{"meta":{"query_hash":"8dd4ac5c7395","filters":{"venue":"Journal of Applied Artificial Intelligence"},"cohort_total":3,"direct_labels_cover":0,"predictions_cover":3,"exported":3,"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/8dd4ac5c7395","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Applied+Artificial+Intelligence"},"results":[{"id":"W4387605156","doi":"10.48185/jaai.v4i1.838","title":"Hybrid Neural Network Models for the Optimization of Induction Hardening Processes","year":2023,"lang":"en","type":"article","venue":"Journal of Applied Artificial Intelligence","topic":"Laser and Thermal Forming Techniques","field":"Engineering","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":"Korea Electrotechnology Research Institute","keywords":"Interpretability; Induction heating; Artificial neural network; Black box; Induction hardening; Computer science; Process (computing); White box; Machine learning; Artificial intelligence; Engineering; Materials science","score_opus":0.05258047746933752,"score_gpt":0.269330008614428,"score_spread":0.2167495311450905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387605156","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026770389,0.0008251515,0.9644025,0.00019824611,0.00005170824,0.00005640662,0.00017638395,0.0003824451,0.007136847],"genre_scores_gemma":[0.8537754,0.00091352925,0.13105635,0.0001261502,0.00007035352,0.00053321023,0.00043325505,0.0001020536,0.012989678],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997813,0.00007539715,0.0000123547115,0.000037971848,0.00006811253,0.000024908317],"domain_scores_gemma":[0.999607,0.00025407577,0.000040011906,0.000017077755,0.000069620146,0.000012194473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006759871,0.0009283822,0.00062747864,0.0004566314,0.00026682872,0.0007876022,0.0010397657,0.0011664513,0.0022011863],"category_scores_gemma":[0.0012905668,0.00042792058,0.0005424667,0.00055651204,0.00048085407,0.00073874736,0.0006632238,0.0009417447,0.00037001408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007722821,0.0000049159844,0.00005391406,0.000008382084,0.0000061856026,0.000004365921,0.000003477806,0.9967173,0.00012145377,0.00086965185,0.00005113675,0.002151463],"study_design_scores_gemma":[0.0000010044065,0.0000026495568,0.0000120350805,8.656626e-7,8.5203044e-7,5.799758e-7,5.297475e-7,0.99946004,0.000038274226,0.00041247677,0.00006991161,8.440884e-7],"about_ca_topic_score_codex":0.008308719,"about_ca_topic_score_gemma":0.0071798693,"teacher_disagreement_score":0.008308719,"about_ca_system_score_codex":0.00078088924,"about_ca_system_score_gemma":0.00064694736,"threshold_uncertainty_score":0.016520679},"labels":[],"label_agreement":null},{"id":"W4392973343","doi":"10.48185/jaai.v5i1.974","title":"Integrative Approaches for Advancing Organoid Engineering: From Mechanobiology to Personalized Therapeutics","year":2024,"lang":"en","type":"article","venue":"Journal of Applied Artificial Intelligence","topic":"3D Printing in Biomedical Research","field":"Engineering","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":"Concordia University","funders":"","keywords":"Organoid; Mechanobiology; Personalized medicine; Computer science; Computational biology; Biology; Neuroscience; Bioinformatics; Cell biology","score_opus":0.07248800878096946,"score_gpt":0.3139535855933529,"score_spread":0.24146557681238345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392973343","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":[0.048227858,0.12728634,0.7823297,0.0065341895,0.0012845035,0.00041996842,0.00027993837,0.0009518362,0.032685738],"genre_scores_gemma":[0.39731535,0.15092975,0.4358242,0.0031780654,0.0007751265,0.0007443933,0.00037385174,0.0002627095,0.010596588],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99944896,0.000125135,0.000031805503,0.00010341548,0.00022407554,0.00006660431],"domain_scores_gemma":[0.9996207,0.0001241245,0.00007611197,0.00006314528,0.000056878056,0.000059018177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010579918,0.00076382374,0.0008664329,0.0011994102,0.0004773533,0.002500983,0.0010446446,0.0010816859,0.0027277532],"category_scores_gemma":[0.0006066927,0.00049285864,0.00083104026,0.00057942944,0.0017006795,0.0020573284,0.0026241813,0.0019785385,0.0008441447],"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":[0.000101424754,0.00016181759,0.00054748554,0.0024542592,0.00009732282,0.00042508496,0.0005314419,0.006736844,0.7200885,0.07397281,0.0025529761,0.19232997],"study_design_scores_gemma":[0.00007320449,0.001466085,0.002365048,0.0009487274,0.00023164645,0.0028877559,0.0009671488,0.025168886,0.5533407,0.08331003,0.3290299,0.00021091932],"about_ca_topic_score_codex":0.00032462427,"about_ca_topic_score_gemma":0.00060196157,"teacher_disagreement_score":0.0027277532,"about_ca_system_score_codex":0.00081190385,"about_ca_system_score_gemma":0.00091586896,"threshold_uncertainty_score":0.009125233},"labels":[],"label_agreement":null},{"id":"W7117893037","doi":"10.48185/jaai.v6i2.1461","title":"Ensemble-based Intrusion Detection System for Electric Vehicles Charging Stations using Machine Learning","year":2025,"lang":"","type":"article","venue":"Journal of Applied Artificial Intelligence","topic":"Smart Grid Security and Resilience","field":"Engineering","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":"Intrusion detection system; Support vector machine; Decision tree; Electric vehicle; Interconnectivity; Convolutional neural network; Artificial neural network; Intrusion; Smart grid","score_opus":0.024409812890412445,"score_gpt":0.2673752427211162,"score_spread":0.24296542983070374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117893037","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":[0.5602168,0.0013511811,0.3958791,0.0008353623,0.00053526036,0.00035384877,0.0028734305,0.032441877,0.005513139],"genre_scores_gemma":[0.93624187,0.00025679398,0.05715842,0.00018297484,0.0000658069,0.00013691511,0.0032882576,0.000067082176,0.0026018622],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999539,0.000034066117,0.00004303442,0.00017252291,0.00013311465,0.00007831656],"domain_scores_gemma":[0.99954635,0.0000657796,0.00007026786,0.000067813,0.00020728764,0.00004239616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042524078,0.0009604581,0.0010382042,0.0013244408,0.00037684315,0.0005312306,0.00097104075,0.0005320472,0.0007105921],"category_scores_gemma":[0.0010576219,0.00026363842,0.00067010935,0.0005443902,0.00012757827,0.0008692974,0.00075745134,0.00080085284,0.00042208255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068248285,0.0008830246,0.057098508,0.00014488521,0.00046109414,0.0007313685,0.00013193408,0.14825535,0.032907456,0.0009373849,0.01687823,0.7408883],"study_design_scores_gemma":[0.000013580523,0.00011527933,0.0051770704,0.0000060023112,0.000048049802,0.0001252297,0.00002281041,0.9799525,0.012427784,0.00040711183,0.0016874863,0.000017096661],"about_ca_topic_score_codex":0.0054229684,"about_ca_topic_score_gemma":0.006027351,"teacher_disagreement_score":0.0054229684,"about_ca_system_score_codex":0.0006342418,"about_ca_system_score_gemma":0.0005695707,"threshold_uncertainty_score":0.010782838},"labels":[],"label_agreement":null}]}