{"meta":{"query_hash":"d776fcbc8ad3","filters":{"venue":"Journal of the Institute of Electronics Engineers of Korea"},"cohort_total":1,"direct_labels_cover":0,"predictions_cover":1,"exported":1,"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/d776fcbc8ad3","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+the+Institute+of+Electronics+Engineers+of+Korea"},"results":[{"id":"W2402952899","doi":"","title":"Pattern Recognition System Combining KNN rules and New Feature Weighting algorithm","year":2005,"lang":"en","type":"article","venue":"Journal of the Institute of Electronics Engineers of Korea","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"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":"Weighting; Overfitting; Pattern recognition (psychology); Feature (linguistics); Computer science; Artificial intelligence; k-nearest neighbors algorithm; Feature vector; Class (philosophy); Numeral system; Algorithm; Artificial neural network","score_opus":0.010195244993446982,"score_gpt":0.20622807676627497,"score_spread":0.196032831772828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2402952899","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":[0.25773546,0.004792016,0.7303414,0.0059006074,0.0006844623,0.00019480877,0.000008075445,0.00003559086,0.00030756718],"genre_scores_gemma":[0.9025706,0.00027019775,0.09679928,0.000054064625,0.00027076254,9.00986e-7,0.0000014049976,0.000007827921,0.000024936795],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920136,0.000015955644,0.00032682362,0.00009031887,0.0002117439,0.00015379295],"domain_scores_gemma":[0.9991206,0.0000425616,0.00047025984,0.00018190758,0.00011836235,0.00006630994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021283921,0.000098581804,0.0002040084,0.00007682674,0.00006349039,0.000031162013,0.0004996935,0.000051653362,3.5466329e-7],"category_scores_gemma":[0.0000151796,0.00006988608,0.00009993754,0.00019382466,0.000030084382,0.00036762547,0.000067039124,0.00029833752,3.8667358e-7],"study_design_candidate":"design_other","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":[0.000009013403,0.00006456256,0.00004800643,0.000072566814,0.0001465387,0.0000055546275,0.0003165194,0.018549316,0.0043040877,0.016253201,0.0027011724,0.9575295],"study_design_scores_gemma":[0.0026237506,0.00062734814,0.0007438579,0.0021509456,0.00026131788,0.0019392789,0.00009884695,0.881891,0.075372756,0.0061170356,0.027593905,0.0005799649],"about_ca_topic_score_codex":0.000009191198,"about_ca_topic_score_gemma":0.0000063194025,"teacher_disagreement_score":0.9569495,"about_ca_system_score_codex":0.00006194446,"about_ca_system_score_gemma":0.00012216029,"threshold_uncertainty_score":0.28498718},"labels":[],"label_agreement":null}]}