809,0.00089347124,0.00033061323,0.0008557107,0.0011033498,0.0007767529,0.0014213502],"category_scores_gemma":[0.0026386972,0.00049606216,0.00033406928,0.00037773026,0.00053005107,0.0006901858,0.00095008704,0.0006826655,0.0007164253],"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":[0.0005914071,0.00015723791,0.011203349,0.00043380389,0.00005692,0.00020354676,0.00042621358,0.007727935,0.50686383,0.0013469674,0.0017090518,0.46927974],"study_design_scores_gemma":[0.00021086536,0.0024669194,0.06987053,0.0002511174,0.00026459442,0.0019918242,0.00049524097,0.41186535,0.46910778,0.0025940049,0.040542744,0.0003390284],"about_ca_topic_score_codex":0.0012318376,"about_ca_topic_score_gemma":0.001598242,"teacher_disagreement_score":0.0014213502,"about_ca_system_score_codex":0.00036006756,"about_ca_system_score_gemma":0.0005229277,"threshold_uncertainty_score":0.005427301},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036770678,0.078874834,0.79473627,0.023593863,0.0068158004,0.000030624993,0.00021837647,0.000692746,0.09136039],"genre_scores_gemma":[0.21380723,0.09708948,0.575135,0.007755426,0.03101814,0.00026086147,0.00048618263,0.0016293996,0.072818324],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99737597,0.00067175005,0.00015578381,0.00052419957,0.0011668642,0.000105448205],"domain_scores_gemma":[0.9956084,0.0024741853,0.00018202708,0.00084839464,0.0006923893,0.00019452392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038978893,0.0014820138,0.0016091312,0.0022595387,0.0010701413,0.004931312,0.0022324794,0.0019514896,0.0096743135],"category_scores_gemma":[0.0118672205,0.00067261944,0.0008573376,0.0038852256,0.0064840904,0.014533162,0.0030999207,0.0063335747,0.0021882933],"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":[0.000012586824,0.000025214651,0.000100372206,0.00012271057,0.000014000112,0.000015936612,0.00007153597,0.00358141,0.0002557737,0.88994944,0.016530149,0.08932095],"study_design_scores_gemma":[0.000003150423,0.000008641034,0.00007071858,0.000042612963,0.000004724634,0.000035869994,0.00003459645,0.012910286,0.0001681144,0.94934756,0.03736379,0.000009927214],"about_ca_topic_score_codex":0.001026812,"about_ca_topic_score_gemma":0.00075437187,"teacher_disagreement_score":0.0096743135,"about_ca_system_score_codex":0.002077809,"about_ca_system_score_gemma":0.00099661,"threshold_uncertainty_score":0.032363772},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03245929,0.00019620814,0.96425337,0.00013979903,0.000033266228,0.000035790817,0.00011376783,0.0020334034,0.0007350553],"genre_scores_gemma":[0.72666156,0.00048277073,0.2677298,0.00020622175,0.00015540906,0.000114959126,0.00089858775,0.00040736186,0.0033433586],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99866045,0.00025672567,0.00008027009,0.0003262732,0.00049907545,0.00017728952],"domain_scores_gemma":[0.9979923,0.00058813597,0.00036183698,0.00030947517,0.0006307149,0.00011762091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012500249,0.0012873926,0.0012005679,0.0027692623,0.00039244114,0.0011012569,0.0014625724,0.0008164529,0.0012468855],"category_scores_gemma":[0.0040817866,0.00033095476,0.001105135,0.0017371137,0.0010035769,0.0022417947,0.0014053857,0.002004489,0.0009675025],"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":[0.00045119465,0.0002527538,0.017801588,0.00011613148,0.00020345063,0.0003285583,0.0002187481,0.1196812,0.037820835,0.012892908,0.0040722196,0.80616045],"study_design_scores_gemma":[0.000009506729,0.000087780405,0.003929214,0.000009480808,0.00002893479,0.0002780161,0.000057999834,0.96252316,0.017013026,0.0142811015,0.0017503989,0.000031376832],"about_ca_topic_score_codex":0.0028405467,"about_ca_topic_score_gemma":0.0031574653,"teacher_disagreement_score":0.0028405467,"about_ca_system_score_codex":0.00074758544,"about_ca_system_score_gemma":0.0008579309,"threshold_uncertainty_score":0.0066108704},"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":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00085495063,0.008590257,0.9440254,0.00056927936,0.0011373735,0.000059757866,0.0018373425,0.024039986,0.018885534],"genre_scores_gemma":[0.030630935,0.014497433,0.8181909,0.0007055361,0.0012514086,0.00040248415,0.0080765085,0.0076262164,0.118618645],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99951553,0.000040991792,0.00003132423,0.000111003515,0.00027577297,0.00002538078],"domain_scores_gemma":[0.9993592,0.00018926503,0.000035384106,0.00019418471,0.0001902847,0.000031692347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059028465,0.0018577796,0.0014863415,0.0024556548,0.00036514105,0.0025720824,0.002397871,0.0016375997,0.047155447],"category_scores_gemma":[0.001706391,0.0010733784,0.00081889634,0.0031606548,0.0006674961,0.0033661628,0.0021305182,0.0017125818,0.028715601],"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":[0.0000415444,0.000023195827,0.00011481776,0.000407518,0.00003689166,0.00007256592,0.000056355355,0.014409116,0.004058056,0.011132039,0.17900941,0.79063845],"study_design_scores_gemma":[0.000035128833,0.000069884016,0.0008298731,0.00030516373,0.000052004063,0.00082705327,0.00006599181,0.21131535,0.011747552,0.0712493,0.70341384,0.00008885286],"about_ca_topic_score_codex":0.0021446224,"about_ca_topic_score_gemma":0.002287281,"teacher_disagreement_score":0.047155447,"about_ca_system_score_codex":0.0004605499,"about_ca_system_score_gemma":0.00056826224,"threshold_uncertainty_score":0.15775067},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035743188,0.00026221364,0.96263796,0.00022004296,0.000019160401,0.00004225034,0.000018884262,0.0002981156,0.00075815234],"genre_scores_gemma":[0.82406366,0.00018239806,0.17379232,0.00017462802,0.000030349505,0.00018012697,0.00009346888,0.00005762104,0.0014252813],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961144,0.00015641375,0.000025138648,0.00008424686,0.000078142715,0.000044669963],"domain_scores_gemma":[0.9981021,0.0013329325,0.00013648864,0.00007383021,0.00030332315,0.000051349376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016358925,0.00077484513,0.00092073117,0.00051141705,0.0002816995,0.00053251017,0.0010332612,0.0010756887,0.0010033728],"category_scores_gemma":[0.0043706787,0.00050473725,0.0005243891,0.0003219997,0.00070445664,0.00065134524,0.0007186469,0.0014060201,0.00018658035],"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":[0.000034409153,0.00004053666,0.0005753034,0.000029367082,0.00002995295,0.000037552043,0.00004036253,0.9664148,0.0014141764,0.0019871378,0.00023379285,0.0291627],"study_design_scores_gemma":[0.0000016738085,0.000008925714,0.00002526709,0.000001586932,0.0000017296884,0.000002894565,0.0000014270373,0.9994512,0.0001297402,0.00035086504,0.000023720508,9.693174e-7],"about_ca_topic_score_codex":0.005252427,"about_ca_topic_score_gemma":0.0053664614,"teacher_disagreement_score":0.005252427,"about_ca_system_score_codex":0.0007521703,"about_ca_system_score_gemma":0.0010102807,"threshold_uncertainty_score":0.010443687},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032551054,0.00043709215,0.9607816,0.0003322803,0.00014938503,0.0001258607,0.0002810916,0.0023022883,0.0030393337],"genre_scores_gemma":[0.67613935,0.00046240512,0.305495,0.00065769575,0.00016086102,0.00022460922,0.0017982493,0.00048398765,0.014577849],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99937207,0.00012392818,0.000021911203,0.00027176962,0.0001378118,0.00007248893],"domain_scores_gemma":[0.99924445,0.00018298918,0.00007165864,0.00027016026,0.0001594642,0.000071227354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00098364,0.0011796879,0.0009519522,0.0007287689,0.00060074474,0.0007664955,0.0023039258,0.0015373966,0.0036221775],"category_scores_gemma":[0.0031294774,0.00045934375,0.0006756545,0.0006918692,0.0008645345,0.0026378466,0.0016959729,0.0022701144,0.0013307102],"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":[0.0003438868,0.00018470132,0.0018959808,0.00016510133,0.00014297762,0.00026011933,0.00014185412,0.41876906,0.023108248,0.01909226,0.0136292735,0.5222665],"study_design_scores_gemma":[0.0000071639206,0.000049376824,0.0002293046,0.000006417093,0.000011622581,0.000073811534,0.000011607594,0.985849,0.0042246426,0.00816407,0.001362493,0.000010457177],"about_ca_topic_score_codex":0.004720676,"about_ca_topic_score_gemma":0.0075858734,"teacher_disagreement_score":0.004720676,"about_ca_system_score_codex":0.000762141,"about_ca_system_score_gemma":0.00070357707,"threshold_uncertainty_score":0.012117386},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012429019,0.56824327,0.39374825,0.0050140494,0.000860704,0.00034309996,0.0013954453,0.0013917445,0.016574444],"genre_scores_gemma":[0.14086968,0.6953253,0.15155281,0.0014544926,0.0009435383,0.0004976882,0.003453715,0.0002636806,0.0056391023],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99873894,0.00024154627,0.00013666901,0.00031192155,0.00049252744,0.00007832465],"domain_scores_gemma":[0.9959622,0.0024011591,0.000239433,0.00029758472,0.0010063925,0.000093306946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002486842,0.0013455702,0.0012368696,0.0045771003,0.00036295838,0.0026836605,0.0025414254,0.0012612615,0.0016137626],"category_scores_gemma":[0.009188414,0.0006198454,0.0011149978,0.00411582,0.00075202016,0.004275242,0.0016229477,0.0020146184,0.00094287685],"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":[0.00005933513,0.00009984594,0.004292046,0.0029806315,0.00016938557,0.00007424421,0.00023939415,0.013939005,0.00079363113,0.024656199,0.012575697,0.94012064],"study_design_scores_gemma":[0.0000571686,0.00069773616,0.012085497,0.00870914,0.00084261753,0.001878863,0.0011604795,0.36321202,0.008083144,0.16161345,0.44135,0.00030991386],"about_ca_topic_score_codex":0.0056029786,"about_ca_topic_score_gemma":0.0053288983,"teacher_disagreement_score":0.0056029786,"about_ca_system_score_codex":0.0013658337,"about_ca_system_score_gemma":0.00270192,"threshold_uncertainty_score":0.0131518245},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6040497,0.0029536637,0.37994325,0.0005042409,0.00024238306,0.00008391638,0.0003836136,0.0035044898,0.008334724],"genre_scores_gemma":[0.94245064,0.0005197469,0.05514066,0.0000622055,0.000018626994,0.000026637343,0.00031131296,0.00003318503,0.0014370921],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994191,0.0001490899,0.000055054683,0.00009573993,0.00018657095,0.00009453577],"domain_scores_gemma":[0.99857235,0.0007007297,0.000117932075,0.00008466168,0.0004421553,0.00008221068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011962792,0.0009743218,0.00057288446,0.0012871823,0.00024231507,0.00075033936,0.000617752,0.0007868573,0.00078294583],"category_scores_gemma":[0.0032411967,0.00017426678,0.00040163146,0.0007085253,0.00022610667,0.0007347787,0.0006033818,0.0006673803,0.0002720895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008298114,0.00030229005,0.011759364,0.0003547709,0.00022878518,0.0001082829,0.00012115536,0.3385131,0.010586998,0.0014423385,0.001866482,0.6338866],"study_design_scores_gemma":[0.000008803598,0.00017244248,0.001939536,0.000016461614,0.00002678469,0.00004130725,0.00003425504,0.99163944,0.005464108,0.0002929007,0.00035562954,0.000008384829],"about_ca_topic_score_codex":0.0071319896,"about_ca_topic_score_gemma":0.005856232,"teacher_disagreement_score":0.0071319896,"about_ca_system_score_codex":0.0006296878,"about_ca_system_score_gemma":0.000709917,"threshold_uncertainty_score":0.014180958},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040381454,0.00018974642,0.9519983,0.00041336234,0.00006926607,0.000108467655,0.00034470588,0.005501423,0.0009934362],"genre_scores_gemma":[0.58173186,0.00018875558,0.4142497,0.00019536902,0.00007180277,0.00014256663,0.00086443045,0.00029377895,0.0022617741],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990503,0.00031595456,0.000067251945,0.0002484058,0.00022898056,0.00008905451],"domain_scores_gemma":[0.9960788,0.0019717666,0.0005533018,0.00054474495,0.000717035,0.0001343106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001945315,0.00090648065,0.0008209243,0.0023080397,0.00046004774,0.0014010172,0.0018284137,0.0009886982,0.0018128591],"category_scores_gemma":[0.007758539,0.0005632441,0.0008716522,0.001372091,0.00063087925,0.0025030773,0.0010825992,0.0015711781,0.00046483648],"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":[0.00023139904,0.0001939106,0.009846706,0.00011310717,0.00021706481,0.0001421523,0.00009427422,0.8073414,0.0031399578,0.008665788,0.0026851592,0.16732909],"study_design_scores_gemma":[0.0000019407053,0.000007503505,0.00020714929,0.0000019203503,0.000003431357,0.00000988416,0.0000043379146,0.99724495,0.00039678006,0.0019716048,0.00014749753,0.0000029308108],"about_ca_topic_score_codex":0.013920649,"about_ca_topic_score_gemma":0.021623217,"teacher_disagreement_score":0.013920649,"about_ca_system_score_codex":0.0015838773,"about_ca_system_score_gemma":0.0011200182,"threshold_uncertainty_score":0.027679205},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021457603,0.00318704,0.96331936,0.00057021837,0.0001779325,0.00016358194,0.0010705823,0.0057203863,0.0043332456],"genre_scores_gemma":[0.5462211,0.0020443592,0.44236407,0.0005217891,0.00023683962,0.00030226682,0.0026929458,0.00025657532,0.005360064],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992662,0.00013188702,0.000035263285,0.00024762255,0.00023191834,0.000087101194],"domain_scores_gemma":[0.99963975,0.00009662623,0.000045858233,0.00006448294,0.00012911155,0.000024252977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011106969,0.0012858681,0.00097149285,0.0013777037,0.00035881068,0.00092296605,0.0013057062,0.0009718446,0.00347011],"category_scores_gemma":[0.0019917456,0.00029433292,0.0012196266,0.0012076735,0.00049858197,0.0013606928,0.0016633159,0.0011752817,0.0008589603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003379876,0.00021996787,0.0024375522,0.00025326913,0.00027956377,0.00032691393,0.00017191851,0.24583356,0.022064226,0.01099874,0.0116808405,0.7053955],"study_design_scores_gemma":[0.000010507432,0.00008049213,0.0016208959,0.000020958953,0.000042235497,0.00011191319,0.000051580933,0.97525907,0.008506109,0.008863632,0.0054063005,0.000026194095],"about_ca_topic_score_codex":0.012957322,"about_ca_topic_score_gemma":0.012188166,"teacher_disagreement_score":0.012957322,"about_ca_system_score_codex":0.0012079737,"about_ca_system_score_gemma":0.000718899,"threshold_uncertainty_score":0.02576381},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021457603,0.00318704,0.96331936,0.00057021837,0.0001779325,0.00016358194,0.0010705823,0.0057203863,0.0043332456],"genre_scores_gemma":[0.5462211,0.0020443592,0.44236407,0.0005217891,0.00023683962,0.00030226682,0.0026929458,0.00025657532,0.005360064],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992662,0.00013188702,0.000035263285,0.00024762255,0.00023191834,0.000087101194],"domain_scores_gemma":[0.99963975,0.00009662623,0.000045858233,0.00006448294,0.00012911155,0.000024252977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011106969,0.0012858681,0.00097149285,0.0013777037,0.00035881068,0.00092296605,0.0013057062,0.0009718446,0.00347011],"category_scores_gemma":[0.0019917456,0.00029433292,0.0012196266,0.0012076735,0.00049858197,0.0013606928,0.0016633159,0.0011752817,0.0008589603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003379876,0.00021996787,0.0024375522,0.00025326913,0.00027956377,0.00032691393,0.00017191851,0.24583356,0.022064226,0.01099874,0.0116808405,0.7053955],"study_design_scores_gemma":[0.000010507432,0.00008049213,0.0016208959,0.000020958953,0.000042235497,0.00011191319,0.000051580933,0.97525907,0.008506109,0.008863632,0.0054063005,0.000026194095],"about_ca_topic_score_codex":0.012957322,"about_ca_topic_score_gemma":0.012188166,"teacher_disagreement_score":0.012957322,"about_ca_system_score_codex":0.0012079737,"about_ca_system_score_gemma":0.000718899,"threshold_uncertainty_score":0.02576381},"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":"review","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031095417,0.28618786,0.12132292,0.18443017,0.13304855,0.00028083276,0.0018980732,0.002473296,0.26724875],"genre_scores_gemma":[0.03296795,0.24160953,0.06540413,0.05233641,0.13047178,0.0003449908,0.0026889741,0.0020785755,0.4720976],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989015,0.00022613826,0.00008116202,0.00023290493,0.00046165608,0.000096632706],"domain_scores_gemma":[0.99526393,0.002221477,0.00017642943,0.00059297297,0.0013160298,0.00042922734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020533078,0.0009603681,0.00070514245,0.0031565756,0.001414022,0.0055354326,0.0018128617,0.00301776,0.052032113],"category_scores_gemma":[0.0056246324,0.00057589554,0.0007684964,0.0038837036,0.002262118,0.010561825,0.0030909774,0.005798666,0.01741761],"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":[0.00005124043,0.000060123617,0.00029012188,0.00052719883,0.00001242176,0.00006915173,0.00021366117,0.00052022265,0.0012110376,0.10082448,0.6004822,0.2957382],"study_design_scores_gemma":[0.0000035527496,0.00001931557,0.00023692996,0.00027019132,0.0000031849193,0.00006959095,0.000059652837,0.00025600774,0.00023696227,0.014802568,0.984029,0.000013026786],"about_ca_topic_score_codex":0.0030699375,"about_ca_topic_score_gemma":0.0057102996,"teacher_disagreement_score":0.052032113,"about_ca_system_score_codex":0.0024198326,"about_ca_system_score_gemma":0.0020479846,"threshold_uncertainty_score":0.17406476},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25326222,0.00035169377,0.74305195,0.00023590369,0.000095779236,0.00007983224,0.00020450604,0.0016349105,0.0010831583],"genre_scores_gemma":[0.94074476,0.00008741884,0.05823756,0.000035249635,0.000022033326,0.00003642874,0.00026786234,0.000032203792,0.00053654],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899954,0.00019269994,0.00006721499,0.00033584528,0.00031169897,0.0000931081],"domain_scores_gemma":[0.99844533,0.0006335917,0.0002730856,0.00018206194,0.0003629145,0.000103105915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001166538,0.0008020599,0.0007299,0.001122731,0.00037206957,0.0009088533,0.001245772,0.0004351126,0.00037536345],"category_scores_gemma":[0.004016282,0.00024152553,0.00036301656,0.0009929285,0.00047479908,0.0011769251,0.0010574351,0.0007765012,0.00013934798],"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":[0.00046184004,0.0002459368,0.030084023,0.00010125842,0.00012080239,0.00033245079,0.0002156532,0.71097666,0.007815728,0.0053210645,0.002029626,0.24229495],"study_design_scores_gemma":[0.0000025710399,0.000028239143,0.0010255582,0.0000015699346,0.0000028862905,0.000025599582,0.000011219372,0.99719125,0.0006828896,0.00088528707,0.00013843187,0.000004506132],"about_ca_topic_score_codex":0.012393328,"about_ca_topic_score_gemma":0.0078310715,"teacher_disagreement_score":0.012393328,"about_ca_system_score_codex":0.0007484394,"about_ca_system_score_gemma":0.00090926257,"threshold_uncertainty_score":0.024642348},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010865348,0.0013859001,0.7101269,0.015883105,0.0005663328,0.00010381204,0.00033564892,0.0011344672,0.25959858],"genre_scores_gemma":[0.6165439,0.0024112102,0.30960843,0.0025561014,0.0005407286,0.00037611203,0.0008430478,0.00091731816,0.06620312],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996442,0.0015537062,0.00020965753,0.0007165154,0.00080258166,0.00027549136],"domain_scores_gemma":[0.9967822,0.0013297629,0.00022695666,0.0011064336,0.0003424519,0.00021215115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034620909,0.0010850825,0.0006315904,0.0013680547,0.0034046974,0.0090266885,0.0023070471,0.0038665708,0.012727997],"category_scores_gemma":[0.008074009,0.00067756255,0.0020017962,0.0015033274,0.01360037,0.015548055,0.007572459,0.004812392,0.0030903514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000057838492,0.0000035234516,0.000093699615,0.000021744021,0.0000035118244,0.000054594755,0.0007896156,0.0010878389,0.00011384776,0.9928323,0.0011798891,0.0038135971],"study_design_scores_gemma":[0.0000071708996,0.00001128595,0.000069332644,0.00006209517,0.000012234647,0.00025338656,0.00053753576,0.010623987,0.0004078634,0.864554,0.123439424,0.000021630445],"about_ca_topic_score_codex":0.004058763,"about_ca_topic_score_gemma":0.0029155589,"teacher_disagreement_score":0.012727997,"about_ca_system_score_codex":0.002958156,"about_ca_system_score_gemma":0.002606291,"threshold_uncertainty_score":0.042579412},"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":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029018132,0.15490831,0.78922606,0.015658256,0.0006335385,0.00025431614,0.0004238344,0.0022703016,0.0076072305],"genre_scores_gemma":[0.45680106,0.16041973,0.36584643,0.0035346139,0.0019605083,0.00030955265,0.0017341418,0.00032431312,0.009069696],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978035,0.0005304208,0.00016474954,0.00046476434,0.00090395566,0.00013251955],"domain_scores_gemma":[0.9941993,0.0031486196,0.00035906295,0.00065036316,0.0014221598,0.00022037947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039552064,0.0012145662,0.0014844432,0.0022503436,0.0004910846,0.0021964745,0.0034318308,0.0019261321,0.0013622793],"category_scores_gemma":[0.008100252,0.0005339452,0.0008535287,0.0024492363,0.0014725674,0.0045774,0.001675057,0.002668218,0.0006638356],"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":[0.00008675808,0.00021462022,0.005368484,0.00064239936,0.00012502006,0.00007993383,0.00010942635,0.026392786,0.0018184147,0.018733144,0.013133278,0.9332958],"study_design_scores_gemma":[0.000025320518,0.00024404777,0.0029520055,0.00057951064,0.000100174766,0.0006205284,0.00031757136,0.8711126,0.008513662,0.067651466,0.04779731,0.00008578917],"about_ca_topic_score_codex":0.0056007206,"about_ca_topic_score_gemma":0.004192904,"teacher_disagreement_score":0.0056007206,"about_ca_system_score_codex":0.0015221041,"about_ca_system_score_gemma":0.0015328001,"threshold_uncertainty_score":0.020917356},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02719582,0.00053627795,0.96538836,0.00094764,0.00019429535,0.000062941464,0.00021862012,0.0023439701,0.0031120598],"genre_scores_gemma":[0.46044308,0.0005596137,0.53456783,0.0005567081,0.00021057123,0.00017296836,0.0007388853,0.00065811514,0.0020922876],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986272,0.00044288646,0.000066011125,0.00024615083,0.0005109926,0.00010684195],"domain_scores_gemma":[0.9973882,0.0012395937,0.00010881053,0.00070422835,0.00040636864,0.00015282587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028087627,0.00062443817,0.0008348918,0.00054194033,0.0008557131,0.0017913884,0.0015942127,0.0005655004,0.0009107934],"category_scores_gemma":[0.009301177,0.00036224147,0.00063904276,0.0008547617,0.0014878729,0.0027346276,0.0028493325,0.0020036383,0.00042235848],"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":[0.00047148098,0.0002537546,0.0082950825,0.00041354378,0.0002867899,0.0004150624,0.0012267986,0.4838706,0.017980738,0.14727311,0.023753496,0.3157596],"study_design_scores_gemma":[0.000027840682,0.00004378313,0.00037880553,0.000015085184,0.000012716597,0.000083005,0.00013431192,0.9247232,0.0050101355,0.06324391,0.006303021,0.000024139548],"about_ca_topic_score_codex":0.0042929463,"about_ca_topic_score_gemma":0.008532076,"teacher_disagreement_score":0.0042929463,"about_ca_system_score_codex":0.0006738067,"about_ca_system_score_gemma":0.0019581825,"threshold_uncertainty_score":0.014854372},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22887778,0.00085547764,0.7662251,0.00048041248,0.000079918536,0.00008844024,0.00009726182,0.0017081421,0.0015874635],"genre_scores_gemma":[0.8237278,0.00018859988,0.17419815,0.00016057116,0.000030312145,0.0001169628,0.0003083394,0.000086471264,0.0011828123],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994999,0.00012832631,0.000037071433,0.00014113082,0.00013572507,0.000057838002],"domain_scores_gemma":[0.99847156,0.0008626365,0.00018423113,0.00011928321,0.0002984845,0.0000636843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001684201,0.0012185612,0.0009791795,0.0013151579,0.00044083354,0.0007689613,0.0015851726,0.0011535271,0.00074426475],"category_scores_gemma":[0.0049552643,0.00047773862,0.0007773364,0.000876684,0.0005396005,0.0011999769,0.00091921334,0.0015148591,0.00017259526],"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":[0.00007345913,0.00011317662,0.0049492386,0.000043286815,0.00009857291,0.000104368344,0.000076050106,0.87294394,0.0032476368,0.0019909476,0.00073058874,0.11562872],"study_design_scores_gemma":[0.0000028718862,0.000011519063,0.00015460445,0.000001576109,0.0000034550599,0.0000066016196,0.0000049785663,0.99899393,0.00028731822,0.0004685283,0.00006295481,0.0000018178797],"about_ca_topic_score_codex":0.0073696543,"about_ca_topic_score_gemma":0.007709292,"teacher_disagreement_score":0.0073696543,"about_ca_system_score_codex":0.0009126098,"about_ca_system_score_gemma":0.0009821338,"threshold_uncertainty_score":0.0146535635},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027994994,0.00047643046,0.96673924,0.00023691423,0.00009082175,0.00006452442,0.0001596113,0.003037883,0.001199563],"genre_scores_gemma":[0.72829425,0.00035154464,0.26390454,0.0007368986,0.00013652677,0.00015670869,0.0011601439,0.00022430299,0.0050351354],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989858,0.00017981311,0.000034762623,0.00034798213,0.00031327532,0.0001382372],"domain_scores_gemma":[0.99929976,0.00021151218,0.000080454796,0.0001335043,0.00022151748,0.00005316672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012439631,0.0012689099,0.0018711631,0.0009622987,0.00035508876,0.0005792947,0.0017663109,0.0009852049,0.00078436173],"category_scores_gemma":[0.0018123196,0.00041898957,0.0012113312,0.0008887177,0.0006636737,0.0011893333,0.0012408403,0.0016484335,0.0005385138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004900787,0.00030471772,0.0073408154,0.00009179582,0.000279447,0.00041973058,0.00010624197,0.3018503,0.028011868,0.0068927286,0.01219042,0.6420219],"study_design_scores_gemma":[0.0000080686295,0.000046071214,0.00053393515,0.0000032674259,0.000013185658,0.0001338981,0.000005204176,0.99231905,0.004170754,0.0020904427,0.00066535326,0.000010736685],"about_ca_topic_score_codex":0.0026955034,"about_ca_topic_score_gemma":0.0034393524,"teacher_disagreement_score":0.0026955034,"about_ca_system_score_codex":0.000751446,"about_ca_system_score_gemma":0.00075233553,"threshold_uncertainty_score":0.006578803},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010639414,0.0001369445,0.9456722,0.00017258804,0.00007650102,0.00039412398,0.001992167,0.03945469,0.0014612658],"genre_scores_gemma":[0.12122949,0.00011699475,0.8671492,0.0002013774,0.0000336469,0.00048636275,0.007883658,0.0017230547,0.0011762001],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981121,0.0003066568,0.00023161824,0.000496443,0.00074675644,0.000106416584],"domain_scores_gemma":[0.99555004,0.0014847483,0.00020392612,0.0016361321,0.0009629886,0.0001622071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026879956,0.001183756,0.00067606376,0.0023584757,0.00049398764,0.002008313,0.002148391,0.0007258049,0.002942866],"category_scores_gemma":[0.010206106,0.00054128084,0.00096417964,0.0017980087,0.0006701554,0.002002486,0.0025210727,0.001602674,0.0022650948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010203267,0.00071967824,0.02281774,0.0005973807,0.00028355466,0.0007393203,0.0009030628,0.08624637,0.040746048,0.031914018,0.04843627,0.7655762],"study_design_scores_gemma":[0.00013545631,0.00016800353,0.0037375179,0.000051267023,0.00004369055,0.0004619926,0.00013724164,0.8715279,0.05584151,0.02944696,0.03837116,0.000077282035],"about_ca_topic_score_codex":0.002061353,"about_ca_topic_score_gemma":0.0019726604,"teacher_disagreement_score":0.002942866,"about_ca_system_score_codex":0.00075354776,"about_ca_system_score_gemma":0.0014527943,"threshold_uncertainty_score":0.014215648},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.206271,0.00044242025,0.78655845,0.00046233667,0.000118091135,0.0000994703,0.00015973633,0.00451123,0.0013772659],"genre_scores_gemma":[0.79062283,0.000068075125,0.20677714,0.0002735655,0.000042982294,0.00006573811,0.00041435464,0.00010899247,0.0016262596],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993773,0.00015556274,0.00004221745,0.00017434584,0.00017033164,0.00008030672],"domain_scores_gemma":[0.9986192,0.0005761521,0.00013610366,0.0002575756,0.00031720765,0.00009370249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024205293,0.00069343025,0.0008076883,0.0005547654,0.0004071244,0.0006539567,0.0014822369,0.00090876024,0.0010151739],"category_scores_gemma":[0.004512881,0.00021692367,0.00053861184,0.00039558177,0.00052175886,0.0011211669,0.0010921214,0.0015748154,0.0003247896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011226495,0.000658545,0.015880039,0.00010200207,0.00017826943,0.0002583739,0.0002723113,0.39063722,0.033618387,0.0089654345,0.0048809876,0.54342574],"study_design_scores_gemma":[0.000007730729,0.000041176896,0.00034396528,0.0000022884965,0.0000056819695,0.000027422453,0.000007756628,0.9934907,0.004784794,0.0009423512,0.00034158857,0.0000045720435],"about_ca_topic_score_codex":0.0055487906,"about_ca_topic_score_gemma":0.0065903277,"teacher_disagreement_score":0.0055487906,"about_ca_system_score_codex":0.00080976397,"about_ca_system_score_gemma":0.0010744311,"threshold_uncertainty_score":0.012801111},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005658174,0.000049903523,0.99179745,0.000029164486,0.000019327943,0.0000285661,0.000056487734,0.0016071529,0.0007536411],"genre_scores_gemma":[0.34669864,0.00024533816,0.64864236,0.000114303584,0.000055721724,0.000090964306,0.00040944127,0.00045649827,0.003286775],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994019,0.0001268637,0.00004069804,0.00015802859,0.0002263502,0.000046014386],"domain_scores_gemma":[0.99914086,0.00031580456,0.00006809585,0.00020258229,0.0002326184,0.00003993867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009016018,0.00063200557,0.0005808638,0.0012384021,0.00021169937,0.0010511857,0.0010666716,0.0005732496,0.0035235742],"category_scores_gemma":[0.0027172253,0.00026276865,0.00076251914,0.0009309514,0.0005034121,0.0015934759,0.0010325836,0.0008647747,0.0013091511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005489142,0.00028640052,0.002125033,0.000256729,0.00011891882,0.0006995719,0.00030936758,0.055761766,0.13787451,0.027934767,0.0043660244,0.76971793],"study_design_scores_gemma":[0.00003470067,0.00015318276,0.0012549568,0.00003301018,0.00007445708,0.0008497612,0.000102799684,0.91099817,0.05520545,0.023339681,0.00790812,0.000045563866],"about_ca_topic_score_codex":0.0013725178,"about_ca_topic_score_gemma":0.0011002152,"teacher_disagreement_score":0.0035235742,"about_ca_system_score_codex":0.00027292845,"about_ca_system_score_gemma":0.00049114734,"threshold_uncertainty_score":0.011787593},"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":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09221821,0.00898394,0.89004403,0.00080021255,0.00013540727,0.00012602907,0.00026978226,0.0030307253,0.0043916227],"genre_scores_gemma":[0.66893774,0.0046324646,0.31957164,0.00032049287,0.00036288766,0.00013665209,0.00060575106,0.0008339116,0.0045984047],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9920002,0.0029707968,0.0005637839,0.0011387201,0.0027862818,0.00054003205],"domain_scores_gemma":[0.9295095,0.054825917,0.002020662,0.007893535,0.0052477927,0.00050254015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00724638,0.0020038092,0.0023205457,0.0022374315,0.00092807185,0.0036077045,0.0035375317,0.0019059886,0.0037863494],"category_scores_gemma":[0.04367648,0.0012019399,0.0010747024,0.0025662808,0.0012795732,0.0082448935,0.0025618332,0.0026909795,0.0011782679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00076572754,0.0004120727,0.0051610926,0.00052790774,0.00029209687,0.00019319463,0.00015944496,0.15985832,0.012246033,0.04192536,0.0027290257,0.7757297],"study_design_scores_gemma":[0.000026744607,0.00021151027,0.0009998324,0.00006257421,0.0001236157,0.00042943368,0.00005856542,0.96563506,0.011339082,0.019860573,0.0012139915,0.000038954313],"about_ca_topic_score_codex":0.0038197124,"about_ca_topic_score_gemma":0.0050125835,"teacher_disagreement_score":0.00724638,"about_ca_system_score_codex":0.0018685965,"about_ca_system_score_gemma":0.002999595,"threshold_uncertainty_score":0.038322926},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18040997,0.0017182997,0.80984503,0.0013200197,0.00021483972,0.00006925333,0.0020824298,0.0009780903,0.0033621073],"genre_scores_gemma":[0.96785396,0.0007129034,0.0232859,0.00019914849,0.00009494905,0.00013330003,0.00229885,0.00004242632,0.0053785685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996668,0.000064838474,0.000025014673,0.00010815315,0.000060674953,0.0000744867],"domain_scores_gemma":[0.9991026,0.00038266173,0.00012234606,0.000048263977,0.00028569115,0.00005846974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001138065,0.00082075293,0.0007053649,0.0009007307,0.00028054378,0.00055590895,0.0012301011,0.0006895501,0.0024338216],"category_scores_gemma":[0.0026203766,0.00028210424,0.00089513365,0.0007118579,0.00037887404,0.0008947533,0.0008024245,0.0016950583,0.0004565885],"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":[0.00018319812,0.000116196104,0.019959437,0.00010503892,0.000097844495,0.0001499352,0.00010262096,0.8676434,0.001108057,0.0077074836,0.004230306,0.098596565],"study_design_scores_gemma":[0.0000030078447,0.000016613283,0.00094781676,0.000006651872,0.000013337187,0.000012536967,0.0000071181935,0.9957957,0.00015432671,0.0027317163,0.00030650818,0.0000045725014],"about_ca_topic_score_codex":0.01490336,"about_ca_topic_score_gemma":0.014131984,"teacher_disagreement_score":0.01490336,"about_ca_system_score_codex":0.001007968,"about_ca_system_score_gemma":0.0012101509,"threshold_uncertainty_score":0.029633224},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028005317,0.00016839324,0.970077,0.00014140079,0.0000527317,0.000019804507,0.000045879195,0.0006867466,0.0008026588],"genre_scores_gemma":[0.65799075,0.00028587398,0.33863083,0.00011969736,0.00014948964,0.00005155524,0.00026388917,0.00019976376,0.0023080776],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993961,0.00017181,0.000036795464,0.00014439967,0.00019731712,0.000053579373],"domain_scores_gemma":[0.9977871,0.0012389814,0.00021550163,0.00034824625,0.00033011124,0.000080015394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014048617,0.000634464,0.0006968115,0.0012258143,0.00026610974,0.0009570591,0.00089576154,0.0006889471,0.0015607695],"category_scores_gemma":[0.006513193,0.00028081486,0.0005499908,0.0011820513,0.0006968573,0.0015765625,0.0013756715,0.0016095196,0.0005571149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041095304,0.00028103625,0.006418573,0.00014393638,0.00015598806,0.000289834,0.00012133114,0.12466854,0.08773417,0.027020035,0.001814131,0.75094146],"study_design_scores_gemma":[0.000006153316,0.000053548778,0.0010158615,0.000004781092,0.000012283995,0.00009658684,0.0000102796785,0.97901136,0.008418061,0.010661102,0.0007009387,0.000008950214],"about_ca_topic_score_codex":0.0012851898,"about_ca_topic_score_gemma":0.0016611306,"teacher_disagreement_score":0.0015607695,"about_ca_system_score_codex":0.00038157628,"about_ca_system_score_gemma":0.0005590919,"threshold_uncertainty_score":0.007429719},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005915811,0.00063498865,0.9920541,0.00017752714,0.000032912678,0.00006195866,0.00015248769,0.0007109807,0.00025936606],"genre_scores_gemma":[0.40587574,0.0018443239,0.5867682,0.00030409574,0.00022399168,0.00065662566,0.0018825025,0.00036355184,0.0020809532],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99670273,0.0014343136,0.0002891948,0.00063755695,0.00077468815,0.0001615409],"domain_scores_gemma":[0.9797314,0.016341247,0.0010764938,0.0012482319,0.0014134889,0.0001891614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008071778,0.0017644815,0.001767183,0.0032697902,0.000507246,0.0017389902,0.0020860923,0.0020910206,0.0020758682],"category_scores_gemma":[0.036108032,0.0005698403,0.0017519498,0.0030816258,0.0012034894,0.0030973654,0.001373295,0.0035468116,0.00093020446],"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":[0.00025651365,0.00016775315,0.0065524527,0.00027132028,0.00021134432,0.00014924146,0.00013774831,0.72648805,0.0019078729,0.035785716,0.0026224172,0.22544956],"study_design_scores_gemma":[0.000005307447,0.000023705641,0.00045209902,0.000012250424,0.000005134092,0.000023722441,0.000010586097,0.98566693,0.00028193262,0.013072305,0.00043703715,0.00000903767],"about_ca_topic_score_codex":0.0053864475,"about_ca_topic_score_gemma":0.0025923145,"teacher_disagreement_score":0.008071778,"about_ca_system_score_codex":0.0016250212,"about_ca_system_score_gemma":0.001087763,"threshold_uncertainty_score":0.04268819},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1671694,0.0008654787,0.78313047,0.00065658084,0.0003773932,0.00031015064,0.00059225253,0.04394541,0.0029528674],"genre_scores_gemma":[0.8392906,0.000108588836,0.15761608,0.00028783956,0.00006666845,0.00023432945,0.0005069782,0.0010291777,0.00085970305],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98978114,0.003659069,0.0006881418,0.0017993272,0.0033100876,0.0007622464],"domain_scores_gemma":[0.96287537,0.02250633,0.0036893093,0.0070045283,0.0032775365,0.00064697606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008812888,0.0012942287,0.0009891674,0.0014889005,0.0008620421,0.0020592276,0.0026990357,0.0013760797,0.0022629888],"category_scores_gemma":[0.059387643,0.00060428795,0.00080294086,0.00079362694,0.001538095,0.0034525306,0.00297945,0.0025488883,0.00082304055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022217594,0.0007584642,0.09920093,0.00079651497,0.00047906773,0.001046045,0.001108875,0.32759124,0.037858594,0.024986692,0.018185725,0.48576614],"study_design_scores_gemma":[0.00005029469,0.00019537317,0.0021024763,0.00003672469,0.000025554433,0.00018156364,0.000041392555,0.96881896,0.01746346,0.009559874,0.0014901129,0.000034134617],"about_ca_topic_score_codex":0.0022215806,"about_ca_topic_score_gemma":0.002172269,"teacher_disagreement_score":0.008812888,"about_ca_system_score_codex":0.0011343575,"about_ca_system_score_gemma":0.0028966928,"threshold_uncertainty_score":0.046607554},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2527951,0.000294143,0.742538,0.0003035626,0.000086195716,0.00004359102,0.0006955818,0.0014277224,0.0018162077],"genre_scores_gemma":[0.81433386,0.00020329173,0.18072847,0.000049356004,0.000063722226,0.00004408058,0.002327615,0.00018231904,0.002067345],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991629,0.00016275837,0.000047958278,0.0001954002,0.00030645574,0.0001244864],"domain_scores_gemma":[0.9990395,0.00020612123,0.00014609426,0.00018255513,0.00036552295,0.00006024722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011334465,0.00071819406,0.00082859886,0.0011755107,0.00035045278,0.0012085951,0.0008087206,0.0010507391,0.00062026066],"category_scores_gemma":[0.0028104952,0.0004021882,0.00066750275,0.0016649257,0.0003374527,0.001046659,0.001105416,0.001081022,0.0004779648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011320595,0.00041724814,0.056529045,0.00032498201,0.0002900595,0.0004083261,0.0003934811,0.37156305,0.12427394,0.0092586195,0.007152939,0.4282563],"study_design_scores_gemma":[0.000012291255,0.00006458147,0.016272673,0.0000074652326,0.000022835442,0.00010955421,0.00004842661,0.97032094,0.009553311,0.0023724327,0.0011825499,0.00003284943],"about_ca_topic_score_codex":0.004993885,"about_ca_topic_score_gemma":0.0062084077,"teacher_disagreement_score":0.004993885,"about_ca_system_score_codex":0.0003669406,"about_ca_system_score_gemma":0.0011396344,"threshold_uncertainty_score":0.009929657},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014328614,0.00042725785,0.9819962,0.00018362116,0.000072596325,0.000052981275,0.0001173576,0.0021283412,0.0006929698],"genre_scores_gemma":[0.36633682,0.00041177814,0.6257733,0.00049929443,0.0001478654,0.00021055712,0.00081622245,0.00023082507,0.005573234],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99909365,0.00017498953,0.000051567713,0.00029900702,0.00028732826,0.00009353179],"domain_scores_gemma":[0.997898,0.0009220412,0.00014740338,0.00036139257,0.00059229217,0.000078872785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001519206,0.0009866017,0.0015024099,0.00066254684,0.0004968482,0.0008335356,0.0023403785,0.0019803897,0.0012899375],"category_scores_gemma":[0.0045165406,0.0008850899,0.0011460241,0.00060442876,0.0005820855,0.0012696618,0.000998585,0.002191594,0.0013108192],"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":[0.00024304453,0.00024648837,0.0041017835,0.0001421049,0.0004927473,0.00023764366,0.00013924859,0.6256765,0.019079743,0.0049680104,0.0061413823,0.33853132],"study_design_scores_gemma":[0.000006973078,0.000032528875,0.0001621239,0.0000035191679,0.000011976478,0.000028901173,0.000003633712,0.9978255,0.0010054808,0.0004967129,0.00041526937,0.000007441465],"about_ca_topic_score_codex":0.022367017,"about_ca_topic_score_gemma":0.02955309,"teacher_disagreement_score":0.022367017,"about_ca_system_score_codex":0.00065838586,"about_ca_system_score_gemma":0.0012215053,"threshold_uncertainty_score":0.044473708},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84713393,0.0015863209,0.09046126,0.0005690303,0.0013238057,0.0003755172,0.0030757382,0.04092269,0.014551708],"genre_scores_gemma":[0.9605555,0.00024294389,0.027252965,0.00032675843,0.00007633219,0.000066068824,0.0047965264,0.00063765893,0.0060451324],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99789447,0.00031875324,0.00016302602,0.0004912787,0.0008586195,0.00027387618],"domain_scores_gemma":[0.99811137,0.00064299314,0.00008835146,0.0002858389,0.0007115456,0.00015989276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011370275,0.0014506944,0.0008343167,0.0010794938,0.00039362715,0.0014993815,0.0020875947,0.0011497457,0.004298951],"category_scores_gemma":[0.003492339,0.00026825332,0.00030360377,0.00086464325,0.00039860542,0.0016784742,0.0009010651,0.0009290291,0.0019033452],"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":[0.01755306,0.0038215439,0.041359294,0.0011626685,0.0012432141,0.0023788866,0.0004682702,0.17038529,0.1411142,0.0073308097,0.105436176,0.50774664],"study_design_scores_gemma":[0.00039112795,0.0027275959,0.015890311,0.000049110415,0.00016710261,0.0006467635,0.0002211142,0.89053273,0.07548536,0.0013065948,0.012473769,0.00010845591],"about_ca_topic_score_codex":0.005173996,"about_ca_topic_score_gemma":0.0040278654,"teacher_disagreement_score":0.005173996,"about_ca_system_score_codex":0.00074148923,"about_ca_system_score_gemma":0.00050111936,"threshold_uncertainty_score":0.014381409},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22054116,0.0005022369,0.77186644,0.00050804514,0.00016695875,0.000066685905,0.00027056568,0.0037675793,0.0023104097],"genre_scores_gemma":[0.8876465,0.00021360647,0.10717053,0.00010655779,0.000037661484,0.000064888605,0.0005807176,0.000089402565,0.0040901178],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997278,0.000036193855,0.000015254925,0.00007071156,0.00009508622,0.00005493111],"domain_scores_gemma":[0.9996561,0.00011625122,0.000040552393,0.000024927198,0.00014043317,0.000021875332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005133496,0.00072005705,0.00044221192,0.00060081866,0.00027115282,0.00039289368,0.0006578213,0.0006442137,0.0009791921],"category_scores_gemma":[0.0012916304,0.0002967669,0.00041965247,0.0004411318,0.0002954874,0.0005455818,0.00048322428,0.0012813892,0.0003653536],"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":[0.000481245,0.00030924042,0.004727779,0.00010913994,0.00010875468,0.0003207624,0.0001239283,0.53531015,0.03562108,0.0025989944,0.0043036924,0.41598523],"study_design_scores_gemma":[0.000001959508,0.000020144606,0.0003628426,0.000002191853,0.0000033996614,0.000015858981,0.0000040054056,0.99687153,0.0022481913,0.0002526621,0.00021399601,0.0000033207118],"about_ca_topic_score_codex":0.011276511,"about_ca_topic_score_gemma":0.008971356,"teacher_disagreement_score":0.011276511,"about_ca_system_score_codex":0.0006478103,"about_ca_system_score_gemma":0.0007927351,"threshold_uncertainty_score":0.022421777},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10057562,0.00024373946,0.8929877,0.00041233067,0.00009620239,0.000117350995,0.00018861075,0.0042487253,0.0011296923],"genre_scores_gemma":[0.89675194,0.00007196195,0.10083673,0.00023045264,0.0000797904,0.00012083719,0.0005424696,0.00033187744,0.001033904],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997995,0.0006166863,0.00011732413,0.00069399615,0.00040166336,0.00017542871],"domain_scores_gemma":[0.991824,0.004074894,0.00074425456,0.0016603152,0.0013378976,0.00035863818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004002359,0.001061171,0.0011618265,0.001076612,0.0004283048,0.0010177541,0.002727477,0.0015335002,0.001073106],"category_scores_gemma":[0.026107196,0.0004990722,0.00061054167,0.00069918024,0.0008509982,0.002794164,0.0017563902,0.002791747,0.0007129714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049809046,0.0006004071,0.030461615,0.00011252422,0.00028595442,0.0004231157,0.00043934453,0.42066848,0.016688365,0.005850089,0.0076874834,0.5162846],"study_design_scores_gemma":[0.0000082925035,0.000038673417,0.0014017624,0.000003977584,0.000008834179,0.00006166901,0.000025375906,0.9922563,0.0023894382,0.0034227744,0.0003709868,0.0000118995285],"about_ca_topic_score_codex":0.0040409225,"about_ca_topic_score_gemma":0.0049636606,"teacher_disagreement_score":0.0040409225,"about_ca_system_score_codex":0.00066518795,"about_ca_system_score_gemma":0.0010055859,"threshold_uncertainty_score":0.021166742},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012885124,0.00018376812,0.9840792,0.00017518328,0.00005049466,0.000068665526,0.000034657605,0.0011013102,0.0014216388],"genre_scores_gemma":[0.6823437,0.00021071288,0.3093741,0.0008616772,0.00012729535,0.00036137045,0.00029119418,0.00050755317,0.005922374],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981123,0.00077614526,0.00006374206,0.0004889729,0.00038475092,0.00017395949],"domain_scores_gemma":[0.9968907,0.0015562837,0.00023549827,0.0007825434,0.00035470238,0.00018022492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027145725,0.0012546488,0.0011415401,0.00065624557,0.00076171913,0.0011329639,0.0020407285,0.0015090457,0.0023472],"category_scores_gemma":[0.0075870687,0.0004863115,0.00075138226,0.0006382241,0.0021152264,0.0028270695,0.0040911227,0.0031274045,0.0010091243],"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":[0.00046452502,0.00031810784,0.002581743,0.00013142996,0.000111779445,0.00019539594,0.0003463476,0.656336,0.026681889,0.03941958,0.006082307,0.2673309],"study_design_scores_gemma":[0.000015840249,0.00006834766,0.00015820794,0.0000093698145,0.0000075833786,0.00005816106,0.000022326189,0.98057884,0.0037881765,0.013772415,0.0015042954,0.000016387332],"about_ca_topic_score_codex":0.000923602,"about_ca_topic_score_gemma":0.001215455,"teacher_disagreement_score":0.0027145725,"about_ca_system_score_codex":0.00073991274,"about_ca_system_score_gemma":0.00095472584,"threshold_uncertainty_score":0.014356196},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24845628,0.0013768908,0.72402817,0.00039550159,0.0002070293,0.00023791776,0.0012769451,0.021151176,0.0028700044],"genre_scores_gemma":[0.753173,0.00018872299,0.23947793,0.00014292327,0.000112388,0.000116732364,0.0028791036,0.00045468577,0.003454504],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99794537,0.00040189625,0.00018241105,0.0004870106,0.000699736,0.00028357704],"domain_scores_gemma":[0.9953252,0.0015267472,0.00064910087,0.0009520224,0.0012958893,0.00025099688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024314297,0.0017090744,0.0014372864,0.0026150257,0.00051436573,0.0013381656,0.0019512781,0.0010330038,0.0014157614],"category_scores_gemma":[0.008455924,0.00034955738,0.00086340716,0.001654139,0.00058207085,0.0018020339,0.0020872739,0.0013910851,0.0010361982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00086948124,0.0005035277,0.020479865,0.00020625102,0.0002916622,0.00021075869,0.00018734239,0.06987698,0.036679626,0.0016777609,0.007675004,0.8613417],"study_design_scores_gemma":[0.000029806706,0.00017126113,0.006079467,0.000013296332,0.00006207585,0.00011320058,0.000059902948,0.9570385,0.031095736,0.0035606558,0.001739095,0.000036983143],"about_ca_topic_score_codex":0.0050794347,"about_ca_topic_score_gemma":0.008657125,"teacher_disagreement_score":0.0050794347,"about_ca_system_score_codex":0.00090765045,"about_ca_system_score_gemma":0.0009776377,"threshold_uncertainty_score":0.012858748},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041987624,0.00081039686,0.92906845,0.00032992216,0.00024550068,0.0004343274,0.00074529095,0.022754986,0.0036235428],"genre_scores_gemma":[0.26509926,0.00035632792,0.72752523,0.00022840223,0.00011971685,0.0004958754,0.0028877486,0.0012039138,0.0020835616],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.995269,0.0010299755,0.0003994378,0.0006934033,0.0022310703,0.0003769663],"domain_scores_gemma":[0.9942477,0.0020096835,0.00070873846,0.0010111415,0.0017319706,0.00029074005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044355397,0.0030570286,0.00263317,0.0034181578,0.0007099085,0.0021968996,0.003943421,0.0016555713,0.0036570097],"category_scores_gemma":[0.01683189,0.00065018755,0.0013459956,0.0021514813,0.00087664893,0.0033827927,0.003981818,0.0018035957,0.0016057334],"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":[0.00093186274,0.00067260495,0.0060381335,0.00060077914,0.00031054148,0.00025792298,0.00021727663,0.35157344,0.014327129,0.010991084,0.023179656,0.5908995],"study_design_scores_gemma":[0.00006851978,0.00013759213,0.0005697498,0.00002406746,0.00002007765,0.00006273725,0.00005907642,0.988913,0.0045644674,0.003646458,0.00191168,0.00002252432],"about_ca_topic_score_codex":0.0035132826,"about_ca_topic_score_gemma":0.004993475,"teacher_disagreement_score":0.0044355397,"about_ca_system_score_codex":0.0011837516,"about_ca_system_score_gemma":0.0027985561,"threshold_uncertainty_score":0.023457646},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007861353,0.0010558288,0.004720043,0.0007283434,0.00094028114,0.00057983154,0.9657596,0.009230452,0.009124327],"genre_scores_gemma":[0.004786689,0.00022085296,0.003952788,0.00022731727,0.00003644107,0.0006359452,0.98647785,0.00041292337,0.003249232],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99735177,0.0004113577,0.00034894416,0.000657639,0.00087518326,0.0003550831],"domain_scores_gemma":[0.99629056,0.00050069665,0.00018186473,0.0015603274,0.001171083,0.00029550825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020564063,0.0033857056,0.0013925869,0.0027343768,0.0012968187,0.001968925,0.0039862115,0.0024265123,0.028032262],"category_scores_gemma":[0.007992556,0.0008206278,0.0021067015,0.0042888857,0.0009241407,0.0019979104,0.0024241866,0.004764475,0.05953072],"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":[0.00023924485,0.00027487875,0.0010365953,0.00044438487,0.000051051185,0.00007547453,0.00002003715,0.0021384226,0.00054542563,0.00078093534,0.9795013,0.014892172],"study_design_scores_gemma":[0.0011785018,0.00038300068,0.008079927,0.00038425464,0.00010481,0.00057540956,0.00016053718,0.015419487,0.008546568,0.006446015,0.9585768,0.00014476021],"about_ca_topic_score_codex":0.015184698,"about_ca_topic_score_gemma":0.030573003,"teacher_disagreement_score":0.028032262,"about_ca_system_score_codex":0.0018653114,"about_ca_system_score_gemma":0.0026815836,"threshold_uncertainty_score":0.0937773},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028492348,0.0061389916,0.039622325,0.0025373867,0.0013148838,0.0010224201,0.86097115,0.043318104,0.016582428],"genre_scores_gemma":[0.04341275,0.00067234837,0.035686,0.0006368654,0.00013688354,0.001164262,0.9132579,0.0012470314,0.0037859925],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974921,0.0007912241,0.00017565695,0.00070365646,0.000646964,0.00019049388],"domain_scores_gemma":[0.99405295,0.002423651,0.0003212442,0.0022715325,0.0006039418,0.0003266546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038256973,0.0047935788,0.001708483,0.0024099285,0.0013214571,0.003617106,0.008606893,0.006517968,0.025504535],"category_scores_gemma":[0.019157479,0.001265921,0.0030553988,0.0017455218,0.0015981384,0.002735209,0.0050735506,0.0044839443,0.020837096],"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":[0.0006210889,0.0005917145,0.0057739317,0.0023180603,0.0005010624,0.0003947955,0.00010417636,0.030267164,0.0008652398,0.005903941,0.9113633,0.04129553],"study_design_scores_gemma":[0.0025159782,0.0007133153,0.018656181,0.0010458912,0.00033564804,0.0016051373,0.00033606283,0.3639312,0.008114721,0.05460685,0.5478093,0.0003297338],"about_ca_topic_score_codex":0.019327026,"about_ca_topic_score_gemma":0.029080585,"teacher_disagreement_score":0.025504535,"about_ca_system_score_codex":0.0025739744,"about_ca_system_score_gemma":0.002117749,"threshold_uncertainty_score":0.08532113},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007861353,0.0010558288,0.004720043,0.0007283434,0.00094028114,0.00057983154,0.9657596,0.009230452,0.009124327],"genre_scores_gemma":[0.004786689,0.00022085296,0.003952788,0.00022731727,0.00003644107,0.0006359452,0.98647785,0.00041292337,0.003249232],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99735177,0.0004113577,0.00034894416,0.000657639,0.00087518326,0.0003550831],"domain_scores_gemma":[0.99629056,0.00050069665,0.00018186473,0.0015603274,0.001171083,0.00029550825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020564063,0.0033857056,0.0013925869,0.0027343768,0.0012968187,0.001968925,0.0039862115,0.0024265123,0.028032262],"category_scores_gemma":[0.007992556,0.0008206278,0.0021067015,0.0042888857,0.0009241407,0.0019979104,0.0024241866,0.004764475,0.05953072],"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":[0.00023924485,0.00027487875,0.0010365953,0.00044438487,0.000051051185,0.00007547453,0.00002003715,0.0021384226,0.00054542563,0.00078093534,0.9795013,0.014892172],"study_design_scores_gemma":[0.0011785018,0.00038300068,0.008079927,0.00038425464,0.00010481,0.00057540956,0.00016053718,0.015419487,0.008546568,0.006446015,0.9585768,0.00014476021],"about_ca_topic_score_codex":0.015184698,"about_ca_topic_score_gemma":0.030573003,"teacher_disagreement_score":0.028032262,"about_ca_system_score_codex":0.0018653114,"about_ca_system_score_gemma":0.0026815836,"threshold_uncertainty_score":0.0937773},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027692122,0.000809321,0.96257263,0.00012862327,0.00008202166,0.000045699104,0.000496629,0.006422283,0.0017506917],"genre_scores_gemma":[0.60496294,0.0007816417,0.38764387,0.00016338733,0.00008669618,0.000099285775,0.0011553584,0.00021335874,0.0048934375],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967647,0.000036327205,0.000029281457,0.00007620092,0.0001318687,0.000049767037],"domain_scores_gemma":[0.99962103,0.00005445158,0.000039270228,0.00008879516,0.00017767139,0.000018733752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033825825,0.000490831,0.00048435273,0.000915094,0.0002469285,0.0008079943,0.0011204338,0.00036893485,0.002644422],"category_scores_gemma":[0.0009601855,0.00016429038,0.00043614124,0.0008322333,0.00017617678,0.0013263886,0.00062081387,0.00051849964,0.000705618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006403343,0.00024934596,0.0030343288,0.00023172348,0.000104404025,0.0002767966,0.00013685788,0.080098726,0.06291804,0.015272809,0.008051097,0.8289855],"study_design_scores_gemma":[0.000019181252,0.00011852583,0.0008135389,0.000015244112,0.00006515071,0.00020993153,0.00004600896,0.9512111,0.03329464,0.007843683,0.0063383225,0.000024681463],"about_ca_topic_score_codex":0.009642154,"about_ca_topic_score_gemma":0.008114137,"teacher_disagreement_score":0.009642154,"about_ca_system_score_codex":0.0004453479,"about_ca_system_score_gemma":0.0010580341,"threshold_uncertainty_score":0.019172072},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00977191,0.000653559,0.0072897016,0.00033483494,0.00046325935,0.00016825041,0.9664446,0.00954798,0.0053257504],"genre_scores_gemma":[0.007692283,0.00013599887,0.004209002,0.0001651808,0.00004993804,0.00019418169,0.9843354,0.00030006576,0.0029178606],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99941003,0.00009202518,0.000039375405,0.00016959387,0.00018388097,0.00010526015],"domain_scores_gemma":[0.9994516,0.00009284248,0.00003914221,0.0001796523,0.00015913139,0.00007760802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005481126,0.0029816295,0.0011300278,0.0012835604,0.00061859103,0.0010024694,0.0026364669,0.0019643481,0.019438643],"category_scores_gemma":[0.0016373753,0.0004659018,0.0012507341,0.0014247423,0.00041899775,0.0006826545,0.0012794543,0.0018285478,0.03166137],"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":[0.00033571885,0.0002448455,0.0017293899,0.00057227834,0.00010203623,0.00015228763,0.000031118434,0.0034389184,0.0027877204,0.0006516666,0.9689229,0.021031223],"study_design_scores_gemma":[0.0007168545,0.00053157535,0.02663643,0.00035033404,0.00016128704,0.0014325246,0.0002473765,0.044849902,0.016518377,0.0068231975,0.90137714,0.00035494726],"about_ca_topic_score_codex":0.009603917,"about_ca_topic_score_gemma":0.025740972,"teacher_disagreement_score":0.019438643,"about_ca_system_score_codex":0.00068992964,"about_ca_system_score_gemma":0.00086105947,"threshold_uncertainty_score":0.06502879},"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":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9252986,0.0022277501,0.018272767,0.0002766541,0.00024481272,0.00052831054,0.033164736,0.00091298594,0.019073416],"genre_scores_gemma":[0.8416751,0.0025447658,0.036334958,0.0005817829,0.00004646048,0.0012272897,0.043366432,0.00046134656,0.07376178],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9994267,0.00007481791,0.00006623709,0.0001478239,0.00018332127,0.00010104034],"domain_scores_gemma":[0.99806255,0.0004776671,0.00040645615,0.000194702,0.00071084744,0.00014780271],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006004322,0.00079746067,0.00038789024,0.00045471647,0.00031362663,0.00047990502,0.0004237302,0.0005570949,0.0060732574],"category_scores_gemma":[0.001046468,0.00028451876,0.0006225891,0.0006865809,0.00019681893,0.0006098037,0.00041853625,0.0012309678,0.004084592],"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":[0.003740381,0.0005551387,0.035600144,0.00031060257,0.00021255093,0.00012258222,0.00016399089,0.0014136508,0.9129385,0.00017231278,0.0040036007,0.040766623],"study_design_scores_gemma":[0.00003610019,0.002454938,0.25076538,0.000039000344,0.00010408535,0.00021468733,0.0004277411,0.0037589525,0.73206466,0.00010858978,0.0099543305,0.00007150578],"about_ca_topic_score_codex":0.0100893155,"about_ca_topic_score_gemma":0.010319105,"teacher_disagreement_score":0.0100893155,"about_ca_system_score_codex":0.0006355561,"about_ca_system_score_gemma":0.00052784936,"threshold_uncertainty_score":0.020317078},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38434428,0.00040182786,0.6080613,0.00043989162,0.000016824344,0.00002217195,0.00007473597,0.00024792823,0.006391063],"genre_scores_gemma":[0.9685254,0.00007413804,0.031044204,0.000017618688,0.000008380189,0.000014317389,0.000021739042,0.000014178792,0.0002798624],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998468,0.000059087964,0.0000072036546,0.000037947906,0.00003044452,0.00001858817],"domain_scores_gemma":[0.99936396,0.0002849174,0.00011833754,0.00012662393,0.000059445258,0.000046818514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053964264,0.00016586504,0.00020495725,0.0006183891,0.00023597018,0.0007683922,0.00030409882,0.00030839068,0.0013266968],"category_scores_gemma":[0.0022996902,0.00016915298,0.00025286738,0.0003436266,0.0015094703,0.0016350799,0.001185591,0.0004152174,0.00013314752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000720231,0.00009101525,0.012212626,0.00026299842,0.00015088794,0.0011115582,0.0028298546,0.07624276,0.15651178,0.610244,0.0017599083,0.13786244],"study_design_scores_gemma":[0.000027178152,0.00009331277,0.013954162,0.00003027787,0.000040085582,0.00057580177,0.00036061718,0.272157,0.011097166,0.6995206,0.0021124808,0.00003129971],"about_ca_topic_score_codex":0.00025092013,"about_ca_topic_score_gemma":0.00024407801,"teacher_disagreement_score":0.0013266968,"about_ca_system_score_codex":0.0002186997,"about_ca_system_score_gemma":0.00020526412,"threshold_uncertainty_score":0.004438281},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008270094,0.0003450443,0.9885079,0.000041920368,0.000043460357,0.000020886424,0.0000721154,0.0018498973,0.0008485706],"genre_scores_gemma":[0.29586995,0.0004332612,0.6984758,0.00010791082,0.00017737171,0.00007172441,0.0010475747,0.00044561745,0.0033708757],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992286,0.00014998835,0.000036727808,0.00022512376,0.0002601062,0.00009944561],"domain_scores_gemma":[0.999169,0.00030442848,0.0000680666,0.00023320537,0.00018715112,0.000038266768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081354915,0.0010001822,0.0011707926,0.0017835371,0.0004584785,0.0008744881,0.0013198038,0.00080761936,0.0019706422],"category_scores_gemma":[0.0018963784,0.00046761075,0.00095717347,0.0017280186,0.00052478514,0.0014138536,0.0012685034,0.0011952604,0.0011346705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043093326,0.00012457956,0.0024574944,0.00015690002,0.00018437141,0.00026109023,0.00005549614,0.10457727,0.02942572,0.0075016352,0.006583562,0.8482409],"study_design_scores_gemma":[0.000013059717,0.000044245262,0.0007426659,0.00000789592,0.000033177726,0.0002890378,0.000009327985,0.97723323,0.004882297,0.014614408,0.0021192082,0.000011426858],"about_ca_topic_score_codex":0.001876833,"about_ca_topic_score_gemma":0.0027727557,"teacher_disagreement_score":0.0019706422,"about_ca_system_score_codex":0.00022320854,"about_ca_system_score_gemma":0.00042271416,"threshold_uncertainty_score":0.0065924525},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16294178,0.00030411876,0.8324428,0.00028982776,0.00004904246,0.000043448385,0.00007257523,0.0004107242,0.003445639],"genre_scores_gemma":[0.98880976,0.00009098887,0.009970494,0.000026743,0.000009778065,0.000029867564,0.00003806936,0.000010144362,0.001014209],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981076,0.000028068725,0.00001060238,0.000075786644,0.000041371357,0.000033501565],"domain_scores_gemma":[0.9997856,0.000071544164,0.000050886694,0.000010820422,0.00006555571,0.00001556495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000515937,0.0005446525,0.0005151984,0.00050456845,0.0003479077,0.000723908,0.0007288637,0.00083329104,0.0009352165],"category_scores_gemma":[0.00082928175,0.00030605774,0.00073266774,0.0003705888,0.0004795536,0.00095417426,0.0005764241,0.00067240023,0.00009353329],"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":[0.000034871395,0.00002831039,0.0012513385,0.000022371114,0.000031280342,0.00008804085,0.00005848922,0.9793459,0.0026233234,0.0031575072,0.00017079424,0.013187791],"study_design_scores_gemma":[6.867759e-7,0.000004812593,0.00015417307,7.057251e-7,0.0000028168606,0.0000038749677,0.0000020306213,0.9994295,0.00010287481,0.00026417678,0.000032446624,0.0000018796808],"about_ca_topic_score_codex":0.014525924,"about_ca_topic_score_gemma":0.005440921,"teacher_disagreement_score":0.014525924,"about_ca_system_score_codex":0.0007816565,"about_ca_system_score_gemma":0.00058452983,"threshold_uncertainty_score":0.028882742},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09437632,0.00017249526,0.9002575,0.00018568747,0.00004461955,0.000045575856,0.00002535005,0.0012001406,0.0036922523],"genre_scores_gemma":[0.9659936,0.000039702252,0.031730145,0.00004386859,0.000008223772,0.000039433242,0.00002083389,0.000021184916,0.0021030514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999851,0.00002809042,0.0000063395505,0.0000426688,0.000041806095,0.000030097188],"domain_scores_gemma":[0.9996131,0.00011818138,0.000077499026,0.000052151852,0.00009669906,0.000042537344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045298034,0.00039923954,0.0004378809,0.0002650574,0.0002785502,0.000335456,0.0008990553,0.00045545,0.0014718216],"category_scores_gemma":[0.0007531084,0.00021983554,0.00027477802,0.00013092901,0.00064204267,0.00048541257,0.0006252126,0.0005576624,0.00019810107],"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":[0.00008177031,0.0000759489,0.0010601012,0.000032047134,0.000027911412,0.000088161614,0.0000519513,0.9294042,0.00858942,0.006285158,0.00078433164,0.053518906],"study_design_scores_gemma":[0.000004595771,0.000021349146,0.00006858962,0.0000013299868,0.0000018772213,0.000007489125,0.0000018966849,0.99840933,0.0005238274,0.0008161358,0.00014183111,0.0000018964334],"about_ca_topic_score_codex":0.0043376326,"about_ca_topic_score_gemma":0.0034566133,"teacher_disagreement_score":0.0043376326,"about_ca_system_score_codex":0.0005808264,"about_ca_system_score_gemma":0.0006956351,"threshold_uncertainty_score":0.008624792},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33030102,0.0004458082,0.66657853,0.00037316902,0.0000399449,0.000027578639,0.00020687157,0.0011144106,0.0009127375],"genre_scores_gemma":[0.9657914,0.00011215796,0.0327924,0.00004797355,0.00003228905,0.000017221448,0.0002794828,0.000027627111,0.00089952047],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938977,0.00015487973,0.00003257692,0.00013763155,0.00017283093,0.00011226948],"domain_scores_gemma":[0.9980562,0.00096280844,0.00036139388,0.000247364,0.0002828708,0.000089403235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013259869,0.00050212536,0.000777967,0.0010784073,0.0002817129,0.0007929704,0.0012829878,0.00076087436,0.00052157714],"category_scores_gemma":[0.004407231,0.00029117372,0.00042845443,0.0008842444,0.00070694706,0.0014189943,0.0009666023,0.0013011551,0.00016665389],"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":[0.00038340676,0.0003297758,0.0367606,0.00009555652,0.00018792103,0.0004982597,0.00017855407,0.6473175,0.01077979,0.017316254,0.002685347,0.28346694],"study_design_scores_gemma":[0.0000020471846,0.000009019405,0.000896573,0.0000014177972,0.0000020918742,0.000023713776,0.000006194792,0.995275,0.0006593134,0.003011818,0.00011044269,0.0000022195377],"about_ca_topic_score_codex":0.0033275967,"about_ca_topic_score_gemma":0.0029924433,"teacher_disagreement_score":0.0033275967,"about_ca_system_score_codex":0.00065893505,"about_ca_system_score_gemma":0.0006857076,"threshold_uncertainty_score":0.007012546},"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":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007030743,0.0070386897,0.008673997,0.07717704,0.6138892,0.0008151084,0.0075132465,0.003687765,0.2805019],"genre_scores_gemma":[0.0024958595,0.00554946,0.0019860952,0.009331755,0.16467243,0.00033909356,0.005827788,0.0023218165,0.8074757],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976604,0.00021994278,0.0001825843,0.0004036463,0.0013567046,0.00017668348],"domain_scores_gemma":[0.9856592,0.0029978908,0.00059413427,0.0012423885,0.006537619,0.0029688615],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003109721,0.0019844442,0.0020304238,0.0028344213,0.0018086479,0.010327344,0.0028949056,0.005204181,0.5195192],"category_scores_gemma":[0.012087572,0.0006784701,0.0011386285,0.0033616056,0.0007840452,0.0067832917,0.0026510565,0.0040112226,0.42427018],"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":[0.00001652145,0.000016225755,0.000035335823,0.00005450121,0.0000038004043,0.000017702123,0.0000020594134,0.00004509278,0.000054347012,0.00026105877,0.98764384,0.011849678],"study_design_scores_gemma":[0.000023639268,0.000022112172,0.00027317432,0.00008021504,0.0000061864457,0.000033847453,0.000013132138,0.00036480042,0.00011750323,0.0013595535,0.99769574,0.000010019438],"about_ca_topic_score_codex":0.00086149067,"about_ca_topic_score_gemma":0.0019717868,"teacher_disagreement_score":0.5195192,"about_ca_system_score_codex":0.0015224991,"about_ca_system_score_gemma":0.001867869,"threshold_uncertainty_score":0.68534756},"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":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20065354,0.011741065,0.7804956,0.0004080699,0.00023907109,0.00016100836,0.00087940996,0.001682053,0.0037402236],"genre_scores_gemma":[0.8157384,0.004815064,0.17284046,0.00007604815,0.00021409437,0.00008074134,0.0024642572,0.00015058221,0.0036203517],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987049,0.0001531437,0.00007551441,0.00017288471,0.00080854056,0.00008500886],"domain_scores_gemma":[0.9976363,0.0007535945,0.0002175678,0.0002042535,0.0011241435,0.00006416631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001401479,0.00068000326,0.00070866715,0.0032489703,0.0003436938,0.0007977122,0.0006980687,0.0005345211,0.0009348686],"category_scores_gemma":[0.0034784954,0.00017035204,0.00082214543,0.002141764,0.00020520031,0.0011991656,0.00031652956,0.0004481556,0.00035274148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029833824,0.0002360687,0.018275945,0.00047278014,0.000313793,0.00018024565,0.000091975795,0.05196053,0.0330713,0.0035182063,0.0034613868,0.88811934],"study_design_scores_gemma":[0.000013041863,0.00042740811,0.050396312,0.00007481214,0.00024206445,0.00083816267,0.00015807596,0.9008273,0.03186242,0.0035307158,0.01157118,0.000058441103],"about_ca_topic_score_codex":0.004208508,"about_ca_topic_score_gemma":0.00393315,"teacher_disagreement_score":0.004208508,"about_ca_system_score_codex":0.00059138983,"about_ca_system_score_gemma":0.0007508501,"threshold_uncertainty_score":0.008368015},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14578664,0.00063135097,0.8458194,0.00029670482,0.00015034966,0.00009992672,0.00020002859,0.0049953666,0.0020201555],"genre_scores_gemma":[0.8249276,0.0003020364,0.17142466,0.00022947979,0.000057679026,0.00008465131,0.00047033126,0.000054723267,0.0024487977],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996482,0.000040947216,0.000024109766,0.00011446799,0.00011541485,0.000056789817],"domain_scores_gemma":[0.99971455,0.00006571517,0.000043999913,0.000030344005,0.00011771226,0.000027662172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004688156,0.000638714,0.0005879704,0.0007193458,0.0002144391,0.00040431652,0.0007575957,0.00046876847,0.00044124262],"category_scores_gemma":[0.00083400996,0.0002742313,0.00040936624,0.00035307638,0.00020641736,0.00056768133,0.0004959083,0.0006857939,0.00020956434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005514819,0.0003899078,0.00801381,0.00011833414,0.00015843648,0.00048402286,0.000108455475,0.18183725,0.08098589,0.0017915373,0.005444058,0.72011685],"study_design_scores_gemma":[0.000007723018,0.000052221403,0.0014335205,0.00000569547,0.0000146781895,0.00006810878,0.0000075040957,0.9867399,0.010913014,0.00029287505,0.00045509828,0.000009685957],"about_ca_topic_score_codex":0.0066910065,"about_ca_topic_score_gemma":0.0066483235,"teacher_disagreement_score":0.0066910065,"about_ca_system_score_codex":0.0006185298,"about_ca_system_score_gemma":0.0006726367,"threshold_uncertainty_score":0.013304114},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43763226,0.00031933142,0.5571644,0.0002857492,0.000061301515,0.0002176586,0.0012640165,0.0020365117,0.0010188256],"genre_scores_gemma":[0.90103996,0.00008509114,0.096104994,0.0000400064,0.00003091602,0.00012442403,0.0020415233,0.000066465,0.00046656985],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965072,0.0011375406,0.00028962104,0.00083801505,0.00091539824,0.0003121079],"domain_scores_gemma":[0.9842138,0.009195796,0.0021768855,0.001848558,0.0022111158,0.00035378916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003067293,0.0007085969,0.00082525954,0.0034118658,0.00046078087,0.0008949886,0.0012364596,0.00064359634,0.0009763556],"category_scores_gemma":[0.020716155,0.00022966694,0.0007292659,0.0026500786,0.00075408514,0.0015034841,0.0014430768,0.001169644,0.00042278727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010860546,0.0007859741,0.29564172,0.0003587025,0.00048661654,0.00074492284,0.001332514,0.06424339,0.017342504,0.005729468,0.003456425,0.6087917],"study_design_scores_gemma":[0.000023440225,0.00019586433,0.07117069,0.000046670008,0.000060381997,0.00051444734,0.0005225928,0.90229464,0.009133737,0.013807739,0.0021817877,0.00004803745],"about_ca_topic_score_codex":0.0037154264,"about_ca_topic_score_gemma":0.0041004084,"teacher_disagreement_score":0.0037154264,"about_ca_system_score_codex":0.0005624048,"about_ca_system_score_gemma":0.000935741,"threshold_uncertainty_score":0.016221642},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021995185,0.0005574746,0.9750709,0.00035778704,0.000053104806,0.00003315967,0.000089301386,0.0007767646,0.0010663334],"genre_scores_gemma":[0.5722456,0.0011290051,0.41645005,0.0006644532,0.00025468387,0.00026781438,0.0009766159,0.00054159196,0.0074701537],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990268,0.00029943444,0.000047807538,0.00015894958,0.00036631536,0.00010073384],"domain_scores_gemma":[0.99807715,0.0011594458,0.00018994238,0.00021809527,0.00028782318,0.00006764626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021909059,0.0014519728,0.001433724,0.00081313256,0.0004499037,0.0011299547,0.0020960425,0.001541526,0.0015950986],"category_scores_gemma":[0.0065432745,0.0005376086,0.00078262505,0.00077239104,0.0008640156,0.0017084627,0.001310818,0.002472596,0.0005734857],"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":[0.00015327141,0.000093049326,0.0012812348,0.000114301554,0.000098728044,0.0002167315,0.0000574444,0.8547405,0.0056316885,0.013130871,0.0038193448,0.12066282],"study_design_scores_gemma":[0.0000028224356,0.00001183121,0.00007324546,0.0000051258535,0.0000042064353,0.000013794923,0.0000021317705,0.9976738,0.0006431012,0.0013405847,0.00022715252,0.0000023003383],"about_ca_topic_score_codex":0.0043588816,"about_ca_topic_score_gemma":0.0053629754,"teacher_disagreement_score":0.0043588816,"about_ca_system_score_codex":0.00083514367,"about_ca_system_score_gemma":0.0015405018,"threshold_uncertainty_score":0.011586726},"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":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7268832,0.00039180418,0.2689643,0.00039286303,0.00007218732,0.00008133277,0.00037137629,0.0013735277,0.0014695146],"genre_scores_gemma":[0.9493981,0.000081979764,0.049159136,0.000036514306,0.000027324828,0.000029248882,0.00046739733,0.000018989134,0.00078143063],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995357,0.00014444739,0.000048140704,0.00010326695,0.00009830467,0.00007014305],"domain_scores_gemma":[0.99803895,0.0009458834,0.00028808403,0.00016859428,0.0004790472,0.0000795935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014367691,0.00049815467,0.0005388624,0.0015358186,0.00029787753,0.0006424044,0.00044395126,0.000534228,0.000539025],"category_scores_gemma":[0.003939654,0.00012853584,0.0005217432,0.00085169054,0.00020110232,0.00065584504,0.0003973523,0.0006873092,0.00029877486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004976623,0.0009162227,0.2086098,0.00012762858,0.0002882143,0.0005094232,0.00034332002,0.17217815,0.011851973,0.0018477766,0.0037686122,0.59906125],"study_design_scores_gemma":[0.0000054866196,0.000077797595,0.01511658,0.000009195283,0.000018673418,0.00011132413,0.000069047615,0.9806574,0.002502359,0.0008985094,0.000525783,0.000007886895],"about_ca_topic_score_codex":0.0034807043,"about_ca_topic_score_gemma":0.004497515,"teacher_disagreement_score":0.0034807043,"about_ca_system_score_codex":0.0003209613,"about_ca_system_score_gemma":0.0004801356,"threshold_uncertainty_score":0.0075984597},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.142757,0.0005010312,0.8084785,0.0003015283,0.00023384481,0.0003465329,0.0013661234,0.04061911,0.0053962953],"genre_scores_gemma":[0.6994118,0.00019541154,0.29321918,0.00017038589,0.0000729064,0.00016900174,0.0028146496,0.00034080056,0.003605912],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958867,0.000029841529,0.000017551933,0.00016382238,0.00014806289,0.00005204485],"domain_scores_gemma":[0.99951494,0.00007643182,0.00008295931,0.000090663896,0.00018406239,0.000050919807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000578348,0.0005812575,0.00060386217,0.001919769,0.0004733909,0.0008333336,0.001602935,0.00054489396,0.0013637177],"category_scores_gemma":[0.0017307305,0.0002464332,0.0004625632,0.00088318053,0.0004087774,0.0012478395,0.0013212916,0.00073144305,0.0005577671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007030307,0.00055218383,0.018528774,0.0001842557,0.00018900227,0.00031150508,0.00023629003,0.096327834,0.06403913,0.011435407,0.02548853,0.782004],"study_design_scores_gemma":[0.00003987338,0.0001872093,0.005264418,0.000010419871,0.000039287108,0.00031718676,0.000040069543,0.9530618,0.028880551,0.003824108,0.008292403,0.000042752603],"about_ca_topic_score_codex":0.005665539,"about_ca_topic_score_gemma":0.0056900303,"teacher_disagreement_score":0.005665539,"about_ca_system_score_codex":0.00070226006,"about_ca_system_score_gemma":0.0009217155,"threshold_uncertainty_score":0.011265099},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046310946,0.006988311,0.91618335,0.004705608,0.0004896239,0.00003729186,0.00025168562,0.002209457,0.02282376],"genre_scores_gemma":[0.71084976,0.00794201,0.26595294,0.0013748,0.00059164636,0.000049761595,0.00043746317,0.00037318355,0.012428435],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993788,0.00010003187,0.000033190925,0.00015528446,0.00026930674,0.000063300555],"domain_scores_gemma":[0.99905497,0.0004054017,0.00008636959,0.00019669188,0.00019984362,0.000056693196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011006823,0.0007267953,0.0004184822,0.0010583265,0.00040764245,0.0018471262,0.0010032057,0.0010448572,0.0023398565],"category_scores_gemma":[0.0040748315,0.0005325919,0.00040373992,0.0010522658,0.001726805,0.0042240433,0.0021765179,0.0019809098,0.0008592149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003601289,0.00008631537,0.004400894,0.00028099073,0.00010838687,0.0007703109,0.00047848027,0.09347084,0.052860297,0.3723929,0.014701607,0.46008888],"study_design_scores_gemma":[0.000015595466,0.00006421885,0.0012053081,0.00008409221,0.000028973294,0.000618155,0.000093244256,0.6323259,0.023053555,0.29985896,0.042609427,0.000042548807],"about_ca_topic_score_codex":0.0014645297,"about_ca_topic_score_gemma":0.001657497,"teacher_disagreement_score":0.0023398565,"about_ca_system_score_codex":0.0012021924,"about_ca_system_score_gemma":0.0005404346,"threshold_uncertainty_score":0.008722603},"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":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029713244,0.003040946,0.004451592,0.90502787,0.06291237,0.0001110676,0.0004517248,0.00040826167,0.020624911],"genre_scores_gemma":[0.16228434,0.012859435,0.0060051135,0.5194867,0.09689518,0.0005487621,0.00093482935,0.0006054411,0.2003802],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99729484,0.0008983119,0.00020229074,0.0004581928,0.0009579175,0.0001883943],"domain_scores_gemma":[0.96955633,0.015027087,0.0019922338,0.0016411612,0.010263335,0.0015198871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003386761,0.0004337491,0.00057329657,0.0010934008,0.0013274312,0.0021293196,0.0011171567,0.007435837,0.04857382],"category_scores_gemma":[0.068024695,0.00021528477,0.00046400112,0.0008998956,0.0027250682,0.0027761871,0.0019539467,0.005896332,0.017432354],"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":[0.00013466363,0.000009061159,0.0004710619,0.00040790855,0.000017830293,0.0003981023,0.00044348842,0.00020405729,0.00039178308,0.010068071,0.9626988,0.024755167],"study_design_scores_gemma":[0.00006236949,0.00006219017,0.001853488,0.00041923943,0.000021470245,0.0008762563,0.0012295461,0.0007142519,0.0007881442,0.022322167,0.9715911,0.00005963861],"about_ca_topic_score_codex":0.0012627586,"about_ca_topic_score_gemma":0.0026603232,"teacher_disagreement_score":0.04857382,"about_ca_system_score_codex":0.0016580564,"about_ca_system_score_gemma":0.0023480265,"threshold_uncertainty_score":0.16249561},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9159376,0.00085273164,0.06774604,0.00064669177,0.00040901618,0.00008685296,0.002675572,0.0016469548,0.009998517],"genre_scores_gemma":[0.99163014,0.00012808766,0.0059279967,0.000083858904,0.00006278532,0.000012709675,0.00082809903,0.000023331457,0.0013029324],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996505,0.00006222593,0.000019681427,0.00011954074,0.000085895124,0.0000622172],"domain_scores_gemma":[0.9995559,0.0001259584,0.000097072254,0.000054406504,0.000101870355,0.00006479579],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002799322,0.00034881636,0.00026753385,0.0010614551,0.00022493521,0.00067644636,0.00032551,0.0005226725,0.0022615185],"category_scores_gemma":[0.0013666735,0.000084959494,0.00020641834,0.00081893656,0.00025052667,0.00039273698,0.00044209752,0.00038743013,0.0011485016],"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":[0.0018228319,0.00044019934,0.24681488,0.0004291578,0.00024317026,0.00536412,0.0013348816,0.039583616,0.069582716,0.0039564515,0.031123057,0.599305],"study_design_scores_gemma":[0.00003791517,0.00041769526,0.36310214,0.00015976653,0.00010890695,0.0087572215,0.0016155265,0.5490766,0.03819546,0.009471002,0.028955547,0.0001021762],"about_ca_topic_score_codex":0.0019414399,"about_ca_topic_score_gemma":0.0027828973,"teacher_disagreement_score":0.0022615185,"about_ca_system_score_codex":0.00025647794,"about_ca_system_score_gemma":0.00019643123,"threshold_uncertainty_score":0.007565558},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21008073,0.00019439938,0.78501177,0.00039150612,0.00007469425,0.00006079536,0.00046514234,0.0017195676,0.0020014818],"genre_scores_gemma":[0.8956204,0.00010454996,0.10272912,0.00009978853,0.00006852226,0.00004375766,0.00035990088,0.000081148544,0.00089275703],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995704,0.00009082081,0.000020551659,0.00009584597,0.00015429394,0.000068052395],"domain_scores_gemma":[0.99797624,0.0006797993,0.000497497,0.00033956062,0.00043152817,0.00007541025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064793776,0.00075785385,0.0005563081,0.0019520462,0.0004324703,0.0008684555,0.0011052723,0.00071996,0.0007388076],"category_scores_gemma":[0.002924473,0.0002846745,0.0005222751,0.0010821332,0.0006155496,0.0014871839,0.0006357191,0.0010302421,0.00025768141],"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":[0.0003638994,0.00052158005,0.0896799,0.00016178157,0.00018714865,0.00032592847,0.000191635,0.5314172,0.09561437,0.010148342,0.0035282923,0.26786],"study_design_scores_gemma":[0.0000055924584,0.000035960802,0.00485114,0.0000067008964,0.000008626856,0.000055635435,0.000020056626,0.97984856,0.0088500185,0.005777931,0.00051968463,0.000020016594],"about_ca_topic_score_codex":0.00354872,"about_ca_topic_score_gemma":0.005876103,"teacher_disagreement_score":0.00354872,"about_ca_system_score_codex":0.00049310416,"about_ca_system_score_gemma":0.000576044,"threshold_uncertainty_score":0.007056117},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030333204,0.00041376005,0.9675619,0.0004401188,0.000036220365,0.000021225786,0.00009696438,0.0006200198,0.0004765963],"genre_scores_gemma":[0.75068474,0.00047090123,0.24569942,0.00033175372,0.00032926997,0.00010105844,0.00070871453,0.0005667929,0.0011074457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.983098,0.0076339245,0.0008424232,0.0029537745,0.004949768,0.0005221556],"domain_scores_gemma":[0.9033233,0.071225464,0.010267425,0.008818399,0.005057407,0.0013080445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0155271515,0.0017411354,0.0021986815,0.0028206273,0.00093550474,0.0031183595,0.003092366,0.0021686782,0.00077424065],"category_scores_gemma":[0.109920815,0.0008601016,0.0011659169,0.0019121242,0.0039960514,0.005776885,0.006014917,0.0050719697,0.00041491137],"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":[0.00040787205,0.00015346837,0.038736388,0.00028663944,0.0003211999,0.0004125915,0.00085594767,0.7254106,0.0038133778,0.08207043,0.0029974922,0.14453398],"study_design_scores_gemma":[0.000006383311,0.00004204805,0.00074921775,0.000025715986,0.000010469592,0.00007877186,0.00003973163,0.9159186,0.0014207367,0.08106185,0.00062680966,0.0000197195],"about_ca_topic_score_codex":0.0026842847,"about_ca_topic_score_gemma":0.0017130736,"teacher_disagreement_score":0.0155271515,"about_ca_system_score_codex":0.0011415858,"about_ca_system_score_gemma":0.0013533108,"threshold_uncertainty_score":0.082116365},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08403849,0.00016431967,0.9135117,0.00026245043,0.00006139323,0.000052730204,0.000074841315,0.0010603009,0.0007737976],"genre_scores_gemma":[0.94630665,0.0000614075,0.052260384,0.000068971494,0.00002826056,0.000037896993,0.0001330913,0.00004781491,0.0010553827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903345,0.00022814295,0.000060255938,0.00028219973,0.00026739697,0.00012856621],"domain_scores_gemma":[0.99363214,0.003932029,0.00051660143,0.00044573218,0.0012003218,0.00027315697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021351622,0.0005551576,0.0010905208,0.0007902179,0.00042085757,0.0007633934,0.0014286995,0.00085218775,0.0010622623],"category_scores_gemma":[0.012024525,0.00027990603,0.0003988496,0.00049706374,0.0010038548,0.0014902509,0.0010822446,0.0016240668,0.00021457285],"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":[0.0006375949,0.00049628335,0.01048908,0.0001181665,0.00008622455,0.00021188888,0.0001640883,0.6216709,0.00968537,0.015048559,0.0029215661,0.3384702],"study_design_scores_gemma":[0.000004473085,0.000030484784,0.00029710203,0.0000023819327,0.0000034258999,0.000016362721,0.0000051930774,0.99584514,0.0009362903,0.0027673615,0.00008828336,0.000003414347],"about_ca_topic_score_codex":0.0076533486,"about_ca_topic_score_gemma":0.0058493754,"teacher_disagreement_score":0.0076533486,"about_ca_system_score_codex":0.0011727487,"about_ca_system_score_gemma":0.0013199521,"threshold_uncertainty_score":0.015217602},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.073941015,0.0015927386,0.9195285,0.0005763573,0.00018200238,0.00008747389,0.00015892206,0.0013295015,0.0026034773],"genre_scores_gemma":[0.93827665,0.00036114646,0.05941433,0.00018238867,0.00006330184,0.00007240882,0.0002447917,0.000026425152,0.0013585092],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994167,0.00008360446,0.000049358587,0.00019200997,0.00016297477,0.00009534471],"domain_scores_gemma":[0.99959666,0.00011776945,0.00006548147,0.000030751984,0.0001625499,0.000026793365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008346213,0.0008124739,0.00084715115,0.0008409895,0.00040185647,0.0006951158,0.0009973239,0.0006547003,0.00074487244],"category_scores_gemma":[0.0011798807,0.0003260983,0.00088234566,0.0005936547,0.00040595848,0.0010450742,0.000857785,0.0009632004,0.0001598764],"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":[0.00033489603,0.00022830848,0.0050104386,0.00016866116,0.00028199513,0.0002938346,0.00026384695,0.6012046,0.013801182,0.0059402147,0.0030782546,0.36939386],"study_design_scores_gemma":[0.0000057155307,0.000059085494,0.0006117784,0.000005137396,0.000027049839,0.00002441967,0.00002778593,0.99408334,0.0022150164,0.0023787452,0.00055391225,0.0000080367945],"about_ca_topic_score_codex":0.0076923375,"about_ca_topic_score_gemma":0.0060560037,"teacher_disagreement_score":0.0076923375,"about_ca_system_score_codex":0.0009045329,"about_ca_system_score_gemma":0.0009400195,"threshold_uncertainty_score":0.015295148},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10282372,0.00064830313,0.8877059,0.0005879964,0.00017725413,0.00009792189,0.0019890647,0.0040604398,0.0019093889],"genre_scores_gemma":[0.78188115,0.0004068596,0.21018782,0.00022240497,0.00013267167,0.000116831594,0.003125452,0.00014940252,0.0037774136],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947613,0.0000814591,0.000030900374,0.00017246057,0.00012642924,0.00011267876],"domain_scores_gemma":[0.99934036,0.00016832189,0.00013391282,0.00012635806,0.00017418615,0.000056876645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045233354,0.001195187,0.0009943121,0.0019706544,0.00030179834,0.00096217316,0.0014269095,0.0011606003,0.0013726027],"category_scores_gemma":[0.0016342018,0.00044778726,0.0008579252,0.0015502069,0.0003770557,0.001266553,0.0015619381,0.0014437475,0.0013447175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039720652,0.00059732597,0.02943034,0.00015616008,0.00022163137,0.00044607665,0.00010736155,0.12404193,0.021242319,0.0043105176,0.017517515,0.80153155],"study_design_scores_gemma":[0.000006208905,0.000040244468,0.003782257,0.000010055467,0.000016052369,0.00013003492,0.0000392814,0.9863034,0.0030541501,0.0057068807,0.00090039324,0.000011030781],"about_ca_topic_score_codex":0.004635067,"about_ca_topic_score_gemma":0.008737521,"teacher_disagreement_score":0.004635067,"about_ca_system_score_codex":0.00045170076,"about_ca_system_score_gemma":0.00071621494,"threshold_uncertainty_score":0.009216189},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059144604,0.0036528534,0.97063273,0.0018696546,0.00032462593,0.000036416463,0.00020968761,0.0010295999,0.016329922],"genre_scores_gemma":[0.47315043,0.007789478,0.44633856,0.001161892,0.00080796704,0.00020052309,0.0008556553,0.0005023168,0.06919317],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974984,0.000054785625,0.000010112686,0.00004716227,0.00010181436,0.000036355723],"domain_scores_gemma":[0.9996842,0.00011952083,0.00003511859,0.000030927622,0.000104533145,0.00002566035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006958937,0.0010283367,0.0005131955,0.0007568842,0.00037090882,0.001028961,0.0012623678,0.000900741,0.0056423047],"category_scores_gemma":[0.0016396951,0.00034835062,0.00053953566,0.00056512916,0.00047235866,0.0014442613,0.0014983111,0.001632018,0.0013739131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004850512,0.00006888355,0.0011529588,0.00013001333,0.00008155891,0.000096234064,0.00010020864,0.34199125,0.0020616879,0.069594845,0.04246633,0.5422074],"study_design_scores_gemma":[0.0000028885559,0.000011007762,0.00029560414,0.000042967404,0.000010450862,0.000045170786,0.000015562846,0.9415875,0.00090821704,0.046250995,0.010817994,0.000011543585],"about_ca_topic_score_codex":0.0055551142,"about_ca_topic_score_gemma":0.007813689,"teacher_disagreement_score":0.0056423047,"about_ca_system_score_codex":0.001387084,"about_ca_system_score_gemma":0.0007699662,"threshold_uncertainty_score":0.01887536},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06833324,0.0016910331,0.124369815,0.0027576322,0.0015098291,0.0015554968,0.753952,0.033496633,0.012334342],"genre_scores_gemma":[0.12644878,0.0003332759,0.13641934,0.0004935003,0.00018421399,0.0019668532,0.722978,0.0021830697,0.008992972],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9961894,0.0011389101,0.0004052231,0.0009327054,0.0011296439,0.00020418382],"domain_scores_gemma":[0.98943615,0.0029423293,0.0004012558,0.003979326,0.0030526589,0.00018821338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062473277,0.0016195254,0.0009837961,0.0020191753,0.00079082284,0.0015859341,0.0033541515,0.0020222862,0.009297175],"category_scores_gemma":[0.025662635,0.0005156268,0.0017432185,0.0026399437,0.0005542091,0.0013563851,0.0016581014,0.002302428,0.012053665],"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":[0.0012260091,0.0006970316,0.02939917,0.0012604387,0.0005230089,0.0005977676,0.00032663328,0.033658553,0.002823412,0.00383059,0.80287147,0.1227859],"study_design_scores_gemma":[0.001581326,0.0007273728,0.059911553,0.0005363843,0.00028957787,0.0013437432,0.00084043277,0.32293016,0.017663686,0.028363265,0.56539404,0.00041846896],"about_ca_topic_score_codex":0.017196426,"about_ca_topic_score_gemma":0.034136664,"teacher_disagreement_score":0.017196426,"about_ca_system_score_codex":0.0009881322,"about_ca_system_score_gemma":0.0014339172,"threshold_uncertainty_score":0.03419262},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06566171,0.0006215129,0.92530787,0.00014950488,0.00012297982,0.00007705384,0.00042199722,0.0025740315,0.0050632446],"genre_scores_gemma":[0.63677174,0.000541116,0.3413248,0.00023438323,0.00012682546,0.00007495637,0.0015663527,0.00016004803,0.019199738],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998072,0.000017603501,0.0000053512317,0.000065163746,0.000049220598,0.00005552333],"domain_scores_gemma":[0.9999013,0.00001641003,0.000009618674,0.000016488844,0.000044477696,0.000011688104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020674986,0.0006625544,0.0007839884,0.00080592994,0.0002514149,0.00046074178,0.0007960098,0.0005776412,0.0014616319],"category_scores_gemma":[0.0002245326,0.00029058746,0.0005927738,0.0006509186,0.00014593214,0.00045655327,0.0005835726,0.00059103227,0.00074888277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025291293,0.00033046395,0.0023002361,0.00006320165,0.000093153256,0.00016861361,0.000038411614,0.05182056,0.06277593,0.0017104705,0.006610461,0.87383556],"study_design_scores_gemma":[0.000005858508,0.000047662477,0.0013035518,0.0000063047846,0.00002482931,0.000071271796,0.000013768631,0.9878818,0.008595384,0.00082332216,0.0012190179,0.0000072718094],"about_ca_topic_score_codex":0.007377479,"about_ca_topic_score_gemma":0.011831787,"teacher_disagreement_score":0.007377479,"about_ca_system_score_codex":0.0004347401,"about_ca_system_score_gemma":0.00045132745,"threshold_uncertainty_score":0.014669061},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012377845,0.00036020862,0.98287934,0.0008923556,0.00017243208,0.00008416958,0.00011580274,0.00019906141,0.0029187547],"genre_scores_gemma":[0.53296316,0.0008374077,0.45439535,0.0010889058,0.00092142756,0.00096791255,0.000543921,0.000481064,0.007800807],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97694314,0.016716577,0.00077964226,0.0024612856,0.0023773494,0.0007221275],"domain_scores_gemma":[0.8417818,0.13525268,0.00505207,0.014215216,0.0026785042,0.001019743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04396657,0.0023841804,0.003660375,0.0017331303,0.0014012707,0.005037311,0.0044536833,0.0044207447,0.0068924963],"category_scores_gemma":[0.1298604,0.0019365815,0.003170203,0.0017571579,0.007285076,0.010715694,0.005968184,0.007450717,0.0007716755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044789872,0.000117629475,0.00068084593,0.00015998386,0.00034507178,0.00010617679,0.00014631827,0.1220127,0.0005223957,0.8413595,0.0024500545,0.031651463],"study_design_scores_gemma":[0.000050905634,0.000043068205,0.00011408059,0.000036123878,0.000040724433,0.000030187872,0.000013059456,0.45861945,0.00036527033,0.5401377,0.00052411406,0.000025259716],"about_ca_topic_score_codex":0.0011008007,"about_ca_topic_score_gemma":0.0010052143,"teacher_disagreement_score":0.04396657,"about_ca_system_score_codex":0.001656679,"about_ca_system_score_gemma":0.0015898304,"threshold_uncertainty_score":0.23252028},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028361492,0.000071838964,0.97058725,0.000099125,0.00001559441,0.0000118942335,0.00004082917,0.00031126273,0.00050068024],"genre_scores_gemma":[0.7171922,0.00015307043,0.2791191,0.00016161015,0.000046683486,0.000056248933,0.00035111324,0.00010566992,0.0028143048],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99979466,0.00004837614,0.000008796627,0.000044049495,0.000075026466,0.000029085466],"domain_scores_gemma":[0.99949956,0.00020191558,0.00007987648,0.00007836979,0.00012121216,0.000019171746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048628898,0.000373301,0.00046549426,0.0003596929,0.00015454924,0.0002967072,0.00049930223,0.00052489195,0.0007426823],"category_scores_gemma":[0.0012484138,0.00019787265,0.00034284554,0.00038253143,0.00047118354,0.00051699864,0.0004774004,0.0007668056,0.00027029277],"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":[0.0001770339,0.0000836025,0.0012117709,0.00006698388,0.00004428892,0.0001076478,0.00008354949,0.66849744,0.06660147,0.008836873,0.0025910952,0.25169823],"study_design_scores_gemma":[0.0000017578035,0.000010579653,0.00013861031,0.0000019837728,0.0000022573875,0.0000116814845,0.0000032326209,0.9951332,0.0035211102,0.0009985915,0.00017456335,0.000002400727],"about_ca_topic_score_codex":0.0019617819,"about_ca_topic_score_gemma":0.002904841,"teacher_disagreement_score":0.0019617819,"about_ca_system_score_codex":0.00028171157,"about_ca_system_score_gemma":0.00036913945,"threshold_uncertainty_score":0.0039007068},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17019238,0.00012556357,0.82430685,0.00037488766,0.000097966076,0.00013527225,0.00043202573,0.0012104553,0.0031245453],"genre_scores_gemma":[0.92369133,0.000062144056,0.07257825,0.000115244016,0.00001716398,0.0001224467,0.00076054165,0.00007066479,0.0025821228],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972266,0.0000717805,0.000009960923,0.00009102741,0.00006724915,0.000037330938],"domain_scores_gemma":[0.99879646,0.0007685048,0.00011378211,0.000118842094,0.0001625462,0.000039794362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008110467,0.0006180209,0.00032212987,0.00041559024,0.0002327665,0.00035213833,0.00095714844,0.0006035273,0.0012641666],"category_scores_gemma":[0.002746765,0.00031198937,0.00058308255,0.00030644843,0.0005447494,0.0004965109,0.00072619924,0.0010706414,0.0002499369],"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":[0.000038007034,0.000021476759,0.0010921408,0.0000101468295,0.0000118696125,0.00004218361,0.000019689605,0.98442036,0.0010804185,0.0014138764,0.0005019837,0.011347779],"study_design_scores_gemma":[0.000001266076,0.000007012386,0.00009864104,9.392289e-7,0.0000012686068,0.0000062084187,0.00000137231,0.999037,0.000435914,0.0003363932,0.00007228807,0.0000016816025],"about_ca_topic_score_codex":0.008230094,"about_ca_topic_score_gemma":0.0070184623,"teacher_disagreement_score":0.008230094,"about_ca_system_score_codex":0.0007459501,"about_ca_system_score_gemma":0.00059113593,"threshold_uncertainty_score":0.016364396},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015280416,0.00040477279,0.97243,0.00077508716,0.00013676652,0.00007095131,0.0006759064,0.0013504045,0.008875688],"genre_scores_gemma":[0.48860344,0.001084182,0.47758603,0.0007315901,0.00012299969,0.0006372814,0.0015893762,0.00038575314,0.029259257],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981576,0.000030843265,0.000007325973,0.00006665335,0.000059840513,0.000019632524],"domain_scores_gemma":[0.9997212,0.000096498545,0.000031350908,0.000034493398,0.00009266662,0.000023665312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042106,0.0006497533,0.000445381,0.00071720296,0.0004885037,0.0011739323,0.0023850356,0.0018886826,0.0055771344],"category_scores_gemma":[0.0013077541,0.0005497288,0.00092208484,0.00087470474,0.00075629697,0.0010441124,0.0010940691,0.0013774702,0.001440375],"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":[0.000030562773,0.000018090876,0.00054005336,0.00002590368,0.000020319894,0.00006780854,0.000031101168,0.96613365,0.0014130884,0.008933204,0.0015792632,0.021206992],"study_design_scores_gemma":[0.0000027017772,0.000005030899,0.000050088132,0.0000033654417,0.0000022234074,0.000013981025,0.0000019822937,0.99658537,0.00020740867,0.002273651,0.0008507847,0.0000034464274],"about_ca_topic_score_codex":0.01804982,"about_ca_topic_score_gemma":0.012055955,"teacher_disagreement_score":0.01804982,"about_ca_system_score_codex":0.0014452821,"about_ca_system_score_gemma":0.0012619416,"threshold_uncertainty_score":0.035889506},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006514662,0.00019962962,0.9911607,0.00006680018,0.000047100486,0.000046098827,0.000027244725,0.0011162775,0.00082141336],"genre_scores_gemma":[0.3364336,0.00039201466,0.65762436,0.00014143718,0.00012650182,0.0002511103,0.00029069028,0.00020849804,0.004531953],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978865,0.0003105732,0.00013572973,0.0006054715,0.00089423126,0.00016754505],"domain_scores_gemma":[0.99858785,0.00040650487,0.00014054087,0.00023823236,0.0005758736,0.00005093015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017425125,0.0010517421,0.0013621016,0.0030241306,0.0008788681,0.0014136517,0.0034176998,0.0013947344,0.0022046922],"category_scores_gemma":[0.002872341,0.00048743156,0.0011942094,0.0026631004,0.0006623606,0.002353206,0.0019204628,0.0008551719,0.0010002505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033260803,0.00027018468,0.0028842175,0.00026851782,0.0003791391,0.00025789317,0.00040022604,0.19429223,0.027336674,0.0228296,0.0042064143,0.74654233],"study_design_scores_gemma":[0.000017113585,0.00008143354,0.0005549802,0.000013113386,0.000038885148,0.00020398508,0.000061003757,0.9785839,0.0084200045,0.0076865675,0.0043046884,0.00003432244],"about_ca_topic_score_codex":0.002179209,"about_ca_topic_score_gemma":0.0015851522,"teacher_disagreement_score":0.0034176998,"about_ca_system_score_codex":0.00078538485,"about_ca_system_score_gemma":0.00078091054,"threshold_uncertainty_score":0.0092154145},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6762767,0.00092512247,0.31553632,0.0013377365,0.00021569578,0.00010573707,0.00025550099,0.0022431316,0.0031039761],"genre_scores_gemma":[0.9828084,0.00009435471,0.016096065,0.00013394047,0.000030313631,0.000019147015,0.0002414101,0.000043122254,0.00053319236],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985864,0.0006144533,0.000054531563,0.00029826697,0.00030455872,0.00014189456],"domain_scores_gemma":[0.9935928,0.0042551127,0.0005732926,0.0007789113,0.0005748167,0.00022505703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004255203,0.0010240147,0.0009248661,0.0013088563,0.00045521217,0.0009083378,0.001119592,0.0009620201,0.0006020855],"category_scores_gemma":[0.008884026,0.00027777554,0.0007638711,0.00055163173,0.001119389,0.0017071083,0.0014298455,0.0018144931,0.00025653103],"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":[0.00035625306,0.00015890985,0.014656064,0.00006392531,0.00009881495,0.00012245815,0.00010284472,0.90798604,0.004053129,0.004229789,0.0020387303,0.06613311],"study_design_scores_gemma":[0.0000033669442,0.000032831467,0.0005022089,0.00000368889,0.00000573564,0.000020072479,0.000008466781,0.9967384,0.0013114145,0.0012760683,0.00009348044,0.0000042555016],"about_ca_topic_score_codex":0.002501066,"about_ca_topic_score_gemma":0.0023617079,"teacher_disagreement_score":0.004255203,"about_ca_system_score_codex":0.0011152502,"about_ca_system_score_gemma":0.00049587496,"threshold_uncertainty_score":0.022503972},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031835746,0.00019871225,0.9654816,0.00025170203,0.000026656866,0.00012784441,0.0001838014,0.00040864546,0.0014852708],"genre_scores_gemma":[0.74137825,0.00030177215,0.25520727,0.00023677354,0.00006351662,0.00028236894,0.0004106111,0.000082291444,0.0020371631],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9932673,0.0019731435,0.00042728422,0.0010505063,0.0026128753,0.00066880987],"domain_scores_gemma":[0.9910495,0.0036855643,0.00078884634,0.0032965255,0.00084514637,0.00033438287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003701985,0.000522968,0.0014877787,0.00092232204,0.0012923632,0.0018730325,0.0026245147,0.001153482,0.0014304012],"category_scores_gemma":[0.011497934,0.00044975255,0.001187371,0.0024929377,0.0014967852,0.0048339316,0.0042274804,0.001745748,0.0005235725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015778731,0.00030202878,0.0052929833,0.0002804368,0.00017320878,0.00067193015,0.0010625788,0.3439281,0.0323184,0.24889892,0.0052799834,0.36021358],"study_design_scores_gemma":[0.00007157388,0.00014042796,0.00052841066,0.000018817098,0.000027067412,0.0003823338,0.00013931615,0.879367,0.013154919,0.10269481,0.0034331486,0.00004222279],"about_ca_topic_score_codex":0.0015474829,"about_ca_topic_score_gemma":0.0012242665,"teacher_disagreement_score":0.003701985,"about_ca_system_score_codex":0.0013039856,"about_ca_system_score_gemma":0.0019798288,"threshold_uncertainty_score":0.019578218},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11644937,0.0014396942,0.8508406,0.0007030254,0.0006407477,0.000109814464,0.0008845055,0.02499454,0.0039376565],"genre_scores_gemma":[0.83118355,0.0003356272,0.1616675,0.00039617668,0.00018177443,0.000055706558,0.001106177,0.00064101623,0.0044325003],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992262,0.00006530301,0.000043308286,0.0003120758,0.00022288955,0.00013014713],"domain_scores_gemma":[0.99887484,0.00026921945,0.00014403493,0.00022855007,0.00038786686,0.000095509284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008430957,0.0016906479,0.001094752,0.0011457321,0.0005048657,0.0010107601,0.0017233554,0.0013722557,0.002947366],"category_scores_gemma":[0.0032084817,0.00047972938,0.00047928127,0.00073718897,0.0003847085,0.0020462614,0.0021272344,0.001930565,0.0020837737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007290086,0.0004105766,0.004771014,0.00028556504,0.000109123815,0.00041634822,0.00013985689,0.029235367,0.12916811,0.0014686743,0.016498702,0.81676763],"study_design_scores_gemma":[0.0000131282295,0.00014534403,0.002304966,0.00002438933,0.00004229866,0.00015550829,0.000025401678,0.9551917,0.03625135,0.0034223641,0.0023979826,0.000025622034],"about_ca_topic_score_codex":0.0047883247,"about_ca_topic_score_gemma":0.009777433,"teacher_disagreement_score":0.0047883247,"about_ca_system_score_codex":0.0005317451,"about_ca_system_score_gemma":0.0012527358,"threshold_uncertainty_score":0.00985992},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025414316,0.00016160701,0.96765196,0.00019960103,0.00007130391,0.00007498206,0.00017809997,0.0053527686,0.00089527137],"genre_scores_gemma":[0.5563711,0.0003150516,0.4368807,0.00029365788,0.00013378849,0.00019216548,0.0013357679,0.0010297955,0.0034479639],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991592,0.00017097207,0.00004400513,0.00024539928,0.00025940518,0.000121032295],"domain_scores_gemma":[0.99600816,0.0017765899,0.00040249518,0.00074705726,0.00093453703,0.00013115583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023604634,0.0012730021,0.00096121704,0.0020894015,0.00069567916,0.0015887318,0.0019074777,0.001155877,0.002105033],"category_scores_gemma":[0.0130713945,0.00049227645,0.00086869457,0.0010675641,0.0010815586,0.0027243646,0.0017009501,0.0026887064,0.0015518367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002972542,0.00026937082,0.013784098,0.00012413961,0.0000945053,0.00018540051,0.0002720109,0.16418584,0.023866417,0.01173042,0.010300923,0.77488965],"study_design_scores_gemma":[0.00000936375,0.000032186283,0.0014368371,0.000016506774,0.00000951863,0.0001178053,0.00003153215,0.9771687,0.011401477,0.0077445977,0.0020113806,0.00002002496],"about_ca_topic_score_codex":0.007826342,"about_ca_topic_score_gemma":0.007087507,"teacher_disagreement_score":0.007826342,"about_ca_system_score_codex":0.0012824049,"about_ca_system_score_gemma":0.0015316906,"threshold_uncertainty_score":0.015561521},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21732238,0.0022057516,0.631775,0.0014141676,0.00049126515,0.00027529133,0.007424397,0.13471444,0.004377283],"genre_scores_gemma":[0.70126617,0.00040271165,0.2750698,0.00039270197,0.00016558483,0.00020050464,0.017638186,0.00096672546,0.0038976115],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980596,0.00041410053,0.0001479615,0.0005543963,0.0006295597,0.00019435877],"domain_scores_gemma":[0.9957463,0.0015435867,0.0002671457,0.001402173,0.0008568186,0.00018396744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026672042,0.0016705896,0.0012701766,0.0024577957,0.0006516603,0.0015025717,0.0029257287,0.0013808346,0.001503358],"category_scores_gemma":[0.010261088,0.00046293487,0.0008788276,0.0015504366,0.00052996643,0.0036626053,0.0019514691,0.0019757533,0.0020199853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007651714,0.0007291074,0.03516696,0.0002807857,0.0002166045,0.00032553048,0.00016862694,0.09649543,0.013166415,0.0021686112,0.047774203,0.8027426],"study_design_scores_gemma":[0.000032893753,0.00012637995,0.003346201,0.000013889995,0.000018864557,0.00013450536,0.000074749085,0.9795999,0.008114235,0.004316683,0.0041950955,0.000026610445],"about_ca_topic_score_codex":0.008824712,"about_ca_topic_score_gemma":0.013054182,"teacher_disagreement_score":0.008824712,"about_ca_system_score_codex":0.00090959057,"about_ca_system_score_gemma":0.0015980637,"threshold_uncertainty_score":0.017546654},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014424904,0.00017142664,0.9844158,0.000075397904,0.000025347406,0.000023169294,0.000026795542,0.00036801363,0.00046914644],"genre_scores_gemma":[0.5532597,0.0005155122,0.4392966,0.0004218181,0.00013384408,0.00020688266,0.0006658863,0.0002878023,0.00521215],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855906,0.0003689631,0.00006262901,0.00047112003,0.0004284421,0.00010979225],"domain_scores_gemma":[0.9984768,0.00050368434,0.0002023305,0.0002883435,0.0004502324,0.00007850881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018491554,0.0010908264,0.0011584045,0.000605325,0.00035931455,0.0007909498,0.0016359307,0.00088277203,0.0008482776],"category_scores_gemma":[0.004809821,0.0004585255,0.0008788259,0.0006371372,0.0010065405,0.0018574656,0.0014888856,0.0014557712,0.00061268423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031988384,0.00027322705,0.0034277607,0.00018836196,0.00021438966,0.00015838495,0.00033595643,0.3811594,0.06589632,0.014579849,0.005602533,0.52784395],"study_design_scores_gemma":[0.000007771386,0.000050797727,0.00029299784,0.000005534944,0.000011704387,0.000042447464,0.000016202457,0.99023306,0.0052859355,0.0033559902,0.0006862002,0.000011308377],"about_ca_topic_score_codex":0.0013182353,"about_ca_topic_score_gemma":0.0015800784,"teacher_disagreement_score":0.0018491554,"about_ca_system_score_codex":0.00051900075,"about_ca_system_score_gemma":0.00077794853,"threshold_uncertainty_score":0.009779394},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016291462,0.0002994133,0.97932374,0.00012727626,0.00008312204,0.000075888376,0.000050707305,0.0024632264,0.0012852437],"genre_scores_gemma":[0.24796218,0.00030370025,0.74644816,0.00015917922,0.000069192814,0.00013638868,0.00047317048,0.0004432058,0.004004814],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840266,0.00021400812,0.00011262399,0.00055113184,0.0005499715,0.00016959607],"domain_scores_gemma":[0.99897194,0.00020535704,0.0001350985,0.00026552376,0.0003590286,0.00006316077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018278863,0.0012844715,0.0013892177,0.0025286616,0.0005616899,0.0014298074,0.002395224,0.00162738,0.002161637],"category_scores_gemma":[0.0035212107,0.00057740445,0.0013208324,0.0020993373,0.00085396145,0.002940533,0.00124247,0.0013603042,0.0011960901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024151393,0.00015881979,0.0025723372,0.00008894279,0.00016980349,0.000109514236,0.000070486065,0.069986224,0.020122,0.013995101,0.0036982284,0.8887871],"study_design_scores_gemma":[0.000015109949,0.0000715146,0.0009271715,0.000007152269,0.000030700226,0.00018120091,0.000014670674,0.9705085,0.021324033,0.0042616143,0.002632572,0.000025671143],"about_ca_topic_score_codex":0.0068191215,"about_ca_topic_score_gemma":0.0056643244,"teacher_disagreement_score":0.0068191215,"about_ca_system_score_codex":0.0014226306,"about_ca_system_score_gemma":0.0016243297,"threshold_uncertainty_score":0.013558865},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054291356,0.00021609834,0.92731774,0.00023433192,0.000112393915,0.00019185847,0.0002856067,0.014802884,0.0025476853],"genre_scores_gemma":[0.61971444,0.0002726154,0.37396368,0.00017887959,0.000070130605,0.00047502617,0.0005736011,0.00019243322,0.0045593074],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990577,0.00013813091,0.000100827885,0.00027902416,0.0003381941,0.00008606281],"domain_scores_gemma":[0.9988813,0.00021407967,0.00017386058,0.000108666434,0.0005392114,0.0000829275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006892282,0.0008463072,0.0009817979,0.0015321391,0.0008446424,0.00092909986,0.0012753438,0.0007594469,0.0015709761],"category_scores_gemma":[0.0027770349,0.00042062975,0.000616222,0.0009412128,0.00027309632,0.0013621676,0.0008857201,0.000929288,0.0010714097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009962054,0.00070776674,0.022524737,0.00031130615,0.00019874517,0.00015359916,0.00078681664,0.05748309,0.031803414,0.0043667685,0.013160549,0.867507],"study_design_scores_gemma":[0.000085671614,0.0002729192,0.009119235,0.00004240304,0.00010286035,0.00017177981,0.00017111942,0.9482089,0.03162925,0.0035941275,0.006466287,0.00013537615],"about_ca_topic_score_codex":0.0061211707,"about_ca_topic_score_gemma":0.0045640077,"teacher_disagreement_score":0.0061211707,"about_ca_system_score_codex":0.00064708886,"about_ca_system_score_gemma":0.0013965273,"threshold_uncertainty_score":0.01217103},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013966501,0.00044495764,0.9799186,0.0004272995,0.00007258755,0.0001760927,0.00053971686,0.002124075,0.002330081],"genre_scores_gemma":[0.45710355,0.0003895001,0.53592503,0.0002319314,0.00016331987,0.0004695339,0.0026925663,0.0006063615,0.002418196],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952625,0.0024935494,0.00028019023,0.00043255254,0.001321301,0.00020982677],"domain_scores_gemma":[0.98706776,0.007911971,0.00050007214,0.0013086295,0.0029507442,0.0002608674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011155001,0.001017998,0.0011083569,0.0016942517,0.0005790686,0.0024133183,0.0020302131,0.001451952,0.002998852],"category_scores_gemma":[0.026663397,0.00040746998,0.0007698892,0.0011903719,0.0007476143,0.0024701874,0.0014006083,0.0013919584,0.0006613646],"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":[0.00035626395,0.00022423486,0.0020883763,0.00013463339,0.0001273812,0.0000694426,0.000057686233,0.78607035,0.0027062015,0.04231934,0.00840702,0.15743904],"study_design_scores_gemma":[0.000007296559,0.000027878747,0.000074471616,0.0000061847245,0.000006356529,0.00000945988,0.0000055199525,0.9946037,0.00049923954,0.0042719226,0.0004846439,0.000003334974],"about_ca_topic_score_codex":0.01083937,"about_ca_topic_score_gemma":0.00785448,"teacher_disagreement_score":0.011155001,"about_ca_system_score_codex":0.0020291738,"about_ca_system_score_gemma":0.00199572,"threshold_uncertainty_score":0.058993936},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031217132,0.000355804,0.96416473,0.00017291836,0.00009962182,0.00007669657,0.000060362636,0.0023763468,0.0014763757],"genre_scores_gemma":[0.30585545,0.0002933001,0.6890149,0.000128467,0.00006636834,0.000101581274,0.000438801,0.0003210023,0.0037801028],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988857,0.00017017755,0.000081541584,0.00036421826,0.00036934364,0.00012896223],"domain_scores_gemma":[0.9990262,0.00022606709,0.00012593536,0.00021785511,0.00033962945,0.0000643645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016415431,0.001037266,0.0011373379,0.0021629517,0.0004312663,0.001432789,0.0020286313,0.0014200683,0.002374346],"category_scores_gemma":[0.0034805653,0.00042007276,0.0010051649,0.0019287345,0.0006724368,0.0024044174,0.00095974596,0.0010026094,0.0010478068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031575747,0.00017962344,0.0029825987,0.000083577754,0.00015456064,0.000118035634,0.00006433277,0.08120811,0.024185227,0.012936644,0.003709777,0.8740618],"study_design_scores_gemma":[0.000010976452,0.00005314473,0.0007385015,0.000004545222,0.00002133512,0.00009691227,0.000010853157,0.9762684,0.018811204,0.002572838,0.0013959346,0.000015222096],"about_ca_topic_score_codex":0.006411204,"about_ca_topic_score_gemma":0.005309472,"teacher_disagreement_score":0.006411204,"about_ca_system_score_codex":0.0012907572,"about_ca_system_score_gemma":0.0012714342,"threshold_uncertainty_score":0.012747765},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11880727,0.00038348293,0.8683266,0.0003624134,0.00015198732,0.00022441178,0.00066421047,0.009048114,0.0020313968],"genre_scores_gemma":[0.628848,0.00015602507,0.3633594,0.00020477564,0.000030169746,0.00040260123,0.0026126727,0.00017759787,0.0042088157],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99969363,0.000042865755,0.000024180517,0.00008254682,0.00010219959,0.00005451799],"domain_scores_gemma":[0.9994178,0.00020842369,0.000031906402,0.000051841336,0.00027379644,0.00001624995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000754438,0.0009434346,0.00052613544,0.0006464842,0.00031878118,0.000462522,0.00082126376,0.0007637514,0.002280758],"category_scores_gemma":[0.0016199518,0.00036746886,0.00060355203,0.00040693252,0.00021306565,0.000620755,0.00048231104,0.0016008355,0.0012020373],"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":[0.00042115684,0.0003114554,0.002874708,0.000136546,0.00012303336,0.0002413412,0.00008523036,0.23673747,0.046803765,0.0015034722,0.0060983817,0.7046634],"study_design_scores_gemma":[0.000013316904,0.00010021597,0.0010098807,0.000008183684,0.000018920748,0.00005166746,0.000011739267,0.98172164,0.015539067,0.0004195634,0.0010952421,0.00001049625],"about_ca_topic_score_codex":0.0070539857,"about_ca_topic_score_gemma":0.0065158843,"teacher_disagreement_score":0.0070539857,"about_ca_system_score_codex":0.0006512465,"about_ca_system_score_gemma":0.0008320244,"threshold_uncertainty_score":0.014025807},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14366336,0.0016032577,0.83289206,0.00078195625,0.0003532319,0.00020345354,0.0012186832,0.013583497,0.005700464],"genre_scores_gemma":[0.6926588,0.0006714205,0.2908035,0.0005114356,0.00006997803,0.00014109256,0.0037106776,0.00025743613,0.011175635],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998185,0.000019421033,0.00000776824,0.00005931031,0.000058899,0.000036124373],"domain_scores_gemma":[0.9998628,0.000025859566,0.000017263847,0.000020895672,0.00005370023,0.000019414707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003163796,0.00072892965,0.00051733485,0.00059333764,0.00026316152,0.00057137804,0.0014601015,0.0006230512,0.0015563664],"category_scores_gemma":[0.00075176684,0.0002830893,0.00045640554,0.00041028394,0.00023494691,0.00090731616,0.0010042618,0.000978828,0.000444499],"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":[0.00043491888,0.00034222804,0.007060269,0.00012997317,0.00018564431,0.00037915923,0.00007725931,0.14670043,0.024106542,0.0041828873,0.01903099,0.79736966],"study_design_scores_gemma":[0.000008855235,0.000058632857,0.00049904897,0.0000060496686,0.000009924356,0.00006120067,0.0000061278606,0.99237484,0.004746744,0.0007698817,0.0014511184,0.0000075699872],"about_ca_topic_score_codex":0.011561986,"about_ca_topic_score_gemma":0.014201023,"teacher_disagreement_score":0.011561986,"about_ca_system_score_codex":0.0008766827,"about_ca_system_score_gemma":0.0008198239,"threshold_uncertainty_score":0.022989392},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.093240306,0.0006826152,0.8993657,0.0006407772,0.00012756257,0.00015327171,0.00017349551,0.0024440472,0.0031722782],"genre_scores_gemma":[0.9354318,0.0001627383,0.062250413,0.00021734175,0.000070819144,0.00008434591,0.00024021132,0.00005444774,0.0014879444],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871635,0.00045021696,0.00006599777,0.00025316083,0.00037929608,0.00013504202],"domain_scores_gemma":[0.9942325,0.0039044332,0.00046272663,0.00064278836,0.0005788277,0.0001786586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025158394,0.0011218901,0.0009772596,0.0010410765,0.00044982048,0.00067499734,0.0016905618,0.00084100803,0.0010572205],"category_scores_gemma":[0.008080167,0.00034627586,0.0005913143,0.00054987916,0.0009775385,0.0013232366,0.0013438117,0.0022688576,0.00034108054],"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":[0.00017895918,0.00014441907,0.00307448,0.000048588863,0.00006059566,0.000099824305,0.00006746867,0.8864524,0.0026394275,0.0046121357,0.001987148,0.10063453],"study_design_scores_gemma":[0.0000023399157,0.000018287794,0.00011852599,0.0000017549241,0.0000034546442,0.00001580155,0.000002468228,0.99792445,0.00053830765,0.0012516197,0.000119989374,0.0000029972105],"about_ca_topic_score_codex":0.0024650055,"about_ca_topic_score_gemma":0.002223884,"teacher_disagreement_score":0.0025158394,"about_ca_system_score_codex":0.0008899545,"about_ca_system_score_gemma":0.00082647137,"threshold_uncertainty_score":0.013305187},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011268288,0.00044390355,0.98730755,0.00013127121,0.000026479769,0.000043210734,0.00009944229,0.00014753365,0.0005323414],"genre_scores_gemma":[0.6771804,0.00097148056,0.31854996,0.00026508077,0.00020571265,0.0003628865,0.0009066692,0.000096972,0.0014609228],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99598557,0.001059379,0.00035129438,0.0006738567,0.0017219204,0.00020786366],"domain_scores_gemma":[0.9965953,0.0020196512,0.00036813781,0.0003360453,0.0005740916,0.000106771804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035883598,0.000963534,0.001507117,0.0029199729,0.00081040466,0.0016564857,0.0014498291,0.000876471,0.0009418677],"category_scores_gemma":[0.00921812,0.00029597967,0.0017419421,0.0022195703,0.001315421,0.0025257405,0.0018499936,0.0011297603,0.00014129799],"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":[0.00035856685,0.00012927278,0.0076623564,0.0004939198,0.000626958,0.00035157517,0.00034536846,0.48653817,0.010412239,0.092109814,0.0036736033,0.39729813],"study_design_scores_gemma":[0.000022656057,0.000109815126,0.002209519,0.000033615877,0.0000690113,0.00013265107,0.000055161774,0.94507635,0.003997188,0.04639456,0.0018458097,0.000053733598],"about_ca_topic_score_codex":0.0031915104,"about_ca_topic_score_gemma":0.0018673055,"teacher_disagreement_score":0.0035883598,"about_ca_system_score_codex":0.0014360559,"about_ca_system_score_gemma":0.0013445606,"threshold_uncertainty_score":0.018977284},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036571436,0.0002362573,0.95599264,0.00016926255,0.00013169629,0.00021641352,0.000095187854,0.005078039,0.0015091243],"genre_scores_gemma":[0.51819575,0.00014108644,0.4768877,0.000243581,0.00013383044,0.0003890899,0.00041693484,0.00050788,0.003084219],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956197,0.00005735557,0.00002156552,0.00011294827,0.00018948552,0.000056643854],"domain_scores_gemma":[0.99903023,0.00026894672,0.00010766628,0.00017619847,0.0003467768,0.00007015821],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074863376,0.0013490211,0.0010809946,0.0014217922,0.00046010234,0.00058465573,0.0018849254,0.0008901567,0.0026656003],"category_scores_gemma":[0.0029141894,0.00032474438,0.00070570986,0.000838668,0.0005066577,0.0012625008,0.0011206917,0.00092696096,0.000552996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004016821,0.0003169261,0.0022173042,0.0001630053,0.00009150494,0.0002123945,0.00014093297,0.046734706,0.057109352,0.005152715,0.008146076,0.8793134],"study_design_scores_gemma":[0.000054977034,0.00015175449,0.0012027986,0.000010715489,0.00003684006,0.00017962113,0.000026535814,0.96186376,0.02832773,0.0047818,0.0033370042,0.000026389744],"about_ca_topic_score_codex":0.0033696636,"about_ca_topic_score_gemma":0.0036754396,"teacher_disagreement_score":0.0033696636,"about_ca_system_score_codex":0.0005488843,"about_ca_system_score_gemma":0.00072291045,"threshold_uncertainty_score":0.008917332},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06824562,0.00018708954,0.92984664,0.00013743708,0.000049508406,0.000019832722,0.000046235098,0.00049454137,0.00097314734],"genre_scores_gemma":[0.8157504,0.00017731123,0.18206955,0.000085016705,0.00005041687,0.000029858758,0.00016408683,0.000033329045,0.0016400611],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99965024,0.0000496174,0.000017114036,0.00009976158,0.00014754721,0.00003566164],"domain_scores_gemma":[0.9994771,0.00019512828,0.00009207876,0.000089723435,0.00012378552,0.000022202792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003831146,0.0003970028,0.00054828345,0.00048423826,0.00024839203,0.0004381813,0.0004974401,0.00055088376,0.0004931735],"category_scores_gemma":[0.0018155695,0.00024541392,0.000288525,0.0004440469,0.0003964308,0.0009182933,0.0005873207,0.0005072769,0.00024041376],"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":[0.00059128384,0.00014310503,0.0060518216,0.0001618206,0.00007114049,0.0002230803,0.00033539472,0.24358556,0.10297559,0.0053220773,0.0017191799,0.63881993],"study_design_scores_gemma":[0.000005336314,0.000053086533,0.0009927154,0.0000058842434,0.0000070551223,0.00007121909,0.000019372364,0.9901428,0.006752634,0.0014981265,0.00044368213,0.000008059861],"about_ca_topic_score_codex":0.0020667375,"about_ca_topic_score_gemma":0.002871572,"teacher_disagreement_score":0.0020667375,"about_ca_system_score_codex":0.00024555763,"about_ca_system_score_gemma":0.00032283424,"threshold_uncertainty_score":0.0041093826},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03788752,0.0001912073,0.958854,0.00028263594,0.00003622321,0.00004485297,0.000137464,0.0016036608,0.00096236257],"genre_scores_gemma":[0.77621883,0.00029384493,0.21734396,0.0005236314,0.00013579027,0.00018883181,0.0011769108,0.0004892421,0.0036290141],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978236,0.0007150043,0.00012917376,0.00054324575,0.0005475785,0.00024149963],"domain_scores_gemma":[0.9919905,0.004460037,0.0007061281,0.0013828349,0.001117624,0.0003429177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046244278,0.0009021531,0.0011595921,0.0022830071,0.00077995117,0.0022578784,0.0029189999,0.0016255071,0.00233179],"category_scores_gemma":[0.02012568,0.0003848798,0.0009288494,0.001913244,0.0019387337,0.004000048,0.002859245,0.00317631,0.0011859433],"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":[0.0010270231,0.00044761796,0.03877272,0.00023753231,0.000175832,0.0006421359,0.0007327851,0.26138008,0.018009242,0.116350435,0.00931733,0.55290717],"study_design_scores_gemma":[0.000009086158,0.000031958854,0.0012283007,0.000011883642,0.000005923126,0.0001749299,0.00004032241,0.9631019,0.0035042444,0.03062913,0.0012416821,0.000020701971],"about_ca_topic_score_codex":0.003902869,"about_ca_topic_score_gemma":0.003056212,"teacher_disagreement_score":0.0046244278,"about_ca_system_score_codex":0.0015321792,"about_ca_system_score_gemma":0.0015418255,"threshold_uncertainty_score":0.02445662},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02258363,0.00010599679,0.9750107,0.000079119,0.000015395572,0.00003376037,0.000085219,0.0010543338,0.001031866],"genre_scores_gemma":[0.623621,0.00014140643,0.3738941,0.000088225206,0.00006503954,0.00012539091,0.00051039684,0.00021145951,0.0013429703],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995546,0.00012474606,0.000016015305,0.000057499074,0.00020186412,0.0000452963],"domain_scores_gemma":[0.99818027,0.001183128,0.0001450093,0.00017375263,0.00023760837,0.00008022071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009944987,0.00066984823,0.0006246816,0.0010064499,0.00029706213,0.00076617335,0.0012667738,0.00047767672,0.002182968],"category_scores_gemma":[0.004668137,0.00044173398,0.0006044691,0.0007230355,0.00047181864,0.0009416012,0.0011641207,0.0009794977,0.00036477658],"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":[0.000074129304,0.000036735182,0.00294398,0.000045206336,0.000042235184,0.000064086176,0.000047919977,0.91740066,0.0021361203,0.010932635,0.0012437803,0.06503248],"study_design_scores_gemma":[0.0000035078758,0.000003397681,0.00008599044,0.0000010029829,0.0000012049933,0.0000061201845,0.0000024625574,0.99720335,0.00017795301,0.002388374,0.00012526484,0.0000015088021],"about_ca_topic_score_codex":0.006727468,"about_ca_topic_score_gemma":0.007169567,"teacher_disagreement_score":0.006727468,"about_ca_system_score_codex":0.0006912648,"about_ca_system_score_gemma":0.0010501762,"threshold_uncertainty_score":0.013376594},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00064116606,0.9820279,0.012292023,0.0006546074,0.0004170858,0.000029542252,0.000091944494,0.00009945392,0.0037463177],"genre_scores_gemma":[0.006316512,0.98540145,0.005974035,0.00030881414,0.0007458269,0.000038860082,0.00019918072,0.000033830813,0.0009815132],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993368,0.00013625367,0.000083241204,0.00014305802,0.00025799972,0.00004253305],"domain_scores_gemma":[0.9975079,0.0017872249,0.00015258206,0.00008682546,0.00041080246,0.000054590357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015990265,0.0016140514,0.0017698347,0.003731596,0.0004310417,0.0017520514,0.001772395,0.0015317518,0.004401331],"category_scores_gemma":[0.00374894,0.0006409984,0.0014007738,0.005029886,0.0007067258,0.002434902,0.0011957678,0.0019338424,0.0021887908],"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":[0.000051032683,0.00010469309,0.00083821075,0.017329358,0.00017252349,0.00016857489,0.00013827886,0.0070712036,0.0010033274,0.01630209,0.021577617,0.935243],"study_design_scores_gemma":[0.000020198671,0.00028022612,0.0027696155,0.011086161,0.00043041314,0.0011843537,0.0001858688,0.016136037,0.00205754,0.030113434,0.9355837,0.00015232578],"about_ca_topic_score_codex":0.0017329504,"about_ca_topic_score_gemma":0.0015747974,"teacher_disagreement_score":0.004401331,"about_ca_system_score_codex":0.00067565806,"about_ca_system_score_gemma":0.0015731249,"threshold_uncertainty_score":0.014723897},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013993275,0.00014008775,0.9825943,0.00006732519,0.00007182247,0.00004528694,0.0001396368,0.0021067723,0.0008415505],"genre_scores_gemma":[0.30947968,0.00029926636,0.6818818,0.00014118665,0.00010727708,0.00017596871,0.0011470368,0.00036402984,0.0064037098],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947816,0.00006153097,0.000025543979,0.00014153331,0.0002126641,0.0000805736],"domain_scores_gemma":[0.99952936,0.000078663266,0.000053387586,0.000074981814,0.0002335751,0.000030089659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078041543,0.00095439114,0.0010313679,0.0020788428,0.0005472599,0.00095058867,0.0015632273,0.0008885349,0.0026699607],"category_scores_gemma":[0.0011796717,0.00047742407,0.0009179474,0.0013584392,0.00040315877,0.0012899388,0.0010767746,0.0010666054,0.0010548603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027190225,0.0003226687,0.0035840988,0.0001311666,0.00009338542,0.000085214444,0.000087670356,0.09352394,0.040000618,0.0047706175,0.0069826483,0.8501462],"study_design_scores_gemma":[0.0000055720066,0.000023127086,0.0007186996,0.0000053934664,0.000010797755,0.000037144215,0.000009843664,0.98981,0.0070324275,0.0011242107,0.0012130387,0.000009874767],"about_ca_topic_score_codex":0.0048495685,"about_ca_topic_score_gemma":0.006619483,"teacher_disagreement_score":0.0048495685,"about_ca_system_score_codex":0.0004873656,"about_ca_system_score_gemma":0.0014577501,"threshold_uncertainty_score":0.00964272},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15443775,0.00046489923,0.841573,0.00037948397,0.00013601086,0.000040187097,0.00029555187,0.0007822056,0.0018909025],"genre_scores_gemma":[0.95090586,0.00020438287,0.044772025,0.000104013125,0.00006993074,0.00005884363,0.0004955547,0.00005116867,0.0033382126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994679,0.0000852407,0.000027620661,0.00018549693,0.00013033253,0.00010338341],"domain_scores_gemma":[0.9982633,0.00085444294,0.00019312381,0.00016941332,0.00042230557,0.000097349184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001170534,0.0005355623,0.0013939071,0.0010252774,0.00076296064,0.0009499445,0.0022029255,0.0010114874,0.0019162784],"category_scores_gemma":[0.0037000598,0.00056011544,0.0006308498,0.0015545713,0.00081247266,0.0010883622,0.00079629954,0.0016074453,0.0004888424],"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":[0.00025921158,0.000069916714,0.007020321,0.000054369826,0.00010046933,0.000095164825,0.00007199858,0.9197851,0.002268128,0.009860919,0.0009986279,0.059415832],"study_design_scores_gemma":[0.0000021852602,0.0000039244233,0.00022698249,0.0000011824965,0.000003964308,0.000007648344,0.0000012991903,0.99895513,0.00010933041,0.00064261345,0.000042938547,0.0000028550623],"about_ca_topic_score_codex":0.035711467,"about_ca_topic_score_gemma":0.030139465,"teacher_disagreement_score":0.035711467,"about_ca_system_score_codex":0.0010724753,"about_ca_system_score_gemma":0.0017299495,"threshold_uncertainty_score":0.07100719},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3702127,0.008109152,0.5771909,0.008056735,0.00070663646,0.0001859419,0.00052896014,0.006001136,0.02900777],"genre_scores_gemma":[0.95112884,0.0018553566,0.0424433,0.0005536962,0.000060347273,0.000027567461,0.00030387272,0.000053847372,0.0035733238],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997477,0.00004706332,0.000013609415,0.000053218617,0.00009902745,0.000039369555],"domain_scores_gemma":[0.99967027,0.00010420243,0.00006997536,0.000047200316,0.000086679735,0.000021804597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053921976,0.00066148624,0.00021772746,0.0005216554,0.0002802262,0.0008717402,0.0005378946,0.0008313123,0.0009922505],"category_scores_gemma":[0.001423378,0.00018325925,0.0003504929,0.0003946563,0.0004394023,0.002063791,0.0007648177,0.0008356968,0.00029977376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002720768,0.00022221422,0.01558457,0.00040339906,0.00012276908,0.0006102269,0.00033104123,0.26808938,0.03721559,0.021115176,0.013270055,0.64276344],"study_design_scores_gemma":[0.00000861356,0.00014398285,0.0036083553,0.00009452917,0.00004612857,0.0002262923,0.00013747801,0.94949675,0.02158588,0.011288936,0.013332676,0.000030308745],"about_ca_topic_score_codex":0.003349742,"about_ca_topic_score_gemma":0.005183009,"teacher_disagreement_score":0.003349742,"about_ca_system_score_codex":0.0007915565,"about_ca_system_score_gemma":0.00066908385,"threshold_uncertainty_score":0.006660521},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09685087,0.0006643208,0.8855754,0.00032167343,0.00021438986,0.00023276926,0.0003343519,0.011246587,0.0045596594],"genre_scores_gemma":[0.7286136,0.00016345158,0.26455265,0.00029063105,0.00008523238,0.00022764709,0.0007221175,0.00015206201,0.005192601],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992065,0.000068861285,0.00004327894,0.00038073177,0.00019729852,0.00010343181],"domain_scores_gemma":[0.9992694,0.00010375902,0.000097924094,0.00013437151,0.00030917162,0.00008544171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008038549,0.0012105273,0.0012683414,0.0012791284,0.0008502132,0.0009484667,0.002227279,0.00082579954,0.0017894009],"category_scores_gemma":[0.0016884643,0.0004866136,0.0006628986,0.0009690953,0.0004621593,0.0018781349,0.0018257744,0.0009165508,0.0007227875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006905725,0.00051214313,0.008991646,0.00014448677,0.00020991168,0.00045414534,0.0003168375,0.18227,0.04816245,0.00658931,0.012311415,0.7393471],"study_design_scores_gemma":[0.00002296405,0.00005789741,0.0013387904,0.0000045534453,0.000028893088,0.000058286598,0.000025530837,0.9872334,0.0072585354,0.0019948003,0.001959757,0.000016650682],"about_ca_topic_score_codex":0.0111003695,"about_ca_topic_score_gemma":0.0094661275,"teacher_disagreement_score":0.0111003695,"about_ca_system_score_codex":0.0012198049,"about_ca_system_score_gemma":0.00117098,"threshold_uncertainty_score":0.02207154},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52170664,0.0020065452,0.443004,0.0015272935,0.00028421177,0.00032987254,0.009507704,0.009381651,0.012252046],"genre_scores_gemma":[0.78714234,0.00046063,0.19182707,0.00032836714,0.00008445826,0.00024077523,0.01691114,0.00031648192,0.002688734],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99758077,0.0008535962,0.00016968236,0.00085625285,0.00037786635,0.00016183284],"domain_scores_gemma":[0.9927221,0.003918165,0.0008123502,0.001540901,0.00067271834,0.0003337422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002656141,0.0018956393,0.0010967797,0.0024703848,0.00063232606,0.0025477319,0.0011651503,0.0021604924,0.002480426],"category_scores_gemma":[0.015237477,0.00041658187,0.0012421446,0.001578715,0.0007390857,0.0049152984,0.0021787393,0.0017016496,0.0013646445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022797077,0.0015575119,0.18359198,0.0015333928,0.0005804233,0.0005666584,0.0024201518,0.06682146,0.024244934,0.009830129,0.021855002,0.6847187],"study_design_scores_gemma":[0.000054102573,0.0008093255,0.1263314,0.00034917876,0.00025347178,0.0007028773,0.0021997115,0.781767,0.02911744,0.03964613,0.018552184,0.00021716492],"about_ca_topic_score_codex":0.008843693,"about_ca_topic_score_gemma":0.012205188,"teacher_disagreement_score":0.008843693,"about_ca_system_score_codex":0.0007039909,"about_ca_system_score_gemma":0.0012106248,"threshold_uncertainty_score":0.017584443},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45193377,0.0003447615,0.5415067,0.00029340913,0.00006404515,0.000074793,0.0002957283,0.0035408898,0.0019458762],"genre_scores_gemma":[0.9721068,0.000051058247,0.02633832,0.000055119854,0.000017560349,0.000041356747,0.00041378348,0.00003476883,0.0009412021],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999553,0.00009335855,0.000022537784,0.00013972686,0.00009363657,0.00009774277],"domain_scores_gemma":[0.9990351,0.00035011276,0.00014631197,0.00012183505,0.00028014457,0.000066441375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083212863,0.000670133,0.0005598774,0.00069381035,0.0003045799,0.00052588526,0.0011901809,0.00065500895,0.00062969467],"category_scores_gemma":[0.002685379,0.00022240046,0.0004168178,0.00047179704,0.00046935616,0.0011878698,0.0007245461,0.0010335678,0.00024896962],"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":[0.0004049117,0.0005224583,0.017580036,0.00010329182,0.00012276927,0.00026476014,0.00024260077,0.55302906,0.017019914,0.0023685156,0.0034722583,0.40486944],"study_design_scores_gemma":[0.0000017740772,0.000016748041,0.00076126296,0.0000016437493,0.0000027705132,0.000007622338,0.000014425244,0.996881,0.0013657073,0.0008636545,0.00007957344,0.0000038427806],"about_ca_topic_score_codex":0.008313757,"about_ca_topic_score_gemma":0.008932731,"teacher_disagreement_score":0.008313757,"about_ca_system_score_codex":0.00087238586,"about_ca_system_score_gemma":0.0008432942,"threshold_uncertainty_score":0.016530693},"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":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037618484,0.0021851182,0.0041800947,0.82395744,0.12501487,0.0001677054,0.0012980822,0.0008134098,0.0386214],"genre_scores_gemma":[0.11744348,0.0067603597,0.005291935,0.39245155,0.08117872,0.00043799338,0.0020008127,0.0011321898,0.39330304],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99760807,0.00070607575,0.00016544934,0.00035747112,0.0009664962,0.00019645873],"domain_scores_gemma":[0.9701536,0.012493709,0.0012140741,0.0013514351,0.013027586,0.0017595754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027510698,0.0004484494,0.00050538866,0.00089128595,0.001133893,0.0019949016,0.0008974283,0.0054530855,0.13240148],"category_scores_gemma":[0.058808427,0.00022032112,0.00043345834,0.0005829922,0.0015380956,0.0015087345,0.0018163103,0.003970412,0.034076184],"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":[0.00014563487,0.000007796176,0.00028126547,0.00021038907,0.000016846358,0.0002554234,0.0002387864,0.00007843633,0.00043360857,0.002599655,0.98190844,0.013823707],"study_design_scores_gemma":[0.00009094507,0.00006460204,0.0019574661,0.00029691757,0.000023761164,0.00082456466,0.00123282,0.00034487346,0.0010633565,0.010411473,0.9836416,0.000047531692],"about_ca_topic_score_codex":0.0014985757,"about_ca_topic_score_gemma":0.0036807135,"teacher_disagreement_score":0.13240148,"about_ca_system_score_codex":0.001143973,"about_ca_system_score_gemma":0.0020503786,"threshold_uncertainty_score":0.44292706},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07599596,0.00046742146,0.92006886,0.0003165043,0.0001413164,0.00004352069,0.0003065752,0.0013976056,0.001262198],"genre_scores_gemma":[0.8636931,0.00017250473,0.13406464,0.00008943993,0.00013127149,0.00008508331,0.0005882871,0.0000787464,0.0010969278],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994468,0.00017088986,0.000030368006,0.00013464475,0.00014333324,0.000073896415],"domain_scores_gemma":[0.9978538,0.0011356329,0.00013920096,0.00032717426,0.00044023755,0.00010391305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012327061,0.0005115711,0.0012312358,0.000815298,0.00035324474,0.0006939565,0.001424085,0.00072141323,0.001793733],"category_scores_gemma":[0.0056473897,0.00032935015,0.0007363857,0.0008763173,0.00027568438,0.001505059,0.001249009,0.002011145,0.00039779325],"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":[0.00031787762,0.00040907014,0.008505711,0.00007481653,0.00032561208,0.0000691006,0.000083171944,0.5929204,0.00640964,0.005939914,0.0056959474,0.3792487],"study_design_scores_gemma":[0.0000024168726,0.00001766217,0.00028427877,0.0000015175809,0.0000076290244,0.0000032035955,0.0000029583184,0.99804,0.00035878108,0.0011931892,0.00008613427,0.0000022520642],"about_ca_topic_score_codex":0.004911242,"about_ca_topic_score_gemma":0.0069418815,"teacher_disagreement_score":0.004911242,"about_ca_system_score_codex":0.00046259147,"about_ca_system_score_gemma":0.00059188576,"threshold_uncertainty_score":0.009765327},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24238858,0.0000972473,0.74226665,0.00018398589,0.00008789392,0.00033472673,0.00038456332,0.007631318,0.0066250814],"genre_scores_gemma":[0.73616713,0.000051820814,0.2583927,0.00004821202,0.000011254313,0.0001743093,0.0005793807,0.000118056094,0.004457227],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999201,0.0000071012028,0.0000028629652,0.000017226286,0.000043289332,0.000009440882],"domain_scores_gemma":[0.9998704,0.000020992024,0.000012217955,0.00002018173,0.000057967194,0.00001822598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018644787,0.00034861604,0.00020778163,0.00029584425,0.00014906473,0.00026449494,0.0005993293,0.00025701526,0.0014194559],"category_scores_gemma":[0.00027559948,0.00015400024,0.00021424636,0.000111577516,0.00013176326,0.00042358745,0.00023461247,0.000273477,0.0003520489],"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":[0.000436441,0.00039534023,0.010351782,0.00027273048,0.00011006515,0.00051102135,0.00030842377,0.25036448,0.33304626,0.0066265934,0.011570123,0.38600677],"study_design_scores_gemma":[0.00002800214,0.0002125095,0.0030018557,0.000006387027,0.000010746664,0.00007803136,0.00002913284,0.9688128,0.023444176,0.00061382627,0.0037450893,0.000017445422],"about_ca_topic_score_codex":0.0036248218,"about_ca_topic_score_gemma":0.0032144666,"teacher_disagreement_score":0.0036248218,"about_ca_system_score_codex":0.0002356246,"about_ca_system_score_gemma":0.00043748933,"threshold_uncertainty_score":0.0072074533},"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":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005634048,0.013744924,0.6521313,0.0029029145,0.0018226709,0.00011218975,0.0043306993,0.01644421,0.30287707],"genre_scores_gemma":[0.047698725,0.0093240505,0.2254271,0.0012395622,0.0005747677,0.00021847451,0.009913865,0.0023775117,0.703226],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99980706,0.0000168618,0.000009180918,0.000054481865,0.00009088087,0.000021507909],"domain_scores_gemma":[0.9997327,0.00006533094,0.00001061847,0.00009117707,0.00007889912,0.000021280539],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00032933033,0.0009254558,0.00057565246,0.0007296184,0.0003356108,0.0013753761,0.0009876818,0.0010676902,0.06657548],"category_scores_gemma":[0.0010084185,0.0005178299,0.0004483874,0.0010986604,0.00032934826,0.001565501,0.0013287371,0.001743402,0.04221025],"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":[0.000018952693,0.00004288524,0.00015176614,0.00012827,0.000019993917,0.000025090716,0.000020955265,0.008881251,0.0018066412,0.023664935,0.18539408,0.77984524],"study_design_scores_gemma":[0.000018499997,0.000056754478,0.000802249,0.00026264187,0.000040157076,0.0002285239,0.000036921192,0.19148813,0.008441982,0.16407627,0.63450575,0.000042183103],"about_ca_topic_score_codex":0.003105186,"about_ca_topic_score_gemma":0.006401076,"teacher_disagreement_score":0.93342453,"about_ca_system_score_codex":0.00048778506,"about_ca_system_score_gemma":0.0007212493,"threshold_uncertainty_score":0.22271717},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034779552,0.00016096402,0.99306387,0.00010907149,0.000024610072,0.000028449715,0.0001052632,0.0023585565,0.0006712024],"genre_scores_gemma":[0.2784471,0.0007530266,0.7081001,0.0005056213,0.0000835799,0.00027333433,0.0017669414,0.0007680907,0.009302279],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99977916,0.000037001595,0.000009427496,0.00006497061,0.00008071455,0.00002875113],"domain_scores_gemma":[0.9996797,0.00011486133,0.000037144844,0.000052813462,0.000081826955,0.00003364765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006197935,0.0014347108,0.0007326192,0.00059813267,0.00026975037,0.0010644849,0.0026322799,0.0015785919,0.0035001142],"category_scores_gemma":[0.0018765554,0.0005816948,0.00063773745,0.00052970817,0.00067064224,0.001588737,0.0016131442,0.0020739587,0.0015234824],"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":[0.00012022632,0.0000890148,0.00081344036,0.00012895426,0.0000766032,0.0001582286,0.00006232634,0.70081276,0.008074024,0.015317013,0.007318884,0.26702857],"study_design_scores_gemma":[0.000002961303,0.000013391569,0.000028491408,0.0000037017696,0.0000020340121,0.000014014462,0.0000032226408,0.9952602,0.0011108556,0.0029484516,0.0006093603,0.000003381243],"about_ca_topic_score_codex":0.0062093465,"about_ca_topic_score_gemma":0.010673764,"teacher_disagreement_score":0.0062093465,"about_ca_system_score_codex":0.0010002361,"about_ca_system_score_gemma":0.0011729443,"threshold_uncertainty_score":0.012346387},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017745625,0.00032553246,0.9776534,0.00035492584,0.00006233573,0.000027324139,0.00007654494,0.001580096,0.0021742021],"genre_scores_gemma":[0.65117776,0.0005406693,0.33920917,0.00031543235,0.00011025578,0.00014102347,0.00045489994,0.00030304314,0.007747723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982446,0.000030316736,0.000009687335,0.000054490556,0.000056435092,0.000024757912],"domain_scores_gemma":[0.9996613,0.00013754219,0.000034552173,0.00007219232,0.000073173185,0.000021269445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044561157,0.0005739475,0.00079057645,0.00048679914,0.0002602889,0.0010643759,0.0015175707,0.00088354544,0.0029867936],"category_scores_gemma":[0.0017609986,0.00042246256,0.0007344472,0.00049887126,0.0005916184,0.0023408595,0.0010790336,0.0015576272,0.00071505626],"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":[0.0001033313,0.00005525211,0.00054652244,0.00013945728,0.000052111078,0.00007398105,0.00008555982,0.719824,0.014078846,0.043166902,0.0047148443,0.21715921],"study_design_scores_gemma":[0.0000031341629,0.000005945311,0.000022656237,0.0000020765517,0.0000026090368,0.0000068917825,0.0000022650609,0.9927999,0.00080124463,0.0060723424,0.00027935277,0.0000016424306],"about_ca_topic_score_codex":0.004736403,"about_ca_topic_score_gemma":0.004578393,"teacher_disagreement_score":0.004736403,"about_ca_system_score_codex":0.00093850406,"about_ca_system_score_gemma":0.00075611577,"threshold_uncertainty_score":0.009991825},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046888214,0.0010484908,0.9476659,0.002118531,0.00007992713,0.00009925656,0.00027294294,0.00094112207,0.00088568137],"genre_scores_gemma":[0.7093102,0.0008169822,0.28696918,0.00074956054,0.000110486086,0.00014887704,0.00075234496,0.00027558598,0.0008668262],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99046963,0.0037570342,0.0008037686,0.0011620212,0.0033317937,0.00047578817],"domain_scores_gemma":[0.95919377,0.024413515,0.0034529553,0.0054842522,0.006764983,0.0006905298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019500572,0.0012557523,0.0012603501,0.0018482378,0.0006650498,0.0041053123,0.0020780317,0.0014483478,0.00087190676],"category_scores_gemma":[0.068602234,0.00054659654,0.0007372817,0.002167922,0.0022692084,0.0074506435,0.0044076433,0.0035386544,0.00023408269],"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":[0.00079962454,0.00035448401,0.021884829,0.000652097,0.00030376646,0.00016444674,0.00044317497,0.60051227,0.009466981,0.05151911,0.00527645,0.30862278],"study_design_scores_gemma":[0.000034321027,0.00015907187,0.0013913121,0.00009693758,0.00003579259,0.0000579039,0.00009070353,0.9288897,0.010039257,0.057395007,0.0017809905,0.000029016723],"about_ca_topic_score_codex":0.003070377,"about_ca_topic_score_gemma":0.0028118053,"teacher_disagreement_score":0.019500572,"about_ca_system_score_codex":0.002685655,"about_ca_system_score_gemma":0.0028082978,"threshold_uncertainty_score":0.1031301},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08091031,0.0014062804,0.90143055,0.0008006386,0.00022096431,0.0002066845,0.00072071597,0.00722032,0.0070835645],"genre_scores_gemma":[0.4243932,0.000782259,0.55509347,0.0012690682,0.00011191442,0.00037672065,0.0033181356,0.0005559429,0.014099333],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997527,0.00004515607,0.000012417506,0.00007179991,0.00006079961,0.000057215497],"domain_scores_gemma":[0.99979097,0.000057526056,0.000028756185,0.000030224612,0.000071306014,0.000021334941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046322265,0.0009898272,0.0007020734,0.0009940048,0.000485759,0.0007006337,0.001940359,0.00075465045,0.0025130732],"category_scores_gemma":[0.0013524172,0.0003653113,0.0007294835,0.00048693584,0.00034153886,0.00096632465,0.0012890747,0.001002576,0.0009820916],"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":[0.0007226046,0.00027022473,0.007818466,0.0002063898,0.00020882246,0.00020177456,0.00016293675,0.2785441,0.015909709,0.0071386155,0.023122024,0.66569436],"study_design_scores_gemma":[0.000021019861,0.00005418754,0.0007834798,0.000016963317,0.000020607285,0.00005838137,0.000027385695,0.991703,0.003140392,0.0015816074,0.002582563,0.000010462044],"about_ca_topic_score_codex":0.012062124,"about_ca_topic_score_gemma":0.026216595,"teacher_disagreement_score":0.012062124,"about_ca_system_score_codex":0.0010549658,"about_ca_system_score_gemma":0.0013689545,"threshold_uncertainty_score":0.023983836},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11071623,0.00036554568,0.8821431,0.00059147424,0.00009837881,0.00013730218,0.00020968073,0.0026899544,0.0030483718],"genre_scores_gemma":[0.9123048,0.00021042874,0.08301545,0.00017679518,0.000044833807,0.00012960109,0.00042246783,0.00007648207,0.0036192855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999468,0.00010221065,0.000027759725,0.00022573245,0.00010433657,0.000071953684],"domain_scores_gemma":[0.9991059,0.00025735528,0.000096763244,0.00016022343,0.00031575828,0.00006408029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012229845,0.00091965613,0.0007619638,0.00095760863,0.00041016057,0.00072283123,0.0017099287,0.0010438797,0.0015052438],"category_scores_gemma":[0.0026960366,0.0002973832,0.0009162587,0.0007011012,0.0006713943,0.0017239897,0.0011653051,0.0016461235,0.00060518796],"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":[0.00024206653,0.00036673647,0.010264429,0.000073503936,0.00013102994,0.00033406614,0.00015098392,0.67506945,0.00910077,0.005698264,0.003105461,0.29546326],"study_design_scores_gemma":[0.0000021107246,0.000029385936,0.00041989164,0.0000018530285,0.000006397416,0.000025425636,0.000006680589,0.9970607,0.0008898652,0.0013631238,0.00019004234,0.0000043889872],"about_ca_topic_score_codex":0.009859422,"about_ca_topic_score_gemma":0.005147046,"teacher_disagreement_score":0.009859422,"about_ca_system_score_codex":0.0011212245,"about_ca_system_score_gemma":0.0008951805,"threshold_uncertainty_score":0.019604027},"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":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003518745,0.23141307,0.043635152,0.39707905,0.23301516,0.00063615025,0.004610631,0.0022221908,0.083869815],"genre_scores_gemma":[0.0415445,0.41996974,0.02992545,0.09756701,0.13072848,0.00045817776,0.01025173,0.0012989988,0.26825604],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99653816,0.0005693108,0.00023842588,0.00035180803,0.0021164478,0.00018579043],"domain_scores_gemma":[0.956115,0.006654273,0.001136079,0.0012500635,0.033167634,0.0016768364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052746446,0.0006971502,0.0008754536,0.0035739664,0.00095788285,0.0027133452,0.0020893817,0.0022206341,0.020047398],"category_scores_gemma":[0.029906167,0.00029460638,0.0006089435,0.0027626455,0.0010556484,0.003577825,0.0017532654,0.0022284046,0.017184414],"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":[0.00003318248,0.00002223144,0.0002617198,0.00081748376,0.000030252813,0.000030510071,0.000020379599,0.00019875684,0.00033807967,0.0028934109,0.82179475,0.17355914],"study_design_scores_gemma":[0.000009438519,0.00003162465,0.00072958873,0.0006083676,0.000043869648,0.000085162435,0.000030919535,0.0005783776,0.00074340764,0.0020378649,0.99508226,0.000019145209],"about_ca_topic_score_codex":0.0048188637,"about_ca_topic_score_gemma":0.010582854,"teacher_disagreement_score":0.020047398,"about_ca_system_score_codex":0.0015756884,"about_ca_system_score_gemma":0.006892761,"threshold_uncertainty_score":0.06706524},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5373943,0.0032508553,0.44465157,0.00046966746,0.0002526996,0.0003350891,0.0030121722,0.004162482,0.0064711366],"genre_scores_gemma":[0.93062705,0.0006081001,0.065635115,0.00008965725,0.00014786876,0.000048523132,0.0019313282,0.000036734047,0.0008756152],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996978,0.000032077864,0.000021280366,0.00011475895,0.00007953299,0.000054604283],"domain_scores_gemma":[0.99956685,0.0000964267,0.00011645077,0.00005475147,0.000115774084,0.000049701204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031584798,0.0008698414,0.00068597327,0.002089167,0.00021376705,0.00047944597,0.00055715867,0.00037353594,0.00045922908],"category_scores_gemma":[0.0010767268,0.00020417667,0.0005311374,0.00062628434,0.00021799687,0.0005900121,0.000567812,0.00053757016,0.0002255974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008240919,0.00060868025,0.13164423,0.0005421066,0.00040391157,0.0016191546,0.00046987322,0.080276676,0.10189685,0.0029749854,0.010112804,0.66862667],"study_design_scores_gemma":[0.000015311962,0.00028831628,0.0784816,0.00010425476,0.00015821753,0.0008701733,0.000305093,0.8791718,0.033046264,0.0020310818,0.0054839235,0.000044040044],"about_ca_topic_score_codex":0.00560389,"about_ca_topic_score_gemma":0.012262078,"teacher_disagreement_score":0.00560389,"about_ca_system_score_codex":0.00034984108,"about_ca_system_score_gemma":0.00042209867,"threshold_uncertainty_score":0.011142552},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039840665,0.00009243932,0.9594778,0.000058602374,0.000017421788,0.00002730042,0.000033688284,0.00024822765,0.00020381238],"genre_scores_gemma":[0.78167,0.00008500253,0.21734765,0.00004726413,0.000043750795,0.000078338024,0.0002620788,0.000075853495,0.00039007122],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969241,0.0007388027,0.0002615938,0.00065558153,0.0011666302,0.00025320347],"domain_scores_gemma":[0.9932775,0.003309351,0.0011301767,0.00072114,0.001379669,0.00018216381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040377993,0.00065494346,0.0022977535,0.0018695261,0.00071859045,0.0017638131,0.002199051,0.0008849221,0.00034154372],"category_scores_gemma":[0.011147788,0.0005665635,0.0014790883,0.0016099012,0.0011082602,0.0017574647,0.0018805491,0.0014018962,0.00011868743],"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":[0.0006099622,0.00026808807,0.008684058,0.00027717082,0.00041234144,0.00031241594,0.00054191466,0.72289383,0.014248207,0.015217372,0.0015622629,0.23497243],"study_design_scores_gemma":[0.000006718008,0.000033316406,0.00046412382,0.000005514364,0.000015285385,0.000031178763,0.000023993864,0.993883,0.0014659647,0.003939629,0.00012345903,0.000007700295],"about_ca_topic_score_codex":0.0035234904,"about_ca_topic_score_gemma":0.002805375,"teacher_disagreement_score":0.0040377993,"about_ca_system_score_codex":0.00076285016,"about_ca_system_score_gemma":0.001574598,"threshold_uncertainty_score":0.021354198},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.084737174,0.00044360044,0.9122248,0.0006676499,0.00007948252,0.00004290035,0.0001307659,0.0005087534,0.0011648702],"genre_scores_gemma":[0.96300364,0.00014917279,0.034941014,0.00023758016,0.000046344598,0.000052446045,0.00023820727,0.000031926043,0.0012995824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959534,0.00013858046,0.000021635951,0.000113707516,0.00007859272,0.00005210678],"domain_scores_gemma":[0.9979462,0.0015408195,0.0001923848,0.000109417655,0.00015615468,0.0000549898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014288273,0.0008374051,0.00070222194,0.0004310076,0.0002726257,0.0004927069,0.001094837,0.0009906593,0.0007419481],"category_scores_gemma":[0.0042185034,0.00040102756,0.0005544141,0.0004043795,0.0008289361,0.0010467398,0.0013581591,0.0015471455,0.00014902394],"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":[0.000069929636,0.000027417218,0.0010679872,0.000032652908,0.000028211776,0.00006448176,0.000044118548,0.970667,0.001397825,0.0021245924,0.00045175603,0.024024041],"study_design_scores_gemma":[0.0000013329085,0.000008756918,0.00009355653,0.0000017601441,0.0000029528044,0.0000061888745,0.0000029893781,0.99838734,0.00031804625,0.001124933,0.000050241622,0.0000019263896],"about_ca_topic_score_codex":0.0048072995,"about_ca_topic_score_gemma":0.0051300745,"teacher_disagreement_score":0.0048072995,"about_ca_system_score_codex":0.00063747185,"about_ca_system_score_gemma":0.00052023825,"threshold_uncertainty_score":0.009558618},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049259737,0.0002569089,0.948828,0.00012114207,0.000025420963,0.00006182572,0.00005793555,0.0005873625,0.0008017379],"genre_scores_gemma":[0.6932519,0.00023710688,0.30345026,0.00016474914,0.000076849195,0.00023277375,0.00042120068,0.0001330365,0.0020321524],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993692,0.00025939266,0.00003366124,0.00014568702,0.00013125325,0.000060698847],"domain_scores_gemma":[0.99763954,0.0015355727,0.00022506091,0.0002592909,0.0002642328,0.00007632989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019611183,0.0009417394,0.0013844195,0.0005937517,0.00042253768,0.0008225917,0.0013916701,0.00096212816,0.0011177687],"category_scores_gemma":[0.0053444495,0.0005936173,0.0006569054,0.000751171,0.00072036,0.0017288292,0.0009871451,0.0013092657,0.0002620661],"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":[0.00016918917,0.000083264764,0.00115238,0.00004596962,0.000046855956,0.00006157414,0.00006286083,0.87967026,0.002822105,0.0051371343,0.0008121738,0.10993618],"study_design_scores_gemma":[0.0000035352136,0.000016100877,0.000056967325,0.0000013279905,0.0000027388185,0.000004747592,0.0000018717311,0.99829584,0.0003749338,0.0011809438,0.000059395432,0.0000015555942],"about_ca_topic_score_codex":0.004472947,"about_ca_topic_score_gemma":0.003898565,"teacher_disagreement_score":0.004472947,"about_ca_system_score_codex":0.0008039293,"about_ca_system_score_gemma":0.0009777495,"threshold_uncertainty_score":0.010371447},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03234881,0.00022235367,0.9645474,0.00013169699,0.00004366511,0.000043477037,0.00008141131,0.0019020422,0.0006791782],"genre_scores_gemma":[0.6550964,0.00029040655,0.3402796,0.00024753084,0.00013761711,0.00014562582,0.0009215561,0.00027720316,0.0026040117],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990459,0.00017196238,0.000041569445,0.00034190452,0.00027200425,0.00012665437],"domain_scores_gemma":[0.9980573,0.00066471624,0.00022586333,0.00035625685,0.00059510476,0.00010077497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015097412,0.0013521822,0.0014153098,0.0020206363,0.0006442739,0.0009255304,0.002025503,0.0010902658,0.00071375555],"category_scores_gemma":[0.004110322,0.00055039045,0.0013191428,0.0013531928,0.0007368244,0.0020888955,0.0015684897,0.00196955,0.00041577648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012688391,0.0002780818,0.00835607,0.00006751909,0.00018192641,0.00012874616,0.0001849031,0.40616965,0.01416672,0.0049075787,0.0042624995,0.56116945],"study_design_scores_gemma":[0.0000016259035,0.000011969326,0.00034096363,0.0000018558151,0.000006461587,0.000016436805,0.000006139415,0.99670285,0.0012787502,0.0014021238,0.00022570013,0.0000051645634],"about_ca_topic_score_codex":0.0100403335,"about_ca_topic_score_gemma":0.009401174,"teacher_disagreement_score":0.0100403335,"about_ca_system_score_codex":0.00090742734,"about_ca_system_score_gemma":0.0012287713,"threshold_uncertainty_score":0.0199638},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28516662,0.00079366466,0.7091128,0.00028545712,0.00013603017,0.000060622566,0.000115818715,0.0034386725,0.0008903741],"genre_scores_gemma":[0.9546322,0.00016855264,0.04474482,0.00003879854,0.000028487531,0.000020840493,0.000107454856,0.0000370438,0.00022176732],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990609,0.00017904426,0.000057612477,0.00019638865,0.00041858305,0.00008738812],"domain_scores_gemma":[0.9981976,0.0007804042,0.00030674244,0.00018338118,0.00043147264,0.00010044053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011702223,0.00056905573,0.0006671269,0.00086835865,0.00028528285,0.0005194279,0.0009459254,0.00046002716,0.00018768733],"category_scores_gemma":[0.003643417,0.0001743991,0.0002290435,0.0008567713,0.0003543676,0.00092814566,0.0006949436,0.00069627346,0.00009665881],"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":[0.0005735896,0.00039270788,0.024788171,0.0001386047,0.0001175586,0.00061906094,0.00022202422,0.5675284,0.039676175,0.0026410043,0.0026203757,0.36068234],"study_design_scores_gemma":[0.0000031540162,0.000038093764,0.0013408622,0.0000021650355,0.000004456894,0.00006623231,0.000015855027,0.9946642,0.0026836356,0.0009621789,0.00021385415,0.00000532915],"about_ca_topic_score_codex":0.0026583627,"about_ca_topic_score_gemma":0.0017788936,"teacher_disagreement_score":0.0026583627,"about_ca_system_score_codex":0.0004278414,"about_ca_system_score_gemma":0.00044171102,"threshold_uncertainty_score":0.0061887503},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48296434,0.0005335818,0.5133108,0.00010541977,0.00012546589,0.00006573413,0.00030039978,0.00075156405,0.0018426972],"genre_scores_gemma":[0.9379269,0.00019525173,0.06022051,0.000020065674,0.00007842832,0.000025548734,0.0005816157,0.000050782786,0.00090093055],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940574,0.00008020942,0.000056512148,0.0001132194,0.00026252397,0.000081811384],"domain_scores_gemma":[0.9985476,0.0003910776,0.00026077114,0.00012371263,0.00058942597,0.00008733831],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051427574,0.00049682974,0.0007121643,0.002974255,0.00034287447,0.0006364074,0.00055102614,0.0005304029,0.0009117788],"category_scores_gemma":[0.0025258728,0.0002068583,0.00055048306,0.0020852268,0.00019606708,0.0009356131,0.00054364995,0.00044740477,0.0003957472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018696018,0.00053009164,0.033971805,0.00034858778,0.00024833076,0.0011309097,0.00032122506,0.13780348,0.14208597,0.0064063943,0.0029460283,0.6723376],"study_design_scores_gemma":[0.000014387873,0.00020724279,0.015933089,0.000011715724,0.000057630634,0.00041509376,0.00008088793,0.9672326,0.013256861,0.0016127429,0.0011528744,0.000024928127],"about_ca_topic_score_codex":0.0017067044,"about_ca_topic_score_gemma":0.0015171878,"teacher_disagreement_score":0.002974255,"about_ca_system_score_codex":0.0002239585,"about_ca_system_score_gemma":0.00056630647,"threshold_uncertainty_score":0.0033935905},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038633547,0.00036998614,0.9943382,0.00017480507,0.00007730814,0.00004234186,0.00003116417,0.00037297918,0.0007298418],"genre_scores_gemma":[0.3156817,0.0014039624,0.6778785,0.00028047775,0.00046143797,0.00032302595,0.00042499564,0.00013021652,0.0034157715],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99788326,0.00053402263,0.00017925019,0.0004291856,0.00084337784,0.00013085676],"domain_scores_gemma":[0.9983882,0.0006523959,0.00015468792,0.000105091975,0.00065247837,0.000047130427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001815496,0.00082738,0.0012802278,0.0018954636,0.00066765293,0.0011265514,0.0015290857,0.0012126738,0.0014820329],"category_scores_gemma":[0.0048175463,0.0003077595,0.001183038,0.0021884847,0.00049642357,0.001336849,0.00076682714,0.0016034426,0.0007149159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012662298,0.00020114124,0.004227266,0.00040608467,0.00018328866,0.00033676854,0.0002098591,0.32418832,0.010666982,0.023484757,0.005213562,0.63075536],"study_design_scores_gemma":[0.0000073915753,0.000040682637,0.00044126128,0.000016270531,0.000019149593,0.00006908487,0.000016653456,0.99238706,0.0015092329,0.0032506576,0.0022282046,0.000014380539],"about_ca_topic_score_codex":0.0030894703,"about_ca_topic_score_gemma":0.0019491563,"teacher_disagreement_score":0.0030894703,"about_ca_system_score_codex":0.000604795,"about_ca_system_score_gemma":0.0010231136,"threshold_uncertainty_score":0.009601414},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008952341,0.0008463649,0.989033,0.00018031604,0.00005035286,0.00003263195,0.00005084719,0.0005424331,0.00031175793],"genre_scores_gemma":[0.5858232,0.002704941,0.40889108,0.00020635057,0.00039939073,0.00022035236,0.0005610001,0.00016009533,0.0010336088],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99633044,0.0010014205,0.00032096976,0.00065321336,0.0015341728,0.00015981584],"domain_scores_gemma":[0.9941754,0.0031212678,0.0010746813,0.0005444504,0.000981532,0.00010266487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002364199,0.0013012566,0.0014071527,0.0031025037,0.00053447706,0.0013566725,0.0017396557,0.0010779606,0.00031830955],"category_scores_gemma":[0.008610723,0.00045568086,0.001278263,0.0036810562,0.0009882518,0.002347124,0.0012135352,0.0014441238,0.00029524407],"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":[0.00011580776,0.0001768666,0.011203704,0.0003968654,0.00033083977,0.00029419368,0.00017806138,0.5128902,0.008281432,0.01079093,0.001921841,0.45341927],"study_design_scores_gemma":[0.000005233572,0.000062419436,0.0010931196,0.000019726673,0.000022871778,0.00009530617,0.000041805877,0.9861665,0.0023713731,0.008931593,0.001173189,0.000016933536],"about_ca_topic_score_codex":0.002426008,"about_ca_topic_score_gemma":0.0018305208,"teacher_disagreement_score":0.0031025037,"about_ca_system_score_codex":0.0007597814,"about_ca_system_score_gemma":0.0008484218,"threshold_uncertainty_score":0.012503266},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19193621,0.0012209668,0.8008595,0.000900442,0.00015982144,0.000056240602,0.00042006493,0.0022934547,0.0021532483],"genre_scores_gemma":[0.9423344,0.0004236391,0.054519545,0.0002561442,0.00007942775,0.000032764452,0.0008400031,0.00009298647,0.0014210204],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996088,0.000069928225,0.00001609265,0.00013521196,0.00009483873,0.00007515107],"domain_scores_gemma":[0.9989034,0.00052252907,0.00017882902,0.00009740297,0.00022698264,0.00007083764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005669219,0.00078459905,0.0007138328,0.0013089554,0.0004034794,0.0005003046,0.001195302,0.0007068474,0.0005120948],"category_scores_gemma":[0.0026131433,0.0003105522,0.00062694843,0.001236369,0.0005431843,0.0012155384,0.00093170296,0.0010427594,0.00015100939],"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":[0.0003842144,0.00023097623,0.02416184,0.00016379089,0.00022910403,0.00047653576,0.00022858287,0.6019742,0.013792793,0.01090212,0.007597183,0.33985862],"study_design_scores_gemma":[0.000002904203,0.000016997945,0.0013159368,0.000003266878,0.000013679154,0.0000381435,0.000013785652,0.99210536,0.00096758275,0.005138048,0.00037878455,0.000005503525],"about_ca_topic_score_codex":0.017148253,"about_ca_topic_score_gemma":0.022098651,"teacher_disagreement_score":0.017148253,"about_ca_system_score_codex":0.0010919757,"about_ca_system_score_gemma":0.0007378999,"threshold_uncertainty_score":0.034096897},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.088693246,0.0002497498,0.90257454,0.00030753063,0.000034380857,0.0001729831,0.00010915964,0.00672942,0.001128936],"genre_scores_gemma":[0.8200421,0.000075098425,0.17812623,0.00022460567,0.000027667482,0.00018147989,0.00029309222,0.00035420523,0.0006754702],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99528104,0.0019268811,0.00033982474,0.0007158269,0.0013397726,0.00039662266],"domain_scores_gemma":[0.9777349,0.014103772,0.0024162075,0.0026706364,0.0026002615,0.00047426388],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004808445,0.0014021004,0.000907018,0.0015820257,0.00037197256,0.0010751336,0.0020773583,0.0010329713,0.0015928994],"category_scores_gemma":[0.029107805,0.00059731654,0.00073585403,0.0007337124,0.0011625172,0.00217469,0.0018264062,0.001753222,0.00039755955],"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":[0.00059031975,0.00050292053,0.017906576,0.00020041622,0.00010965875,0.0003483534,0.00024636777,0.49936607,0.023535809,0.0064723818,0.0029073698,0.4478137],"study_design_scores_gemma":[0.000025739097,0.00008233911,0.0006776422,0.000012999791,0.000017773624,0.00008458009,0.00001771661,0.98459387,0.010624232,0.0034570287,0.00039452163,0.000011491316],"about_ca_topic_score_codex":0.002728633,"about_ca_topic_score_gemma":0.0035947452,"teacher_disagreement_score":0.004808445,"about_ca_system_score_codex":0.0014761596,"about_ca_system_score_gemma":0.0025626412,"threshold_uncertainty_score":0.025429785},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13532011,0.0002504023,0.853751,0.0012743927,0.0001539311,0.00021480353,0.0003096169,0.004717676,0.0040080897],"genre_scores_gemma":[0.5863479,0.00009551552,0.40917566,0.00022533518,0.000056639074,0.00034626023,0.00079759903,0.00033635576,0.0026187557],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986921,0.00048069193,0.000086589636,0.00019860052,0.00045736792,0.000084641244],"domain_scores_gemma":[0.9926037,0.0046274555,0.00030342367,0.001636866,0.0006844615,0.00014422339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018888508,0.0005375963,0.00058142433,0.0003725727,0.00039290902,0.0006790183,0.0015813411,0.00072482263,0.0072747828],"category_scores_gemma":[0.011128544,0.0003981962,0.00062174234,0.000389182,0.0006071013,0.0026209846,0.001691659,0.0019107024,0.0012890857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00168854,0.00044877062,0.0030402213,0.00040301034,0.00011400813,0.00037523304,0.00023352104,0.30008996,0.04635957,0.04758435,0.008871781,0.59079117],"study_design_scores_gemma":[0.00004629932,0.00007787565,0.00023255653,0.000012577043,0.0000068114255,0.000038145048,0.000026062897,0.9771748,0.011754669,0.009730472,0.0008928216,0.0000069334455],"about_ca_topic_score_codex":0.00048725135,"about_ca_topic_score_gemma":0.0007798642,"teacher_disagreement_score":0.0072747828,"about_ca_system_score_codex":0.0005445962,"about_ca_system_score_gemma":0.0006656018,"threshold_uncertainty_score":0.024336576},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83173335,0.00046600017,0.15546349,0.0064957356,0.0001975062,0.00012550643,0.00080704305,0.0006456455,0.0040656775],"genre_scores_gemma":[0.99134773,0.000078044446,0.007756046,0.0000615887,0.000041453757,0.0000145447975,0.00014359171,0.000014601906,0.00054248964],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904746,0.0003348469,0.00005772532,0.00019552319,0.00024518633,0.000119218006],"domain_scores_gemma":[0.9923881,0.0057188235,0.00042397054,0.0005639326,0.00056475715,0.00034032052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026037984,0.0005406739,0.0009836059,0.000640537,0.00074956357,0.0017148888,0.001319592,0.002584421,0.001316127],"category_scores_gemma":[0.011078458,0.00037614524,0.0004828679,0.0012321931,0.0015863978,0.002456726,0.001200667,0.0019768642,0.00018772045],"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":[0.00061580323,0.00043206374,0.032949645,0.00011563883,0.00010212859,0.0045789005,0.0003913929,0.9067236,0.0014253964,0.015161716,0.004340577,0.033163022],"study_design_scores_gemma":[0.000021578804,0.000029912728,0.0021272986,0.000002729301,0.0000064371775,0.0002078891,0.00010436307,0.9901557,0.00030229054,0.006790659,0.00024356604,0.0000075264975],"about_ca_topic_score_codex":0.010166217,"about_ca_topic_score_gemma":0.0063287867,"teacher_disagreement_score":0.010166217,"about_ca_system_score_codex":0.000779101,"about_ca_system_score_gemma":0.0005823322,"threshold_uncertainty_score":0.02021408},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017574241,0.00041353639,0.97968125,0.00013993992,0.00006447542,0.000034281584,0.000096941956,0.00079674856,0.0011985447],"genre_scores_gemma":[0.5804263,0.00063582533,0.40952787,0.0003600329,0.00016902169,0.00019779614,0.0011816118,0.00024229498,0.0072593284],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941874,0.00012238197,0.00003783794,0.00017815371,0.00016081515,0.00008205969],"domain_scores_gemma":[0.9990715,0.0003418191,0.00008395023,0.0001964263,0.00026623384,0.0000400418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096138276,0.000908581,0.00092717964,0.00038052152,0.000304011,0.00051681534,0.001377768,0.00096618984,0.0022519769],"category_scores_gemma":[0.0022973828,0.0003392832,0.0007666094,0.0006371876,0.0004935485,0.0012395664,0.0009808275,0.0020865758,0.0008896539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027633423,0.0001880318,0.0013004915,0.0001455821,0.0001520512,0.00012845064,0.00008890416,0.40550023,0.023864144,0.009537102,0.0057077087,0.553111],"study_design_scores_gemma":[0.000003920771,0.000019996534,0.00018396646,0.0000041507287,0.0000071962336,0.000026250455,0.0000049641103,0.99501956,0.0027745818,0.001426956,0.0005224187,0.0000060406683],"about_ca_topic_score_codex":0.0039339513,"about_ca_topic_score_gemma":0.0053304858,"teacher_disagreement_score":0.0039339513,"about_ca_system_score_codex":0.0004792173,"about_ca_system_score_gemma":0.00091446424,"threshold_uncertainty_score":0.007822156},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060753964,0.00044418388,0.82517177,0.00018531054,0.0002535164,0.00025154036,0.001720332,0.10638724,0.00483213],"genre_scores_gemma":[0.520502,0.0003891108,0.4622831,0.00047959705,0.000098755336,0.00032239655,0.0049534542,0.0018773,0.009094152],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99971944,0.000020492678,0.000016064301,0.00007019736,0.0001304672,0.000043309377],"domain_scores_gemma":[0.9996214,0.00009001074,0.00004675529,0.000056652785,0.00013031054,0.00005480487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024122083,0.000770812,0.00056800636,0.00106664,0.00026217208,0.0006257223,0.0015599575,0.0005969108,0.0032201388],"category_scores_gemma":[0.00094735407,0.00031402335,0.00034862803,0.00042027063,0.00022898824,0.0011029079,0.001237163,0.0009084685,0.0014721019],"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":[0.002423222,0.0010612874,0.015862342,0.0004768507,0.00023802288,0.0009610273,0.00033589953,0.037420627,0.18709323,0.007961372,0.046921715,0.69924444],"study_design_scores_gemma":[0.00010661089,0.0004089915,0.0057242224,0.000034964218,0.000070956085,0.0005822287,0.000059821145,0.8593233,0.09791607,0.0063558547,0.02929023,0.00012670044],"about_ca_topic_score_codex":0.0011878025,"about_ca_topic_score_gemma":0.0013541623,"teacher_disagreement_score":0.0032201388,"about_ca_system_score_codex":0.0002657183,"about_ca_system_score_gemma":0.00038414283,"threshold_uncertainty_score":0.010772467},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24368769,0.0004346896,0.7461841,0.00036921934,0.00008947703,0.00013352295,0.0013195609,0.006417894,0.0013637483],"genre_scores_gemma":[0.87112874,0.00025302533,0.12251795,0.00010007169,0.000085600506,0.000113668815,0.003266469,0.00018343775,0.0023511164],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996941,0.00008375665,0.000018553492,0.00010025448,0.00006786314,0.00003554358],"domain_scores_gemma":[0.9991522,0.0004952054,0.000115881776,0.000074263204,0.00012475996,0.000037777878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007972927,0.0008760239,0.0005953461,0.0011005601,0.00025228556,0.00049363344,0.001235861,0.0004943539,0.00058970856],"category_scores_gemma":[0.0022088995,0.00026295538,0.00060722,0.00067268935,0.00025974796,0.00084119866,0.0006352766,0.00085306197,0.00029820995],"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":[0.0003589926,0.00025876076,0.017593857,0.000074507705,0.000187762,0.00016205125,0.000081493345,0.7663964,0.004136622,0.0019623607,0.0039122943,0.2048749],"study_design_scores_gemma":[0.0000023403622,0.000011395974,0.0004581198,9.109691e-7,0.0000029048774,0.000012285471,0.0000029436803,0.9986462,0.00037286882,0.00034232016,0.0001457133,0.000002014285],"about_ca_topic_score_codex":0.012566381,"about_ca_topic_score_gemma":0.01570269,"teacher_disagreement_score":0.012566381,"about_ca_system_score_codex":0.00069591723,"about_ca_system_score_gemma":0.0006287058,"threshold_uncertainty_score":0.024986506},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021933565,0.0004057164,0.975679,0.0003023263,0.000049496983,0.000020108671,0.00006700877,0.0005411505,0.0010016394],"genre_scores_gemma":[0.9365104,0.00037571785,0.059670307,0.00026444683,0.00008645425,0.00007001546,0.00027050925,0.000112586764,0.0026395817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912125,0.00035492386,0.000033352015,0.00021297534,0.00017772014,0.000099763194],"domain_scores_gemma":[0.9966577,0.0025759966,0.00027138894,0.00017960224,0.00024193212,0.000073357696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018101395,0.0010245533,0.000954182,0.0006240755,0.0002980613,0.0006605158,0.0012673422,0.0009216066,0.0009121108],"category_scores_gemma":[0.0051162094,0.0005192685,0.0007542123,0.00048673793,0.0013713463,0.0010192115,0.0014391204,0.002338876,0.0002445898],"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":[0.000049994385,0.000019540124,0.0007779006,0.000022943297,0.0000361623,0.00005880944,0.000038872557,0.9764708,0.0010150274,0.0060497075,0.00047935813,0.014980719],"study_design_scores_gemma":[8.6863037e-7,0.0000045568304,0.00005926155,0.0000014156444,0.0000021101166,0.000007788169,0.0000015572474,0.997815,0.00015645805,0.0018939703,0.000055168242,0.0000019056355],"about_ca_topic_score_codex":0.003927156,"about_ca_topic_score_gemma":0.0034222421,"teacher_disagreement_score":0.003927156,"about_ca_system_score_codex":0.0009665955,"about_ca_system_score_gemma":0.00048734702,"threshold_uncertainty_score":0.009573042},"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":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003565017,0.0016364566,0.00713503,0.053938348,0.9213515,0.000045632994,0.009595484,0.0034132586,0.002527736],"genre_scores_gemma":[0.041661218,0.012103974,0.054055687,0.15505834,0.40886867,0.00081458595,0.033789475,0.018977482,0.27467063],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99353516,0.000885822,0.0009679921,0.0010455182,0.0030646974,0.00050079834],"domain_scores_gemma":[0.9228215,0.011526553,0.0024466726,0.0047899457,0.05646665,0.0019485805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055438043,0.002971585,0.0025108447,0.004942399,0.0032366477,0.0056066513,0.004579047,0.0059826006,0.09021189],"category_scores_gemma":[0.11922405,0.0014051722,0.0022753465,0.004282887,0.0024475523,0.0033799985,0.0030284468,0.00963873,0.05950079],"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":[0.000028501214,0.000003251235,0.00007277603,0.00012466808,0.000017243014,0.00006399443,0.000028265447,0.000054111184,0.00007764001,0.00060454075,0.99385154,0.0050734705],"study_design_scores_gemma":[0.00008996557,0.000020922518,0.0008758538,0.00045832936,0.000070685666,0.00048873865,0.000089075984,0.00082182547,0.0008815328,0.0036841147,0.9924418,0.00007716526],"about_ca_topic_score_codex":0.02175256,"about_ca_topic_score_gemma":0.026339078,"teacher_disagreement_score":0.09021189,"about_ca_system_score_codex":0.0026267548,"about_ca_system_score_gemma":0.007340179,"threshold_uncertainty_score":0.30178887},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14936799,0.00042558403,0.8442094,0.0004290706,0.000049256847,0.00018331305,0.00029108638,0.0015778576,0.0034665049],"genre_scores_gemma":[0.8618513,0.00026161296,0.13642196,0.00005428871,0.000020441767,0.000057602516,0.00030303068,0.000059110065,0.0009707067],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994636,0.00012780493,0.000027846172,0.000101184865,0.00019896522,0.00008049171],"domain_scores_gemma":[0.9978485,0.0010979667,0.00047166742,0.000094834104,0.00037612586,0.00011086627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008666116,0.0010053071,0.0006077883,0.0021311317,0.00052591634,0.0009471127,0.0006545082,0.0006302302,0.0015863978],"category_scores_gemma":[0.0037140786,0.0003274703,0.0004473611,0.0006461756,0.00032664498,0.00088102673,0.000673289,0.0007329363,0.0004227939],"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":[0.00093196804,0.000513281,0.03407842,0.0006934998,0.000100286816,0.0010427497,0.00071916496,0.48741007,0.13218424,0.018345257,0.0062711043,0.31770995],"study_design_scores_gemma":[0.000018698425,0.00016229608,0.006199489,0.00004020153,0.00003424759,0.00026249062,0.0002271981,0.9572805,0.02171627,0.012249028,0.0017789899,0.00003049962],"about_ca_topic_score_codex":0.003351607,"about_ca_topic_score_gemma":0.0046670097,"teacher_disagreement_score":0.003351607,"about_ca_system_score_codex":0.0006165501,"about_ca_system_score_gemma":0.001955087,"threshold_uncertainty_score":0.006664157},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.094197564,0.00021811208,0.8993179,0.00026167659,0.000032821707,0.00010164719,0.00017647898,0.00045756612,0.0052362327],"genre_scores_gemma":[0.808624,0.00036101852,0.17958066,0.00019671187,0.000056308556,0.00027217707,0.00031092478,0.00025389515,0.010344411],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99800926,0.000753897,0.00009873645,0.0005267177,0.00040479162,0.00020670731],"domain_scores_gemma":[0.9897552,0.0054245093,0.0014852479,0.0022106038,0.0006452456,0.0004792041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032953937,0.0006049584,0.0010581319,0.0012492286,0.0007243448,0.0019233575,0.0023655016,0.001432918,0.0060371235],"category_scores_gemma":[0.0153776035,0.00069863274,0.00113783,0.0011241216,0.0024747516,0.005222657,0.0019441581,0.0018261117,0.0009496083],"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":[0.00032274908,0.000084134655,0.0038778842,0.00015237287,0.000078541656,0.00015587447,0.00041982724,0.4029664,0.007997428,0.53363806,0.0013026961,0.04900411],"study_design_scores_gemma":[0.000035943773,0.00008479309,0.0003615719,0.000028139075,0.00002607711,0.00011572293,0.000050958773,0.73437214,0.0031919966,0.25937042,0.0023393033,0.000022973125],"about_ca_topic_score_codex":0.0013412277,"about_ca_topic_score_gemma":0.0014227363,"teacher_disagreement_score":0.0060371235,"about_ca_system_score_codex":0.0018825918,"about_ca_system_score_gemma":0.0009277716,"threshold_uncertainty_score":0.02019614},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.069874294,0.0006101572,0.92147046,0.0027591563,0.00015930813,0.00023816524,0.0004104152,0.0034084488,0.0010695856],"genre_scores_gemma":[0.6566009,0.00024465294,0.33953997,0.0007440151,0.00014316362,0.00026134905,0.0009743643,0.00024298203,0.0012486865],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.964779,0.019145738,0.0035361517,0.005977329,0.0055431523,0.0010186699],"domain_scores_gemma":[0.813381,0.12310845,0.016712444,0.031978417,0.013529851,0.0012898404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07188791,0.002201668,0.002083408,0.0037540046,0.0021430433,0.004765069,0.0039239046,0.0026877453,0.0016148008],"category_scores_gemma":[0.16702111,0.0010577978,0.0016283961,0.0036673674,0.002456981,0.0066158343,0.004239505,0.0044967956,0.0010714178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053492363,0.0007931414,0.17712352,0.0007667226,0.0012012419,0.00070710434,0.0017170636,0.36976585,0.0050879573,0.013801724,0.0066262837,0.42187452],"study_design_scores_gemma":[0.000048085087,0.00033884993,0.010900929,0.00027103056,0.00014569084,0.00029503056,0.0005043245,0.9353401,0.009530745,0.03749841,0.0050350083,0.00009178176],"about_ca_topic_score_codex":0.0062732724,"about_ca_topic_score_gemma":0.005966423,"teacher_disagreement_score":0.07188791,"about_ca_system_score_codex":0.002091806,"about_ca_system_score_gemma":0.0039454293,"threshold_uncertainty_score":0.38018417},"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":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008225716,0.008036808,0.028821828,0.0031792163,0.0009300615,0.000055326662,0.0002599927,0.00046151018,0.9574327],"genre_scores_gemma":[0.018484462,0.012527614,0.02479689,0.0016400582,0.00070145895,0.0001227517,0.0007970672,0.00021532053,0.9407144],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996482,0.00005984561,0.000013385669,0.00006017537,0.00019889743,0.00001957797],"domain_scores_gemma":[0.9996475,0.00013833641,0.000015592304,0.00009932205,0.00007549606,0.000023636794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040760986,0.0007055747,0.00041003918,0.0009468938,0.00052420696,0.003131053,0.000867144,0.0008492683,0.0714296],"category_scores_gemma":[0.0013098875,0.0002931869,0.00024265445,0.0012313941,0.00091416715,0.002617681,0.0011076825,0.0013765102,0.041587763],"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":[0.000013379111,0.00004633322,0.0001974586,0.00026517143,0.000013316113,0.000051065846,0.00016449817,0.0009980521,0.0006721162,0.22250442,0.31358838,0.4614859],"study_design_scores_gemma":[0.0000032025873,0.000009680878,0.0001982622,0.00014141541,0.000004701962,0.00009169208,0.00006354717,0.0013491965,0.00037152492,0.0986426,0.89911926,0.00000488401],"about_ca_topic_score_codex":0.000940545,"about_ca_topic_score_gemma":0.0016202102,"teacher_disagreement_score":0.0714296,"about_ca_system_score_codex":0.0007812082,"about_ca_system_score_gemma":0.00078725547,"threshold_uncertainty_score":0.23895586},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35590675,0.0010895281,0.62766945,0.00048077942,0.00042109058,0.00017550685,0.00048304102,0.008216817,0.0055571105],"genre_scores_gemma":[0.83000594,0.0003648941,0.16226974,0.00023401309,0.00007439159,0.00005661373,0.00079801277,0.00009923784,0.0060972017],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997789,0.000019103234,0.000014173847,0.00007229858,0.00007316156,0.00004226391],"domain_scores_gemma":[0.99978346,0.00004084795,0.000027855589,0.000028213983,0.000093005896,0.000026700975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033936146,0.0005706373,0.0004846446,0.0008419088,0.00017810853,0.00059881713,0.00068059296,0.00051533495,0.0011464669],"category_scores_gemma":[0.0007939023,0.00022073028,0.0003964852,0.0005093225,0.00018663943,0.0010355652,0.00064897264,0.0006282191,0.00051282195],"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":[0.00036248288,0.00025952145,0.00627522,0.000102925755,0.000085055144,0.00021479452,0.00011462088,0.032764737,0.06261605,0.0016203618,0.0042665447,0.8913177],"study_design_scores_gemma":[0.00002749881,0.0003393418,0.0045667286,0.00001836586,0.00006557145,0.00022293217,0.0000791806,0.952341,0.037366305,0.001275963,0.0036670975,0.000029975137],"about_ca_topic_score_codex":0.005014246,"about_ca_topic_score_gemma":0.007679732,"teacher_disagreement_score":0.005014246,"about_ca_system_score_codex":0.0003985393,"about_ca_system_score_gemma":0.00067971396,"threshold_uncertainty_score":0.009970129},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041052524,0.0005062291,0.9554122,0.00032909747,0.00008931199,0.00004840398,0.00007171336,0.0010674061,0.0014230487],"genre_scores_gemma":[0.93583804,0.0002416788,0.06146001,0.00013661443,0.00007773418,0.000103555016,0.00016254492,0.0000471016,0.0019326956],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990551,0.00022131922,0.00007453446,0.00031090103,0.00022270306,0.00011546292],"domain_scores_gemma":[0.995321,0.003160269,0.0005700847,0.00025128873,0.00055596704,0.00014132605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002053943,0.0010857577,0.001592653,0.0009167503,0.00042369205,0.0010260087,0.0014483181,0.001538821,0.0010713266],"category_scores_gemma":[0.008342872,0.00060049206,0.0010869276,0.00066358066,0.00096104393,0.0013013924,0.0013878986,0.0022137794,0.0003030231],"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":[0.0001417643,0.000059843307,0.0032823263,0.00006213723,0.000110771805,0.00011136996,0.000103117534,0.91548795,0.0012948617,0.0057590003,0.0005138431,0.07307307],"study_design_scores_gemma":[0.0000032025034,0.000011742736,0.00012157304,0.0000019786107,0.0000034995721,0.000006209666,0.0000022859708,0.9984946,0.00018100705,0.0011114408,0.000058110672,0.000004303636],"about_ca_topic_score_codex":0.013260866,"about_ca_topic_score_gemma":0.005634728,"teacher_disagreement_score":0.013260866,"about_ca_system_score_codex":0.0010334782,"about_ca_system_score_gemma":0.0012769769,"threshold_uncertainty_score":0.026367366},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.081060156,0.000727104,0.91206557,0.00032160705,0.0001703912,0.0001277953,0.00016003812,0.0032228867,0.002144411],"genre_scores_gemma":[0.48819944,0.00035979613,0.5078229,0.0003522749,0.00014758491,0.00010162011,0.00054469303,0.0002809522,0.0021908255],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99718374,0.00068000227,0.0002163019,0.00046125418,0.0011920512,0.00026659766],"domain_scores_gemma":[0.97646797,0.01512083,0.0018466761,0.002360417,0.0038268068,0.00037734216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005140574,0.0016675037,0.0013981602,0.0026790076,0.00080236525,0.0021047667,0.0019261553,0.0013189442,0.0029250472],"category_scores_gemma":[0.022484725,0.0005068381,0.0007703101,0.0011276063,0.00089139846,0.0020462566,0.0017652947,0.0016546667,0.002058392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016519477,0.00052201835,0.028863413,0.0005469759,0.00020399249,0.0012778722,0.0005972591,0.06933123,0.095389985,0.011906859,0.007142862,0.7825656],"study_design_scores_gemma":[0.00009441722,0.00083862816,0.020643672,0.000107341424,0.00029488644,0.0027427184,0.00030948207,0.8145301,0.14197204,0.009757791,0.008571499,0.00013747258],"about_ca_topic_score_codex":0.00081619504,"about_ca_topic_score_gemma":0.0014457075,"teacher_disagreement_score":0.005140574,"about_ca_system_score_codex":0.0003573605,"about_ca_system_score_gemma":0.0011707536,"threshold_uncertainty_score":0.027186275},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024173023,0.00018623228,0.9743123,0.00024908918,0.000011440238,0.000060210245,0.00019328027,0.00038150226,0.00043287175],"genre_scores_gemma":[0.5149072,0.00032048006,0.48117244,0.00021411876,0.000075833756,0.00035350406,0.0017645544,0.00021355406,0.0009783434],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973242,0.0010757815,0.00018644659,0.00055771467,0.00069227547,0.00016354887],"domain_scores_gemma":[0.97347295,0.022518318,0.0014785613,0.0011499035,0.0011415388,0.00023878725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043606055,0.0013319668,0.0013305056,0.0025894383,0.0006264924,0.0018132483,0.0013509085,0.0016977912,0.003257103],"category_scores_gemma":[0.028441291,0.00070329144,0.0018811444,0.001241539,0.0013111589,0.0025288034,0.0019762942,0.0025378426,0.0004157975],"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":[0.00028287736,0.00013931196,0.0091718165,0.00034306105,0.00014988914,0.00045629786,0.0005383311,0.7478698,0.0041947626,0.04646672,0.0021818427,0.18820529],"study_design_scores_gemma":[0.000010122636,0.000030921194,0.00027521624,0.000028613771,0.000017164572,0.00003797381,0.000023366356,0.96403205,0.00094695267,0.034074984,0.00051567034,0.0000069118523],"about_ca_topic_score_codex":0.0014660953,"about_ca_topic_score_gemma":0.0018909042,"teacher_disagreement_score":0.0043606055,"about_ca_system_score_codex":0.0010432543,"about_ca_system_score_gemma":0.0017305689,"threshold_uncertainty_score":0.023061335},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007358171,0.0011125086,0.97883546,0.0010029373,0.00014400424,0.00030865092,0.0016266623,0.007559353,0.0020523001],"genre_scores_gemma":[0.18764691,0.0012255831,0.8018554,0.00051979424,0.00032637082,0.0007330913,0.0053586,0.00033273286,0.0020015072],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9939727,0.002028641,0.0006301739,0.0014483512,0.0016891788,0.00023094624],"domain_scores_gemma":[0.98815095,0.006512628,0.001028757,0.0020950707,0.001995559,0.00021709329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006173765,0.0021057795,0.0015281818,0.0077612265,0.0009814706,0.0036816087,0.0021316917,0.0016470279,0.0035108814],"category_scores_gemma":[0.022507727,0.00087443314,0.0022713416,0.0043956367,0.0010056371,0.0027164978,0.0025341203,0.0035996109,0.0035432545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019573196,0.0004911362,0.013188626,0.00063283823,0.00061004556,0.00024347485,0.00025617325,0.1508283,0.0030121452,0.010129289,0.016429162,0.80398303],"study_design_scores_gemma":[0.000023328004,0.000096799195,0.0027668632,0.00016687816,0.000049117985,0.00013753309,0.00012023475,0.9210099,0.004334172,0.05831986,0.012920642,0.00005468836],"about_ca_topic_score_codex":0.0039202143,"about_ca_topic_score_gemma":0.0031743676,"teacher_disagreement_score":0.0077612265,"about_ca_system_score_codex":0.001406375,"about_ca_system_score_gemma":0.0026361735,"threshold_uncertainty_score":0.03265035},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1264278,0.0007294449,0.83601665,0.0006553657,0.00011824988,0.000277827,0.0016430394,0.032748915,0.0013827286],"genre_scores_gemma":[0.45561782,0.00020212613,0.5400087,0.00019162941,0.000048648748,0.00014075679,0.002145278,0.00018935479,0.0014556565],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99732924,0.00047962365,0.0002757277,0.0007873395,0.0009555171,0.00017258467],"domain_scores_gemma":[0.99217415,0.0028350565,0.0008200105,0.0016799244,0.00221385,0.00027701416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024239097,0.0010206855,0.0015389388,0.003489438,0.00064711913,0.0013045401,0.0023951922,0.0013453357,0.0012766661],"category_scores_gemma":[0.0138131585,0.00042800765,0.0012325734,0.0024306676,0.0004614303,0.0020772377,0.0013269186,0.0015160418,0.0015403801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000335739,0.0003959139,0.07709959,0.00023857121,0.00016998514,0.00039827198,0.00041413223,0.028564453,0.018422393,0.002329936,0.009806726,0.8618242],"study_design_scores_gemma":[0.000024698767,0.000116149,0.0088713225,0.000023523044,0.00004232174,0.00039000457,0.00011008182,0.97367823,0.009672527,0.0027779771,0.004245755,0.00004738571],"about_ca_topic_score_codex":0.011570724,"about_ca_topic_score_gemma":0.015578052,"teacher_disagreement_score":0.011570724,"about_ca_system_score_codex":0.00077788025,"about_ca_system_score_gemma":0.001373397,"threshold_uncertainty_score":0.023006737},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014225254,0.00038640876,0.9827969,0.00016862944,0.0000651943,0.000044133412,0.00021803382,0.0007210679,0.001374327],"genre_scores_gemma":[0.6650522,0.0009689286,0.3266465,0.00012159671,0.00018397313,0.00016600816,0.0009895086,0.00032506074,0.005546251],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994752,0.00006963221,0.000028393337,0.0001346306,0.0002442609,0.000047949205],"domain_scores_gemma":[0.9989454,0.0005447306,0.00009916081,0.000105898755,0.00026364194,0.00004110554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059018395,0.00084724795,0.0010775821,0.0013667003,0.0004269376,0.0016580999,0.002335018,0.0010432826,0.0014083043],"category_scores_gemma":[0.0021059287,0.0006566391,0.00086611474,0.0019321964,0.0007134043,0.0015692957,0.000908362,0.0010752579,0.00044865825],"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":[0.00014906973,0.00018657064,0.0032805824,0.00019680959,0.00010406784,0.00026912548,0.000100411955,0.6978875,0.013412672,0.018609332,0.0041709915,0.26163286],"study_design_scores_gemma":[0.0000018991608,0.00001537095,0.00030132465,0.000004429194,0.000007786165,0.00005201572,0.000013767411,0.9905205,0.0017239094,0.0065512056,0.00080127956,0.0000065720333],"about_ca_topic_score_codex":0.0033373127,"about_ca_topic_score_gemma":0.0033642296,"teacher_disagreement_score":0.0033373127,"about_ca_system_score_codex":0.0007556411,"about_ca_system_score_gemma":0.0007077668,"threshold_uncertainty_score":0.006635785},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8545714,0.00015202073,0.14111198,0.0003661455,0.000021649517,0.00012399929,0.0003022114,0.0005731138,0.002777434],"genre_scores_gemma":[0.9702622,0.000024852081,0.028993374,0.0000534884,0.000008481775,0.000052264095,0.00027621537,0.000037650734,0.00029145568],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9979401,0.00096183195,0.00011637074,0.0005042586,0.0003763809,0.00010097322],"domain_scores_gemma":[0.98579943,0.00932992,0.0018438876,0.0017517273,0.0008547013,0.00042041836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041774707,0.0006618526,0.0003820802,0.0012568466,0.00030158175,0.0013072622,0.0010094153,0.0010498731,0.0018568867],"category_scores_gemma":[0.0249126,0.00034381275,0.00065466826,0.0004461954,0.0010500961,0.0021756059,0.0014677445,0.000993289,0.0002174511],"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":[0.0036238658,0.0021145341,0.3209288,0.00062365615,0.0012184515,0.00035658255,0.0041638585,0.21071649,0.09326987,0.020900287,0.0021070198,0.33997652],"study_design_scores_gemma":[0.0000597392,0.0010702562,0.1297955,0.00002810474,0.00013313274,0.00018872022,0.00059781806,0.8322913,0.018187292,0.016757872,0.0008129829,0.000077264114],"about_ca_topic_score_codex":0.0030237813,"about_ca_topic_score_gemma":0.0036948025,"teacher_disagreement_score":0.0041774707,"about_ca_system_score_codex":0.0011125072,"about_ca_system_score_gemma":0.0006154487,"threshold_uncertainty_score":0.02209282},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.112089306,0.0010885703,0.8680584,0.00016472646,0.000192932,0.00032440663,0.00087149604,0.012805294,0.00440485],"genre_scores_gemma":[0.72132033,0.00051887357,0.2680748,0.0003198674,0.000091297494,0.00014487791,0.002463741,0.00027344812,0.0067927013],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99975556,0.000023887686,0.000009308667,0.00010038406,0.00006159642,0.00004922698],"domain_scores_gemma":[0.99981683,0.000050963274,0.000017920613,0.000027386643,0.000058461115,0.0000285169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035413893,0.000830314,0.0006010151,0.0012021855,0.00020416244,0.00049845484,0.00085066864,0.00042914363,0.002840976],"category_scores_gemma":[0.00068188296,0.00020942371,0.0005301342,0.0005050501,0.00023294344,0.0007540147,0.0006682284,0.0005923203,0.00093605847],"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":[0.00039085106,0.00023908861,0.0022327788,0.00013758054,0.00011447968,0.00020224456,0.00008241449,0.01793277,0.10312235,0.0014104514,0.009398209,0.8647367],"study_design_scores_gemma":[0.00003521655,0.00019977333,0.00468514,0.000012682928,0.00005528782,0.0002635409,0.00006239768,0.9361966,0.052963186,0.0015236179,0.0039785197,0.00002408335],"about_ca_topic_score_codex":0.011366392,"about_ca_topic_score_gemma":0.0189287,"teacher_disagreement_score":0.011366392,"about_ca_system_score_codex":0.00051767,"about_ca_system_score_gemma":0.00048427816,"threshold_uncertainty_score":0.022600472},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.074168846,0.040332593,0.3403804,0.0066582877,0.003646858,0.003637258,0.24039604,0.22807309,0.062706664],"genre_scores_gemma":[0.110628486,0.005033239,0.43646133,0.0013646362,0.00040309265,0.0031704516,0.42742965,0.009141886,0.006367308],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9832171,0.004029673,0.0026393714,0.0021423332,0.0072032204,0.0007683119],"domain_scores_gemma":[0.97748303,0.009968114,0.0014822413,0.0038461122,0.0062613334,0.00095920946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009667878,0.0040589576,0.001722036,0.009733874,0.0012615613,0.0040068342,0.0076436778,0.0036738403,0.006979063],"category_scores_gemma":[0.04585392,0.00088302733,0.0018091489,0.009764142,0.001124623,0.0032034835,0.0041146707,0.0025788941,0.008109467],"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":[0.00111002,0.00083009887,0.005991322,0.0042187134,0.00040254722,0.000261337,0.00018406153,0.058403824,0.0051959017,0.012276294,0.5815665,0.32955936],"study_design_scores_gemma":[0.0011041155,0.0008417747,0.014199038,0.001144606,0.00027207917,0.0016145967,0.00033710556,0.4814062,0.036436092,0.041110422,0.42124984,0.0002841682],"about_ca_topic_score_codex":0.011766743,"about_ca_topic_score_gemma":0.012776942,"teacher_disagreement_score":0.011766743,"about_ca_system_score_codex":0.0030304787,"about_ca_system_score_gemma":0.003412289,"threshold_uncertainty_score":0.051129222},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66985023,0.0018366709,0.3170703,0.0012706643,0.00032355697,0.00012595579,0.0023018764,0.0033150404,0.0039057294],"genre_scores_gemma":[0.9491913,0.0004630572,0.043390766,0.00023094364,0.00005303003,0.00005890683,0.0029841743,0.000046790712,0.0035811013],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994856,0.00011900942,0.000037994985,0.00012026032,0.00011924026,0.00011776479],"domain_scores_gemma":[0.9994387,0.00015657475,0.000095635034,0.00008818239,0.00016913758,0.000051655283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001044586,0.0007754416,0.00064146897,0.001293477,0.00035382074,0.0005426836,0.0013238266,0.00076372415,0.0010305577],"category_scores_gemma":[0.0020887773,0.00024411734,0.0007140529,0.00072085275,0.0002555202,0.0008407215,0.0011583946,0.0011236994,0.0006668564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069906714,0.00069712155,0.09508286,0.00018847239,0.0003204987,0.0014205785,0.00037045174,0.23089728,0.0074715544,0.0018705367,0.013522061,0.64745957],"study_design_scores_gemma":[0.000012095698,0.00010513781,0.0076928763,0.000031152893,0.00004821214,0.00020047475,0.00015862881,0.98424464,0.0042050793,0.0018396535,0.0014432798,0.00001866428],"about_ca_topic_score_codex":0.005951332,"about_ca_topic_score_gemma":0.0067468616,"teacher_disagreement_score":0.005951332,"about_ca_system_score_codex":0.00047302642,"about_ca_system_score_gemma":0.00083296676,"threshold_uncertainty_score":0.011833429},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029417516,0.00037548272,0.9840732,0.000087801986,0.00019247379,0.00017023116,0.00074917823,0.0054521505,0.005957877],"genre_scores_gemma":[0.041017838,0.00043525634,0.91213655,0.000107857486,0.00010761316,0.00024053213,0.0053425548,0.0024550755,0.038156718],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787354,0.00034075344,0.00014383813,0.00072502805,0.0006965229,0.00022035658],"domain_scores_gemma":[0.99803644,0.0003492047,0.000072942865,0.00084900175,0.00061400654,0.00007842437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012358273,0.001587426,0.0021897033,0.0039728587,0.0026610203,0.0028236557,0.004132702,0.0016581338,0.022821886],"category_scores_gemma":[0.0030744344,0.001253581,0.0024290748,0.007142718,0.0010690158,0.0027008858,0.0028204704,0.001724867,0.026352026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041039396,0.00016778246,0.00067495124,0.00043488672,0.00023137161,0.00022180534,0.00026953593,0.04629378,0.019597568,0.03111554,0.04721667,0.8533657],"study_design_scores_gemma":[0.00004805558,0.00017280149,0.0013192557,0.000086253174,0.00023205065,0.0008483638,0.00034203357,0.7259798,0.0571539,0.09346197,0.12021237,0.00014316158],"about_ca_topic_score_codex":0.0059070243,"about_ca_topic_score_gemma":0.013063492,"teacher_disagreement_score":0.022821886,"about_ca_system_score_codex":0.0011015334,"about_ca_system_score_gemma":0.002626049,"threshold_uncertainty_score":0.076346755},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006793991,0.00049512944,0.99098504,0.0001635518,0.000037613467,0.000121150355,0.00011298497,0.0008380575,0.00045251998],"genre_scores_gemma":[0.100048654,0.0004616607,0.89769626,0.00009372455,0.00007175167,0.0003022674,0.00049956114,0.00018382956,0.0006423617],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9936167,0.002417541,0.00041551748,0.0008271318,0.002438113,0.00028498025],"domain_scores_gemma":[0.9910664,0.0037553797,0.0007286571,0.0023482968,0.0018804452,0.00022065193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006429354,0.0011129042,0.0012311912,0.004133183,0.0014945954,0.0015515412,0.0021126994,0.00077796966,0.0025227435],"category_scores_gemma":[0.015028492,0.0006780683,0.0013680487,0.0039863572,0.001281053,0.002881281,0.0026385002,0.001453314,0.0016125783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043632826,0.00026158892,0.0074634613,0.00052788295,0.00018406837,0.00030088608,0.0006672598,0.05178097,0.024629172,0.023835223,0.008017616,0.88189554],"study_design_scores_gemma":[0.00010747083,0.00026202315,0.008371035,0.00015709706,0.00012392025,0.0009649866,0.00076596776,0.8384922,0.047836043,0.07720236,0.025584713,0.000132317],"about_ca_topic_score_codex":0.002672701,"about_ca_topic_score_gemma":0.0031168896,"teacher_disagreement_score":0.006429354,"about_ca_system_score_codex":0.0007133856,"about_ca_system_score_gemma":0.0019283913,"threshold_uncertainty_score":0.034002125},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020786151,0.0005406632,0.9748813,0.0005572972,0.000088270775,0.000058379926,0.00032683357,0.0008170437,0.0019441159],"genre_scores_gemma":[0.7145398,0.00054064085,0.27969995,0.00035575416,0.0001641257,0.00019455088,0.0010476208,0.00014855094,0.003308977],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991059,0.00018815324,0.0000754756,0.00015530217,0.0003773607,0.00009779235],"domain_scores_gemma":[0.99748814,0.0015491052,0.00013119412,0.00032143618,0.0004327662,0.0000774173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014243929,0.0007842339,0.0013466938,0.0010579701,0.0005753151,0.0012789584,0.0018137903,0.0012679942,0.004676826],"category_scores_gemma":[0.008569541,0.000554038,0.000637474,0.0012798577,0.0009237961,0.0031625107,0.0025776154,0.002788377,0.00064931257],"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":[0.00042544137,0.00017200348,0.000688234,0.00015481729,0.00007576905,0.00011642416,0.00011053809,0.5085808,0.0031302315,0.045580663,0.0046933196,0.43627164],"study_design_scores_gemma":[0.000008347441,0.000011804121,0.000045986646,0.000010937243,0.0000065287536,0.000013688881,0.0000061869846,0.9822067,0.0009775197,0.01643559,0.0002716722,0.0000050922417],"about_ca_topic_score_codex":0.0067693247,"about_ca_topic_score_gemma":0.009425915,"teacher_disagreement_score":0.0067693247,"about_ca_system_score_codex":0.0010181274,"about_ca_system_score_gemma":0.0022064804,"threshold_uncertainty_score":0.015645504},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09319108,0.0005813349,0.90044725,0.00017123061,0.000075860975,0.000054006086,0.0003252176,0.003575363,0.0015786189],"genre_scores_gemma":[0.81061125,0.00039922894,0.18436338,0.0001614345,0.000076743454,0.000044484717,0.0012657292,0.00015697913,0.002920859],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997806,0.000038653005,0.000008715903,0.00008514467,0.000045666977,0.000041319796],"domain_scores_gemma":[0.9994967,0.00018896896,0.000058645626,0.000087209395,0.0001333938,0.00003500127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057879963,0.00072069,0.00070901704,0.0010495486,0.0001659136,0.00047373577,0.00082052854,0.00046761433,0.001660253],"category_scores_gemma":[0.0017441207,0.00019089744,0.0005736481,0.0008091554,0.00032158123,0.001009095,0.0005500585,0.0008226948,0.0008565701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042469226,0.00021569329,0.008028988,0.00007896774,0.00008642303,0.000082035265,0.00005654684,0.17503408,0.021707984,0.002128317,0.0040045916,0.7881518],"study_design_scores_gemma":[0.000009020565,0.00007260573,0.0019876335,0.000005762259,0.000012927007,0.000053915468,0.000016213668,0.99092007,0.004792054,0.0014240758,0.0006972059,0.000008582991],"about_ca_topic_score_codex":0.006002975,"about_ca_topic_score_gemma":0.005808645,"teacher_disagreement_score":0.006002975,"about_ca_system_score_codex":0.00039057987,"about_ca_system_score_gemma":0.00048175,"threshold_uncertainty_score":0.011936069},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03531353,0.0011002104,0.9550517,0.00023780379,0.00021584102,0.00006815507,0.00032232914,0.006775392,0.0009150155],"genre_scores_gemma":[0.46742952,0.0009837993,0.5236895,0.00030850433,0.00022454766,0.0001160477,0.0012174611,0.00042417043,0.005606442],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950504,0.00007902257,0.000035791032,0.00016451803,0.00014312236,0.00007254354],"domain_scores_gemma":[0.998544,0.00081594073,0.00010182053,0.00013133774,0.0003158827,0.000091042864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008096521,0.0010685209,0.001140166,0.00077600376,0.0003255851,0.0007882456,0.0016668235,0.001262734,0.003331415],"category_scores_gemma":[0.0037012924,0.0004584407,0.0006914661,0.0008060491,0.00030839746,0.0009923152,0.0014498588,0.0019144561,0.0014687373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042777188,0.00021126802,0.0034376848,0.00011987763,0.00003631223,0.00018039615,0.000061271254,0.044069406,0.018725181,0.00095150346,0.0054823584,0.92629683],"study_design_scores_gemma":[0.000027434355,0.00018660966,0.001782618,0.000023506747,0.000031093663,0.00031146593,0.000032939566,0.9774139,0.014692371,0.003797721,0.0016844732,0.000015885755],"about_ca_topic_score_codex":0.0023233858,"about_ca_topic_score_gemma":0.0033772367,"teacher_disagreement_score":0.003331415,"about_ca_system_score_codex":0.00041951888,"about_ca_system_score_gemma":0.0009549987,"threshold_uncertainty_score":0.011144698},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005866739,0.00007870464,0.99896646,0.000023869514,0.0000084245785,0.0000047918884,0.00002211674,0.00007749109,0.00023152486],"genre_scores_gemma":[0.19936268,0.00093118474,0.785573,0.0001621787,0.00020979998,0.00020968837,0.00089591934,0.0004981483,0.01215735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894387,0.00036419954,0.000054866214,0.0002545861,0.0002991661,0.00008336447],"domain_scores_gemma":[0.99857175,0.00075246533,0.00014870019,0.00018933191,0.0002937205,0.00004411237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002022523,0.0008977323,0.0016012982,0.0007474145,0.0004390985,0.0013989767,0.0026513797,0.0013064911,0.0029346978],"category_scores_gemma":[0.004367283,0.0009777038,0.0014484257,0.0013210551,0.0011407112,0.002124765,0.0024678186,0.0021722028,0.001163935],"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":[0.00016092099,0.00005851194,0.00058307766,0.00018558775,0.00018255583,0.000118838696,0.00012766248,0.63512677,0.007398025,0.15980153,0.0035794594,0.19267708],"study_design_scores_gemma":[0.0000034384987,0.000017524682,0.00012314541,0.000007250846,0.000012788115,0.00003224869,0.0000060867083,0.97591156,0.001026596,0.021587053,0.0012577146,0.0000145855],"about_ca_topic_score_codex":0.00512599,"about_ca_topic_score_gemma":0.005271628,"teacher_disagreement_score":0.00512599,"about_ca_system_score_codex":0.00089128385,"about_ca_system_score_gemma":0.0014691799,"threshold_uncertainty_score":0.010696232},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47323325,0.00029254932,0.5245979,0.00008143717,0.00004642562,0.000036809914,0.00018949105,0.0007244177,0.00079777424],"genre_scores_gemma":[0.94428915,0.00012162387,0.05471214,0.000014345472,0.000016695434,0.000019614481,0.00026483843,0.000023674884,0.00053799216],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997483,0.00004718802,0.000016108008,0.00008219046,0.00006667091,0.000039616196],"domain_scores_gemma":[0.99950504,0.00022760266,0.00008921178,0.00004976927,0.0001118337,0.000016477485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003403578,0.0005410183,0.00032183825,0.0009231683,0.0001497143,0.00036971387,0.00041406992,0.0003082252,0.0002223521],"category_scores_gemma":[0.001119744,0.00021027449,0.00045988095,0.0005669068,0.0002395395,0.00036551314,0.0003271527,0.0005311255,0.00008850938],"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":[0.0003219085,0.00024566418,0.030424982,0.00012428242,0.00020239915,0.00037551342,0.00031694525,0.5251761,0.066183165,0.0021524013,0.0013148102,0.3731618],"study_design_scores_gemma":[0.0000019348531,0.00002013051,0.009667919,0.000003936188,0.000009563683,0.00003602137,0.00002776543,0.9853489,0.0042261756,0.00045465463,0.00019587627,0.0000071405516],"about_ca_topic_score_codex":0.004190424,"about_ca_topic_score_gemma":0.005038532,"teacher_disagreement_score":0.004190424,"about_ca_system_score_codex":0.00024770334,"about_ca_system_score_gemma":0.00034479744,"threshold_uncertainty_score":0.008332074},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2021872,0.0034023596,0.77041644,0.015671456,0.00014734063,0.000098417906,0.00093811797,0.0016607849,0.005477834],"genre_scores_gemma":[0.7983476,0.001711476,0.19753535,0.0005950059,0.00009213211,0.00005920747,0.0005502483,0.0001541512,0.0009548331],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899834,0.0005937692,0.000045420937,0.00014815902,0.00016494603,0.000049400165],"domain_scores_gemma":[0.99037,0.00750175,0.00076179195,0.0006001809,0.00064521714,0.00012109178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047174757,0.0010278627,0.0005070647,0.002303437,0.0003707904,0.0019672092,0.00075100857,0.0009815131,0.0015448917],"category_scores_gemma":[0.01963837,0.00031346342,0.00071211846,0.0019362192,0.0017325999,0.0037295953,0.0010003347,0.002108929,0.00031718233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023518759,0.00019427596,0.08054347,0.00064234994,0.0003806193,0.00058750995,0.0030350222,0.31241247,0.0052809855,0.09409458,0.009254783,0.4933387],"study_design_scores_gemma":[0.000018440764,0.000082503706,0.017524479,0.00016606426,0.000043097436,0.0001546176,0.0014367306,0.8196143,0.0027002757,0.15057975,0.007633243,0.00004655564],"about_ca_topic_score_codex":0.006029729,"about_ca_topic_score_gemma":0.004929434,"teacher_disagreement_score":0.006029729,"about_ca_system_score_codex":0.0011304268,"about_ca_system_score_gemma":0.0006591587,"threshold_uncertainty_score":0.024948657},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008163979,0.611179,0.3457521,0.014277543,0.0013204846,0.00019437287,0.00085701165,0.0016254772,0.016630031],"genre_scores_gemma":[0.044180147,0.77727115,0.16315804,0.003307421,0.0022080373,0.00027219817,0.0020574008,0.0003726054,0.0071729957],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99817336,0.00045484622,0.00019417453,0.00033906373,0.0006942764,0.00014423003],"domain_scores_gemma":[0.9940578,0.0032826827,0.00027076824,0.0005734701,0.0015848143,0.00023044719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003402627,0.0017563584,0.00182961,0.0039703064,0.00085879624,0.003331806,0.0027526047,0.0019911248,0.0050926907],"category_scores_gemma":[0.010265279,0.0010120609,0.0010631497,0.0070379293,0.0012296359,0.008404155,0.0025862984,0.0030860135,0.0025048126],"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":[0.000074292635,0.00011549679,0.0025398794,0.0037673332,0.00010126043,0.00005815679,0.00019718257,0.0102986535,0.00077885203,0.061487097,0.039168775,0.8814129],"study_design_scores_gemma":[0.000035386824,0.00034542786,0.003857262,0.0062190183,0.00023604864,0.0007879228,0.00085614726,0.14792617,0.0045937467,0.24802579,0.58692706,0.00019004087],"about_ca_topic_score_codex":0.0036878686,"about_ca_topic_score_gemma":0.004138814,"teacher_disagreement_score":0.0050926907,"about_ca_system_score_codex":0.0020668418,"about_ca_system_score_gemma":0.0029446362,"threshold_uncertainty_score":0.017995},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002097257,0.0004985188,0.9846704,0.0028277982,0.000100548335,0.000036850834,0.00006576605,0.00020983191,0.009493121],"genre_scores_gemma":[0.09929779,0.0009165674,0.89397067,0.0010992822,0.0003771672,0.00030118576,0.0002415922,0.0001258603,0.0036698855],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9906233,0.003701856,0.0007525393,0.0014191638,0.0032243128,0.0002787608],"domain_scores_gemma":[0.97707,0.01467085,0.0010166538,0.0036347408,0.0032253792,0.00038246496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009993646,0.0011255078,0.000763437,0.0027159038,0.00205416,0.0044576745,0.0038570287,0.0028064265,0.002602252],"category_scores_gemma":[0.0190963,0.00075139693,0.0016891162,0.0023552857,0.016103948,0.010921106,0.0032192431,0.0069282963,0.0011218693],"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":[0.00000333874,0.0000074493983,0.00012953747,0.000051107232,0.000006196724,0.000017503859,0.00010268995,0.0024442114,0.00025000048,0.98953015,0.000884676,0.006573144],"study_design_scores_gemma":[0.000006029172,0.000017290422,0.000110701716,0.00002800732,0.000006711047,0.00011123975,0.000044012813,0.02385315,0.00055299257,0.9648149,0.010441865,0.000013187723],"about_ca_topic_score_codex":0.0016306324,"about_ca_topic_score_gemma":0.0012752388,"teacher_disagreement_score":0.009993646,"about_ca_system_score_codex":0.0026088147,"about_ca_system_score_gemma":0.0022153186,"threshold_uncertainty_score":0.052852094},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007464331,0.00085478567,0.9873524,0.00014460058,0.000081468796,0.0000364088,0.00027547925,0.0022188388,0.0015717687],"genre_scores_gemma":[0.24043564,0.0016667899,0.7451877,0.00024603304,0.0003710716,0.00018640645,0.0041078767,0.0006099786,0.007188351],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985176,0.0002347202,0.00008908413,0.00047197894,0.00059569976,0.00009093633],"domain_scores_gemma":[0.99801564,0.0008896996,0.00022170423,0.00035976162,0.00042110888,0.00009212404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012750808,0.0012883419,0.0013675615,0.0021007464,0.00050336745,0.0015039087,0.0029363446,0.0012240222,0.0034133191],"category_scores_gemma":[0.0039160247,0.00044495985,0.0013667791,0.0023948539,0.0009890321,0.0024215414,0.0023719696,0.002271547,0.0015338989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000100055564,0.00012135797,0.0029618314,0.0002057568,0.000113707094,0.00018852804,0.00013011195,0.059965767,0.01117743,0.022879856,0.012929052,0.8892265],"study_design_scores_gemma":[0.0000055724886,0.000033503176,0.0011365574,0.000018897927,0.000016073527,0.00033151577,0.000036187037,0.9546481,0.007898539,0.027935315,0.007915283,0.000024589834],"about_ca_topic_score_codex":0.0013771967,"about_ca_topic_score_gemma":0.0016203861,"teacher_disagreement_score":0.0034133191,"about_ca_system_score_codex":0.0007381735,"about_ca_system_score_gemma":0.0006810929,"threshold_uncertainty_score":0.0114187},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042566586,0.00042417488,0.9545143,0.00018701833,0.000075828684,0.000057737067,0.00045240228,0.0011093994,0.0006124795],"genre_scores_gemma":[0.72258246,0.00060964626,0.27227375,0.00018288125,0.00024256845,0.00016729202,0.0021574576,0.00022966422,0.0015542711],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998444,0.0003803723,0.00015043466,0.0003936183,0.0004986148,0.00013298898],"domain_scores_gemma":[0.9937186,0.0023002862,0.001464825,0.00072938774,0.0014022674,0.00038457775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020712218,0.0011893711,0.0011889146,0.0021332318,0.00041802588,0.0014124883,0.0011375465,0.0008542447,0.0013494863],"category_scores_gemma":[0.0139368065,0.00027375927,0.0006303798,0.0016108052,0.00091523264,0.0025500827,0.001360873,0.0011959232,0.00060695724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014323326,0.0001993312,0.040008377,0.00051469734,0.00023228959,0.0005980105,0.00039413117,0.23080073,0.07134565,0.03061347,0.0076892725,0.6161717],"study_design_scores_gemma":[0.000022142694,0.00028437492,0.0069237943,0.00002467639,0.00005482837,0.00045343066,0.00009888392,0.9675002,0.011493555,0.010497893,0.0025579003,0.000088384884],"about_ca_topic_score_codex":0.0023995487,"about_ca_topic_score_gemma":0.0020855523,"teacher_disagreement_score":0.0023995487,"about_ca_system_score_codex":0.0006591103,"about_ca_system_score_gemma":0.0010920553,"threshold_uncertainty_score":0.010953784},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029547103,0.00038349268,0.9685661,0.00021403837,0.0000668641,0.000034966506,0.00011873015,0.0006980491,0.0003705574],"genre_scores_gemma":[0.77268046,0.00045364766,0.22565341,0.00012566334,0.00014487341,0.00006718389,0.00039859134,0.00010721048,0.00036881256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99833316,0.0004168865,0.0001744509,0.00030668377,0.000597047,0.000171717],"domain_scores_gemma":[0.9940758,0.0033645488,0.0006835854,0.001044141,0.0006262991,0.00020560827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029108648,0.00075961446,0.0020651203,0.002795727,0.00075404125,0.0031529914,0.0017222622,0.00095292,0.0005573465],"category_scores_gemma":[0.015672533,0.00054602895,0.0010177153,0.004059823,0.00095802196,0.0033315623,0.0025868213,0.0017938082,0.00019387974],"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":[0.00045753559,0.00028235398,0.020211166,0.00027772845,0.00041287442,0.000459559,0.00033189173,0.6784279,0.007821557,0.0375948,0.0026222055,0.25110048],"study_design_scores_gemma":[0.0000043533705,0.0000105465115,0.00061551324,0.0000074130066,0.000011914678,0.000031639698,0.000023722005,0.981557,0.00048359213,0.01706975,0.00017597816,0.000008627462],"about_ca_topic_score_codex":0.007270903,"about_ca_topic_score_gemma":0.005161628,"teacher_disagreement_score":0.007270903,"about_ca_system_score_codex":0.001039627,"about_ca_system_score_gemma":0.0009237618,"threshold_uncertainty_score":0.01539433},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09095778,0.0017075517,0.8901867,0.0017567842,0.00026378853,0.00027870125,0.0017871265,0.010779176,0.0022822954],"genre_scores_gemma":[0.7072794,0.0005119499,0.28566048,0.00028356805,0.00019261576,0.00029069372,0.0033265946,0.0006177754,0.0018369964],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99496895,0.0025112166,0.0003998524,0.00081155193,0.0010531854,0.00025530512],"domain_scores_gemma":[0.9575218,0.03201675,0.0019154013,0.0032411835,0.004882941,0.00042196273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010341667,0.0030861718,0.0015347368,0.006124258,0.0010573973,0.0027692202,0.0016684016,0.0014942175,0.0030075582],"category_scores_gemma":[0.056156173,0.0004516304,0.0017326197,0.0031416141,0.0008632019,0.003155681,0.002519457,0.0030373775,0.0008382829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007419044,0.00068737456,0.03834755,0.00046846093,0.00092639547,0.0003823281,0.0008982889,0.3586287,0.006082566,0.012379204,0.014912368,0.5655449],"study_design_scores_gemma":[0.000022179955,0.00008334495,0.0018594459,0.000036625737,0.00005288702,0.00007178031,0.00006218411,0.98600775,0.002797946,0.0076073613,0.0013634325,0.00003494032],"about_ca_topic_score_codex":0.01790587,"about_ca_topic_score_gemma":0.01385988,"teacher_disagreement_score":0.01790587,"about_ca_system_score_codex":0.0017180585,"about_ca_system_score_gemma":0.0021097772,"threshold_uncertainty_score":0.054692566},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.129427,0.0013898483,0.85205394,0.0010514173,0.00031924588,0.0002372787,0.0031310383,0.009332491,0.003057713],"genre_scores_gemma":[0.61159974,0.00050719327,0.3717556,0.0007401192,0.0002494245,0.00025286997,0.0089889085,0.00083407306,0.0050720656],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986337,0.00021878995,0.000108807886,0.0005104263,0.00036991804,0.00015840726],"domain_scores_gemma":[0.9945932,0.0018659392,0.00065623294,0.0015902693,0.0010737558,0.00022063885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019301,0.0011745191,0.0011751828,0.002157498,0.0006659003,0.0022013874,0.002043632,0.001359606,0.001997781],"category_scores_gemma":[0.007823569,0.00033997968,0.0010608167,0.0018665058,0.0008591441,0.0029783882,0.0016041076,0.00198213,0.0017481344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009393813,0.000653416,0.048311446,0.0003652748,0.00016009808,0.00031124862,0.00037209684,0.12171608,0.029426761,0.0072568688,0.023294566,0.7671927],"study_design_scores_gemma":[0.00002349328,0.00009425252,0.005107776,0.00003409771,0.000021867365,0.00020083363,0.00020169854,0.96410507,0.012721477,0.011398218,0.006058584,0.00003257765],"about_ca_topic_score_codex":0.0032055762,"about_ca_topic_score_gemma":0.00622006,"teacher_disagreement_score":0.0032055762,"about_ca_system_score_codex":0.0011697982,"about_ca_system_score_gemma":0.0011047118,"threshold_uncertainty_score":0.010207474},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013200888,0.0006367788,0.98305607,0.0013057268,0.000065376254,0.00009727006,0.00017931528,0.0007402436,0.00071836734],"genre_scores_gemma":[0.50303406,0.0009150007,0.49227878,0.00054182013,0.00023725243,0.00046764224,0.0007878649,0.00017606237,0.0015616368],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996123,0.001845557,0.00024682833,0.00067486876,0.00094206067,0.00016762197],"domain_scores_gemma":[0.98776466,0.008874053,0.0007481391,0.001046252,0.0012360787,0.0003308851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0072909067,0.0010193135,0.0013075585,0.0017196105,0.0007045919,0.0030423747,0.0017940934,0.0016773264,0.0012039366],"category_scores_gemma":[0.017654698,0.0007236852,0.0011439845,0.002203171,0.0013620777,0.0028323303,0.0022075116,0.0025855508,0.00049333286],"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":[0.0001715753,0.00026468275,0.006982536,0.00022206268,0.00017352842,0.00024127077,0.00020225633,0.802133,0.0017449126,0.05647463,0.004881046,0.12650841],"study_design_scores_gemma":[0.0000040939085,0.000011946067,0.0001865718,0.000008309293,0.0000061762853,0.000014044986,0.000013493901,0.9735738,0.00028772702,0.025166174,0.00072218705,0.0000054541615],"about_ca_topic_score_codex":0.005097148,"about_ca_topic_score_gemma":0.0058817132,"teacher_disagreement_score":0.0072909067,"about_ca_system_score_codex":0.001610352,"about_ca_system_score_gemma":0.0021962915,"threshold_uncertainty_score":0.038558424},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05995086,0.00026577958,0.9368491,0.00018065504,0.000028744516,0.000036676196,0.00028923433,0.0018784016,0.0005205086],"genre_scores_gemma":[0.6816823,0.00022074205,0.31587908,0.000040073952,0.00003717059,0.0001240939,0.0011468056,0.00021050058,0.0006593038],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994407,0.00018709904,0.000042900218,0.00012638728,0.00014422226,0.000058633283],"domain_scores_gemma":[0.99820995,0.0009799931,0.00022549236,0.0003384722,0.00017966199,0.00006644141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00130753,0.0004553849,0.00070964947,0.0008428029,0.00036940732,0.0008287237,0.00096476683,0.00044999397,0.000977668],"category_scores_gemma":[0.0061454414,0.000347396,0.0005031322,0.0014016251,0.00034188465,0.0013148949,0.00084726216,0.0012795561,0.00040421376],"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":[0.0004294897,0.00017323603,0.010719297,0.00007488293,0.00007692157,0.0001105621,0.00019648232,0.662778,0.012764074,0.016753277,0.0026224114,0.29330128],"study_design_scores_gemma":[0.0000044523094,0.000013816367,0.0011462902,0.0000031103477,0.0000036583797,0.000012200438,0.000017938017,0.99088717,0.0020060018,0.005206022,0.00069368054,0.0000055943137],"about_ca_topic_score_codex":0.008663073,"about_ca_topic_score_gemma":0.0072827837,"teacher_disagreement_score":0.008663073,"about_ca_system_score_codex":0.0008899968,"about_ca_system_score_gemma":0.0011045088,"threshold_uncertainty_score":0.017225266},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034701075,0.00039701498,0.9619037,0.0004933586,0.00016716261,0.000031884167,0.000084085346,0.0011239514,0.0010978293],"genre_scores_gemma":[0.8209567,0.0003880603,0.17605773,0.00023407537,0.00022258949,0.00003165145,0.000315137,0.00023573762,0.0015584383],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987208,0.00023955801,0.000075786375,0.00040338168,0.00043264238,0.00012777047],"domain_scores_gemma":[0.9964803,0.0012522738,0.00025547456,0.00065353216,0.0011826686,0.00017577507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016691778,0.00088300556,0.0012410614,0.00093105034,0.0004637305,0.0015030897,0.0016408727,0.0008798229,0.0009836119],"category_scores_gemma":[0.0064874236,0.0003116199,0.0005928212,0.0007381975,0.0008103645,0.0025786664,0.0019351642,0.001963581,0.00045630088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048566525,0.00033064737,0.024071835,0.00018681452,0.0002472187,0.0005175405,0.00038864484,0.1972715,0.055029415,0.018597823,0.006207167,0.69666576],"study_design_scores_gemma":[0.0000056237586,0.000055928365,0.0018566424,0.000011029965,0.000024806797,0.000091418646,0.00005903259,0.98306686,0.004950527,0.008850364,0.0010141975,0.0000135635055],"about_ca_topic_score_codex":0.0067622466,"about_ca_topic_score_gemma":0.005668747,"teacher_disagreement_score":0.0067622466,"about_ca_system_score_codex":0.00044382518,"about_ca_system_score_gemma":0.0008744848,"threshold_uncertainty_score":0.013445735},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05382078,0.00027979855,0.94093263,0.00057755725,0.00011004581,0.0000792201,0.000314311,0.0010152122,0.0028704866],"genre_scores_gemma":[0.8885275,0.00017946519,0.10276923,0.00028474798,0.00005986742,0.0001557109,0.000842245,0.0001566908,0.0070245685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974054,0.000073552044,0.000008552566,0.00008952004,0.000054755084,0.000033057226],"domain_scores_gemma":[0.9994717,0.00033651834,0.000052879528,0.000046479385,0.000058712492,0.000033617594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007062955,0.00077252754,0.00046380147,0.0002602368,0.0001804291,0.00042415434,0.00077523803,0.00076802645,0.0017590935],"category_scores_gemma":[0.0020094535,0.00037372828,0.0006307455,0.00022329029,0.0006656275,0.0004200137,0.0008948015,0.0013228056,0.00037850943],"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":[0.00005735098,0.000016814718,0.0005611939,0.00001625327,0.000016926213,0.000061747756,0.000022578475,0.97674155,0.0016390771,0.004441017,0.001070822,0.015354654],"study_design_scores_gemma":[0.0000025328368,0.000005022561,0.000050140967,0.0000015668986,0.0000014730365,0.0000062587123,0.0000010665893,0.9986971,0.00030448384,0.0008133682,0.000115010276,0.0000019461615],"about_ca_topic_score_codex":0.004872362,"about_ca_topic_score_gemma":0.004526766,"teacher_disagreement_score":0.004872362,"about_ca_system_score_codex":0.0006557457,"about_ca_system_score_gemma":0.00055558636,"threshold_uncertainty_score":0.00968796},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021874601,0.00021336485,0.97646266,0.00013095753,0.000045367473,0.000028284734,0.0000750142,0.0006595058,0.0005102766],"genre_scores_gemma":[0.6868703,0.0002328894,0.30342096,0.00019559271,0.0001020061,0.00009684408,0.00050157146,0.000101486585,0.008478346],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991559,0.00014928862,0.000054293836,0.0002094573,0.00026337805,0.00016775787],"domain_scores_gemma":[0.9987993,0.00045019726,0.0000987806,0.00016267694,0.00041823366,0.000070715454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012308183,0.0008088336,0.0011598875,0.0011809207,0.00048783084,0.0008521723,0.0018727302,0.0013884122,0.0021478042],"category_scores_gemma":[0.0017207527,0.00056389836,0.0010688163,0.00092950795,0.00041549635,0.0012207859,0.0014171245,0.002034089,0.00055762014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047818312,0.0005073733,0.0077559333,0.00016370964,0.00032633197,0.00026309481,0.000120287186,0.32303882,0.0364584,0.01242982,0.0047726743,0.61368537],"study_design_scores_gemma":[0.0000020630948,0.000027307582,0.00028482388,0.0000019813153,0.000010316654,0.000021859903,0.0000027903077,0.99566215,0.0020585875,0.0017120035,0.00021210323,0.000004120576],"about_ca_topic_score_codex":0.005215722,"about_ca_topic_score_gemma":0.007939662,"teacher_disagreement_score":0.005215722,"about_ca_system_score_codex":0.00068813376,"about_ca_system_score_gemma":0.0011328096,"threshold_uncertainty_score":0.010370731},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06976836,0.0016596974,0.92003006,0.0003662945,0.0001754546,0.000087396744,0.0012324788,0.0029272933,0.0037529399],"genre_scores_gemma":[0.8621558,0.0014099622,0.12788945,0.00038920203,0.00011199849,0.00016835719,0.0028073825,0.0000716006,0.004996184],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996666,0.00006547487,0.000023104469,0.0001254974,0.00007416714,0.00004522898],"domain_scores_gemma":[0.99971884,0.000082745646,0.00005047423,0.000046026387,0.00007277958,0.000029051103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036308353,0.0008800434,0.0005862184,0.0009546321,0.00015301719,0.0005664714,0.0007583119,0.00054905436,0.0012445878],"category_scores_gemma":[0.0011921511,0.00022207921,0.0005384103,0.00084263633,0.00026472326,0.0006651807,0.0008134429,0.0007492936,0.0007191128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024586188,0.00041960867,0.014995321,0.0002304051,0.00021643525,0.00020424988,0.00014676717,0.08859827,0.020551305,0.0027748,0.006901154,0.8647159],"study_design_scores_gemma":[0.000012498239,0.00010545677,0.009855033,0.00004173289,0.00003276983,0.00013622854,0.00005982078,0.97043556,0.008372719,0.007622296,0.003303041,0.000022799317],"about_ca_topic_score_codex":0.004219325,"about_ca_topic_score_gemma":0.007441928,"teacher_disagreement_score":0.004219325,"about_ca_system_score_codex":0.00045565958,"about_ca_system_score_gemma":0.00040430357,"threshold_uncertainty_score":0.008389533},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01863682,0.0003562724,0.97296655,0.0003226442,0.00010983028,0.000067054025,0.00037440698,0.006065006,0.0011014303],"genre_scores_gemma":[0.31415817,0.00060606014,0.67124915,0.0005088732,0.00022927232,0.00024368608,0.004945993,0.00137455,0.006684281],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987883,0.00023192551,0.000068515874,0.00020808988,0.00057406223,0.00012924032],"domain_scores_gemma":[0.997242,0.0009680493,0.00027396114,0.0008069347,0.0005595372,0.00014949898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013378011,0.0018387046,0.0017516437,0.0016752364,0.00070670084,0.0016080475,0.0028123374,0.001386897,0.0028284222],"category_scores_gemma":[0.009261072,0.0006357251,0.0013085235,0.001608634,0.0011657892,0.0024662649,0.0026705298,0.0030594508,0.0017951193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047121046,0.00027278162,0.004565931,0.00023469777,0.00026257618,0.0004926451,0.00026575293,0.41693383,0.015267327,0.026479037,0.03282025,0.50193405],"study_design_scores_gemma":[0.00001533459,0.000036727582,0.00014603352,0.000009777732,0.0000122459305,0.00006216708,0.000023987392,0.98573965,0.0025017448,0.009621949,0.0018217035,0.000008653656],"about_ca_topic_score_codex":0.008982228,"about_ca_topic_score_gemma":0.014999009,"teacher_disagreement_score":0.008982228,"about_ca_system_score_codex":0.0007602079,"about_ca_system_score_gemma":0.0019438723,"threshold_uncertainty_score":0.017859936},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.824696,0.0014994356,0.16623168,0.0006427483,0.00020892908,0.0001357408,0.0008533816,0.0019376379,0.0037944023],"genre_scores_gemma":[0.9805462,0.00026323684,0.017718688,0.00008295614,0.000011669356,0.00006673364,0.00065721595,0.000050076134,0.0006031321],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996019,0.000081202816,0.000039767325,0.00010822876,0.000107011125,0.00006184287],"domain_scores_gemma":[0.99768317,0.0014303686,0.00017695529,0.00016628628,0.00044194338,0.00010128338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010652717,0.00094083673,0.00085790805,0.0008985757,0.00026476767,0.00072584156,0.0010623473,0.0011521928,0.00088407844],"category_scores_gemma":[0.006768767,0.00043385744,0.00086498196,0.0005617183,0.00048560597,0.0010902871,0.00048488533,0.00094675674,0.00022948504],"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":[0.00026018242,0.0001000638,0.0031204966,0.00010498153,0.00005523756,0.00004924883,0.000044343713,0.97533447,0.0020306488,0.0006169213,0.00047547757,0.017808013],"study_design_scores_gemma":[0.0000033660156,0.00002061512,0.00031948218,0.000004324387,0.0000028446518,0.0000056461995,0.0000051258107,0.9989717,0.00048654285,0.00013876874,0.00003783436,0.0000037726036],"about_ca_topic_score_codex":0.019827336,"about_ca_topic_score_gemma":0.014780192,"teacher_disagreement_score":0.019827336,"about_ca_system_score_codex":0.0012312339,"about_ca_system_score_gemma":0.0011104764,"threshold_uncertainty_score":0.039423883},"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":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0138508845,0.16260143,0.1832173,0.0075898236,0.0018839281,0.00009680993,0.00017460954,0.00033092752,0.6302542],"genre_scores_gemma":[0.2686811,0.25073755,0.16941237,0.0018775569,0.002313089,0.00014581953,0.00025383377,0.00015870943,0.30641994],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99978536,0.000060453185,0.00001055058,0.000025077406,0.00010593111,0.000012545671],"domain_scores_gemma":[0.99953663,0.00035503754,0.000019346468,0.000022900605,0.000055003744,0.000011108046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041741604,0.00042617638,0.00027041545,0.0009791651,0.0003397961,0.001742491,0.0004815235,0.0008370489,0.0049539874],"category_scores_gemma":[0.0008590668,0.00020616186,0.0003287378,0.0022440345,0.0008926446,0.0012446464,0.00058086985,0.0011587704,0.0009049381],"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":[0.000028718769,0.000106957166,0.00077417097,0.0006171338,0.00003238044,0.00027357647,0.0004593465,0.02308624,0.002419129,0.3695393,0.0412453,0.56141776],"study_design_scores_gemma":[0.000005860092,0.00012412247,0.0023522342,0.00063679775,0.000025808984,0.000569456,0.0004198022,0.048495613,0.002095374,0.43038607,0.5148516,0.000037276004],"about_ca_topic_score_codex":0.0013039992,"about_ca_topic_score_gemma":0.001826815,"teacher_disagreement_score":0.0049539874,"about_ca_system_score_codex":0.00058952894,"about_ca_system_score_gemma":0.00045949072,"threshold_uncertainty_score":0.016572714},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12231222,0.00038098227,0.8719588,0.00027697312,0.00013577865,0.000075992684,0.000052328967,0.0023903793,0.002416692],"genre_scores_gemma":[0.93120307,0.000096750206,0.06715056,0.00010814357,0.000025494088,0.00006606341,0.000056342316,0.000023894067,0.0012696539],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984276,0.000022464417,0.000008368514,0.00004498699,0.00006301978,0.000018378794],"domain_scores_gemma":[0.99976355,0.00007490581,0.000043940385,0.000018125478,0.00008107581,0.000018379318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032102127,0.00034460358,0.0004299589,0.00047191963,0.0003029795,0.00037620784,0.0007427269,0.0004333154,0.00066842174],"category_scores_gemma":[0.000756293,0.000117830736,0.00024335997,0.0002124129,0.0002411107,0.00035371163,0.00039803184,0.00034104247,0.00013908501],"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":[0.00042296402,0.00038470604,0.009243865,0.00012592156,0.00012921775,0.00040973857,0.00019425365,0.43780082,0.104212664,0.0072850604,0.0032778976,0.4365129],"study_design_scores_gemma":[0.0000062420886,0.00006588727,0.0006286203,0.0000027794676,0.000010189585,0.000058899583,0.000009503283,0.99337876,0.0043330686,0.000904425,0.00059655,0.0000050384406],"about_ca_topic_score_codex":0.0013384388,"about_ca_topic_score_gemma":0.0016405781,"teacher_disagreement_score":0.0013384388,"about_ca_system_score_codex":0.0004340546,"about_ca_system_score_gemma":0.0004177847,"threshold_uncertainty_score":0.0031493306},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84324825,0.0009049545,0.14911486,0.00040402,0.00036063863,0.00010717532,0.001429512,0.0014922495,0.002938278],"genre_scores_gemma":[0.98725265,0.00016059249,0.009654912,0.000034000266,0.000034460452,0.00003240175,0.00094640977,0.000012953553,0.0018714893],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997634,0.000029682848,0.00001439068,0.00007060464,0.00005124038,0.00007061407],"domain_scores_gemma":[0.9995926,0.0001387381,0.000047782916,0.00004633099,0.0001388321,0.000035752888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044725108,0.00063204864,0.00049246557,0.0010883278,0.00025836163,0.00040667717,0.0006400915,0.0006637033,0.001139737],"category_scores_gemma":[0.0010233369,0.00019750999,0.00068767776,0.0005976939,0.00020621474,0.00047883304,0.0005064066,0.0008112376,0.0005016981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010397129,0.0011175884,0.10068142,0.00014079649,0.00029341137,0.00042418562,0.00013151717,0.16726466,0.016619435,0.0015135518,0.0074981726,0.7032757],"study_design_scores_gemma":[0.000006031198,0.00007363912,0.017797444,0.000013411639,0.00002689763,0.00006073695,0.000029774947,0.9780426,0.0028584416,0.00073934003,0.0003414361,0.000010211555],"about_ca_topic_score_codex":0.011950007,"about_ca_topic_score_gemma":0.01062231,"teacher_disagreement_score":0.011950007,"about_ca_system_score_codex":0.0006492083,"about_ca_system_score_gemma":0.0006752866,"threshold_uncertainty_score":0.023760915},"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":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07196997,0.43282714,0.43633392,0.02511061,0.0010176942,0.00016653677,0.0011351921,0.0010403557,0.030398563],"genre_scores_gemma":[0.41491574,0.4345049,0.13634993,0.0029488984,0.0011900552,0.0001864473,0.0021991741,0.0002536041,0.0074512134],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977361,0.00064925494,0.00017346493,0.00032772988,0.0009948347,0.00011870483],"domain_scores_gemma":[0.99391174,0.0039956276,0.00020725092,0.00049377594,0.00123793,0.00015373185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005284278,0.0013309739,0.0010768259,0.0021155674,0.00046598862,0.002785055,0.0013546583,0.001250659,0.0017571946],"category_scores_gemma":[0.011617548,0.0006788475,0.0008169128,0.0030620613,0.00091178634,0.0060779196,0.0015150496,0.003717344,0.00079493184],"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":[0.00014700071,0.0001758771,0.009356213,0.0018373397,0.00019012767,0.00010478121,0.00025673932,0.047807373,0.0021511777,0.062385276,0.014930554,0.8606576],"study_design_scores_gemma":[0.00003401502,0.000761222,0.007600255,0.0035798065,0.00037592443,0.000524976,0.00093879795,0.64278275,0.011421038,0.16022027,0.17161469,0.00014628429],"about_ca_topic_score_codex":0.0036599855,"about_ca_topic_score_gemma":0.004673016,"teacher_disagreement_score":0.005284278,"about_ca_system_score_codex":0.0014455789,"about_ca_system_score_gemma":0.0014893301,"threshold_uncertainty_score":0.027946293},"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":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024459248,0.02658083,0.3742355,0.0020511884,0.0032039355,0.00016970531,0.0012093127,0.015946643,0.57415694],"genre_scores_gemma":[0.025349826,0.022086766,0.106197976,0.0011037273,0.001360616,0.00011947852,0.0016860192,0.0019142032,0.8401814],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996039,0.00002794397,0.00001202159,0.000054353834,0.00027628336,0.000025428646],"domain_scores_gemma":[0.99951243,0.0001788565,0.000020441788,0.000055110464,0.00020079175,0.000032293632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003446341,0.0012624279,0.00048801457,0.002174065,0.00037578234,0.0039024837,0.0011580543,0.0010590112,0.062130988],"category_scores_gemma":[0.0013726373,0.0004974533,0.0004168083,0.0019465236,0.00050940434,0.001978801,0.0011671453,0.0012725397,0.035064444],"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":[0.00003658891,0.000027901711,0.00009558711,0.00028065292,0.000008325001,0.000053025662,0.00010870411,0.0020141203,0.0036578428,0.031844556,0.2695471,0.6923256],"study_design_scores_gemma":[0.000006448931,0.000040959527,0.00037955053,0.00027872858,0.000016152824,0.00040576162,0.000103981365,0.016760191,0.0043143784,0.04824791,0.92941314,0.00003271532],"about_ca_topic_score_codex":0.0015160721,"about_ca_topic_score_gemma":0.0018723978,"teacher_disagreement_score":0.062130988,"about_ca_system_score_codex":0.00062507886,"about_ca_system_score_gemma":0.00039655704,"threshold_uncertainty_score":0.20784885},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16621897,0.0062955017,0.7508398,0.0012173508,0.00040778576,0.000105666266,0.0037512982,0.004603626,0.06655999],"genre_scores_gemma":[0.7808892,0.006348771,0.16250262,0.00013704036,0.00041488747,0.00007232727,0.0047951518,0.0005351639,0.0443048],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9998074,0.00002983693,0.0000092858745,0.00003643453,0.00009429008,0.00002281611],"domain_scores_gemma":[0.99943024,0.00032363937,0.00007929528,0.000045167217,0.000106097985,0.000015431948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003651802,0.00053611794,0.0004213883,0.0021053345,0.00023022095,0.0010493711,0.00059884,0.0003390381,0.0050359843],"category_scores_gemma":[0.0015417984,0.00020317886,0.00042569218,0.002480629,0.0002155485,0.0009163102,0.00031125674,0.0004911445,0.001313393],"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":[0.00016062659,0.000087872606,0.019780928,0.00035544904,0.00010352748,0.0003462033,0.00024285576,0.13424043,0.012667819,0.029807566,0.023513509,0.7786932],"study_design_scores_gemma":[0.000010316823,0.00011752282,0.047934942,0.0000947542,0.000073245305,0.000829336,0.0003852124,0.8245849,0.01327692,0.075534955,0.03710191,0.000055897297],"about_ca_topic_score_codex":0.0017392491,"about_ca_topic_score_gemma":0.002006985,"teacher_disagreement_score":0.0050359843,"about_ca_system_score_codex":0.00035264564,"about_ca_system_score_gemma":0.00029072657,"threshold_uncertainty_score":0.016847074},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64605546,0.0021328868,0.3412974,0.001191435,0.00021704887,0.00016451017,0.0016642484,0.005089524,0.0021874798],"genre_scores_gemma":[0.9469523,0.0002932753,0.049040027,0.00014934262,0.00003215396,0.00009729576,0.0018583909,0.000076977696,0.0015001926],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989064,0.0002143539,0.00011396231,0.00031337113,0.00030699218,0.00014500784],"domain_scores_gemma":[0.99326766,0.0037451256,0.0011468338,0.00037905114,0.0012052081,0.0002560765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002463661,0.0015342407,0.0006440231,0.0015568971,0.0003723142,0.0006189358,0.0014925865,0.0011747574,0.0010094681],"category_scores_gemma":[0.010682212,0.00042894893,0.0005559325,0.0007628549,0.00043133183,0.0013052617,0.00073103234,0.001942965,0.00030036768],"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":[0.00052315264,0.00035937718,0.038843863,0.00015705073,0.000104118095,0.00019405645,0.0001434565,0.77785075,0.0029411274,0.0009846344,0.0043059755,0.17359245],"study_design_scores_gemma":[0.000004377181,0.000033268432,0.0010774754,0.000008938029,0.000007720744,0.00001034993,0.000012629235,0.9970795,0.00096626463,0.0006429538,0.00015216554,0.000004422418],"about_ca_topic_score_codex":0.021111565,"about_ca_topic_score_gemma":0.027576517,"teacher_disagreement_score":0.021111565,"about_ca_system_score_codex":0.0020200065,"about_ca_system_score_gemma":0.0010620266,"threshold_uncertainty_score":0.041977406},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.081015125,0.0002153869,0.9181485,0.0001184031,0.000013429228,0.000009975035,0.000078969286,0.00010203686,0.0002982146],"genre_scores_gemma":[0.8785749,0.00034972746,0.11978381,0.000053235526,0.00008450774,0.000030135554,0.00030475087,0.000033896533,0.00078501593],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993069,0.00024958246,0.000041325322,0.00022734431,0.00012713819,0.000047668458],"domain_scores_gemma":[0.99426174,0.0041588754,0.0007899199,0.00042791254,0.00025986787,0.00010158596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024450773,0.0004914128,0.0006387386,0.0012356079,0.00035860002,0.0007674094,0.001008041,0.00069741934,0.0006895468],"category_scores_gemma":[0.013134999,0.00031931896,0.0005672968,0.0012729416,0.0008136282,0.0019302829,0.0010397597,0.0012735886,0.000119605196],"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":[0.00030472217,0.00011641874,0.02866281,0.00020022051,0.0002964331,0.00029465617,0.0003561038,0.71368176,0.009644148,0.07548937,0.0008095657,0.1701438],"study_design_scores_gemma":[0.0000049943974,0.00001771519,0.001788631,0.000003883806,0.000010666742,0.00001775035,0.000017474204,0.9817135,0.00071374624,0.015511725,0.00019370085,0.0000061330106],"about_ca_topic_score_codex":0.0018036315,"about_ca_topic_score_gemma":0.0014123578,"teacher_disagreement_score":0.0024450773,"about_ca_system_score_codex":0.00046611813,"about_ca_system_score_gemma":0.0003267431,"threshold_uncertainty_score":0.01293093},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005535951,0.00005645595,0.9933014,0.000070575996,0.000016342761,0.00005660345,0.000047403908,0.0006998475,0.00021548278],"genre_scores_gemma":[0.1782202,0.00008912388,0.8186475,0.00018369552,0.00009953549,0.00045189483,0.00073849416,0.00023460043,0.0013350148],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968696,0.0010862574,0.000260676,0.0008021388,0.0008113087,0.00016998056],"domain_scores_gemma":[0.9934651,0.0031538876,0.00081775704,0.00074417907,0.0016543196,0.00016470884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005849101,0.0011910786,0.0018728497,0.0017516179,0.0009752222,0.001148575,0.0027220414,0.0015154883,0.0014277399],"category_scores_gemma":[0.010134635,0.0007307092,0.0015640327,0.0011792363,0.0012092665,0.0019688255,0.0018522278,0.0028071003,0.0009672362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002956865,0.0002799708,0.0039190375,0.00019516454,0.00025495532,0.00012521945,0.00026700087,0.38587126,0.008105373,0.008433867,0.005667241,0.5865853],"study_design_scores_gemma":[0.000012967635,0.00003208015,0.00027429912,0.000007095425,0.000008019692,0.000038667305,0.0000137155575,0.9933615,0.0016621439,0.0040762313,0.0005046483,0.000008638917],"about_ca_topic_score_codex":0.0020094947,"about_ca_topic_score_gemma":0.0023310327,"teacher_disagreement_score":0.005849101,"about_ca_system_score_codex":0.0008314276,"about_ca_system_score_gemma":0.0021877685,"threshold_uncertainty_score":0.03093344},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043581877,0.00022142223,0.95447296,0.00026325427,0.000051690975,0.000015164675,0.000115789415,0.000650129,0.0006276948],"genre_scores_gemma":[0.9187823,0.00021633728,0.078077525,0.00008641075,0.00005823933,0.000045328165,0.00030917593,0.00008017429,0.002344498],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995733,0.000104175735,0.00002359826,0.00009516478,0.00011886446,0.00008479707],"domain_scores_gemma":[0.99915004,0.00041366005,0.00010492882,0.00009193791,0.00019916892,0.000040235318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007687247,0.0006318393,0.0008207893,0.0005753329,0.00029104383,0.0008223621,0.001293477,0.0006978628,0.0011458052],"category_scores_gemma":[0.002811506,0.00041425688,0.00054854015,0.0009577597,0.00042358806,0.0014548413,0.0010500231,0.0019342648,0.00025562642],"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":[0.000117424075,0.00009946577,0.0025402792,0.000045002365,0.00005698801,0.000054586904,0.00003394026,0.8803461,0.0027784684,0.00994468,0.001816603,0.10216652],"study_design_scores_gemma":[5.80085e-7,0.0000025699064,0.00006905394,6.3628386e-7,0.0000010943643,0.0000028725342,0.0000013934599,0.9983354,0.00013499142,0.0014068133,0.000043773674,7.608795e-7],"about_ca_topic_score_codex":0.006836369,"about_ca_topic_score_gemma":0.008274818,"teacher_disagreement_score":0.006836369,"about_ca_system_score_codex":0.00087201816,"about_ca_system_score_gemma":0.0008980168,"threshold_uncertainty_score":0.013593137},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11633856,0.0024963976,0.83324325,0.0008048823,0.00048271366,0.0002877891,0.0026149526,0.029189654,0.014541854],"genre_scores_gemma":[0.61508757,0.0007924205,0.35222584,0.00094748795,0.0001100454,0.00031192214,0.008044612,0.00068599085,0.021794053],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99979657,0.000014514102,0.000009045125,0.000094651594,0.00005149628,0.00003375473],"domain_scores_gemma":[0.9998115,0.00003518498,0.000022604174,0.000042550208,0.00007049715,0.000017704626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002722449,0.0009647659,0.0005447039,0.0005053371,0.00034210586,0.0005660863,0.0017454679,0.0008036217,0.002901466],"category_scores_gemma":[0.0008063579,0.00039447067,0.0006405819,0.00042651597,0.0003780662,0.0011796111,0.0010428781,0.0008147078,0.0015052997],"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":[0.0004389658,0.00024154648,0.0041084914,0.00029378952,0.00019625419,0.00032021457,0.00014103127,0.20179158,0.054994073,0.0053661456,0.037050806,0.6950571],"study_design_scores_gemma":[0.000012305923,0.000107156964,0.0010293816,0.000019067586,0.000031346302,0.00010029573,0.000026705091,0.97887415,0.010689369,0.0026498851,0.0064369068,0.000023354296],"about_ca_topic_score_codex":0.01242788,"about_ca_topic_score_gemma":0.020947127,"teacher_disagreement_score":0.01242788,"about_ca_system_score_codex":0.00080338126,"about_ca_system_score_gemma":0.0007157855,"threshold_uncertainty_score":0.024711072},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01629906,0.000073729345,0.98220485,0.00014665796,0.00002874951,0.000052155294,0.0001369974,0.0006045298,0.00045322746],"genre_scores_gemma":[0.18129878,0.00027658063,0.81606287,0.000109038505,0.000045215005,0.00014495086,0.0008183443,0.00038952322,0.0008547229],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988361,0.00040090765,0.00009263341,0.00024153592,0.0003427046,0.00008609156],"domain_scores_gemma":[0.9956273,0.0021202553,0.00036751322,0.000905647,0.00085170683,0.00012767682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032495542,0.0011989395,0.0007982163,0.0023509616,0.0005339811,0.001627935,0.0012461323,0.0013543101,0.0028527365],"category_scores_gemma":[0.01882124,0.00050715194,0.0010544804,0.0019822696,0.000958564,0.00263237,0.0012085411,0.0021922772,0.0010887074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046148855,0.00035653723,0.007863182,0.00051632145,0.00015218466,0.00054235716,0.0005637386,0.23634152,0.13015322,0.043150302,0.004103795,0.57579535],"study_design_scores_gemma":[0.000011403738,0.000090859976,0.0019005754,0.00004039396,0.00001418704,0.00026181564,0.00012712652,0.9490171,0.02924635,0.01535051,0.0038962904,0.000043346274],"about_ca_topic_score_codex":0.0044384045,"about_ca_topic_score_gemma":0.004765942,"teacher_disagreement_score":0.0044384045,"about_ca_system_score_codex":0.0007531845,"about_ca_system_score_gemma":0.0014309189,"threshold_uncertainty_score":0.01718551},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017019507,0.0002689357,0.9811163,0.000090630994,0.00009184338,0.000017518454,0.000041180916,0.001078216,0.0002758634],"genre_scores_gemma":[0.69318366,0.00033273443,0.30438143,0.00009577233,0.00010748571,0.00006454234,0.00031383257,0.00013977342,0.0013807593],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992169,0.00013508614,0.000060181483,0.00016812752,0.00033562022,0.00008406781],"domain_scores_gemma":[0.9983742,0.00065207726,0.00015079182,0.00015076608,0.0006163514,0.00005571572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010838427,0.0006521338,0.0014269875,0.00096508075,0.000313661,0.00085658557,0.0010456069,0.0007695599,0.00058946974],"category_scores_gemma":[0.00410454,0.00042369164,0.0006904056,0.00097618013,0.00035530986,0.0017163794,0.00092829426,0.0010137028,0.00032391047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022229562,0.00020893569,0.0059269113,0.00016130993,0.00016402389,0.00009581966,0.000115260795,0.38340652,0.014811959,0.0029522567,0.0017087029,0.590226],"study_design_scores_gemma":[0.0000033841293,0.0000199377,0.00041682232,0.0000032488042,0.00000666028,0.000014503975,0.000006033093,0.9966875,0.001843434,0.0006752818,0.00031802146,0.0000053459007],"about_ca_topic_score_codex":0.009266087,"about_ca_topic_score_gemma":0.008598435,"teacher_disagreement_score":0.009266087,"about_ca_system_score_codex":0.0005352375,"about_ca_system_score_gemma":0.0009775818,"threshold_uncertainty_score":0.018424273},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02087431,0.00019471835,0.97705907,0.00022558619,0.0000357327,0.000024504821,0.000109886685,0.0008647351,0.00061147776],"genre_scores_gemma":[0.5501054,0.00052913174,0.44151947,0.00027232696,0.00020135855,0.00016243405,0.0012255986,0.00054712204,0.0054370235],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99784184,0.000815435,0.00014512696,0.00045078425,0.0005512637,0.00019558173],"domain_scores_gemma":[0.99325585,0.003822136,0.0004093015,0.0008321402,0.0013533827,0.00032719894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004201047,0.00092922687,0.0015914139,0.0027352662,0.0008947051,0.0020388316,0.002228498,0.0015189521,0.0020114416],"category_scores_gemma":[0.014503715,0.0005008739,0.0013609715,0.0022364757,0.0025355057,0.0036284197,0.002666372,0.0033978734,0.00083644455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064914086,0.00033903428,0.007772864,0.00015461887,0.00015472033,0.00016928653,0.00030792976,0.31597397,0.010123195,0.1239283,0.006526092,0.53390086],"study_design_scores_gemma":[0.000007486455,0.000028107119,0.0003623823,0.000007722819,0.000008229676,0.000032883152,0.000019334882,0.94402194,0.0017784928,0.053227518,0.00048887846,0.000017064342],"about_ca_topic_score_codex":0.0066991434,"about_ca_topic_score_gemma":0.008991044,"teacher_disagreement_score":0.0066991434,"about_ca_system_score_codex":0.0020451322,"about_ca_system_score_gemma":0.002067851,"threshold_uncertainty_score":0.022217512},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0101722535,0.00013430328,0.98590106,0.00006998302,0.000052237807,0.000032015694,0.00010194557,0.0031019982,0.00043428733],"genre_scores_gemma":[0.2231944,0.00023930405,0.7697808,0.00009134278,0.00014128732,0.00009224638,0.0008684555,0.0006546197,0.0049375105],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990663,0.00011697209,0.00004743249,0.0003127784,0.00033796066,0.00011859407],"domain_scores_gemma":[0.998357,0.0006872769,0.0001385534,0.00037944395,0.00034161014,0.000096185984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070121983,0.0011175415,0.001500244,0.0011012276,0.00040202355,0.0012359557,0.002330568,0.0011887347,0.0025431653],"category_scores_gemma":[0.002858371,0.00060902047,0.0009074684,0.0012380724,0.0006260548,0.0018579005,0.0018297622,0.0016160043,0.0021225126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027000965,0.00022440589,0.0016736885,0.0000945887,0.000095046875,0.00012295524,0.00006100225,0.04235386,0.043476082,0.0031939386,0.0058488045,0.9025856],"study_design_scores_gemma":[0.0000087476865,0.00003923785,0.0007970475,0.0000033785986,0.000011098438,0.00013517469,0.000016077845,0.9833898,0.0100635,0.0045109116,0.0010150607,0.000009997672],"about_ca_topic_score_codex":0.0028843337,"about_ca_topic_score_gemma":0.0064868894,"teacher_disagreement_score":0.0028843337,"about_ca_system_score_codex":0.0005321664,"about_ca_system_score_gemma":0.0010030407,"threshold_uncertainty_score":0.008507669},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020249322,0.00021111216,0.9783076,0.00006033319,0.000029742796,0.000017463724,0.00004836031,0.0005880409,0.00048794466],"genre_scores_gemma":[0.5899509,0.00047319374,0.40487957,0.0001580984,0.00007146695,0.000115551054,0.0006206442,0.00012185666,0.003608678],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962187,0.00007078759,0.00002432196,0.00010470791,0.00012959118,0.000048601654],"domain_scores_gemma":[0.9994649,0.00019258205,0.00006163492,0.000077520286,0.0001805735,0.000022804816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065151375,0.0006428068,0.000601825,0.00046157418,0.00019874926,0.00034921162,0.00068946235,0.00048581275,0.0006482457],"category_scores_gemma":[0.0015477409,0.000289549,0.0005396725,0.00051086943,0.00037577964,0.0008162499,0.0006088742,0.001109064,0.00028286106],"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":[0.00020485469,0.000121285026,0.0036745837,0.00008977486,0.00013926855,0.000096422256,0.00011279614,0.47980675,0.032778956,0.007301882,0.0026597457,0.47301367],"study_design_scores_gemma":[0.0000025413435,0.000017998644,0.0003480856,0.0000027957128,0.0000075233424,0.000019038296,0.0000037354584,0.99528164,0.0030659768,0.0008499149,0.00039663614,0.000004099892],"about_ca_topic_score_codex":0.0051558455,"about_ca_topic_score_gemma":0.0066059395,"teacher_disagreement_score":0.0051558455,"about_ca_system_score_codex":0.0004632269,"about_ca_system_score_gemma":0.00077104557,"threshold_uncertainty_score":0.010251641},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1812386,0.00081480027,0.7844846,0.0004516817,0.00016183851,0.00016793703,0.0038783168,0.021305365,0.0074967844],"genre_scores_gemma":[0.8474345,0.00043956414,0.14672393,0.00004782058,0.000051188657,0.00007938029,0.0034466018,0.00018856386,0.0015883805],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996977,0.00004683513,0.000021416405,0.000081501865,0.00011307656,0.00003943086],"domain_scores_gemma":[0.99963474,0.000059698075,0.00005379505,0.00005632911,0.00015602676,0.00003942988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026267747,0.0005843586,0.00043459135,0.0022035355,0.00020295446,0.00081328786,0.00064178236,0.000431292,0.0017706908],"category_scores_gemma":[0.0008980279,0.00013748516,0.00036267252,0.0017993503,0.00019707582,0.0005733092,0.00051939074,0.0003712481,0.00092150783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041143197,0.0001744475,0.026851257,0.00036921207,0.00014576358,0.00040172148,0.00022670071,0.121749245,0.063868225,0.004825922,0.015566606,0.7654095],"study_design_scores_gemma":[0.000010835622,0.0000772807,0.012417908,0.00003408365,0.00003312173,0.00024917486,0.00014433765,0.9518427,0.01952434,0.0045454646,0.011098984,0.000021753227],"about_ca_topic_score_codex":0.006355131,"about_ca_topic_score_gemma":0.005782412,"teacher_disagreement_score":0.006355131,"about_ca_system_score_codex":0.00045617152,"about_ca_system_score_gemma":0.00046100476,"threshold_uncertainty_score":0.012636304},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47631088,0.0069216387,0.35569805,0.0018267184,0.0020363955,0.001012916,0.05974344,0.08213053,0.014319537],"genre_scores_gemma":[0.52799404,0.0005946269,0.30101565,0.0008467439,0.00019379257,0.00038230632,0.15899394,0.001172638,0.008806243],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884844,0.00015606855,0.000051200706,0.00038810153,0.00039938994,0.00015676189],"domain_scores_gemma":[0.9989291,0.00024030615,0.0000674196,0.00028946451,0.00039731254,0.00007645253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014722856,0.0018126181,0.0010756196,0.002166671,0.00048406704,0.0012074353,0.002615476,0.0016349771,0.0038748626],"category_scores_gemma":[0.0039559165,0.00046317896,0.0011610786,0.0013175274,0.0007046433,0.0014050102,0.0014937965,0.0019351073,0.0027103506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016142268,0.0015268237,0.013483477,0.0008028324,0.0005949371,0.0006959538,0.0001847662,0.22802168,0.032966897,0.0039934115,0.1538788,0.56223625],"study_design_scores_gemma":[0.000100343095,0.00020374596,0.003989508,0.000036886468,0.00002698746,0.00030673112,0.00007553821,0.9690536,0.010455059,0.0033169403,0.01238493,0.000049699684],"about_ca_topic_score_codex":0.01679285,"about_ca_topic_score_gemma":0.03027376,"teacher_disagreement_score":0.01679285,"about_ca_system_score_codex":0.001047411,"about_ca_system_score_gemma":0.00096894754,"threshold_uncertainty_score":0.033390224},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1975675,0.0002933586,0.7980953,0.0002266571,0.000083631036,0.000051691957,0.000102714504,0.0010903107,0.002488792],"genre_scores_gemma":[0.92258126,0.00010927871,0.07535615,0.00009505588,0.000030947303,0.000035293284,0.000101783815,0.000028402257,0.001661885],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997702,0.000029210622,0.000012836977,0.00007998427,0.00006519491,0.000042481104],"domain_scores_gemma":[0.99973565,0.00006769447,0.000051837455,0.00002490428,0.000084938416,0.00003487548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033682305,0.0004771147,0.0005372959,0.00056038913,0.00024064242,0.00040271724,0.00067409803,0.00041082603,0.00055551063],"category_scores_gemma":[0.0008021615,0.0001795992,0.00023511362,0.00038504126,0.00022765638,0.0005020859,0.0004986459,0.00041985878,0.00017533131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008816161,0.0005552504,0.0338139,0.00010558332,0.00014483067,0.00037826132,0.00014610417,0.29629803,0.05716641,0.003426548,0.003832536,0.6032509],"study_design_scores_gemma":[0.0000052928513,0.000044876706,0.0021661797,0.000002694755,0.000009518169,0.000030096688,0.000009293372,0.9935581,0.0032173367,0.0007198466,0.00023017172,0.0000065541935],"about_ca_topic_score_codex":0.003412129,"about_ca_topic_score_gemma":0.004619084,"teacher_disagreement_score":0.003412129,"about_ca_system_score_codex":0.00031816756,"about_ca_system_score_gemma":0.00044587185,"threshold_uncertainty_score":0.0067845583},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12225578,0.0020241095,0.8709254,0.0005716967,0.0001366367,0.000033289816,0.00035813072,0.0009866661,0.002708351],"genre_scores_gemma":[0.97425914,0.00041893305,0.022517214,0.00014645151,0.000055058546,0.000038236743,0.00031979042,0.000032422206,0.002212855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998492,0.000021857993,0.000008910476,0.000047334495,0.000044086784,0.000028599805],"domain_scores_gemma":[0.99958605,0.00021302118,0.000059574486,0.000025629828,0.00009642672,0.000019306137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046572043,0.0006693747,0.00057936565,0.00050455134,0.0002182225,0.00043174325,0.0010958804,0.00071387563,0.00094200147],"category_scores_gemma":[0.0013780581,0.0003114194,0.0005481758,0.0004363296,0.00037977484,0.00087984896,0.0006285857,0.0011328695,0.0001474717],"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":[0.00008716848,0.000055799526,0.0020023126,0.000045141747,0.000043049095,0.0000930335,0.000028941264,0.9424011,0.0027319475,0.0018243908,0.0009138459,0.04977338],"study_design_scores_gemma":[8.709994e-7,0.000003558196,0.00013566553,0.0000014516331,0.0000030976473,0.0000039009024,8.55932e-7,0.9990337,0.00018123745,0.00059382705,0.000040383118,0.0000014262602],"about_ca_topic_score_codex":0.009555399,"about_ca_topic_score_gemma":0.010247804,"teacher_disagreement_score":0.009555399,"about_ca_system_score_codex":0.0007356647,"about_ca_system_score_gemma":0.0006725348,"threshold_uncertainty_score":0.018999577},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04829625,0.00035011765,0.9414866,0.00013322722,0.00022778423,0.00006577563,0.0002694022,0.006988111,0.00218285],"genre_scores_gemma":[0.6722243,0.0002881181,0.32000044,0.00021042625,0.00014292284,0.000093839946,0.00086251803,0.00031975014,0.005857772],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991755,0.00008648048,0.0000336209,0.00026271443,0.00032154296,0.00012007623],"domain_scores_gemma":[0.9992316,0.00021755187,0.000085217944,0.00015883804,0.00025613006,0.000050762843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000657685,0.00086086616,0.0014027948,0.0018949078,0.00036981187,0.00079886545,0.0015007427,0.0008972761,0.0016835862],"category_scores_gemma":[0.0014776706,0.00030166746,0.00069294404,0.0014518,0.00047212705,0.0014186612,0.0012794202,0.00084811996,0.0009463693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006636611,0.00031159417,0.008885478,0.00011482384,0.00021572097,0.00031142408,0.000068599235,0.077062145,0.03518133,0.0046327617,0.0052840454,0.86726844],"study_design_scores_gemma":[0.000011519497,0.00008495254,0.0026259406,0.0000107145315,0.000036379428,0.00026609452,0.000019221578,0.97732055,0.012087705,0.005880291,0.0016356418,0.000021121245],"about_ca_topic_score_codex":0.0044598654,"about_ca_topic_score_gemma":0.00528583,"teacher_disagreement_score":0.0044598654,"about_ca_system_score_codex":0.0006496239,"about_ca_system_score_gemma":0.0007062097,"threshold_uncertainty_score":0.0088678},"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":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018504899,0.28139916,0.66813624,0.005659058,0.0007939115,0.00044893747,0.0011743982,0.0030011237,0.020882268],"genre_scores_gemma":[0.36203927,0.32433382,0.2982191,0.0028379844,0.002107949,0.00044769232,0.003546997,0.00053905416,0.005928104],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99364555,0.0014183252,0.00064094,0.001340583,0.0026904293,0.00026422567],"domain_scores_gemma":[0.9816896,0.012663586,0.00073359394,0.0023915372,0.002286676,0.00023501604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051403744,0.0014171273,0.0016981652,0.0037265029,0.00080482836,0.0034407142,0.003569476,0.0020257516,0.0019619127],"category_scores_gemma":[0.02255828,0.0006977381,0.0016598428,0.004742946,0.0013344289,0.007100529,0.002134106,0.002315137,0.001319098],"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":[0.00011845651,0.0001283487,0.004173785,0.0018652743,0.00016385471,0.00013002683,0.00022400386,0.00919781,0.0025206925,0.0161294,0.010878357,0.95447016],"study_design_scores_gemma":[0.00008228734,0.0009486845,0.016233848,0.0037311227,0.00073142524,0.008559009,0.00170033,0.3842347,0.03933546,0.11139285,0.43265608,0.00039422885],"about_ca_topic_score_codex":0.0027533195,"about_ca_topic_score_gemma":0.0010502383,"teacher_disagreement_score":0.0051403744,"about_ca_system_score_codex":0.0014369395,"about_ca_system_score_gemma":0.0016446335,"threshold_uncertainty_score":0.027185261},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049612757,0.00030850887,0.9484216,0.00028168948,0.000037613405,0.000026932537,0.000045443045,0.0002772533,0.0009881468],"genre_scores_gemma":[0.91166204,0.00034701676,0.0850548,0.00018205358,0.00006292347,0.000059491747,0.00018356493,0.000040602397,0.0024075885],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994647,0.00015004312,0.000028724931,0.00015931981,0.00014193392,0.00005534959],"domain_scores_gemma":[0.99841106,0.00094748265,0.00017597563,0.0001360191,0.0002751175,0.000054229567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013012685,0.0006751267,0.0006533677,0.00036812943,0.00023225305,0.0005971623,0.000982766,0.0007951671,0.0005921462],"category_scores_gemma":[0.004130087,0.00038100596,0.00054895045,0.0003826868,0.0007510856,0.0010079268,0.0011195095,0.0017266481,0.00015223278],"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":[0.0001633118,0.000071149814,0.002835991,0.000055064593,0.00008431871,0.00012849958,0.00010028165,0.87626696,0.005685619,0.0076654213,0.0009388199,0.10600463],"study_design_scores_gemma":[0.0000011753135,0.000008556506,0.00013334658,0.0000020400032,0.0000038439407,0.000011656651,0.0000021592846,0.9986076,0.0004995156,0.00065506576,0.0000725109,0.000002506831],"about_ca_topic_score_codex":0.0024942257,"about_ca_topic_score_gemma":0.0019177574,"teacher_disagreement_score":0.0024942257,"about_ca_system_score_codex":0.00053798174,"about_ca_system_score_gemma":0.0004585443,"threshold_uncertainty_score":0.0068818927},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22917715,0.00046344902,0.75901484,0.00023369312,0.00011615112,0.00003714704,0.00016689305,0.002107553,0.0086832],"genre_scores_gemma":[0.9371626,0.000113255075,0.059963368,0.000028057791,0.000027093096,0.000016728654,0.00021445825,0.0001233205,0.0023511609],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998229,0.000028482664,0.000005319646,0.000036170106,0.00008347834,0.000023612103],"domain_scores_gemma":[0.99985933,0.00005088953,0.000016218415,0.00001990162,0.000045542358,0.000008131243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021464693,0.0003768078,0.0005852247,0.00064516114,0.00038388252,0.0007355981,0.0007056019,0.0005053766,0.001036572],"category_scores_gemma":[0.0006121522,0.00030752426,0.00047314476,0.0007781186,0.000358991,0.00081682287,0.0005670032,0.00063968473,0.00039432163],"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":[0.0002620612,0.00019131353,0.0065911585,0.00014781591,0.00008732482,0.00015819738,0.00026623564,0.59281445,0.042766634,0.009338335,0.003452106,0.34392437],"study_design_scores_gemma":[0.000001917577,0.00001253964,0.0014547547,0.0000023253453,0.000003877807,0.000023602317,0.000015906591,0.9946221,0.0020758733,0.0012132245,0.00056755764,0.000006340554],"about_ca_topic_score_codex":0.005441254,"about_ca_topic_score_gemma":0.006368098,"teacher_disagreement_score":0.005441254,"about_ca_system_score_codex":0.0004381243,"about_ca_system_score_gemma":0.000391993,"threshold_uncertainty_score":0.010819137},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.077351466,0.00070323737,0.9174855,0.0001331141,0.00014472747,0.00010361609,0.0006187326,0.0019237427,0.0015359558],"genre_scores_gemma":[0.57554245,0.0005455003,0.41831678,0.00007510884,0.00019510718,0.00011099286,0.0018014057,0.0001971006,0.0032155574],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942446,0.00011685924,0.000039967836,0.00015985625,0.00018913431,0.00006989359],"domain_scores_gemma":[0.9990722,0.00022867873,0.00011055028,0.0001245398,0.00041093925,0.000053183823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000610284,0.0006962141,0.00086113985,0.004517673,0.000545561,0.0009588023,0.0009131456,0.0009052645,0.0011212303],"category_scores_gemma":[0.0017695676,0.00024531755,0.0007094936,0.0028833065,0.00027186467,0.0007934149,0.0005246406,0.00069864304,0.0005816042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001018509,0.0005559227,0.013423222,0.00037348073,0.00039904923,0.00041529987,0.00027519654,0.15555224,0.16293779,0.008649697,0.007978058,0.6484215],"study_design_scores_gemma":[0.000010896949,0.00010577162,0.008058761,0.0000110959245,0.00007366838,0.00028265518,0.000073610085,0.97143847,0.014677978,0.003197681,0.0020372118,0.00003216802],"about_ca_topic_score_codex":0.003892603,"about_ca_topic_score_gemma":0.0039135795,"teacher_disagreement_score":0.004517673,"about_ca_system_score_codex":0.00044215232,"about_ca_system_score_gemma":0.0005684396,"threshold_uncertainty_score":0.0077399015},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99236673,0.000107983884,0.0056778076,0.00012969713,0.000021405955,0.000014686038,0.00080198655,0.00006893722,0.000810672],"genre_scores_gemma":[0.9951303,0.00006708038,0.004025808,0.000015352018,0.000018936216,0.000007280885,0.0005802353,0.000006052124,0.00014895212],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99982435,0.00004758179,0.000016162418,0.000045070417,0.000044959233,0.000021834594],"domain_scores_gemma":[0.9992347,0.00034533098,0.00016117963,0.00007992343,0.0001248625,0.000054128745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035344803,0.00020309248,0.00019182522,0.0008679041,0.0001877183,0.00043539598,0.00024173335,0.00035546842,0.00038776585],"category_scores_gemma":[0.0009546248,0.0001085963,0.00025670987,0.001114683,0.00017830734,0.00050845277,0.00022128026,0.00029612164,0.000100083285],"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":[0.0007502284,0.0005602923,0.82511455,0.00017070406,0.00023948906,0.0005212323,0.0003510062,0.009838656,0.05544371,0.0013193763,0.0017445721,0.103946164],"study_design_scores_gemma":[0.000015189173,0.00035754728,0.8630593,0.0000143605785,0.000081103964,0.00041857242,0.0005238564,0.120439574,0.0118350005,0.0019553655,0.0012772527,0.000022939232],"about_ca_topic_score_codex":0.0018366587,"about_ca_topic_score_gemma":0.0023417117,"teacher_disagreement_score":0.0018366587,"about_ca_system_score_codex":0.00017328023,"about_ca_system_score_gemma":0.00018851306,"threshold_uncertainty_score":0.003651917},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019023716,0.0002830557,0.9537707,0.00054769777,0.00010593362,0.00030900512,0.00030688362,0.018491441,0.007161559],"genre_scores_gemma":[0.23370278,0.00029296186,0.7592143,0.00041306243,0.000059655315,0.00030112156,0.00085771194,0.0003320399,0.004826301],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947745,0.00011739872,0.000049048496,0.00013671113,0.00018894026,0.00003041601],"domain_scores_gemma":[0.99859387,0.0007435174,0.00010295165,0.00014519092,0.00033898087,0.00007540124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094529433,0.000680071,0.00041060973,0.0012206929,0.0005192551,0.001384606,0.0013924935,0.0009170434,0.004753695],"category_scores_gemma":[0.0035640988,0.00028420915,0.0004487671,0.0005296728,0.00046694666,0.0012435179,0.0010181109,0.0009310751,0.0016209995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000418707,0.0005739055,0.0037990343,0.00048976997,0.0001300694,0.00061822456,0.0007563551,0.09816072,0.06570727,0.017217632,0.023472382,0.788656],"study_design_scores_gemma":[0.000052147414,0.00013150467,0.0007410861,0.000051372346,0.00003646115,0.00022724265,0.00009884418,0.95212686,0.017398113,0.010841374,0.018260928,0.00003406861],"about_ca_topic_score_codex":0.0029038633,"about_ca_topic_score_gemma":0.003053453,"teacher_disagreement_score":0.004753695,"about_ca_system_score_codex":0.00060207746,"about_ca_system_score_gemma":0.0013807439,"threshold_uncertainty_score":0.015902758},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027666854,0.00019102811,0.9639839,0.00016230275,0.00006934135,0.00014270158,0.00079808454,0.006331849,0.0006539666],"genre_scores_gemma":[0.2581741,0.00021117748,0.73229545,0.00021873561,0.000062876774,0.0005084669,0.005903798,0.0012891219,0.0013362467],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99557436,0.0010044764,0.00044146768,0.0012400695,0.0014955183,0.00024414598],"domain_scores_gemma":[0.98630255,0.0035181732,0.0010318066,0.006068846,0.0028824944,0.00019606025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003627413,0.001391391,0.0012457833,0.0023967035,0.0010085818,0.001588027,0.0024195886,0.0011560268,0.0016238465],"category_scores_gemma":[0.020036055,0.0006909305,0.0015576873,0.002122439,0.0012293626,0.0024387492,0.0030453554,0.0028521423,0.0019151816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006029928,0.00046492604,0.027863996,0.0006397978,0.00051946635,0.00038161088,0.0008757017,0.08419274,0.10060935,0.011313191,0.015451289,0.75708497],"study_design_scores_gemma":[0.0000415957,0.00033563838,0.01587371,0.00009521923,0.00009892635,0.0007539587,0.00032494875,0.7432831,0.17564422,0.03302971,0.030388374,0.00013060184],"about_ca_topic_score_codex":0.001990707,"about_ca_topic_score_gemma":0.0026225445,"teacher_disagreement_score":0.003627413,"about_ca_system_score_codex":0.00068609137,"about_ca_system_score_gemma":0.0018321091,"threshold_uncertainty_score":0.019183874},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8694369,0.0012252321,0.1216493,0.0005923315,0.00022837157,0.0003381226,0.0014322803,0.003069161,0.0020283014],"genre_scores_gemma":[0.9342732,0.00027873123,0.0618956,0.00018832451,0.000047258953,0.00013762961,0.0024998642,0.00017632802,0.00050307566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99133915,0.0030684823,0.0007870034,0.0021201805,0.0021013152,0.00058378495],"domain_scores_gemma":[0.94756025,0.033773735,0.0040739286,0.008136758,0.0054795435,0.00097566243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007929396,0.0018832011,0.0011169454,0.001661484,0.0008995163,0.0014695115,0.002249924,0.0016478138,0.00058150967],"category_scores_gemma":[0.05394289,0.00040605833,0.00073537865,0.0014607838,0.0015166851,0.002436233,0.0023405189,0.0023339386,0.00043013107],"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":[0.0032608176,0.0027416656,0.06939686,0.0012833487,0.0007655107,0.0006884824,0.00095675513,0.6779253,0.044057146,0.0042906236,0.009677128,0.18495636],"study_design_scores_gemma":[0.00011571271,0.0011255275,0.010282731,0.00009869646,0.000116823074,0.00040848996,0.00038739856,0.9239247,0.056115698,0.0053446647,0.001988401,0.000091161164],"about_ca_topic_score_codex":0.0054737376,"about_ca_topic_score_gemma":0.005302001,"teacher_disagreement_score":0.007929396,"about_ca_system_score_codex":0.0012217823,"about_ca_system_score_gemma":0.0018706761,"threshold_uncertainty_score":0.041935146},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054676592,0.0004787482,0.94174004,0.00030182413,0.000063113606,0.000036099766,0.00009088966,0.0017155691,0.0008970488],"genre_scores_gemma":[0.826516,0.0002968939,0.1707206,0.00034377552,0.00009614332,0.00005837992,0.00032757272,0.00014961872,0.0014910088],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890745,0.0002548178,0.00008580607,0.00027542913,0.00037192885,0.000104540115],"domain_scores_gemma":[0.9954064,0.0021966454,0.00045811178,0.00046967494,0.0013160387,0.00015307538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002653553,0.0011328036,0.00094984093,0.0010645934,0.000352468,0.0011374608,0.0012782381,0.0009104967,0.0008410284],"category_scores_gemma":[0.010317698,0.0005380248,0.00058896624,0.0006692593,0.0007062639,0.0020539449,0.0015368946,0.0020162822,0.00030004026],"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":[0.0002463499,0.00017067234,0.006890928,0.00007857568,0.00014937688,0.00010582683,0.000198054,0.6049514,0.011338714,0.0055804965,0.0021750568,0.3681145],"study_design_scores_gemma":[0.0000036792233,0.000028091901,0.00040711017,0.000007200987,0.0000110204355,0.000021647395,0.000008076013,0.9941492,0.0031779138,0.0019430099,0.00023514446,0.000007815704],"about_ca_topic_score_codex":0.0049302336,"about_ca_topic_score_gemma":0.004888172,"teacher_disagreement_score":0.0049302336,"about_ca_system_score_codex":0.00089022925,"about_ca_system_score_gemma":0.0015211464,"threshold_uncertainty_score":0.014033496},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24711324,0.00051372044,0.7460549,0.0002629932,0.00015488347,0.00009950892,0.0010938725,0.0017803303,0.0029266735],"genre_scores_gemma":[0.9438285,0.000108733264,0.052424137,0.00006623616,0.00006839563,0.000075520526,0.0013734625,0.0000631593,0.0019918615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964035,0.00006981741,0.000020545296,0.00014153602,0.00007969894,0.000048080725],"domain_scores_gemma":[0.99883,0.00050028967,0.00017232196,0.00017114996,0.00026583258,0.0000603964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009022037,0.0005433099,0.00079599686,0.0010792927,0.00046521486,0.00061191426,0.0012828563,0.0008954113,0.0013398214],"category_scores_gemma":[0.0026339365,0.00025119015,0.00053277035,0.0010777002,0.0003715751,0.0015010429,0.00067318085,0.000913502,0.0005371448],"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":[0.00037224256,0.00052969466,0.021542864,0.000114108385,0.00016754173,0.00021415675,0.0000976722,0.60736233,0.006931526,0.0062836483,0.007374548,0.34900963],"study_design_scores_gemma":[0.0000015456047,0.000015479718,0.00058238395,0.00000253741,0.00000473228,0.000015231851,0.000003927994,0.9965264,0.00041684342,0.0022989511,0.00012923598,0.0000026564376],"about_ca_topic_score_codex":0.003109416,"about_ca_topic_score_gemma":0.00446617,"teacher_disagreement_score":0.003109416,"about_ca_system_score_codex":0.0006092329,"about_ca_system_score_gemma":0.00046453328,"threshold_uncertainty_score":0.0061826706},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71882385,0.003128959,0.26599342,0.0006824609,0.000046471687,0.00028887406,0.0015894787,0.00088845415,0.0085581085],"genre_scores_gemma":[0.96506137,0.0013955829,0.030292151,0.000042586606,0.000028969538,0.00007737095,0.0015160926,0.000048960774,0.0015369328],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995608,0.000115408824,0.000030117304,0.00007159032,0.00018985012,0.000032247397],"domain_scores_gemma":[0.9985598,0.000723019,0.00025480954,0.00014754095,0.00027893134,0.000035893092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012036024,0.0008034376,0.0003441333,0.0025349318,0.00030936484,0.00073314796,0.0005641341,0.00041826823,0.000806147],"category_scores_gemma":[0.0029767046,0.00018835477,0.0008526536,0.0017415814,0.00035093926,0.0009002944,0.0005046858,0.0005189234,0.00016256163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021977938,0.00026497032,0.20451522,0.0008539971,0.0008388637,0.0009109827,0.0013396876,0.3123322,0.017627146,0.014826753,0.0033029923,0.44296744],"study_design_scores_gemma":[0.000020399046,0.00063279964,0.16812785,0.00014359197,0.00032940324,0.0005477553,0.0011671579,0.7977584,0.011738903,0.008448057,0.010995161,0.00009056491],"about_ca_topic_score_codex":0.007865555,"about_ca_topic_score_gemma":0.004418657,"teacher_disagreement_score":0.007865555,"about_ca_system_score_codex":0.00072102086,"about_ca_system_score_gemma":0.0005593158,"threshold_uncertainty_score":0.015639544},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017547473,0.00013148252,0.9776537,0.00056093396,0.000037127043,0.000028089797,0.000341596,0.0024535372,0.0012460827],"genre_scores_gemma":[0.67315024,0.00030862837,0.31755215,0.00041309954,0.00009882057,0.00020611676,0.0013953083,0.00048871496,0.0063869297],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958223,0.00017673065,0.000017268567,0.000075640375,0.00010361496,0.00004440045],"domain_scores_gemma":[0.9971596,0.0021213435,0.00014025651,0.00029998703,0.00021784284,0.00006093665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015050346,0.00058960763,0.0004058507,0.0005070869,0.000272462,0.000742157,0.0009912679,0.00079760386,0.0061508752],"category_scores_gemma":[0.006653326,0.00027159654,0.0007350925,0.0003112303,0.0005023767,0.0008145657,0.0018022028,0.0010982868,0.000544421],"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":[0.0002528926,0.00008553039,0.005449384,0.00015196126,0.000100674246,0.00033635364,0.00037135635,0.7106148,0.0052909977,0.046330202,0.010630659,0.22038521],"study_design_scores_gemma":[0.000007866727,0.000009720005,0.00026813912,0.0000059553518,0.000006748948,0.000023526127,0.000009711981,0.9764553,0.00063401344,0.021125045,0.0014503291,0.000003748289],"about_ca_topic_score_codex":0.004012116,"about_ca_topic_score_gemma":0.0064934627,"teacher_disagreement_score":0.0061508752,"about_ca_system_score_codex":0.0007400781,"about_ca_system_score_gemma":0.00084847404,"threshold_uncertainty_score":0.020576715},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025510011,0.00021941647,0.95917064,0.00022706497,0.000104213526,0.000119881,0.00039467818,0.012262667,0.0019914107],"genre_scores_gemma":[0.33920586,0.00021276431,0.65496635,0.00034854846,0.00010626529,0.00018596112,0.0012760368,0.00026858313,0.0034295956],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994616,0.00006194586,0.000035789646,0.00021260421,0.00018858463,0.00003946379],"domain_scores_gemma":[0.9992593,0.000111102025,0.00010761766,0.00013780127,0.00029034223,0.00009391415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000712498,0.00072915404,0.0009590394,0.0010838081,0.0004042009,0.0010484388,0.0017501335,0.00081402913,0.0014294636],"category_scores_gemma":[0.0014737233,0.00036767207,0.00045720235,0.00074074103,0.0004404805,0.0017106549,0.0016518122,0.001092855,0.0011035942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006932324,0.00061237556,0.012047644,0.00029037238,0.00029383024,0.0004793133,0.0003237056,0.05201174,0.15368441,0.0074624675,0.017975077,0.75412583],"study_design_scores_gemma":[0.000027773554,0.00018952503,0.002894246,0.000010579168,0.000029481342,0.0003758337,0.000036211557,0.96964586,0.017763505,0.002289176,0.0066910526,0.000046744608],"about_ca_topic_score_codex":0.0027854312,"about_ca_topic_score_gemma":0.005138855,"teacher_disagreement_score":0.0027854312,"about_ca_system_score_codex":0.00043947285,"about_ca_system_score_gemma":0.0009822957,"threshold_uncertainty_score":0.0055384636},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031173289,0.00039236833,0.9667213,0.000096503994,0.00005440841,0.000013636925,0.00006797115,0.0008374875,0.0006429577],"genre_scores_gemma":[0.80345005,0.00046800237,0.19126166,0.00015928407,0.000080431004,0.000052994954,0.00052709196,0.0001721797,0.0038283325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997067,0.000050109393,0.000014650746,0.000111913774,0.000077538294,0.000039063216],"domain_scores_gemma":[0.9993911,0.00027607067,0.00006387348,0.00010370607,0.0001430333,0.00002224753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046148864,0.00076069345,0.00082469627,0.0005327308,0.00020966367,0.00034762442,0.0008748769,0.00073250197,0.001006195],"category_scores_gemma":[0.0013574454,0.00038351456,0.00066180783,0.0005766318,0.00041309829,0.0008659666,0.00067307026,0.0010600954,0.00037580598],"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":[0.00029717913,0.00015180213,0.0017653065,0.00011525864,0.00013569083,0.00013619891,0.00008642177,0.5152308,0.025790479,0.005036853,0.0030505136,0.44820347],"study_design_scores_gemma":[0.0000017725649,0.000015930325,0.00036061884,0.0000022265706,0.00000924865,0.000014621443,0.0000032732123,0.99756265,0.0009121773,0.00095101196,0.00016369393,0.0000027754816],"about_ca_topic_score_codex":0.0055241436,"about_ca_topic_score_gemma":0.0071682706,"teacher_disagreement_score":0.0055241436,"about_ca_system_score_codex":0.00032632623,"about_ca_system_score_gemma":0.000562001,"threshold_uncertainty_score":0.010984004},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.106012546,0.00024640476,0.8900732,0.00024837736,0.00008443243,0.00006965752,0.00019154625,0.0020059156,0.0010678528],"genre_scores_gemma":[0.9305369,0.000213476,0.06756445,0.000081013604,0.000047409692,0.000075142154,0.00034420195,0.000052929347,0.0010845576],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990113,0.00014581802,0.00006522809,0.00028715993,0.00038172834,0.0001087989],"domain_scores_gemma":[0.99880874,0.00043505925,0.000251927,0.00020365376,0.0002451065,0.000055430126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078519137,0.0009187821,0.0009905961,0.0016321354,0.00044167478,0.0010270723,0.0012764472,0.00097390515,0.00036498858],"category_scores_gemma":[0.0037920948,0.0002840526,0.00076259614,0.0010756529,0.0006234825,0.0019443219,0.0013912639,0.0011372854,0.0002659568],"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":[0.00025559837,0.00036163055,0.047229156,0.00010909409,0.00015163615,0.0009513482,0.0002366341,0.6969443,0.013806427,0.013767307,0.0026991535,0.22348782],"study_design_scores_gemma":[0.0000021456378,0.000021076856,0.001368994,0.0000037701927,0.00000685726,0.00011628112,0.000017391283,0.9931532,0.0011508083,0.00375109,0.0004014538,0.0000068661398],"about_ca_topic_score_codex":0.0027672166,"about_ca_topic_score_gemma":0.0020860208,"teacher_disagreement_score":0.0027672166,"about_ca_system_score_codex":0.00066765293,"about_ca_system_score_gemma":0.0006906445,"threshold_uncertainty_score":0.005502224},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011306164,0.00019596856,0.98486495,0.00014532624,0.00004573944,0.00004933083,0.0000257549,0.0018251643,0.0015416032],"genre_scores_gemma":[0.76573426,0.00019851103,0.22985217,0.00043250158,0.000057717574,0.00027290307,0.00017500434,0.00047740093,0.0027996493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992526,0.00025160596,0.000041793828,0.00015868817,0.00019416401,0.000101082114],"domain_scores_gemma":[0.99842596,0.0007496843,0.00020699487,0.00019651518,0.00028274852,0.00013811441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001841879,0.0015699406,0.0011645606,0.00050667627,0.00035791233,0.0010211958,0.0018778928,0.0012884187,0.0021515521],"category_scores_gemma":[0.005384649,0.00074059877,0.00067327876,0.00027802805,0.0013240573,0.0011941952,0.001864013,0.0027484675,0.0008295153],"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":[0.00009648978,0.00006555141,0.00087634625,0.000088588684,0.000056803485,0.00009669576,0.000103755236,0.937804,0.0034363274,0.005895316,0.0015659314,0.049914114],"study_design_scores_gemma":[0.00001075973,0.00002387526,0.000035923345,0.000005742732,0.0000042397955,0.000008900355,0.0000042460592,0.9977253,0.00041329756,0.0015094947,0.00025463908,0.0000035749094],"about_ca_topic_score_codex":0.0033050475,"about_ca_topic_score_gemma":0.003979366,"teacher_disagreement_score":0.0033050475,"about_ca_system_score_codex":0.00074338395,"about_ca_system_score_gemma":0.0020382826,"threshold_uncertainty_score":0.009740889},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035754807,0.00017183187,0.962152,0.00017342501,0.00003361519,0.000033375996,0.00002298438,0.000498033,0.0011599647],"genre_scores_gemma":[0.9128671,0.000078043086,0.0858397,0.00013860506,0.00005212671,0.00005454285,0.000051829637,0.00009304154,0.0008250315],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99769634,0.0008961378,0.00008252243,0.00045538758,0.0005885877,0.0002810166],"domain_scores_gemma":[0.9954389,0.0026443976,0.0003700846,0.0005834054,0.0006929227,0.0002702881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042090993,0.0008284461,0.0013353215,0.0006033274,0.0006175578,0.0012367759,0.0019936212,0.0009593656,0.00091079774],"category_scores_gemma":[0.013280611,0.00045983272,0.00044790938,0.00042475623,0.0012256042,0.0020572776,0.0021673867,0.0017558944,0.0002102831],"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":[0.00018548644,0.00011429362,0.0013950935,0.000033694683,0.000031528343,0.000048809612,0.00008965407,0.9450765,0.0033732236,0.009679986,0.00055097666,0.039420754],"study_design_scores_gemma":[0.000008629992,0.000027825747,0.00007759885,0.0000022251502,0.0000032191786,0.000008097044,0.0000044850785,0.9960985,0.00065923465,0.0029504737,0.00015666851,0.0000029551325],"about_ca_topic_score_codex":0.0045727897,"about_ca_topic_score_gemma":0.0037402802,"teacher_disagreement_score":0.0045727897,"about_ca_system_score_codex":0.0010338125,"about_ca_system_score_gemma":0.001840179,"threshold_uncertainty_score":0.02226013},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0573192,0.00086599524,0.93761575,0.00031714802,0.0001316605,0.000042320124,0.00020369976,0.0010785268,0.0024257854],"genre_scores_gemma":[0.88913697,0.0007280245,0.104425974,0.00017448376,0.000065871,0.00008015387,0.0006046499,0.00009788175,0.0046860967],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963367,0.000061914354,0.000026544345,0.00012935464,0.00010826235,0.000040166055],"domain_scores_gemma":[0.99946314,0.00018024113,0.00007227216,0.000045041248,0.00021913419,0.000020210155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004872258,0.0007219517,0.00048560766,0.0007002825,0.00024996765,0.00057115755,0.00097484316,0.0005715036,0.00091034744],"category_scores_gemma":[0.00228347,0.00031448712,0.00055843283,0.0008224322,0.00033060662,0.0011151539,0.00051172427,0.0007887509,0.00025486277],"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":[0.00016760237,0.00007757112,0.0034871702,0.00012374051,0.00010666986,0.00016428386,0.000071051145,0.73301953,0.013285158,0.0053437576,0.0028906984,0.24126275],"study_design_scores_gemma":[0.0000020747323,0.00001077252,0.00034873802,0.000002273809,0.0000073300266,0.00001421932,0.0000027448673,0.99750525,0.0012198911,0.0006556766,0.00022608938,0.0000050255067],"about_ca_topic_score_codex":0.015497441,"about_ca_topic_score_gemma":0.0124777835,"teacher_disagreement_score":0.015497441,"about_ca_system_score_codex":0.0008452527,"about_ca_system_score_gemma":0.0007613378,"threshold_uncertainty_score":0.030814469},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018510595,0.00048499735,0.9468484,0.0006454614,0.00060150534,0.0001378672,0.0010529478,0.014164971,0.017553257],"genre_scores_gemma":[0.49218124,0.00042302633,0.46944678,0.0005748534,0.00038555535,0.00029857835,0.0032464974,0.0020279968,0.031415485],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992723,0.00009012084,0.000044256245,0.00023828995,0.00025769367,0.00009734104],"domain_scores_gemma":[0.9985812,0.00020188942,0.000043966338,0.00067499164,0.00044373632,0.000054206437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008146344,0.0014207574,0.0009820884,0.0009718626,0.00066769175,0.0016016619,0.0023103282,0.0015623225,0.016711244],"category_scores_gemma":[0.0051714266,0.00066912436,0.0007799615,0.0007403797,0.0006930042,0.0025146978,0.0020838145,0.0018388709,0.0069417176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006538199,0.00015515194,0.0009548029,0.00019901338,0.00016589479,0.00031386362,0.000036995203,0.4559887,0.018062014,0.040279187,0.026370632,0.45681986],"study_design_scores_gemma":[0.00002007966,0.000036893354,0.00022825837,0.000010935607,0.000025272971,0.00008926605,0.000006142421,0.9698901,0.008836568,0.015582043,0.005260126,0.0000143111165],"about_ca_topic_score_codex":0.011505751,"about_ca_topic_score_gemma":0.010579963,"teacher_disagreement_score":0.016711244,"about_ca_system_score_codex":0.0006986118,"about_ca_system_score_gemma":0.0016598335,"threshold_uncertainty_score":0.055904627},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036699913,0.001456787,0.9552091,0.0026819329,0.00013134537,0.00011647723,0.00052248297,0.0011043724,0.00207759],"genre_scores_gemma":[0.6632919,0.002338803,0.32446003,0.001491033,0.00049896096,0.00030747056,0.0028638658,0.00070700736,0.0040408983],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99731845,0.0013577783,0.00011683808,0.000591468,0.00041454448,0.00020083078],"domain_scores_gemma":[0.9750709,0.019983811,0.0009923646,0.0022468874,0.0012133718,0.00049272116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004242014,0.0015103128,0.0015703322,0.001970454,0.00054431055,0.0035578571,0.002652054,0.00235825,0.002566321],"category_scores_gemma":[0.018506186,0.0009661281,0.0033581436,0.0014817457,0.0019683815,0.0038976579,0.0019163487,0.0037833292,0.0008975519],"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":[0.0001319673,0.0005267805,0.02104528,0.00028278443,0.00058454927,0.0002765814,0.0009104398,0.8148109,0.0024820766,0.050953094,0.0048083626,0.10318716],"study_design_scores_gemma":[0.0000038289995,0.000015595042,0.00046364672,0.000015601123,0.000014687746,0.000027797081,0.00002611404,0.9825429,0.00024019179,0.015557928,0.0010796612,0.000012071468],"about_ca_topic_score_codex":0.013910932,"about_ca_topic_score_gemma":0.013597088,"teacher_disagreement_score":0.013910932,"about_ca_system_score_codex":0.00192161,"about_ca_system_score_gemma":0.0018739,"threshold_uncertainty_score":0.027659893},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21003902,0.0048271306,0.7620689,0.002271607,0.0007094099,0.00020305095,0.0014820637,0.007483346,0.01091544],"genre_scores_gemma":[0.8491428,0.00065759337,0.13136314,0.00077739,0.0001681878,0.00019866947,0.0030115733,0.000210241,0.01447033],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915075,0.00016706409,0.000058038604,0.00023265179,0.00019364698,0.0001977937],"domain_scores_gemma":[0.9989064,0.00036648242,0.000071708484,0.00014005712,0.00044855775,0.00006684131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017102634,0.001381716,0.0012933397,0.0010963201,0.0005033991,0.0011328743,0.0019077465,0.0021101858,0.0043450054],"category_scores_gemma":[0.0028067613,0.000378722,0.0012018174,0.00094074704,0.00040236342,0.0013846132,0.0012002902,0.0021754724,0.0017874645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048771186,0.0005149557,0.0030807883,0.00013964281,0.00016231077,0.00018427236,0.000076586424,0.47681546,0.00831041,0.0029526586,0.015025267,0.49224997],"study_design_scores_gemma":[0.0000067756155,0.000022899805,0.00015276726,0.000004443476,0.000006445286,0.000010422446,0.00000342792,0.9981091,0.00077266764,0.0005955262,0.00031113467,0.0000043585915],"about_ca_topic_score_codex":0.017288579,"about_ca_topic_score_gemma":0.013021269,"teacher_disagreement_score":0.017288579,"about_ca_system_score_codex":0.0012664999,"about_ca_system_score_gemma":0.0015893128,"threshold_uncertainty_score":0.034375846},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12868935,0.0006100682,0.85911787,0.00032428236,0.0002822327,0.00019767726,0.0006046092,0.008612103,0.0015617737],"genre_scores_gemma":[0.64671177,0.00021671788,0.348998,0.00011594719,0.0001432967,0.00011087873,0.0017422575,0.00022394914,0.0017371664],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99796945,0.00027828437,0.00014512325,0.0006422962,0.0007199248,0.00024490283],"domain_scores_gemma":[0.996442,0.0010406924,0.00046504842,0.00079827214,0.0010469989,0.00020698585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015323397,0.0011218837,0.0017171295,0.0029086499,0.00067317893,0.0013700338,0.0024872902,0.00095493405,0.0007542594],"category_scores_gemma":[0.005524023,0.00033373758,0.0009907428,0.0025498874,0.00047808234,0.0016180496,0.0014793025,0.0017970453,0.0007506347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084076694,0.0009049632,0.04438348,0.00025024908,0.0004915866,0.00049966574,0.0003231103,0.10997665,0.040168628,0.0042551714,0.0125296675,0.78537613],"study_design_scores_gemma":[0.000011770426,0.000114104165,0.0061099445,0.000008768687,0.000030261073,0.00032468134,0.00007922079,0.9770856,0.011827426,0.0019874715,0.0023920257,0.000028763017],"about_ca_topic_score_codex":0.0053117545,"about_ca_topic_score_gemma":0.0066946647,"teacher_disagreement_score":0.0053117545,"about_ca_system_score_codex":0.0005824532,"about_ca_system_score_gemma":0.0008949456,"threshold_uncertainty_score":0.010561705},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54782444,0.006719068,0.31939593,0.010080482,0.0007997863,0.00079115294,0.012936963,0.013668274,0.08778384],"genre_scores_gemma":[0.790605,0.0024922509,0.18069725,0.00064478326,0.00015240324,0.00023822003,0.0032160687,0.00070477265,0.02124919],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998938,0.00020158922,0.00006344079,0.00011161591,0.0006061355,0.000079151614],"domain_scores_gemma":[0.9980147,0.0009483936,0.000079342615,0.00026077367,0.00061142154,0.000085416825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014342566,0.00072991394,0.0005260161,0.0015763247,0.0007545297,0.0013213595,0.0012445037,0.0021938602,0.0061241593],"category_scores_gemma":[0.0037906002,0.0002471068,0.00081185007,0.0019706103,0.0006023432,0.00086035376,0.00088682375,0.0007574034,0.001802727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001298289,0.0009085923,0.06813586,0.0015857689,0.00027432258,0.01096841,0.000939266,0.15374994,0.020660985,0.019268045,0.10155653,0.6206539],"study_design_scores_gemma":[0.00019346652,0.0006167172,0.020745507,0.00034941875,0.00019774873,0.0066962824,0.0009823627,0.74202335,0.048853546,0.020539649,0.15868084,0.000121165634],"about_ca_topic_score_codex":0.013786966,"about_ca_topic_score_gemma":0.02487356,"teacher_disagreement_score":0.013786966,"about_ca_system_score_codex":0.0008604234,"about_ca_system_score_gemma":0.0008229359,"threshold_uncertainty_score":0.027413428},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27545425,0.040449172,0.51011103,0.07735168,0.0010966238,0.00093515794,0.00093061884,0.00086797256,0.09280345],"genre_scores_gemma":[0.67937595,0.020334432,0.28884152,0.0032641809,0.0007130496,0.00045871493,0.0004884467,0.00026317776,0.0062605096],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.982496,0.011752473,0.0008302836,0.00089850137,0.003516653,0.0005060811],"domain_scores_gemma":[0.95219755,0.039209396,0.0015096203,0.0030535096,0.0034933372,0.00053671276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019436715,0.0009976152,0.0008323443,0.0029624924,0.002309529,0.005703276,0.0025530597,0.004718092,0.0019625085],"category_scores_gemma":[0.026395064,0.00047414054,0.0011844542,0.004076703,0.0052460534,0.005797687,0.0036547587,0.0035860501,0.0005449714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068169745,0.0006193634,0.062300276,0.003373288,0.00029099407,0.028914627,0.01580361,0.07267251,0.006197043,0.31987706,0.032178953,0.45709065],"study_design_scores_gemma":[0.000164647,0.00086955004,0.018989298,0.0047858157,0.00028100368,0.030866912,0.0276694,0.19747333,0.022439029,0.2588079,0.4371917,0.00046141003],"about_ca_topic_score_codex":0.005033763,"about_ca_topic_score_gemma":0.007673285,"teacher_disagreement_score":0.019436715,"about_ca_system_score_codex":0.0022889583,"about_ca_system_score_gemma":0.0019861148,"threshold_uncertainty_score":0.10279244},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03954338,0.0018492807,0.40807793,0.004191031,0.0035788557,0.00092355645,0.011152538,0.14717707,0.38350636],"genre_scores_gemma":[0.3065604,0.0017645953,0.21052648,0.00242909,0.0006124062,0.00076480425,0.014844511,0.007675715,0.45482206],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995115,0.000042922078,0.000020644558,0.00011851279,0.00019358374,0.00011280048],"domain_scores_gemma":[0.9993723,0.000093517374,0.000038473965,0.00020506821,0.000193122,0.00009754864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007103267,0.00077442214,0.00054369296,0.0011699448,0.0009169936,0.0010022164,0.0014580507,0.0008545748,0.11254699],"category_scores_gemma":[0.002010035,0.00035528617,0.00057852664,0.00041129743,0.00038923748,0.0013511362,0.001965966,0.0014326144,0.035883367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001145195,0.0002591654,0.002284378,0.00031457623,0.00006957577,0.00060533796,0.00025753482,0.008968295,0.029046984,0.037455473,0.48739326,0.4322003],"study_design_scores_gemma":[0.00022650915,0.00027836187,0.0021729327,0.00010623805,0.00007064801,0.00070883817,0.00013755307,0.021094438,0.025677059,0.011739558,0.93770325,0.00008457637],"about_ca_topic_score_codex":0.0022026394,"about_ca_topic_score_gemma":0.0027257719,"teacher_disagreement_score":0.11254699,"about_ca_system_score_codex":0.0005387302,"about_ca_system_score_gemma":0.00087114406,"threshold_uncertainty_score":0.37650716},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44746748,0.0003402537,0.54857016,0.00081294635,0.00002855764,0.00009781808,0.0002623168,0.0013132412,0.0011072383],"genre_scores_gemma":[0.9550615,0.00006644931,0.04425521,0.00004626927,0.000024438505,0.00003391886,0.0002592464,0.000040938467,0.00021193104],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990214,0.00039045207,0.000067086716,0.0001778972,0.00023675816,0.00010646794],"domain_scores_gemma":[0.9887482,0.008346863,0.0010247399,0.0008093422,0.0009023983,0.00016844695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021982181,0.0007004233,0.0006113636,0.0018440535,0.00037336806,0.0012320346,0.00061023363,0.0004909575,0.00075447356],"category_scores_gemma":[0.014493659,0.00021862674,0.0006480637,0.00092515966,0.0005667228,0.0017715186,0.000944059,0.0012143725,0.00019719536],"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":[0.00086786726,0.0008591292,0.10217429,0.00026295334,0.00022077249,0.0004989603,0.00067248225,0.38142097,0.03982846,0.011833237,0.0019651058,0.4593957],"study_design_scores_gemma":[0.000008059806,0.000076387296,0.008080107,0.0000070400783,0.000020525178,0.000052498974,0.000048957576,0.98263675,0.0033985514,0.0053396528,0.00032121665,0.000010186091],"about_ca_topic_score_codex":0.0022107142,"about_ca_topic_score_gemma":0.0019011091,"teacher_disagreement_score":0.0022107142,"about_ca_system_score_codex":0.0006061904,"about_ca_system_score_gemma":0.0006152768,"threshold_uncertainty_score":0.011625409},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8689496,0.0012266785,0.1221119,0.0005919312,0.00022881129,0.0003387601,0.0014336853,0.0030849017,0.0020338763],"genre_scores_gemma":[0.9341638,0.0002789129,0.062003538,0.00018807346,0.000047205136,0.00013772825,0.0024995557,0.00017696568,0.00050425547],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9913523,0.0030620466,0.0007860507,0.0021166347,0.002100051,0.000583036],"domain_scores_gemma":[0.94775,0.033623658,0.0040604575,0.008126299,0.0054666237,0.00097298395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007905298,0.0018815857,0.001116855,0.0016591651,0.0008984588,0.0014690176,0.0022463857,0.0016453571,0.0005821965],"category_scores_gemma":[0.053840294,0.00040532468,0.00073454174,0.0014603997,0.0015157025,0.002435204,0.0023397885,0.0023313963,0.00043093445],"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":[0.0032586644,0.0027389552,0.06926247,0.0012844707,0.00076502707,0.0006889036,0.00095788296,0.6776779,0.044095855,0.004298009,0.009698607,0.18527327],"study_design_scores_gemma":[0.000115462695,0.0011240939,0.010271239,0.00009867952,0.00011670136,0.0004090589,0.00038784195,0.92390794,0.056138,0.0053476044,0.001992255,0.000091049165],"about_ca_topic_score_codex":0.0054665213,"about_ca_topic_score_gemma":0.0052944995,"teacher_disagreement_score":0.007905298,"about_ca_system_score_codex":0.0012200304,"about_ca_system_score_gemma":0.00186743,"threshold_uncertainty_score":0.04180777},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019379849,0.00017282578,0.9697007,0.0001927224,0.000039416977,0.00011401101,0.0009526944,0.008439508,0.0010083631],"genre_scores_gemma":[0.3666232,0.00025386503,0.61891454,0.00023227259,0.00005679483,0.00035636447,0.009488624,0.0015491383,0.0025252395],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996452,0.00060258264,0.0002837474,0.00069015485,0.0016541727,0.00031735073],"domain_scores_gemma":[0.99351585,0.0016460569,0.0008681591,0.0012996495,0.0023731897,0.0002971288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029580449,0.0015557195,0.0017010625,0.0042769006,0.0012302329,0.0025631008,0.0033674592,0.0015068717,0.002306185],"category_scores_gemma":[0.016068958,0.0007637063,0.0015752863,0.004338671,0.0010081447,0.0024054777,0.0037328994,0.0022748413,0.0024493784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011327618,0.00040844773,0.027502354,0.0004255553,0.0004929078,0.0007669615,0.00089532766,0.3822151,0.017066902,0.020804094,0.02571472,0.5225749],"study_design_scores_gemma":[0.000022407694,0.00003497181,0.0017063279,0.000021890519,0.000016224023,0.0001410924,0.000117974385,0.975049,0.0073208283,0.010829492,0.004695298,0.00004448553],"about_ca_topic_score_codex":0.012501266,"about_ca_topic_score_gemma":0.016141994,"teacher_disagreement_score":0.012501266,"about_ca_system_score_codex":0.0012518717,"about_ca_system_score_gemma":0.0025435148,"threshold_uncertainty_score":0.024856985},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50112706,0.00051584723,0.4889075,0.00057052705,0.00009332706,0.00014760299,0.00041924868,0.0065372027,0.00168177],"genre_scores_gemma":[0.86007136,0.0001377027,0.13739218,0.00011654688,0.000030918214,0.00008827473,0.0006038003,0.0001417197,0.0014174329],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983188,0.0005261424,0.00012867486,0.0004688801,0.00043801861,0.00011948059],"domain_scores_gemma":[0.99464697,0.0029009795,0.0004815776,0.0006839612,0.0011705244,0.00011588235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021564274,0.00069404976,0.0006052566,0.0009661019,0.00052008504,0.0008344845,0.00095260056,0.00079575187,0.00048063637],"category_scores_gemma":[0.009783179,0.00047581663,0.0005888933,0.00072913355,0.00046940922,0.0017040482,0.00072026637,0.0014090254,0.00051479466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064795255,0.0004581204,0.030652046,0.00021545845,0.00026137338,0.00038823095,0.00086221437,0.3047163,0.056018803,0.0023276592,0.0033863978,0.60006547],"study_design_scores_gemma":[0.000005326317,0.00004667668,0.0025407858,0.0000054253796,0.000013423936,0.000035141795,0.000025251386,0.98686194,0.009175827,0.00086212397,0.00041662942,0.000011354014],"about_ca_topic_score_codex":0.011652248,"about_ca_topic_score_gemma":0.015135639,"teacher_disagreement_score":0.011652248,"about_ca_system_score_codex":0.0010845404,"about_ca_system_score_gemma":0.0008552696,"threshold_uncertainty_score":0.023168862},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12326114,0.00035260536,0.8647786,0.00012982728,0.000047769307,0.00013111709,0.00024252755,0.009207671,0.0018486868],"genre_scores_gemma":[0.8815376,0.00019275345,0.11647919,0.00006225098,0.000031425683,0.000088411965,0.00031637395,0.0003280068,0.00096391985],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99867755,0.00018914099,0.000082873514,0.00029092355,0.00068105856,0.00007841318],"domain_scores_gemma":[0.9961622,0.001211892,0.0008245704,0.0008379758,0.00082455,0.00013885475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076045096,0.0010120242,0.0006137324,0.0017608561,0.00033560247,0.0010881528,0.0009376652,0.00037073932,0.00067532837],"category_scores_gemma":[0.005305779,0.00027296666,0.00029881758,0.0008641157,0.00032972745,0.0011825856,0.0007649762,0.0006460118,0.00036755766],"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":[0.0008228481,0.0005497207,0.06469157,0.0004699547,0.00024513202,0.0006454721,0.00083975063,0.1024605,0.25509003,0.005209816,0.004337073,0.5646381],"study_design_scores_gemma":[0.000017885532,0.00024367521,0.016531168,0.000037532773,0.00008505566,0.00042334932,0.00015261504,0.84947944,0.12223554,0.0054568057,0.005275015,0.0000618721],"about_ca_topic_score_codex":0.0014192841,"about_ca_topic_score_gemma":0.0021299112,"teacher_disagreement_score":0.0017608561,"about_ca_system_score_codex":0.0005045956,"about_ca_system_score_gemma":0.0007656366,"threshold_uncertainty_score":0.004021704},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029474596,0.0007039829,0.9665391,0.00020516673,0.00007212993,0.00008230342,0.00006618168,0.0013056236,0.0015510964],"genre_scores_gemma":[0.41050532,0.00072693377,0.58259934,0.00042126022,0.0001138888,0.00029197795,0.00051993324,0.0003541438,0.004467171],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99935895,0.00009868195,0.000053083437,0.000106995925,0.000299472,0.00008275735],"domain_scores_gemma":[0.99925524,0.00024446973,0.00011919687,0.00011141608,0.00023509956,0.000034491924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012704132,0.0016567325,0.0011010787,0.00076830655,0.0004893745,0.00069954013,0.0020883896,0.0011910604,0.0011740677],"category_scores_gemma":[0.003098658,0.00060602947,0.0007400488,0.0006316798,0.0005844335,0.0011872244,0.0010570689,0.0019892345,0.00041327608],"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":[0.00020411606,0.0001959431,0.0015705929,0.00015595755,0.00013912431,0.0002976035,0.00013620006,0.50614583,0.029893883,0.010555678,0.0048727393,0.44583234],"study_design_scores_gemma":[0.000014799933,0.00005194262,0.00022129732,0.000015564294,0.000021202068,0.000058157442,0.000009237752,0.9899995,0.0061551086,0.0024474773,0.0009993014,0.0000064561295],"about_ca_topic_score_codex":0.00467931,"about_ca_topic_score_gemma":0.009393313,"teacher_disagreement_score":0.00467931,"about_ca_system_score_codex":0.0006872818,"about_ca_system_score_gemma":0.0014705237,"threshold_uncertainty_score":0.009304166},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018882044,0.0005187026,0.9703344,0.00020894496,0.00012282726,0.00007370447,0.0003331544,0.008471335,0.0010549349],"genre_scores_gemma":[0.29040298,0.000628445,0.69596577,0.0005104914,0.00018697562,0.00020017821,0.003215229,0.0010891947,0.0078006927],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994796,0.000058302732,0.000024320378,0.00021564576,0.00016259644,0.00005952816],"domain_scores_gemma":[0.9993356,0.00013955611,0.000086320055,0.00022296794,0.0001765688,0.00003888869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009600455,0.0015487825,0.001472207,0.0011532058,0.0004002324,0.00088558503,0.0029271564,0.0014172718,0.0033914798],"category_scores_gemma":[0.0026298473,0.00058958994,0.0013015276,0.001121772,0.00061972375,0.0020384688,0.0017501027,0.0023785038,0.0017826757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002642251,0.00026006464,0.00210012,0.0001483048,0.00022466705,0.00016765723,0.00007179845,0.12505837,0.025687667,0.0036762527,0.014258905,0.82808197],"study_design_scores_gemma":[0.000017236778,0.00005798371,0.0007115656,0.0000122041165,0.000025708554,0.00014359155,0.000013673777,0.9835137,0.009162586,0.0039018404,0.0024214305,0.000018414123],"about_ca_topic_score_codex":0.005062594,"about_ca_topic_score_gemma":0.0071645896,"teacher_disagreement_score":0.005062594,"about_ca_system_score_codex":0.000761357,"about_ca_system_score_gemma":0.0010066048,"threshold_uncertainty_score":0.011345625},"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":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2806037,0.023191642,0.62719566,0.014451085,0.00056104065,0.00013917238,0.001195511,0.0015549011,0.051107336],"genre_scores_gemma":[0.95670426,0.004179172,0.032395896,0.0004348427,0.00016068878,0.000044467623,0.0004996679,0.00010082822,0.0054800827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99865013,0.00035878393,0.00004779426,0.00019806769,0.0005859699,0.0001593195],"domain_scores_gemma":[0.9914818,0.0052471966,0.0004719219,0.00087278796,0.0016791259,0.00024717554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031373221,0.0009081357,0.000513174,0.0009690464,0.0003991043,0.0019900836,0.0010324793,0.0009714915,0.0023899134],"category_scores_gemma":[0.016148107,0.00034168377,0.0004380477,0.0010088235,0.0007992362,0.00333176,0.0010678403,0.002028416,0.00067090656],"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":[0.00038656034,0.0003156076,0.020194197,0.00040327024,0.00018659414,0.00018492388,0.0001586072,0.42806175,0.008682802,0.118196845,0.012004643,0.41122428],"study_design_scores_gemma":[0.000004059626,0.00004919211,0.0025710904,0.000047639263,0.000020350775,0.000045274264,0.00003638885,0.9554429,0.0022802672,0.036523115,0.0029708426,0.000008918892],"about_ca_topic_score_codex":0.005874524,"about_ca_topic_score_gemma":0.004974876,"teacher_disagreement_score":0.005874524,"about_ca_system_score_codex":0.0016968279,"about_ca_system_score_gemma":0.0011471481,"threshold_uncertainty_score":0.016591907},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74944115,0.0003805264,0.24532792,0.00028665896,0.000122000514,0.000054609427,0.0013707256,0.0014448754,0.0015715297],"genre_scores_gemma":[0.95394933,0.00012331146,0.042743016,0.000024743875,0.00008627521,0.000019538842,0.0020952327,0.00004275081,0.00091567804],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997019,0.000048602764,0.000021068341,0.00008949584,0.00008679763,0.000052046376],"domain_scores_gemma":[0.9990758,0.00035858923,0.00017750353,0.00015596255,0.00017260453,0.00005960248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045116953,0.000384575,0.00046158474,0.0018738536,0.00022485288,0.000671521,0.0005433083,0.0004948202,0.000495544],"category_scores_gemma":[0.001617556,0.00018827163,0.0004006634,0.0011593308,0.00021987224,0.0005134962,0.000477253,0.00061170396,0.00029519145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009746332,0.00093039725,0.17069143,0.00026417754,0.0004613577,0.0010372612,0.00039912906,0.13353416,0.11481682,0.0060015763,0.006738387,0.5641507],"study_design_scores_gemma":[0.000015843194,0.00008292441,0.051660288,0.000010375485,0.000044479373,0.00025127147,0.0000879587,0.9332464,0.009955356,0.0031969398,0.0014298725,0.000018301695],"about_ca_topic_score_codex":0.0020955268,"about_ca_topic_score_gemma":0.0026239518,"teacher_disagreement_score":0.0020955268,"about_ca_system_score_codex":0.00018967237,"about_ca_system_score_gemma":0.00045302484,"threshold_uncertainty_score":0.0041666627},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17672051,0.009726876,0.7938533,0.0013854508,0.00050695305,0.000081401064,0.0016746154,0.0011846083,0.014866292],"genre_scores_gemma":[0.9049234,0.0025949094,0.07998639,0.000095100055,0.0002839442,0.00012989812,0.0009872639,0.00022243592,0.01077672],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997614,0.00006528318,0.000013799816,0.000059010614,0.00007744322,0.000022977867],"domain_scores_gemma":[0.9983223,0.0012954694,0.00012647155,0.0000878221,0.00013163596,0.00003623837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009799841,0.0006540998,0.0009977121,0.0007395867,0.00027576508,0.00088856235,0.0011046049,0.00082772964,0.0030154756],"category_scores_gemma":[0.005276286,0.00030075567,0.0005406595,0.0011705903,0.0004072162,0.001194197,0.00043708188,0.0012474984,0.00075191405],"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":[0.00021207082,0.00010912806,0.008139019,0.00017813333,0.00009886494,0.000040685372,0.000044460594,0.7200877,0.00224764,0.0239564,0.006285498,0.23860033],"study_design_scores_gemma":[0.0000020366942,0.000020487696,0.0016391175,0.0000134513275,0.000004751162,0.000009366115,0.000004844168,0.98564523,0.00035806175,0.011784978,0.0005102968,0.000007390237],"about_ca_topic_score_codex":0.0050254376,"about_ca_topic_score_gemma":0.004647679,"teacher_disagreement_score":0.0050254376,"about_ca_system_score_codex":0.00087701355,"about_ca_system_score_gemma":0.00036021692,"threshold_uncertainty_score":0.010087788},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015735343,0.00017976007,0.98261917,0.00018101554,0.000041763295,0.000013735098,0.00005151038,0.000591987,0.0005857015],"genre_scores_gemma":[0.83662736,0.0003709276,0.15686141,0.00026970287,0.0001799105,0.00006132336,0.00043451707,0.0002779345,0.0049169413],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995135,0.00013464924,0.000016427963,0.00015330379,0.00011606583,0.00006604635],"domain_scores_gemma":[0.9986607,0.0007903943,0.00013678531,0.00011519411,0.00022593372,0.00007099902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093946885,0.00078880764,0.0009993379,0.0011350634,0.00032031263,0.00078423356,0.0019762847,0.0012631814,0.0017441806],"category_scores_gemma":[0.003156187,0.00043176668,0.00092050084,0.00094944,0.00074841833,0.0013911885,0.0013031971,0.0017243483,0.00040406277],"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":[0.00011559619,0.00008318772,0.0015178273,0.000066933026,0.00009377295,0.00013817081,0.00007460441,0.8619835,0.005761141,0.0251607,0.0021595894,0.10284497],"study_design_scores_gemma":[8.9025275e-7,0.000003790955,0.00006817309,7.694876e-7,0.0000031485358,0.0000074138893,0.0000012222441,0.9967347,0.00021612842,0.0028802285,0.000081863975,0.0000015881085],"about_ca_topic_score_codex":0.0062600225,"about_ca_topic_score_gemma":0.006561209,"teacher_disagreement_score":0.0062600225,"about_ca_system_score_codex":0.0007193276,"about_ca_system_score_gemma":0.00062935444,"threshold_uncertainty_score":0.012447178},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2606225,0.00619107,0.6099187,0.0014298322,0.001228252,0.0007345122,0.015409728,0.091142796,0.013322648],"genre_scores_gemma":[0.6693701,0.001318861,0.29000318,0.0007243826,0.00022741733,0.00036474026,0.028008431,0.00064184703,0.009340971],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995882,0.0000491429,0.000017708237,0.00014540784,0.0001296045,0.000069915775],"domain_scores_gemma":[0.999701,0.00007919235,0.000042482177,0.000052815347,0.00007460719,0.000049970145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066635385,0.0015996225,0.00076782494,0.001762394,0.0003226939,0.0009938087,0.0014453564,0.0007363422,0.0014587952],"category_scores_gemma":[0.0015955179,0.00040474226,0.0005458949,0.00081933575,0.00028050313,0.0009226345,0.0013483667,0.0016783535,0.0009987706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011157427,0.001030146,0.024424355,0.0005395593,0.00050156016,0.0008758708,0.0002926571,0.05324187,0.062155858,0.0022394918,0.07902115,0.77456176],"study_design_scores_gemma":[0.000049759572,0.00025029332,0.007355917,0.000049599148,0.000044905948,0.00030552183,0.000067720786,0.9626838,0.018370239,0.0024103292,0.008363593,0.00004836566],"about_ca_topic_score_codex":0.008739677,"about_ca_topic_score_gemma":0.015288921,"teacher_disagreement_score":0.008739677,"about_ca_system_score_codex":0.00066260615,"about_ca_system_score_gemma":0.00076463836,"threshold_uncertainty_score":0.017377615},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15264641,0.0031135844,0.83679295,0.0018970777,0.00017016317,0.00015768858,0.0010682049,0.0019710849,0.002182917],"genre_scores_gemma":[0.91857177,0.00097048114,0.0766036,0.00023978157,0.00024419528,0.00017514844,0.001273873,0.000072962124,0.00184817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990683,0.00034081252,0.00007258857,0.00025418887,0.00014857734,0.000115502226],"domain_scores_gemma":[0.9933542,0.005159595,0.0006045957,0.00025578178,0.0004899979,0.00013583664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002870087,0.001816357,0.0011719173,0.0031624022,0.00065225275,0.0014506523,0.0020309803,0.001771355,0.0011689885],"category_scores_gemma":[0.008044329,0.00075229106,0.00088631525,0.0020027023,0.00078762317,0.0018313219,0.0009085264,0.0026543317,0.00047189445],"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":[0.000024539719,0.00005385664,0.0030314303,0.000027327074,0.00004284336,0.000022749444,0.000019434297,0.97577614,0.00011292298,0.0007779115,0.0004157165,0.019695109],"study_design_scores_gemma":[0.000001504081,0.00000664619,0.00019029401,0.0000039243164,0.0000026080152,0.000002880336,0.0000061759774,0.9977549,0.00004629254,0.0019231152,0.000059448168,0.000002204567],"about_ca_topic_score_codex":0.015842484,"about_ca_topic_score_gemma":0.009778246,"teacher_disagreement_score":0.015842484,"about_ca_system_score_codex":0.0016964985,"about_ca_system_score_gemma":0.0009517999,"threshold_uncertainty_score":0.031500578},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030327596,0.0003322163,0.96758145,0.0003463203,0.00006438281,0.000017893879,0.000096328615,0.00052785466,0.0007059476],"genre_scores_gemma":[0.7966931,0.0004839222,0.19801474,0.00022573385,0.00016009172,0.000054401335,0.00029033353,0.00017956046,0.0038979475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996111,0.0001091295,0.000020526502,0.000100972284,0.000098940385,0.00005939297],"domain_scores_gemma":[0.99816763,0.0011566689,0.00016261144,0.0001748477,0.0002394897,0.00009868866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014211783,0.0007842298,0.00082517695,0.0010372852,0.00033068258,0.001062695,0.001162039,0.0011565363,0.0015395817],"category_scores_gemma":[0.0060170526,0.0005177398,0.00058213854,0.0008486467,0.00073187466,0.001544152,0.0017842836,0.0023483683,0.00036623012],"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":[0.00030205626,0.00022742667,0.0058187395,0.00018565546,0.00012345662,0.00016586752,0.00014190322,0.48621985,0.016184717,0.032597393,0.004769125,0.45326388],"study_design_scores_gemma":[0.000002929797,0.000009593653,0.00017488211,0.000004478254,0.0000036277147,0.000008828114,0.000002445685,0.9907933,0.0010119756,0.007825048,0.00015968621,0.0000031757454],"about_ca_topic_score_codex":0.0024962623,"about_ca_topic_score_gemma":0.0024826203,"teacher_disagreement_score":0.0024962623,"about_ca_system_score_codex":0.0006494478,"about_ca_system_score_gemma":0.0007070414,"threshold_uncertainty_score":0.0075160265},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041395514,0.00011303232,0.9943309,0.00012229543,0.00002162502,0.000021807164,0.0000795677,0.0007377493,0.00043353732],"genre_scores_gemma":[0.6156122,0.00049557,0.37728685,0.0003510026,0.00020091938,0.00023695816,0.00059625617,0.00052604574,0.0046942425],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993223,0.00017127629,0.000040440693,0.00019940281,0.00020732339,0.00005940872],"domain_scores_gemma":[0.9991443,0.00037365334,0.00011127416,0.00010985954,0.0002094281,0.000051475545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021671846,0.0011911126,0.0013331205,0.0010590935,0.00049914356,0.0015493245,0.0018489979,0.0013006936,0.0028023764],"category_scores_gemma":[0.0035839311,0.00050039426,0.0014833057,0.00061174773,0.00064639427,0.0024416253,0.0014385594,0.0016461712,0.0008249768],"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":[0.00056385127,0.00019146851,0.002306715,0.00017508934,0.00042132696,0.00025385444,0.000245152,0.65608567,0.015014948,0.041289315,0.00452603,0.27892655],"study_design_scores_gemma":[0.0000042865477,0.000027899016,0.00024220174,0.0000035630117,0.000020056406,0.000025401423,0.0000040565874,0.9911175,0.00094560446,0.0071200933,0.00048032062,0.00000905037],"about_ca_topic_score_codex":0.0048338603,"about_ca_topic_score_gemma":0.004503219,"teacher_disagreement_score":0.0048338603,"about_ca_system_score_codex":0.0007610371,"about_ca_system_score_gemma":0.0009203816,"threshold_uncertainty_score":0.011461258},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056370914,0.0014103767,0.9064973,0.0011571966,0.0006011329,0.00017539252,0.0011347939,0.008480207,0.024172733],"genre_scores_gemma":[0.7604008,0.000942457,0.17417829,0.0007764967,0.00032820262,0.000295868,0.0036019632,0.000534237,0.058941644],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936265,0.00006008112,0.000030572686,0.00027233758,0.00012496153,0.00014932323],"domain_scores_gemma":[0.99943775,0.00008374635,0.000047654517,0.00010503043,0.00026594818,0.00005986383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007467862,0.001587791,0.001012383,0.001002055,0.0006738581,0.0012272396,0.0022819357,0.0012152466,0.010086591],"category_scores_gemma":[0.0014672477,0.0005529981,0.0012560107,0.0009648192,0.0006544075,0.0026628382,0.0012944575,0.0014379271,0.0027532277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008416619,0.00037491822,0.0032573056,0.000377074,0.00026360812,0.0004475096,0.00029877576,0.11468232,0.056880563,0.03517466,0.04640438,0.7409972],"study_design_scores_gemma":[0.00005285724,0.00020453564,0.00206085,0.000029158626,0.00014386652,0.00014036027,0.000058787115,0.94561857,0.02282557,0.020499518,0.008311207,0.000054711858],"about_ca_topic_score_codex":0.017919838,"about_ca_topic_score_gemma":0.014665851,"teacher_disagreement_score":0.017919838,"about_ca_system_score_codex":0.001632454,"about_ca_system_score_gemma":0.0016074368,"threshold_uncertainty_score":0.03563106},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004016917,0.00007982167,0.9954444,0.000028468487,0.000017282497,0.000022097778,0.000015487054,0.00017607126,0.00019936309],"genre_scores_gemma":[0.15731095,0.00015393106,0.8412396,0.00006055407,0.000040282044,0.000083089966,0.00010445956,0.0000673048,0.0009398236],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863005,0.00026171052,0.0000987924,0.0003200117,0.0006193592,0.000070007016],"domain_scores_gemma":[0.9987883,0.0003445863,0.00014992026,0.00012600508,0.00055184914,0.00003936735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012743608,0.00044046412,0.0007204586,0.0012579066,0.0004795536,0.00068230485,0.0014175416,0.00088334584,0.0006917574],"category_scores_gemma":[0.003848111,0.0003353997,0.00059672364,0.0010637464,0.00047072588,0.0012416448,0.0007977913,0.0006570597,0.00038349463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012116119,0.00012195424,0.004367389,0.00015957838,0.00010981006,0.0001275808,0.00015965968,0.34936857,0.025390573,0.008759065,0.0016784461,0.6096361],"study_design_scores_gemma":[0.0000066008843,0.000046840705,0.00063993194,0.000006565657,0.000008023465,0.000110984765,0.00002339746,0.9909061,0.0041071656,0.002626293,0.0014980644,0.00001995874],"about_ca_topic_score_codex":0.0046603535,"about_ca_topic_score_gemma":0.006033491,"teacher_disagreement_score":0.0046603535,"about_ca_system_score_codex":0.00052998035,"about_ca_system_score_gemma":0.000911465,"threshold_uncertainty_score":0.009266496},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06970185,0.00046916617,0.88627595,0.0004951917,0.00015059735,0.00018448281,0.0009612647,0.040356133,0.0014053981],"genre_scores_gemma":[0.62774944,0.00021266907,0.36415544,0.00085128617,0.000071030205,0.00031985535,0.0026849452,0.0009932759,0.0029619753],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989729,0.0001865711,0.00004798172,0.0004992142,0.00019235659,0.00010104707],"domain_scores_gemma":[0.9982564,0.000829538,0.00013838745,0.00033814015,0.00033565817,0.0001018213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013710121,0.0017614723,0.0011046971,0.0009870713,0.00031401287,0.0012629204,0.002555193,0.001372472,0.0029058494],"category_scores_gemma":[0.006963238,0.000620805,0.0012245284,0.0005113171,0.00065720314,0.0033945807,0.002329505,0.0025042112,0.0016251536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00087756565,0.0006287138,0.007819748,0.0003850063,0.00014371506,0.00051608635,0.0007139217,0.16115268,0.036260754,0.0074965768,0.015341824,0.7686634],"study_design_scores_gemma":[0.00003550401,0.00013850746,0.00052766904,0.000015877527,0.000025813195,0.00009194622,0.00009390448,0.97559506,0.009520968,0.011890586,0.0020343412,0.000029803517],"about_ca_topic_score_codex":0.0043688463,"about_ca_topic_score_gemma":0.006540687,"teacher_disagreement_score":0.0043688463,"about_ca_system_score_codex":0.00092920347,"about_ca_system_score_gemma":0.0013585122,"threshold_uncertainty_score":0.009721041},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040503677,0.00055797765,0.9543635,0.0006472623,0.00019742665,0.00018891296,0.00036580264,0.001446274,0.0017292153],"genre_scores_gemma":[0.5246147,0.00038032504,0.46722636,0.00054820464,0.0003979885,0.00022842942,0.0012416141,0.00021371465,0.005148709],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9947883,0.00095237035,0.00026572245,0.0016209283,0.0019842307,0.00038843296],"domain_scores_gemma":[0.9920814,0.0028929724,0.0013524026,0.00094867084,0.002249897,0.00047473845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002938476,0.0013390069,0.0020618504,0.0046566436,0.0011606341,0.0021015722,0.0032078442,0.0023414306,0.0013804514],"category_scores_gemma":[0.0124791525,0.00044340483,0.0011255479,0.0043304926,0.000727298,0.0024507875,0.0020986283,0.0024904807,0.0012066287],"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":[0.0005602763,0.001358278,0.10495173,0.00035952806,0.0006396622,0.00095956837,0.00083491363,0.09771756,0.011330972,0.012027727,0.014451054,0.7548088],"study_design_scores_gemma":[0.000039658487,0.00030063238,0.010701892,0.000036632657,0.00008425089,0.00072941976,0.00034153194,0.9596391,0.0052162497,0.015321463,0.007498592,0.00009069196],"about_ca_topic_score_codex":0.0052841404,"about_ca_topic_score_gemma":0.0059067444,"teacher_disagreement_score":0.0052841404,"about_ca_system_score_codex":0.0008193424,"about_ca_system_score_gemma":0.0014952922,"threshold_uncertainty_score":0.015540361},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007312123,0.00021708684,0.9735131,0.00013064565,0.00012362632,0.00009442362,0.0009968781,0.016421054,0.0011910094],"genre_scores_gemma":[0.12805659,0.0002561141,0.8638417,0.0001321111,0.0000744543,0.00022805014,0.0023370513,0.0008825619,0.0041913074],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984529,0.00022964225,0.00022276676,0.0002724675,0.00074398203,0.000078148754],"domain_scores_gemma":[0.9964406,0.0013411219,0.00028010327,0.0010978269,0.00070196745,0.00013837662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013776073,0.00093810435,0.0008604258,0.0022546195,0.00038901062,0.0018577548,0.0018538091,0.00092431734,0.006275645],"category_scores_gemma":[0.0057416954,0.00053427916,0.0008064339,0.0023157662,0.00047402777,0.0021897983,0.0019558307,0.0013052642,0.0030014222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063824,0.0002314572,0.0057171187,0.0003266299,0.00009957594,0.00030775592,0.00020860003,0.01661978,0.022832159,0.015901854,0.018846873,0.9182698],"study_design_scores_gemma":[0.00008247831,0.0002536422,0.0025244805,0.000087327986,0.0000667582,0.0009948035,0.00013073639,0.88160527,0.03755216,0.04716257,0.029478343,0.00006144262],"about_ca_topic_score_codex":0.0014668397,"about_ca_topic_score_gemma":0.0019733075,"teacher_disagreement_score":0.006275645,"about_ca_system_score_codex":0.00047707584,"about_ca_system_score_gemma":0.0006553618,"threshold_uncertainty_score":0.020994127},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18178204,0.00080173754,0.8044217,0.00047606096,0.00011847404,0.00020122423,0.00028650605,0.008465356,0.0034468742],"genre_scores_gemma":[0.902466,0.00018902618,0.09301953,0.00026321312,0.000092943286,0.00008961014,0.0005431422,0.00011161429,0.0032249533],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989391,0.00019169743,0.000052521635,0.00036828744,0.00029623337,0.00015216217],"domain_scores_gemma":[0.9983896,0.00049161527,0.0003133518,0.000264054,0.0004431687,0.00009818802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001192602,0.0012231814,0.0009955718,0.0014638423,0.00040387735,0.00080172904,0.0016433727,0.0008751083,0.0008492319],"category_scores_gemma":[0.0028966705,0.00045608738,0.0008342015,0.0006246462,0.0005030989,0.0016621565,0.0013942046,0.0013289567,0.00051682297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047018216,0.0008584632,0.02154304,0.00013979367,0.00024287807,0.00027788422,0.00029061464,0.14810531,0.034761924,0.0023029086,0.006465827,0.7845411],"study_design_scores_gemma":[0.000009171476,0.00009548616,0.0042616604,0.000008669619,0.00002537163,0.0000872518,0.000021779024,0.98638237,0.0068142302,0.001585578,0.00069210865,0.000016427508],"about_ca_topic_score_codex":0.003911972,"about_ca_topic_score_gemma":0.0058685844,"teacher_disagreement_score":0.003911972,"about_ca_system_score_codex":0.0007039466,"about_ca_system_score_gemma":0.00091371953,"threshold_uncertainty_score":0.007778406},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056547716,0.00014093761,0.9414023,0.0001537611,0.000026267846,0.000021320076,0.00009258512,0.000765098,0.00084992894],"genre_scores_gemma":[0.820385,0.0001886544,0.17551872,0.00014723279,0.000042120788,0.000060323397,0.0005422379,0.00009222479,0.0030235413],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997131,0.00005164611,0.000014967512,0.00009464279,0.000086203734,0.000039387458],"domain_scores_gemma":[0.999498,0.0002077195,0.000073896954,0.000056848414,0.00014078252,0.000022672075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005690644,0.00073447806,0.00054200616,0.0005729139,0.00023411284,0.00037317435,0.0008364138,0.0005856033,0.00056146324],"category_scores_gemma":[0.0018729308,0.00039158665,0.00065698277,0.00035280234,0.00046814975,0.00087118725,0.0006894792,0.0013024526,0.00023449663],"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":[0.00016459858,0.00012327534,0.0046212543,0.000052580424,0.00011557477,0.00010923454,0.00008440339,0.76535827,0.020190595,0.005192054,0.0016550657,0.20233315],"study_design_scores_gemma":[0.0000013465825,0.0000082367515,0.0003387267,0.0000019397467,0.0000029245336,0.000011113344,0.0000018780847,0.9977233,0.0010722596,0.00073807297,0.0000974741,0.0000028706854],"about_ca_topic_score_codex":0.007997779,"about_ca_topic_score_gemma":0.009748456,"teacher_disagreement_score":0.007997779,"about_ca_system_score_codex":0.0005913836,"about_ca_system_score_gemma":0.0008810074,"threshold_uncertainty_score":0.01590246},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03021959,0.00039638797,0.9670012,0.00021801992,0.000037972306,0.00006475671,0.0002519069,0.0009913153,0.00081882835],"genre_scores_gemma":[0.65070295,0.00069982634,0.3434314,0.00021417676,0.00017468167,0.00020991688,0.0018556981,0.0003508888,0.0023603872],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900204,0.00022406528,0.00006058709,0.00033586996,0.00030562576,0.000071825416],"domain_scores_gemma":[0.9952076,0.002314764,0.0008745064,0.00071292784,0.00065713766,0.0002330623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014339419,0.0012435438,0.0010705079,0.003889853,0.00087893516,0.00126421,0.0019780933,0.001265531,0.001470112],"category_scores_gemma":[0.0084587345,0.00051741645,0.0013264586,0.0032329746,0.0011258471,0.0030626378,0.0021573184,0.001982816,0.00046877872],"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":[0.00020463786,0.00024693832,0.020082705,0.0003759325,0.0003427729,0.0005191159,0.00049498805,0.5148427,0.012776902,0.03848685,0.0058045317,0.4058219],"study_design_scores_gemma":[0.0000076313645,0.000044479428,0.0018993805,0.000018190249,0.000032129337,0.00010440677,0.000061828825,0.97418857,0.0017671036,0.020013435,0.0018454876,0.000017333055],"about_ca_topic_score_codex":0.0050188205,"about_ca_topic_score_gemma":0.007440593,"teacher_disagreement_score":0.0050188205,"about_ca_system_score_codex":0.0010780354,"about_ca_system_score_gemma":0.00097162795,"threshold_uncertainty_score":0.009979248},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30519217,0.00068232056,0.6898681,0.000372314,0.00017357591,0.000047740115,0.0006566054,0.0018561268,0.0011510745],"genre_scores_gemma":[0.95593196,0.00012768258,0.042399302,0.000072007104,0.00003611127,0.000022943761,0.0005661052,0.000026921874,0.00081693666],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977237,0.000029010467,0.000010465424,0.00007133122,0.00006613323,0.000050617404],"domain_scores_gemma":[0.99969566,0.000088010216,0.000054486132,0.000034902765,0.00009133357,0.00003576162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003168819,0.00054869126,0.00058787595,0.00054942945,0.00017025931,0.00030782376,0.0007103376,0.00035502648,0.00043911109],"category_scores_gemma":[0.00089011056,0.00024666448,0.00030565038,0.00048496094,0.00017201924,0.0005033761,0.0006078149,0.00078942615,0.0001886659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009126423,0.00088299304,0.03232473,0.00013169217,0.00020606183,0.00058392173,0.00016035842,0.22758834,0.09086667,0.0023382388,0.009823018,0.63418126],"study_design_scores_gemma":[0.0000031382187,0.0000361352,0.002467439,0.000002253306,0.000006182741,0.000049317252,0.000009590768,0.99384576,0.0028946397,0.00048441015,0.00019703324,0.0000039965935],"about_ca_topic_score_codex":0.0040978836,"about_ca_topic_score_gemma":0.007286104,"teacher_disagreement_score":0.0040978836,"about_ca_system_score_codex":0.00028824346,"about_ca_system_score_gemma":0.000538269,"threshold_uncertainty_score":0.008148074},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13879888,0.00024248927,0.8591498,0.00013620993,0.000053482738,0.000029166436,0.00005281893,0.00053317257,0.0010039732],"genre_scores_gemma":[0.9270046,0.00006630469,0.071903065,0.00003065285,0.000019172425,0.000025785914,0.00009464127,0.0000271226,0.0008288363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994849,0.00010142062,0.00003148664,0.00009138225,0.00023677449,0.000054069646],"domain_scores_gemma":[0.99919564,0.00031504247,0.000096428965,0.00007995523,0.00028863197,0.000024301204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008521301,0.00033924213,0.0005915888,0.00097351125,0.00035405997,0.0004954467,0.00061748596,0.00047419433,0.0004504669],"category_scores_gemma":[0.0018799119,0.0001800924,0.0004650645,0.0007126181,0.00031531692,0.00088787096,0.00060159015,0.00052341557,0.00012377415],"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":[0.0002565495,0.00022142312,0.005000885,0.000043564636,0.0000969048,0.000103235914,0.00008742896,0.50875044,0.03285927,0.0060552997,0.0007752699,0.44574967],"study_design_scores_gemma":[0.0000026164628,0.000030850835,0.00076824904,0.0000013179332,0.000007977033,0.000017122804,0.0000041708245,0.9943997,0.003436789,0.0012163013,0.000109987726,0.0000050061835],"about_ca_topic_score_codex":0.0032781966,"about_ca_topic_score_gemma":0.0031368213,"teacher_disagreement_score":0.0032781966,"about_ca_system_score_codex":0.00069305405,"about_ca_system_score_gemma":0.00050356233,"threshold_uncertainty_score":0.0065182447},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26359344,0.0040744618,0.69934154,0.0009584473,0.0010893005,0.00011659165,0.0029794974,0.010770164,0.017076569],"genre_scores_gemma":[0.83188725,0.0013583759,0.15017058,0.00028252092,0.00032206645,0.00006289278,0.0027956746,0.00025831815,0.012862256],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998752,0.000018189638,0.0000040378513,0.000038213915,0.000028718154,0.000035670993],"domain_scores_gemma":[0.99990034,0.000029762343,0.000011241369,0.000013159938,0.00003480616,0.000010689531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002535368,0.0008176593,0.00047947158,0.0004583494,0.00013209727,0.00047094518,0.0006967979,0.00067286275,0.0025503323],"category_scores_gemma":[0.00049602386,0.0002704539,0.00042523103,0.00050793303,0.00015473636,0.0006262482,0.00048054932,0.000623385,0.0011038155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045133373,0.0002772767,0.0017040014,0.00016390011,0.00012209632,0.00017477236,0.000058533027,0.065308265,0.05244051,0.0014277787,0.017381443,0.86048996],"study_design_scores_gemma":[0.000009219563,0.000076542914,0.0017214406,0.000011883705,0.00003912805,0.00007835822,0.000017692946,0.97844076,0.01645575,0.001131942,0.0020049242,0.000012383559],"about_ca_topic_score_codex":0.0064925314,"about_ca_topic_score_gemma":0.009387796,"teacher_disagreement_score":0.0064925314,"about_ca_system_score_codex":0.00037953304,"about_ca_system_score_gemma":0.0004157658,"threshold_uncertainty_score":0.012909472},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10387523,0.0011519691,0.884253,0.0008388481,0.00023380178,0.00011958956,0.0015082234,0.006576526,0.0014428144],"genre_scores_gemma":[0.52508503,0.0003125083,0.46890625,0.00017940793,0.00013278791,0.00010183969,0.0036340193,0.00024083395,0.0014073387],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99654144,0.00071529334,0.00028868509,0.00081993535,0.0013789285,0.00025579642],"domain_scores_gemma":[0.99152946,0.0035052185,0.001140232,0.0012071388,0.0022561033,0.00036178489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023191045,0.00080100814,0.0017329976,0.0033146937,0.0005712726,0.001835923,0.0021824017,0.0010297985,0.0010250849],"category_scores_gemma":[0.012658064,0.00034610112,0.001033088,0.003601987,0.0007336323,0.0018165566,0.0017339694,0.0016396341,0.0009160505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077882106,0.0004618866,0.05017883,0.0008298702,0.00048182343,0.00064704084,0.00037486674,0.18998596,0.032076243,0.014108315,0.024965689,0.68511075],"study_design_scores_gemma":[0.000022182034,0.00007385336,0.003295305,0.000029871542,0.000022390726,0.00026960004,0.00017578932,0.9690149,0.00919436,0.0143168485,0.0035559575,0.000028890985],"about_ca_topic_score_codex":0.0029843934,"about_ca_topic_score_gemma":0.0030513927,"teacher_disagreement_score":0.0033146937,"about_ca_system_score_codex":0.0007080211,"about_ca_system_score_gemma":0.0010908024,"threshold_uncertainty_score":0.012264729},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02030915,0.00021003427,0.97804594,0.00017082163,0.000027603204,0.00002753537,0.000034708162,0.00055841665,0.00061577867],"genre_scores_gemma":[0.72646844,0.00035244698,0.27031946,0.00026263873,0.000113863476,0.000099615325,0.00028055286,0.0001528941,0.001950074],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993374,0.00019135843,0.000036779376,0.00020786998,0.00017253961,0.00005411562],"domain_scores_gemma":[0.9976839,0.0012840392,0.00026658413,0.0003461224,0.00032187233,0.00009757199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001715445,0.00093725964,0.0008218795,0.000981523,0.00032664966,0.0009489151,0.0014055911,0.0009268046,0.0009818658],"category_scores_gemma":[0.007486346,0.00035919005,0.0005979597,0.0008909188,0.0011437497,0.002290356,0.0017246307,0.0023450458,0.00039812463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026350445,0.00028015816,0.006300485,0.0001245986,0.00012822662,0.00025301985,0.00018335483,0.4350964,0.028692793,0.037533633,0.002368925,0.48877496],"study_design_scores_gemma":[0.000003832402,0.000035949026,0.00018390523,0.0000033084098,0.0000057089665,0.000032816166,0.000005466123,0.9917795,0.002483373,0.0051191105,0.0003416542,0.0000053120834],"about_ca_topic_score_codex":0.0026018461,"about_ca_topic_score_gemma":0.00290838,"teacher_disagreement_score":0.0026018461,"about_ca_system_score_codex":0.0008360588,"about_ca_system_score_gemma":0.00088805787,"threshold_uncertainty_score":0.009072304},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036998335,0.0009727891,0.9570497,0.0008283454,0.00007832568,0.000034426223,0.00010246475,0.00062089803,0.003314736],"genre_scores_gemma":[0.94192946,0.00069013477,0.053229034,0.000302716,0.00010076843,0.00005893622,0.00018657441,0.00006595711,0.003436495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99972147,0.000103643535,0.000009742979,0.00005722495,0.00006613964,0.00004172421],"domain_scores_gemma":[0.9990343,0.0006705403,0.000086369364,0.00008686267,0.00009039027,0.000031548025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009366892,0.00070558174,0.0005226182,0.00044372046,0.00018195773,0.00048793267,0.00082453917,0.0007264858,0.00088121183],"category_scores_gemma":[0.0021149907,0.0003229484,0.00049358734,0.000342145,0.000870272,0.0008490519,0.0009151847,0.0017081156,0.0001993395],"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":[0.000032319906,0.00001852648,0.0006509382,0.000019398261,0.00003188852,0.000049503837,0.00002201732,0.969462,0.001313213,0.010084721,0.00061648106,0.017699026],"study_design_scores_gemma":[7.378067e-7,0.0000059415775,0.00005681674,0.0000018625069,0.0000023736739,0.000007565616,0.0000011621662,0.99670875,0.00021328877,0.002891727,0.00010810887,0.0000017311914],"about_ca_topic_score_codex":0.0027340972,"about_ca_topic_score_gemma":0.0026537422,"teacher_disagreement_score":0.0027340972,"about_ca_system_score_codex":0.0007926581,"about_ca_system_score_gemma":0.0003812332,"threshold_uncertainty_score":0.005751133},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09764098,0.00028374203,0.89688694,0.00029805073,0.00005441304,0.00006102766,0.00028522254,0.0024232564,0.002066311],"genre_scores_gemma":[0.9322118,0.0001865922,0.06404757,0.0001115956,0.000030631305,0.000060584196,0.0005236417,0.00007809269,0.0027494438],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997986,0.00003652856,0.000011222691,0.0000673002,0.000056205095,0.000030091938],"domain_scores_gemma":[0.9997234,0.000109381304,0.000028629958,0.000033820892,0.00008626226,0.000018505862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042251693,0.00059852644,0.00052856177,0.00063690223,0.00017860034,0.00057831657,0.0009730351,0.00038394184,0.0011949218],"category_scores_gemma":[0.00094882475,0.00029073848,0.0005100962,0.00039192406,0.00030844862,0.0010592822,0.0004940915,0.0005530132,0.000363097],"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":[0.00023017949,0.00016462631,0.0042101336,0.000050401908,0.000087071574,0.00010482074,0.000042754134,0.8542511,0.010734125,0.0052625863,0.0018013223,0.12306085],"study_design_scores_gemma":[0.0000018409849,0.000010198284,0.00009115735,5.6820414e-7,0.0000032466953,0.000007728184,0.0000014429675,0.99869233,0.00053773716,0.00058176613,0.00007075618,0.0000012031794],"about_ca_topic_score_codex":0.011889988,"about_ca_topic_score_gemma":0.016489057,"teacher_disagreement_score":0.011889988,"about_ca_system_score_codex":0.0008539781,"about_ca_system_score_gemma":0.00084769947,"threshold_uncertainty_score":0.023641586},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033162154,0.000419419,0.9646611,0.00014338412,0.000042490297,0.000034042656,0.00006533014,0.0010309814,0.0004411528],"genre_scores_gemma":[0.54224956,0.00081976724,0.4537967,0.0002229099,0.00014415744,0.000047742848,0.0003950888,0.00024132346,0.0020828187],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958616,0.000059627524,0.000020224945,0.00009645064,0.0001937055,0.00004377592],"domain_scores_gemma":[0.99900275,0.00044398612,0.00016530739,0.00016103422,0.00017608514,0.000050761773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054275733,0.00070168445,0.0006770758,0.00083611265,0.00017207353,0.0005529968,0.0008169817,0.0005937263,0.00077645795],"category_scores_gemma":[0.0022478846,0.0002553575,0.0006482954,0.00049918756,0.00050244277,0.00045658436,0.0006983448,0.0010698115,0.0003771578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035003194,0.00013071201,0.003179736,0.00023625769,0.00011154448,0.00043448142,0.00023183903,0.1641003,0.2642045,0.0034022245,0.0025720631,0.5610463],"study_design_scores_gemma":[0.000012420288,0.00010789199,0.0019445892,0.000011999448,0.00003508845,0.00069864397,0.000018552313,0.9335086,0.059889164,0.0022412546,0.0015118315,0.000019909556],"about_ca_topic_score_codex":0.0011538354,"about_ca_topic_score_gemma":0.0015937202,"teacher_disagreement_score":0.0011538354,"about_ca_system_score_codex":0.00031870976,"about_ca_system_score_gemma":0.00038279663,"threshold_uncertainty_score":0.0028704405},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016691187,0.00008470673,0.981501,0.00008527752,0.000034075456,0.000020278823,0.00005790418,0.00076442165,0.00076121057],"genre_scores_gemma":[0.7213517,0.00016011302,0.27215323,0.0001374093,0.000040759667,0.00008483774,0.00034229297,0.00013185645,0.005597831],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996044,0.00007441644,0.000018113227,0.000119366174,0.00014069253,0.00004299006],"domain_scores_gemma":[0.99960583,0.00009247521,0.000057398254,0.00014872718,0.00007295055,0.00002267491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042562647,0.00045265636,0.00039336737,0.00033729605,0.00024495716,0.00042007555,0.00067705225,0.00044766202,0.0020922727],"category_scores_gemma":[0.0014661122,0.00014618658,0.0003887515,0.0003153963,0.00059722376,0.0010628606,0.0010316495,0.0011754188,0.000684532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032212943,0.00017693557,0.0018723217,0.000090792775,0.000072386385,0.00023305211,0.0001405411,0.1652197,0.09233007,0.0376326,0.0040979437,0.69781154],"study_design_scores_gemma":[0.000010810131,0.00008119166,0.00059706025,0.000008033393,0.00001129119,0.0001962667,0.000017238566,0.9558201,0.030384146,0.010138101,0.0027193443,0.00001637812],"about_ca_topic_score_codex":0.00085991644,"about_ca_topic_score_gemma":0.0010792763,"teacher_disagreement_score":0.0020922727,"about_ca_system_score_codex":0.0003866433,"about_ca_system_score_gemma":0.00046856384,"threshold_uncertainty_score":0.0069993734},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02288459,0.000105929874,0.9757901,0.0001250332,0.00002835194,0.000013403791,0.000029970302,0.000678512,0.00034400352],"genre_scores_gemma":[0.80668736,0.00017980693,0.19074075,0.0002254542,0.00007714777,0.000046588375,0.00032079982,0.0003645051,0.0013576882],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996932,0.0007981119,0.00018706026,0.00074805086,0.0010691617,0.00026563916],"domain_scores_gemma":[0.98472637,0.009079452,0.0013429717,0.0023936378,0.00217655,0.0002811472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043935585,0.0011745531,0.0019492862,0.0013752575,0.00069618813,0.0017570257,0.0023044695,0.0019114973,0.00073875795],"category_scores_gemma":[0.0298994,0.0006705595,0.00094250817,0.0012483348,0.0013256107,0.0037135207,0.0036327597,0.003910553,0.0004554976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004489408,0.00051596866,0.019850615,0.00022067253,0.00040268112,0.00047745698,0.00045551945,0.41424796,0.024498748,0.022468604,0.003813736,0.51259905],"study_design_scores_gemma":[0.0000049521614,0.00003458714,0.0012997068,0.000009039234,0.000017435515,0.0001201544,0.000019705489,0.98397875,0.0040979856,0.010027329,0.00037605822,0.000014278752],"about_ca_topic_score_codex":0.0029044899,"about_ca_topic_score_gemma":0.003420545,"teacher_disagreement_score":0.0043935585,"about_ca_system_score_codex":0.00071869453,"about_ca_system_score_gemma":0.0013766617,"threshold_uncertainty_score":0.023235679},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25994924,0.0010028835,0.7335061,0.00067496544,0.00015548652,0.00013676904,0.00070391624,0.0029102736,0.0009603811],"genre_scores_gemma":[0.8416727,0.00029876558,0.15369888,0.00013303808,0.00022003264,0.00014416515,0.0016803402,0.00023107081,0.00192102],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99729615,0.0013560947,0.00017631066,0.00059880514,0.00037614876,0.00019642539],"domain_scores_gemma":[0.985413,0.009999172,0.0018873881,0.001163,0.0012146711,0.0003227721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007238714,0.0012446494,0.0011811834,0.00406762,0.00052550016,0.0012044401,0.0025307622,0.0014122637,0.0018878363],"category_scores_gemma":[0.022380734,0.00040007127,0.0011595418,0.0022258752,0.00055468525,0.0016474318,0.0014024763,0.0019508166,0.0018121686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010822944,0.00047223447,0.11355542,0.00027545966,0.00061204506,0.00075271376,0.00027086984,0.4269158,0.008590008,0.0033300407,0.006003739,0.43813935],"study_design_scores_gemma":[0.000011066653,0.000038975726,0.0037084648,0.000011040742,0.00001654564,0.00009121113,0.000028129685,0.9932909,0.00085542334,0.0015959297,0.00033627948,0.000015953134],"about_ca_topic_score_codex":0.0034582336,"about_ca_topic_score_gemma":0.002032269,"teacher_disagreement_score":0.007238714,"about_ca_system_score_codex":0.00048451687,"about_ca_system_score_gemma":0.0005754009,"threshold_uncertainty_score":0.038282454},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09679214,0.00091144326,0.89545214,0.00038029568,0.00025415368,0.0000646909,0.0004544116,0.0033562002,0.0023344862],"genre_scores_gemma":[0.9021602,0.0003808319,0.09425773,0.00016320328,0.00008570282,0.00006853251,0.0011690528,0.00007208647,0.0016426531],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993487,0.000069433896,0.00003969807,0.00020842497,0.00021281996,0.000121028126],"domain_scores_gemma":[0.9996209,0.000059812133,0.00007493842,0.00007067872,0.00014429998,0.000029282623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068752706,0.0011867863,0.0009983919,0.0013878464,0.0003588863,0.0008494143,0.0011886554,0.0006521238,0.0005732059],"category_scores_gemma":[0.0013432845,0.0004400923,0.00084607577,0.0013106719,0.00039314115,0.0015525172,0.001433554,0.001355381,0.00033983705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029483472,0.00026490082,0.009727125,0.00018361483,0.00023846485,0.00029468632,0.00021956976,0.38575408,0.03384996,0.004452006,0.0056256517,0.55909514],"study_design_scores_gemma":[0.0000047160433,0.000041279407,0.0019388201,0.000009617995,0.000016629358,0.000032332824,0.000043936252,0.98766035,0.0057799234,0.0032924942,0.0011663169,0.000013615009],"about_ca_topic_score_codex":0.006401862,"about_ca_topic_score_gemma":0.0070918,"teacher_disagreement_score":0.006401862,"about_ca_system_score_codex":0.00077118154,"about_ca_system_score_gemma":0.000878753,"threshold_uncertainty_score":0.012729228},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.157621,0.0018227171,0.83333457,0.0009205563,0.00026392395,0.0001083374,0.0004288789,0.0026463645,0.0028537351],"genre_scores_gemma":[0.92480916,0.00031383752,0.070748076,0.0003090217,0.000124398,0.00010381013,0.0007301396,0.000102892416,0.0027586366],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957186,0.0001281532,0.000026248787,0.00011490043,0.00010018497,0.000058700287],"domain_scores_gemma":[0.9992399,0.0003066361,0.00009939885,0.000083590625,0.00023002543,0.000040517887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015972774,0.0010859024,0.0010435933,0.0010436355,0.0002673775,0.0006100287,0.0017220351,0.00080279086,0.0010020322],"category_scores_gemma":[0.0027367182,0.0003005636,0.000708605,0.0006480579,0.00041211298,0.0015681852,0.0006753149,0.0011606114,0.00035968743],"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":[0.0005147811,0.00027843588,0.0067363917,0.00012451147,0.0002091452,0.00020863363,0.00012795805,0.6309448,0.0064024287,0.0043760524,0.0043250327,0.3457518],"study_design_scores_gemma":[0.0000043769046,0.000037332535,0.0002894898,0.000004560081,0.000011240763,0.000014085503,0.000007318719,0.9971716,0.00056959665,0.0016848368,0.00020102251,0.000004557342],"about_ca_topic_score_codex":0.003529798,"about_ca_topic_score_gemma":0.0051681898,"teacher_disagreement_score":0.003529798,"about_ca_system_score_codex":0.0007387298,"about_ca_system_score_gemma":0.0005686751,"threshold_uncertainty_score":0.0084472895},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016822182,0.00030940815,0.9791326,0.00019446539,0.00007994564,0.000044783166,0.00028376185,0.0025016486,0.00063117145],"genre_scores_gemma":[0.5184746,0.00068202993,0.4724558,0.0003843303,0.00022431734,0.00016666573,0.0025889052,0.0005330741,0.004490327],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993056,0.00008040909,0.000046889414,0.0002805472,0.00020915548,0.00007726896],"domain_scores_gemma":[0.9990558,0.0002806435,0.0001659423,0.00013788663,0.00028774186,0.00007203632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007005268,0.0011579643,0.0010641909,0.0024754829,0.00040741172,0.00078172877,0.0018470013,0.00095669396,0.0013369662],"category_scores_gemma":[0.0026903527,0.00036506986,0.0013835205,0.002502055,0.000561759,0.0019527272,0.0010424143,0.0017283255,0.00068135187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022990257,0.0002242905,0.008879259,0.00017216265,0.00027967713,0.00037229466,0.00015019502,0.16131707,0.020832732,0.011633455,0.011619866,0.7842891],"study_design_scores_gemma":[0.000006056795,0.0000203025,0.0007647648,0.0000041909884,0.000015042596,0.000094670424,0.000011105083,0.9915418,0.001937246,0.0044244477,0.0011707201,0.000009701658],"about_ca_topic_score_codex":0.008623472,"about_ca_topic_score_gemma":0.0098879235,"teacher_disagreement_score":0.008623472,"about_ca_system_score_codex":0.0007589907,"about_ca_system_score_gemma":0.0010745836,"threshold_uncertainty_score":0.017146587},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02086037,0.0007139119,0.9758202,0.00014217921,0.0000930825,0.00003909533,0.00002763621,0.0008684616,0.0014350474],"genre_scores_gemma":[0.4839166,0.0013408344,0.5108688,0.00023331317,0.00013617086,0.00010321268,0.00012896283,0.000102246304,0.0031698274],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922967,0.00015720894,0.00004402406,0.00020460061,0.0003001171,0.00006430649],"domain_scores_gemma":[0.9992582,0.000273959,0.000098837285,0.00009732046,0.0002446081,0.000027006701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057405443,0.00052239734,0.00081168534,0.0012071242,0.00022106833,0.0006881197,0.0008707254,0.00081050204,0.0008652853],"category_scores_gemma":[0.0018298423,0.00023304207,0.0005355104,0.0015324667,0.0007706122,0.0010288948,0.0005898505,0.00087155955,0.000544151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002732755,0.00015915223,0.00331359,0.00029265723,0.00009589575,0.00044644412,0.00016802132,0.09429499,0.10906456,0.020597652,0.0024862487,0.76880753],"study_design_scores_gemma":[0.0000066305647,0.000081584214,0.0014152566,0.000012267318,0.000025897052,0.0003015772,0.000022063849,0.9628258,0.025464471,0.007486961,0.0023303404,0.000027203207],"about_ca_topic_score_codex":0.0011828977,"about_ca_topic_score_gemma":0.00081380893,"teacher_disagreement_score":0.0012071242,"about_ca_system_score_codex":0.00041225643,"about_ca_system_score_gemma":0.0003832881,"threshold_uncertainty_score":0.003035903},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07292818,0.0015813342,0.902508,0.0006947202,0.00024816894,0.0001601094,0.0028402042,0.014424105,0.0046152403],"genre_scores_gemma":[0.7200146,0.0007688387,0.2598976,0.00090295536,0.00015180587,0.00032035375,0.007309413,0.00039905985,0.010235367],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964345,0.000060948307,0.000016799224,0.00013084791,0.000079576836,0.00006833638],"domain_scores_gemma":[0.9996439,0.00009127763,0.00004899962,0.00005205785,0.00012429796,0.000039442803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064362085,0.0013224335,0.00065790943,0.0010769084,0.00032135076,0.0006996782,0.0016852844,0.0010096427,0.0020882336],"category_scores_gemma":[0.0015857704,0.0003507466,0.0006821797,0.00071198883,0.00035929075,0.0013138077,0.0011682693,0.0014485512,0.0008505029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005743606,0.00054233865,0.0083753485,0.0002001825,0.0002584915,0.00028063162,0.00012782888,0.20326121,0.017049514,0.0043036398,0.031637736,0.73338866],"study_design_scores_gemma":[0.000010487077,0.000056329256,0.00089128636,0.000013279279,0.000018684968,0.00004881604,0.000018097178,0.99051195,0.0036211275,0.0028794983,0.0019169432,0.0000134825195],"about_ca_topic_score_codex":0.011705723,"about_ca_topic_score_gemma":0.019021505,"teacher_disagreement_score":0.011705723,"about_ca_system_score_codex":0.0010721157,"about_ca_system_score_gemma":0.0010625002,"threshold_uncertainty_score":0.023275197},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059167504,0.0006511876,0.9318586,0.00046626927,0.00010750889,0.00017099483,0.0005094618,0.0060474044,0.0010209921],"genre_scores_gemma":[0.6202494,0.00026181745,0.37520182,0.00025891454,0.00013196233,0.0002227988,0.0018079304,0.00029018839,0.0015751509],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984182,0.000372341,0.00009774135,0.00039139687,0.0005928412,0.00012746101],"domain_scores_gemma":[0.9962633,0.0019131134,0.00044241763,0.0005493287,0.0006588022,0.00017299822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001965711,0.0014201489,0.0016314308,0.0020950064,0.00071810745,0.0013735514,0.0026681216,0.0011728242,0.001568],"category_scores_gemma":[0.009443905,0.00040683636,0.0010960648,0.0017176651,0.00091730914,0.002439733,0.0027753226,0.0021851137,0.0006450041],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069144147,0.00041619816,0.027201824,0.00027839976,0.00035015124,0.0004400358,0.0005039947,0.42502925,0.007817154,0.014258313,0.012531334,0.51048195],"study_design_scores_gemma":[0.000018938299,0.000077311706,0.00056917104,0.000008653948,0.000012227252,0.00007599459,0.000042477124,0.99064714,0.0014360325,0.0062457398,0.00085515284,0.000011023797],"about_ca_topic_score_codex":0.0041027046,"about_ca_topic_score_gemma":0.004192894,"teacher_disagreement_score":0.0041027046,"about_ca_system_score_codex":0.00068842305,"about_ca_system_score_gemma":0.0016923517,"threshold_uncertainty_score":0.010395825},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33533126,0.0016431593,0.6562257,0.00087380334,0.00026413225,0.00007362784,0.00039108857,0.003498725,0.0016985368],"genre_scores_gemma":[0.9156959,0.00036266964,0.08169429,0.00014170865,0.00007421424,0.000035101384,0.0005953374,0.00014698034,0.0012536803],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99794954,0.0006490693,0.00012558006,0.00038327885,0.0006226483,0.00026986242],"domain_scores_gemma":[0.98915267,0.0068502184,0.00077481504,0.0013192021,0.0016100126,0.00029311507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004743045,0.0012190429,0.0010637201,0.0007929954,0.00039495988,0.0011969712,0.0015941086,0.0015106486,0.0008225702],"category_scores_gemma":[0.020480577,0.0004017429,0.00052598957,0.00083993556,0.000884228,0.0021880714,0.0019322407,0.0019442027,0.00030334943],"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":[0.0017058464,0.0004242245,0.014593643,0.00023076535,0.00032165262,0.00016470796,0.00010395692,0.5536233,0.017144758,0.002615851,0.0033786164,0.40569267],"study_design_scores_gemma":[0.00000675739,0.000097496704,0.0006106153,0.0000069090265,0.000018840921,0.000029141063,0.000010939223,0.9937588,0.004073567,0.0012552819,0.00012687113,0.0000048077004],"about_ca_topic_score_codex":0.0047539,"about_ca_topic_score_gemma":0.0037698955,"teacher_disagreement_score":0.0047539,"about_ca_system_score_codex":0.00093412877,"about_ca_system_score_gemma":0.0010927025,"threshold_uncertainty_score":0.0250839},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004636426,0.00014718309,0.9943592,0.00031919542,0.000014561293,0.000023931894,0.000031716496,0.00029287723,0.00017484595],"genre_scores_gemma":[0.37243515,0.00035515864,0.6249154,0.00034724636,0.0002241633,0.00016937974,0.0002455314,0.00011572683,0.0011923101],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99643886,0.0014974345,0.00024147105,0.0007444875,0.00078565377,0.00029207938],"domain_scores_gemma":[0.98998535,0.006550127,0.0010812116,0.00082125934,0.00122199,0.00034013108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005523994,0.0012611005,0.0016531487,0.0029145968,0.0010310399,0.0027894687,0.0033026105,0.002279737,0.0010630246],"category_scores_gemma":[0.01429675,0.00073123747,0.0018438814,0.0019692502,0.0027547025,0.0037964263,0.0029393057,0.003995632,0.0004317949],"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":[0.00010096695,0.00021231415,0.008010762,0.00012697362,0.00019982937,0.00021875894,0.00034465702,0.8094454,0.0023755122,0.054663856,0.0014797193,0.12282121],"study_design_scores_gemma":[0.000003533435,0.00001708981,0.00020249501,0.0000064799383,0.0000058693527,0.000015187068,0.000010159957,0.9821846,0.0002195831,0.017062623,0.00026430734,0.0000080409145],"about_ca_topic_score_codex":0.008428644,"about_ca_topic_score_gemma":0.0054874006,"teacher_disagreement_score":0.008428644,"about_ca_system_score_codex":0.0014953897,"about_ca_system_score_gemma":0.0018606058,"threshold_uncertainty_score":0.029214025},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25262648,0.029554648,0.54502726,0.0033215308,0.0032085106,0.0054088426,0.060788438,0.027860291,0.07220406],"genre_scores_gemma":[0.63679564,0.0056951344,0.26136005,0.0007140221,0.0007454147,0.0032220732,0.07398573,0.0022759798,0.015205905],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96919894,0.0065474096,0.0032192061,0.0024976293,0.017388405,0.0011484165],"domain_scores_gemma":[0.96110976,0.017594224,0.003299683,0.0040061073,0.012813707,0.0011764866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010586255,0.002533418,0.0021096999,0.010943576,0.0010003181,0.0035514003,0.0020777776,0.0015709813,0.0059416266],"category_scores_gemma":[0.06008482,0.00028022914,0.0012794075,0.0073931497,0.0008416654,0.0035912541,0.001772573,0.0014939898,0.0033848013],"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":[0.0014134586,0.0009877784,0.045612123,0.0023894391,0.0006600109,0.00027208216,0.00028786468,0.06719509,0.006471717,0.0151978,0.08568074,0.7738319],"study_design_scores_gemma":[0.00020384749,0.0031139003,0.048464324,0.0009806717,0.0006217368,0.0030501576,0.0012175372,0.7080291,0.036919635,0.04019601,0.15682632,0.00037677778],"about_ca_topic_score_codex":0.0031495253,"about_ca_topic_score_gemma":0.0027628136,"teacher_disagreement_score":0.010943576,"about_ca_system_score_codex":0.0021865093,"about_ca_system_score_gemma":0.0016511118,"threshold_uncertainty_score":0.055986166},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0138287535,0.00039922653,0.9834086,0.00027668677,0.000069123555,0.000042952302,0.00007260429,0.00065152213,0.001250502],"genre_scores_gemma":[0.687303,0.000570852,0.30390307,0.00085970445,0.00024921785,0.00017332264,0.00058369705,0.0003442547,0.006012824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886036,0.0003505311,0.00006232042,0.00024109945,0.00037486613,0.00011095953],"domain_scores_gemma":[0.99816424,0.00097253,0.00019377096,0.00027837238,0.00029201622,0.00009922019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020673738,0.001256625,0.0014305734,0.00074146525,0.00045584032,0.000676652,0.0021954505,0.0013216283,0.0017190597],"category_scores_gemma":[0.004850657,0.00047732398,0.0009614679,0.00050475437,0.0011284956,0.0018676859,0.0024345312,0.0024846203,0.0003131623],"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":[0.00021208818,0.00008521634,0.0014020386,0.000099225006,0.00011520855,0.00015960139,0.00009040806,0.8197639,0.007122353,0.0170813,0.0039395783,0.1499291],"study_design_scores_gemma":[0.000004231285,0.000028148277,0.00008044671,0.000006342365,0.000007507427,0.00003472628,0.000002700399,0.99493945,0.0012653824,0.003300384,0.00032350197,0.0000071904547],"about_ca_topic_score_codex":0.0017017233,"about_ca_topic_score_gemma":0.0016113021,"teacher_disagreement_score":0.0021954505,"about_ca_system_score_codex":0.00073231803,"about_ca_system_score_gemma":0.0008496111,"threshold_uncertainty_score":0.010933459},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0069823526,0.00020319902,0.9678967,0.000097498276,0.00012797366,0.00007711652,0.00063059246,0.023238795,0.0007458127],"genre_scores_gemma":[0.14258467,0.00015763541,0.8450647,0.00025639808,0.00014616814,0.00026564306,0.0038832694,0.00174547,0.005896149],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875236,0.00027254794,0.00006317954,0.00040368285,0.00038528893,0.00012288868],"domain_scores_gemma":[0.9976169,0.0008697999,0.000107911605,0.00078868284,0.000504184,0.000112568414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019022423,0.0018824763,0.0017218433,0.0017304388,0.0007619936,0.0013601021,0.0032902495,0.0018781116,0.0048658424],"category_scores_gemma":[0.0062404606,0.00091675296,0.0013537981,0.0012329434,0.0008733307,0.0023914601,0.0025174327,0.002663532,0.0030826675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061216234,0.0003044066,0.003067454,0.00021501364,0.00031812946,0.00018954955,0.0000836144,0.10124146,0.008752855,0.005537435,0.03190297,0.8477748],"study_design_scores_gemma":[0.000022902035,0.000046344634,0.00034325087,0.00000784029,0.000016808719,0.0000965603,0.0000109188195,0.9836945,0.005032866,0.0079017375,0.0028095962,0.000016654247],"about_ca_topic_score_codex":0.005582392,"about_ca_topic_score_gemma":0.009693226,"teacher_disagreement_score":0.005582392,"about_ca_system_score_codex":0.0006493092,"about_ca_system_score_gemma":0.001557275,"threshold_uncertainty_score":0.01627791},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7577113,0.0018627502,0.22903827,0.00044465467,0.00030890107,0.0001943527,0.00064216426,0.0019582382,0.0078392895],"genre_scores_gemma":[0.97625244,0.00019358905,0.02216544,0.00009210729,0.000034500863,0.00003496452,0.00038363753,0.00013393624,0.00070941244],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9966557,0.0011256442,0.0002545241,0.0008020478,0.0009117509,0.00025029219],"domain_scores_gemma":[0.98748255,0.0067823897,0.0018497107,0.0019188348,0.0015530271,0.0004134803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042836233,0.00094169006,0.0005754113,0.0010332621,0.00035637422,0.0013894453,0.0007497215,0.0010747854,0.0018148326],"category_scores_gemma":[0.03371372,0.0002394799,0.0004754025,0.00042422855,0.0012764904,0.0017801685,0.0017939977,0.0008871014,0.00055262557],"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":[0.0029450548,0.00046163975,0.0822285,0.0015752336,0.0009164235,0.0006452938,0.0017666032,0.31283146,0.12628627,0.0056630345,0.0060896394,0.45859084],"study_design_scores_gemma":[0.00007548439,0.0025226376,0.10374114,0.0002994034,0.00033885718,0.001376943,0.00094373815,0.75304085,0.11450812,0.01653432,0.0063824654,0.00023606082],"about_ca_topic_score_codex":0.0020117958,"about_ca_topic_score_gemma":0.002178474,"teacher_disagreement_score":0.0042836233,"about_ca_system_score_codex":0.0006251575,"about_ca_system_score_gemma":0.00039904396,"threshold_uncertainty_score":0.022654235},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.099457495,0.0009901302,0.88686645,0.00035182305,0.00019899949,0.00014728091,0.0012735998,0.008907374,0.0018068929],"genre_scores_gemma":[0.6725835,0.00043351325,0.31642506,0.00027815782,0.00027909008,0.00020701069,0.0049432316,0.00073134823,0.004119112],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998019,0.00033230003,0.00011129691,0.0007386245,0.00062311307,0.00017581244],"domain_scores_gemma":[0.9960975,0.0012303333,0.0004953019,0.001206579,0.0008007581,0.00016951394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027251795,0.001456719,0.0014237548,0.003059787,0.00069335796,0.0019539003,0.0018056115,0.0010980504,0.0015437689],"category_scores_gemma":[0.009090624,0.0003784385,0.0013965885,0.0021329254,0.0008948893,0.00258425,0.0023403605,0.002140404,0.0021615906],"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":[0.0005636624,0.00036624246,0.026179288,0.0002907721,0.0003472143,0.0004014496,0.00033915622,0.033077177,0.06321669,0.005939398,0.018214935,0.851064],"study_design_scores_gemma":[0.000057467805,0.00034212615,0.014274929,0.000039177605,0.000104304774,0.0008462448,0.00016268832,0.9163123,0.02909988,0.030046267,0.008602962,0.000111669964],"about_ca_topic_score_codex":0.0012705293,"about_ca_topic_score_gemma":0.002407718,"teacher_disagreement_score":0.003059787,"about_ca_system_score_codex":0.00070572365,"about_ca_system_score_gemma":0.000784706,"threshold_uncertainty_score":0.0144123435},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02091478,0.00032524165,0.9768685,0.00019885284,0.00005598566,0.000029681602,0.00015881562,0.0007222755,0.0007258922],"genre_scores_gemma":[0.80660796,0.0008571679,0.1833948,0.0002707005,0.00010388428,0.00012251397,0.0010861069,0.00021294253,0.0073440126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968505,0.000049074402,0.000015886186,0.00012434245,0.000082015904,0.000043704327],"domain_scores_gemma":[0.999305,0.0003477983,0.00010311804,0.00005191415,0.00013939264,0.00005278833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062893896,0.00094580237,0.00087887445,0.0010453445,0.000318536,0.00057766814,0.0016283395,0.00092455914,0.0016650709],"category_scores_gemma":[0.0023592857,0.00039868543,0.00092398626,0.0009552963,0.00069989957,0.0010377646,0.0006819753,0.0016793247,0.00045479363],"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":[0.00014391991,0.00008966376,0.003237147,0.000098858975,0.000082564715,0.00012907814,0.00008832038,0.84501904,0.008052496,0.019315941,0.002638279,0.121104725],"study_design_scores_gemma":[0.0000019244533,0.0000061242818,0.00013819741,0.0000014580361,0.000004020261,0.000010585927,0.0000016678136,0.99755126,0.00028140264,0.0018170307,0.00018303467,0.0000033014903],"about_ca_topic_score_codex":0.02014637,"about_ca_topic_score_gemma":0.018536745,"teacher_disagreement_score":0.02014637,"about_ca_system_score_codex":0.0010778265,"about_ca_system_score_gemma":0.0010802781,"threshold_uncertainty_score":0.040058196},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14251775,0.0012993594,0.85341895,0.00024838286,0.000099927696,0.00005094213,0.000113445625,0.0010070121,0.0012442769],"genre_scores_gemma":[0.9191162,0.000426189,0.078212574,0.00010605042,0.00007479311,0.000066537286,0.00040098082,0.000027845585,0.001568742],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994935,0.00013336742,0.000040497183,0.0001038506,0.00015130852,0.000077439],"domain_scores_gemma":[0.9988575,0.000495368,0.00011881242,0.0001016782,0.0003961007,0.000030514719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012638952,0.0007572208,0.0011167092,0.00092121237,0.00033359663,0.0006451599,0.00089493446,0.00060883764,0.00038710027],"category_scores_gemma":[0.0025614458,0.00027056196,0.00088739564,0.0007232343,0.00018205683,0.0007392975,0.00044534553,0.0008499082,0.00021336082],"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":[0.00013072311,0.00014997115,0.009348353,0.00004693191,0.00021158157,0.00011846238,0.000077764395,0.6802542,0.00394181,0.0018031835,0.0017227909,0.30219415],"study_design_scores_gemma":[0.0000013911373,0.000026045334,0.0005082375,0.0000021858627,0.000013420331,0.000010430634,0.0000038280054,0.99863225,0.00038592724,0.00026569507,0.00014736885,0.00000319166],"about_ca_topic_score_codex":0.006433266,"about_ca_topic_score_gemma":0.005771993,"teacher_disagreement_score":0.006433266,"about_ca_system_score_codex":0.00034371327,"about_ca_system_score_gemma":0.0006322815,"threshold_uncertainty_score":0.012791693},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85627687,0.0032612248,0.1329073,0.00033778374,0.0003285244,0.00015954679,0.0007418451,0.0038221069,0.002164792],"genre_scores_gemma":[0.9665149,0.00045218336,0.03149489,0.000031191845,0.00006335392,0.000036292102,0.00080840435,0.000081813356,0.00051693094],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9953121,0.0013371179,0.0004369158,0.0006372064,0.0020712612,0.00020539136],"domain_scores_gemma":[0.9528285,0.033382677,0.0023586766,0.0023795576,0.008444644,0.00060583017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053842585,0.0008214603,0.0009934722,0.0043640453,0.00047600924,0.0012998316,0.0013970787,0.00090263924,0.00057339383],"category_scores_gemma":[0.021535812,0.00021496316,0.00054454297,0.0023738206,0.00047966948,0.0023173895,0.000612458,0.0007538781,0.0002750606],"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":[0.00407959,0.0022565955,0.16593231,0.0015861635,0.0010430209,0.00041754128,0.0010766154,0.07228071,0.02665531,0.0023272708,0.004923112,0.7174217],"study_design_scores_gemma":[0.00008474796,0.0015903903,0.07180727,0.00006509773,0.00023008406,0.0008305075,0.0008825939,0.90546286,0.015471389,0.0010937596,0.0023930173,0.00008828864],"about_ca_topic_score_codex":0.0037257774,"about_ca_topic_score_gemma":0.0042955885,"teacher_disagreement_score":0.0053842585,"about_ca_system_score_codex":0.00074078655,"about_ca_system_score_gemma":0.0007507967,"threshold_uncertainty_score":0.028474987},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023978412,0.0004440188,0.96913624,0.00049868366,0.00006704218,0.000109232366,0.00043578612,0.004503842,0.0008268052],"genre_scores_gemma":[0.7125982,0.0005082973,0.27823073,0.0009964922,0.00024729,0.00029268864,0.0034184929,0.00052286976,0.0031848985],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859315,0.00034293046,0.00007241144,0.000522883,0.00030014195,0.00016856118],"domain_scores_gemma":[0.9967996,0.0013819729,0.0004519858,0.00058967096,0.00059456367,0.0001820899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002291375,0.0013406607,0.0012852706,0.0017758599,0.00056940375,0.0012463126,0.0031627452,0.0016153429,0.002119243],"category_scores_gemma":[0.009768754,0.00060171005,0.0013655468,0.0013322453,0.001164941,0.0026496174,0.0027009195,0.0034205478,0.00065428554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038998644,0.000387118,0.009644339,0.00025404312,0.00027336105,0.00029566255,0.00026868933,0.34087667,0.008062537,0.018739605,0.014109989,0.6066979],"study_design_scores_gemma":[0.000014166318,0.000039862538,0.0004099424,0.0000110913,0.000014869029,0.000041059247,0.000016795779,0.98831195,0.0012652489,0.009180345,0.0006838067,0.000010880966],"about_ca_topic_score_codex":0.007914874,"about_ca_topic_score_gemma":0.007917544,"teacher_disagreement_score":0.007914874,"about_ca_system_score_codex":0.0014021791,"about_ca_system_score_gemma":0.0019032031,"threshold_uncertainty_score":0.015737593},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0423032,0.00013876666,0.95490986,0.00016344752,0.00006548999,0.00006200569,0.00020575321,0.001078457,0.0010730913],"genre_scores_gemma":[0.7679928,0.0001955078,0.22819807,0.00010137889,0.000096592514,0.00011593899,0.0008825259,0.000110407156,0.0023067768],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992841,0.00015096896,0.00006506261,0.00015184312,0.00026817463,0.0000799649],"domain_scores_gemma":[0.9986104,0.00027153298,0.00020057648,0.0002864647,0.0005294833,0.00010152341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082858943,0.00066918915,0.0005361509,0.0010190887,0.00040908207,0.0007526166,0.0008828965,0.00043392362,0.0012846508],"category_scores_gemma":[0.0038788328,0.00020677292,0.0006035368,0.0011411182,0.0007603655,0.0010865595,0.0013623772,0.00086739316,0.00051697844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006728989,0.00014529897,0.011521191,0.00015439156,0.00012510785,0.000307956,0.0002844596,0.2620233,0.050423257,0.031712357,0.0061324025,0.6364974],"study_design_scores_gemma":[0.00000893289,0.00009164401,0.0016294833,0.0000054433667,0.000009901914,0.00010639452,0.0000472334,0.98248833,0.0073578577,0.006408222,0.0018246415,0.00002199255],"about_ca_topic_score_codex":0.004162238,"about_ca_topic_score_gemma":0.005485301,"teacher_disagreement_score":0.004162238,"about_ca_system_score_codex":0.00051321555,"about_ca_system_score_gemma":0.0014583472,"threshold_uncertainty_score":0.008276045},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88584137,0.016197816,0.048252303,0.0019424473,0.00070004474,0.0005478522,0.011902247,0.025483115,0.009132816],"genre_scores_gemma":[0.8662659,0.0022198863,0.08192795,0.0012571409,0.00012014481,0.00033785414,0.044244155,0.001033261,0.0025937387],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9917892,0.0021030472,0.00083922246,0.001982224,0.002764398,0.0005219729],"domain_scores_gemma":[0.9831149,0.008782233,0.0012487732,0.0033358466,0.0029701102,0.00054813974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007018362,0.00308005,0.0010529362,0.0036782154,0.0007700109,0.0014484739,0.0043864036,0.0022340722,0.001452719],"category_scores_gemma":[0.03009481,0.0006796612,0.0014439883,0.0024986418,0.0018560579,0.004100663,0.0024358043,0.0023318944,0.0008554612],"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":[0.0022799226,0.002254241,0.10265206,0.0047243116,0.0016814743,0.00083658966,0.00052482716,0.36290702,0.019521717,0.006069791,0.07874124,0.41780692],"study_design_scores_gemma":[0.00047919416,0.0019764525,0.030112078,0.0005193504,0.00038805767,0.0009117238,0.00033592258,0.89469916,0.04193434,0.0059063374,0.02257733,0.00016020102],"about_ca_topic_score_codex":0.014145503,"about_ca_topic_score_gemma":0.020008087,"teacher_disagreement_score":0.014145503,"about_ca_system_score_codex":0.0031183048,"about_ca_system_score_gemma":0.002320378,"threshold_uncertainty_score":0.037117064},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00045424618,0.97877616,0.008161228,0.0011153321,0.00084587577,0.000043720127,0.00006508833,0.00007892388,0.01045946],"genre_scores_gemma":[0.003490893,0.9856425,0.005120643,0.00060384703,0.0010292076,0.00004806157,0.00016383885,0.000032297274,0.0038687086],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99939775,0.000105177176,0.000065091976,0.000105936,0.0002797424,0.00004628663],"domain_scores_gemma":[0.99822015,0.0010862802,0.00012177433,0.000074729425,0.0004322002,0.000064859836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010235738,0.0011289971,0.00077278895,0.003908089,0.00043593923,0.0015961553,0.0012592011,0.0013703267,0.0069293203],"category_scores_gemma":[0.003081367,0.0006773701,0.000593464,0.004622929,0.00062669866,0.004479396,0.00092661695,0.002177186,0.0047640246],"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":[0.000024023173,0.00006652408,0.00046517604,0.005298311,0.0000455473,0.00007344966,0.00009543295,0.0009128296,0.0008096042,0.01751922,0.04007529,0.9346146],"study_design_scores_gemma":[0.0000045107677,0.00007714512,0.0006774701,0.0034520703,0.00005118084,0.00084958796,0.00009076568,0.0014039259,0.00083586574,0.0071092844,0.9854177,0.000030358335],"about_ca_topic_score_codex":0.0018074457,"about_ca_topic_score_gemma":0.0023880554,"teacher_disagreement_score":0.0069293203,"about_ca_system_score_codex":0.00077020365,"about_ca_system_score_gemma":0.0012142279,"threshold_uncertainty_score":0.023180842},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023846276,0.0011429337,0.9696107,0.00012982781,0.00012727798,0.000030696934,0.00014407803,0.0015018946,0.0034663815],"genre_scores_gemma":[0.51682186,0.0015241468,0.4726128,0.00009782957,0.00021219427,0.00005249595,0.0006204674,0.00016470363,0.007893559],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973077,0.000037442016,0.000011688139,0.00007941926,0.00011061514,0.0000301093],"domain_scores_gemma":[0.99975246,0.000095117044,0.0000358279,0.000036824702,0.00006755037,0.000012187457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002509929,0.0005784925,0.00060031115,0.0010574894,0.00017051665,0.0006953226,0.00089591945,0.0005571773,0.00095924735],"category_scores_gemma":[0.0010774441,0.00027598732,0.0004364251,0.0010842192,0.00026280174,0.0007550299,0.00056728814,0.0005990501,0.0005450257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016950729,0.00006706192,0.0032193055,0.0000734794,0.000056354034,0.00020148464,0.00006658645,0.04544317,0.03421375,0.0055827834,0.0055950163,0.9053116],"study_design_scores_gemma":[0.0000052651526,0.000076824326,0.0021640377,0.000014333859,0.00002716125,0.0005683315,0.000028944423,0.9702602,0.015928432,0.0064853616,0.00442455,0.000016464486],"about_ca_topic_score_codex":0.0013742888,"about_ca_topic_score_gemma":0.0014951541,"teacher_disagreement_score":0.0013742888,"about_ca_system_score_codex":0.00032038163,"about_ca_system_score_gemma":0.0001491189,"threshold_uncertainty_score":0.0032089949},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95156956,0.0014242138,0.029734006,0.0013393869,0.00030403037,0.00015043434,0.011907783,0.0018556198,0.0017150847],"genre_scores_gemma":[0.9499023,0.00042785137,0.01716896,0.00016454369,0.00009997989,0.0000830345,0.031163005,0.000026825255,0.00096346834],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927765,0.00014557716,0.000074974436,0.00021711712,0.00017420363,0.0001105688],"domain_scores_gemma":[0.99842024,0.00054786465,0.00020018652,0.00028348534,0.00041028624,0.0001379075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016080283,0.0010896102,0.000651574,0.001575311,0.0004965409,0.00065038406,0.0012464711,0.0011786809,0.00045128178],"category_scores_gemma":[0.003747284,0.00016422976,0.000764201,0.0013996897,0.00044151375,0.0008906866,0.00078166765,0.0010341865,0.00030483352],"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":[0.0011952069,0.0012807813,0.11644064,0.00037206095,0.00033976315,0.00121856,0.00021099825,0.696222,0.0067667738,0.0017960593,0.02908625,0.14507088],"study_design_scores_gemma":[0.000024787643,0.00022863677,0.020271262,0.000024709896,0.00004255065,0.00025857388,0.0001114513,0.9730344,0.0029132331,0.0008855772,0.00217617,0.000028658951],"about_ca_topic_score_codex":0.019144302,"about_ca_topic_score_gemma":0.017801205,"teacher_disagreement_score":0.019144302,"about_ca_system_score_codex":0.0012217963,"about_ca_system_score_gemma":0.00083677453,"threshold_uncertainty_score":0.03806573},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32427827,0.0007898854,0.64847034,0.000313131,0.00029299367,0.00041998294,0.0009038548,0.014143759,0.010387786],"genre_scores_gemma":[0.85916966,0.00031417672,0.12741363,0.00037347467,0.000045648558,0.00024364582,0.0011796138,0.00006698689,0.011193097],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998355,0.000010235588,0.000010931145,0.000060587812,0.000049502465,0.00003317191],"domain_scores_gemma":[0.99990416,0.000014036551,0.000015135849,0.000011755905,0.000037280606,0.000017544635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021232502,0.00047673853,0.00035396192,0.000394306,0.00021136216,0.00024500105,0.0007496039,0.0004211959,0.0030023414],"category_scores_gemma":[0.000300848,0.00020117826,0.00025859303,0.00023990338,0.00013478992,0.00039819317,0.00046658894,0.0003769373,0.0008747039],"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":[0.0007094876,0.0006932179,0.007960523,0.00022974641,0.00011858202,0.0005123924,0.00017013458,0.015105259,0.22960944,0.00086179277,0.0068464493,0.7371829],"study_design_scores_gemma":[0.000077291996,0.0011660191,0.02544982,0.000057804238,0.00015240692,0.00067199697,0.00011953159,0.8564205,0.10538203,0.001018339,0.009404423,0.00007984025],"about_ca_topic_score_codex":0.005904981,"about_ca_topic_score_gemma":0.0083915675,"teacher_disagreement_score":0.005904981,"about_ca_system_score_codex":0.00036930843,"about_ca_system_score_gemma":0.0005262957,"threshold_uncertainty_score":0.011741221},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19791603,0.0008401446,0.75084513,0.001671627,0.00015951987,0.00027697897,0.0011014725,0.0037926796,0.04339644],"genre_scores_gemma":[0.84843874,0.00052000606,0.14392126,0.00017082535,0.00008513312,0.00008026575,0.0015670607,0.00021834082,0.0049983747],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877137,0.00032347476,0.00006999429,0.00023451578,0.00047139416,0.00012923063],"domain_scores_gemma":[0.9971079,0.0016209146,0.0005473392,0.00037930836,0.00027078204,0.00007374205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011784812,0.0010073147,0.00039789866,0.0020458242,0.00043629445,0.0028053708,0.0010369414,0.0011782285,0.00401403],"category_scores_gemma":[0.008859096,0.00031685564,0.0009154415,0.0010434914,0.00096185185,0.0040302416,0.0018140621,0.0015386055,0.0013307831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004440403,0.00030486257,0.05722722,0.00050738157,0.00028668877,0.0019367555,0.0034071899,0.087072775,0.027123943,0.108515285,0.014796839,0.6983771],"study_design_scores_gemma":[0.000018251993,0.00019398217,0.021002121,0.00021158079,0.00017382545,0.0011768475,0.0017135389,0.81674427,0.010177752,0.12911972,0.019378735,0.00008936963],"about_ca_topic_score_codex":0.000990783,"about_ca_topic_score_gemma":0.0012211249,"teacher_disagreement_score":0.00401403,"about_ca_system_score_codex":0.00058953767,"about_ca_system_score_gemma":0.00045693183,"threshold_uncertainty_score":0.01342833},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39570507,0.00022506613,0.60025346,0.00009072359,0.000084672705,0.000061551546,0.00038566807,0.0011314512,0.0020622802],"genre_scores_gemma":[0.9886232,0.000076603814,0.010419752,0.000007789103,0.000009223345,0.0000119583065,0.0002865481,0.000012667716,0.00055235357],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998678,0.000022689119,0.000008589712,0.0000393057,0.00003643659,0.000025190266],"domain_scores_gemma":[0.99981576,0.000064714186,0.000019314828,0.000017379558,0.00007240205,0.000010519563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028621455,0.0004983323,0.00039363958,0.00081678556,0.00010335894,0.000341943,0.00047413827,0.00024926366,0.0006039433],"category_scores_gemma":[0.00091613823,0.00013222656,0.00044223873,0.00047723998,0.0001673528,0.0005268016,0.00032245126,0.00033992474,0.0003569618],"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":[0.00040160934,0.0002208425,0.025503451,0.00009707118,0.00011067274,0.00017418923,0.00010121123,0.5652849,0.030385451,0.002177841,0.0012830001,0.37425974],"study_design_scores_gemma":[0.0000026323748,0.00003666321,0.0025842797,0.000002304101,0.000010115755,0.00002937813,0.00001561054,0.9937936,0.0029095737,0.00045177704,0.00015935663,0.0000047070944],"about_ca_topic_score_codex":0.0054435898,"about_ca_topic_score_gemma":0.0055550328,"teacher_disagreement_score":0.0054435898,"about_ca_system_score_codex":0.0002882616,"about_ca_system_score_gemma":0.0002989726,"threshold_uncertainty_score":0.010823846},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05586378,0.00029116037,0.93655646,0.0001495842,0.00016954573,0.000070812785,0.00013164151,0.0044048387,0.0023622853],"genre_scores_gemma":[0.5907436,0.00016601784,0.40212023,0.00016405067,0.000097318305,0.00008780831,0.00046844882,0.00022326894,0.0059291567],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997714,0.000028594852,0.000009084785,0.00007877955,0.00006773634,0.000044496486],"domain_scores_gemma":[0.9998129,0.000031549196,0.000018135017,0.000022866448,0.000096127944,0.000018446657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039124076,0.0008350891,0.0007919996,0.00066429534,0.00031110365,0.0005727603,0.0008501638,0.0006802684,0.0019633193],"category_scores_gemma":[0.00070859416,0.00030290004,0.00042962003,0.0004467636,0.00017954441,0.0005604901,0.00069677655,0.0007319204,0.0008144156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037451801,0.00022707653,0.0030937411,0.00009202262,0.000094257084,0.00014260193,0.00009877512,0.091113135,0.06677558,0.0012259571,0.0047765556,0.83198583],"study_design_scores_gemma":[0.000007974693,0.00003921203,0.001039108,0.0000055373207,0.00001446223,0.000032731925,0.000015688844,0.9871782,0.010368956,0.0003374042,0.0009548122,0.0000060178286],"about_ca_topic_score_codex":0.0054910686,"about_ca_topic_score_gemma":0.0077078626,"teacher_disagreement_score":0.0054910686,"about_ca_system_score_codex":0.0002901675,"about_ca_system_score_gemma":0.00053687784,"threshold_uncertainty_score":0.01091826},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04266697,0.0005286201,0.9506415,0.00014395422,0.00017022803,0.000104554696,0.0005001627,0.004133823,0.0011101057],"genre_scores_gemma":[0.3326338,0.00076766056,0.65567327,0.00021194515,0.0002552186,0.00018650426,0.0026992175,0.00028291077,0.007289584],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948263,0.00003460568,0.000032271422,0.00016128436,0.00022048301,0.0000685737],"domain_scores_gemma":[0.9995222,0.000078798,0.000052451705,0.000050240244,0.0002560585,0.00004022026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043152497,0.0012906694,0.0011385791,0.0034417869,0.0004902728,0.0007173896,0.0013268762,0.0010042972,0.0014570617],"category_scores_gemma":[0.0009608251,0.00048656302,0.00075837184,0.0017980951,0.0003240195,0.0011170512,0.0009580664,0.00065824215,0.0011877917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032554,0.0002508799,0.007311644,0.00014736687,0.00015873778,0.00027282018,0.00007164172,0.03590601,0.06606716,0.0013392331,0.005389688,0.8827594],"study_design_scores_gemma":[0.000018209554,0.00008744712,0.00499901,0.000014489677,0.00005545796,0.0003923811,0.000042324755,0.97157514,0.018048856,0.0013939444,0.0033456834,0.000027056858],"about_ca_topic_score_codex":0.009463589,"about_ca_topic_score_gemma":0.012667967,"teacher_disagreement_score":0.009463589,"about_ca_system_score_codex":0.00047537984,"about_ca_system_score_gemma":0.0009353929,"threshold_uncertainty_score":0.018817008},"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":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020712296,0.34345898,0.6158977,0.003064647,0.0012089526,0.00021732516,0.001958084,0.0029264386,0.01055559],"genre_scores_gemma":[0.24819253,0.47558874,0.25887635,0.0016898259,0.003108251,0.00028007745,0.0063077165,0.0006736446,0.0052828896],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988028,0.0002016294,0.00013604041,0.00037367115,0.00042756894,0.000058255584],"domain_scores_gemma":[0.99581605,0.0029296442,0.0002669324,0.0002914315,0.0006066151,0.000089230736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001194561,0.0012978141,0.0015119769,0.0055502933,0.0003810312,0.0015748949,0.0019635959,0.0012787818,0.0015497162],"category_scores_gemma":[0.006826835,0.00053932436,0.0014280764,0.007841745,0.00065624685,0.0032113378,0.0007765394,0.0012134127,0.0010071574],"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":[0.00006933569,0.0001351296,0.006898451,0.0027545332,0.00020174094,0.00018920006,0.00016992357,0.026206851,0.0016273529,0.014975054,0.017619373,0.9291531],"study_design_scores_gemma":[0.000032412237,0.00038716994,0.014136325,0.0018754018,0.00043785686,0.0029488306,0.0006033133,0.55001223,0.007210545,0.11537195,0.30674222,0.00024178941],"about_ca_topic_score_codex":0.004316626,"about_ca_topic_score_gemma":0.0029121896,"teacher_disagreement_score":0.0055502933,"about_ca_system_score_codex":0.00068405695,"about_ca_system_score_gemma":0.0008870473,"threshold_uncertainty_score":0.008583009},"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":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1210846,0.00141837,0.04299493,0.00073757814,0.0010986069,0.0012803602,0.7799471,0.039939053,0.011499403],"genre_scores_gemma":[0.051627792,0.00032455954,0.03814934,0.00010342104,0.00007834718,0.00056320045,0.90389913,0.0005290299,0.0047251834],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988269,0.00010039206,0.00011247903,0.00031383487,0.0005172718,0.00012903128],"domain_scores_gemma":[0.9983069,0.00020119708,0.00011109674,0.0006841776,0.00056792825,0.0001287305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086244754,0.0014687316,0.0008118319,0.0026429824,0.0006606348,0.0009751126,0.0021870534,0.0011051891,0.0039602835],"category_scores_gemma":[0.0025741924,0.00041348222,0.00093933556,0.002606829,0.00038498914,0.000883449,0.001164062,0.0015107478,0.007187143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061287417,0.001119773,0.0144186765,0.00085204776,0.00021395438,0.00019886334,0.0001237672,0.01701153,0.010287474,0.0014866667,0.72914386,0.22453056],"study_design_scores_gemma":[0.00061288296,0.0011230622,0.0903885,0.00036745964,0.00022338307,0.0018172357,0.00064594956,0.21990988,0.06699348,0.007840489,0.609691,0.00038665472],"about_ca_topic_score_codex":0.013635335,"about_ca_topic_score_gemma":0.026089083,"teacher_disagreement_score":0.013635335,"about_ca_system_score_codex":0.00077506487,"about_ca_system_score_gemma":0.0012659271,"threshold_uncertainty_score":0.027111948},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029877512,0.00009464225,0.88572544,0.00012263653,0.000053665623,0.00031853115,0.000407034,0.08119995,0.0022006861],"genre_scores_gemma":[0.4732487,0.00019064375,0.5108229,0.00020506995,0.00006948706,0.00064133125,0.0013513621,0.008492675,0.004977885],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99897707,0.0001616742,0.00006560472,0.00020731009,0.00047834968,0.00010999755],"domain_scores_gemma":[0.9980678,0.00075935456,0.0001669123,0.00043736864,0.00038932863,0.0001792741],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017258255,0.0016285846,0.000723888,0.001289176,0.00039867876,0.0019053591,0.0014631845,0.0003961127,0.0045227734],"category_scores_gemma":[0.006151904,0.0005308077,0.0008101314,0.0004659598,0.0005184087,0.0024165246,0.0023828049,0.0011840984,0.0012431927],"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":[0.0021795838,0.00066620467,0.022324732,0.0007283132,0.00018325941,0.0007431278,0.0040767156,0.034293965,0.29327357,0.0174402,0.023589281,0.60050106],"study_design_scores_gemma":[0.00012974945,0.00062520837,0.018542849,0.000103810446,0.0001109664,0.0003709393,0.0006225598,0.69292766,0.22533986,0.015952244,0.04489906,0.00037501607],"about_ca_topic_score_codex":0.0019159165,"about_ca_topic_score_gemma":0.002170495,"teacher_disagreement_score":0.0045227734,"about_ca_system_score_codex":0.00047954786,"about_ca_system_score_gemma":0.00062827335,"threshold_uncertainty_score":0.015130162},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5541911,0.005002494,0.4219754,0.00977429,0.00040415477,0.00009214789,0.0008196722,0.0012038684,0.0065368046],"genre_scores_gemma":[0.96957624,0.00063407014,0.027928106,0.0002941931,0.00014639407,0.000020233338,0.00040724166,0.000047093385,0.0009462758],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975471,0.00072759885,0.00013374403,0.00039929472,0.00097391853,0.0002182765],"domain_scores_gemma":[0.9793605,0.013862949,0.0014273729,0.001755349,0.0031788931,0.0004149903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005177072,0.0007714549,0.00060447655,0.0019572256,0.00068656,0.001896894,0.0011492595,0.0011975927,0.0010508603],"category_scores_gemma":[0.032506015,0.00032909965,0.0005475113,0.0016972763,0.0015753013,0.0041466476,0.0012474486,0.0032338998,0.00027482334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012460662,0.000434879,0.12698658,0.0004416713,0.0003580688,0.0016380731,0.0006469521,0.32220158,0.017413063,0.08363346,0.017140897,0.4278587],"study_design_scores_gemma":[0.000008566181,0.00006248941,0.009173085,0.000045065484,0.000020146403,0.00024379461,0.000095315685,0.9597336,0.0056734113,0.02291577,0.002008542,0.000020119176],"about_ca_topic_score_codex":0.0044354238,"about_ca_topic_score_gemma":0.0042034374,"teacher_disagreement_score":0.005177072,"about_ca_system_score_codex":0.0016748465,"about_ca_system_score_gemma":0.0008566982,"threshold_uncertainty_score":0.027379274},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7467733,0.0036678272,0.23137334,0.0015490397,0.00045187128,0.00023351342,0.0038221022,0.0044131908,0.0077158646],"genre_scores_gemma":[0.9787465,0.000435727,0.017497782,0.00015729891,0.00007045081,0.000044704415,0.0017063079,0.000019012914,0.0013222],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973387,0.00005745035,0.00002994985,0.00007082638,0.000049544004,0.000058302714],"domain_scores_gemma":[0.9994825,0.00017730655,0.000104692714,0.000027888746,0.00016374674,0.000043785574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048324294,0.0007647408,0.00063100917,0.0008385238,0.00014421626,0.0004615948,0.0006064972,0.0005101757,0.0009725907],"category_scores_gemma":[0.0015686234,0.00020090817,0.0005370887,0.00032059613,0.000120603865,0.0005980933,0.00050670595,0.0006362452,0.0004854424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019302504,0.00084049086,0.13358554,0.0005902486,0.00047434776,0.0017572448,0.00032435468,0.20455399,0.040899027,0.0014277754,0.017519647,0.5960971],"study_design_scores_gemma":[0.000018790839,0.00016691598,0.012378678,0.000027625303,0.000064720196,0.00017685631,0.00006187724,0.98138064,0.0043002223,0.0007448575,0.0006581634,0.000020735533],"about_ca_topic_score_codex":0.005684435,"about_ca_topic_score_gemma":0.007399234,"teacher_disagreement_score":0.005684435,"about_ca_system_score_codex":0.00039759706,"about_ca_system_score_gemma":0.00053185225,"threshold_uncertainty_score":0.01130271},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03392518,0.0006314844,0.9603902,0.0005220071,0.000060193397,0.00006839819,0.00012569404,0.0012584991,0.0030182917],"genre_scores_gemma":[0.70843685,0.00044633058,0.28476903,0.0005670237,0.00007866269,0.00020180117,0.0005403112,0.000373251,0.0045867423],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944407,0.00023364028,0.000019339179,0.00013705563,0.00010630049,0.000059659713],"domain_scores_gemma":[0.9977374,0.0018433706,0.000105769286,0.00011881916,0.00013193047,0.000062704785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015748987,0.0010714184,0.0006546876,0.00064040394,0.00035000138,0.00095405965,0.0014398496,0.0013341531,0.0028757257],"category_scores_gemma":[0.00411634,0.0008301151,0.0010674675,0.00032066554,0.00085906597,0.0008226493,0.0012667207,0.0015134851,0.00057503715],"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":[0.000056154266,0.000033380617,0.0007695905,0.000040267932,0.00003479997,0.00004999263,0.00004972578,0.955848,0.0013406073,0.002831943,0.0007045772,0.03824107],"study_design_scores_gemma":[0.000003161043,0.000009157481,0.000038776234,0.0000037566822,0.0000030065269,0.000009528125,0.0000035027551,0.9977324,0.00027967925,0.0017262908,0.0001886156,0.0000020717641],"about_ca_topic_score_codex":0.005621875,"about_ca_topic_score_gemma":0.007944182,"teacher_disagreement_score":0.005621875,"about_ca_system_score_codex":0.00097323494,"about_ca_system_score_gemma":0.00082376983,"threshold_uncertainty_score":0.011178315},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03481195,0.00011995553,0.96161413,0.00016160295,0.000047560097,0.000051998166,0.00022103846,0.002515665,0.0004561663],"genre_scores_gemma":[0.46024713,0.00021099103,0.53487194,0.00011741292,0.0001512801,0.00015702492,0.001353713,0.0005086637,0.0023818363],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968644,0.00045973453,0.00022366006,0.0006874047,0.0015158628,0.00024894704],"domain_scores_gemma":[0.9929367,0.0020416572,0.0013594379,0.002049396,0.0013923744,0.00022045124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016470945,0.000860368,0.0014119854,0.0022705854,0.00055128115,0.0016650305,0.0018048923,0.0012319771,0.0008411638],"category_scores_gemma":[0.010739954,0.0005328681,0.0009808855,0.0019364228,0.0007819724,0.0020505777,0.0025797654,0.0018297632,0.0012365839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007261214,0.00043262186,0.022239618,0.00030688278,0.00028218518,0.0005031795,0.00030352944,0.05631086,0.13616346,0.010285058,0.008187374,0.7642591],"study_design_scores_gemma":[0.000031911037,0.00019668549,0.008602328,0.000026532949,0.000059921484,0.0009485026,0.0001356948,0.9132407,0.05160876,0.021293882,0.0038050343,0.000049987837],"about_ca_topic_score_codex":0.0009562429,"about_ca_topic_score_gemma":0.0015974111,"teacher_disagreement_score":0.0022705854,"about_ca_system_score_codex":0.00040364717,"about_ca_system_score_gemma":0.001347943,"threshold_uncertainty_score":0.008710802},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02286427,0.00030263275,0.9741062,0.00032737336,0.00008575624,0.00003794846,0.00016111285,0.0015115476,0.00060322526],"genre_scores_gemma":[0.7595861,0.00043649686,0.2342973,0.0004841633,0.00015747905,0.000104145176,0.0010137196,0.00023345453,0.003687186],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911946,0.00026738047,0.000034606237,0.00026663186,0.00022262865,0.000089232744],"domain_scores_gemma":[0.9980952,0.0010450155,0.00023458869,0.00027325787,0.0002444735,0.0001073155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015308537,0.0010635995,0.0011060123,0.0012611937,0.00041495962,0.00083218096,0.0020321761,0.0010733897,0.0010459612],"category_scores_gemma":[0.0045801923,0.00039939012,0.0008747577,0.00095945393,0.0009672324,0.0015732477,0.0016694481,0.0025217587,0.00040063055],"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":[0.00021054913,0.00020566354,0.0063238097,0.00009578181,0.00018267304,0.00025058902,0.00010229113,0.724307,0.0053797127,0.013043325,0.007981795,0.24191682],"study_design_scores_gemma":[0.000002354041,0.000011018622,0.00015218122,0.0000019016613,0.0000033617366,0.000021425849,0.0000037535226,0.9958841,0.0005251552,0.0031052504,0.0002857052,0.0000036679257],"about_ca_topic_score_codex":0.0039532925,"about_ca_topic_score_gemma":0.003621336,"teacher_disagreement_score":0.0039532925,"about_ca_system_score_codex":0.0007261321,"about_ca_system_score_gemma":0.000678841,"threshold_uncertainty_score":0.008096039},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005561472,0.00019999246,0.9187095,0.00007051375,0.00014319339,0.00010187006,0.0009807213,0.072526015,0.0017067097],"genre_scores_gemma":[0.06402591,0.00020590921,0.9226087,0.0001981662,0.000071235474,0.00020020331,0.0034205916,0.0038350113,0.0054342956],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990669,0.000053716674,0.00003139011,0.000208431,0.0005755058,0.00006411881],"domain_scores_gemma":[0.99935144,0.00017057057,0.00006369855,0.00019558362,0.00016694044,0.000051729992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050258264,0.0020055682,0.0016413695,0.0013832472,0.0003569887,0.0013907724,0.0034842023,0.0010737814,0.012054075],"category_scores_gemma":[0.0014843472,0.001069836,0.0012206481,0.0011525118,0.00045676087,0.0012146542,0.002904707,0.0013982494,0.0076258057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052222837,0.00014714318,0.0012952448,0.00025592183,0.00017034287,0.00019875483,0.00009222847,0.020939138,0.07193612,0.0026166686,0.048603192,0.853223],"study_design_scores_gemma":[0.0000760521,0.00023676959,0.0016849717,0.000028199267,0.00006139195,0.00080689194,0.00004581582,0.88217795,0.0789172,0.006319325,0.029563308,0.00008224722],"about_ca_topic_score_codex":0.0019704239,"about_ca_topic_score_gemma":0.0035170226,"teacher_disagreement_score":0.012054075,"about_ca_system_score_codex":0.00035439935,"about_ca_system_score_gemma":0.000663938,"threshold_uncertainty_score":0.040324926},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83007586,0.00025135127,0.16656359,0.00022829422,0.00004199268,0.00004480152,0.0012802891,0.000775784,0.0007381371],"genre_scores_gemma":[0.9834359,0.000058200658,0.014891791,0.00001770472,0.000028548355,0.000017770015,0.0013171802,0.000015674597,0.00021706361],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999231,0.00023373085,0.00006416335,0.00022992732,0.00017009422,0.00007103476],"domain_scores_gemma":[0.99579394,0.0019771096,0.0011561251,0.00046048724,0.00047319013,0.00013920684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013836833,0.00044827265,0.00039838374,0.0023418998,0.00015809773,0.00070424826,0.00061683194,0.0004361269,0.00036098214],"category_scores_gemma":[0.005108514,0.00014033631,0.0004316957,0.001106391,0.0003403865,0.0006657321,0.0006055442,0.00062190293,0.00018704025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053033076,0.0005603803,0.56082624,0.00018164703,0.00045187544,0.00033725484,0.00032974692,0.2375576,0.011868188,0.004520287,0.0026907166,0.18014574],"study_design_scores_gemma":[0.000005525121,0.00007878071,0.08403602,0.000017211876,0.00002454721,0.00010358413,0.000080069105,0.9095182,0.0025083697,0.0030787047,0.00052959943,0.000019443512],"about_ca_topic_score_codex":0.0043870723,"about_ca_topic_score_gemma":0.0043242057,"teacher_disagreement_score":0.0043870723,"about_ca_system_score_codex":0.0004204389,"about_ca_system_score_gemma":0.0004543278,"threshold_uncertainty_score":0.00872308},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018450381,0.00056523253,0.9745592,0.0008287394,0.00015632095,0.000049893275,0.00022684515,0.0015407326,0.0036226858],"genre_scores_gemma":[0.6628072,0.00047297435,0.31636247,0.00045617993,0.00040157276,0.00014895535,0.0008184751,0.00058658514,0.017945526],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99707174,0.0011775077,0.00013899426,0.0005498751,0.00078224816,0.00027969189],"domain_scores_gemma":[0.9921306,0.005698513,0.00045238293,0.00054788316,0.00095948647,0.00021110172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004609255,0.00092168426,0.0017376051,0.001813048,0.00079841644,0.0027255428,0.0032281626,0.0027196398,0.0062186276],"category_scores_gemma":[0.023235982,0.0007002031,0.00072192115,0.0020114388,0.0011364304,0.0041200574,0.0039356165,0.00292032,0.0013247846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006732577,0.00022305356,0.007275238,0.00018784982,0.00012324803,0.00019601887,0.00020147885,0.25708172,0.0021322318,0.05969381,0.015985275,0.6562268],"study_design_scores_gemma":[0.0000083782215,0.000010199392,0.00031958055,0.0000101998785,0.0000062993176,0.000026505742,0.0000138412815,0.97008014,0.0006525193,0.028124765,0.0007374376,0.000010016293],"about_ca_topic_score_codex":0.0054967687,"about_ca_topic_score_gemma":0.0047499156,"teacher_disagreement_score":0.0062186276,"about_ca_system_score_codex":0.0013077823,"about_ca_system_score_gemma":0.00095152063,"threshold_uncertainty_score":0.024376392},"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":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19702806,0.025138369,0.12805559,0.025596302,0.020122431,0.0011537869,0.20823874,0.13264583,0.26202098],"genre_scores_gemma":[0.22909848,0.002330094,0.108323,0.005970917,0.0011772766,0.00047697392,0.5836099,0.01620323,0.05281015],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99494416,0.00096787576,0.00025465921,0.0015442733,0.0015298626,0.00075908465],"domain_scores_gemma":[0.9957885,0.001130906,0.000095283576,0.0012267326,0.0014320677,0.00032648616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030235932,0.003218102,0.0027951712,0.0027677643,0.004078382,0.0051315837,0.0031632292,0.0058848225,0.01529002],"category_scores_gemma":[0.011223798,0.00071130117,0.0019258023,0.0029689993,0.0015726044,0.004173301,0.0040497715,0.0036046938,0.016709412],"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":[0.0008676255,0.00023206676,0.001152396,0.000642347,0.00015144052,0.00038819696,0.00025533626,0.007212098,0.0041874326,0.008114566,0.8192558,0.15754065],"study_design_scores_gemma":[0.00035664023,0.00022250564,0.0051826136,0.00063061,0.0002907148,0.0014695522,0.0019516448,0.14617407,0.028213708,0.06359625,0.75173163,0.00018012544],"about_ca_topic_score_codex":0.068237916,"about_ca_topic_score_gemma":0.12592904,"teacher_disagreement_score":0.068237916,"about_ca_system_score_codex":0.0026828528,"about_ca_system_score_gemma":0.004181257,"threshold_uncertainty_score":0.13568145},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12092182,0.0015136818,0.65049595,0.0012577156,0.0011921754,0.001816582,0.020888204,0.14335293,0.058561042],"genre_scores_gemma":[0.64653075,0.0011334204,0.28327087,0.0012927745,0.0005322003,0.001553077,0.033439334,0.0016467611,0.030600792],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996499,0.000042297164,0.000023015875,0.00010162017,0.00013401831,0.000049156522],"domain_scores_gemma":[0.9995664,0.000044340086,0.00004183431,0.00006865019,0.00022598729,0.000052768566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047953898,0.0006873027,0.00056400045,0.0014161243,0.0003713941,0.00079166994,0.00081301323,0.0004704222,0.010478837],"category_scores_gemma":[0.0010183773,0.00022894415,0.0002647197,0.0005514238,0.00017327926,0.00090264704,0.0008192601,0.00060414796,0.0039730207],"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":[0.0013950644,0.0005252147,0.009957652,0.000424606,0.00019738123,0.0006378275,0.00038494304,0.0062597743,0.07858046,0.005289367,0.24281168,0.653536],"study_design_scores_gemma":[0.00033616275,0.0008991605,0.035230543,0.00025709902,0.00022327926,0.0015153935,0.0003515803,0.54882723,0.15010288,0.006299251,0.25575525,0.00020224591],"about_ca_topic_score_codex":0.0034450088,"about_ca_topic_score_gemma":0.0047476976,"teacher_disagreement_score":0.010478837,"about_ca_system_score_codex":0.0005772243,"about_ca_system_score_gemma":0.0005801619,"threshold_uncertainty_score":0.03505516},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10133323,0.0001765589,0.89659107,0.00008739324,0.000043126685,0.000031784435,0.00004272611,0.0005518642,0.0011422967],"genre_scores_gemma":[0.77433574,0.0001394437,0.22238883,0.000059877115,0.000021587462,0.000025704785,0.00009965971,0.000066662455,0.002862536],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999788,0.000026481635,0.000009138883,0.00005176947,0.00010695524,0.000017751414],"domain_scores_gemma":[0.99954164,0.0001989559,0.00007761478,0.0000372196,0.00012544807,0.000019195018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000511845,0.00030658636,0.00029774095,0.00034020012,0.00019867632,0.00039213317,0.00066870905,0.0004227047,0.0004434454],"category_scores_gemma":[0.0011464189,0.00017019991,0.00024948953,0.00026065746,0.00035030246,0.0005401362,0.00037717918,0.000612226,0.00019056935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038531807,0.00008564441,0.0057623554,0.00015759133,0.00007403251,0.00022524092,0.00019603378,0.24103285,0.5188205,0.0063853264,0.0005719829,0.22630306],"study_design_scores_gemma":[0.0000026350376,0.000031654075,0.0010438832,0.0000038508456,0.0000048597985,0.000045852008,0.000008686842,0.92865175,0.06912486,0.00063310453,0.00044037224,0.000008420684],"about_ca_topic_score_codex":0.0026839208,"about_ca_topic_score_gemma":0.0029225647,"teacher_disagreement_score":0.0026839208,"about_ca_system_score_codex":0.0006004122,"about_ca_system_score_gemma":0.0003520739,"threshold_uncertainty_score":0.0053366423},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011415702,0.00053939415,0.98492706,0.00021440767,0.00010992641,0.00002413713,0.00013746398,0.001558901,0.0010729288],"genre_scores_gemma":[0.39493096,0.0007473591,0.58999634,0.0004849043,0.00026800393,0.00012104897,0.00087236834,0.0002913806,0.012287725],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937433,0.0001057838,0.00003293104,0.00014438303,0.00024963787,0.00009300332],"domain_scores_gemma":[0.998965,0.00038927488,0.000059278216,0.00015297701,0.00036496244,0.00006836768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010070009,0.0007625994,0.00089177274,0.001081195,0.00039246544,0.0010594008,0.002072181,0.001003272,0.002229799],"category_scores_gemma":[0.0016906937,0.00043749952,0.00062039745,0.0013337148,0.00046239767,0.0017750881,0.0017389065,0.0016208652,0.0006881918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020705664,0.0003004614,0.002374214,0.00010680493,0.00020131588,0.000110008696,0.000062147985,0.2090734,0.011123484,0.010783897,0.010192347,0.7554648],"study_design_scores_gemma":[0.0000029294808,0.000019698024,0.00016641596,0.0000029846883,0.000007879161,0.000019394243,0.000004276502,0.9940878,0.0011415371,0.003885367,0.0006571508,0.0000045174984],"about_ca_topic_score_codex":0.004897812,"about_ca_topic_score_gemma":0.008124366,"teacher_disagreement_score":0.004897812,"about_ca_system_score_codex":0.000660142,"about_ca_system_score_gemma":0.0008493225,"threshold_uncertainty_score":0.0097385645},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12484624,0.00010788109,0.873832,0.0001500109,0.000020598716,0.000032698827,0.000023130402,0.00039757372,0.0005899408],"genre_scores_gemma":[0.95103014,0.000037679718,0.048369702,0.00003957015,0.000014315663,0.000033323253,0.00004191971,0.000028519282,0.00040488233],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993709,0.0002471085,0.000031827352,0.00017404635,0.00011087013,0.00006521542],"domain_scores_gemma":[0.9957644,0.0028812662,0.00052428414,0.0002583611,0.00045011696,0.00012150441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019939637,0.0006327291,0.00075132964,0.00039458953,0.00024210516,0.0005996021,0.00088154024,0.0005773247,0.00048661107],"category_scores_gemma":[0.007604398,0.00031854055,0.00046984392,0.000252877,0.00097186596,0.0009261986,0.00080004585,0.001325372,0.00009621932],"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":[0.0000723792,0.000077577904,0.0029152706,0.000026660897,0.000047436988,0.00005031336,0.000056436707,0.95955044,0.003224566,0.0032898418,0.0001775317,0.030511403],"study_design_scores_gemma":[0.0000015820049,0.000010233258,0.00011958695,0.0000010598595,0.0000011931286,0.0000037354548,0.0000012786103,0.9988312,0.00034948482,0.00065947714,0.000019418998,0.0000016218023],"about_ca_topic_score_codex":0.0030299874,"about_ca_topic_score_gemma":0.002289186,"teacher_disagreement_score":0.0030299874,"about_ca_system_score_codex":0.0007216643,"about_ca_system_score_gemma":0.0006775038,"threshold_uncertainty_score":0.010545254},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037168857,0.00040023233,0.93653274,0.0011691807,0.00045811894,0.00008078244,0.0023210282,0.0075425054,0.014326543],"genre_scores_gemma":[0.706281,0.00048879324,0.2437014,0.0006276849,0.00023339131,0.0001610685,0.005278081,0.00066249934,0.04256612],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969375,0.000041507556,0.00001370328,0.00013955383,0.00006968863,0.00004180008],"domain_scores_gemma":[0.9994554,0.0001334948,0.00004497548,0.00016162872,0.00016191538,0.00004268195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003634312,0.00067484815,0.000554094,0.00038792664,0.00023865479,0.0007176005,0.0010484993,0.001210035,0.013933333],"category_scores_gemma":[0.0019215259,0.0003405204,0.00044601338,0.0007464525,0.00043536717,0.0012748726,0.0008734064,0.0015248597,0.0073369904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015135668,0.00012385318,0.0015148732,0.00013153396,0.00006345069,0.00012564627,0.0000648879,0.029523684,0.059359305,0.009346268,0.020244857,0.8793503],"study_design_scores_gemma":[0.000012187116,0.00008674688,0.0040956303,0.000022816612,0.000025144825,0.00018406675,0.000028768813,0.94597423,0.019530833,0.024512846,0.0055090897,0.000017614271],"about_ca_topic_score_codex":0.005133395,"about_ca_topic_score_gemma":0.0051062265,"teacher_disagreement_score":0.013933333,"about_ca_system_score_codex":0.00043280615,"about_ca_system_score_gemma":0.0007156105,"threshold_uncertainty_score":0.046611607},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7730539,0.0014432864,0.21652165,0.0005040963,0.00017291085,0.00024668878,0.0014945092,0.0011830248,0.00537993],"genre_scores_gemma":[0.9825838,0.00017449338,0.01600855,0.000021298676,0.0000230585,0.00003150536,0.00090403593,0.000023307044,0.0002299759],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99761766,0.000916646,0.00014960225,0.0004761441,0.00056655676,0.0002733387],"domain_scores_gemma":[0.9918069,0.0048704487,0.0010930151,0.0007435764,0.0009951735,0.00049084343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035349573,0.0014005137,0.0012622133,0.0023705857,0.0004305912,0.0013578202,0.00109128,0.0008056467,0.00076406373],"category_scores_gemma":[0.012753878,0.0002437968,0.00056114304,0.0017966075,0.00067289645,0.0018147341,0.0014753648,0.0007370032,0.00012046916],"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":[0.0004921338,0.0001291005,0.012843835,0.00010349494,0.00011641376,0.00008464768,0.00004106314,0.94167566,0.0011182204,0.002003364,0.00065939594,0.040732678],"study_design_scores_gemma":[0.000017298313,0.00020877403,0.002534999,0.000010947785,0.00003503622,0.000030605916,0.00006891173,0.9950394,0.0011348292,0.00067117705,0.00023930492,0.000008685489],"about_ca_topic_score_codex":0.01590016,"about_ca_topic_score_gemma":0.008976979,"teacher_disagreement_score":0.01590016,"about_ca_system_score_codex":0.001509793,"about_ca_system_score_gemma":0.0019165054,"threshold_uncertainty_score":0.031615257},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023750832,0.00037377037,0.95314866,0.0009955519,0.00014561137,0.00013399607,0.00012443117,0.002617253,0.018709902],"genre_scores_gemma":[0.69391334,0.0003934427,0.29729137,0.00036327532,0.00010997415,0.00021941376,0.0004109038,0.00063457264,0.0066637723],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978701,0.0008510959,0.00013842588,0.00038748296,0.00047543916,0.00027735002],"domain_scores_gemma":[0.99535465,0.0016227608,0.00035421323,0.0017627607,0.00053738017,0.00036823816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038277714,0.0008298806,0.0009536967,0.0015890964,0.0014803656,0.0061607794,0.0030376979,0.002133591,0.008103287],"category_scores_gemma":[0.009137166,0.000615461,0.0012430724,0.0011552932,0.003270516,0.009476846,0.008088188,0.0028600106,0.0016439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033313315,0.00025469429,0.004499308,0.00031815263,0.00019337038,0.00031011147,0.0027359647,0.0655663,0.007938921,0.7876334,0.003465656,0.12675093],"study_design_scores_gemma":[0.000026026988,0.00010907258,0.00048406134,0.00009463105,0.00007006456,0.00010491248,0.0006019391,0.45338538,0.005320876,0.52561486,0.014157146,0.00003102053],"about_ca_topic_score_codex":0.0023720902,"about_ca_topic_score_gemma":0.0025320998,"teacher_disagreement_score":0.008103287,"about_ca_system_score_codex":0.0012216851,"about_ca_system_score_gemma":0.0024436226,"threshold_uncertainty_score":0.027108192},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08042054,0.0010607041,0.9100731,0.0004949811,0.00008710302,0.00009966687,0.00015497375,0.0021627839,0.0054461774],"genre_scores_gemma":[0.8677306,0.00059004204,0.12620655,0.00020386971,0.000028119011,0.00021067268,0.00041881265,0.00011457014,0.004496689],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997166,0.000036282476,0.000015574567,0.00007917207,0.00008365618,0.00006861312],"domain_scores_gemma":[0.9997732,0.00005728907,0.00003037248,0.000021603551,0.00009804571,0.000019472767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005317519,0.0011003158,0.00069497444,0.000608173,0.0003460721,0.0006866342,0.0015099648,0.0008404756,0.0017589092],"category_scores_gemma":[0.0010678383,0.0004534571,0.00057287933,0.00049981655,0.00047986477,0.0013588818,0.00076909835,0.0010991519,0.00032483137],"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":[0.000049810675,0.000036197278,0.0009364925,0.000044161425,0.000039193757,0.000043948916,0.00001804269,0.93465155,0.002215453,0.002169991,0.0013061091,0.058489114],"study_design_scores_gemma":[0.0000026953817,0.000008979946,0.000051874118,0.0000016521002,0.0000039456013,0.000003757727,0.0000014885212,0.99915934,0.0002968148,0.00036628832,0.00010187637,0.0000012711918],"about_ca_topic_score_codex":0.019562716,"about_ca_topic_score_gemma":0.018593645,"teacher_disagreement_score":0.019562716,"about_ca_system_score_codex":0.0015812931,"about_ca_system_score_gemma":0.0025138478,"threshold_uncertainty_score":0.038897693},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9935801,0.00013740292,0.005266301,0.00003061987,0.000004004047,0.000009629477,0.000058085418,0.000032882424,0.00088088785],"genre_scores_gemma":[0.9983748,0.000054703913,0.0013346492,0.000011470106,0.0000029876535,0.00000648722,0.00005022743,0.000006783601,0.00015775648],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99990153,0.000023368513,0.000004827565,0.000032977427,0.000018340234,0.000018859735],"domain_scores_gemma":[0.9997496,0.00011819538,0.00005877386,0.00003179705,0.000021196012,0.000020453896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012720074,0.00012631754,0.00013915278,0.00015811506,0.00009779119,0.00027707277,0.000098579054,0.00018313751,0.000986587],"category_scores_gemma":[0.0012272589,0.00010539048,0.00009405562,0.00009302149,0.00024372195,0.00033870162,0.0002595472,0.00017172884,0.0000849971],"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":[0.00039374814,0.000034366865,0.016038992,0.00010307488,0.000051283547,0.00013040802,0.0007306696,0.000907261,0.95456976,0.00083871686,0.00022808593,0.025973542],"study_design_scores_gemma":[0.00005430165,0.00036947857,0.86180925,0.000027112801,0.00007228453,0.0006609948,0.00062609126,0.025375705,0.10440775,0.0045294682,0.0020192286,0.00004832854],"about_ca_topic_score_codex":0.0004504839,"about_ca_topic_score_gemma":0.00058388786,"teacher_disagreement_score":0.000986587,"about_ca_system_score_codex":0.0001114089,"about_ca_system_score_gemma":0.000080263395,"threshold_uncertainty_score":0.0033004284},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02020936,0.00012565526,0.9781706,0.00013984223,0.000027232401,0.000038516995,0.000047382047,0.00074712513,0.000494345],"genre_scores_gemma":[0.5949354,0.00016838513,0.40266228,0.00015737185,0.00008275029,0.00013683492,0.00034079392,0.00019475316,0.0013213593],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987031,0.00030846588,0.00006347464,0.00039279673,0.00038823573,0.00014384957],"domain_scores_gemma":[0.9965622,0.0019061657,0.00045592897,0.00037048914,0.00063490093,0.00007031751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019356709,0.00082298846,0.0006937535,0.0015744084,0.00049130205,0.00086411077,0.0013328558,0.0011150754,0.0013459001],"category_scores_gemma":[0.0064865914,0.00029819176,0.0010609804,0.0010596996,0.00085509894,0.0012666972,0.0010208334,0.0016244682,0.0004227204],"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":[0.0003253607,0.00023381863,0.006815513,0.0001762095,0.00012446173,0.00025862193,0.0003657471,0.2877044,0.030888373,0.009820368,0.0024799097,0.6608072],"study_design_scores_gemma":[0.0000048338643,0.000030747913,0.0009374512,0.0000073667006,0.000014338762,0.000051223582,0.00001677377,0.9896834,0.0037884293,0.004747954,0.00070441025,0.000013072386],"about_ca_topic_score_codex":0.0037306193,"about_ca_topic_score_gemma":0.0032286197,"teacher_disagreement_score":0.0037306193,"about_ca_system_score_codex":0.0009214683,"about_ca_system_score_gemma":0.0009844534,"threshold_uncertainty_score":0.0102369785},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05228075,0.0013705053,0.9297847,0.00242739,0.00017260567,0.00024771658,0.0005829949,0.005898772,0.0072345496],"genre_scores_gemma":[0.60047007,0.00044243564,0.39440995,0.0004161609,0.00011216351,0.00027452322,0.0017401417,0.00070087455,0.0014336843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9934186,0.0032603906,0.0003407691,0.0010857593,0.0015848437,0.00030962116],"domain_scores_gemma":[0.9849561,0.0085706515,0.0014121394,0.0021706694,0.002403592,0.0004867952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009817717,0.0018484831,0.0013882258,0.0031994358,0.00081750815,0.0052416855,0.003481343,0.0025174578,0.0030415272],"category_scores_gemma":[0.037595455,0.0006857426,0.0009115318,0.0019348292,0.0026727174,0.0035939475,0.0042607347,0.0023596461,0.0013941965],"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":[0.0002667801,0.00020680128,0.007657426,0.0005168709,0.00024027606,0.00014152582,0.00048007225,0.7771397,0.0034380856,0.04484728,0.006754739,0.15831031],"study_design_scores_gemma":[0.000009224321,0.000044319655,0.0005063791,0.000040924697,0.00000984632,0.000017716413,0.00008703658,0.9633834,0.0012454812,0.032938804,0.0017069463,0.000009964865],"about_ca_topic_score_codex":0.0076746615,"about_ca_topic_score_gemma":0.0055114585,"teacher_disagreement_score":0.009817717,"about_ca_system_score_codex":0.002138205,"about_ca_system_score_gemma":0.0023076455,"threshold_uncertainty_score":0.051921666},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02012886,0.0019265838,0.8697562,0.00065580267,0.0008806944,0.00045611695,0.008051953,0.02272286,0.0754209],"genre_scores_gemma":[0.16149399,0.0014890948,0.71174335,0.00053155853,0.0007384935,0.00033109915,0.032700114,0.0034361803,0.08753607],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978095,0.000016844453,0.0000101508485,0.00008880599,0.00007094194,0.00003235324],"domain_scores_gemma":[0.9995622,0.000099618934,0.000019026682,0.00016643511,0.000106055035,0.00004667078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030852715,0.0008495282,0.0005711137,0.0008699707,0.00059266645,0.0008954309,0.0010008829,0.00050794176,0.036926985],"category_scores_gemma":[0.0012321972,0.0003428305,0.00052426517,0.001236137,0.00022853567,0.0016210102,0.00090184965,0.0010825488,0.02013858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004891887,0.0001779965,0.00075991615,0.0002766791,0.00004780962,0.00014554903,0.000034729106,0.016844835,0.01641092,0.012515763,0.12046035,0.8318363],"study_design_scores_gemma":[0.00014889635,0.00022748695,0.0022984988,0.00011670535,0.00010311044,0.0012598507,0.000091205504,0.6181405,0.029419892,0.121427685,0.22671588,0.0000502489],"about_ca_topic_score_codex":0.0015549888,"about_ca_topic_score_gemma":0.004699126,"teacher_disagreement_score":0.036926985,"about_ca_system_score_codex":0.0002263301,"about_ca_system_score_gemma":0.00066289224,"threshold_uncertainty_score":0.12353307},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061450582,0.00047755,0.92442423,0.00035008148,0.000115848175,0.0001017694,0.0007909959,0.010793588,0.0014953463],"genre_scores_gemma":[0.59906405,0.0004172207,0.38857135,0.00031098642,0.00008147657,0.00024378012,0.0034076523,0.00048122555,0.007422302],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997929,0.000020193795,0.000008586669,0.000082816194,0.0000617055,0.000033863264],"domain_scores_gemma":[0.9996215,0.00009896331,0.000047833448,0.00006093907,0.00014157912,0.000029228067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053723773,0.0010509519,0.0006924493,0.00092082366,0.00035167404,0.00060584873,0.0018926203,0.00084733806,0.0020702158],"category_scores_gemma":[0.0014994893,0.0004995929,0.0007744557,0.00062637625,0.00045239218,0.0011203726,0.001050704,0.0009615573,0.00086338236],"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":[0.00035484176,0.0001574284,0.0051286686,0.00008741661,0.00013922625,0.00026730838,0.00009365268,0.5187806,0.01750808,0.005005312,0.010146159,0.44233137],"study_design_scores_gemma":[0.0000031414763,0.00001866782,0.0002308064,0.0000033624722,0.0000067716705,0.000037514073,0.0000046118485,0.9953595,0.0024614185,0.0014354418,0.0004339022,0.000004783701],"about_ca_topic_score_codex":0.013253755,"about_ca_topic_score_gemma":0.020089094,"teacher_disagreement_score":0.013253755,"about_ca_system_score_codex":0.0011661785,"about_ca_system_score_gemma":0.001192769,"threshold_uncertainty_score":0.02635318},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9024715,0.00015641323,0.09527801,0.00014726855,0.0000332493,0.000025967958,0.00038227218,0.00064483273,0.000860471],"genre_scores_gemma":[0.9869233,0.000043884083,0.012206176,0.000008233906,0.000016372,0.000016037615,0.00055823487,0.000011571771,0.00021629836],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99974734,0.000045618275,0.000020300482,0.00007512494,0.00006836603,0.000043291304],"domain_scores_gemma":[0.9993543,0.0001408447,0.0001941121,0.00009622204,0.00018517149,0.000029391635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042668308,0.00038558856,0.00028122746,0.0015909456,0.00021110919,0.00041029492,0.00041567086,0.00025540232,0.00025544054],"category_scores_gemma":[0.0014452526,0.00014148244,0.00042541148,0.001367154,0.00014669933,0.00047968826,0.0003634731,0.00034625395,0.00009034912],"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":[0.0002839604,0.00024585085,0.35404804,0.00010350484,0.00028435414,0.00063365325,0.0005587239,0.23525015,0.017996347,0.002417607,0.00288465,0.38529316],"study_design_scores_gemma":[0.0000069138773,0.00006015792,0.09662434,0.0000072525395,0.000041172923,0.00010739395,0.00019494681,0.8982041,0.0029288563,0.0008475293,0.0009530292,0.00002431694],"about_ca_topic_score_codex":0.0114659,"about_ca_topic_score_gemma":0.009932521,"teacher_disagreement_score":0.0114659,"about_ca_system_score_codex":0.00031347238,"about_ca_system_score_gemma":0.00045829875,"threshold_uncertainty_score":0.02279836},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09092887,0.00026885766,0.9056256,0.0002971879,0.00005587703,0.00002501436,0.00013710497,0.00079328526,0.0018681724],"genre_scores_gemma":[0.95651764,0.00015302461,0.040714283,0.00006767327,0.000019984445,0.000035253775,0.00012308615,0.000033177446,0.002335888],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998053,0.00003546901,0.000012758582,0.00006086616,0.000060524304,0.000025121231],"domain_scores_gemma":[0.9996542,0.00014960657,0.000050346836,0.00003622717,0.00008917311,0.000020622338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005948299,0.0004133492,0.00045674693,0.00042033033,0.00020690386,0.0005806883,0.0011498552,0.00058135786,0.0010282756],"category_scores_gemma":[0.0010915784,0.00023797719,0.00055131456,0.0005488163,0.00038942153,0.0008000058,0.00053548877,0.0008187329,0.0001678887],"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":[0.00007723452,0.0000444983,0.001406658,0.000022704504,0.000040502953,0.00005321739,0.000019231942,0.9410452,0.00615443,0.005274208,0.00043742088,0.045424663],"study_design_scores_gemma":[4.96282e-7,0.000003658201,0.00004133771,4.038355e-7,0.0000011783061,0.0000037606856,4.6973412e-7,0.999273,0.00020202332,0.0004346322,0.000038072605,8.7051285e-7],"about_ca_topic_score_codex":0.0056007337,"about_ca_topic_score_gemma":0.0043295757,"teacher_disagreement_score":0.0056007337,"about_ca_system_score_codex":0.0006413171,"about_ca_system_score_gemma":0.0005947758,"threshold_uncertainty_score":0.011136234},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8038334,0.008362419,0.103680804,0.00490504,0.0003803253,0.00044622173,0.06579443,0.0015329091,0.011064357],"genre_scores_gemma":[0.88906544,0.0020655585,0.05398662,0.00028955188,0.00016468663,0.00015283214,0.053441696,0.000058165144,0.00077551533],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99653757,0.0007452219,0.00058408215,0.00078241347,0.0010608769,0.0002898315],"domain_scores_gemma":[0.9857218,0.007636228,0.0013401122,0.002308093,0.0024878813,0.0005058765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045716567,0.00054563617,0.00086258765,0.006413811,0.00063800457,0.0033312615,0.0014493667,0.0007846229,0.0009736773],"category_scores_gemma":[0.015314449,0.0002190482,0.00061368063,0.006463915,0.0004419614,0.0033775915,0.0019140518,0.000949702,0.00045509325],"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":[0.0007467539,0.0007653165,0.5434456,0.0011634468,0.00046498497,0.0013479432,0.0011258492,0.058884684,0.0034396816,0.01156259,0.03147741,0.34557563],"study_design_scores_gemma":[0.000052021474,0.00029522652,0.2087002,0.0006192685,0.00031352846,0.0013382931,0.0064602625,0.6701097,0.0059865275,0.028346058,0.077643275,0.00013568121],"about_ca_topic_score_codex":0.009333911,"about_ca_topic_score_gemma":0.010490913,"teacher_disagreement_score":0.009333911,"about_ca_system_score_codex":0.00093609054,"about_ca_system_score_gemma":0.0012196556,"threshold_uncertainty_score":0.024177551},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38641495,0.002265885,0.5917647,0.0010489888,0.00013654925,0.00013990611,0.0012818314,0.014791273,0.0021559577],"genre_scores_gemma":[0.90098137,0.00026296815,0.09440447,0.00032915283,0.00004450722,0.000050700884,0.0023216784,0.00012291815,0.00148234],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903345,0.00018099818,0.00008265675,0.00031069515,0.00026285398,0.00012938293],"domain_scores_gemma":[0.9962369,0.0016683531,0.0006538538,0.0006065168,0.00073011563,0.000104196224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001403995,0.0016212024,0.0006956938,0.0015661162,0.00038400458,0.0007601279,0.0016532141,0.0012520619,0.0009004818],"category_scores_gemma":[0.0060477084,0.00040518542,0.0009428059,0.000630594,0.0005979702,0.0019679216,0.001122484,0.0017999645,0.00033029565],"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":[0.00052915,0.0003969265,0.036709577,0.00023047303,0.00024480146,0.00059877866,0.00027725662,0.5539766,0.0125639755,0.0029034046,0.007820678,0.38374838],"study_design_scores_gemma":[0.000008799711,0.000039790808,0.0012029221,0.000010115317,0.000018119903,0.00003590236,0.000017562981,0.99355066,0.003123112,0.0016871312,0.0002976795,0.000008146899],"about_ca_topic_score_codex":0.023884509,"about_ca_topic_score_gemma":0.030621989,"teacher_disagreement_score":0.023884509,"about_ca_system_score_codex":0.0016970752,"about_ca_system_score_gemma":0.0014318335,"threshold_uncertainty_score":0.047490954},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05737947,0.0002486006,0.92162704,0.0010210921,0.00006861292,0.0004519936,0.0001591135,0.017194968,0.0018490576],"genre_scores_gemma":[0.27136216,0.00015767151,0.72249967,0.00090318866,0.000025785579,0.0005032681,0.00070418726,0.0029272065,0.00091680826],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9640129,0.020102954,0.0027555206,0.0037697135,0.007846645,0.0015121892],"domain_scores_gemma":[0.86386806,0.07140093,0.00718053,0.042483553,0.012649194,0.0024176962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029736599,0.0021291117,0.00087988743,0.0022964813,0.001012548,0.004751471,0.0069493973,0.0025356298,0.0023073326],"category_scores_gemma":[0.16251267,0.0026724557,0.002189115,0.0009719733,0.0053666956,0.014697963,0.01214614,0.0072125904,0.0010203667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017223711,0.001631794,0.052611,0.0015101519,0.0004458914,0.0015800904,0.009163085,0.120545164,0.044764765,0.09559547,0.013915562,0.65651464],"study_design_scores_gemma":[0.00021353833,0.0012980374,0.003787874,0.00056174584,0.00013921573,0.00071504596,0.0012134978,0.79810435,0.077059254,0.09207801,0.024609895,0.00021956187],"about_ca_topic_score_codex":0.0043198946,"about_ca_topic_score_gemma":0.0055419933,"teacher_disagreement_score":0.029736599,"about_ca_system_score_codex":0.0028788012,"about_ca_system_score_gemma":0.0066022538,"threshold_uncertainty_score":0.157264},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020325772,0.00010776087,0.958618,0.00015236044,0.000018926325,0.00010355446,0.00060334074,0.019500421,0.0005699012],"genre_scores_gemma":[0.2204577,0.000097781805,0.77141905,0.00025935506,0.000028827522,0.0003126964,0.0044009765,0.0015287165,0.0014949483],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947508,0.0016730184,0.00042330901,0.001969949,0.0009125046,0.00027043372],"domain_scores_gemma":[0.98535407,0.0066877436,0.001172246,0.005247058,0.0013610092,0.00017774638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005433959,0.0017346893,0.0012645983,0.0017873786,0.0006175561,0.001656593,0.0045888247,0.0014400079,0.0020375233],"category_scores_gemma":[0.02189638,0.0011395226,0.0019646867,0.0014600703,0.0013431516,0.0037453296,0.0028006968,0.0030001295,0.0019004559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040951243,0.00040991552,0.018551258,0.00032312225,0.00023676861,0.00039215482,0.00050131965,0.23967499,0.020816568,0.006777069,0.012528536,0.6993787],"study_design_scores_gemma":[0.000018972758,0.000048646703,0.0012637794,0.000018376757,0.000017432612,0.00011947469,0.00005657973,0.9755809,0.01224365,0.00845851,0.0021521938,0.000021563552],"about_ca_topic_score_codex":0.00516454,"about_ca_topic_score_gemma":0.009242806,"teacher_disagreement_score":0.005433959,"about_ca_system_score_codex":0.0013606022,"about_ca_system_score_gemma":0.001998927,"threshold_uncertainty_score":0.028737903},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052779447,0.0040058545,0.9144475,0.0037096571,0.00058407953,0.00006387446,0.00036658422,0.0017968608,0.022246081],"genre_scores_gemma":[0.83005995,0.0036106925,0.15611409,0.0012504833,0.00035410663,0.000059785365,0.00092513754,0.0001499844,0.0074758655],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997342,0.000056899822,0.000011805381,0.000053523596,0.00009598123,0.000047625395],"domain_scores_gemma":[0.999668,0.00012124126,0.000035391597,0.00006395872,0.000085316904,0.0000260584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042864055,0.0005573813,0.00033477452,0.0006696781,0.00022033244,0.0009901753,0.0007245599,0.00074121874,0.0014417644],"category_scores_gemma":[0.0011489503,0.00017924608,0.00032687926,0.0005929744,0.0004897957,0.0019646895,0.0012582199,0.0015172593,0.00088981923],"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":[0.00020328823,0.00012210905,0.006450547,0.00020954174,0.00009096478,0.00042401283,0.00025186027,0.18635921,0.02335889,0.08891264,0.01570595,0.677911],"study_design_scores_gemma":[0.000009341401,0.00012489647,0.001032703,0.00005635085,0.000033787313,0.00028090455,0.00010284683,0.8840118,0.007019758,0.06421597,0.043080043,0.00003166255],"about_ca_topic_score_codex":0.001961305,"about_ca_topic_score_gemma":0.0037043833,"teacher_disagreement_score":0.001961305,"about_ca_system_score_codex":0.0004699655,"about_ca_system_score_gemma":0.0005217407,"threshold_uncertainty_score":0.004823208},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027312997,0.00042755183,0.9937264,0.00014871631,0.00013106033,0.00003677804,0.000045967467,0.0005340149,0.0022181678],"genre_scores_gemma":[0.0493518,0.0012677432,0.9386586,0.00023897583,0.00026562889,0.00007483066,0.0001341757,0.0001484725,0.009859733],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99953794,0.00005033762,0.00002033952,0.00010052448,0.00025787577,0.000033042674],"domain_scores_gemma":[0.99953544,0.00015923266,0.000044114917,0.000086621774,0.00014554072,0.000029185694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004144226,0.0007227134,0.00076613546,0.0017468759,0.00033641726,0.0015096649,0.0015439212,0.0011331592,0.003082291],"category_scores_gemma":[0.0010717206,0.00038521507,0.0008927989,0.0017052566,0.0008154421,0.0012701348,0.0013234244,0.0013501341,0.0015126582],"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":[0.00008659519,0.00010694813,0.00076832104,0.0002938437,0.00009405325,0.0002429792,0.0001375981,0.013483959,0.13970989,0.06379987,0.009498426,0.7717775],"study_design_scores_gemma":[0.000028037612,0.00027151112,0.0026500593,0.000063340536,0.00013475688,0.003040466,0.00011329218,0.75547373,0.09614835,0.0908725,0.05111364,0.00009032458],"about_ca_topic_score_codex":0.00066925096,"about_ca_topic_score_gemma":0.0010285376,"teacher_disagreement_score":0.003082291,"about_ca_system_score_codex":0.0003641097,"about_ca_system_score_gemma":0.00036051872,"threshold_uncertainty_score":0.010311365},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8137204,0.053304184,0.11362768,0.0014955405,0.0010861944,0.00041734232,0.00391418,0.0021827358,0.010251641],"genre_scores_gemma":[0.9006751,0.006304979,0.08336034,0.00024743006,0.00032681206,0.00013766396,0.0058999616,0.00019462428,0.0028530457],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950525,0.0019861516,0.000684075,0.0005792048,0.0015119271,0.00018609554],"domain_scores_gemma":[0.93047273,0.061312314,0.00083473185,0.0011319245,0.0058523263,0.00039599877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011346588,0.0010464899,0.0014687966,0.003491959,0.0006799044,0.0016246467,0.001497622,0.0012180106,0.001769138],"category_scores_gemma":[0.025433123,0.0002482701,0.0012397392,0.002172452,0.00046511152,0.002530264,0.0006142925,0.00094726746,0.0005622587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00980117,0.0011995416,0.058433656,0.0017674868,0.0016271503,0.0002239346,0.00036656493,0.12539724,0.0046841,0.002932093,0.014056501,0.77951056],"study_design_scores_gemma":[0.00024811085,0.003210859,0.032042097,0.00024873464,0.0012672124,0.00037946354,0.00066700927,0.9444398,0.0085231,0.0052747447,0.0035919521,0.00010697311],"about_ca_topic_score_codex":0.009290291,"about_ca_topic_score_gemma":0.008322588,"teacher_disagreement_score":0.011346588,"about_ca_system_score_codex":0.001722742,"about_ca_system_score_gemma":0.0011207792,"threshold_uncertainty_score":0.060007215},"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":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000042186486,0.0011100724,0.00009819247,0.040349647,0.957707,0.000012898647,0.00006265814,0.000043924018,0.00057342084],"genre_scores_gemma":[0.0007705971,0.0019535657,0.00019987224,0.03509706,0.9525207,0.00002902332,0.00008611709,0.0000717602,0.0092712855],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.994069,0.00068766804,0.00082387077,0.0008146308,0.0029677541,0.0006371843],"domain_scores_gemma":[0.9686983,0.009277524,0.0027044725,0.0012678509,0.014460813,0.0035909337],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0061702956,0.003363192,0.0034504512,0.0041234395,0.0031078337,0.0071064914,0.003692018,0.023189547,0.024146944],"category_scores_gemma":[0.03647707,0.0016411672,0.0035292478,0.0020739222,0.0020815716,0.0036526464,0.002065134,0.02785401,0.01806706],"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":[0.000030930652,0.000010435436,0.00003243186,0.000093481875,0.000014376449,0.00011338502,0.000006684197,0.000016454738,0.0000771696,0.00012601035,0.996825,0.002653697],"study_design_scores_gemma":[0.00009591666,0.000056557652,0.0008727371,0.00034778807,0.000083354586,0.00041075549,0.000049287435,0.0002674443,0.00030489406,0.00076830684,0.99670726,0.00003577058],"about_ca_topic_score_codex":0.002748367,"about_ca_topic_score_gemma":0.0054157255,"teacher_disagreement_score":0.97681046,"about_ca_system_score_codex":0.0033931779,"about_ca_system_score_gemma":0.0043192604,"threshold_uncertainty_score":0.08077955},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025989702,0.00013535233,0.99270695,0.000051722538,0.00014071986,0.000042909312,0.00009580733,0.003614595,0.00061289355],"genre_scores_gemma":[0.09154115,0.00023508312,0.90077883,0.00017079712,0.00022947586,0.00016743882,0.0006632171,0.00086395495,0.0053500542],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860233,0.0002594008,0.00007503889,0.0003471076,0.0006280357,0.00008813977],"domain_scores_gemma":[0.998185,0.00083715154,0.00011357893,0.00044568037,0.0003479,0.000070615766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019047435,0.0012452726,0.001319646,0.0014141015,0.0005874918,0.001690433,0.0032300625,0.001546951,0.0049682525],"category_scores_gemma":[0.0047884155,0.00062200375,0.0013147258,0.0012269422,0.0007246811,0.0018560287,0.0016765792,0.002218617,0.0021711187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047987606,0.0002495726,0.0010511236,0.00016360659,0.00015833524,0.00015702532,0.00012531693,0.07060956,0.034040783,0.02100598,0.015752971,0.8562059],"study_design_scores_gemma":[0.000020964895,0.00005489255,0.00027287283,0.000008850932,0.000021775546,0.000093841634,0.000012768238,0.97371656,0.010844788,0.010607226,0.004326566,0.000018845378],"about_ca_topic_score_codex":0.0024121741,"about_ca_topic_score_gemma":0.003134128,"teacher_disagreement_score":0.0049682525,"about_ca_system_score_codex":0.0006158433,"about_ca_system_score_gemma":0.00061614305,"threshold_uncertainty_score":0.016620457},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04093927,0.00015508053,0.956368,0.00028936408,0.000050371287,0.00003434303,0.000058676353,0.0008046761,0.0013001821],"genre_scores_gemma":[0.87967795,0.00013803013,0.11770183,0.00021573278,0.00004170636,0.0000516375,0.00013934069,0.00010274475,0.0019309643],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949694,0.00016274571,0.000017859304,0.00012613597,0.0001416352,0.000054613825],"domain_scores_gemma":[0.9988078,0.00068069674,0.00013400865,0.00019525495,0.00013835815,0.000043903485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011443959,0.00067779346,0.0005052706,0.00042973435,0.00027891056,0.00059922965,0.0011044887,0.0006623558,0.0012166939],"category_scores_gemma":[0.003563132,0.0002673505,0.0005301981,0.00037117873,0.0011287174,0.0013452941,0.0011743205,0.0015224767,0.00022593598],"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":[0.00015476653,0.00010158795,0.002995079,0.0000654366,0.00009070584,0.00014464525,0.000110895424,0.803985,0.020680226,0.051223446,0.0015220757,0.11892613],"study_design_scores_gemma":[0.0000022025606,0.000018268465,0.00010620925,0.0000018390978,0.0000034268614,0.000021270655,0.0000029971395,0.9912594,0.00217979,0.006183544,0.00021684174,0.0000043504815],"about_ca_topic_score_codex":0.0020682032,"about_ca_topic_score_gemma":0.002958852,"teacher_disagreement_score":0.0020682032,"about_ca_system_score_codex":0.0008399472,"about_ca_system_score_gemma":0.0006077625,"threshold_uncertainty_score":0.006094277},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0081096105,0.8740691,0.10098023,0.0055742087,0.0007635876,0.000095091884,0.00045304623,0.00045757688,0.009497584],"genre_scores_gemma":[0.059782837,0.8950281,0.039230708,0.0014243071,0.0010332451,0.00007407549,0.0006876447,0.000105818515,0.0026332538],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99847895,0.0002711686,0.00020053805,0.00025796515,0.00070785364,0.00008345499],"domain_scores_gemma":[0.99440706,0.0037686746,0.0002742641,0.00022638153,0.001187695,0.00013593902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035861472,0.0013720596,0.0013714104,0.0035153197,0.00036807067,0.0019070575,0.0017824899,0.0013156829,0.002307212],"category_scores_gemma":[0.009037866,0.00075946574,0.0010556525,0.0044378485,0.0008727226,0.004246895,0.0016212518,0.0026722841,0.0010864458],"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":[0.0000737491,0.00011235125,0.0038591647,0.0041207112,0.00016610885,0.00008480475,0.000114317576,0.010592737,0.0008623965,0.012443293,0.014686587,0.95288384],"study_design_scores_gemma":[0.00005170164,0.0007239465,0.011035971,0.011277315,0.0008308926,0.0016998188,0.0006036067,0.19576505,0.01088841,0.0771943,0.68968123,0.00024775264],"about_ca_topic_score_codex":0.0029927734,"about_ca_topic_score_gemma":0.0026165808,"teacher_disagreement_score":0.0035861472,"about_ca_system_score_codex":0.0011763362,"about_ca_system_score_gemma":0.0015997094,"threshold_uncertainty_score":0.018965542},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3044262,0.0008455015,0.68705827,0.0005943297,0.0001883695,0.00003466362,0.0004615579,0.0033403495,0.0030507073],"genre_scores_gemma":[0.9190897,0.00027714504,0.07416625,0.00012252906,0.000111453395,0.000025511341,0.0008965731,0.000128703,0.00518216],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998036,0.000026642956,0.000008525889,0.000069500886,0.000048980626,0.00004266767],"domain_scores_gemma":[0.9996722,0.000109001885,0.000045188448,0.000059369984,0.00009001961,0.000024345929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037172638,0.0006171856,0.000749144,0.0005586085,0.00023132905,0.0004940515,0.0009666949,0.00060965377,0.0012679748],"category_scores_gemma":[0.0010044269,0.0002991414,0.00044468083,0.0008062184,0.00029373303,0.0009950707,0.00075130776,0.0012373816,0.0007898148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029191436,0.0005704634,0.009552353,0.00006932377,0.00020528107,0.00014529562,0.0000885099,0.32120237,0.017535992,0.003554336,0.008532639,0.63825154],"study_design_scores_gemma":[0.0000021101998,0.000011336877,0.00060135097,0.0000014155057,0.000006415857,0.000008076196,0.0000052804903,0.9974794,0.0009779684,0.0006759669,0.00022836096,0.0000023394923],"about_ca_topic_score_codex":0.00520647,"about_ca_topic_score_gemma":0.009325409,"teacher_disagreement_score":0.00520647,"about_ca_system_score_codex":0.0003087505,"about_ca_system_score_gemma":0.00054757897,"threshold_uncertainty_score":0.0103523135},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9791836,0.00026706493,0.0005095548,0.00061500043,0.000052838903,0.000021731934,0.0004653002,0.000076922246,0.018807871],"genre_scores_gemma":[0.99797183,0.0000936212,0.00016481536,0.000057541332,0.000015943215,0.000004441061,0.00020208317,0.00002015238,0.0014695971],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99981374,0.000021901913,0.000016520027,0.00006258768,0.00003567196,0.00004959902],"domain_scores_gemma":[0.99961877,0.000012567252,0.000102295795,0.00005341151,0.0001157525,0.0000972381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029899998,0.0003487,0.00021228817,0.00069192884,0.0008484962,0.0015965893,0.00051185815,0.00034477183,0.005059602],"category_scores_gemma":[0.0013436383,0.00011023085,0.00017541274,0.00081115216,0.0013314773,0.00038534586,0.0009645536,0.00040014525,0.00037769804],"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":[0.00081028475,0.00019639688,0.7797392,0.00072190556,0.0003059174,0.004706154,0.0069634216,0.0076397457,0.05853478,0.012225768,0.0043284995,0.1238279],"study_design_scores_gemma":[0.000011930731,0.0000744636,0.9751916,0.00004192814,0.000029660816,0.00032117558,0.003339362,0.00093656726,0.0014167764,0.00082259846,0.017801762,0.000012166902],"about_ca_topic_score_codex":0.04341991,"about_ca_topic_score_gemma":0.098768115,"teacher_disagreement_score":0.04341991,"about_ca_system_score_codex":0.0018517985,"about_ca_system_score_gemma":0.0013466201,"threshold_uncertainty_score":0.08633435},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046613365,0.00027406268,0.9927239,0.00024182163,0.000040942552,0.000019643832,0.00021847202,0.0013750511,0.00044477615],"genre_scores_gemma":[0.37015995,0.0010835133,0.6188797,0.00059184845,0.00026324048,0.00029866345,0.0023714409,0.0004643131,0.0058873175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996375,0.000121929435,0.000027277501,0.000103367536,0.000069968126,0.00004000092],"domain_scores_gemma":[0.9988368,0.0007124523,0.0000967264,0.00012807152,0.00017835743,0.000047624457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018328418,0.0010783729,0.0010203404,0.00077555585,0.00029038943,0.0008986643,0.0021958558,0.0013074229,0.0024792925],"category_scores_gemma":[0.0049379454,0.00049645384,0.0009477526,0.0009097808,0.0005076772,0.0015510574,0.0011530619,0.0028356307,0.0007944382],"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":[0.00007604177,0.000048117774,0.0007709644,0.000048371287,0.00006852409,0.000050198454,0.000030264351,0.8587769,0.00079708494,0.009952408,0.0038224682,0.12555856],"study_design_scores_gemma":[0.0000021082185,0.0000031874372,0.000024194933,0.0000021731375,0.000001781869,0.00000282373,9.312206e-7,0.996944,0.00016690715,0.0026127354,0.00023760191,0.0000014925661],"about_ca_topic_score_codex":0.010376742,"about_ca_topic_score_gemma":0.011570991,"teacher_disagreement_score":0.010376742,"about_ca_system_score_codex":0.001082918,"about_ca_system_score_gemma":0.0011608895,"threshold_uncertainty_score":0.020632684},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49767926,0.0034795864,0.48254958,0.0014003556,0.00023688392,0.00025394445,0.00074322516,0.007348575,0.0063085095],"genre_scores_gemma":[0.92897964,0.00032997713,0.06643396,0.00032839022,0.000059630715,0.00018063588,0.0011406392,0.0001162544,0.0024308723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932945,0.00023680022,0.000052341013,0.0001919747,0.00008639562,0.00010318954],"domain_scores_gemma":[0.9983462,0.0008352741,0.00014618557,0.00017767628,0.00043017065,0.000064463595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018741827,0.0021862993,0.0011574142,0.0018611114,0.0005502774,0.0011633493,0.0014235068,0.001272364,0.0014418791],"category_scores_gemma":[0.005557085,0.00063600054,0.0012719769,0.0010140593,0.00044729392,0.001458959,0.00091777724,0.0018096329,0.00052648736],"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":[0.00017215865,0.00027929185,0.012936124,0.00007540179,0.00020011551,0.00013314457,0.0000877198,0.82269144,0.0025087006,0.00096925773,0.0034067768,0.15653977],"study_design_scores_gemma":[0.000011940246,0.00003619899,0.00066795223,0.0000094358875,0.000022149303,0.0000127665235,0.000012477117,0.99772364,0.000510263,0.0008234536,0.00016225413,0.0000074269733],"about_ca_topic_score_codex":0.016296292,"about_ca_topic_score_gemma":0.017372897,"teacher_disagreement_score":0.016296292,"about_ca_system_score_codex":0.0010895929,"about_ca_system_score_gemma":0.0015929246,"threshold_uncertainty_score":0.032402873},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22049245,0.0026149913,0.760311,0.0010797137,0.0007706875,0.0003177993,0.0008493621,0.004793627,0.00877037],"genre_scores_gemma":[0.82250166,0.00075375737,0.17090638,0.0003775084,0.0002798107,0.0001469071,0.0017198897,0.000083394545,0.0032306958],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990965,0.00020185785,0.000058263377,0.0001752128,0.00033316997,0.00013502517],"domain_scores_gemma":[0.9985447,0.0006369508,0.0001338741,0.00015793928,0.0004855903,0.000040939714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002230545,0.0012443169,0.0010775463,0.002064292,0.00046817918,0.0014031216,0.0011214104,0.0011191422,0.0010140404],"category_scores_gemma":[0.004275869,0.00023273987,0.00086528447,0.0014067454,0.0002640681,0.0016498639,0.0006707451,0.0012734757,0.0016538461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024786795,0.0006358474,0.025304325,0.00019056474,0.00014060424,0.00021894682,0.00009696647,0.22582605,0.011495464,0.0026734737,0.010955912,0.722214],"study_design_scores_gemma":[0.000008124225,0.00008237966,0.002763561,0.000024077435,0.00002374191,0.00007245367,0.00003466949,0.9882941,0.0044498937,0.0018448115,0.0023899535,0.000012152275],"about_ca_topic_score_codex":0.0029155144,"about_ca_topic_score_gemma":0.003791931,"teacher_disagreement_score":0.0029155144,"about_ca_system_score_codex":0.0004919546,"about_ca_system_score_gemma":0.0005776935,"threshold_uncertainty_score":0.011796355},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049867244,0.0013155302,0.9874752,0.00068850495,0.00023849234,0.000045739416,0.00010507658,0.002377254,0.0027673393],"genre_scores_gemma":[0.12632935,0.0011218906,0.86098784,0.00067050685,0.00063805276,0.00012590643,0.0003046542,0.0018599373,0.0079619195],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9911583,0.003285873,0.00036395463,0.0018439671,0.0029277757,0.00042014066],"domain_scores_gemma":[0.9722392,0.009881587,0.0011549297,0.00922319,0.0068956506,0.00060542446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011650616,0.0016906913,0.0023030753,0.004555318,0.0014090885,0.0053967983,0.0077500045,0.002924309,0.0077296444],"category_scores_gemma":[0.043177463,0.0016569811,0.0016841191,0.0030193655,0.0034356017,0.01012178,0.007852709,0.0064938506,0.0043838075],"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":[0.00033420548,0.00017610258,0.003664244,0.00036273286,0.00024083228,0.00013502395,0.00050716696,0.06369078,0.0068229823,0.1910734,0.0111415535,0.72185093],"study_design_scores_gemma":[0.00004522446,0.00016243792,0.0012116095,0.00016906406,0.00010477844,0.00027882805,0.00014682686,0.681202,0.016409678,0.2592858,0.04086047,0.00012332361],"about_ca_topic_score_codex":0.0043264646,"about_ca_topic_score_gemma":0.0033681807,"teacher_disagreement_score":0.011650616,"about_ca_system_score_codex":0.0026292722,"about_ca_system_score_gemma":0.002923632,"threshold_uncertainty_score":0.06161511},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1377493,0.0008921421,0.8580811,0.00020942252,0.00047300928,0.00003994517,0.000297399,0.0009843846,0.0012732759],"genre_scores_gemma":[0.72656786,0.0005294033,0.2692401,0.00007118585,0.0003016737,0.0000612449,0.00073037384,0.000086830405,0.0024113578],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956864,0.00006085234,0.000039233655,0.00008979766,0.00018051716,0.00006095962],"domain_scores_gemma":[0.99890685,0.00040909144,0.000121463614,0.00006374719,0.0004507854,0.00004802131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008552211,0.00058483856,0.00063919084,0.0018343034,0.0002907748,0.0006655517,0.00049334747,0.00069507444,0.00083824614],"category_scores_gemma":[0.0023519213,0.00015660262,0.00046787874,0.001197895,0.00021394854,0.0007940306,0.00029195624,0.00064636744,0.0005563688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00085631676,0.00022556909,0.007494883,0.00018668083,0.00006515785,0.00025030255,0.00016151181,0.029918808,0.105329975,0.0014501606,0.003813833,0.8502468],"study_design_scores_gemma":[0.000021866264,0.00015475802,0.013054411,0.000020473299,0.000054399625,0.0002753136,0.00006555793,0.962371,0.020513952,0.0009567146,0.0024716752,0.000039915092],"about_ca_topic_score_codex":0.0018905515,"about_ca_topic_score_gemma":0.0014749855,"teacher_disagreement_score":0.0018905515,"about_ca_system_score_codex":0.00018908913,"about_ca_system_score_gemma":0.0003238018,"threshold_uncertainty_score":0.0045229197},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58787555,0.00017145267,0.4010606,0.0013090462,0.00007026432,0.000068238005,0.00028093724,0.0034231201,0.0057407445],"genre_scores_gemma":[0.97973526,0.000034644618,0.018946806,0.00017577957,0.000012769109,0.000025755126,0.00013022304,0.0001780072,0.0007608389],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983889,0.00061072636,0.00010747266,0.00043492572,0.00032148694,0.00013658569],"domain_scores_gemma":[0.98321205,0.009329611,0.001323164,0.005087112,0.00068921864,0.00035881094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025551685,0.0006032734,0.00064338295,0.0004652625,0.000419304,0.0016611132,0.0012972893,0.0009925725,0.0025080768],"category_scores_gemma":[0.027485786,0.0004979527,0.0007782102,0.00033089163,0.0023462097,0.004626805,0.002698149,0.0029354875,0.00041001046],"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":[0.0015337483,0.00036427373,0.048902977,0.0006194945,0.00034739505,0.0015969127,0.009701292,0.38659397,0.13827455,0.11947683,0.0070297006,0.2855589],"study_design_scores_gemma":[0.00003632205,0.00022391378,0.006232761,0.000021681522,0.00004346641,0.00032184034,0.0005303361,0.8188284,0.027152283,0.14478949,0.0017659328,0.000053585423],"about_ca_topic_score_codex":0.0022632407,"about_ca_topic_score_gemma":0.0018974112,"teacher_disagreement_score":0.0025551685,"about_ca_system_score_codex":0.00082550297,"about_ca_system_score_gemma":0.0005423061,"threshold_uncertainty_score":0.013513148},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009632657,0.00013969296,0.98932356,0.00008941952,0.000026675974,0.000038018992,0.000018732699,0.0004094746,0.00032174625],"genre_scores_gemma":[0.28842533,0.0003072071,0.7072793,0.00020561113,0.000058600843,0.00026482326,0.000294371,0.00020452379,0.0029602821],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993093,0.0001417709,0.000055127755,0.00023871253,0.00017112434,0.00008401074],"domain_scores_gemma":[0.998159,0.0009802198,0.00014251325,0.00014403058,0.00049188297,0.00008233527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020497455,0.0010751135,0.0014013168,0.0010128403,0.00047572027,0.0010928503,0.0021929636,0.0019260736,0.0013387972],"category_scores_gemma":[0.005578375,0.0007372136,0.0010557496,0.00062926364,0.00081467093,0.0015816295,0.0015394825,0.0026225708,0.0006322553],"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":[0.00021472522,0.00011959205,0.0020117084,0.00011407136,0.00013470482,0.000098826116,0.00021020087,0.58481735,0.011986821,0.009608455,0.0015476516,0.38913593],"study_design_scores_gemma":[0.0000045430043,0.000018034883,0.000121623205,0.0000059851104,0.000006161633,0.000022823724,0.0000063541265,0.9975044,0.001260586,0.0008810213,0.00016290355,0.0000055468686],"about_ca_topic_score_codex":0.009448921,"about_ca_topic_score_gemma":0.0077252053,"teacher_disagreement_score":0.009448921,"about_ca_system_score_codex":0.0010890679,"about_ca_system_score_gemma":0.0019348778,"threshold_uncertainty_score":0.01878786},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034468714,0.00057350885,0.9611115,0.0006361858,0.00005190773,0.000021111806,0.00017183635,0.00035693918,0.0026082858],"genre_scores_gemma":[0.90922827,0.0008612242,0.08596696,0.00033504615,0.00021201037,0.00014904296,0.00046488523,0.00039787826,0.0023846063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843127,0.00056521344,0.00007752879,0.00023684904,0.0005562973,0.00013290234],"domain_scores_gemma":[0.98901165,0.008226054,0.0007924381,0.00069667486,0.0010071757,0.00026603416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005064547,0.00088736304,0.0010059634,0.0017810515,0.0006172453,0.0022726832,0.0011566675,0.0011945621,0.0017106461],"category_scores_gemma":[0.021901004,0.00072436413,0.0008580806,0.00091322867,0.0022764262,0.0036523931,0.002321429,0.0027175131,0.00020138438],"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":[0.00004936824,0.000017574659,0.0016802828,0.00007624778,0.00006340652,0.000070542555,0.00007577088,0.8389145,0.0010555063,0.13775499,0.0014281815,0.018813627],"study_design_scores_gemma":[0.0000012979708,0.0000047915155,0.00014773487,0.000011118414,0.0000026601283,0.0000060146717,0.0000046297637,0.94540066,0.00027626738,0.053952027,0.00018671273,0.000006163251],"about_ca_topic_score_codex":0.0043689483,"about_ca_topic_score_gemma":0.0032478014,"teacher_disagreement_score":0.005064547,"about_ca_system_score_codex":0.0020224536,"about_ca_system_score_gemma":0.0011313274,"threshold_uncertainty_score":0.026784182},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0051258546,0.00027658965,0.98356795,0.00012191511,0.00012928188,0.000041469364,0.0004860651,0.009441719,0.0008091001],"genre_scores_gemma":[0.082589544,0.0003630021,0.906636,0.00011164644,0.00011184423,0.0001029879,0.0015737804,0.0012581565,0.007253004],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997559,0.000035395016,0.000012285711,0.00007701121,0.0000934649,0.000025925074],"domain_scores_gemma":[0.9995485,0.00016974148,0.000034393503,0.00010280016,0.000113458475,0.000031080654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060206425,0.0010114826,0.0008930418,0.0008580442,0.0004322067,0.0008854087,0.0013883078,0.0010220532,0.0048745135],"category_scores_gemma":[0.0017274504,0.00044567042,0.0007525076,0.0007889674,0.00038522834,0.00082356465,0.0010621176,0.001263572,0.002206535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038957706,0.00010505874,0.0009869398,0.00021600898,0.00016757881,0.00017582219,0.00008204925,0.055280346,0.04710105,0.011004813,0.032763846,0.851727],"study_design_scores_gemma":[0.00002066548,0.00004487154,0.0005294256,0.000010865036,0.000022123595,0.00017936004,0.000013536656,0.9564302,0.022073433,0.012423422,0.008228278,0.00002385406],"about_ca_topic_score_codex":0.003178772,"about_ca_topic_score_gemma":0.0055386196,"teacher_disagreement_score":0.0048745135,"about_ca_system_score_codex":0.0004092285,"about_ca_system_score_gemma":0.0007294832,"threshold_uncertainty_score":0.016306937},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12971891,0.0005248515,0.6816353,0.00025687943,0.00016244223,0.0004548195,0.0035825127,0.16985343,0.013810747],"genre_scores_gemma":[0.6868321,0.0002456194,0.2910598,0.00029524122,0.000045095632,0.0003239036,0.008236872,0.0014842596,0.011477092],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972075,0.000023440436,0.000018172974,0.00010580331,0.00009618181,0.00003568538],"domain_scores_gemma":[0.99974996,0.000049788403,0.000034803616,0.000055966753,0.0000799947,0.000029500707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035154182,0.00060405093,0.00037923342,0.0007013294,0.00015511572,0.00066139235,0.001035726,0.00024599492,0.0033023723],"category_scores_gemma":[0.0007366949,0.00025345656,0.0002653216,0.0002894502,0.00019513624,0.0008434276,0.0007409489,0.00036818592,0.0013926452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017260169,0.00040675284,0.025391707,0.00063273316,0.00022207419,0.0004605405,0.0007030425,0.028223628,0.2111214,0.007056155,0.049352884,0.67470306],"study_design_scores_gemma":[0.00014189632,0.0005113556,0.022930225,0.00008029768,0.00010241064,0.0006624897,0.00015687913,0.7482827,0.14941223,0.004960489,0.072635606,0.0001234559],"about_ca_topic_score_codex":0.004798766,"about_ca_topic_score_gemma":0.005907548,"teacher_disagreement_score":0.004798766,"about_ca_system_score_codex":0.00053749967,"about_ca_system_score_gemma":0.00054210797,"threshold_uncertainty_score":0.011047542},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015569155,0.0004191436,0.92326576,0.0017447362,0.0002594922,0.0005834653,0.0033798509,0.06509966,0.0036909906],"genre_scores_gemma":[0.043872178,0.0007988049,0.93167377,0.0012389637,0.00027137966,0.00082326063,0.012656395,0.004583897,0.0040812856],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956204,0.0007256013,0.0006058236,0.00057998777,0.0021640144,0.0003041909],"domain_scores_gemma":[0.9929328,0.0017922807,0.0005319179,0.0022532393,0.0014487288,0.0010410396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008290481,0.001988745,0.0014788884,0.00469903,0.0026164986,0.008308967,0.005382686,0.0024284963,0.007308467],"category_scores_gemma":[0.014243507,0.0017491124,0.002548098,0.004246375,0.0023041833,0.009795506,0.008884606,0.005397833,0.0038994781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010891412,0.0007296457,0.009354137,0.0016275033,0.0007910989,0.002498964,0.0027590927,0.050760765,0.020762404,0.2372514,0.33378553,0.33859032],"study_design_scores_gemma":[0.0002292649,0.00018137331,0.0024139124,0.000491785,0.00013769236,0.0009276927,0.0004605833,0.49081594,0.012143261,0.20196383,0.28990987,0.0003247365],"about_ca_topic_score_codex":0.009599619,"about_ca_topic_score_gemma":0.012669493,"teacher_disagreement_score":0.009599619,"about_ca_system_score_codex":0.0017155274,"about_ca_system_score_gemma":0.004397365,"threshold_uncertainty_score":0.04384482},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009684462,0.00027127546,0.9858549,0.0003695727,0.000044457316,0.00007228408,0.00017126597,0.0030662739,0.00046552028],"genre_scores_gemma":[0.35998744,0.00038809245,0.63108724,0.001774177,0.00022805197,0.00046411212,0.0020504447,0.0011476303,0.0028728815],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9952472,0.0014566176,0.00024802767,0.0015510797,0.001095285,0.00040167978],"domain_scores_gemma":[0.9877643,0.0063221063,0.00091582665,0.0028551323,0.001685129,0.0004574347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0072524883,0.0020987901,0.0021922877,0.002579082,0.0010860587,0.0026915567,0.0061108754,0.003439743,0.0012782204],"category_scores_gemma":[0.027714547,0.0013286948,0.0017328077,0.0015229182,0.0025401455,0.0046292916,0.005846332,0.006567618,0.0013756682],"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":[0.00061475585,0.00074269355,0.018996628,0.0003217799,0.00035718305,0.00046469105,0.0007280275,0.4162086,0.017736088,0.018513411,0.01544444,0.5098718],"study_design_scores_gemma":[0.000014735924,0.000052463525,0.0005960403,0.0000233499,0.000017582115,0.00010625399,0.000035062312,0.9826927,0.003371778,0.011473554,0.001594057,0.000022488553],"about_ca_topic_score_codex":0.006538977,"about_ca_topic_score_gemma":0.009548385,"teacher_disagreement_score":0.0072524883,"about_ca_system_score_codex":0.0013421457,"about_ca_system_score_gemma":0.0024660185,"threshold_uncertainty_score":0.03835529},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052345317,0.0021992717,0.86680317,0.0009077601,0.0010448842,0.0006965532,0.018430442,0.05035398,0.007218707],"genre_scores_gemma":[0.23293689,0.00091245293,0.69119465,0.00082070695,0.000470721,0.0006489646,0.06499123,0.0014578768,0.0065664416],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99527293,0.00053287135,0.00026877335,0.0018472815,0.001608915,0.00046919633],"domain_scores_gemma":[0.9966348,0.00062699616,0.0003506943,0.0015128949,0.00067309575,0.00020161814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018332587,0.0032687804,0.0025279063,0.0030438576,0.0009852181,0.0026450912,0.0054143104,0.0027207562,0.0031064127],"category_scores_gemma":[0.007590405,0.00075739255,0.0017373404,0.002713745,0.0011826515,0.0032310267,0.004956861,0.0035900152,0.0044235582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00091576105,0.0008693472,0.02467715,0.0010070212,0.00042664976,0.0006625415,0.00017513627,0.04998731,0.033043027,0.008924061,0.14980105,0.72951096],"study_design_scores_gemma":[0.00012596573,0.0005212858,0.010177287,0.00010915314,0.00011747681,0.0035739716,0.00022726115,0.8357554,0.04511396,0.02857493,0.07556465,0.00013863739],"about_ca_topic_score_codex":0.00450874,"about_ca_topic_score_gemma":0.006970968,"teacher_disagreement_score":0.0054143104,"about_ca_system_score_codex":0.00087414274,"about_ca_system_score_gemma":0.0017080349,"threshold_uncertainty_score":0.01039201},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06122265,0.0009656125,0.9298041,0.00035213362,0.00014444081,0.000071673385,0.0003228603,0.003569436,0.0035471139],"genre_scores_gemma":[0.8236146,0.0003036677,0.16709347,0.00036736036,0.00007556786,0.00007888238,0.0007662145,0.00011765945,0.0075825294],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997621,0.000023160155,0.000009289644,0.00009350462,0.000055974215,0.000055966422],"domain_scores_gemma":[0.9997037,0.00008877482,0.000031782984,0.000036651756,0.00010964579,0.00002939406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005832918,0.0011238706,0.00071269216,0.00087181956,0.00039666818,0.0006206675,0.001733366,0.0012191202,0.00213964],"category_scores_gemma":[0.0011115146,0.0003875818,0.00066352845,0.00055182056,0.0005448919,0.0015729548,0.0011864422,0.0011643992,0.0006917785],"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":[0.00042523834,0.00027257216,0.0031590336,0.00013331024,0.00020276349,0.00033799023,0.000099675985,0.30480826,0.042547468,0.006769256,0.00589477,0.6353497],"study_design_scores_gemma":[0.0000050284484,0.000050021878,0.00032295426,0.0000075261323,0.000022137798,0.00007050564,0.0000092647015,0.9893601,0.0063771238,0.003069033,0.0006952362,0.000011088248],"about_ca_topic_score_codex":0.0075214445,"about_ca_topic_score_gemma":0.0117582735,"teacher_disagreement_score":0.0075214445,"about_ca_system_score_codex":0.0009766254,"about_ca_system_score_gemma":0.0006841668,"threshold_uncertainty_score":0.014955342},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029142868,0.0008234902,0.96415293,0.00040824348,0.0001776905,0.00015087059,0.00012525132,0.0029839631,0.0020346872],"genre_scores_gemma":[0.7906687,0.0004438344,0.20391516,0.0007297723,0.00023182374,0.00034004982,0.00053308246,0.00066828716,0.0024692283],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99806803,0.0004720421,0.00009327072,0.00061730016,0.0005383903,0.00021103318],"domain_scores_gemma":[0.9925942,0.003664104,0.00076639315,0.0016253735,0.0010450626,0.00030485477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003324268,0.0019250914,0.0015890797,0.00085258775,0.00073840603,0.0015614429,0.0040124725,0.0017000053,0.0022764672],"category_scores_gemma":[0.022197213,0.000623898,0.00096016127,0.000758072,0.0021564604,0.0036218092,0.0036947923,0.0044047153,0.0009298947],"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":[0.00028616405,0.00021970556,0.0029887278,0.00019763122,0.00012485955,0.00011297507,0.00022820458,0.7996516,0.008959311,0.009693564,0.004887505,0.17264967],"study_design_scores_gemma":[0.000010568585,0.00007773409,0.0002125068,0.000014616937,0.000012652028,0.000044951346,0.000016243139,0.9896964,0.0031607517,0.0060790638,0.00065882667,0.000015747986],"about_ca_topic_score_codex":0.0026623989,"about_ca_topic_score_gemma":0.0022547834,"teacher_disagreement_score":0.0040124725,"about_ca_system_score_codex":0.001395418,"about_ca_system_score_gemma":0.0015810069,"threshold_uncertainty_score":0.017580628},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010123628,0.00016714241,0.9881909,0.00025904784,0.00003411287,0.000038177106,0.000100311125,0.0006086032,0.00047812378],"genre_scores_gemma":[0.49766585,0.00073036173,0.4917377,0.00037834552,0.00017267656,0.00025393613,0.0009823584,0.0004872195,0.00759145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989593,0.00019665917,0.000059156348,0.00034029598,0.00032762822,0.00011700062],"domain_scores_gemma":[0.9977906,0.0008462482,0.00031165418,0.00034548924,0.00059317297,0.000112876995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019623206,0.0009878691,0.0013991977,0.0016349248,0.000605359,0.0014107246,0.0032009643,0.002020698,0.0014259191],"category_scores_gemma":[0.0053097866,0.000577644,0.0014195936,0.0012552321,0.0014641316,0.002332589,0.0015041383,0.002484754,0.0007814938],"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":[0.00020969665,0.00011335872,0.004823573,0.00014439742,0.00012320638,0.00022613538,0.00033736584,0.6829464,0.018814575,0.046207834,0.004316115,0.24173735],"study_design_scores_gemma":[0.0000041519356,0.000012379241,0.00016918295,0.000003841172,0.0000060641423,0.000047562222,0.000007175051,0.99239916,0.0012177618,0.0055199466,0.0006026536,0.000010114855],"about_ca_topic_score_codex":0.012465145,"about_ca_topic_score_gemma":0.012096819,"teacher_disagreement_score":0.012465145,"about_ca_system_score_codex":0.0015682165,"about_ca_system_score_gemma":0.0016559394,"threshold_uncertainty_score":0.024785161},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8839014,0.0031933428,0.103093654,0.0003279891,0.00017647546,0.000059994778,0.0013243462,0.00064662175,0.0072761457],"genre_scores_gemma":[0.9715146,0.00083892024,0.0245657,0.000040143746,0.00004677212,0.000013885086,0.0007146347,0.000027479893,0.0022377],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99984646,0.000025386618,0.000009018926,0.000037080536,0.000047234797,0.000034753903],"domain_scores_gemma":[0.9997937,0.000063073385,0.000038086087,0.000014358024,0.00006717944,0.000023493623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021576439,0.00033478218,0.00031782012,0.0007428211,0.00013239411,0.00045802246,0.00031916646,0.0003180808,0.00085394847],"category_scores_gemma":[0.0010298162,0.00011063176,0.00031507504,0.0006475383,0.000118692864,0.00027596683,0.00026968555,0.00032157724,0.00030540684],"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":[0.00068058725,0.00018475938,0.25141174,0.00016856595,0.000176645,0.0015427254,0.00064427184,0.012469196,0.022361482,0.0013131318,0.0068234527,0.70222336],"study_design_scores_gemma":[0.0000140156,0.0005306163,0.6537436,0.00009216043,0.00015933586,0.008641447,0.0015732772,0.31043476,0.014648034,0.00481297,0.005287062,0.00006264187],"about_ca_topic_score_codex":0.00566501,"about_ca_topic_score_gemma":0.007786284,"teacher_disagreement_score":0.00566501,"about_ca_system_score_codex":0.00021924065,"about_ca_system_score_gemma":0.00019119598,"threshold_uncertainty_score":0.011264086},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002607126,0.00019818086,0.9957087,0.000078461286,0.00002811435,0.0000139716585,0.000029813567,0.0007424567,0.0005932748],"genre_scores_gemma":[0.15723659,0.0004349395,0.8360171,0.00028290125,0.00022875698,0.00009565841,0.0004343041,0.00044510287,0.004824568],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833965,0.00043885817,0.00008797519,0.00043699157,0.0005880726,0.00010852044],"domain_scores_gemma":[0.9975764,0.0009397463,0.00024962283,0.00058144756,0.0005803971,0.00007236242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022411942,0.0008822994,0.0016044924,0.0013200206,0.00042690826,0.0017612928,0.003210941,0.0016914867,0.0015528869],"category_scores_gemma":[0.004658319,0.0005624292,0.001348612,0.0013897662,0.0010081395,0.00160979,0.001887325,0.0020064397,0.0012828746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010076373,0.00021031752,0.0016057675,0.00019799224,0.00024693322,0.00010806505,0.00012338768,0.17840922,0.016962817,0.026330246,0.010701416,0.765003],"study_design_scores_gemma":[0.000002667414,0.000023015376,0.0002502143,0.0000055009227,0.000011623174,0.000052299623,0.0000071619493,0.9816436,0.0037388834,0.0127987405,0.0014579297,0.0000083252335],"about_ca_topic_score_codex":0.0010752252,"about_ca_topic_score_gemma":0.0014352027,"teacher_disagreement_score":0.003210941,"about_ca_system_score_codex":0.0005326848,"about_ca_system_score_gemma":0.0005854136,"threshold_uncertainty_score":0.011852682},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38546085,0.02213335,0.5693756,0.0018134526,0.00055436505,0.00017132597,0.0010277883,0.0058769286,0.013586414],"genre_scores_gemma":[0.84527946,0.0039664377,0.14145854,0.00040685345,0.0001623869,0.00008820684,0.0014020491,0.0005955169,0.0066405446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936336,0.00022264308,0.000042447016,0.00015066566,0.0001284965,0.00009239487],"domain_scores_gemma":[0.99364436,0.004652362,0.00029705587,0.00046218178,0.00078226853,0.00016178733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003080785,0.0015643449,0.0016433696,0.0008418851,0.00042481473,0.0016901296,0.0023738411,0.0020109306,0.0023217902],"category_scores_gemma":[0.007840565,0.0005622786,0.00076438265,0.0011099756,0.00057210197,0.0031152437,0.0009283764,0.0017055145,0.0006444311],"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":[0.0010416991,0.00038099973,0.0022251701,0.000301193,0.00020165545,0.000052190368,0.000039275386,0.6903823,0.0011857686,0.0036143712,0.004966127,0.29560918],"study_design_scores_gemma":[0.0000098737055,0.00007238076,0.00013958651,0.000012199284,0.000020957466,0.000009792421,0.000010381775,0.9982786,0.00046426538,0.000810707,0.000167738,0.0000034984216],"about_ca_topic_score_codex":0.017810144,"about_ca_topic_score_gemma":0.016962746,"teacher_disagreement_score":0.017810144,"about_ca_system_score_codex":0.0012769677,"about_ca_system_score_gemma":0.0017419369,"threshold_uncertainty_score":0.035412967},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13273345,0.0014703663,0.8552799,0.00045175914,0.0002845774,0.00011319427,0.0006558611,0.0053701345,0.003640703],"genre_scores_gemma":[0.86251795,0.00047593823,0.12849763,0.00040221293,0.00014595583,0.00013601751,0.0027346085,0.00014465877,0.004945075],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992749,0.00020012166,0.000034880093,0.00029017706,0.00011576466,0.00008412707],"domain_scores_gemma":[0.99923337,0.00033699846,0.00008037415,0.00016601532,0.00013472492,0.00004846948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081215956,0.0010712276,0.0010140429,0.000618192,0.00028896684,0.0007238042,0.0013294594,0.000801569,0.0011414808],"category_scores_gemma":[0.0021500539,0.0003228422,0.0011052202,0.0006819099,0.000565595,0.0010259574,0.0010931127,0.0016025138,0.0010066289],"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":[0.00027141732,0.0004876686,0.007705148,0.00012969067,0.00018206263,0.00017188287,0.00016113828,0.5044137,0.008575416,0.0022211163,0.006758986,0.46892163],"study_design_scores_gemma":[0.0000069404164,0.000033727112,0.0009581133,0.0000070038755,0.000008393741,0.000041833722,0.00002854167,0.99398935,0.0021023003,0.0018517118,0.00096229994,0.000009839291],"about_ca_topic_score_codex":0.004788129,"about_ca_topic_score_gemma":0.0054717786,"teacher_disagreement_score":0.004788129,"about_ca_system_score_codex":0.00056562974,"about_ca_system_score_gemma":0.0004978934,"threshold_uncertainty_score":0.009520531},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009022408,0.011149132,0.86964285,0.0029354796,0.0012219598,0.0000614085,0.00035543108,0.0022018543,0.1034094],"genre_scores_gemma":[0.45567793,0.0146968635,0.31756592,0.0015561697,0.0019451972,0.00009365716,0.000733468,0.0006676936,0.20706312],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984086,0.000029358,0.0000060998113,0.000032026175,0.0000800751,0.000011590111],"domain_scores_gemma":[0.9994721,0.0002878387,0.000024452593,0.00006946512,0.00011808866,0.000028104048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048972823,0.00045479494,0.00042795378,0.00057117303,0.00020389787,0.0011082898,0.0009292345,0.00073885685,0.019070005],"category_scores_gemma":[0.0016587701,0.00016300757,0.00031283556,0.00061126804,0.00058070105,0.0009370994,0.0009063702,0.0007898208,0.0026034776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008457437,0.000036611887,0.00088010763,0.00014832539,0.000025032105,0.0001374672,0.00005414453,0.03249065,0.003537001,0.061393015,0.033898905,0.86731416],"study_design_scores_gemma":[0.000022658356,0.0001562036,0.002098029,0.00014865815,0.000036426736,0.0012632702,0.000100524805,0.55277663,0.008100253,0.29410475,0.14115065,0.000041910407],"about_ca_topic_score_codex":0.0011437312,"about_ca_topic_score_gemma":0.0011546451,"teacher_disagreement_score":0.019070005,"about_ca_system_score_codex":0.0005022276,"about_ca_system_score_gemma":0.00031401764,"threshold_uncertainty_score":0.06379551},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01819746,0.00042455466,0.97546786,0.00023856503,0.00007792637,0.000036627862,0.00045971043,0.004063323,0.0010340628],"genre_scores_gemma":[0.6933289,0.00036014954,0.2993271,0.00036753665,0.000065849024,0.00010428834,0.0012628521,0.00020921906,0.0049741343],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975973,0.000037895312,0.000011728677,0.00006825347,0.000086439504,0.00003602922],"domain_scores_gemma":[0.99955577,0.00017948453,0.000041058192,0.00003600051,0.00015717435,0.000030502519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005926874,0.00095526397,0.00081096403,0.00085758074,0.00033364657,0.00076171116,0.0016130214,0.0010139134,0.0025382678],"category_scores_gemma":[0.0015893391,0.0004289418,0.0005246706,0.000503934,0.0003435844,0.0010411489,0.000981753,0.0011664891,0.0006452017],"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":[0.00038453017,0.00013218728,0.003121437,0.00016909816,0.000116792675,0.00013182497,0.000047920283,0.64078957,0.009346222,0.0031950865,0.0059092194,0.3366562],"study_design_scores_gemma":[0.000004136419,0.00001269883,0.00012742958,0.000003966065,0.000006198206,0.000009784057,0.0000031595448,0.99722147,0.0012192626,0.001125262,0.0002635283,0.0000029962048],"about_ca_topic_score_codex":0.014310128,"about_ca_topic_score_gemma":0.014777811,"teacher_disagreement_score":0.014310128,"about_ca_system_score_codex":0.00090548623,"about_ca_system_score_gemma":0.00083570124,"threshold_uncertainty_score":0.028453648},"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":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12999079,0.014586799,0.26072362,0.2682365,0.021180311,0.00026156724,0.0046972428,0.047758095,0.25256503],"genre_scores_gemma":[0.8324651,0.004596793,0.06080028,0.023128912,0.0067479354,0.00011532308,0.0020364888,0.004539789,0.06556936],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9952413,0.00106783,0.00023455457,0.00052649656,0.0024648854,0.00046497377],"domain_scores_gemma":[0.97354287,0.007639329,0.0019658424,0.0062402706,0.008940557,0.0016711667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004671752,0.0008610151,0.00058906677,0.0011178773,0.001230881,0.0056660916,0.0013017061,0.003041215,0.01601978],"category_scores_gemma":[0.03481074,0.00045512238,0.0005217778,0.00064428797,0.0014374829,0.009675538,0.0011283959,0.0036630856,0.0078043044],"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":[0.0009896869,0.0003224616,0.020549057,0.0005610593,0.0001553046,0.00035513134,0.001055819,0.004350907,0.007824959,0.062381282,0.37003547,0.531419],"study_design_scores_gemma":[0.0002186049,0.00062264345,0.015070117,0.0011592763,0.00044492257,0.0022730131,0.0028303026,0.03515458,0.03531938,0.16114628,0.74544454,0.00031642691],"about_ca_topic_score_codex":0.0037135158,"about_ca_topic_score_gemma":0.0026941448,"teacher_disagreement_score":0.01601978,"about_ca_system_score_codex":0.0009558166,"about_ca_system_score_gemma":0.0014589209,"threshold_uncertainty_score":0.05359155},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7781777,0.003425541,0.20582463,0.0011110512,0.00039518194,0.00039182568,0.0013835421,0.005016976,0.0042735995],"genre_scores_gemma":[0.949317,0.00032901464,0.046278343,0.0002387691,0.000070827395,0.000086746106,0.00218616,0.00010399462,0.001389164],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953891,0.0017921905,0.00037213895,0.0011366728,0.0009649775,0.00034495007],"domain_scores_gemma":[0.9838214,0.010226932,0.0011807381,0.0014670509,0.0025616144,0.00074222474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008369729,0.002151465,0.0012419056,0.0016596359,0.00073875167,0.0014508382,0.0028316665,0.0022636186,0.0007899514],"category_scores_gemma":[0.018386183,0.00037990318,0.00092115096,0.00075921504,0.0013869632,0.0027977445,0.0024834813,0.0021281242,0.0005242887],"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":[0.003163103,0.0024781069,0.043060336,0.0006398263,0.0007305868,0.00019435612,0.0002519235,0.6538676,0.0048584845,0.0021005021,0.0068082497,0.28184697],"study_design_scores_gemma":[0.000026771793,0.00027743154,0.0015880108,0.000025404743,0.000029894647,0.000038108315,0.00004046416,0.9947831,0.0020037538,0.00094803475,0.00022591966,0.000013181968],"about_ca_topic_score_codex":0.008802006,"about_ca_topic_score_gemma":0.009685079,"teacher_disagreement_score":0.008802006,"about_ca_system_score_codex":0.001485902,"about_ca_system_score_gemma":0.0014939258,"threshold_uncertainty_score":0.0442639},"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":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011880841,0.0060301577,0.22572763,0.7083542,0.010412701,0.00010989233,0.0002527161,0.0016298234,0.035602003],"genre_scores_gemma":[0.3935178,0.019460125,0.37459108,0.10881881,0.0154628735,0.00039293623,0.0009951916,0.0013786745,0.08538245],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9965752,0.0011001354,0.00013563893,0.00036588297,0.0014646517,0.000358445],"domain_scores_gemma":[0.97646755,0.009289751,0.0015304367,0.0042325077,0.0068334066,0.0016463515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049672676,0.00066110934,0.0011960759,0.0012017734,0.0030770106,0.007928804,0.0015841962,0.008300551,0.015420525],"category_scores_gemma":[0.036098324,0.0005925004,0.00080404873,0.0009933899,0.0030758202,0.017911088,0.004061283,0.019081002,0.008775661],"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":[0.00017225849,0.000398246,0.004888098,0.00068662263,0.00017674232,0.00054935436,0.0011027292,0.005584407,0.0110273585,0.3498562,0.2356022,0.38995582],"study_design_scores_gemma":[0.00003172577,0.00011760985,0.0022174595,0.0002536798,0.000040542327,0.00045533586,0.0021987872,0.008669882,0.0038663077,0.62243307,0.35960943,0.0001061114],"about_ca_topic_score_codex":0.0022376229,"about_ca_topic_score_gemma":0.0026263464,"teacher_disagreement_score":0.015420525,"about_ca_system_score_codex":0.0013591548,"about_ca_system_score_gemma":0.005096159,"threshold_uncertainty_score":0.051586807},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018190207,0.00039562007,0.971521,0.00029617015,0.00010332991,0.000094472896,0.00046825674,0.0076848054,0.0012460651],"genre_scores_gemma":[0.344263,0.0004963545,0.6456759,0.0006303838,0.00010534433,0.00049133704,0.003059652,0.001193006,0.0040850914],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947006,0.000102127546,0.000031991924,0.0001603138,0.00018741115,0.0000481001],"domain_scores_gemma":[0.9990688,0.000283411,0.00009919765,0.0003647053,0.00013234625,0.000051591418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013151998,0.0016423286,0.0007415017,0.0007648975,0.00038297582,0.0010408573,0.0022927404,0.0009281964,0.002897668],"category_scores_gemma":[0.0041431696,0.0006123897,0.0009552576,0.00071622524,0.0009753555,0.0023601344,0.0035635503,0.0027984802,0.001279218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044394846,0.0003268461,0.0028365296,0.00037018573,0.0002803845,0.0002531329,0.00023184529,0.39688066,0.05690666,0.021001678,0.018829688,0.5016384],"study_design_scores_gemma":[0.000020815558,0.00007312292,0.00018970422,0.000015522955,0.000015742165,0.00005986331,0.000013814594,0.97115606,0.015037713,0.009945219,0.003457073,0.000015372014],"about_ca_topic_score_codex":0.0016291537,"about_ca_topic_score_gemma":0.0027127618,"teacher_disagreement_score":0.002897668,"about_ca_system_score_codex":0.00073862984,"about_ca_system_score_gemma":0.0010912337,"threshold_uncertainty_score":0.009693682},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011354483,0.0005283634,0.9338319,0.00023920868,0.00012051901,0.00021229996,0.0034449466,0.047825683,0.0024426046],"genre_scores_gemma":[0.24672554,0.0007198586,0.73464745,0.00046812443,0.00009461149,0.00052335236,0.011045072,0.0015065117,0.004269447],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981946,0.000019720474,0.000011461054,0.000061377184,0.00006844018,0.000019560384],"domain_scores_gemma":[0.99976784,0.00007408084,0.00002979864,0.000050962233,0.000054669545,0.00002260465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034637793,0.0012450733,0.0006984879,0.0010810688,0.00035490262,0.00096088,0.0016708721,0.0007914659,0.0026135063],"category_scores_gemma":[0.0016957975,0.0004423901,0.00089008,0.0009021832,0.00030822813,0.0014610926,0.0012330466,0.0012145032,0.0008156986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008785227,0.00035426603,0.010725714,0.001115149,0.00077879574,0.0011114145,0.00055319665,0.17392246,0.036279004,0.024214378,0.09804714,0.65202],"study_design_scores_gemma":[0.000054575437,0.00006191099,0.0026881706,0.000052321717,0.00006166904,0.00026930383,0.00007736166,0.9509499,0.009592892,0.014730824,0.021402389,0.000058664475],"about_ca_topic_score_codex":0.012752966,"about_ca_topic_score_gemma":0.032865133,"teacher_disagreement_score":0.012752966,"about_ca_system_score_codex":0.0006387204,"about_ca_system_score_gemma":0.0007391336,"threshold_uncertainty_score":0.025357485},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036439545,0.00018442889,0.960055,0.00029330814,0.000029293198,0.00009405326,0.00006681107,0.0013515045,0.001486122],"genre_scores_gemma":[0.9350299,0.00008579767,0.06278135,0.00011572688,0.00004342776,0.00017173245,0.00015914586,0.00011918065,0.0014935799],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980983,0.00075378956,0.000074274925,0.00047542658,0.0003861788,0.00021199534],"domain_scores_gemma":[0.9960061,0.0022104862,0.00034964044,0.0007738234,0.00044136797,0.00021865964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030873355,0.0008893912,0.0015840778,0.0007193153,0.00094656734,0.000985941,0.0021016253,0.000916023,0.0023169774],"category_scores_gemma":[0.01027296,0.0006378791,0.0006723612,0.0007799646,0.0012306296,0.0018517015,0.0019524661,0.0018147773,0.00047051348],"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":[0.000099863515,0.000063562584,0.0010432907,0.000031069234,0.0000319123,0.00007853162,0.00005589951,0.95604765,0.0015558741,0.0045245495,0.00081424887,0.03565358],"study_design_scores_gemma":[0.000007522243,0.000011409904,0.00008150057,0.0000010458855,0.000002479456,0.0000066931275,0.0000043303144,0.99661106,0.00029675913,0.0028545323,0.00012014751,0.0000025442673],"about_ca_topic_score_codex":0.0066888807,"about_ca_topic_score_gemma":0.006641761,"teacher_disagreement_score":0.0066888807,"about_ca_system_score_codex":0.001355982,"about_ca_system_score_gemma":0.00189336,"threshold_uncertainty_score":0.01632762},"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":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000023267958,0.0029202558,0.00009265904,0.018531827,0.9756526,0.000020178551,0.00004229854,0.000045018187,0.0026719763],"genre_scores_gemma":[0.00024922122,0.0028314937,0.00008218836,0.00923784,0.97486037,0.000020786001,0.000047003068,0.0000417372,0.0126293395],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9954283,0.00055880303,0.00044717418,0.0005183958,0.0027232068,0.0003241462],"domain_scores_gemma":[0.9842089,0.004689273,0.0008851503,0.00045410305,0.006748095,0.0030145622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043126256,0.0035212797,0.0037169007,0.0034955218,0.0030276277,0.009106165,0.003121011,0.010987533,0.034923],"category_scores_gemma":[0.016068552,0.00108045,0.002572917,0.00173019,0.0021422184,0.004819186,0.0015410901,0.016052313,0.028912975],"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":[0.00001860982,0.000009753308,0.0000145897375,0.00010216667,0.0000081003245,0.00004145407,0.0000046384553,0.000017563372,0.000034792964,0.00021141181,0.99573743,0.0037995037],"study_design_scores_gemma":[0.00003522899,0.000019215162,0.00021457877,0.00025766133,0.000022528433,0.00013319014,0.000020342692,0.00014963005,0.000079761536,0.001061814,0.99799454,0.000011447665],"about_ca_topic_score_codex":0.0011844876,"about_ca_topic_score_gemma":0.0040171063,"teacher_disagreement_score":0.034923,"about_ca_system_score_codex":0.002725291,"about_ca_system_score_gemma":0.0031399317,"threshold_uncertainty_score":0.11682904},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033357684,0.0005584426,0.9611897,0.00033591775,0.00016090447,0.00006489533,0.00041103704,0.0027434684,0.0011780228],"genre_scores_gemma":[0.71252596,0.0007248541,0.27933022,0.00030151647,0.0002215976,0.00019042665,0.0020627778,0.00029002418,0.0043525994],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937844,0.00012944045,0.000033210163,0.00019344337,0.00015892094,0.00010653128],"domain_scores_gemma":[0.99904615,0.00029627583,0.00014116538,0.0001290361,0.000319921,0.00006751752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011231091,0.0012203342,0.0008243879,0.0012721773,0.0004020107,0.0005802449,0.0013849614,0.0006202358,0.0009147514],"category_scores_gemma":[0.0029310891,0.00034577743,0.0009909803,0.0015881669,0.0005761945,0.0009846273,0.0012687945,0.0016528598,0.0003473768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032671308,0.00030221493,0.018396832,0.00019989749,0.00045520588,0.0003565955,0.00017240015,0.41123623,0.017136607,0.012585651,0.013295981,0.5255357],"study_design_scores_gemma":[0.0000027309557,0.000017457569,0.00077482016,0.0000038765947,0.000010773795,0.0000271808,0.000009306317,0.9952514,0.0014352344,0.0017449463,0.00071533816,0.000006922453],"about_ca_topic_score_codex":0.016047824,"about_ca_topic_score_gemma":0.019819325,"teacher_disagreement_score":0.016047824,"about_ca_system_score_codex":0.0009311035,"about_ca_system_score_gemma":0.0015464746,"threshold_uncertainty_score":0.03190887},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06970625,0.0020479364,0.712313,0.00043636863,0.00031106523,0.0007953623,0.026714416,0.18475814,0.0029174609],"genre_scores_gemma":[0.31178328,0.0006519343,0.6133794,0.00037011635,0.00018612972,0.000859013,0.060987815,0.007964869,0.0038175043],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.996443,0.00040110486,0.00036548957,0.00064120797,0.0019130452,0.00023613493],"domain_scores_gemma":[0.9917419,0.0018186815,0.0011330292,0.0022423528,0.0027508477,0.00031318265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004899876,0.0023772698,0.001731946,0.0071098334,0.000639518,0.0036484473,0.0029229883,0.0017611253,0.0035581852],"category_scores_gemma":[0.019275965,0.00060800987,0.0016297688,0.0033938307,0.00053490925,0.004041105,0.003147629,0.0016422184,0.002473949],"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":[0.001463768,0.00075232534,0.043707564,0.0015965302,0.0013306481,0.00027624925,0.00025028855,0.03721485,0.04684359,0.006599778,0.12171758,0.73824686],"study_design_scores_gemma":[0.0002018872,0.0008420877,0.036459845,0.00021412368,0.00023901943,0.0007488719,0.0001911561,0.81693685,0.08902632,0.015844008,0.039035298,0.00026057244],"about_ca_topic_score_codex":0.004034545,"about_ca_topic_score_gemma":0.007025032,"teacher_disagreement_score":0.0071098334,"about_ca_system_score_codex":0.0011954671,"about_ca_system_score_gemma":0.0010035062,"threshold_uncertainty_score":0.025913298},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7882653,0.0017573918,0.1930425,0.0036726443,0.0003604438,0.00050425547,0.0031126714,0.004708596,0.0045762016],"genre_scores_gemma":[0.90886545,0.00020832544,0.087658405,0.00042436828,0.00008752333,0.00021785335,0.0019558656,0.00019807494,0.00038407496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9912315,0.0057385773,0.00044285753,0.0013237881,0.0009849804,0.0002782489],"domain_scores_gemma":[0.76483285,0.21184304,0.004671977,0.01125612,0.005942588,0.001453399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.037415512,0.0010287517,0.0010359791,0.0019490413,0.0011839877,0.0023487324,0.003094984,0.0018638666,0.0023700262],"category_scores_gemma":[0.1541006,0.00051266304,0.0011643852,0.0015211051,0.0022858276,0.00201134,0.0029044899,0.0030480565,0.00040188426],"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":[0.0015459122,0.00033697774,0.07248804,0.0005032792,0.0008210501,0.00022256527,0.00027963798,0.8436951,0.0013600414,0.012355892,0.0047375974,0.061654005],"study_design_scores_gemma":[0.00011102064,0.00018694853,0.0024632204,0.000032603144,0.000057771467,0.000055420744,0.000064192085,0.9902362,0.0010539751,0.0051415213,0.00058126875,0.000015782081],"about_ca_topic_score_codex":0.0093538435,"about_ca_topic_score_gemma":0.006566896,"teacher_disagreement_score":0.037415512,"about_ca_system_score_codex":0.0015270269,"about_ca_system_score_gemma":0.0033351234,"threshold_uncertainty_score":0.19787455},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007046374,0.00023995753,0.991155,0.00008039372,0.000024376617,0.000032077867,0.00008416058,0.0005452236,0.00079247064],"genre_scores_gemma":[0.63270026,0.00063824013,0.36136284,0.0003375126,0.00014412066,0.00022157069,0.00081737794,0.0002711018,0.0035069748],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987087,0.00020124619,0.000074680065,0.00042073434,0.00046209106,0.00013257666],"domain_scores_gemma":[0.99897254,0.00030833523,0.00018085325,0.00014976926,0.00032061088,0.00006789854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016576793,0.0013223188,0.0012935787,0.0018510892,0.00068875344,0.0014051596,0.0020197239,0.0012400487,0.0019828386],"category_scores_gemma":[0.005001568,0.0005329321,0.0014929275,0.0017951817,0.00080424105,0.0025708608,0.0023955673,0.0013486049,0.00060581585],"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":[0.00028051928,0.00007728235,0.0022390587,0.00012123935,0.00014968649,0.00016340635,0.00024488918,0.49240023,0.021256415,0.014263403,0.002740826,0.4660631],"study_design_scores_gemma":[0.00000409413,0.00003385465,0.00041513165,0.000010103088,0.000026892152,0.000059456328,0.000017928478,0.9866846,0.0055521065,0.0063591963,0.0008176084,0.000019078088],"about_ca_topic_score_codex":0.007170948,"about_ca_topic_score_gemma":0.0059974296,"teacher_disagreement_score":0.007170948,"about_ca_system_score_codex":0.0016736152,"about_ca_system_score_gemma":0.0011838903,"threshold_uncertainty_score":0.014258385},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06726794,0.00021572586,0.9290997,0.0001125983,0.000054422402,0.0000421735,0.00020567495,0.0016361168,0.0013656996],"genre_scores_gemma":[0.87207264,0.0002116136,0.1248125,0.00005569438,0.000033533903,0.00006142072,0.000561655,0.00009438843,0.002096496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998022,0.000024701845,0.000010061583,0.00007685438,0.000060148257,0.000026063763],"domain_scores_gemma":[0.9996419,0.00011567748,0.00005114408,0.000036842936,0.00013111626,0.000023407181],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036534455,0.00072657585,0.0005326142,0.0011760646,0.0002499207,0.00051000994,0.0007479695,0.00039959134,0.001380809],"category_scores_gemma":[0.0011669496,0.00023063291,0.0006126775,0.0009846719,0.00022514162,0.00095611846,0.00044930988,0.00059079065,0.00029089264],"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":[0.00018428742,0.00010074877,0.005326648,0.00007223864,0.000065225584,0.00016925266,0.00007080967,0.68275315,0.011122828,0.0043999297,0.002173982,0.29356092],"study_design_scores_gemma":[0.0000012958299,0.0000060970488,0.00022753415,0.0000010128978,0.0000029855564,0.000005117282,0.0000021639069,0.99878114,0.0005046663,0.00037978042,0.00008650673,0.0000017709918],"about_ca_topic_score_codex":0.015905004,"about_ca_topic_score_gemma":0.013381205,"teacher_disagreement_score":0.015905004,"about_ca_system_score_codex":0.00058602233,"about_ca_system_score_gemma":0.0006449131,"threshold_uncertainty_score":0.031624854},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5201196,0.022170126,0.36572188,0.016581332,0.008293212,0.0012208376,0.013655428,0.03730429,0.014933286],"genre_scores_gemma":[0.6556608,0.005767388,0.2709557,0.0015893084,0.0025334482,0.00039991367,0.040465128,0.0036621522,0.018966192],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.98933613,0.00258378,0.00071276154,0.0018912932,0.0047791274,0.0006968317],"domain_scores_gemma":[0.9691795,0.010662092,0.00056979974,0.0033747626,0.014361505,0.0018523062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01662762,0.001919085,0.0018750981,0.003077066,0.0016203198,0.0054481896,0.003740419,0.002692627,0.0027556848],"category_scores_gemma":[0.02531054,0.00070481864,0.0018923583,0.0021425895,0.001613132,0.0033225808,0.00223454,0.00241824,0.002606901],"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":[0.0011551458,0.00091962144,0.0113304695,0.0015885239,0.00029985284,0.00093946373,0.0009110975,0.06559918,0.0197556,0.0028568043,0.14925164,0.74539274],"study_design_scores_gemma":[0.00028376817,0.0009184563,0.028051442,0.00063111185,0.00018240974,0.001049505,0.002233351,0.76330185,0.05439502,0.013649741,0.13494481,0.00035854013],"about_ca_topic_score_codex":0.02025989,"about_ca_topic_score_gemma":0.0194634,"teacher_disagreement_score":0.02025989,"about_ca_system_score_codex":0.002250731,"about_ca_system_score_gemma":0.0044108294,"threshold_uncertainty_score":0.08793634},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44450173,0.00081184367,0.5459287,0.00043026736,0.0003442625,0.00013237573,0.0005492753,0.0039930805,0.0033084482],"genre_scores_gemma":[0.95998985,0.000086163775,0.038823783,0.00004078419,0.000056387216,0.000022293973,0.00022595556,0.0000376691,0.00071723724],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975425,0.000034148932,0.000016820277,0.00006120251,0.000093236486,0.00004041375],"domain_scores_gemma":[0.9988035,0.00046966123,0.00020274751,0.00006515485,0.00035180248,0.000107101696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042135254,0.00074665307,0.0006260999,0.001420897,0.00045850343,0.0007087323,0.0004259926,0.00047785454,0.0014787272],"category_scores_gemma":[0.0022741982,0.00018647345,0.00018474809,0.00040424307,0.00014683045,0.00064051384,0.00039455434,0.00075847557,0.0005675547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004275632,0.0006414503,0.037897862,0.00033821983,0.000115759125,0.00069211225,0.0003943642,0.05649198,0.28081334,0.0028997608,0.01026251,0.60517704],"study_design_scores_gemma":[0.000044972367,0.0005961056,0.027737308,0.000027963655,0.00007678094,0.00051232445,0.00018771325,0.9048337,0.060591675,0.0027660339,0.002581945,0.000043403663],"about_ca_topic_score_codex":0.00070965214,"about_ca_topic_score_gemma":0.0011802595,"teacher_disagreement_score":0.0014787272,"about_ca_system_score_codex":0.00021184132,"about_ca_system_score_gemma":0.0005791186,"threshold_uncertainty_score":0.004946828},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012024611,0.0009768908,0.9506104,0.00016811887,0.0003331501,0.00012300284,0.0008733466,0.005860772,0.029029693],"genre_scores_gemma":[0.18146136,0.0009907585,0.755225,0.00040222556,0.00037180455,0.00018690212,0.0067658746,0.0015305503,0.05306551],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968755,0.00002874485,0.000009930932,0.00012368507,0.00010584996,0.000044338103],"domain_scores_gemma":[0.99958247,0.000109440385,0.000020724005,0.0001438671,0.000109075074,0.000034438588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003466736,0.0008060759,0.00064186007,0.0009896363,0.0005581665,0.00085073593,0.0013732597,0.0006374181,0.018159261],"category_scores_gemma":[0.00093423243,0.00034000594,0.0005663583,0.0011940241,0.00029837748,0.0012394955,0.0010011389,0.0010939279,0.010811424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030793753,0.0001738846,0.0007450355,0.00015561754,0.000054489636,0.00010072406,0.000027023763,0.033600885,0.023533743,0.0095031075,0.034399904,0.8973977],"study_design_scores_gemma":[0.000061813735,0.00013948538,0.0017197647,0.000056884197,0.00008387768,0.00076425815,0.000049462495,0.8521516,0.024618426,0.04633908,0.07397648,0.00003890814],"about_ca_topic_score_codex":0.0013204507,"about_ca_topic_score_gemma":0.00352342,"teacher_disagreement_score":0.018159261,"about_ca_system_score_codex":0.00019094876,"about_ca_system_score_gemma":0.00050915504,"threshold_uncertainty_score":0.060748816},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3109131,0.0032715728,0.65977234,0.0010643555,0.00056097994,0.00040718602,0.0021426452,0.015895564,0.005972353],"genre_scores_gemma":[0.8067584,0.0005520445,0.18283731,0.0003297265,0.000101952486,0.0003984082,0.0034219117,0.00027867622,0.0053215767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994562,0.00017880416,0.000036134883,0.00015745572,0.000108485685,0.00006290807],"domain_scores_gemma":[0.9984571,0.00076256215,0.00012497493,0.00008852605,0.0005062122,0.000060685506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024394835,0.0014140429,0.00086018397,0.0010561323,0.000365109,0.0013014475,0.0015683367,0.0012309948,0.0025594938],"category_scores_gemma":[0.004417709,0.0005583474,0.001231156,0.00055893284,0.00027588394,0.0008666774,0.00084753626,0.0017188281,0.0014346506],"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":[0.0006015773,0.00047497384,0.019726222,0.0004183587,0.00043471478,0.00016922831,0.00018286605,0.60446316,0.010142762,0.0014444197,0.011705662,0.3502361],"study_design_scores_gemma":[0.0000039633605,0.0000358186,0.0010617438,0.000012245403,0.000009951285,0.000011926088,0.000012455643,0.9972602,0.0008318592,0.00034304347,0.00041192566,0.00000487128],"about_ca_topic_score_codex":0.01422597,"about_ca_topic_score_gemma":0.011186583,"teacher_disagreement_score":0.01422597,"about_ca_system_score_codex":0.001135259,"about_ca_system_score_gemma":0.0011021848,"threshold_uncertainty_score":0.028286338},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016245587,0.00022388142,0.98085487,0.00014486723,0.000032460666,0.000041756466,0.00012126871,0.0018922135,0.00044313347],"genre_scores_gemma":[0.6069383,0.00041903782,0.38784453,0.00018476626,0.00017490232,0.00014540764,0.0008932064,0.00027352243,0.003126456],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920976,0.00016027447,0.000052045667,0.00024101685,0.00024585018,0.00009096154],"domain_scores_gemma":[0.997666,0.0011141779,0.00034042276,0.0003483827,0.00040765732,0.00012334104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017532611,0.001109586,0.0009975524,0.0022459924,0.0005553902,0.0012497427,0.0018267913,0.0009745688,0.0014802135],"category_scores_gemma":[0.005491352,0.000551921,0.0010165287,0.0017390108,0.0006802499,0.0020813686,0.0016425225,0.00202593,0.00059003267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064519397,0.0003762778,0.013541543,0.00020142659,0.00027708177,0.00034829596,0.00027480206,0.3853215,0.022269018,0.018592643,0.005090432,0.55306184],"study_design_scores_gemma":[0.0000051470934,0.000021869671,0.0005172531,0.0000044103585,0.000013746449,0.000031196396,0.000011148821,0.9916087,0.0023313651,0.004970624,0.00047634306,0.000008199069],"about_ca_topic_score_codex":0.0038607405,"about_ca_topic_score_gemma":0.006799538,"teacher_disagreement_score":0.0038607405,"about_ca_system_score_codex":0.0007975811,"about_ca_system_score_gemma":0.0011063915,"threshold_uncertainty_score":0.009272218},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.086174436,0.00058797805,0.9051426,0.002099865,0.0003217153,0.00010168427,0.00084507297,0.0010983212,0.0036283867],"genre_scores_gemma":[0.8588793,0.0005271119,0.13007846,0.00027177949,0.00032883158,0.00017559707,0.0008893045,0.000116367926,0.008733269],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937314,0.00015784525,0.000043526998,0.00019857284,0.0001607454,0.0000662506],"domain_scores_gemma":[0.99801636,0.0010290864,0.00019267743,0.00021160003,0.00048347213,0.00006665476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013990932,0.0006440915,0.0009184994,0.0013448808,0.0005389238,0.0017399647,0.0018758783,0.0017336996,0.0032932095],"category_scores_gemma":[0.0055699064,0.00026056354,0.001084353,0.0015574078,0.0005407337,0.0023383354,0.0007624799,0.0018348509,0.0009672679],"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":[0.00045194165,0.00059823744,0.03020475,0.0002179216,0.00022891234,0.00037962527,0.0002518102,0.4904151,0.005003004,0.050341103,0.012649031,0.4092585],"study_design_scores_gemma":[0.000004700715,0.000022671627,0.00073796767,0.000005845004,0.000012843916,0.000047763282,0.000011518912,0.98600525,0.00034064814,0.012245721,0.0005581144,0.0000069602183],"about_ca_topic_score_codex":0.0051721605,"about_ca_topic_score_gemma":0.0035461516,"teacher_disagreement_score":0.0051721605,"about_ca_system_score_codex":0.0011510113,"about_ca_system_score_gemma":0.0009786199,"threshold_uncertainty_score":0.011016905},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88779604,0.00041654045,0.10818039,0.00009358911,0.00009966652,0.000121267134,0.0012137375,0.00084681803,0.0012319778],"genre_scores_gemma":[0.9812362,0.000120849814,0.015975319,0.000016793469,0.00001898664,0.00005331597,0.0017153223,0.000022068452,0.0008411916],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957174,0.000097247255,0.000037416645,0.00012555982,0.00007868356,0.00008933259],"domain_scores_gemma":[0.9988324,0.0005117672,0.00015546296,0.00008073283,0.00036143218,0.000058168705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010656685,0.0009845734,0.0005568916,0.0017487145,0.00022032279,0.0006469326,0.0004179217,0.0004939872,0.00080825767],"category_scores_gemma":[0.0035323752,0.0001817845,0.0005688968,0.0008369037,0.00021218578,0.0005747177,0.00039263463,0.0004379794,0.0007656647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011867548,0.0006942728,0.2975319,0.00027379883,0.00038042813,0.00021870968,0.00026567956,0.313117,0.020450994,0.00036778886,0.002296058,0.36321664],"study_design_scores_gemma":[0.000014849695,0.0003906619,0.122540385,0.000062257466,0.00005312254,0.00011425451,0.000228527,0.86933696,0.0060188095,0.0005308921,0.0006794888,0.00002981883],"about_ca_topic_score_codex":0.009101472,"about_ca_topic_score_gemma":0.009599469,"teacher_disagreement_score":0.009101472,"about_ca_system_score_codex":0.00028563692,"about_ca_system_score_gemma":0.0006269795,"threshold_uncertainty_score":0.018096983},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005368746,0.52320033,0.4579493,0.0019451273,0.0006478791,0.0000859741,0.0003951517,0.000734062,0.009673463],"genre_scores_gemma":[0.13002536,0.68199855,0.17476386,0.000942227,0.0023486419,0.0003045939,0.0012342986,0.00045337726,0.007929113],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99950445,0.00012431313,0.00006801637,0.000119609256,0.00015092957,0.000032579726],"domain_scores_gemma":[0.99904937,0.0005628134,0.00006440082,0.00006585608,0.0002315158,0.000026054548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015464099,0.0021708978,0.0012885162,0.0011213447,0.0002725857,0.0015762397,0.0020074702,0.0014832104,0.0023356047],"category_scores_gemma":[0.0031927668,0.0009172828,0.0011863353,0.0020225989,0.0005469856,0.0022255809,0.0011173072,0.002029279,0.0012862283],"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":[0.0000811149,0.00009189314,0.001216143,0.0032496112,0.00028248556,0.00012288924,0.00009314408,0.32023537,0.0017849841,0.033639252,0.009941017,0.629262],"study_design_scores_gemma":[0.000021373662,0.0002725482,0.0011090948,0.0016363472,0.0002997679,0.00030917992,0.000063080275,0.8449403,0.0032421418,0.04152636,0.10646724,0.000112579146],"about_ca_topic_score_codex":0.0067061493,"about_ca_topic_score_gemma":0.004981109,"teacher_disagreement_score":0.0067061493,"about_ca_system_score_codex":0.00089157384,"about_ca_system_score_gemma":0.0011857772,"threshold_uncertainty_score":0.013334215},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97104454,0.00041758223,0.025746968,0.0002843685,0.00003616029,0.000057720867,0.00043625335,0.00021666443,0.0017598263],"genre_scores_gemma":[0.99679536,0.00013164699,0.002138332,0.00001821858,0.000008866537,0.000010732471,0.0003377689,0.000022537144,0.00053647696],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999316,0.00030308502,0.000031401058,0.00016570195,0.000093853756,0.00008996935],"domain_scores_gemma":[0.99142003,0.0066581923,0.00034511933,0.0004928963,0.00087894086,0.00020482835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025502392,0.00067962497,0.0005495962,0.0010353521,0.00038388144,0.0015300469,0.0013440273,0.00067936233,0.0030107514],"category_scores_gemma":[0.014692854,0.00029454683,0.0006785991,0.00089612656,0.00048638126,0.0026295849,0.0007320564,0.0010902435,0.0007600891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0045728376,0.0017567921,0.47825277,0.0004187972,0.0005234022,0.0006602691,0.0028733278,0.1657301,0.0033219217,0.013481745,0.0062897033,0.32211834],"study_design_scores_gemma":[0.00004283795,0.0006539056,0.06492695,0.000043756816,0.00020427293,0.00018888195,0.0014791599,0.9178916,0.0014052534,0.012320199,0.00080992014,0.00003328608],"about_ca_topic_score_codex":0.03040476,"about_ca_topic_score_gemma":0.016266646,"teacher_disagreement_score":0.03040476,"about_ca_system_score_codex":0.0015323055,"about_ca_system_score_gemma":0.0014373199,"threshold_uncertainty_score":0.06045556},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03299278,0.0018574538,0.9575585,0.0002548905,0.00036903663,0.000056756235,0.00043256508,0.0040457663,0.0024323028],"genre_scores_gemma":[0.6646766,0.0013778178,0.32104635,0.00039430198,0.00059508387,0.00011764219,0.0016626043,0.0003043607,0.009825283],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948394,0.000068979934,0.000032571912,0.00017214922,0.00014906544,0.000093282324],"domain_scores_gemma":[0.9991825,0.00032949238,0.000055011424,0.0001657152,0.00021199461,0.000055234843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091088674,0.0008997027,0.0012204144,0.0014391121,0.00044903305,0.0009905777,0.0015048627,0.00097635,0.004201182],"category_scores_gemma":[0.0021052354,0.00025781462,0.0008337366,0.0017402273,0.00031264682,0.0014056588,0.0015597801,0.0011655088,0.0012010931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035491714,0.00023148327,0.0014632915,0.00009802507,0.00011758084,0.00016832273,0.000066448134,0.032539546,0.024700394,0.0041208016,0.007296512,0.9288426],"study_design_scores_gemma":[0.000008898369,0.00007703632,0.0010814478,0.000011746306,0.000051223473,0.000151046,0.000025852214,0.97937965,0.008602626,0.007972085,0.0026266605,0.000011685401],"about_ca_topic_score_codex":0.007147989,"about_ca_topic_score_gemma":0.007967243,"teacher_disagreement_score":0.007147989,"about_ca_system_score_codex":0.00064892374,"about_ca_system_score_gemma":0.0007442853,"threshold_uncertainty_score":0.014212787},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010651201,0.0003066996,0.98638713,0.00033334212,0.000027552573,0.000054298773,0.000104419465,0.001012931,0.0011224148],"genre_scores_gemma":[0.6429129,0.00043261802,0.35210475,0.000509805,0.00011493635,0.0002267326,0.0005174606,0.0001597699,0.003020937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924064,0.00019585871,0.000035786412,0.0002365889,0.00022619929,0.0000649454],"domain_scores_gemma":[0.9979061,0.001342762,0.00020076465,0.0002064893,0.0002803978,0.00006343947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015814856,0.0009890571,0.00070367067,0.0010305675,0.0005652522,0.0009609935,0.0018941433,0.0011232926,0.0026796549],"category_scores_gemma":[0.004854998,0.00033661939,0.001093397,0.00058934075,0.0010607713,0.0014905859,0.0015398925,0.00252966,0.0003269796],"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":[0.00018129709,0.000128691,0.0038751953,0.00024678692,0.0002300602,0.00023964503,0.0003160819,0.6659106,0.0064954497,0.061103567,0.003758109,0.2575145],"study_design_scores_gemma":[0.0000034662562,0.000020532532,0.00020023307,0.000008763747,0.000009944045,0.000027937853,0.000008616094,0.9824752,0.00069936883,0.016005391,0.00053466234,0.000005889439],"about_ca_topic_score_codex":0.005712528,"about_ca_topic_score_gemma":0.00613135,"teacher_disagreement_score":0.005712528,"about_ca_system_score_codex":0.0012934105,"about_ca_system_score_gemma":0.0012646014,"threshold_uncertainty_score":0.011358559},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007701013,0.0015613183,0.9281576,0.010657353,0.0006082003,0.0015006069,0.0022257008,0.0054419823,0.04214609],"genre_scores_gemma":[0.09789368,0.0013827388,0.8844118,0.0008089028,0.0003321987,0.0010198118,0.0040062806,0.00045420162,0.009690439],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9887271,0.00543665,0.0013295243,0.0012259814,0.002722689,0.0005580886],"domain_scores_gemma":[0.95671463,0.021427918,0.0046622404,0.0051975106,0.010515292,0.0014823304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012362667,0.0016359875,0.0007687902,0.01036351,0.0041449363,0.009076971,0.0031034697,0.0019982012,0.014440251],"category_scores_gemma":[0.05251234,0.0008835235,0.002219128,0.0047536315,0.0027788973,0.008581176,0.006117273,0.0037664887,0.0038323726],"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":[0.00014990737,0.00032918114,0.015801536,0.001248972,0.00016706304,0.00067997095,0.006475958,0.02338308,0.0013558451,0.4883523,0.08827811,0.3737782],"study_design_scores_gemma":[0.000048123642,0.00011382521,0.005510783,0.0012744367,0.000105537896,0.00075800694,0.0109079955,0.21354179,0.002215765,0.47552294,0.2898438,0.00015695374],"about_ca_topic_score_codex":0.01119983,"about_ca_topic_score_gemma":0.0104199955,"teacher_disagreement_score":0.014440251,"about_ca_system_score_codex":0.0055622784,"about_ca_system_score_gemma":0.0074243476,"threshold_uncertainty_score":0.06538087},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63918763,0.00022830916,0.33441365,0.0006375517,0.00016492199,0.0004936426,0.0012363818,0.020916738,0.0027212584],"genre_scores_gemma":[0.8796177,0.00005692142,0.11859135,0.000053637188,0.000021947822,0.00009545499,0.0004959763,0.00017794757,0.00088907976],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929976,0.0002268236,0.000028507886,0.00017842937,0.00017693652,0.00008953683],"domain_scores_gemma":[0.99865973,0.0004984011,0.00008440329,0.00019810474,0.0003916377,0.00016769246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010586829,0.0008080205,0.0006535629,0.0007369976,0.00044264275,0.0007923431,0.0011102215,0.00095726154,0.0032117406],"category_scores_gemma":[0.003376834,0.00022855026,0.0003539778,0.00069255393,0.00037778588,0.00083499955,0.00088688463,0.00081170467,0.00068963226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030079118,0.002128909,0.03147962,0.0003645769,0.0002832237,0.0007152731,0.0009667373,0.15235396,0.09638079,0.0025315646,0.015472883,0.6943146],"study_design_scores_gemma":[0.000092851704,0.00046210794,0.0086182505,0.000009908186,0.000042477943,0.000135592,0.00018242002,0.97056603,0.017346874,0.0009660148,0.0015492686,0.00002829765],"about_ca_topic_score_codex":0.0067423037,"about_ca_topic_score_gemma":0.0077001564,"teacher_disagreement_score":0.0067423037,"about_ca_system_score_codex":0.00063617603,"about_ca_system_score_gemma":0.0006906873,"threshold_uncertainty_score":0.013406098},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08535309,0.00095766864,0.89871496,0.00029833108,0.0003240105,0.00019680007,0.000697474,0.00941771,0.0040400014],"genre_scores_gemma":[0.7371453,0.00029569835,0.25024024,0.00029723445,0.00017831761,0.00032451283,0.0022230123,0.000213251,0.009082567],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992415,0.00009171687,0.00004723693,0.00023007054,0.0002386121,0.00015083519],"domain_scores_gemma":[0.99939346,0.00012231086,0.000046281755,0.00013183402,0.0002709613,0.000035248104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012748197,0.0011675062,0.0013579238,0.0011432976,0.0005037051,0.00083878066,0.0019832633,0.0008718757,0.0028550636],"category_scores_gemma":[0.0019679721,0.0004430854,0.0012102702,0.0013637994,0.00027958068,0.0012985002,0.00086725724,0.0011908534,0.0012828435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033370906,0.00019203749,0.003311903,0.000054643213,0.00018509835,0.00010558763,0.00004961505,0.13435842,0.012841128,0.0021730731,0.008638614,0.83775616],"study_design_scores_gemma":[0.000007681799,0.00004083422,0.0005868086,0.000003904104,0.000024399136,0.000027721559,0.0000084057165,0.99234796,0.004835545,0.0010872798,0.0010215484,0.000007982423],"about_ca_topic_score_codex":0.012439427,"about_ca_topic_score_gemma":0.01649232,"teacher_disagreement_score":0.012439427,"about_ca_system_score_codex":0.0010675081,"about_ca_system_score_gemma":0.0010825301,"threshold_uncertainty_score":0.02473402},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12758504,0.0015950622,0.8525683,0.00056027307,0.00035898635,0.00011571341,0.00090932107,0.004557043,0.011750213],"genre_scores_gemma":[0.9235433,0.0005086051,0.06337636,0.0003523814,0.00005112352,0.00016277713,0.001209842,0.000088580855,0.010707052],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998596,0.000009790827,0.0000056383146,0.00005315214,0.000035539197,0.000036367484],"domain_scores_gemma":[0.99990976,0.000016899452,0.000010365707,0.000011781021,0.000041511135,0.000009700235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021215362,0.00080787105,0.0005204833,0.00032771798,0.00019963944,0.000506424,0.0013276587,0.00069860945,0.002239504],"category_scores_gemma":[0.00042812456,0.00029746912,0.000692608,0.000342307,0.0001970634,0.00043326916,0.0005666683,0.00063864456,0.0006944229],"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":[0.00043437444,0.00023204745,0.0042319843,0.00014067533,0.00021531824,0.00032406987,0.000080153615,0.5990762,0.037196916,0.002587461,0.0061689857,0.34931192],"study_design_scores_gemma":[0.0000029282885,0.000043261804,0.00048329993,0.000005561724,0.000013730274,0.000023595558,0.0000037666723,0.9960551,0.0024960458,0.00036145246,0.0005061617,0.0000051530724],"about_ca_topic_score_codex":0.019393427,"about_ca_topic_score_gemma":0.016272796,"teacher_disagreement_score":0.019393427,"about_ca_system_score_codex":0.0006802059,"about_ca_system_score_gemma":0.0006466817,"threshold_uncertainty_score":0.038561046},"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":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.736133,0.00038949944,0.25993982,0.00014488357,0.00010453162,0.0009824394,0.0005317367,0.00013910493,0.0016350724],"genre_scores_gemma":[0.7102067,0.00032180894,0.28510728,0.0000946057,0.00008724742,0.0017586694,0.000716486,0.000078613295,0.0016285079],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9968081,0.0013398897,0.00036679555,0.0007191268,0.0006149105,0.00015108603],"domain_scores_gemma":[0.9965323,0.0010348897,0.00035869904,0.00046999662,0.0014444602,0.00015963509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042441334,0.0006432928,0.00074760144,0.002568827,0.0008981589,0.0007482864,0.0008888503,0.0009228222,0.0019079681],"category_scores_gemma":[0.0058090826,0.00030496373,0.0006748677,0.0016825069,0.0007141954,0.0005563738,0.0012083391,0.000423495,0.00034338623],"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":[0.0024956171,0.0016205009,0.48872775,0.0008608328,0.00040998944,0.0004840091,0.004050014,0.004057518,0.11762431,0.0014699561,0.00093626097,0.3772632],"study_design_scores_gemma":[0.00053264253,0.008628657,0.91340446,0.00018417485,0.00090602046,0.0037525902,0.0055503785,0.037105527,0.020215217,0.0031223844,0.0063996487,0.00019820716],"about_ca_topic_score_codex":0.0033124888,"about_ca_topic_score_gemma":0.011242796,"teacher_disagreement_score":0.0042441334,"about_ca_system_score_codex":0.0003571483,"about_ca_system_score_gemma":0.0012383513,"threshold_uncertainty_score":0.02244538},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26016074,0.0014178308,0.7296622,0.0005315094,0.00012404751,0.0001160014,0.00050971593,0.0033045406,0.0041734288],"genre_scores_gemma":[0.9364328,0.00018220254,0.059823845,0.00013741374,0.00002887334,0.000049180213,0.00064013695,0.00003778107,0.0026677386],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994875,0.0001083959,0.000029149887,0.00016522664,0.00011139203,0.00009825232],"domain_scores_gemma":[0.99958354,0.00008248942,0.000063479485,0.0000738286,0.00016460205,0.000032091575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079086877,0.0007750937,0.00042955927,0.00074273185,0.00029395125,0.00054528576,0.0009936042,0.0005472915,0.0012242846],"category_scores_gemma":[0.001425076,0.00030684366,0.0006249144,0.00046817763,0.0002642434,0.00082031963,0.00081129785,0.0008166673,0.0004550955],"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":[0.00028806922,0.00021569936,0.0070881383,0.00008422238,0.0001552974,0.00013935805,0.00009227084,0.5118363,0.014553097,0.0029828432,0.0038525613,0.4587121],"study_design_scores_gemma":[0.0000019372897,0.000030873405,0.00070118136,0.000004252709,0.000009179766,0.0000089948335,0.00000829223,0.9968274,0.0014965804,0.00065892475,0.00024784126,0.0000043707987],"about_ca_topic_score_codex":0.018285725,"about_ca_topic_score_gemma":0.023547152,"teacher_disagreement_score":0.018285725,"about_ca_system_score_codex":0.0011571578,"about_ca_system_score_gemma":0.00095258106,"threshold_uncertainty_score":0.036358595},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42131534,0.00020306875,0.5641265,0.0004248895,0.00007035747,0.00037916895,0.0010926691,0.010075922,0.0023121515],"genre_scores_gemma":[0.7611741,0.00005340563,0.23621969,0.00008235607,0.000016967508,0.00016236333,0.0012678951,0.00007091728,0.0009523076],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993025,0.00015666857,0.0000592537,0.00023126396,0.0001606565,0.00008961926],"domain_scores_gemma":[0.9976037,0.0013003221,0.00015889853,0.0002730251,0.00059531606,0.00006879313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020323987,0.0007199109,0.00051385583,0.0012914398,0.0005782463,0.0007121864,0.0012819556,0.00062619837,0.00072507514],"category_scores_gemma":[0.005279859,0.00022065055,0.0006209731,0.0007874528,0.00032278683,0.0010186286,0.00064354116,0.0011157474,0.0003957981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058389496,0.0009839181,0.0661073,0.00013219485,0.00012727588,0.0004686824,0.00042284283,0.41698673,0.011698599,0.0018731722,0.0037836523,0.49683172],"study_design_scores_gemma":[0.000007771475,0.000054576503,0.0024957864,0.000005015316,0.000007864012,0.000038983577,0.000036114063,0.99329764,0.0030226132,0.0006615791,0.0003644175,0.000007674902],"about_ca_topic_score_codex":0.0204674,"about_ca_topic_score_gemma":0.024379846,"teacher_disagreement_score":0.0204674,"about_ca_system_score_codex":0.0007878105,"about_ca_system_score_gemma":0.0011791706,"threshold_uncertainty_score":0.04069656},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023938816,0.0005985814,0.9739189,0.00009417316,0.00006872597,0.00003350107,0.00006639206,0.00029617827,0.0009847601],"genre_scores_gemma":[0.5822933,0.0004410115,0.41323066,0.00007063287,0.00012482081,0.00010049846,0.00025628062,0.00007492874,0.0034078779],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987117,0.00027490343,0.0000781515,0.00029592027,0.000566732,0.00007272221],"domain_scores_gemma":[0.9979553,0.0010360434,0.00017273682,0.00019606284,0.0005703608,0.00006949371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015065193,0.00038893943,0.0011479099,0.0019616652,0.0007298267,0.0010605353,0.0010786323,0.000895413,0.0010854251],"category_scores_gemma":[0.0047631995,0.00025526743,0.0006375682,0.0014429936,0.0008205461,0.0015058911,0.00066784414,0.00096501823,0.00022952202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060578826,0.00024568487,0.004016851,0.00031424518,0.00017090584,0.00027964383,0.00024880722,0.31968412,0.016887987,0.097793765,0.004818378,0.5549338],"study_design_scores_gemma":[0.000005179316,0.000034113502,0.00038346378,0.000006746809,0.000011161831,0.00004892507,0.000027621028,0.9810286,0.0019598752,0.015612143,0.000871908,0.000010236725],"about_ca_topic_score_codex":0.004839272,"about_ca_topic_score_gemma":0.005010018,"teacher_disagreement_score":0.004839272,"about_ca_system_score_codex":0.00075034436,"about_ca_system_score_gemma":0.0007435236,"threshold_uncertainty_score":0.009622216},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016893355,0.00065017317,0.8996729,0.0003939821,0.0002362883,0.00020995844,0.0024247873,0.07773104,0.0017874298],"genre_scores_gemma":[0.22712637,0.00033078314,0.75279444,0.00071077,0.0001245768,0.00046443442,0.010740263,0.0024680952,0.0052403347],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988439,0.00021599626,0.00005355059,0.00054687355,0.00023276029,0.0001067979],"domain_scores_gemma":[0.9987004,0.000564158,0.00008688085,0.00031377922,0.0002491697,0.0000855281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012750238,0.0019847767,0.0012071594,0.0014556174,0.0004949822,0.0011122365,0.0044875685,0.0017592382,0.0045093605],"category_scores_gemma":[0.0060490957,0.0008073674,0.0012997487,0.0007779181,0.00070004334,0.0023485168,0.0025695292,0.0032760252,0.0028279717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045649143,0.0004651836,0.004604242,0.00039588066,0.0003102342,0.0003051921,0.00016760237,0.18695475,0.020059358,0.0057439036,0.06178264,0.7187546],"study_design_scores_gemma":[0.000027072527,0.000042593867,0.00036377317,0.000010364405,0.000011600124,0.00006183598,0.00002390008,0.9860239,0.0054454,0.004139921,0.003832953,0.000016701724],"about_ca_topic_score_codex":0.008297317,"about_ca_topic_score_gemma":0.01629588,"teacher_disagreement_score":0.008297317,"about_ca_system_score_codex":0.0011209666,"about_ca_system_score_gemma":0.001390795,"threshold_uncertainty_score":0.01649803},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009064573,0.00011790679,0.98815745,0.00013895823,0.00004063271,0.000034895154,0.00006433442,0.0010007736,0.0013803783],"genre_scores_gemma":[0.74705637,0.00018539983,0.24742843,0.00025137688,0.00006489819,0.0002892556,0.00043723284,0.00019392063,0.004093124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961793,0.00008462273,0.000016481688,0.00011918079,0.00010502582,0.000056816207],"domain_scores_gemma":[0.999067,0.00041448057,0.00009208154,0.000120118966,0.00023982278,0.00006651901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007748396,0.0006799003,0.00084706803,0.0003964724,0.0004665164,0.000786382,0.001782361,0.0007413353,0.0025489964],"category_scores_gemma":[0.0023629586,0.00033263883,0.00057615223,0.00038428546,0.0006270485,0.0009234568,0.0016051639,0.0015918973,0.00066990627],"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":[0.00013608336,0.00006990316,0.0009545799,0.00008640124,0.000058765643,0.00006501265,0.0000837519,0.8562658,0.0074012503,0.01591473,0.0031789925,0.11578473],"study_design_scores_gemma":[0.0000036863457,0.0000092975,0.00003934559,0.0000015914006,0.0000020081975,0.0000054181073,0.00000253928,0.99764806,0.00038709154,0.0016493397,0.00024898266,0.0000027431352],"about_ca_topic_score_codex":0.007337236,"about_ca_topic_score_gemma":0.007889778,"teacher_disagreement_score":0.007337236,"about_ca_system_score_codex":0.00058761355,"about_ca_system_score_gemma":0.0013183515,"threshold_uncertainty_score":0.014589071},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014787086,0.0003539563,0.9710496,0.00037446932,0.00011207664,0.000053791475,0.0005172459,0.011456559,0.0012952451],"genre_scores_gemma":[0.4585282,0.0002649609,0.5304783,0.0002740775,0.00014009868,0.00013396962,0.002021934,0.00065225636,0.007506233],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906176,0.00014728832,0.000050922165,0.00029068484,0.00032535728,0.00012390755],"domain_scores_gemma":[0.9982749,0.0005954085,0.00010343052,0.0004186548,0.00049294514,0.000114659895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010578729,0.0010092311,0.001281036,0.0013493502,0.00075418496,0.001239025,0.0028563903,0.0014310852,0.0057865996],"category_scores_gemma":[0.004354833,0.0005242827,0.00070455635,0.001106809,0.0005541832,0.0034840864,0.0028504692,0.0035232324,0.0020876613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005099927,0.00034637708,0.0028854613,0.00019268622,0.00012689759,0.00022239513,0.00014587045,0.06429478,0.013854046,0.011250495,0.015055478,0.8911154],"study_design_scores_gemma":[0.000019761457,0.000044812394,0.0003771947,0.000011655113,0.000025680025,0.000082103856,0.000032154476,0.9713917,0.010818977,0.014055856,0.0031243307,0.000015748745],"about_ca_topic_score_codex":0.008444964,"about_ca_topic_score_gemma":0.019663237,"teacher_disagreement_score":0.008444964,"about_ca_system_score_codex":0.00083297625,"about_ca_system_score_gemma":0.0021303094,"threshold_uncertainty_score":0.019358099},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04479772,0.00028446133,0.9388542,0.0001264053,0.00012571969,0.000113317845,0.00025898372,0.01438914,0.0010500272],"genre_scores_gemma":[0.54265684,0.00017882577,0.44712186,0.00046572302,0.000152492,0.00025784614,0.002574087,0.0014840051,0.0051083635],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989466,0.00017157198,0.000036770678,0.0005227994,0.00020309105,0.0001192037],"domain_scores_gemma":[0.99823415,0.00055969076,0.00015553972,0.00051377056,0.00040508556,0.00013173561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009602541,0.0015518944,0.0011564107,0.0009437613,0.00051440246,0.000739721,0.0031031822,0.0012104299,0.0016791512],"category_scores_gemma":[0.0041916,0.00040034365,0.0008524058,0.0006754079,0.0009657609,0.0016656362,0.0020393818,0.0020123436,0.0012716408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007747233,0.00044409555,0.0048606405,0.00027133612,0.00018968976,0.00040700455,0.0003900509,0.10744688,0.09266204,0.0030770553,0.01503194,0.7744446],"study_design_scores_gemma":[0.00002030751,0.000098070945,0.0010316645,0.0000082707165,0.000022838423,0.00012796378,0.000049977036,0.97354555,0.019800924,0.0034421925,0.0018316073,0.000020612932],"about_ca_topic_score_codex":0.004387828,"about_ca_topic_score_gemma":0.0074825776,"teacher_disagreement_score":0.004387828,"about_ca_system_score_codex":0.0005552806,"about_ca_system_score_gemma":0.0009539438,"threshold_uncertainty_score":0.00872457},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033381242,0.00028053156,0.96238536,0.00014503297,0.000095347714,0.000108924156,0.000250184,0.0025633727,0.00078997156],"genre_scores_gemma":[0.5327744,0.00053609564,0.4613586,0.00012762808,0.0001537713,0.00025761046,0.0011564628,0.000125176,0.0035103466],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997199,0.000028562854,0.00001652317,0.00008618664,0.000093937815,0.00005481367],"domain_scores_gemma":[0.9997004,0.000042105552,0.000040181534,0.000020824644,0.00015651064,0.000039981314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003655111,0.0008488512,0.00083193317,0.0020726556,0.00029546008,0.0008088917,0.000803291,0.0005824932,0.0009559123],"category_scores_gemma":[0.000687196,0.00021619645,0.00077788736,0.00066839426,0.00021379866,0.00069592346,0.00066615577,0.0007328277,0.0005177068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006373547,0.0006117823,0.007263436,0.00020381535,0.0001340546,0.0004736698,0.00017589949,0.06859238,0.12768781,0.0049783154,0.008252795,0.78098875],"study_design_scores_gemma":[0.00001419535,0.00009167983,0.0033887492,0.000011825578,0.000039986724,0.00012224648,0.000062152045,0.97704905,0.016105404,0.0017129036,0.0013819485,0.000019913088],"about_ca_topic_score_codex":0.0055557624,"about_ca_topic_score_gemma":0.005958097,"teacher_disagreement_score":0.0055557624,"about_ca_system_score_codex":0.00039946797,"about_ca_system_score_gemma":0.000874904,"threshold_uncertainty_score":0.011046827},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06451405,0.00023677525,0.9306636,0.0002887999,0.000038946848,0.00004413483,0.00012675612,0.0033841515,0.0007027465],"genre_scores_gemma":[0.8685666,0.00012494385,0.12796557,0.00016273417,0.00004000077,0.00007032248,0.00043844554,0.00013262306,0.002498883],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994259,0.000072795876,0.000023030081,0.00024915911,0.00015433138,0.0000749614],"domain_scores_gemma":[0.998727,0.00046542083,0.00017644664,0.0002497783,0.00031216122,0.00006914549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008150149,0.00091840007,0.0009414092,0.0010321892,0.0003499208,0.00076385733,0.0024927384,0.0011190546,0.0007513515],"category_scores_gemma":[0.0025415947,0.00042288384,0.00082137994,0.0007949887,0.00085445505,0.0014791392,0.0013918377,0.0013487656,0.0003188301],"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":[0.0003127065,0.00029472267,0.012738565,0.00013506667,0.000100964215,0.0004092508,0.00031463118,0.60238963,0.030744271,0.008706932,0.005369379,0.33848396],"study_design_scores_gemma":[0.0000045185507,0.000018826791,0.00032038603,0.0000017554885,0.0000056073186,0.000044449152,0.000009538488,0.9938548,0.002947938,0.002511344,0.00027646488,0.0000042303905],"about_ca_topic_score_codex":0.005661841,"about_ca_topic_score_gemma":0.0063508414,"teacher_disagreement_score":0.005661841,"about_ca_system_score_codex":0.0010908787,"about_ca_system_score_gemma":0.00081354723,"threshold_uncertainty_score":0.011257768},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042394823,0.00019878808,0.972785,0.00010756897,0.000029096866,0.00010226607,0.00024175039,0.021829879,0.00046615853],"genre_scores_gemma":[0.04253463,0.00017892323,0.95045733,0.00019382799,0.00002307121,0.0002785058,0.0012132702,0.0038684811,0.0012519386],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988857,0.00018678072,0.000082609215,0.00026460044,0.00047820422,0.000102050224],"domain_scores_gemma":[0.99869955,0.0005347659,0.00013302162,0.00033979115,0.0001937795,0.00009907913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015271781,0.0022323562,0.00162821,0.0016595428,0.00075473817,0.0020353456,0.005445382,0.0021233773,0.0048963483],"category_scores_gemma":[0.004366474,0.0019159646,0.0024820725,0.0010796459,0.0013796345,0.0016308856,0.0037271017,0.0025669632,0.0031344097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00065976096,0.00020959026,0.003458231,0.0006657081,0.00050630106,0.00053711876,0.00079640944,0.24536972,0.051513303,0.012215476,0.032363497,0.6517048],"study_design_scores_gemma":[0.000041041036,0.00004520382,0.00029345884,0.00001669781,0.000024232831,0.0002690586,0.000034907524,0.9723345,0.01622146,0.0056617837,0.005010681,0.000046885354],"about_ca_topic_score_codex":0.01029616,"about_ca_topic_score_gemma":0.021589924,"teacher_disagreement_score":0.01029616,"about_ca_system_score_codex":0.0012394777,"about_ca_system_score_gemma":0.0025291713,"threshold_uncertainty_score":0.020472467},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021949891,0.00018865519,0.97631866,0.00027889432,0.00003217942,0.000023928404,0.000060503284,0.00046108366,0.00068621285],"genre_scores_gemma":[0.88352114,0.0003599691,0.111311525,0.0002938449,0.00010525162,0.00009692111,0.00032425876,0.00011152509,0.0038756374],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992944,0.00020906661,0.000031749674,0.00017553403,0.00021839023,0.00007084631],"domain_scores_gemma":[0.9979594,0.0012206228,0.00027181362,0.00018191813,0.0002805572,0.00008573085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018078164,0.0008761278,0.00091531157,0.0009013033,0.0003018372,0.00068385067,0.001679857,0.0008956916,0.00094813376],"category_scores_gemma":[0.0049017756,0.00038007688,0.0008959711,0.0007584174,0.00122927,0.0013151404,0.0015045975,0.0021081122,0.0003114871],"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":[0.00009842725,0.000061142964,0.0024289354,0.00004261528,0.00006616227,0.00010975191,0.000078386285,0.9115874,0.0038631072,0.016023101,0.0012180391,0.06442293],"study_design_scores_gemma":[0.0000014937298,0.000009412847,0.00008805126,0.0000016929113,0.0000035512312,0.000015510876,0.0000015206205,0.9973122,0.0003356976,0.0021345974,0.00009371506,0.0000026008138],"about_ca_topic_score_codex":0.002579152,"about_ca_topic_score_gemma":0.0025757528,"teacher_disagreement_score":0.002579152,"about_ca_system_score_codex":0.0006767536,"about_ca_system_score_gemma":0.0006912292,"threshold_uncertainty_score":0.009560823},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050003346,0.0037180122,0.9287456,0.0012790111,0.0002996472,0.00017779873,0.0028941953,0.0066315606,0.0062507284],"genre_scores_gemma":[0.8082583,0.0012734592,0.17649142,0.00097304164,0.00016984355,0.0003618015,0.0077282353,0.00020427404,0.0045396327],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99821377,0.00025908658,0.00016107914,0.00063701236,0.000528646,0.00020036915],"domain_scores_gemma":[0.9969495,0.0009635843,0.00025822152,0.0008264744,0.0008687974,0.00013341494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021631904,0.0017875326,0.0021162552,0.004085991,0.0010176819,0.0024236192,0.0047791535,0.0027483932,0.004021584],"category_scores_gemma":[0.0065657296,0.00081523624,0.0016556587,0.0026281157,0.0010611038,0.0043120584,0.0035209619,0.0027379158,0.001404439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003493623,0.00068398693,0.003525367,0.00031770603,0.00045103958,0.00050000864,0.00011760034,0.34290245,0.0037143012,0.010366572,0.017196344,0.6198753],"study_design_scores_gemma":[0.00001956549,0.00006539231,0.000480965,0.000048255308,0.000066946304,0.000054130192,0.000032890068,0.9740622,0.0026002596,0.020042788,0.0025064975,0.000020082376],"about_ca_topic_score_codex":0.015785005,"about_ca_topic_score_gemma":0.01772259,"teacher_disagreement_score":0.015785005,"about_ca_system_score_codex":0.002408113,"about_ca_system_score_gemma":0.0024448342,"threshold_uncertainty_score":0.031386256},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.102542944,0.00030082167,0.8949598,0.00022038764,0.000041684154,0.000029718589,0.00015663351,0.0011465626,0.00060142105],"genre_scores_gemma":[0.91241324,0.00017067006,0.08556792,0.000085367545,0.0000581029,0.000037610633,0.0005772212,0.00005739346,0.0010325338],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992624,0.00017391976,0.00004222194,0.0002291314,0.00018215671,0.00011020456],"domain_scores_gemma":[0.99844754,0.00056685833,0.00021680334,0.00022102124,0.0004612716,0.0000864615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013917497,0.00094923936,0.0012097838,0.0011709515,0.00033307614,0.000796591,0.0016932074,0.0008095788,0.0004396384],"category_scores_gemma":[0.0033008314,0.00039296973,0.000976148,0.0010368164,0.00036999825,0.0014527397,0.001146633,0.001708906,0.00021910942],"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":[0.00020580603,0.0002243941,0.010131008,0.000035453613,0.00018992079,0.0001220284,0.000078384706,0.7165682,0.006959338,0.0026480323,0.0023267306,0.26051068],"study_design_scores_gemma":[6.8192145e-7,0.0000071044155,0.00019033802,9.5358837e-7,0.0000035254031,0.0000051251477,0.0000018258966,0.9989435,0.0003528794,0.00045467826,0.000037968948,0.0000014004173],"about_ca_topic_score_codex":0.010167575,"about_ca_topic_score_gemma":0.008627971,"teacher_disagreement_score":0.010167575,"about_ca_system_score_codex":0.00079318666,"about_ca_system_score_gemma":0.0007210376,"threshold_uncertainty_score":0.020216763},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49326214,0.0004246974,0.49656925,0.00032468088,0.00019709508,0.000048100406,0.0003435636,0.0063555366,0.0024749038],"genre_scores_gemma":[0.9349027,0.00008144611,0.06267255,0.00006984063,0.00006175652,0.000017377579,0.0007447082,0.00011976736,0.0013298687],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909186,0.00017219216,0.000040378076,0.000291936,0.00025459545,0.00014894886],"domain_scores_gemma":[0.9979399,0.00095829304,0.00015477029,0.00039182,0.0004079048,0.00014730095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011244548,0.000726247,0.0016034265,0.0007329786,0.00050585635,0.0010484552,0.0011429698,0.001083774,0.0007763655],"category_scores_gemma":[0.003993105,0.00027671029,0.00043663254,0.0008666385,0.00046727926,0.0012578586,0.0012631153,0.0010877734,0.000631149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024518077,0.0012113837,0.018348632,0.00018777921,0.00026129978,0.00053099357,0.00021553502,0.1530592,0.065797664,0.0016901097,0.00567392,0.7505717],"study_design_scores_gemma":[0.000010649511,0.00009884403,0.0028416293,0.0000024963376,0.000014087712,0.00015124383,0.000040124272,0.9874626,0.008090607,0.0009825762,0.00029302642,0.000012073808],"about_ca_topic_score_codex":0.0030157333,"about_ca_topic_score_gemma":0.0038385687,"teacher_disagreement_score":0.0030157333,"about_ca_system_score_codex":0.00026660622,"about_ca_system_score_gemma":0.0008173426,"threshold_uncertainty_score":0.0059963465},"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":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02255983,0.26222417,0.6903065,0.0038387678,0.00055033946,0.00017217112,0.000149791,0.0016072721,0.018591184],"genre_scores_gemma":[0.28519,0.24508134,0.4618911,0.0016005384,0.001618194,0.00021008676,0.0003890516,0.00020208558,0.003817478],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984169,0.0003916964,0.00009973794,0.00028870258,0.00071863655,0.00008432372],"domain_scores_gemma":[0.9981741,0.0009755013,0.00014932253,0.00016348885,0.0004810675,0.00005649873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021596116,0.0007992462,0.0009863959,0.0047713104,0.00045125172,0.0022994876,0.0013016199,0.0013369833,0.0006929788],"category_scores_gemma":[0.002885206,0.0006291176,0.0008369668,0.0032551545,0.0009811452,0.0032934302,0.000981666,0.0014910713,0.00050095265],"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":[0.000058197307,0.00015298842,0.0035020784,0.0014321575,0.00011594985,0.000103613645,0.0002738886,0.0072908853,0.008596191,0.021919556,0.0051751737,0.9513793],"study_design_scores_gemma":[0.000043306838,0.0011346166,0.01631876,0.0027950923,0.000373016,0.0032346996,0.0011749738,0.33406872,0.057636127,0.12183992,0.4609423,0.00043854682],"about_ca_topic_score_codex":0.0012181492,"about_ca_topic_score_gemma":0.0009947892,"teacher_disagreement_score":0.0047713104,"about_ca_system_score_codex":0.0008903387,"about_ca_system_score_gemma":0.00057148916,"threshold_uncertainty_score":0.011421263},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0091359075,0.00010026709,0.98911357,0.00012008757,0.00004097101,0.00005410175,0.00022641785,0.00094161276,0.0002670927],"genre_scores_gemma":[0.18676203,0.00013569313,0.8074469,0.00026920924,0.00010745241,0.0003074281,0.0031469234,0.00034950842,0.0014747476],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959508,0.0011525789,0.00039594973,0.0011162707,0.0011397605,0.00024458396],"domain_scores_gemma":[0.99250376,0.0029813254,0.0005257554,0.001714334,0.001998747,0.000276093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003765276,0.0011765206,0.0019471426,0.0019949423,0.00086777227,0.0016690062,0.0031212529,0.0014876159,0.0019097676],"category_scores_gemma":[0.016575214,0.0006146304,0.0016832551,0.0022665586,0.0010439458,0.0027792195,0.0024921729,0.0025008067,0.00118919],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041669176,0.00035843195,0.011195001,0.00026581585,0.00027757842,0.0002634966,0.0003484719,0.36107415,0.015928254,0.024046153,0.010792589,0.57503337],"study_design_scores_gemma":[0.000014530314,0.000062108295,0.0005268095,0.00001127309,0.000008297595,0.00007436069,0.00002161394,0.984861,0.003759115,0.008871516,0.0017734623,0.000015818738],"about_ca_topic_score_codex":0.0044679027,"about_ca_topic_score_gemma":0.006050942,"teacher_disagreement_score":0.0044679027,"about_ca_system_score_codex":0.0009789668,"about_ca_system_score_gemma":0.0019792684,"threshold_uncertainty_score":0.019912899},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046389364,0.00047523956,0.9486844,0.00031488377,0.00006833977,0.000042898682,0.00014796555,0.0025516667,0.001325197],"genre_scores_gemma":[0.90258676,0.0003582821,0.093012504,0.0003366317,0.000065539636,0.00004808573,0.0004757454,0.0002046689,0.0029117824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996463,0.000085142165,0.000012761663,0.000105741674,0.0000833745,0.00006663919],"domain_scores_gemma":[0.99917585,0.00043504298,0.00010982162,0.000092919065,0.00013052241,0.00005582377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008990472,0.00093286834,0.0007363829,0.0009600121,0.00032487628,0.0005962805,0.0018192979,0.0010542701,0.0014830763],"category_scores_gemma":[0.0023763743,0.00045563915,0.00088761596,0.0006012348,0.00084225903,0.0015128792,0.0013214307,0.0014962116,0.00038498658],"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":[0.00017751196,0.00010950502,0.0053819185,0.00007980355,0.00014635432,0.00024562923,0.00013510142,0.76964176,0.011325268,0.011674412,0.0036953317,0.19738743],"study_design_scores_gemma":[0.0000024635117,0.000016688495,0.00020240076,0.0000031287093,0.000008879132,0.000027739761,0.0000034098514,0.9947312,0.0012620327,0.003550703,0.00018759203,0.000003798396],"about_ca_topic_score_codex":0.0077208206,"about_ca_topic_score_gemma":0.007898357,"teacher_disagreement_score":0.0077208206,"about_ca_system_score_codex":0.0009731506,"about_ca_system_score_gemma":0.0006963156,"threshold_uncertainty_score":0.015351772},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12404969,0.00032366716,0.8713994,0.00043860707,0.00013085619,0.0000477706,0.000302258,0.000978152,0.002329568],"genre_scores_gemma":[0.97120464,0.00014624652,0.025609918,0.000097428616,0.000045229037,0.000030594656,0.0004032839,0.00004575446,0.0024170072],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967,0.00007235403,0.000009437189,0.000117438474,0.00007767111,0.000053119562],"domain_scores_gemma":[0.999046,0.00057988294,0.00012190867,0.000081697435,0.00012545334,0.00004502228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005492276,0.00064380065,0.0006050821,0.0011462551,0.00030256005,0.000471015,0.0012662098,0.0008579097,0.00090349204],"category_scores_gemma":[0.0016987773,0.00038557738,0.00079287565,0.00078825536,0.00060830143,0.0006626305,0.00083971594,0.0010691494,0.00028425522],"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":[0.00009307799,0.000047268113,0.0027621384,0.000023137038,0.000052588264,0.00013757545,0.000035867826,0.95805675,0.0016870707,0.0050047617,0.0011171895,0.030982621],"study_design_scores_gemma":[8.116842e-7,0.0000038976004,0.00017651259,8.589699e-7,0.0000031173308,0.000012262934,0.0000019181266,0.9985593,0.00017161311,0.0010086057,0.000059342587,0.0000017727764],"about_ca_topic_score_codex":0.007188674,"about_ca_topic_score_gemma":0.007252666,"teacher_disagreement_score":0.007188674,"about_ca_system_score_codex":0.0006707167,"about_ca_system_score_gemma":0.00047983116,"threshold_uncertainty_score":0.014293671},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08966287,0.0012559575,0.9020698,0.000628123,0.000220405,0.000050558683,0.0006744032,0.0029375844,0.0025003268],"genre_scores_gemma":[0.94016635,0.00039228398,0.054214314,0.0003074257,0.000099827186,0.00014210565,0.0010339436,0.00011474488,0.003529004],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999778,0.000043680222,0.000012708675,0.00009322759,0.000033637647,0.000038746242],"domain_scores_gemma":[0.9996433,0.0001838967,0.000043060674,0.000028220707,0.00008858864,0.000012993954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047791604,0.0009601755,0.00058626366,0.0003764916,0.00023015995,0.0005938969,0.0012505726,0.0007799459,0.0014856714],"category_scores_gemma":[0.0018599931,0.00037177632,0.00066631165,0.00046881224,0.00036624237,0.0009234159,0.0008410669,0.0016308624,0.00046716165],"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":[0.00018236088,0.000089305584,0.0020365382,0.0001303559,0.00009923539,0.00013379048,0.00011862001,0.8406416,0.0070472197,0.0032642959,0.003042036,0.14321464],"study_design_scores_gemma":[0.0000024344486,0.000013387873,0.00019350882,0.000004188126,0.000008165366,0.000007829607,0.0000046429955,0.99786866,0.00043409216,0.001289841,0.00016989505,0.000003439166],"about_ca_topic_score_codex":0.012228545,"about_ca_topic_score_gemma":0.0142574385,"teacher_disagreement_score":0.012228545,"about_ca_system_score_codex":0.0006318694,"about_ca_system_score_gemma":0.0008092368,"threshold_uncertainty_score":0.024314761},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019015057,0.00055810914,0.97835594,0.00021553047,0.000059630107,0.000050990715,0.00021346768,0.00093209994,0.00059910095],"genre_scores_gemma":[0.28654101,0.0010303156,0.7074376,0.00022382643,0.0001554374,0.00022361458,0.0019451255,0.00020482721,0.002238196],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976221,0.0005040664,0.0001612252,0.00041612613,0.0010643493,0.0002320766],"domain_scores_gemma":[0.99550337,0.001974627,0.00051791925,0.0006829128,0.0010708364,0.00025044088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020458496,0.0010691646,0.0017669735,0.0032160485,0.0010785399,0.0016341475,0.0012808973,0.0012385648,0.001939866],"category_scores_gemma":[0.009928991,0.000429453,0.0013236633,0.0038693156,0.0011091924,0.0023006343,0.0026778523,0.00195307,0.0011025964],"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":[0.00058333547,0.00028563847,0.0104259,0.0005023358,0.00023532148,0.0008407544,0.0009446268,0.15995567,0.02718832,0.029143468,0.011033126,0.75886154],"study_design_scores_gemma":[0.000012593352,0.00008295237,0.0021827419,0.000029538845,0.00001595405,0.00037458498,0.0002510531,0.9645909,0.0041920557,0.023800371,0.00442114,0.00004609709],"about_ca_topic_score_codex":0.0031393436,"about_ca_topic_score_gemma":0.0032812937,"teacher_disagreement_score":0.0032160485,"about_ca_system_score_codex":0.0005497364,"about_ca_system_score_gemma":0.0012027462,"threshold_uncertainty_score":0.010819674},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06453844,0.0007118477,0.92159355,0.00058677245,0.00023343734,0.00025093777,0.00065992546,0.0021445565,0.009280473],"genre_scores_gemma":[0.7130928,0.00030646153,0.2741501,0.0002300693,0.00020130714,0.00021869822,0.002351611,0.00034177856,0.0091072],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996705,0.00074180716,0.00017178684,0.0006851349,0.001281545,0.00041466067],"domain_scores_gemma":[0.9959365,0.0015275996,0.00029303756,0.00086899387,0.0012584509,0.00011534403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002984785,0.0010366712,0.0018223563,0.0022911832,0.0017914737,0.001898861,0.0035144112,0.0019328044,0.0037064955],"category_scores_gemma":[0.0064605754,0.00028662497,0.001441078,0.0017184821,0.0010712056,0.0022520572,0.0013611296,0.0022473969,0.0013614467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008049046,0.0005744289,0.010471658,0.0002816671,0.00027877218,0.0003927851,0.0004451895,0.1691675,0.01297196,0.08147204,0.026752643,0.6963864],"study_design_scores_gemma":[0.000012479419,0.000058621903,0.002145591,0.000021635049,0.000029012888,0.00009105786,0.00009311985,0.9616785,0.0038297907,0.029585734,0.002429743,0.000024740988],"about_ca_topic_score_codex":0.011637513,"about_ca_topic_score_gemma":0.013840257,"teacher_disagreement_score":0.011637513,"about_ca_system_score_codex":0.0016963087,"about_ca_system_score_gemma":0.0021122028,"threshold_uncertainty_score":0.023139536},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00085083756,0.016713014,0.0003116461,0.00440963,0.006338305,0.000030637806,0.0035550753,0.00027663872,0.96751416],"genre_scores_gemma":[0.0014100949,0.0019784155,0.000085953536,0.00015313803,0.00021289027,0.000006778128,0.00030593024,0.00008153142,0.9957653],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996278,0.000022822633,0.000015324464,0.000074471434,0.00019376955,0.00006574978],"domain_scores_gemma":[0.9997038,0.000034554763,0.000019873432,0.00002368574,0.00015552827,0.00006245452],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003200544,0.00091049616,0.00064112456,0.0021175786,0.0034693368,0.004916178,0.00073064084,0.001605002,0.30087033],"category_scores_gemma":[0.0014708666,0.0004305733,0.0003348859,0.0030390788,0.00062516227,0.002207185,0.0013618901,0.0022467899,0.12067524],"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":[0.000033896544,0.000008881927,0.00022751476,0.00008023155,0.000004391266,0.00008249788,0.00009899982,0.00007551395,0.0001013601,0.009596151,0.9399963,0.04969441],"study_design_scores_gemma":[0.0000012309243,0.0000021272301,0.00034528188,0.000044940214,7.6916234e-7,0.000018431996,0.000028513778,0.000017658556,0.000033947992,0.00034237752,0.9991622,0.000002507147],"about_ca_topic_score_codex":0.096698985,"about_ca_topic_score_gemma":0.42928508,"teacher_disagreement_score":0.903301,"about_ca_system_score_codex":0.0029336,"about_ca_system_score_gemma":0.0020076432,"threshold_uncertainty_score":0.9972236},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9518139,0.00021951913,0.046105254,0.000049182334,0.0000044846906,0.000018252284,0.0000584917,0.0001824935,0.0015484744],"genre_scores_gemma":[0.9842487,0.000056333043,0.0152267665,0.000007930375,0.00000953492,0.0000064707947,0.00006410884,0.000011030005,0.0003690909],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989414,0.00001707243,0.000003967588,0.000036961974,0.00002424421,0.000023684444],"domain_scores_gemma":[0.99924797,0.00021808391,0.00022006054,0.00011348559,0.000069113565,0.00013130705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003351545,0.0002581597,0.00035291672,0.0013197063,0.00036155162,0.0006330717,0.0005152841,0.0003042937,0.0010093335],"category_scores_gemma":[0.0014485643,0.00017414054,0.00013217324,0.00041773266,0.00049011,0.0008402314,0.0006405559,0.00031881765,0.0003328444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040579506,0.00011107305,0.66773915,0.00014853894,0.00007416015,0.0014887186,0.0009331554,0.0064108204,0.13943493,0.020539975,0.00071058923,0.1620031],"study_design_scores_gemma":[0.000077178,0.00035298846,0.623145,0.000066416476,0.0000985064,0.0042009926,0.0015522905,0.18098256,0.101013124,0.08219855,0.006236071,0.000076271506],"about_ca_topic_score_codex":0.0005346103,"about_ca_topic_score_gemma":0.0009029825,"teacher_disagreement_score":0.0013197063,"about_ca_system_score_codex":0.00019879019,"about_ca_system_score_gemma":0.00013715753,"threshold_uncertainty_score":0.0033766031},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00092307804,0.002142901,0.021146962,0.0020152696,0.004447246,0.0016713444,0.030807825,0.0063521927,0.9304932],"genre_scores_gemma":[0.008465367,0.004547717,0.03905641,0.002741866,0.0009812937,0.0014750012,0.09684362,0.0045492873,0.8413395],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9903425,0.000826635,0.0008037992,0.0004604595,0.0070206802,0.00054582866],"domain_scores_gemma":[0.98914623,0.00044079867,0.0002961432,0.00082893507,0.009105922,0.00018203662],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0034692101,0.002115179,0.0008881041,0.006841098,0.0019159174,0.0060717873,0.0032944572,0.0036891582,0.2514944],"category_scores_gemma":[0.009934651,0.0010033576,0.0013328571,0.0061223805,0.0009646523,0.0066816956,0.002306953,0.0026169117,0.33865145],"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":[0.000056048357,0.0000746124,0.00018429905,0.00054942723,0.000007918182,0.00010216733,0.00014281813,0.00039890586,0.0013913792,0.014854241,0.8721428,0.11009528],"study_design_scores_gemma":[0.0000051583515,0.0000118597,0.000264349,0.00013229297,0.0000035184419,0.000046952566,0.000043607248,0.000062334715,0.0003986069,0.00096322916,0.9980573,0.000010678541],"about_ca_topic_score_codex":0.022530163,"about_ca_topic_score_gemma":0.012059908,"teacher_disagreement_score":0.7485056,"about_ca_system_score_codex":0.0028243368,"about_ca_system_score_gemma":0.007699704,"threshold_uncertainty_score":0.8413326},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7081458,0.019186852,0.016138162,0.0009841803,0.0018079478,0.0010198464,0.017319575,0.0009282213,0.23446938],"genre_scores_gemma":[0.9448994,0.0062620663,0.012812406,0.0005492611,0.0002208466,0.00033721814,0.010506046,0.00008403041,0.024328666],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9998282,0.000009456703,0.000020564354,0.000058819696,0.000059111775,0.000023708351],"domain_scores_gemma":[0.9998481,0.000026602074,0.000046728135,0.00001955729,0.000034569082,0.000024387675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000113433656,0.0007725548,0.00035353733,0.0017438965,0.0015708999,0.00053550024,0.00053837185,0.00058202207,0.01045477],"category_scores_gemma":[0.0004471856,0.00021870199,0.00032610918,0.0012814264,0.00053857645,0.0009700924,0.0007049248,0.00087697926,0.004085298],"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":[0.0011501234,0.00015823905,0.07506827,0.0014124381,0.00038345947,0.0072380616,0.0024384272,0.0025867464,0.12653928,0.0060336916,0.033094797,0.7438965],"study_design_scores_gemma":[0.0002124391,0.00034881814,0.53109485,0.0009629558,0.0005621869,0.027403412,0.0030619178,0.0018773226,0.009627601,0.002570961,0.42218438,0.000093051225],"about_ca_topic_score_codex":0.009273198,"about_ca_topic_score_gemma":0.017013628,"teacher_disagreement_score":0.01045477,"about_ca_system_score_codex":0.0005977582,"about_ca_system_score_gemma":0.0006232692,"threshold_uncertainty_score":0.034974694},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09739526,0.0011553295,0.89540565,0.00024491458,0.00023338999,0.00008591499,0.00040465035,0.0028433262,0.002231464],"genre_scores_gemma":[0.7009573,0.0006770057,0.2938922,0.00014360421,0.0003187979,0.000076493576,0.0012334828,0.00014384983,0.0025574109],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926645,0.00007482809,0.000038913306,0.0002263767,0.00029914003,0.000094240684],"domain_scores_gemma":[0.99932706,0.00019677846,0.00013191355,0.00008433461,0.0002089102,0.000050988274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048981764,0.0010104472,0.0009490096,0.0024922525,0.0003040142,0.00075820927,0.0010091494,0.0009404424,0.00069873163],"category_scores_gemma":[0.0018971583,0.0002400505,0.00069812825,0.0013694109,0.0003788466,0.0012385608,0.0010804891,0.0008669651,0.000595799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004434363,0.00021784747,0.007254096,0.00018639363,0.000114717215,0.00039251716,0.00012932552,0.035097297,0.08240426,0.0012666621,0.0026747568,0.8698187],"study_design_scores_gemma":[0.000022720813,0.0001842192,0.009716394,0.000028267881,0.00006744479,0.0006064037,0.00012805438,0.9514163,0.028984271,0.0044795116,0.0043264073,0.000039961164],"about_ca_topic_score_codex":0.0023674571,"about_ca_topic_score_gemma":0.0028023885,"teacher_disagreement_score":0.0024922525,"about_ca_system_score_codex":0.0003045709,"about_ca_system_score_gemma":0.0004524437,"threshold_uncertainty_score":0.0047073364},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2102711,0.0016199248,0.7443566,0.001826649,0.00071998994,0.000082690145,0.01598947,0.018963614,0.0061700665],"genre_scores_gemma":[0.8293629,0.00058552274,0.12938899,0.00023317154,0.0002766637,0.000094303934,0.029889839,0.00096058514,0.009208036],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966526,0.000054049706,0.000020862914,0.00008220707,0.00012460933,0.00005298781],"domain_scores_gemma":[0.99893636,0.0003951591,0.000093433446,0.00025230952,0.00025772842,0.00006501739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088628905,0.000671297,0.0007373608,0.0012164267,0.00029193758,0.0012080818,0.0013543598,0.0009960803,0.004855532],"category_scores_gemma":[0.0048939553,0.00033763787,0.0006508935,0.0012134942,0.00035673098,0.0013597348,0.0009396005,0.0012819018,0.001295565],"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":[0.0008388145,0.00041795446,0.019805081,0.00031384663,0.0004326697,0.0005338105,0.000118216965,0.4698116,0.011195551,0.03396824,0.11797267,0.3445916],"study_design_scores_gemma":[0.000014422776,0.00001644896,0.0008141354,0.000005290821,0.0000071172494,0.00003942594,0.000006264882,0.9876016,0.0011568671,0.008511716,0.0018212271,0.0000055235846],"about_ca_topic_score_codex":0.0068878243,"about_ca_topic_score_gemma":0.008417888,"teacher_disagreement_score":0.0068878243,"about_ca_system_score_codex":0.0005038288,"about_ca_system_score_gemma":0.0010344288,"threshold_uncertainty_score":0.016243398},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005752484,0.28204852,0.6530462,0.022074405,0.01595415,0.0000811393,0.00020125712,0.0006517106,0.02019019],"genre_scores_gemma":[0.26343343,0.24833812,0.3326719,0.0103347525,0.05346294,0.00048573478,0.000878288,0.0006998003,0.08969499],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858475,0.0006585983,0.00006773096,0.00028363185,0.00033416078,0.000071161594],"domain_scores_gemma":[0.9972121,0.0018620709,0.000084037834,0.00013318182,0.00062751764,0.000081089376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031466417,0.0007763355,0.0009320723,0.0013487526,0.0003765842,0.0018795781,0.00075091334,0.00182108,0.0039797877],"category_scores_gemma":[0.006133983,0.00040254218,0.000551308,0.0018204508,0.0012694696,0.002706408,0.0010112927,0.0033494907,0.0016627429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001266554,0.000089246896,0.0009838524,0.0012826923,0.000195269,0.0002921338,0.00026376316,0.021788828,0.0043049506,0.30659318,0.12139457,0.54268485],"study_design_scores_gemma":[0.000023875391,0.00015227828,0.0015917459,0.0005903037,0.00006441021,0.00045614244,0.00013296712,0.10775803,0.0043481565,0.5471046,0.3376694,0.00010809285],"about_ca_topic_score_codex":0.0005078885,"about_ca_topic_score_gemma":0.0004730156,"teacher_disagreement_score":0.0039797877,"about_ca_system_score_codex":0.0010326736,"about_ca_system_score_gemma":0.00040828707,"threshold_uncertainty_score":0.01664126},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11233913,0.0023364767,0.84425557,0.001506425,0.00034496226,0.00027697755,0.017539881,0.018224165,0.0031763313],"genre_scores_gemma":[0.79278487,0.0009271939,0.16051018,0.00057130057,0.0004896394,0.00071834086,0.03524707,0.0013436895,0.007407704],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99721044,0.00096595014,0.00022797781,0.0007745467,0.00051590987,0.0003051763],"domain_scores_gemma":[0.98981804,0.0066395393,0.00070325384,0.0012636152,0.0012701134,0.00030546624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032284984,0.0013532741,0.0011768182,0.003130891,0.0006778183,0.0022242046,0.001965071,0.0015680642,0.003675768],"category_scores_gemma":[0.018844295,0.00044608314,0.0017936032,0.0020117136,0.0006376095,0.0030736977,0.0018403209,0.0025975222,0.003527796],"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":[0.0022160055,0.0013966525,0.061825342,0.0013081373,0.0010127024,0.0011437247,0.001257386,0.2406811,0.017975729,0.056448612,0.07637282,0.53836185],"study_design_scores_gemma":[0.000043852066,0.000091270995,0.0023351018,0.000037123926,0.00004414486,0.00018814504,0.000067392844,0.9608878,0.0016968352,0.030032966,0.00453896,0.00003641526],"about_ca_topic_score_codex":0.0077249203,"about_ca_topic_score_gemma":0.009864022,"teacher_disagreement_score":0.0077249203,"about_ca_system_score_codex":0.0011133426,"about_ca_system_score_gemma":0.0012904253,"threshold_uncertainty_score":0.017074168},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09739526,0.0011553295,0.89540565,0.00024491458,0.00023338999,0.00008591499,0.00040465035,0.0028433262,0.002231464],"genre_scores_gemma":[0.7009573,0.0006770057,0.2938922,0.00014360421,0.0003187979,0.000076493576,0.0012334828,0.00014384983,0.0025574109],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926645,0.00007482809,0.000038913306,0.0002263767,0.00029914003,0.000094240684],"domain_scores_gemma":[0.99932706,0.00019677846,0.00013191355,0.00008433461,0.0002089102,0.000050988274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048981764,0.0010104472,0.0009490096,0.0024922525,0.0003040142,0.00075820927,0.0010091494,0.0009404424,0.00069873163],"category_scores_gemma":[0.0018971583,0.0002400505,0.00069812825,0.0013694109,0.0003788466,0.0012385608,0.0010804891,0.0008669651,0.000595799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004434363,0.00021784747,0.007254096,0.00018639363,0.000114717215,0.00039251716,0.00012932552,0.035097297,0.08240426,0.0012666621,0.0026747568,0.8698187],"study_design_scores_gemma":[0.000022720813,0.0001842192,0.009716394,0.000028267881,0.00006744479,0.0006064037,0.00012805438,0.9514163,0.028984271,0.0044795116,0.0043264073,0.000039961164],"about_ca_topic_score_codex":0.0023674571,"about_ca_topic_score_gemma":0.0028023885,"teacher_disagreement_score":0.0024922525,"about_ca_system_score_codex":0.0003045709,"about_ca_system_score_gemma":0.0004524437,"threshold_uncertainty_score":0.0047073364},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9307042,0.0019078758,0.008884359,0.0095148785,0.00023080158,0.017557634,0.01487453,0.0007888341,0.015536992],"genre_scores_gemma":[0.8988079,0.0018430426,0.06584983,0.0034029922,0.00019617409,0.011225533,0.01316249,0.000103466235,0.0054086098],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9829555,0.008434204,0.001397361,0.0010781881,0.0036255952,0.0025091826],"domain_scores_gemma":[0.9366352,0.0070604533,0.015994173,0.004551827,0.010252885,0.025505358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016251834,0.00050007197,0.00038177584,0.0014937652,0.0019458698,0.0018048382,0.0017742891,0.00038724774,0.0049043535],"category_scores_gemma":[0.022141539,0.00039819238,0.00040628348,0.00419919,0.0009206701,0.0011861654,0.0028608388,0.0011027534,0.0005942664],"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":[0.0018083071,0.0046552224,0.63348144,0.0013044014,0.00016111784,0.0002870205,0.0037355,0.0014364853,0.0019591555,0.0010151198,0.02999166,0.3201645],"study_design_scores_gemma":[0.0007643666,0.0028023717,0.9818792,0.00032916144,0.000048172933,0.000115055554,0.0025061984,0.0009724051,0.0005664205,0.000105824765,0.009888026,0.000022756887],"about_ca_topic_score_codex":0.10075061,"about_ca_topic_score_gemma":0.15331535,"teacher_disagreement_score":0.10075061,"about_ca_system_score_codex":0.014443561,"about_ca_system_score_gemma":0.06334924,"threshold_uncertainty_score":0.20032841},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005289806,0.0004981985,0.0029449323,0.0002312366,0.00016511447,0.00008407325,0.9765345,0.0070376415,0.007214459],"genre_scores_gemma":[0.0037790737,0.000093197996,0.003607739,0.000059809794,0.000015818965,0.000089668334,0.99071395,0.00018554337,0.0014551],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988193,0.00011706078,0.000115549,0.0003640083,0.00041739727,0.0001667095],"domain_scores_gemma":[0.99879116,0.00015875288,0.000116348194,0.0003584888,0.00047878092,0.000096483185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005874971,0.0022874877,0.00093021325,0.0038102167,0.0009776955,0.0013507175,0.0021580956,0.0015315857,0.015719341],"category_scores_gemma":[0.0023940997,0.00040388427,0.0009897923,0.005505286,0.0004919307,0.0017055325,0.0015140134,0.001545189,0.026662268],"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":[0.000118122734,0.000085866326,0.0020106686,0.00045538743,0.000041502524,0.0001611522,0.00005731686,0.0010284109,0.0016246564,0.0013498863,0.9725988,0.020468364],"study_design_scores_gemma":[0.00010086329,0.000047630812,0.015750244,0.00015897938,0.00003583771,0.0004818306,0.0003014089,0.0083077345,0.0041739764,0.0032350535,0.96732324,0.0000831875],"about_ca_topic_score_codex":0.035888713,"about_ca_topic_score_gemma":0.09714482,"teacher_disagreement_score":0.035888713,"about_ca_system_score_codex":0.0014094246,"about_ca_system_score_gemma":0.0016684034,"threshold_uncertainty_score":0.071359634},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17488603,0.0014391078,0.68177867,0.0030448816,0.00025077726,0.0004550028,0.029443586,0.007264184,0.10143776],"genre_scores_gemma":[0.75215894,0.0006647198,0.21899876,0.00016081013,0.00004695376,0.0001758146,0.007504421,0.0005287452,0.019760875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99977094,0.000049954626,0.000018290837,0.00006678207,0.00007527618,0.000018730074],"domain_scores_gemma":[0.9997073,0.00008899214,0.000049683542,0.000048796697,0.00009025298,0.000014935442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005009006,0.00033503887,0.00020150002,0.0009847953,0.00036639356,0.0018145976,0.00051681267,0.00048988045,0.006738227],"category_scores_gemma":[0.002011457,0.00030752193,0.0004284128,0.0018696098,0.0005410479,0.00083065475,0.0013300565,0.00042231297,0.0008027679],"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":[0.00020201252,0.00007473324,0.045844648,0.00067823654,0.00011964486,0.0023740188,0.0036517996,0.44113398,0.009228513,0.06980745,0.02805111,0.39883387],"study_design_scores_gemma":[0.00007262315,0.000100192614,0.04767337,0.00024586025,0.000072699164,0.0005553301,0.002052702,0.7435509,0.002004535,0.055777226,0.14782569,0.00006889008],"about_ca_topic_score_codex":0.102924004,"about_ca_topic_score_gemma":0.14724101,"teacher_disagreement_score":0.102924004,"about_ca_system_score_codex":0.0018002044,"about_ca_system_score_gemma":0.0015889812,"threshold_uncertainty_score":0.2046498},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9956518,0.0012005252,0.00011341586,0.00050323666,0.000020142072,0.000021367923,0.0009538072,0.0000067485767,0.0015290291],"genre_scores_gemma":[0.99911827,0.00023875569,0.0000767499,0.00007857095,0.000013463303,0.00001443618,0.00037036237,7.0747e-7,0.000088785295],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994229,0.00017170192,0.00007883505,0.000075930766,0.00014310145,0.000107563865],"domain_scores_gemma":[0.9981346,0.00040280554,0.000988286,0.000053380856,0.00017571045,0.00024508566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008000538,0.00020946846,0.00034073618,0.0006153982,0.0003975061,0.0007899827,0.00023681718,0.0003807521,0.0025715798],"category_scores_gemma":[0.0053604455,0.00009880954,0.0006411347,0.0009915025,0.00022438534,0.0004989692,0.00057878473,0.00070042635,0.00019897679],"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":[0.000035639827,0.000042422784,0.99526817,0.000024633247,0.00006400611,0.000027068962,0.00010344502,0.00007878746,0.000029594787,0.000021745751,0.0002175188,0.0040870463],"study_design_scores_gemma":[0.0000069658367,0.00009686407,0.9989524,0.000015984986,0.000027699374,0.00012674148,0.00018747832,0.00023528846,0.00001523818,0.000064607084,0.000266865,0.0000039487813],"about_ca_topic_score_codex":0.0029714068,"about_ca_topic_score_gemma":0.0028317,"teacher_disagreement_score":0.0029714068,"about_ca_system_score_codex":0.00036204502,"about_ca_system_score_gemma":0.00028540826,"threshold_uncertainty_score":0.008602798},"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":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001156437,0.00007301769,0.00007649167,0.0001350155,0.000029130284,0.000017161972,0.9968285,0.00015182736,0.0015323064],"genre_scores_gemma":[0.002740199,0.0000926092,0.00036959152,0.000036191694,0.000008530009,0.000046460726,0.9947826,0.000026815322,0.0018969377],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9988386,0.000086135224,0.00011017116,0.00023302602,0.00041813962,0.00031396459],"domain_scores_gemma":[0.9968182,0.00039004712,0.0002209739,0.00040112255,0.001814719,0.00035485282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008106086,0.0017844025,0.00097167055,0.0042104106,0.0014231055,0.002050852,0.0023954008,0.0014742848,0.011100715],"category_scores_gemma":[0.0051333024,0.00040216517,0.00086427014,0.009027643,0.0006253628,0.0009595475,0.0011773586,0.0018913327,0.012783289],"study_design_candidate":"observational","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":[0.00006403791,0.0000353739,0.008754916,0.00028854696,0.000027976892,0.000034126104,0.00008651573,0.00044297296,0.00012207784,0.00080659223,0.9847596,0.004577223],"study_design_scores_gemma":[0.000117080046,0.000017269524,0.06400619,0.00028619517,0.000037554706,0.000119159246,0.00084248086,0.001453213,0.00066274597,0.0009825577,0.93141806,0.0000574579],"about_ca_topic_score_codex":0.82970375,"about_ca_topic_score_gemma":0.9137518,"teacher_disagreement_score":0.17029625,"about_ca_system_score_codex":0.0086549595,"about_ca_system_score_gemma":0.012362848,"threshold_uncertainty_score":0.34259826},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005752484,0.28204852,0.6530462,0.022074405,0.01595415,0.0000811393,0.00020125712,0.0006517106,0.02019019],"genre_scores_gemma":[0.26343343,0.24833812,0.3326719,0.0103347525,0.05346294,0.00048573478,0.000878288,0.0006998003,0.08969499],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858475,0.0006585983,0.00006773096,0.00028363185,0.00033416078,0.000071161594],"domain_scores_gemma":[0.9972121,0.0018620709,0.000084037834,0.00013318182,0.00062751764,0.000081089376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031466417,0.0007763355,0.0009320723,0.0013487526,0.0003765842,0.0018795781,0.00075091334,0.00182108,0.0039797877],"category_scores_gemma":[0.006133983,0.00040254218,0.000551308,0.0018204508,0.0012694696,0.002706408,0.0010112927,0.0033494907,0.0016627429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001266554,0.000089246896,0.0009838524,0.0012826923,0.000195269,0.0002921338,0.00026376316,0.021788828,0.0043049506,0.30659318,0.12139457,0.54268485],"study_design_scores_gemma":[0.000023875391,0.00015227828,0.0015917459,0.0005903037,0.00006441021,0.00045614244,0.00013296712,0.10775803,0.0043481565,0.5471046,0.3376694,0.00010809285],"about_ca_topic_score_codex":0.0005078885,"about_ca_topic_score_gemma":0.0004730156,"teacher_disagreement_score":0.0039797877,"about_ca_system_score_codex":0.0010326736,"about_ca_system_score_gemma":0.00040828707,"threshold_uncertainty_score":0.01664126},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022291148,0.00072766724,0.00051629095,0.00061431224,0.00007908933,0.0045580035,0.9899358,0.00017408226,0.003171878],"genre_scores_gemma":[0.016876068,0.010109199,0.029400114,0.004463709,0.0004840117,0.23835444,0.65257084,0.0010568138,0.046684757],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99648786,0.0009791164,0.0014450344,0.00040207696,0.00045249128,0.00023339617],"domain_scores_gemma":[0.89454716,0.08971231,0.005756735,0.0012871751,0.007904339,0.0007922372],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00800123,0.0016400629,0.0033025148,0.01408378,0.0012803095,0.0026706452,0.0022524034,0.0022689407,0.79227877],"category_scores_gemma":[0.08015132,0.001083564,0.0029403388,0.019921707,0.00047094226,0.003606174,0.0026475075,0.0011905811,0.048488587],"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":[0.0006023424,0.00006256627,0.0010547558,0.294595,0.0002877892,0.00013462386,0.0005254712,0.0004579822,0.00013136715,0.0025234716,0.6742846,0.025340058],"study_design_scores_gemma":[0.007203463,0.0002833693,0.01125744,0.19655938,0.0018055897,0.0002912182,0.0016208108,0.00088132796,0.0004665149,0.010650628,0.7687561,0.00022422645],"about_ca_topic_score_codex":0.015813567,"about_ca_topic_score_gemma":0.037052475,"teacher_disagreement_score":0.79227877,"about_ca_system_score_codex":0.0042054234,"about_ca_system_score_gemma":0.0092610065,"threshold_uncertainty_score":0.2962891},"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":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04017915,0.00416053,0.0025630964,0.06667772,0.050238486,0.00026184323,0.017414922,0.0048343563,0.81367],"genre_scores_gemma":[0.090407,0.001360011,0.0009120917,0.0045416807,0.00701428,0.00009331592,0.014972168,0.0019587548,0.8787406],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99876875,0.00013416361,0.000031347186,0.000110161556,0.0007348188,0.00022069461],"domain_scores_gemma":[0.9958476,0.00054088247,0.00014340626,0.00019946344,0.0012668545,0.002001879],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024957233,0.0009995217,0.00084018253,0.0016012196,0.003141495,0.00626899,0.00088666327,0.0021711106,0.24632171],"category_scores_gemma":[0.0042731822,0.00029353594,0.0005254471,0.001236038,0.00049875904,0.0017986251,0.0032028,0.0028504638,0.05058615],"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":[0.00011929246,0.00006145003,0.0005938022,0.000033003784,0.000007617556,0.00008829702,0.00009639633,0.00015563761,0.00016370227,0.0006114465,0.98513734,0.012931971],"study_design_scores_gemma":[0.000027627764,0.000103378545,0.00529407,0.000071425035,0.000009576672,0.000060194456,0.00072967494,0.0009535866,0.00033759285,0.0005884114,0.99180603,0.00001837854],"about_ca_topic_score_codex":0.012581762,"about_ca_topic_score_gemma":0.06145137,"teacher_disagreement_score":0.24632171,"about_ca_system_score_codex":0.0016834451,"about_ca_system_score_gemma":0.0016845352,"threshold_uncertainty_score":0.8240282},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044982817,0.00013032806,0.94058824,0.00017959837,0.00008346383,0.000052842577,0.00020473685,0.012758979,0.0010189757],"genre_scores_gemma":[0.6603686,0.000105919695,0.33428976,0.0002294972,0.000044820357,0.00010333581,0.0006503413,0.00020022408,0.004007431],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968505,0.000020539714,0.000017250672,0.00012148233,0.0001157222,0.000039985312],"domain_scores_gemma":[0.9996606,0.00006328477,0.00005183984,0.00005577128,0.00013780138,0.000030788873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047444232,0.00063433824,0.00059000566,0.00072388694,0.00033881373,0.00048991386,0.0012435503,0.00077241455,0.0019750483],"category_scores_gemma":[0.00090872514,0.0003000434,0.0004735497,0.00043015528,0.0002943753,0.00093330705,0.0011161391,0.0010088639,0.0006610331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054711296,0.00035503163,0.0058634463,0.00011064635,0.00012600735,0.0004162668,0.0001336099,0.12370978,0.13243933,0.004427372,0.006909998,0.7249615],"study_design_scores_gemma":[0.000007721969,0.00006331728,0.00079177215,0.0000034177817,0.000011177869,0.000067068184,0.0000070974875,0.97798014,0.018371021,0.0013633897,0.0013226062,0.000011222324],"about_ca_topic_score_codex":0.0034309488,"about_ca_topic_score_gemma":0.004850641,"teacher_disagreement_score":0.0034309488,"about_ca_system_score_codex":0.0008702244,"about_ca_system_score_gemma":0.0007609559,"threshold_uncertainty_score":0.00682199},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9905738,0.001003941,0.000104604274,0.0018547131,0.000019057301,0.00003370932,0.0010942625,0.0000058820237,0.005309972],"genre_scores_gemma":[0.99581367,0.00040949348,0.000105089326,0.00016937549,0.000006341385,0.000013687638,0.00026183322,0.0000035366218,0.0032170138],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.9994949,0.0001089199,0.000010312747,0.000056993063,0.00006545039,0.00026339182],"domain_scores_gemma":[0.99878854,0.00013821464,0.00023361688,0.000032288313,0.0003556828,0.0004517116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067648885,0.00023093347,0.00022771544,0.0008194345,0.002698416,0.0013603442,0.0010927254,0.00039426412,0.005298584],"category_scores_gemma":[0.0019516634,0.000182456,0.00025354527,0.001885655,0.0011642666,0.00040707394,0.0008388474,0.00075276004,0.00029124034],"study_design_candidate":"qualitative","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":[0.00008503549,0.00006300671,0.9652083,0.000031025393,0.00004991947,0.00023202415,0.0088356845,0.00023077136,0.0002846775,0.0007472227,0.0036244323,0.02060783],"study_design_scores_gemma":[0.0000032400258,0.0000130184735,0.99058,0.000027967913,0.000013424566,0.0000208902,0.0074572316,0.00025334934,0.000040126422,0.000043999993,0.001537752,0.00000899073],"about_ca_topic_score_codex":0.9965733,"about_ca_topic_score_gemma":0.9987179,"teacher_disagreement_score":0.026788756,"about_ca_system_score_codex":0.026788756,"about_ca_system_score_gemma":0.01461304,"threshold_uncertainty_score":0.19436693},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11745038,0.0058828294,0.69531256,0.0046689487,0.0018924098,0.0012260359,0.008482664,0.059883744,0.105200425],"genre_scores_gemma":[0.41291144,0.0039279163,0.46098483,0.001639138,0.00055217365,0.00077629805,0.01708186,0.009391014,0.09273543],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9960294,0.00057206716,0.00040292702,0.0012859452,0.0013506634,0.000359185],"domain_scores_gemma":[0.99496114,0.0012632475,0.00027718337,0.0019793229,0.0012299076,0.00028927816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022614906,0.0017083025,0.001493492,0.0019720907,0.0016423849,0.0064635263,0.0025255147,0.002147689,0.03382825],"category_scores_gemma":[0.010016412,0.001074515,0.0014290301,0.0020931768,0.0011167412,0.0067811776,0.004561716,0.0026482213,0.018081976],"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":[0.0014551947,0.0005027311,0.011635103,0.00229121,0.00037414374,0.0007144487,0.0015364746,0.014833531,0.08692337,0.017466964,0.074173644,0.78809315],"study_design_scores_gemma":[0.00023102855,0.0012489929,0.025849275,0.0008023332,0.0004470037,0.0021166704,0.0030204088,0.1681012,0.16172953,0.04109604,0.5949669,0.0003906897],"about_ca_topic_score_codex":0.005194294,"about_ca_topic_score_gemma":0.008570728,"teacher_disagreement_score":0.03382825,"about_ca_system_score_codex":0.0012458018,"about_ca_system_score_gemma":0.001953244,"threshold_uncertainty_score":0.11316681},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021253206,0.001233076,0.9616785,0.00043706148,0.00023266813,0.00007615465,0.00043244398,0.00385283,0.010804127],"genre_scores_gemma":[0.60305107,0.0024762123,0.3557522,0.00061709085,0.00027629148,0.00024560964,0.0022002717,0.00040765302,0.03497361],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997327,0.000041148374,0.000011435408,0.000084323154,0.00007981508,0.000050587983],"domain_scores_gemma":[0.99971753,0.000055160795,0.000034222365,0.000089549154,0.000075889584,0.000027587803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004069341,0.0012236002,0.00048354198,0.0005540937,0.00028759043,0.000940709,0.0016643086,0.00092498603,0.0039845705],"category_scores_gemma":[0.0009777932,0.00043993857,0.0007117826,0.0005301208,0.00049030746,0.001223729,0.001243834,0.0017375909,0.0016693638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025962712,0.00017440201,0.0014319265,0.00022149259,0.00020801867,0.0002474711,0.000080930164,0.3530942,0.030687593,0.065120354,0.020494001,0.52797997],"study_design_scores_gemma":[0.000005131949,0.000031447762,0.00028611883,0.000014976449,0.000017188459,0.000058201622,0.000005512907,0.97925466,0.0038465841,0.009326396,0.007143124,0.000010591704],"about_ca_topic_score_codex":0.0052132267,"about_ca_topic_score_gemma":0.00831865,"teacher_disagreement_score":0.0052132267,"about_ca_system_score_codex":0.0008183222,"about_ca_system_score_gemma":0.00083142874,"threshold_uncertainty_score":0.013329744},"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":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8289498,0.0008985785,0.041073162,0.009678482,0.0005287004,0.002476118,0.003465897,0.015207208,0.09772204],"genre_scores_gemma":[0.8743776,0.0008975853,0.081608646,0.0010440699,0.000141748,0.0010730372,0.002548486,0.0003325804,0.037976276],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99930537,0.00033893995,0.000052124265,0.000056275225,0.0001630729,0.00008411599],"domain_scores_gemma":[0.9971731,0.0008062624,0.00019035488,0.00030054658,0.00044660023,0.0010831597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012392121,0.0002959416,0.0003014866,0.0006611859,0.00067508256,0.0024687967,0.00073895295,0.00055309065,0.024226934],"category_scores_gemma":[0.0051918332,0.0001563761,0.00041667282,0.00067363883,0.00033838517,0.0016257842,0.0025373588,0.0008345668,0.0046682414],"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":[0.0008397635,0.0031928814,0.04697428,0.0013833545,0.000034138906,0.0010769755,0.011950964,0.00046590983,0.009900551,0.0027844957,0.0664498,0.854947],"study_design_scores_gemma":[0.00074707274,0.0074595613,0.2345414,0.0015970188,0.0002303436,0.0051104696,0.03228023,0.015247335,0.030653177,0.007377381,0.66440177,0.0003542595],"about_ca_topic_score_codex":0.00063044496,"about_ca_topic_score_gemma":0.00092456175,"teacher_disagreement_score":0.024226934,"about_ca_system_score_codex":0.00039319418,"about_ca_system_score_gemma":0.0016035249,"threshold_uncertainty_score":0.08104718},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6815568,0.0016813914,0.2968986,0.0010471097,0.00067092513,0.0002733167,0.00206236,0.009942463,0.0058670514],"genre_scores_gemma":[0.94402343,0.00020445013,0.050608028,0.00021931162,0.000076548546,0.00006542701,0.0033796697,0.00014158715,0.0012815375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99893683,0.0004110823,0.000053114174,0.00027168,0.00020198386,0.00012533703],"domain_scores_gemma":[0.9977209,0.0012439283,0.00013445724,0.00028391287,0.00049378065,0.00012301632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024913042,0.0014304476,0.0007925331,0.0009300617,0.00030283423,0.0007373878,0.0013526542,0.00080621924,0.0007382115],"category_scores_gemma":[0.0053208596,0.00019628293,0.0005770634,0.0006898682,0.0005368597,0.001037868,0.00071151677,0.0014302792,0.0004727316],"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":[0.0009524766,0.00056609605,0.03845069,0.00028008182,0.00034134832,0.00044283547,0.00017696204,0.65874696,0.01227255,0.0021613727,0.010219382,0.27538934],"study_design_scores_gemma":[0.000009198274,0.000047342226,0.0020765157,0.0000072064768,0.000009320504,0.00004208208,0.000018519322,0.995152,0.0019222395,0.00039207083,0.000315233,0.000008230189],"about_ca_topic_score_codex":0.008066862,"about_ca_topic_score_gemma":0.010670301,"teacher_disagreement_score":0.008066862,"about_ca_system_score_codex":0.00065595296,"about_ca_system_score_gemma":0.0005112998,"threshold_uncertainty_score":0.016039789},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035080545,0.0009423647,0.9586013,0.00014083362,0.000094394636,0.000052062103,0.000012134268,0.00024320785,0.004833132],"genre_scores_gemma":[0.94860846,0.00077705533,0.0477638,0.0000623789,0.00005216388,0.00005221873,0.00003606704,0.0000120969235,0.0026358978],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998636,0.00001818776,0.000009483536,0.000037382695,0.00005490003,0.000016307726],"domain_scores_gemma":[0.99985087,0.00003818759,0.000025755618,0.000016830825,0.000055310495,0.000013092218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030869662,0.00041553003,0.00033310248,0.00032032482,0.00018949757,0.0005275765,0.0005898044,0.00028273702,0.0010645377],"category_scores_gemma":[0.0003406027,0.00015705363,0.00031424736,0.00035344128,0.00030885322,0.0005694426,0.0003554752,0.00043914746,0.00014615926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021213341,0.0002305407,0.0019609584,0.00060127856,0.00011514568,0.00033969438,0.00020744593,0.40215966,0.09932557,0.032576587,0.0015042047,0.4607667],"study_design_scores_gemma":[0.000017354434,0.00023674271,0.00046791625,0.000015438638,0.000020649468,0.000078905316,0.000017106966,0.9880386,0.0068973373,0.0019888687,0.0022120967,0.000008982333],"about_ca_topic_score_codex":0.0012327718,"about_ca_topic_score_gemma":0.00081186247,"teacher_disagreement_score":0.0012327718,"about_ca_system_score_codex":0.00018448792,"about_ca_system_score_gemma":0.00036096407,"threshold_uncertainty_score":0.0035612583},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024714075,0.0000536725,0.97401583,0.00011072012,0.000023285638,0.0000085191205,0.000026622747,0.0005629061,0.0004844813],"genre_scores_gemma":[0.8042159,0.00016083155,0.19280238,0.00009268621,0.000086723245,0.00003702454,0.00021998043,0.00021061704,0.0021738275],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992662,0.00029529192,0.000041001622,0.00013945477,0.00019206089,0.000066100125],"domain_scores_gemma":[0.9964981,0.002252051,0.00029533904,0.0005194451,0.00036538747,0.00006967885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013617908,0.00067834527,0.0007988635,0.0004847934,0.00021654178,0.00085825066,0.00073656015,0.00077187264,0.0014594982],"category_scores_gemma":[0.007828301,0.00035394894,0.0006176529,0.00055841205,0.00044534175,0.0014680723,0.0013043985,0.0014989304,0.00048804667],"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":[0.0004821463,0.0002564126,0.004633423,0.00013246783,0.00019017678,0.00017367509,0.0001805,0.5408081,0.03849314,0.044090867,0.0018831327,0.36867595],"study_design_scores_gemma":[0.0000036725567,0.000023790055,0.00025900206,0.0000017554496,0.000009229443,0.000011705739,0.0000038898615,0.99284005,0.002301966,0.0043063806,0.00023525598,0.0000033274098],"about_ca_topic_score_codex":0.0015030748,"about_ca_topic_score_gemma":0.0013693093,"teacher_disagreement_score":0.0015030748,"about_ca_system_score_codex":0.000255066,"about_ca_system_score_gemma":0.00064700533,"threshold_uncertainty_score":0.00720191},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011088441,0.03555649,0.90663207,0.009501231,0.002047442,0.00011155858,0.0011990521,0.003205976,0.030657744],"genre_scores_gemma":[0.10216808,0.0476201,0.7845884,0.0020575176,0.0060147806,0.0003972058,0.0028368046,0.004509378,0.04980775],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99218917,0.0028787765,0.00045830142,0.0015904398,0.0026302051,0.00025318319],"domain_scores_gemma":[0.97566414,0.011845747,0.0008282244,0.004276799,0.0067608245,0.0006241936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015220943,0.0017122965,0.0018025648,0.0088057965,0.0009039418,0.0077288067,0.0031038842,0.0023637153,0.014742362],"category_scores_gemma":[0.022692364,0.0013263482,0.0025742732,0.007889845,0.0031816668,0.00536751,0.0026512132,0.0049114055,0.009665989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021460801,0.00009027578,0.008277989,0.00118312,0.00028943017,0.000119064985,0.00052772,0.01769212,0.013023192,0.12698767,0.031380408,0.80021435],"study_design_scores_gemma":[0.00007619638,0.00028130555,0.028749825,0.0013678623,0.00022256158,0.0011183273,0.0006426903,0.19018537,0.022971129,0.08418171,0.6699217,0.00028132595],"about_ca_topic_score_codex":0.017075079,"about_ca_topic_score_gemma":0.007742341,"teacher_disagreement_score":0.017075079,"about_ca_system_score_codex":0.0024873943,"about_ca_system_score_gemma":0.002453727,"threshold_uncertainty_score":0.08049697},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4169115,0.0009706094,0.5781452,0.0015102557,0.00008771197,0.00009919883,0.00020451372,0.0008695638,0.0012014598],"genre_scores_gemma":[0.9713274,0.00010595255,0.027687367,0.00018576394,0.000041030562,0.0000402775,0.00018056273,0.00004360331,0.0003880708],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99788254,0.0010092172,0.000099781384,0.00042663474,0.00038600294,0.00019573656],"domain_scores_gemma":[0.97789985,0.017966984,0.0012048617,0.00094524334,0.0014612123,0.0005217708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008636634,0.0013436236,0.0010541937,0.00090206275,0.00052498706,0.0010432219,0.0015983431,0.0015605808,0.0008410167],"category_scores_gemma":[0.0331188,0.00057425525,0.00046204365,0.0004471466,0.0014804644,0.0020873088,0.00208585,0.0021970868,0.00012871332],"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":[0.00020872778,0.00007013509,0.008260443,0.000044845976,0.00007325875,0.00005896917,0.00006937123,0.95491505,0.00095502735,0.0017004886,0.00051002164,0.0331337],"study_design_scores_gemma":[0.0000033469692,0.00002787148,0.00027784688,0.0000035205226,0.0000036980327,0.000006851397,0.0000054032002,0.9970318,0.00036073005,0.0022351048,0.000040992225,0.000002914013],"about_ca_topic_score_codex":0.0075751795,"about_ca_topic_score_gemma":0.007928541,"teacher_disagreement_score":0.008636634,"about_ca_system_score_codex":0.0017612015,"about_ca_system_score_gemma":0.0012220389,"threshold_uncertainty_score":0.045675457},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014007059,0.00009450708,0.98486775,0.00015723826,0.000030856136,0.000022511562,0.000029508632,0.00035365185,0.00043696357],"genre_scores_gemma":[0.4976764,0.00017819714,0.49929872,0.0001557918,0.0000762204,0.0000910233,0.00015869553,0.00027917832,0.002085757],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931955,0.00021985706,0.00004343539,0.0001291448,0.00022189038,0.00006605394],"domain_scores_gemma":[0.99766785,0.0012153893,0.00021386039,0.00046732082,0.00032465215,0.00011089897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014817188,0.0005470184,0.0007417515,0.0007380339,0.0006090849,0.0007795952,0.0016301692,0.0007508454,0.001748157],"category_scores_gemma":[0.006065511,0.000361986,0.0005988654,0.0008532928,0.0012142863,0.0018011592,0.0014510854,0.0018750625,0.00032823798],"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":[0.00024800468,0.00011899349,0.003342125,0.00011396541,0.00012816861,0.00016985525,0.00023108213,0.6288657,0.027131652,0.15909748,0.0017566538,0.1787963],"study_design_scores_gemma":[0.0000026074208,0.000008498168,0.00004962862,0.0000012845484,0.0000014299156,0.000006817564,0.0000032743083,0.989042,0.0009805813,0.00968835,0.00021154864,0.000004015073],"about_ca_topic_score_codex":0.004777008,"about_ca_topic_score_gemma":0.00450296,"teacher_disagreement_score":0.004777008,"about_ca_system_score_codex":0.00087060756,"about_ca_system_score_gemma":0.0010044933,"threshold_uncertainty_score":0.009498417},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019749673,0.00039843353,0.9776055,0.00030272256,0.000058981637,0.000051862473,0.00016599489,0.0010058507,0.0006610328],"genre_scores_gemma":[0.6031718,0.000590417,0.39185217,0.0003462249,0.00021304256,0.00022818692,0.0012670459,0.00030655434,0.0020245672],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964198,0.0010917208,0.00022127795,0.0008942642,0.0011717415,0.00020119458],"domain_scores_gemma":[0.99341935,0.003484201,0.0008054646,0.00082694227,0.0012716105,0.00019242823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002440316,0.0013295537,0.0015493769,0.0027162163,0.00067300996,0.002190015,0.0024692477,0.0013725824,0.0005138448],"category_scores_gemma":[0.010504236,0.0005254118,0.0009500761,0.002914385,0.0013268694,0.0026789338,0.0019705915,0.0024721199,0.0005168912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002919545,0.00035231566,0.016236259,0.00035189372,0.0003981858,0.00024075474,0.00046756415,0.45821384,0.0070251473,0.0228733,0.006869767,0.48667908],"study_design_scores_gemma":[0.000005056194,0.00003740371,0.0011732265,0.000011812882,0.000009495529,0.00006839286,0.00005547281,0.9800228,0.0013704875,0.016097996,0.0011295023,0.000018315632],"about_ca_topic_score_codex":0.0073563135,"about_ca_topic_score_gemma":0.006303486,"teacher_disagreement_score":0.0073563135,"about_ca_system_score_codex":0.00133982,"about_ca_system_score_gemma":0.0016067887,"threshold_uncertainty_score":0.01462698},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.085598074,0.00022428796,0.90978086,0.00014930031,0.000094078234,0.00003942803,0.0001846104,0.0024066751,0.0015227338],"genre_scores_gemma":[0.82701176,0.00020923285,0.16900544,0.0000684305,0.000097415395,0.00006091085,0.0006631256,0.00011646927,0.0027671189],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991405,0.00012538195,0.00003796977,0.00017960991,0.00036835112,0.00014807556],"domain_scores_gemma":[0.9986633,0.0004565592,0.00017479576,0.0002633906,0.00040344463,0.000038467024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067882484,0.0007546901,0.00064258446,0.0017593799,0.0005320563,0.0008244877,0.001174354,0.000775657,0.0007288206],"category_scores_gemma":[0.0030303549,0.0002459372,0.0005644673,0.0011050076,0.00040652312,0.0012861884,0.0007020269,0.0010832569,0.00048318817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000518201,0.00035440194,0.035792664,0.00013582253,0.00026040064,0.00052614236,0.00021195188,0.15918918,0.099846065,0.010795916,0.0052706525,0.6870986],"study_design_scores_gemma":[0.0000060313223,0.000051507595,0.005097329,0.000007687028,0.000024576824,0.0002663982,0.000030276286,0.9759951,0.013344513,0.0038126132,0.0013485374,0.000015323449],"about_ca_topic_score_codex":0.0040493887,"about_ca_topic_score_gemma":0.0056404886,"teacher_disagreement_score":0.0040493887,"about_ca_system_score_codex":0.0005362125,"about_ca_system_score_gemma":0.0006332896,"threshold_uncertainty_score":0.008051634},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062762066,0.00054607075,0.97699547,0.00014202404,0.000067236266,0.00012591902,0.00065551995,0.014620645,0.0005709395],"genre_scores_gemma":[0.07801684,0.00036601673,0.913531,0.0002599939,0.00008626301,0.00023804492,0.0025270432,0.001622458,0.0033522672],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987978,0.00029603977,0.000060611917,0.00024603118,0.00048175803,0.00011782592],"domain_scores_gemma":[0.99900025,0.00025393994,0.000073308365,0.0001894878,0.00041554283,0.000067425746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001758989,0.001681799,0.0020776708,0.003068709,0.00068090245,0.0014598825,0.0047097113,0.0025963392,0.0045473934],"category_scores_gemma":[0.0038876382,0.0009093778,0.0019393999,0.0021291177,0.00058722025,0.001354156,0.0024062914,0.0018769401,0.0035548208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040895186,0.00019912065,0.0010865239,0.00025779384,0.00025477272,0.00017380105,0.000093292336,0.108017005,0.014358717,0.0042900825,0.020612866,0.850247],"study_design_scores_gemma":[0.000016174512,0.000053671978,0.00033555483,0.000017693603,0.000020593661,0.00010620892,0.000016752016,0.98744917,0.006140157,0.0023371757,0.0034839944,0.000022839713],"about_ca_topic_score_codex":0.012905909,"about_ca_topic_score_gemma":0.019383958,"teacher_disagreement_score":0.012905909,"about_ca_system_score_codex":0.0008571012,"about_ca_system_score_gemma":0.0016917734,"threshold_uncertainty_score":0.025661528},"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":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5199288,0.001618991,0.4716868,0.0018361394,0.00014778713,0.0009684951,0.0012055221,0.00054442324,0.0020630849],"genre_scores_gemma":[0.93633515,0.00031825778,0.06112928,0.00024020679,0.000029413113,0.0005069305,0.0004956591,0.00006819366,0.00087678246],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98890567,0.009326578,0.0002305556,0.0008740558,0.00037540027,0.00028772574],"domain_scores_gemma":[0.89768964,0.08884185,0.0031970567,0.0073698927,0.002290377,0.00061120966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05230466,0.0012081974,0.0015039587,0.0013577536,0.00083401275,0.0016258446,0.002059506,0.001937623,0.0035293787],"category_scores_gemma":[0.12083023,0.0005262067,0.00361387,0.0014802559,0.0014118709,0.0024053277,0.0028666912,0.0034862293,0.0002546534],"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":[0.0016400097,0.0002416871,0.049035802,0.00044528584,0.0022670953,0.0003671705,0.0007891221,0.8747821,0.00064076966,0.025734153,0.0020045012,0.04205225],"study_design_scores_gemma":[0.00013433947,0.0003833983,0.008823987,0.00014925248,0.00047135216,0.00012494366,0.00018100381,0.9546021,0.0005586449,0.033419628,0.0010884315,0.000062941894],"about_ca_topic_score_codex":0.02410712,"about_ca_topic_score_gemma":0.008952671,"teacher_disagreement_score":0.05230466,"about_ca_system_score_codex":0.0024030714,"about_ca_system_score_gemma":0.0023134614,"threshold_uncertainty_score":0.27661675},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13463704,0.0002161277,0.8620514,0.00016811075,0.000035762718,0.00005476459,0.00036530956,0.0009447312,0.0015266386],"genre_scores_gemma":[0.91146404,0.00020632448,0.086363986,0.0000409079,0.000039737948,0.00006364075,0.0003460728,0.000070961185,0.0014044193],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996785,0.00005303968,0.000017498238,0.000101192716,0.000100177116,0.000049574814],"domain_scores_gemma":[0.9994209,0.00015836608,0.00012843333,0.00008302891,0.00016442186,0.000044841592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034938214,0.00058686885,0.00044897656,0.0013766184,0.00016312822,0.0007414098,0.00041007594,0.00038947648,0.0010965029],"category_scores_gemma":[0.00209005,0.0001211673,0.0005050467,0.0008317703,0.00037805614,0.0007466182,0.0005974299,0.0004193224,0.00025360307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010244112,0.00029969847,0.02649451,0.00016694944,0.00012217404,0.0008433981,0.00048656162,0.0469545,0.22319037,0.009792714,0.0024248727,0.6881998],"study_design_scores_gemma":[0.000011829019,0.00016861859,0.02654565,0.000019992955,0.00006845828,0.0005016141,0.00014607649,0.9406796,0.022457788,0.008307308,0.001061427,0.000031650903],"about_ca_topic_score_codex":0.0023791625,"about_ca_topic_score_gemma":0.0018907071,"teacher_disagreement_score":0.0023791625,"about_ca_system_score_codex":0.000273089,"about_ca_system_score_gemma":0.0003951141,"threshold_uncertainty_score":0.004730642},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15862356,0.00060467457,0.83806497,0.00027938077,0.00007222552,0.000039000028,0.00017890979,0.0013010274,0.00083620107],"genre_scores_gemma":[0.8555104,0.0002856505,0.14230365,0.000105701074,0.000051727457,0.000026565784,0.00029538348,0.000052724656,0.0013681964],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959606,0.0000663696,0.000023374389,0.00009461529,0.00012570593,0.00009387021],"domain_scores_gemma":[0.9990282,0.0003890208,0.00012164322,0.00009946967,0.00030939013,0.000052254323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062918384,0.000546101,0.00074582105,0.0010400629,0.00022765856,0.00066136866,0.0007556663,0.000692192,0.00050476973],"category_scores_gemma":[0.0021000507,0.00023368375,0.00058363134,0.0007431237,0.00026365378,0.00085189915,0.0007358743,0.0008324387,0.00025681857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040084973,0.0004591476,0.008832337,0.00011731204,0.00008584745,0.00016339889,0.000073896386,0.08639532,0.07801316,0.0020226217,0.0028444733,0.8205916],"study_design_scores_gemma":[0.0000037363238,0.00005557068,0.0017438745,0.00000441777,0.000010979289,0.0000572839,0.000009044995,0.9877528,0.009106917,0.001000621,0.00024960976,0.0000052314335],"about_ca_topic_score_codex":0.004301107,"about_ca_topic_score_gemma":0.0047256076,"teacher_disagreement_score":0.004301107,"about_ca_system_score_codex":0.00041207115,"about_ca_system_score_gemma":0.00052788405,"threshold_uncertainty_score":0.008552134},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014750644,0.00025964834,0.97708136,0.00023306644,0.00009962212,0.000072178314,0.0003676647,0.0053667077,0.0017690546],"genre_scores_gemma":[0.50381947,0.00023369066,0.48399642,0.00075055443,0.0001561901,0.00026622397,0.0029847678,0.0009284591,0.0068641603],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99869555,0.00033177107,0.000037082526,0.00045008396,0.00034233008,0.00014336749],"domain_scores_gemma":[0.99851686,0.0005599668,0.000118592165,0.00029402607,0.00043006428,0.00008054478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017773126,0.0012213669,0.0007513934,0.0013682958,0.0007347402,0.0009414456,0.0025240807,0.0012877887,0.0025267187],"category_scores_gemma":[0.0038967838,0.000411201,0.000945347,0.0009071049,0.0009569397,0.0019525096,0.002952168,0.002078545,0.000738075],"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":[0.0004265673,0.00021381701,0.0037612978,0.00013613628,0.00021024398,0.00018747235,0.00019626999,0.396624,0.016658941,0.025589017,0.01760804,0.5383882],"study_design_scores_gemma":[0.0000060935954,0.000026837994,0.00027280304,0.000004606452,0.00001004369,0.00004528659,0.000012743773,0.986013,0.0023227555,0.009253864,0.002020986,0.000011057441],"about_ca_topic_score_codex":0.009721982,"about_ca_topic_score_gemma":0.013098167,"teacher_disagreement_score":0.009721982,"about_ca_system_score_codex":0.00116344,"about_ca_system_score_gemma":0.001281333,"threshold_uncertainty_score":0.0193308},"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":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010485129,0.015987089,0.96499544,0.0007581999,0.00017273985,0.000092152004,0.00046740886,0.00244809,0.0045938296],"genre_scores_gemma":[0.6340504,0.033714544,0.315291,0.00079729117,0.000554846,0.00031397512,0.0034986811,0.0009802104,0.010798986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"systematic_review","domain_scores_codex":[0.9991048,0.00023009501,0.00008615169,0.00023220133,0.0002640829,0.00008269726],"domain_scores_gemma":[0.99871814,0.00067589397,0.0000752431,0.00016340055,0.00032879633,0.00003845894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017807287,0.0017108854,0.001221862,0.0014771353,0.00034645404,0.0016688346,0.002687795,0.0011455428,0.002467754],"category_scores_gemma":[0.004320534,0.00087253633,0.0016989415,0.0015879333,0.00059769646,0.0026818325,0.0011839399,0.0019471063,0.0010567527],"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":[0.00019654866,0.000110830464,0.0027955985,0.0006050255,0.00023880527,0.000099373174,0.000113530594,0.5019461,0.0027650215,0.023976577,0.007275076,0.4598775],"study_design_scores_gemma":[0.0000035436692,0.0000443783,0.00023968316,0.00004278052,0.00003097159,0.000047423993,0.000011519306,0.98941773,0.00095520256,0.006006543,0.0031889954,0.0000112059415],"about_ca_topic_score_codex":0.013432589,"about_ca_topic_score_gemma":0.010567487,"teacher_disagreement_score":0.013432589,"about_ca_system_score_codex":0.001904177,"about_ca_system_score_gemma":0.001472782,"threshold_uncertainty_score":0.026708841},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056341738,0.0004061531,0.9379936,0.00017861782,0.0000799377,0.000060723105,0.00011619769,0.003929954,0.0008930491],"genre_scores_gemma":[0.79064244,0.00020457323,0.20680149,0.00011218918,0.000036135883,0.000057446166,0.00030985658,0.00017735989,0.0016585215],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994018,0.00011674119,0.000047468246,0.00015657496,0.0001794639,0.00009805557],"domain_scores_gemma":[0.9984561,0.00066780834,0.00016032826,0.00035526173,0.00027490096,0.000085495856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011338454,0.0005580266,0.00064108305,0.00070630433,0.00047774776,0.0009387137,0.0021841596,0.00054538366,0.0015202659],"category_scores_gemma":[0.004463432,0.00027498035,0.0003274922,0.0007464283,0.00064008817,0.0024685077,0.0009939986,0.00084181176,0.00055233337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011203956,0.00021838541,0.0057034316,0.0002338442,0.000064313535,0.00044205043,0.000242484,0.17638607,0.05388837,0.024347562,0.009592236,0.727761],"study_design_scores_gemma":[0.000025641972,0.00010013612,0.00060293305,0.000011865906,0.00002516854,0.00020514448,0.00005139472,0.9469287,0.033663742,0.014904163,0.0034661624,0.00001502898],"about_ca_topic_score_codex":0.0039224434,"about_ca_topic_score_gemma":0.0068886494,"teacher_disagreement_score":0.0039224434,"about_ca_system_score_codex":0.00094234163,"about_ca_system_score_gemma":0.0015170801,"threshold_uncertainty_score":0.007799208},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009746468,0.00009702037,0.9870955,0.00009338601,0.000076021475,0.000029686686,0.00006464423,0.0021166292,0.00068070256],"genre_scores_gemma":[0.33120516,0.00014701979,0.6609031,0.00018559078,0.000119395685,0.00011553645,0.00067673787,0.00029024883,0.006357115],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984282,0.00013331552,0.00009834833,0.00029616995,0.00089095155,0.00015302609],"domain_scores_gemma":[0.99820626,0.00022968932,0.00018523891,0.0003662065,0.0009302516,0.00008221816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009856753,0.00088420056,0.0014470187,0.0014723676,0.00052630674,0.0011699854,0.0019827934,0.0012903885,0.0023241055],"category_scores_gemma":[0.0038645822,0.00035488335,0.0009023546,0.0012732653,0.0005401698,0.0015556797,0.0019639912,0.00145502,0.0017667842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069910905,0.00032050503,0.0054736156,0.00016033943,0.00015047664,0.00046855325,0.00014984312,0.07501767,0.10959482,0.007333345,0.0067258435,0.793906],"study_design_scores_gemma":[0.000026966718,0.0001790314,0.0015182262,0.00001092063,0.000029357923,0.00045334094,0.000041818847,0.94179505,0.047700543,0.0025536336,0.0056513757,0.000039758714],"about_ca_topic_score_codex":0.0011759144,"about_ca_topic_score_gemma":0.001718545,"teacher_disagreement_score":0.0023241055,"about_ca_system_score_codex":0.00033085412,"about_ca_system_score_gemma":0.0007510726,"threshold_uncertainty_score":0.0077748895},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38507596,0.0017065173,0.6063415,0.0008495795,0.000242203,0.000045778346,0.0012179684,0.0027664264,0.0017540451],"genre_scores_gemma":[0.95939726,0.00032082357,0.036442775,0.000084678606,0.0000574672,0.000025616915,0.0012280317,0.000051515577,0.002391962],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969447,0.0000523853,0.000022525266,0.00008146453,0.0000827422,0.000066451765],"domain_scores_gemma":[0.99894625,0.0005281741,0.00012527846,0.000111178706,0.0002456869,0.00004344895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079593086,0.00074798666,0.0005639322,0.0009079298,0.0002401551,0.00059826125,0.00092390215,0.0007633057,0.0008137177],"category_scores_gemma":[0.0024860674,0.0003595652,0.0005462502,0.0008969457,0.0002709298,0.0009012269,0.0006143135,0.0017440626,0.0003107835],"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":[0.00025838419,0.00033937246,0.017890135,0.00006917901,0.000121253375,0.00010847037,0.000054129592,0.69172233,0.005749232,0.0035117199,0.0044885473,0.27568728],"study_design_scores_gemma":[0.0000013417805,0.000008507824,0.0005027655,0.0000020647096,0.0000033221868,0.0000051779534,0.0000025211993,0.9981839,0.00038057452,0.00081069843,0.00009745234,0.0000016805334],"about_ca_topic_score_codex":0.014668189,"about_ca_topic_score_gemma":0.015910266,"teacher_disagreement_score":0.014668189,"about_ca_system_score_codex":0.0007671864,"about_ca_system_score_gemma":0.0007979802,"threshold_uncertainty_score":0.029165626},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6094134,0.007047898,0.3784197,0.0005671646,0.00011216396,0.00009549474,0.00019387108,0.00070060237,0.0034497755],"genre_scores_gemma":[0.9460765,0.0014478702,0.051544443,0.000033974982,0.00006353607,0.000018527437,0.00023570585,0.0000624864,0.0005169894],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976326,0.0008431741,0.00016867551,0.00037076775,0.0008675615,0.000117157],"domain_scores_gemma":[0.9756884,0.018673936,0.0014710095,0.0010757514,0.002860994,0.00022988721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054278416,0.00063818187,0.0010449169,0.0037622275,0.00046903262,0.0014623022,0.0011659547,0.0010637393,0.00057175424],"category_scores_gemma":[0.020054605,0.00022518412,0.0007107716,0.004124358,0.0006889484,0.0030074783,0.0005271198,0.00073828426,0.00014539337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015909282,0.0005459709,0.080003954,0.0007418515,0.00087674713,0.0003450368,0.001037455,0.23707582,0.01069104,0.017781356,0.0012823902,0.6480275],"study_design_scores_gemma":[0.000014749539,0.00021993804,0.022293469,0.000036373855,0.00014223634,0.00027186738,0.00030149968,0.96699375,0.0026912754,0.0063706464,0.00063538546,0.000028878498],"about_ca_topic_score_codex":0.007441058,"about_ca_topic_score_gemma":0.0046833516,"teacher_disagreement_score":0.007441058,"about_ca_system_score_codex":0.0010118975,"about_ca_system_score_gemma":0.00075788493,"threshold_uncertainty_score":0.028705478},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040047397,0.590655,0.39449373,0.0020517549,0.001232251,0.00009821607,0.00031754037,0.00082960475,0.0063171713],"genre_scores_gemma":[0.0856474,0.7216337,0.17932059,0.0018139469,0.0041687866,0.00022236124,0.0016748386,0.00028677483,0.005231648],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99857545,0.00025460316,0.00015894581,0.000396231,0.00055722886,0.000057514884],"domain_scores_gemma":[0.9965245,0.0018312464,0.0002720894,0.00022923392,0.001073838,0.00006920744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001883418,0.0012492744,0.0016254715,0.0028590858,0.00029655395,0.0018662909,0.0020631156,0.0013636481,0.0016626464],"category_scores_gemma":[0.0069974214,0.00062541256,0.0011138386,0.0034223283,0.0006868872,0.0029362666,0.0008052744,0.0015883831,0.0015426456],"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":[0.000087010194,0.0000566822,0.0011785475,0.0042923028,0.00018731163,0.00012910095,0.00007595108,0.007962111,0.0023592687,0.014006682,0.015472826,0.9541923],"study_design_scores_gemma":[0.000075993354,0.0006780827,0.0069867363,0.0050688284,0.0008218193,0.0039773057,0.000308654,0.24006376,0.018009499,0.07999304,0.6435944,0.0004218559],"about_ca_topic_score_codex":0.0012018386,"about_ca_topic_score_gemma":0.00077996374,"teacher_disagreement_score":0.0028590858,"about_ca_system_score_codex":0.00062827393,"about_ca_system_score_gemma":0.0010175951,"threshold_uncertainty_score":0.009960592},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13591659,0.002592106,0.85065037,0.0015759243,0.00027178496,0.000097389595,0.0008124462,0.0019461478,0.0061371964],"genre_scores_gemma":[0.92141235,0.00076083094,0.07041612,0.000444413,0.00014925418,0.000079142905,0.00094577466,0.00007286579,0.0057191597],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970216,0.00007973603,0.0000122702495,0.000102395,0.000056220793,0.000047208257],"domain_scores_gemma":[0.9993217,0.00037650822,0.0000636062,0.00004437631,0.00015895965,0.00003490069],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060867605,0.0007754047,0.00056878186,0.0010976169,0.00036206815,0.00058527297,0.0011516288,0.0010555972,0.0017545694],"category_scores_gemma":[0.0024415522,0.00028491236,0.0005597051,0.00094908284,0.00042895263,0.0011975294,0.0007301185,0.0011226508,0.00032044583],"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":[0.00029839322,0.0001947348,0.0063062855,0.00013126397,0.00015404541,0.0002469796,0.000102858074,0.67266154,0.0075768116,0.013702593,0.008207767,0.29041672],"study_design_scores_gemma":[0.0000023995035,0.000011539572,0.00033559214,0.0000032240232,0.000009740564,0.00001747874,0.000004010923,0.99643373,0.0003252075,0.0025927643,0.00026090615,0.0000034289026],"about_ca_topic_score_codex":0.014866812,"about_ca_topic_score_gemma":0.016894674,"teacher_disagreement_score":0.014866812,"about_ca_system_score_codex":0.0012183901,"about_ca_system_score_gemma":0.0006744524,"threshold_uncertainty_score":0.029560506},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051907692,0.02027288,0.9214505,0.0007815677,0.00012752312,0.0010334013,0.00024124932,0.0010347688,0.0031504869],"genre_scores_gemma":[0.15328145,0.006647287,0.83771914,0.00022451964,0.0000928272,0.00061501516,0.00057693047,0.00023983167,0.00060300523],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.98358583,0.008728096,0.0014876997,0.0020529209,0.0038774023,0.00026800347],"domain_scores_gemma":[0.93696266,0.04018518,0.0031215765,0.006983796,0.012441803,0.0003050996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02880421,0.0018147747,0.0016449639,0.0053574587,0.00090957317,0.0029751712,0.002254896,0.0010217966,0.00085471565],"category_scores_gemma":[0.065282606,0.0008547275,0.0021403765,0.0039410843,0.0013046048,0.005124237,0.002019257,0.002540618,0.00037976558],"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":[0.00017250654,0.0007867517,0.010636516,0.002331412,0.0005060149,0.00008837921,0.00040449368,0.03107795,0.011572296,0.011029291,0.0011354751,0.9302589],"study_design_scores_gemma":[0.0004255163,0.0093461145,0.036152143,0.008155026,0.001573356,0.0021190785,0.001724339,0.59891224,0.19770303,0.045070454,0.09818136,0.000637283],"about_ca_topic_score_codex":0.0019652515,"about_ca_topic_score_gemma":0.0020489788,"teacher_disagreement_score":0.02880421,"about_ca_system_score_codex":0.0013920107,"about_ca_system_score_gemma":0.003846351,"threshold_uncertainty_score":0.15233308},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7920764,0.0023258014,0.1959255,0.0005119931,0.0001856774,0.0002768206,0.00048516708,0.003928089,0.004284569],"genre_scores_gemma":[0.8952323,0.00056409294,0.10218674,0.000060841907,0.000035719393,0.00007312234,0.00092465593,0.00013776025,0.000784758],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99696034,0.0011084522,0.0002487854,0.00062909944,0.0008268303,0.00022655538],"domain_scores_gemma":[0.9944989,0.0026867168,0.0003246915,0.0007857179,0.0015194687,0.00018446824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045175445,0.0011471994,0.00090562855,0.0028614565,0.00070079527,0.0012263112,0.0013496097,0.0010659904,0.00071117136],"category_scores_gemma":[0.009300206,0.00025653982,0.00077314215,0.0021953657,0.00050097343,0.0015618957,0.0011489185,0.00069624156,0.0005376706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019153864,0.00070898107,0.031385664,0.00042741353,0.00097082806,0.00010662343,0.0004581076,0.4382281,0.013935757,0.0028072558,0.0040537096,0.50500214],"study_design_scores_gemma":[0.00003932129,0.00045599515,0.010424577,0.000020616846,0.00008538164,0.00011801726,0.00024129746,0.9771063,0.009447966,0.0011250435,0.00088730163,0.000048133],"about_ca_topic_score_codex":0.01119044,"about_ca_topic_score_gemma":0.00868693,"teacher_disagreement_score":0.01119044,"about_ca_system_score_codex":0.001258173,"about_ca_system_score_gemma":0.0009599906,"threshold_uncertainty_score":0.02389133},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058393102,0.00068497553,0.9382475,0.00048473332,0.00008883147,0.000028927776,0.00014822715,0.0011459148,0.0007778629],"genre_scores_gemma":[0.8857786,0.00037059237,0.11092595,0.0002301697,0.00006373941,0.00006651608,0.00023905755,0.000053682255,0.002271651],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996778,0.00007432352,0.000022786713,0.00009237944,0.0000787461,0.00005403662],"domain_scores_gemma":[0.99936193,0.00031717154,0.000086413835,0.000052162366,0.00014798639,0.00003444695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008487736,0.00053507957,0.000491226,0.00048720324,0.00017162913,0.0004812316,0.0007932051,0.00080102036,0.00079998415],"category_scores_gemma":[0.002190809,0.0003006413,0.0004118646,0.00057686324,0.00032896857,0.00083396584,0.00068222685,0.0011001052,0.00020707933],"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":[0.00034470088,0.0002735552,0.0072610253,0.00019157528,0.00014836912,0.0002975843,0.00015954796,0.51955473,0.024437731,0.0058558024,0.0031048532,0.43837047],"study_design_scores_gemma":[0.0000022600293,0.000027861886,0.00046910744,0.0000054337497,0.0000065323757,0.000028134824,0.00000637308,0.995148,0.0019943274,0.0020541504,0.00025392292,0.0000038686953],"about_ca_topic_score_codex":0.0037848353,"about_ca_topic_score_gemma":0.004266058,"teacher_disagreement_score":0.0037848353,"about_ca_system_score_codex":0.0007617569,"about_ca_system_score_gemma":0.0007392453,"threshold_uncertainty_score":0.007525623},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035944294,0.00027204934,0.951937,0.00023690282,0.00016521363,0.00007010057,0.0003753989,0.010586592,0.00041248908],"genre_scores_gemma":[0.6118739,0.0002887382,0.38231507,0.00041616263,0.0001545158,0.0002016954,0.0017925607,0.000554551,0.0024028427],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985146,0.00036405763,0.00012838014,0.0004365678,0.00043646398,0.0001198998],"domain_scores_gemma":[0.996612,0.0015781726,0.0004435513,0.00063991797,0.00055309513,0.00017335975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020640544,0.0014898294,0.0009822976,0.0011481134,0.00030770348,0.0009985903,0.0012861094,0.0011093825,0.0018922861],"category_scores_gemma":[0.010224321,0.00031221344,0.00089274487,0.0010170436,0.0007009155,0.0029806925,0.0019224507,0.0027084735,0.0013462931],"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":[0.00084455794,0.00058874243,0.007552396,0.00024703945,0.00015217223,0.00057119277,0.00033242526,0.16493072,0.047553074,0.00717993,0.009303024,0.7607448],"study_design_scores_gemma":[0.000015406404,0.0001770062,0.0010759261,0.000009597442,0.000010408173,0.0001234787,0.000036865105,0.9783942,0.009421363,0.009563144,0.0011469492,0.000025740854],"about_ca_topic_score_codex":0.0011805727,"about_ca_topic_score_gemma":0.0015117386,"teacher_disagreement_score":0.0020640544,"about_ca_system_score_codex":0.00045713614,"about_ca_system_score_gemma":0.00083835877,"threshold_uncertainty_score":0.010915875},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50484663,0.0002682908,0.49161223,0.00010008162,0.000037577163,0.000041170828,0.00008280844,0.001654307,0.0013569029],"genre_scores_gemma":[0.91780996,0.00008738097,0.080768,0.000029702898,0.00001061335,0.0000151904715,0.00017159407,0.000034372897,0.0010732417],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975437,0.000027478274,0.0000120196155,0.000078391866,0.00008914642,0.00003856859],"domain_scores_gemma":[0.99959487,0.00010844693,0.00007488604,0.000042123997,0.00015908456,0.000020656758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003642463,0.0004965857,0.0005195242,0.0015484006,0.00018915531,0.00053050293,0.0006793394,0.00071609,0.00045723293],"category_scores_gemma":[0.00064910995,0.00019816379,0.0004335141,0.0005714178,0.00036185817,0.00070664764,0.00031031214,0.00031488074,0.00016050725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048079953,0.00028039384,0.018329706,0.00014462332,0.00011277742,0.00045596348,0.00016236202,0.29162484,0.15596852,0.0015436759,0.002193963,0.5287023],"study_design_scores_gemma":[0.0000029092773,0.000041959433,0.004311556,0.0000045325346,0.000009787446,0.00005326458,0.000023415894,0.98262286,0.012397018,0.00033322794,0.00019241478,0.0000070122983],"about_ca_topic_score_codex":0.004132559,"about_ca_topic_score_gemma":0.005346549,"teacher_disagreement_score":0.004132559,"about_ca_system_score_codex":0.00044358807,"about_ca_system_score_gemma":0.00040909354,"threshold_uncertainty_score":0.008217037},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014361571,0.00028963908,0.9794708,0.00052474556,0.00014305995,0.00008698541,0.00019776417,0.0015950932,0.0033303],"genre_scores_gemma":[0.42312926,0.00057227927,0.56574214,0.0007560544,0.00014382708,0.00027391317,0.0011389216,0.0002778907,0.007965856],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997346,0.000068251364,0.000015438634,0.00005097721,0.00010019182,0.00003046785],"domain_scores_gemma":[0.9995332,0.00015279348,0.000028691526,0.00009310188,0.00015411114,0.00003821148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006777508,0.00042104215,0.0004103661,0.0002689176,0.00030123262,0.00066464837,0.0009700805,0.0007863644,0.005701862],"category_scores_gemma":[0.0020077636,0.00017831312,0.0004558153,0.00034187257,0.00037952926,0.00084376324,0.0008097293,0.0010394121,0.0013534114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006491634,0.00024793553,0.0033063588,0.00025486806,0.00013936494,0.0002955684,0.00013659812,0.38567862,0.07865937,0.024482392,0.02645449,0.4796953],"study_design_scores_gemma":[0.0000070787332,0.00003789853,0.0003620632,0.0000062607073,0.000005233267,0.00004413517,0.000011724026,0.9856878,0.008480967,0.0023850491,0.0029639194,0.0000078014355],"about_ca_topic_score_codex":0.0048988275,"about_ca_topic_score_gemma":0.005325432,"teacher_disagreement_score":0.005701862,"about_ca_system_score_codex":0.0005027199,"about_ca_system_score_gemma":0.0006988262,"threshold_uncertainty_score":0.019074678},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01973877,0.00018230229,0.9757159,0.0002653353,0.000047916128,0.00006061246,0.00007571429,0.00321698,0.00069653126],"genre_scores_gemma":[0.55843186,0.00038472633,0.43593428,0.00040627026,0.00014509406,0.00022049049,0.00076765736,0.0008903877,0.002819163],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838495,0.0004513192,0.00008043344,0.00041469603,0.0005205369,0.00014803684],"domain_scores_gemma":[0.9930248,0.0035315354,0.0007258132,0.0015081814,0.0010082134,0.00020146035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037069588,0.0010442492,0.0007875875,0.001892586,0.00086629216,0.0019372285,0.0020317575,0.0012750723,0.0014566275],"category_scores_gemma":[0.019452108,0.00056515745,0.00070329953,0.0010522372,0.001939531,0.0038104989,0.002692796,0.0031031778,0.0008973569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026260305,0.00029975225,0.014221034,0.00012892086,0.00008955279,0.0002495704,0.00049693533,0.24505645,0.01844094,0.035566803,0.008566884,0.67662054],"study_design_scores_gemma":[0.000007665673,0.000037395956,0.0010367349,0.000019106179,0.0000062737718,0.0000941138,0.00003938854,0.96420217,0.00994633,0.02214343,0.0024471672,0.000020161759],"about_ca_topic_score_codex":0.005196581,"about_ca_topic_score_gemma":0.0049977084,"teacher_disagreement_score":0.005196581,"about_ca_system_score_codex":0.0018715113,"about_ca_system_score_gemma":0.0018265367,"threshold_uncertainty_score":0.019604504},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025810178,0.0002865686,0.97220874,0.00015948103,0.000038932925,0.000018930814,0.00014420504,0.00077729486,0.000555683],"genre_scores_gemma":[0.6443605,0.0007499155,0.35032442,0.00012260494,0.000110253095,0.000068358866,0.001138641,0.00029472946,0.0028306523],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994661,0.00016327827,0.00003066018,0.0001555509,0.00013348974,0.000051004266],"domain_scores_gemma":[0.9984648,0.00088723295,0.00016410125,0.0001789248,0.00025580663,0.00004908982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009792205,0.0007267676,0.00055416103,0.0009860073,0.00018108592,0.00071697234,0.0008294114,0.00045872902,0.0009900914],"category_scores_gemma":[0.0040592817,0.00024444362,0.00085927837,0.0009945376,0.00044166303,0.0017094575,0.0007651807,0.0015018437,0.00047263046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024054864,0.00014628243,0.0051159724,0.00013017083,0.00016368706,0.00013582951,0.00016095855,0.40718278,0.018469818,0.022352401,0.003000638,0.542901],"study_design_scores_gemma":[0.0000017995247,0.0000147264045,0.00034937603,0.0000037416683,0.0000063282655,0.000026885573,0.0000074887803,0.99413824,0.0017874708,0.0033255373,0.00033373642,0.0000046439463],"about_ca_topic_score_codex":0.004108347,"about_ca_topic_score_gemma":0.0042965515,"teacher_disagreement_score":0.004108347,"about_ca_system_score_codex":0.00053039903,"about_ca_system_score_gemma":0.00062772416,"threshold_uncertainty_score":0.008168876},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14370476,0.00052415935,0.85053396,0.000776445,0.000055715944,0.00007153543,0.0002278931,0.0019346514,0.002170858],"genre_scores_gemma":[0.94688356,0.00025076646,0.05090097,0.00019788889,0.00006688869,0.000044846685,0.00035271927,0.000072006034,0.0012303201],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935895,0.00014855877,0.000028006507,0.00021455475,0.00017113365,0.000078870384],"domain_scores_gemma":[0.99763453,0.0011688832,0.00042938726,0.0003013109,0.00034563837,0.00012026539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091872347,0.00079486065,0.0007330208,0.002259344,0.00046356834,0.00086904474,0.0017180895,0.0010483724,0.0008723006],"category_scores_gemma":[0.0057381466,0.00029987554,0.00065699895,0.0013112569,0.0010690261,0.0028410598,0.0016715492,0.0012802948,0.0002283257],"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":[0.00018979669,0.00014125007,0.012692134,0.00012007143,0.00012522406,0.00019811312,0.00019319836,0.6915591,0.009730795,0.024241043,0.0033751656,0.2574342],"study_design_scores_gemma":[0.0000041831177,0.00002433068,0.0008358047,0.0000047285257,0.000013788127,0.00004075446,0.000015319316,0.97777563,0.0011939933,0.01971258,0.0003730978,0.0000057342886],"about_ca_topic_score_codex":0.0067172856,"about_ca_topic_score_gemma":0.009247034,"teacher_disagreement_score":0.0067172856,"about_ca_system_score_codex":0.0014959024,"about_ca_system_score_gemma":0.00067232497,"threshold_uncertainty_score":0.013356388},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031061832,0.00015959416,0.9636678,0.00018630852,0.00006564796,0.00003260784,0.0002584098,0.0038609817,0.00070683344],"genre_scores_gemma":[0.5130289,0.00024340578,0.4811815,0.0001598282,0.00015180667,0.00007643422,0.0010155225,0.0004350601,0.0037075912],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993911,0.00012219991,0.000044925266,0.00012393456,0.0002614413,0.000056425102],"domain_scores_gemma":[0.9985266,0.000509861,0.00019448792,0.00018908734,0.0005347457,0.000045265075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009962835,0.0007233535,0.00086890365,0.001196044,0.00029608948,0.0010645452,0.0007628887,0.00062789203,0.0028718342],"category_scores_gemma":[0.0038766505,0.00024605496,0.0005316536,0.0007479324,0.000298206,0.00063226494,0.0007220016,0.0010038988,0.00076813484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005492693,0.0001951571,0.0065646516,0.00016856872,0.00008806729,0.00037468164,0.00010781321,0.06592887,0.15125073,0.006189315,0.007134475,0.76144844],"study_design_scores_gemma":[0.000015198818,0.00008128226,0.0034854289,0.00001307807,0.00002611002,0.00015620467,0.000026775635,0.95544225,0.032937028,0.0044426317,0.003355181,0.000018805482],"about_ca_topic_score_codex":0.0011482118,"about_ca_topic_score_gemma":0.0017618749,"teacher_disagreement_score":0.0028718342,"about_ca_system_score_codex":0.0003529273,"about_ca_system_score_gemma":0.00061296864,"threshold_uncertainty_score":0.009607255},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1621037,0.0025540825,0.7984216,0.00092374,0.0004849016,0.00028054466,0.0012012314,0.014500787,0.01952929],"genre_scores_gemma":[0.8966764,0.0008983868,0.095436,0.00045326148,0.000095043746,0.00014763643,0.0010814304,0.000095393174,0.005116341],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997404,0.00003571537,0.000013009478,0.000058735666,0.00010350149,0.000048631253],"domain_scores_gemma":[0.99977535,0.000035404883,0.00003423897,0.000024008703,0.0001054894,0.000025513782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025235798,0.00049802527,0.00044379767,0.0008233281,0.000324434,0.0005679455,0.0007330386,0.00057251356,0.0011914349],"category_scores_gemma":[0.00045359498,0.00022710356,0.00034608415,0.00044589778,0.00013946788,0.0005462403,0.0005008013,0.00057520514,0.00051543984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063526334,0.00042404523,0.020202834,0.0005499737,0.00020923544,0.0008934056,0.00038329393,0.033964373,0.11835149,0.0033234544,0.03349443,0.78756815],"study_design_scores_gemma":[0.00006182384,0.00028412833,0.01672854,0.00008719366,0.00014878793,0.0006579768,0.00011477721,0.90986097,0.053444784,0.0024704044,0.016056376,0.00008432961],"about_ca_topic_score_codex":0.0058656638,"about_ca_topic_score_gemma":0.0075586326,"teacher_disagreement_score":0.0058656638,"about_ca_system_score_codex":0.00057774555,"about_ca_system_score_gemma":0.00062194443,"threshold_uncertainty_score":0.01166302},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070771193,0.00023182144,0.9892379,0.0014274914,0.000048476093,0.00009165249,0.00022204018,0.00023183049,0.0014316238],"genre_scores_gemma":[0.46550173,0.0011562126,0.52820635,0.0005934647,0.00023219852,0.0007528744,0.00069130876,0.00008068122,0.0027852678],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987109,0.00046165317,0.000106168016,0.0002867507,0.00037742942,0.00005710428],"domain_scores_gemma":[0.9967253,0.0018520629,0.00035862485,0.00029278288,0.00063233287,0.0001388714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031354409,0.0007793702,0.0005375115,0.0016895256,0.0003645548,0.0029640587,0.002236862,0.0015101683,0.0013613993],"category_scores_gemma":[0.006647659,0.00037360823,0.0008916474,0.0019119103,0.0017432426,0.0042116866,0.0011273425,0.0022161189,0.0004977813],"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":[0.00011553258,0.00021127038,0.007203842,0.00033995925,0.0001395556,0.00030397557,0.0008190994,0.3486138,0.0051712207,0.555925,0.0032993108,0.077857435],"study_design_scores_gemma":[0.000007893014,0.00006682179,0.0008249175,0.000044440716,0.000019969239,0.000100615965,0.00009838752,0.91727036,0.00055660855,0.07720678,0.0037819466,0.000021292408],"about_ca_topic_score_codex":0.0041956822,"about_ca_topic_score_gemma":0.0030578428,"teacher_disagreement_score":0.0041956822,"about_ca_system_score_codex":0.0012183082,"about_ca_system_score_gemma":0.0014005497,"threshold_uncertainty_score":0.016582012},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056602444,0.00028471975,0.9403741,0.0002303531,0.00005192485,0.00005249896,0.00006732532,0.00071387587,0.0016226997],"genre_scores_gemma":[0.7496895,0.00018684153,0.2468359,0.0000983197,0.00007564178,0.00008452392,0.00036801584,0.0001463694,0.0025149905],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986494,0.00030184968,0.00006940675,0.00029864727,0.00054461695,0.0001360334],"domain_scores_gemma":[0.99778783,0.0009488673,0.00021956992,0.00040988252,0.00052806176,0.00010572602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014779838,0.00052539323,0.0013923256,0.0015685114,0.0006847366,0.0013868687,0.0018974708,0.0014859338,0.0017196056],"category_scores_gemma":[0.0062102466,0.00039762206,0.0007549051,0.0010710591,0.00093289104,0.001871702,0.0021029618,0.0016725123,0.0005990715],"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":[0.00052454195,0.0002830515,0.005037123,0.00027144386,0.00010282043,0.00012477585,0.0002364059,0.16509244,0.04142268,0.039015118,0.004648319,0.7432412],"study_design_scores_gemma":[0.0000053069757,0.000031272764,0.0007374864,0.0000074989957,0.000008889529,0.00006551294,0.000014062629,0.9862272,0.004526985,0.007882743,0.0004851767,0.00000785079],"about_ca_topic_score_codex":0.001782378,"about_ca_topic_score_gemma":0.0018716531,"teacher_disagreement_score":0.0018974708,"about_ca_system_score_codex":0.00082187675,"about_ca_system_score_gemma":0.0008139535,"threshold_uncertainty_score":0.007816434},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021918813,0.0020822377,0.96912843,0.00038249907,0.0003278281,0.00004793773,0.00029005785,0.002475118,0.0033470895],"genre_scores_gemma":[0.6964514,0.0017623059,0.2892797,0.00018168027,0.00036298076,0.000084891064,0.00077691313,0.00027883222,0.010821216],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994449,0.00007364427,0.000026165731,0.000121942925,0.00026486826,0.000068473615],"domain_scores_gemma":[0.9991505,0.00031009302,0.00009915639,0.000104137034,0.00030256555,0.000033571458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041850636,0.0008657273,0.0009772847,0.0010888054,0.00038565713,0.0009039208,0.00141108,0.0010148943,0.0026500407],"category_scores_gemma":[0.0022636477,0.0002335227,0.0006395098,0.0012042869,0.00041849658,0.0013859306,0.0009484257,0.0014995907,0.0011405167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029106415,0.00014592991,0.005253286,0.00025915372,0.000085843574,0.0003459102,0.00013982641,0.09218943,0.031594504,0.017302778,0.013786088,0.8386063],"study_design_scores_gemma":[0.000006535371,0.0001037259,0.0023619446,0.000029880064,0.000027060167,0.00046069938,0.00008106365,0.95781416,0.011233432,0.020038597,0.007812614,0.00003026898],"about_ca_topic_score_codex":0.0016618621,"about_ca_topic_score_gemma":0.0012764619,"teacher_disagreement_score":0.0026500407,"about_ca_system_score_codex":0.0005558536,"about_ca_system_score_gemma":0.00037364013,"threshold_uncertainty_score":0.008865297},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9651851,0.0022770842,0.029946823,0.0003634003,0.00015023952,0.00004731055,0.0005822056,0.000280923,0.0011667566],"genre_scores_gemma":[0.98680776,0.000602312,0.011732099,0.00006327632,0.00007768682,0.000017234383,0.00028978367,0.000013827943,0.00039608128],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971765,0.00005635958,0.000047013455,0.00006192008,0.00007987464,0.000037186168],"domain_scores_gemma":[0.9991881,0.0003809238,0.00016236697,0.00003580009,0.00016705983,0.00006562571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054241886,0.00046988804,0.0007183473,0.0016160196,0.0002404131,0.00084465527,0.00030844452,0.00087368686,0.0005385459],"category_scores_gemma":[0.001791252,0.00023210431,0.0005639836,0.0009088269,0.00010773453,0.00033150168,0.00025087947,0.0006368564,0.00015449148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023562068,0.0003940594,0.8240706,0.00012671942,0.00027270484,0.0012394156,0.00018225977,0.0029961453,0.008997287,0.00025486975,0.0016561488,0.15745363],"study_design_scores_gemma":[0.00013387082,0.00081071915,0.82684296,0.00007148825,0.0005696082,0.0035758037,0.0003745461,0.15739845,0.007665391,0.0009807616,0.0015149132,0.00006145969],"about_ca_topic_score_codex":0.0049876883,"about_ca_topic_score_gemma":0.0031808293,"teacher_disagreement_score":0.0049876883,"about_ca_system_score_codex":0.00029329705,"about_ca_system_score_gemma":0.0003238632,"threshold_uncertainty_score":0.009917319},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3989927,0.0020745858,0.5939329,0.00018689432,0.00022326253,0.00006365597,0.00067275256,0.0008793367,0.0029739537],"genre_scores_gemma":[0.8898297,0.001165326,0.10616564,0.000050246752,0.00017637823,0.000034248646,0.00061525777,0.000069572554,0.0018935206],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998424,0.000016932381,0.000010238999,0.00003747885,0.000062437924,0.000030496778],"domain_scores_gemma":[0.99950933,0.00017138383,0.00010013581,0.00004931554,0.00012821387,0.00004162626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002505422,0.00031578835,0.00030918643,0.0014558752,0.00019037719,0.00039784086,0.00034687808,0.0003515735,0.00088926876],"category_scores_gemma":[0.0011749839,0.00010836881,0.00020434338,0.0012203476,0.00018311392,0.0005098923,0.000260311,0.0003194243,0.00030818937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015513153,0.00017982721,0.016576659,0.00031544923,0.00008839397,0.00073205016,0.00015642804,0.022573184,0.30005598,0.0045295367,0.0026000724,0.6506412],"study_design_scores_gemma":[0.000037661408,0.0003949851,0.059987865,0.00005168862,0.00010283431,0.0019474108,0.00022805741,0.80438346,0.121081695,0.005510809,0.0062288833,0.00004471767],"about_ca_topic_score_codex":0.0008678602,"about_ca_topic_score_gemma":0.0011364731,"teacher_disagreement_score":0.0014558752,"about_ca_system_score_codex":0.00019005073,"about_ca_system_score_gemma":0.00025260184,"threshold_uncertainty_score":0.0029749274},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022139775,0.00028046398,0.9756159,0.00016396712,0.00003638281,0.000028639426,0.00017470162,0.00079103064,0.00076918403],"genre_scores_gemma":[0.6952247,0.0010011945,0.28803334,0.00014751685,0.00023424391,0.00023032809,0.0030243201,0.0006331459,0.011471229],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99771726,0.000658438,0.00010660117,0.0006784281,0.00061631604,0.00022296677],"domain_scores_gemma":[0.99098444,0.0056345486,0.0008207838,0.0012513734,0.0010640823,0.00024479986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035867607,0.0011524425,0.0016048293,0.0020949694,0.00048506702,0.00250964,0.003127828,0.0016591105,0.0023848962],"category_scores_gemma":[0.02008406,0.00092235726,0.0014930796,0.0029020812,0.0012256112,0.002932062,0.002097591,0.0024218077,0.0010478252],"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":[0.00021388079,0.00013091163,0.012561812,0.00009637108,0.00021195279,0.0001851178,0.00017410153,0.6924232,0.0018548812,0.07813066,0.0042138845,0.2098032],"study_design_scores_gemma":[0.0000031582854,0.00001219933,0.00042549384,0.000005912362,0.000009669064,0.000041904037,0.00000901347,0.9724779,0.00032073818,0.026308877,0.0003788548,0.000006383137],"about_ca_topic_score_codex":0.004862752,"about_ca_topic_score_gemma":0.0058070566,"teacher_disagreement_score":0.004862752,"about_ca_system_score_codex":0.0013259541,"about_ca_system_score_gemma":0.0008643035,"threshold_uncertainty_score":0.01896882},"labels":[],"label_agreement":null}]}