{"meta":{"query_hash":"71827c68f51f","filters":{"venue":"International Journal of Fuzzy Logic and Intelligent Systems"},"cohort_total":2,"direct_labels_cover":0,"predictions_cover":2,"exported":2,"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/71827c68f51f","api":"https://metacan.xera.ac/api/v1/cohort?venue=International+Journal+of+Fuzzy+Logic+and+Intelligent+Systems"},"results":[{"id":"W4383878148","doi":"10.5391/ijfis.2023.23.2.130","title":"Fuzzy Location Algorithm for Cross-Country and Evolving Faults in EHV Transmission Line","year":2023,"lang":"en","type":"article","venue":"International Journal of Fuzzy Logic and Intelligent Systems","topic":"Smart Parking Systems Research","field":"Engineering","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":"Cross country; Algorithm; Line (geometry); Fuzzy logic; Transmission line; Computer science; Mathematics; Artificial intelligence; Telecommunications; Economics; Demographic economics; Geometry","score_opus":0.032647636480372194,"score_gpt":0.32985085149981974,"score_spread":0.2972032150194476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383878148","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":[0.5134718,0.029318657,0.44070786,0.00048262734,0.010339697,0.0014160698,0.00008507426,0.00022827342,0.003949975],"genre_scores_gemma":[0.99625266,0.0020860336,0.0005721252,0.000012685749,0.00071867625,0.00002311646,0.000017821429,0.000023425455,0.0002934885],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983499,0.000043555774,0.0007413699,0.00015271817,0.0004930606,0.0002194148],"domain_scores_gemma":[0.998914,0.00023718373,0.00013354712,0.000071564595,0.00054437306,0.00009932788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00115306,0.00014238678,0.00025897752,0.0005353448,0.00004487138,0.00016273229,0.00022315058,0.00011639167,0.0000046128157],"category_scores_gemma":[0.00013562512,0.00011972565,0.00005252,0.00023115432,0.000041760646,0.00027626927,0.000028169978,0.00020726885,0.000009710121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044822635,0.00023453278,0.04621422,0.0024730728,0.0010587593,0.00048183475,0.0046087313,0.35811314,0.012925214,0.0047540516,0.007739282,0.5609489],"study_design_scores_gemma":[0.0015458919,0.00029759298,0.013670392,0.0020213607,0.000018335206,0.00055197027,0.0010933818,0.9569488,0.0014015081,0.0023383712,0.019775143,0.000337239],"about_ca_topic_score_codex":0.000057920082,"about_ca_topic_score_gemma":0.000004226234,"teacher_disagreement_score":0.59883565,"about_ca_system_score_codex":0.00017425527,"about_ca_system_score_gemma":0.000035428384,"threshold_uncertainty_score":0.48822704},"labels":[],"label_agreement":null},{"id":"W4403080156","doi":"10.5391/ijfis.2024.24.3.194","title":"Features Exploitation of YOLOv5-Based Freeze Backbone for Performance Improvement of UAV Object Detection","year":2024,"lang":"en","type":"article","venue":"International Journal of Fuzzy Logic and Intelligent Systems","topic":"Advanced Algorithms and Applications","field":"Engineering","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":"Universitas Telkom","keywords":"Object detection; Artificial intelligence; Computer science; Object (grammar); Computer vision; Pattern recognition (psychology)","score_opus":0.01572773192980015,"score_gpt":0.26268991144800535,"score_spread":0.2469621795182052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403080156","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":[0.21335633,0.0048157964,0.77879727,0.000073300165,0.0021390303,0.00029291172,0.000058723028,0.000028191786,0.0004384187],"genre_scores_gemma":[0.99705887,0.0007629383,0.0018157563,0.0000074095433,0.0002493665,0.000026700878,0.000006923895,0.00001103804,0.00006102082],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999124,0.0000064271435,0.0005041142,0.00007703258,0.00021544467,0.000072973125],"domain_scores_gemma":[0.9993041,0.00009309022,0.00017159706,0.00005342287,0.00034741688,0.000030341644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019574756,0.000088509354,0.00016393652,0.00018259992,0.000017306193,0.000026171643,0.00011214258,0.000044072272,0.0000049524133],"category_scores_gemma":[0.000022071106,0.00007007092,0.000090602116,0.000074567186,0.000027538617,0.00014274249,0.000008119311,0.000087950866,9.074406e-7],"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":[0.00018144489,0.00012739944,0.00016822404,0.0014253347,0.00064319005,0.000004599438,0.0005673517,0.3290258,0.25104073,0.010951072,0.00050848455,0.40535638],"study_design_scores_gemma":[0.0007298385,0.0013464048,0.0012584109,0.0013420929,0.00010030154,0.00010246853,0.0012902375,0.29851115,0.6827909,0.0041419663,0.008113242,0.00027299253],"about_ca_topic_score_codex":0.000013717171,"about_ca_topic_score_gemma":0.0000024115188,"teacher_disagreement_score":0.7837025,"about_ca_system_score_codex":0.00007271896,"about_ca_system_score_gemma":0.000020155445,"threshold_uncertainty_score":0.2857409},"labels":[],"label_agreement":null}]}