{"meta":{"query_hash":"660ebf9b2ca6","filters":{"venue":"Journal of Computational and Cognitive Engineering"},"cohort_total":5,"direct_labels_cover":0,"predictions_cover":5,"exported":5,"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/660ebf9b2ca6","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Computational+and+Cognitive+Engineering"},"results":[{"id":"W4323530389","doi":"10.47852/bonviewjcce3202491","title":"Dynamic Failure Analysis of Ship Energy Systems Using an Adaptive Machine Learning Formalism","year":2023,"lang":"en","type":"article","venue":"Journal of Computational and Cognitive Engineering","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Propulsion; Probabilistic logic; Bayesian network; Computer science; Reliability engineering; Mechanical system; Engineering; Machine learning; Artificial intelligence; Aerospace engineering","score_opus":0.04450663735114614,"score_gpt":0.31509138760253763,"score_spread":0.2705847502513915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323530389","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48836657,0.00035923085,0.51112676,0.000036664092,0.00005678367,0.000013828304,0.000022243643,0.0000075999988,0.00001032941],"genre_scores_gemma":[0.99776363,0.000048738682,0.0020708696,0.000008163467,0.000038852497,5.9745014e-7,0.000025994925,0.000007594267,0.000035557754],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99799454,0.00014241159,0.0007204614,0.00014501122,0.00086618646,0.00013141353],"domain_scores_gemma":[0.99707663,0.0012725736,0.0005285764,0.000046535417,0.00097156316,0.00010413366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013286894,0.00011309166,0.00048538606,0.0017582433,0.00012210729,0.00011125791,0.0001441942,0.00004386216,0.000014550508],"category_scores_gemma":[0.00038623292,0.00008837725,0.00023376284,0.0024393946,0.000038843355,0.00043907473,0.000041356405,0.00015960765,0.000001091083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040874023,0.000013916868,0.0022772565,0.00000387747,0.00084070466,0.000019583933,0.00045320095,0.9895417,0.00008487326,0.00057952665,0.0000020947307,0.0061423574],"study_design_scores_gemma":[0.00022391138,0.00011070988,0.033509314,0.000047261583,0.00055954716,0.00003459579,0.0019218408,0.96145797,0.000009459554,0.0019901912,0.000043215943,0.00009196734],"about_ca_topic_score_codex":0.000038890656,"about_ca_topic_score_gemma":0.00001813329,"teacher_disagreement_score":0.5093971,"about_ca_system_score_codex":0.000020330217,"about_ca_system_score_gemma":0.000050576775,"threshold_uncertainty_score":0.36039197},"labels":[],"label_agreement":null},{"id":"W4391420261","doi":"10.47852/bonviewjcce42022066","title":"A Machine Learning Model to Predict Cyberattacks in Connected and Autonomous Vehicles","year":2024,"lang":"en","type":"article","venue":"Journal of Computational and Cognitive Engineering","topic":"Advanced Malware Detection Techniques","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":"McMaster University","funders":"","keywords":"Computer science; Ransomware; Malware; Computer security; Artificial intelligence; Machine learning","score_opus":0.007843163208252577,"score_gpt":0.24253007450270525,"score_spread":0.23468691129445268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391420261","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16408075,0.0008551415,0.83464295,0.00022085929,0.00004572156,0.00004838451,0.000002461483,0.00008576745,0.000017970775],"genre_scores_gemma":[0.91351205,0.000021617721,0.08635597,0.000060702405,0.000028239467,0.0000037937464,6.808776e-7,0.0000066234766,0.000010331688],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946785,0.000014800174,0.00019443526,0.00011897665,0.00011937192,0.00008458292],"domain_scores_gemma":[0.99948806,0.00027317967,0.000035650115,0.000016896103,0.00012033781,0.00006585212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015516757,0.00007967213,0.00011472235,0.000326652,0.000033972134,0.00012349224,0.000057292928,0.000022656912,6.917513e-7],"category_scores_gemma":[0.00009469369,0.000075594195,0.000019735457,0.00021518153,0.000011805308,0.00036217063,0.00005551549,0.0002353255,4.006533e-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":[0.0000138332725,0.000010421495,0.00019726373,0.000030050778,0.00002558291,0.00007656545,0.0007170062,0.91397226,0.00076851656,0.0038223139,0.0000069218822,0.08035927],"study_design_scores_gemma":[0.00018189587,0.00012162886,0.0030704113,0.0002356798,0.0000049322102,0.00024728535,0.000012199607,0.9886021,0.0004723987,0.0068430738,0.00012709812,0.00008128103],"about_ca_topic_score_codex":0.000001417895,"about_ca_topic_score_gemma":5.673007e-7,"teacher_disagreement_score":0.7494313,"about_ca_system_score_codex":0.000025286907,"about_ca_system_score_gemma":0.00004595142,"threshold_uncertainty_score":0.3082642},"labels":[],"label_agreement":null},{"id":"W4403411078","doi":"10.47852/bonviewjcce42023602","title":"Deep Learning-Based Approach for Monitoring and Controlling Fake Reviews","year":2024,"lang":"en","type":"article","venue":"Journal of Computational and Cognitive Engineering","topic":"Misinformation and Its Impacts","field":"Social Sciences","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Deep learning; Data science; Artificial intelligence; Psychology","score_opus":0.024745408207651372,"score_gpt":0.3048650806192021,"score_spread":0.2801196724115507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403411078","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028424433,0.0074278936,0.96347034,0.00012702572,0.0001398547,0.00011996128,0.0000012307274,0.000015597305,0.0002736782],"genre_scores_gemma":[0.9889383,0.00024245172,0.0104288915,0.000028712999,0.0003244434,0.0000019021259,0.000002249824,0.000004632304,0.00002844561],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995093,0.000025307121,0.00019329975,0.00004441775,0.00014300556,0.00008469279],"domain_scores_gemma":[0.9991838,0.00050357945,0.000067724424,0.000006029931,0.00015963864,0.00007924383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065268256,0.000054234002,0.0001182718,0.00010451038,0.00014423522,0.00018834302,0.000021803084,0.000023805324,0.0000038110866],"category_scores_gemma":[0.00047261568,0.000045872028,0.000044261265,0.00008093527,0.000025975187,0.0002479482,0.000003478713,0.00011365805,4.614072e-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":[0.000052706564,0.000018256114,0.00025879042,0.0003757714,0.000098225144,0.0000037113327,0.016001701,0.73998064,0.000045110803,0.0047899964,0.00003915062,0.23833592],"study_design_scores_gemma":[0.0005543037,0.000075551485,0.0016273726,0.0004176677,0.00004568155,0.000012258864,0.0022698622,0.984087,0.000020806538,0.00036488086,0.010433924,0.000090712754],"about_ca_topic_score_codex":0.0000010087471,"about_ca_topic_score_gemma":1.1591488e-7,"teacher_disagreement_score":0.96051383,"about_ca_system_score_codex":0.000015304213,"about_ca_system_score_gemma":0.00005536796,"threshold_uncertainty_score":0.1870607},"labels":[],"label_agreement":null},{"id":"W4409402469","doi":"10.47852/bonviewjcce52024104","title":"Legal Text Analytics for Reasonable Notice Period Prediction","year":2025,"lang":"en","type":"article","venue":"Journal of Computational and Cognitive Engineering","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Queen's University; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Notice; Period (music); Analytics; Computer science; Data science; Political science; Law; Philosophy","score_opus":0.017458569748571937,"score_gpt":0.30906539209582157,"score_spread":0.2916068223472496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409402469","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10598273,0.00027806836,0.8883809,0.0011566236,0.00054415804,0.00012348339,0.000013981791,0.000015837682,0.0035042528],"genre_scores_gemma":[0.9948828,0.000014367678,0.0043783984,0.00007449904,0.00030847918,0.0000027423537,0.000002118292,0.0000036608963,0.00033290073],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942124,0.000019458123,0.00021627368,0.0000612036,0.00017835236,0.00010346613],"domain_scores_gemma":[0.99858665,0.00059432973,0.000079044614,0.000012469315,0.0006779452,0.000049551694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003835284,0.000051594616,0.000100491794,0.00010682762,0.00024031864,0.000102793514,0.000050946634,0.000036519803,0.000013988795],"category_scores_gemma":[0.0007341874,0.00005196132,0.000050786603,0.00016811234,0.000083216146,0.0002497223,0.000009586339,0.00009590522,9.44445e-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":[0.00017747271,0.00009619481,0.0019052515,0.00006804097,0.00027680333,0.000013193769,0.004081733,0.37988558,0.00011298675,0.56588924,0.000748639,0.04674484],"study_design_scores_gemma":[0.0010071625,0.00039766796,0.021338401,0.00090243435,0.00044929975,0.000047886206,0.014457864,0.76503116,0.0006929749,0.06162088,0.13369358,0.00036070525],"about_ca_topic_score_codex":0.000018771058,"about_ca_topic_score_gemma":0.000009931614,"teacher_disagreement_score":0.8889001,"about_ca_system_score_codex":0.000042183747,"about_ca_system_score_gemma":0.00021410317,"threshold_uncertainty_score":0.21189213},"labels":[],"label_agreement":null},{"id":"W4414553343","doi":"10.47852/bonviewjcce52024527","title":"A 3D Irrigation Canal Alignment Optimization Model for a Steep-Sloping Area with Rectangular Inclined Drops","year":2025,"lang":"en","type":"article","venue":"Journal of Computational and Cognitive Engineering","topic":"Hydraulic flow and structures","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Terrain; Particle swarm optimization; Geospatial analysis; Identification (biology); Feature (linguistics); Genetic algorithm","score_opus":0.005340999225694679,"score_gpt":0.2055735607563664,"score_spread":0.20023256153067173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414553343","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.109367445,0.00025764512,0.88997847,0.000060014496,0.000090266454,0.000113034264,0.000011459945,0.000023646176,0.00009803632],"genre_scores_gemma":[0.9187499,0.000008965594,0.081081405,0.000049975682,0.00005780204,0.000009363845,0.000017557173,0.000011746385,0.000013255279],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994293,0.0000063954826,0.0002468674,0.00007292506,0.00014268668,0.000101823534],"domain_scores_gemma":[0.999508,0.00012935433,0.000055038632,0.000019601803,0.000242184,0.000045820463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000092441755,0.00011111849,0.00016208127,0.00017136832,0.00006107703,0.000041998996,0.000034695324,0.00003599333,0.00000237562],"category_scores_gemma":[0.000036904014,0.000097383105,0.00003562001,0.000117408315,0.000013668943,0.0001223065,0.000007197471,0.00009380143,4.4838167e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050325427,0.0000058666747,0.000039705556,0.000076807264,0.00016973738,0.000003349779,0.00024854383,0.99609417,0.00010194155,0.0005671968,0.0000173688,0.0026249657],"study_design_scores_gemma":[0.0009090211,0.000055593,0.0005971538,0.00033228548,0.00007938698,0.000024874793,0.00005048665,0.9961846,0.00015708066,0.0014735075,0.000030307361,0.000105743806],"about_ca_topic_score_codex":0.00000133951,"about_ca_topic_score_gemma":0.0000024864873,"teacher_disagreement_score":0.8093825,"about_ca_system_score_codex":0.00005370031,"about_ca_system_score_gemma":0.00007336088,"threshold_uncertainty_score":0.39711678},"labels":[],"label_agreement":null}]}