{"id":"W2289065632","doi":"","title":"국가기록관의 포털의 패싯 내비게이션 기능에 관한 연구","year":2015,"lang":"ko","type":"article","venue":"한국도서관정보학회 동계 학술발표회","topic":"Technology and Data Analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Facet (psychology); World Wide Web; Computer science; Selection (genetic algorithm); Function (biology); Information retrieval; Subject (documents); Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001225534,0.0005844383,0.0007778629,0.0005911505,0.0003913754,0.0004548721,0.003817912,0.0007656505,0.0003989936],"category_scores_gemma":[0.0004930835,0.0005700152,0.0003432143,0.002174914,0.0005101564,0.001198878,0.001921506,0.0009032299,0.007982039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001941266,"about_ca_system_score_gemma":0.0005138206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005108588,"about_ca_topic_score_gemma":0.0002594892,"domain_scores_codex":[0.9954417,0.0002744147,0.0007651653,0.00146492,0.0008825323,0.001171277],"domain_scores_gemma":[0.9951403,0.0001608561,0.000362156,0.00334743,0.0003527234,0.0006365256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001381621,0.001302277,0.08069516,0.0001329452,0.001021403,0.001495017,0.004562421,0.0003487738,0.0005268914,0.1234558,0.5709273,0.2153939],"study_design_scores_gemma":[0.004471815,0.001328127,0.02120977,0.0002807033,0.0009208317,0.0004967967,0.001345816,0.05586313,0.007527937,0.07255994,0.8306059,0.003389205],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3828883,0.01077551,0.478663,0.06337892,0.01193425,0.001434061,0.000632147,0.004945414,0.04534846],"genre_scores_gemma":[0.9744775,0.0001637037,0.01335186,0.002438768,0.0005484531,0.00002933812,0.00008593794,0.00004986443,0.008854525],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5915893,"threshold_uncertainty_score":0.9996752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03131441793246742,"score_gpt":0.2628208633613944,"score_spread":0.231506445428927,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}