{"meta":{"query_hash":"052e042a1331","filters":{"venue":"Journal of Cyber Security and Mobility"},"cohort_total":15,"direct_labels_cover":0,"predictions_cover":15,"exported":15,"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/052e042a1331","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Cyber+Security+and+Mobility"},"results":[{"id":"W2111749454","doi":"10.13052/jcsm2245-1439.321","title":"Characterizing Evaluation Practices of Intrusion Detection Methods for Smartphones","year":2014,"lang":"en","type":"article","venue":"Journal of Cyber Security and Mobility","topic":"Advanced Malware Detection Techniques","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 New Brunswick","funders":"University of Hail; LG Display","keywords":"Android (operating system); Popularity; Malware; Computer science; Computer security; Mobile malware; Mobile device; Internet privacy; Intrusion detection system; Android malware; Intrusion prevention system; Open research; World Wide Web; Operating system","score_opus":0.029876356597615634,"score_gpt":0.383975858717625,"score_spread":0.3540995021200094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111749454","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3169555,0.000118807766,0.6821942,0.00016908378,0.00031835315,0.00018981873,5.2303045e-7,0.000020005067,0.00003372166],"genre_scores_gemma":[0.7820489,0.000049706498,0.21776919,0.000039904164,0.00007494834,0.00001293803,2.3468787e-7,0.0000030625808,0.0000011604217],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99853885,0.00051826803,0.0004675667,0.00017085359,0.00021103713,0.00009341242],"domain_scores_gemma":[0.99673635,0.00065180403,0.0015468053,0.00022012324,0.00079202215,0.00005288716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0072274124,0.000083269515,0.0002319906,0.00012088233,0.00008993898,0.000042089556,0.00018251677,0.00007624481,0.0000030039769],"category_scores_gemma":[0.0027528708,0.0000715037,0.000083611645,0.0001420586,0.000044887485,0.0012407748,0.00007724968,0.00016575205,9.481989e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000097888784,0.000095255,0.00008555987,0.00007714539,0.000012983327,9.434378e-8,0.0010047846,0.000005974328,0.14850742,0.0005105179,0.0000036107112,0.84959877],"study_design_scores_gemma":[0.0005340379,0.0008290802,0.006720985,0.000051756215,0.000048029877,0.00006948167,0.00009355765,0.04258521,0.83874184,0.10310906,0.007100038,0.00011689888],"about_ca_topic_score_codex":0.000017954473,"about_ca_topic_score_gemma":0.000018102808,"teacher_disagreement_score":0.8494819,"about_ca_system_score_codex":0.000047451296,"about_ca_system_score_gemma":0.00004333962,"threshold_uncertainty_score":0.32956424},"labels":[],"label_agreement":null},{"id":"W2765541515","doi":"10.13052/jcsm2245-1439.612","title":"Packet Momentum for Identificationof Anonymity Networks","year":2017,"lang":"en","type":"article","venue":"Journal of Cyber Security and Mobility","topic":"Internet Traffic Analysis and Secure E-voting","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":"Dalhousie University","funders":"National Institute for Materials Science; Natural Sciences and Engineering Research Council of Canada; Dalhousie University","keywords":"Anonymity; Computer science; Momentum (technical analysis); Network packet; Computer network; Physics; Computer security; Economics; Financial economics","score_opus":0.01562853291371671,"score_gpt":0.27228759937154934,"score_spread":0.25665906645783265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765541515","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49965793,0.0003329062,0.4981834,0.00085654133,0.00062322343,0.00012919198,0.0000032075511,0.000010578707,0.00020301448],"genre_scores_gemma":[0.9978754,0.000060660914,0.0016546774,0.000074640055,0.00024646416,0.0000037942605,0.0000010382076,0.0000037002505,0.00007964179],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877733,0.000073467294,0.0005034201,0.0002383258,0.00021187619,0.00019559603],"domain_scores_gemma":[0.9981798,0.00012834957,0.00075358135,0.00041337006,0.00039961125,0.00012526994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021609005,0.00011200999,0.00029619178,0.000048431022,0.0004245174,0.00056731474,0.00084716623,0.00008060412,0.000009881559],"category_scores_gemma":[0.00024166823,0.0000913646,0.00022595377,0.000042029187,0.00011389274,0.0008991029,0.00017148897,0.00021382567,8.781368e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038785208,0.00257355,0.017854353,0.00033653283,0.001044104,0.00007018883,0.013506179,0.0028871037,0.0003060844,0.8545445,0.01912971,0.08735983],"study_design_scores_gemma":[0.0017940617,0.00036019573,0.05503557,0.00011324537,0.00015393824,0.000092132956,0.00031761164,0.9159791,0.00053876656,0.016650012,0.008524698,0.00044070385],"about_ca_topic_score_codex":0.0000145022705,"about_ca_topic_score_gemma":0.00009621949,"teacher_disagreement_score":0.91309196,"about_ca_system_score_codex":0.00003236181,"about_ca_system_score_gemma":0.000046150297,"threshold_uncertainty_score":0.54706293},"labels":[],"label_agreement":null},{"id":"W2767145233","doi":"10.13052/jcsm2245-1439.611","title":"Biometric Authentication Using Mouseand Eye Movement Data","year":2017,"lang":"en","type":"article","venue":"Journal of Cyber Security and Mobility","topic":"User Authentication and Security Systems","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":"New York Institute of Technology","funders":"","keywords":"Biometrics; Computer science; Authentication (law); Eye movement; Movement (music); Computer security; Computer vision; Artificial intelligence; Art; Aesthetics","score_opus":0.07198741733302855,"score_gpt":0.3444033800237408,"score_spread":0.2724159626907123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767145233","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9703156,0.0005762478,0.026825838,0.001338349,0.000603048,0.00017286652,0.000015954001,0.000015343077,0.00013674928],"genre_scores_gemma":[0.99798065,0.00007794838,0.0017090088,0.00008976834,0.00010064329,8.631656e-7,0.0000018555573,0.0000042160136,0.000035026973],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843276,0.000121416226,0.00053793745,0.0003019087,0.00043603595,0.00016996426],"domain_scores_gemma":[0.9968293,0.00006003226,0.0007698315,0.0018862034,0.0002795838,0.00017505977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023365326,0.00011255668,0.00025078026,0.00019948625,0.00038485698,0.00066447305,0.0019308808,0.00006761667,0.000009170748],"category_scores_gemma":[0.00036008138,0.00009481013,0.000064857086,0.00016263645,0.00012644776,0.0018586603,0.0007528654,0.00017366835,0.0000032726964],"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":[0.0003399175,0.01253247,0.3966225,0.0018745019,0.0017011451,0.00018309026,0.31253564,0.000019119008,0.035733342,0.099176764,0.0045805364,0.13470094],"study_design_scores_gemma":[0.0029432494,0.00031203125,0.44750777,0.00021564323,0.00020018204,0.00012867039,0.0006921147,0.46539262,0.0033492676,0.062492546,0.01607052,0.00069537404],"about_ca_topic_score_codex":0.00014046897,"about_ca_topic_score_gemma":0.000030938456,"teacher_disagreement_score":0.46537352,"about_ca_system_score_codex":0.000049415954,"about_ca_system_score_gemma":0.00009911647,"threshold_uncertainty_score":0.6407529},"labels":[],"label_agreement":null},{"id":"W2772142659","doi":"10.13052/jcsm2245-1439.625","title":"Rethinking the Use of Resource Hints in HTML5: Is Faster Always Better!?","year":2017,"lang":"en","type":"article","venue":"Journal of Cyber Security and Mobility","topic":"Web Application Security Vulnerabilities","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":"HTML5; Resource (disambiguation); Computer science; World Wide Web","score_opus":0.05762398292172741,"score_gpt":0.2780927411801867,"score_spread":0.22046875825845927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772142659","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9874108,0.00016720711,0.0022217713,0.009494646,0.00012431969,0.0001819139,0.000007601919,0.000008467741,0.00038330557],"genre_scores_gemma":[0.99624825,0.00003847812,0.0025804755,0.00102168,0.000071690745,0.0000040549853,1.9430553e-7,0.0000050239482,0.000030154648],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99805963,0.00030719544,0.0007069031,0.00026527877,0.00046428668,0.00019670943],"domain_scores_gemma":[0.9968994,0.000585815,0.00083466526,0.0013305643,0.0002611839,0.00008837276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027659566,0.00013530136,0.00034150135,0.00008277084,0.00027213086,0.00039038842,0.0013405338,0.000108125845,0.000015367099],"category_scores_gemma":[0.000514021,0.000095846546,0.00012605324,0.00009048744,0.00044347995,0.0014280115,0.00056192436,0.0006057971,0.0000011144746],"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":[0.0004110587,0.0019363046,0.28379008,0.00055756466,0.00024659914,0.00007197512,0.5278684,0.00010266761,0.0019977577,0.06476677,0.012859796,0.10539105],"study_design_scores_gemma":[0.0019066368,0.00027735272,0.46409443,0.0003724384,0.000043816097,0.0001783203,0.0009923781,0.006698778,0.010433394,0.3807874,0.1337197,0.0004953562],"about_ca_topic_score_codex":0.00032560746,"about_ca_topic_score_gemma":0.00022556956,"teacher_disagreement_score":0.526876,"about_ca_system_score_codex":0.000044630797,"about_ca_system_score_gemma":0.000070430564,"threshold_uncertainty_score":0.39085087},"labels":[],"label_agreement":null},{"id":"W2898625021","doi":"10.13052/jcsm2245-1439.812","title":"Unsupervised Monitoring of Networkand Service Behaviour Using SelfOrganizing Maps","year":2018,"lang":"en","type":"article","venue":"Journal of Cyber Security and Mobility","topic":"Network Security and Intrusion Detection","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":"Dalhousie University","funders":"National Institute for Materials Science; Dalhousie University; Public Safety Canada; Defence Research and Development Canada","keywords":"Botnet; Computer science; Unsupervised learning; Anomaly detection; Service (business); Analytics; Intrusion detection system; Data mining; Web analytics; Machine learning; Web service; Artificial intelligence; World Wide Web; The Internet; Web intelligence","score_opus":0.021042244202755127,"score_gpt":0.25706722442713426,"score_spread":0.23602498022437912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898625021","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9869636,0.00056624814,0.011045446,0.00012697124,0.0011006168,0.00009449634,0.0000019203512,0.000020745065,0.000079913036],"genre_scores_gemma":[0.9924379,0.000108497414,0.0066714934,0.00006356522,0.0007088951,4.8755345e-7,2.1296795e-7,0.000006959889,0.0000019964575],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984741,0.00015156495,0.0005984082,0.00021676924,0.00033171964,0.00022742354],"domain_scores_gemma":[0.9982455,0.000081755985,0.00042827215,0.00031021985,0.0007839877,0.00015026693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011930731,0.00014166576,0.0003199379,0.0001041684,0.00022240347,0.00009070334,0.0004113616,0.0001284846,0.000013143049],"category_scores_gemma":[0.00004206326,0.00012892808,0.00008957926,0.00053079566,0.00009893485,0.00094321993,0.00023939004,0.00034111977,9.1361795e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001225394,0.003600435,0.7192646,0.001167039,0.00063597277,0.00013270817,0.11206296,0.0012527822,0.066498965,0.007895037,0.00065911154,0.08560495],"study_design_scores_gemma":[0.007659796,0.004489388,0.4392123,0.002411408,0.00059526134,0.0026261006,0.004378368,0.17256035,0.29080516,0.06659034,0.0065615945,0.002109958],"about_ca_topic_score_codex":0.00015978668,"about_ca_topic_score_gemma":0.00005442943,"teacher_disagreement_score":0.28005236,"about_ca_system_score_codex":0.000051860938,"about_ca_system_score_gemma":0.00008805108,"threshold_uncertainty_score":0.52575344},"labels":[],"label_agreement":null},{"id":"W2899464318","doi":"10.13052/jcsm2245-1439.814","title":"A Survey on User Profiling Model forAnomaly Detection in Cyberspace","year":2018,"lang":"en","type":"article","venue":"Journal of Cyber Security and Mobility","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Profiling (computer programming); Computer science; Cyberspace; Computer security; Parsing; Data science; World Wide Web; The Internet; Artificial intelligence","score_opus":0.018619168858445886,"score_gpt":0.260443240331123,"score_spread":0.2418240714726771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899464318","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93482476,0.000108858665,0.06394399,0.00017910935,0.00045697973,0.00015587965,0.000002475437,0.000020525647,0.0003074365],"genre_scores_gemma":[0.9977264,0.000050905408,0.001875271,0.00016894018,0.00015124475,0.0000027305384,3.1254214e-7,0.0000054418674,0.00001875372],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984143,0.0003225986,0.0004449582,0.0002851006,0.0003052599,0.00022778464],"domain_scores_gemma":[0.9988148,0.00017309263,0.0002676368,0.00027289978,0.00035021934,0.000121306184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029017215,0.00013821857,0.0002600783,0.00019259364,0.00014598052,0.00010349881,0.0002916888,0.00014005449,0.0000052298938],"category_scores_gemma":[0.00023681317,0.000120438475,0.00008270199,0.00042088082,0.000104293074,0.0008564097,0.000110428016,0.00048386812,0.0000032568357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.016545145,0.014052806,0.14437832,0.0007209152,0.0006241285,0.00028066995,0.10477863,0.018766457,0.039122302,0.06923094,0.004782295,0.58671737],"study_design_scores_gemma":[0.0023108365,0.0026855513,0.13806818,0.00017436229,0.000019875075,0.00011279358,0.0001365545,0.75488603,0.053870875,0.04610632,0.001104358,0.0005242372],"about_ca_topic_score_codex":0.00024300213,"about_ca_topic_score_gemma":0.003653186,"teacher_disagreement_score":0.73611957,"about_ca_system_score_codex":0.000094015864,"about_ca_system_score_gemma":0.00008302285,"threshold_uncertainty_score":0.49113384},"labels":[],"label_agreement":null},{"id":"W3121006491","doi":"10.13052/2245-1439.812","title":"Unsupervised Monitoring of Network and Service Behaviour Using Self Organizing Maps","year":2018,"lang":"en","type":"article","venue":"Journal of Cyber Security and Mobility","topic":"Network Security and Intrusion Detection","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":"Dalhousie University","funders":"National Institute for Materials Science; Dalhousie University; Public Safety Canada","keywords":"Botnet; Computer science; Unsupervised learning; Anomaly detection; Analytics; Intrusion detection system; Service (business); Data mining; Visualization; Machine learning; Web analytics; Artificial intelligence; Web service; The Internet; World Wide Web; Web intelligence","score_opus":0.016561933780178586,"score_gpt":0.2476809571292785,"score_spread":0.23111902334909992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121006491","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9941779,0.00085069705,0.003844339,0.00016640451,0.0007970057,0.000092463495,0.0000012770471,0.000023909039,0.000046020083],"genre_scores_gemma":[0.98325723,0.00019408045,0.015754057,0.00009108946,0.0006954705,4.336624e-7,1.2893793e-7,0.0000063832135,0.0000011381635],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986198,0.00016086425,0.00051008165,0.00021913773,0.00027181895,0.00021831221],"domain_scores_gemma":[0.99854755,0.0000920681,0.00035562195,0.00025433148,0.00059814175,0.00015227043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012418116,0.00013372643,0.00030258702,0.000073679104,0.00024634541,0.00009514792,0.00028420534,0.00011973199,0.00000917418],"category_scores_gemma":[0.00003693191,0.00012221574,0.00005552339,0.00045132384,0.00008827286,0.00081103336,0.00027606048,0.00031241777,5.1631895e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008524275,0.0026670345,0.76069045,0.0013517569,0.0006357261,0.00011573667,0.13249962,0.0005507149,0.035203993,0.009735178,0.00064255047,0.05505484],"study_design_scores_gemma":[0.007007735,0.0044284794,0.6444619,0.0024744403,0.0007089496,0.003238053,0.0039720465,0.11506505,0.10495251,0.104634434,0.007003567,0.0020528322],"about_ca_topic_score_codex":0.00012998049,"about_ca_topic_score_gemma":0.000049736776,"teacher_disagreement_score":0.12852757,"about_ca_system_score_codex":0.000040349227,"about_ca_system_score_gemma":0.000072132745,"threshold_uncertainty_score":0.49838135},"labels":[],"label_agreement":null},{"id":"W3176760417","doi":"10.13052/jcsm2245-1439.1043","title":"Evaluating and Improving a Content Delivery Network (CDN) Workflow using Stochastic Modelling","year":2021,"lang":"en","type":"article","venue":"Journal of Cyber Security and Mobility","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":0,"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":"Workflow; Computer science; Reliability (semiconductor); Component (thermodynamics); Content delivery network; Process (computing); Content delivery; Reliability engineering; The Internet; Computer network; Database; Server; Engineering; Operating system","score_opus":0.0815722565650304,"score_gpt":0.28473105858288206,"score_spread":0.20315880201785166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176760417","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63236403,0.005427987,0.36176124,0.00008184269,0.00028753243,0.000056753906,8.8410303e-7,0.000010604777,0.000009122169],"genre_scores_gemma":[0.97532195,0.00009798126,0.024189295,0.0001701115,0.00020553813,0.0000010212103,2.8703388e-7,0.0000059261706,0.00000790502],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983047,0.00024447127,0.00051583466,0.00031774247,0.0003465851,0.00027065078],"domain_scores_gemma":[0.99843353,0.00030915995,0.0003113535,0.00025639328,0.0005138616,0.00017567677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019255102,0.00014923904,0.00034584635,0.00005064261,0.00026450228,0.00027229992,0.00018836366,0.00007480293,0.0000025947886],"category_scores_gemma":[0.0001685781,0.00013679394,0.00012845642,0.00016002622,0.000060116585,0.000671045,0.00030078748,0.00043182794,2.0180863e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040188202,0.0006709092,0.004350361,0.00031863968,0.00038064565,0.00041834897,0.00917067,0.75148034,0.01872385,0.0036195572,0.000037213096,0.21042758],"study_design_scores_gemma":[0.00060624006,0.000117134514,0.00040132442,0.000239405,0.00006841923,0.00048081833,0.00026276984,0.9911591,0.00009474676,0.0064097373,0.000007747812,0.00015260381],"about_ca_topic_score_codex":0.00016395179,"about_ca_topic_score_gemma":0.000035797548,"teacher_disagreement_score":0.3429579,"about_ca_system_score_codex":0.000064742766,"about_ca_system_score_gemma":0.00020972542,"threshold_uncertainty_score":0.5578295},"labels":[],"label_agreement":null},{"id":"W3184298967","doi":"10.13052/2245-1439.611","title":"Biometric Authentication Using Mouse and Eye Movement Data","year":2017,"lang":"en","type":"article","venue":"Journal of Cyber Security and Mobility","topic":"User Authentication and Security Systems","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":"New York Institute of Technology","funders":"","keywords":"Computer science; Biometrics; Salient; Artificial neural network; Eye movement; Artificial intelligence; Classifier (UML); Modalities; Authentication (law); Data set; Machine learning; Computer vision; Pattern recognition (psychology); Computer security","score_opus":0.06238039796970234,"score_gpt":0.3341156197576858,"score_spread":0.2717352217879834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184298967","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98596644,0.00063661963,0.011786239,0.0010884339,0.00029874596,0.00014269778,0.00001378104,0.000011130879,0.00005590127],"genre_scores_gemma":[0.9981387,0.00013529729,0.0015411599,0.00008370589,0.00006307997,7.5155396e-7,0.0000013984809,0.0000035255935,0.000032328408],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99870765,0.000103387356,0.00044387762,0.00028201178,0.00032250813,0.00014057207],"domain_scores_gemma":[0.9975958,0.00005483809,0.0005988091,0.0013830756,0.0002013378,0.00016616218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019625416,0.00010128452,0.00022358258,0.00017384336,0.00033802862,0.00061424467,0.0011685909,0.000060711198,0.0000046629684],"category_scores_gemma":[0.0002978214,0.00008553193,0.00003948766,0.00011751916,0.0001401872,0.0016573414,0.0007379726,0.00014884655,0.0000012778964],"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":[0.00026529105,0.007937524,0.41245365,0.0018734736,0.0012214154,0.00010490073,0.29838327,0.00000619412,0.054533456,0.0886277,0.0019512319,0.1326419],"study_design_scores_gemma":[0.0029627657,0.00033302695,0.5064755,0.00018870966,0.00018921394,0.00015795337,0.0008974752,0.4249741,0.0047758534,0.048422273,0.009925011,0.0006980765],"about_ca_topic_score_codex":0.00014788317,"about_ca_topic_score_gemma":0.000030283392,"teacher_disagreement_score":0.4249679,"about_ca_system_score_codex":0.000031854113,"about_ca_system_score_gemma":0.00006223689,"threshold_uncertainty_score":0.5923175},"labels":[],"label_agreement":null},{"id":"W4229730853","doi":"10.13052/2245-1439.523","title":"SMS-Based Mobile Botnet Detection Framework Using Intelligent Agents","year":2017,"lang":"en","type":"article","venue":"Journal of Cyber Security and Mobility","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":0,"is_retracted":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":"Botnet; Android (operating system); Exploit; Malware; Computer science; Computer security; Phishing; Short Message Service; Android malware; Computer network; The Internet; World Wide Web; Operating system","score_opus":0.02727318133157571,"score_gpt":0.3039755979040534,"score_spread":0.2767024165724777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229730853","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7379862,0.0003711937,0.2598453,0.0001266591,0.0014015819,0.00015605365,0.0000015760062,0.000020947104,0.00009051542],"genre_scores_gemma":[0.9940711,0.00015963409,0.0052612345,0.00017868693,0.0003133197,0.0000040984996,1.8909591e-7,0.0000066671646,0.0000050483627],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99836063,0.00017963552,0.0005301746,0.00029228022,0.0003935352,0.00024375085],"domain_scores_gemma":[0.99789405,0.00012763773,0.0007574893,0.0007379163,0.0002754552,0.00020746056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014149516,0.00016784816,0.00030424548,0.00011150936,0.0007387465,0.00047186887,0.00070646044,0.00020129209,0.000043114036],"category_scores_gemma":[0.00026430655,0.0001497007,0.00018436002,0.00011850035,0.00017013132,0.001143299,0.00025344,0.00063569064,0.0000032290513],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012693121,0.00406328,0.014180572,0.0005180872,0.00039251245,0.00025315926,0.014406994,0.009169943,0.0076183197,0.0075142314,0.0006168456,0.9399967],"study_design_scores_gemma":[0.0021762084,0.002743641,0.040317696,0.00075462623,0.00016411878,0.000566355,0.0003348472,0.64120996,0.10504587,0.16426231,0.04133196,0.001092383],"about_ca_topic_score_codex":0.00013593317,"about_ca_topic_score_gemma":0.00008459953,"teacher_disagreement_score":0.93890435,"about_ca_system_score_codex":0.000105073595,"about_ca_system_score_gemma":0.00008538716,"threshold_uncertainty_score":0.6104618},"labels":[],"label_agreement":null},{"id":"W4238994784","doi":"10.13052/2245-1439.814","title":"A Survey on User Profiling Model for Anomaly Detection in Cyberspace","year":2018,"lang":"en","type":"article","venue":"Journal of Cyber Security and Mobility","topic":"Network Security and Intrusion Detection","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 New Brunswick","funders":"","keywords":"Profiling (computer programming); Computer science; Parsing; Computer security; Cyberspace; Anomaly detection; Data science; Data mining; World Wide Web; The Internet; Artificial intelligence","score_opus":0.023347652619345106,"score_gpt":0.27187222767670605,"score_spread":0.24852457505736095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238994784","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8105176,0.00008078505,0.18851385,0.00016379525,0.0004127265,0.00022234196,0.0000044237995,0.000015187865,0.00006926708],"genre_scores_gemma":[0.9958892,0.000034272995,0.0036995828,0.00016373851,0.00017816167,0.000006596111,4.6571827e-7,0.0000055679825,0.000022444645],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986475,0.00022059782,0.00041317003,0.00027659745,0.00022419459,0.00021791414],"domain_scores_gemma":[0.9987214,0.0002540349,0.0002594159,0.00024766472,0.0004140554,0.00010338656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029693989,0.00012564224,0.00024587012,0.00016292988,0.00015017355,0.000091795155,0.0002520769,0.00013110481,0.0000025453182],"category_scores_gemma":[0.00031510554,0.00011048665,0.0000900962,0.00030020485,0.00008451005,0.0006847611,0.00007829724,0.0003297896,0.0000011885779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.02649993,0.014910227,0.11654259,0.0011634444,0.00071313523,0.000102364684,0.12277882,0.021648033,0.04043743,0.075642385,0.0076556406,0.57190603],"study_design_scores_gemma":[0.0013556983,0.0016023444,0.047164038,0.0000730814,0.0000110350275,0.000032160046,0.000056130146,0.9047259,0.02120632,0.022800745,0.0007337448,0.00023877241],"about_ca_topic_score_codex":0.00016185975,"about_ca_topic_score_gemma":0.0033230921,"teacher_disagreement_score":0.8830779,"about_ca_system_score_codex":0.00008481273,"about_ca_system_score_gemma":0.00008130045,"threshold_uncertainty_score":0.45055148},"labels":[],"label_agreement":null},{"id":"W4244224115","doi":"10.13052/2245-1439.625","title":"Rethinking the Use of Resource Hints in HTML5: Is Faster Always Better!?","year":2017,"lang":"en","type":"article","venue":"Journal of Cyber Security and Mobility","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Malware; Resource (disambiguation); Reputation; World Wide Web; Vulnerability (computing); HTML5; Computer security; Compromise; Analytics; JavaScript; Internet privacy; Data science","score_opus":0.04533363533656373,"score_gpt":0.282386143697253,"score_spread":0.23705250836068928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244224115","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95016396,0.00010955915,0.046393648,0.002845896,0.00014952483,0.00014188662,0.0000035498215,0.000018642031,0.00017331891],"genre_scores_gemma":[0.9870237,0.000052282277,0.01220393,0.00065258483,0.000043338223,0.0000019980948,5.589311e-8,0.00000421764,0.000017861616],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99872184,0.00016443724,0.00047868732,0.00019184253,0.0002962152,0.0001470034],"domain_scores_gemma":[0.9979542,0.00024240151,0.00072015764,0.00084282807,0.00018224707,0.00005816388],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014490634,0.0001035829,0.0002487267,0.00007988596,0.00019706991,0.00018522503,0.0008317934,0.00008784511,0.0000059814734],"category_scores_gemma":[0.00041953387,0.00007244862,0.00009568478,0.00007246828,0.00022575099,0.0014507688,0.00043181857,0.00051746244,2.7818237e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007182623,0.0014511573,0.11621504,0.00042836188,0.00022201115,0.0002842862,0.17123492,0.00016253533,0.0071444833,0.009254574,0.0073274663,0.6855569],"study_design_scores_gemma":[0.0018231171,0.00075788173,0.2625682,0.0007230148,0.000044158223,0.0004639111,0.0003382724,0.005515027,0.116172075,0.5077043,0.103285305,0.00060474675],"about_ca_topic_score_codex":0.00011403384,"about_ca_topic_score_gemma":0.000111079695,"teacher_disagreement_score":0.68495214,"about_ca_system_score_codex":0.00003963459,"about_ca_system_score_gemma":0.000030900454,"threshold_uncertainty_score":0.29543692},"labels":[],"label_agreement":null},{"id":"W4246931262","doi":"10.13052/2245-1439.612","title":"Packet Momentum for Identification of Anonymity Networks","year":2017,"lang":"en","type":"article","venue":"Journal of Cyber Security and Mobility","topic":"Internet Traffic Analysis and Secure E-voting","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":"Dalhousie University","funders":"National Institute for Materials Science; Natural Sciences and Engineering Research Council of Canada; Dalhousie University","keywords":"Anonymity; Encryption; Obfuscation; Computer science; Network packet; Computer security; The Internet; Identification (biology); Computer network; Internet privacy; World Wide Web","score_opus":0.013117635112655924,"score_gpt":0.26908685572554214,"score_spread":0.25596922061288624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246931262","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59532666,0.00022015821,0.40364403,0.00029212466,0.00034784817,0.000092771494,0.000003372089,0.0000044376775,0.000068584624],"genre_scores_gemma":[0.9989193,0.00005518673,0.0008409968,0.00002510451,0.00012944815,0.000002003429,7.528335e-7,0.0000027109602,0.000024491683],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877554,0.00006869529,0.0006321384,0.00017819826,0.000204103,0.00014130161],"domain_scores_gemma":[0.9979021,0.000111301364,0.0011416768,0.0003389046,0.00042833344,0.00007769641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023843206,0.000088346715,0.00029765445,0.000047364458,0.00020807721,0.00020540017,0.00064880005,0.00007150431,0.000004337896],"category_scores_gemma":[0.00024050004,0.00007197417,0.0002053581,0.00004120774,0.00011271445,0.0006435343,0.0001273922,0.0001578468,2.3087998e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00074435194,0.0041427705,0.031313322,0.0008689667,0.0014024922,0.00004446688,0.022486323,0.008177637,0.0023597516,0.8035498,0.008046859,0.116863236],"study_design_scores_gemma":[0.0012159897,0.00034694714,0.056318652,0.00009525195,0.0001226605,0.000035929945,0.0002350881,0.9300732,0.002290191,0.007531175,0.0014966916,0.00023823818],"about_ca_topic_score_codex":0.000015695594,"about_ca_topic_score_gemma":0.000054043394,"teacher_disagreement_score":0.92189556,"about_ca_system_score_codex":0.00002145097,"about_ca_system_score_gemma":0.000037795362,"threshold_uncertainty_score":0.29350215},"labels":[],"label_agreement":null},{"id":"W4248958414","doi":"10.13052/2245-1439.732","title":"Understanding Android Financial Malware Attacks:Taxonomy, Characterization, and Challenges","year":2018,"lang":"en","type":"article","venue":"Journal of Cyber Security and Mobility","topic":"Advanced Malware Detection Techniques","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 New Brunswick","funders":"International Islamic University Malaysia; Ministry of Education, India","keywords":"Malware; Cryptovirology; Android (operating system); Computer science; Computer security; Android malware; Operating system","score_opus":0.07957883716074342,"score_gpt":0.2611241760929446,"score_spread":0.18154533893220115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248958414","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2513023,0.0006542453,0.74618363,0.0011577662,0.00028616298,0.00015910825,0.0000047250514,0.000058829497,0.00019322018],"genre_scores_gemma":[0.991475,0.0017420554,0.006418674,0.00013526044,0.00021017854,0.0000052165224,3.7471403e-7,0.000004734443,0.0000085117],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990352,0.000078989644,0.00032747004,0.00023739664,0.00016645552,0.00015449025],"domain_scores_gemma":[0.99906164,0.00006797465,0.00028285343,0.00021329784,0.0002554154,0.00011883292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006449311,0.000119536286,0.00022576224,0.00010720187,0.00018162615,0.00008507374,0.0002022745,0.00009348277,0.000007840602],"category_scores_gemma":[0.00013273256,0.00010882272,0.0000433129,0.00012306446,0.00019631762,0.0011092711,0.0001594695,0.00020249614,5.7995106e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009082243,0.0015594746,0.020722676,0.0012513704,0.00022255752,0.00019909517,0.034959476,0.000004464805,0.013207409,0.2579043,0.0014322771,0.66762865],"study_design_scores_gemma":[0.0034825096,0.0052242554,0.24894616,0.00058177183,0.00008416962,0.0026201876,0.0014764431,0.002748338,0.048245344,0.40484703,0.28017238,0.0015714166],"about_ca_topic_score_codex":0.0000029491375,"about_ca_topic_score_gemma":0.000035240188,"teacher_disagreement_score":0.7401727,"about_ca_system_score_codex":0.00007428752,"about_ca_system_score_gemma":0.00006200748,"threshold_uncertainty_score":0.44376618},"labels":[],"label_agreement":null},{"id":"W4293870309","doi":"10.13052/jcsm2245-1439.1135","title":"Can We Detect Malicious Behaviours in Encrypted DNS Tunnels Using Network Flow Entropy?","year":2022,"lang":"en","type":"article","venue":"Journal of Cyber Security and Mobility","topic":"Internet Traffic Analysis and Secure E-voting","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":"Dalhousie University","funders":"National Institute for Materials Science; Natural Sciences and Engineering Research Council of Canada; Dalhousie University","keywords":"Byte; Computer science; Entropy (arrow of time); Network packet; Encryption; Approximate entropy; Classifier (UML); Data mining; Artificial intelligence; Theoretical computer science; Pattern recognition (psychology); Computer network; Operating system; Physics","score_opus":0.012209198792556192,"score_gpt":0.23781740316967004,"score_spread":0.22560820437711385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293870309","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9704966,0.0010506981,0.027281798,0.00048343345,0.00052315404,0.000118584176,0.000007827417,0.000016845317,0.000021043132],"genre_scores_gemma":[0.9966273,0.000040096635,0.0029782641,0.00017470754,0.0001591142,0.0000030576643,0.0000012494475,0.000007584431,0.000008683327],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99754184,0.0005159598,0.0007370118,0.00031445135,0.0004914233,0.00039932178],"domain_scores_gemma":[0.99896723,0.00012110012,0.0004181876,0.0002093814,0.00012688951,0.00015722304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021250458,0.0001779737,0.00045895763,0.00016541747,0.00026469844,0.000117211894,0.0006001035,0.00006747138,0.00008828681],"category_scores_gemma":[0.00004539386,0.00016466995,0.00023533178,0.0004787172,0.0000628309,0.00026896968,0.0003659657,0.00081823795,3.437397e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003816291,0.0021959606,0.03446507,0.000098165976,0.0004659554,0.0017990565,0.06947557,0.82069993,0.0008119088,0.031339012,0.0009899704,0.0372778],"study_design_scores_gemma":[0.0008779822,0.00036927348,0.0060076555,0.00006646778,0.00008587768,0.00056856003,0.0007617131,0.98764336,0.000089401095,0.0025578432,0.00066152855,0.00031036435],"about_ca_topic_score_codex":0.00031959228,"about_ca_topic_score_gemma":0.0007203012,"teacher_disagreement_score":0.16694343,"about_ca_system_score_codex":0.00023333024,"about_ca_system_score_gemma":0.0001495936,"threshold_uncertainty_score":0.6715046},"labels":[],"label_agreement":null}]}