{"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022660326,0.00018102776,0.97548455,0.00017088442,0.000012716497,0.000023845889,0.000046006127,0.00007693034,0.0013436903],"genre_scores_gemma":[0.9091704,0.00056202547,0.087189,0.00006552818,0.000052680443,0.00015209365,0.00016214668,0.000040758598,0.0026053593],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942786,0.00025323324,0.000026991122,0.00009276531,0.00014834547,0.000050687086],"domain_scores_gemma":[0.9983322,0.0011986807,0.00018443534,0.00006769343,0.00017743808,0.00003955548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018428452,0.00076514354,0.00072513154,0.0011138603,0.00040272434,0.0010108371,0.0011115273,0.00077152334,0.0009997773],"category_scores_gemma":[0.0041347574,0.0003951653,0.0011119578,0.0006577367,0.001079687,0.001203646,0.00094902446,0.0011571975,0.00015731742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000876648,0.000009734048,0.0006917667,0.000014782402,0.000026114185,0.000031532116,0.000024854464,0.98008895,0.00032934398,0.013650754,0.00009218679,0.005031256],"study_design_scores_gemma":[6.879034e-7,0.000002849731,0.00009002293,0.0000015992937,0.000001903907,0.000002882905,0.0000015749616,0.9946432,0.000034308636,0.005174434,0.000044877932,0.0000017385602],"about_ca_topic_score_codex":0.00972369,"about_ca_topic_score_gemma":0.0054175677,"teacher_disagreement_score":0.00972369,"about_ca_system_score_codex":0.0013251707,"about_ca_system_score_gemma":0.0011256706,"threshold_uncertainty_score":0.019334197},"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":"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":[0.5729992,0.0008061708,0.41811493,0.0013449833,0.00020418805,0.0002407174,0.0016297799,0.0015307065,0.003129379],"genre_scores_gemma":[0.96465576,0.00020066512,0.031224482,0.00011789948,0.000058716698,0.00018786923,0.0013531888,0.00002653405,0.0021747455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969673,0.00007197097,0.000020129617,0.00009222521,0.00005186446,0.00006716793],"domain_scores_gemma":[0.998823,0.0007624481,0.00011560366,0.000042654538,0.00021716906,0.000039089813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011762589,0.00068906165,0.0005931415,0.0009803225,0.00045266567,0.0006843109,0.0011218244,0.0009985993,0.0011398232],"category_scores_gemma":[0.0029657108,0.0003440918,0.0007229931,0.000727514,0.00030971927,0.000731957,0.00041159787,0.001382509,0.00034139154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000082157145,0.00014077277,0.0111766765,0.000025259249,0.000048177626,0.00004729691,0.000027672577,0.96222174,0.0004077558,0.0010340684,0.0011570618,0.023631306],"study_design_scores_gemma":[0.0000019548324,0.000011674936,0.00048273522,0.0000019013714,0.0000027396622,0.0000034700083,0.0000028900886,0.9990453,0.00005817084,0.0003326586,0.00005501625,0.0000016057332],"about_ca_topic_score_codex":0.02451313,"about_ca_topic_score_gemma":0.015327923,"teacher_disagreement_score":0.02451313,"about_ca_system_score_codex":0.00088828435,"about_ca_system_score_gemma":0.0010724621,"threshold_uncertainty_score":0.048740864},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3829231,0.0066311923,0.59387547,0.0014211163,0.0005547976,0.00030242745,0.0016643626,0.005353265,0.007274333],"genre_scores_gemma":[0.9454878,0.0007433783,0.0478814,0.00028599353,0.00019340885,0.000107772736,0.00118723,0.00005065224,0.00406241],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897826,0.00019447443,0.00009413968,0.0003125657,0.0002857913,0.00013466997],"domain_scores_gemma":[0.997528,0.00080950966,0.0005355193,0.0001584294,0.00088509376,0.00008354058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015015819,0.0012438566,0.0010512322,0.0011779638,0.0002664911,0.0008488294,0.0012651422,0.00088293984,0.0007602199],"category_scores_gemma":[0.0044605513,0.0004062521,0.00054407504,0.000652782,0.00031892737,0.00095857756,0.00059170945,0.0011931435,0.000557541],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009322586,0.00086805574,0.021566505,0.0003839995,0.00037307903,0.0003962201,0.00015070636,0.32307097,0.016369715,0.001492501,0.010050736,0.62434524],"study_design_scores_gemma":[0.0000068929166,0.000067091765,0.0013979579,0.000010526272,0.000027153683,0.000028756553,0.000009315022,0.9952461,0.0023336948,0.00033140398,0.0005332057,0.00000782464],"about_ca_topic_score_codex":0.007323385,"about_ca_topic_score_gemma":0.006431648,"teacher_disagreement_score":0.007323385,"about_ca_system_score_codex":0.001045491,"about_ca_system_score_gemma":0.0008869197,"threshold_uncertainty_score":0.014561534},"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":"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":[0.6745467,0.0028677047,0.19381468,0.0074361037,0.00053954567,0.00064269383,0.06879444,0.027093615,0.024264546],"genre_scores_gemma":[0.8962633,0.00041080688,0.05588403,0.00022943917,0.0002141635,0.00019334412,0.041944202,0.00019824064,0.004662496],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999363,0.00012988137,0.0000639041,0.00018354434,0.0001879769,0.00007167329],"domain_scores_gemma":[0.99613535,0.001669165,0.00073071895,0.00042911057,0.00073923165,0.000296482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008738168,0.0006731886,0.00033026197,0.0033575022,0.00040643787,0.00078281556,0.0008680646,0.0006637815,0.004662453],"category_scores_gemma":[0.009224445,0.00013115005,0.00034969006,0.002047818,0.0002492002,0.0013362418,0.00071228546,0.0011820383,0.0019502373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00076960644,0.00091537077,0.15294944,0.00038853812,0.000096709446,0.0007016718,0.00066593685,0.114715196,0.006479562,0.0063505466,0.10356295,0.6124044],"study_design_scores_gemma":[0.000048377016,0.00012476138,0.036696386,0.000054292133,0.000021675523,0.00012470868,0.00033094583,0.9310645,0.005112288,0.00928292,0.017103037,0.000036135774],"about_ca_topic_score_codex":0.02855885,"about_ca_topic_score_gemma":0.047332533,"teacher_disagreement_score":0.02855885,"about_ca_system_score_codex":0.0011877351,"about_ca_system_score_gemma":0.0012866454,"threshold_uncertainty_score":0.056785226},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10590234,0.0007938953,0.8574872,0.0007549013,0.000107391235,0.0001720694,0.0012367831,0.000637267,0.03290821],"genre_scores_gemma":[0.8977922,0.00073429727,0.08749191,0.00016156542,0.000031747863,0.00057205296,0.0008176673,0.00011154039,0.012286891],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998305,0.00003856726,0.000009996581,0.00005083805,0.000040511997,0.000029523233],"domain_scores_gemma":[0.99969447,0.00015313222,0.000038211383,0.000013412702,0.00007907402,0.00002163269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035018026,0.00067065307,0.0008735367,0.0005879836,0.00049339846,0.0016561885,0.0010123806,0.0021129376,0.00440368],"category_scores_gemma":[0.0007697491,0.0007041305,0.0011582016,0.00060239836,0.00058539794,0.0004648285,0.0008738234,0.00079567306,0.0004704646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000074175387,0.0000059508284,0.00022428302,0.000018035307,0.0000038624007,0.000038353395,0.000011610958,0.99743456,0.00030211537,0.00062086806,0.00013361587,0.0011993689],"study_design_scores_gemma":[0.0000033680487,0.000004485328,0.000078065175,0.0000028440797,0.0000033239814,0.0000057427774,0.000007662483,0.99939394,0.000051987983,0.0001733561,0.00027261692,0.000002620661],"about_ca_topic_score_codex":0.026515376,"about_ca_topic_score_gemma":0.017243562,"teacher_disagreement_score":0.026515376,"about_ca_system_score_codex":0.00086757174,"about_ca_system_score_gemma":0.0016666942,"threshold_uncertainty_score":0.052722037},"labels":[],"label_agreement":null}]}