{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":2,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":2,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"71827c68f51f","filters":{"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"A. Naresh Kumar","is_ca":false},{"name":"M. Chakravarthy","is_ca":false},{"name":"M. Suresh Kumar","is_ca":false},{"name":"M. Nagaraju","is_ca":false},{"name":"M Ramesha","is_ca":false},{"name":"Bharathi Gururaj","is_ca":false},{"name":"Uday Kiran Elemasetty","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03264763648037219,"gpt":0.3298508514998197,"spread":0.2972032150194476,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00115306,0.0001423868,0.0002589775,0.0005353448,0.00004487138,0.0001627323,0.0002231506,0.0001163917,0.000004612816],"category_scores_gemma":[0.0001356251,0.0001197256,0.00005252,0.0002311543,0.00004176065,0.0002762693,0.00002816998,0.0002072689,0.000009710121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001742553,"about_ca_system_score_gemma":0.00003542838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005792008,"about_ca_topic_score_gemma":0.000004226234,"domain_scores_codex":[0.9983499,0.00004355577,0.0007413699,0.0001527182,0.0004930606,0.0002194148],"domain_scores_gemma":[0.998914,0.0002371837,0.0001335471,0.00007156459,0.0005443731,0.00009932788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004482263,0.0002345328,0.04621422,0.002473073,0.001058759,0.0004818348,0.004608731,0.3581131,0.01292521,0.004754052,0.007739282,0.5609489],"study_design_scores_gemma":[0.001545892,0.000297593,0.01367039,0.002021361,0.00001833521,0.0005519703,0.001093382,0.9569488,0.001401508,0.002338371,0.01977514,0.000337239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5134718,0.02931866,0.4407079,0.0004826273,0.0103397,0.00141607,0.00008507426,0.0002282734,0.003949975],"genre_scores_gemma":[0.9962527,0.002086034,0.0005721252,0.00001268575,0.0007186763,0.00002311646,0.00001782143,0.00002342545,0.0002934885],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5988356,"threshold_uncertainty_score":0.488227,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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)","authors":[{"name":"Nur Qomariyati Laily","is_ca":true},{"name":"Jannah Nurul","is_ca":true},{"name":"Adhi Wibowo Suryo","is_ca":true},{"name":"Suprapto Siadari Thomhert","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01572773192980015,"gpt":0.2626899114480054,"spread":0.2469621795182052,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001957476,0.00008850935,0.0001639365,0.0001825999,0.00001730619,0.00002617164,0.0001121426,0.00004407227,0.000004952413],"category_scores_gemma":[0.00002207111,0.00007007092,0.00009060212,0.00007456719,0.00002753862,0.0001427425,0.000008119311,0.00008795087,9.074406e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007271896,"about_ca_system_score_gemma":0.00002015544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001371717,"about_ca_topic_score_gemma":0.000002411519,"domain_scores_codex":[0.999124,0.000006427143,0.0005041142,0.00007703258,0.0002154447,0.00007297313],"domain_scores_gemma":[0.9993041,0.00009309022,0.0001715971,0.00005342287,0.0003474169,0.00003034164],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001814449,0.0001273994,0.000168224,0.001425335,0.0006431901,0.000004599438,0.0005673517,0.3290258,0.2510407,0.01095107,0.0005084845,0.4053564],"study_design_scores_gemma":[0.0007298385,0.001346405,0.001258411,0.001342093,0.0001003015,0.0001024685,0.001290238,0.2985111,0.6827909,0.004141966,0.008113242,0.0002729925],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2133563,0.004815796,0.7787973,0.00007330017,0.00213903,0.0002929117,0.00005872303,0.00002819179,0.0004384187],"genre_scores_gemma":[0.9970589,0.0007629383,0.001815756,0.000007409543,0.0002493665,0.00002670088,0.000006923895,0.00001103804,0.00006102082],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7837025,"threshold_uncertainty_score":0.2857409,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}