{"id":"W231320235","doi":"","title":"D-8-11 Memory-Retriever〜履歴を利用したローカルファイル検索システム(D-8. 人工知能と知識処理, 情報・システム1)","year":2005,"lang":"ja","type":"article","venue":"電子情報通信学会総合大会講演論文集","topic":"Military Technology and Strategies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Labrador Retriever; Computer science; Business; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004011876,0.0005394907,0.0003845816,0.0005965693,0.001071187,0.001992463,0.001319303,0.001109788,0.06313013],"category_scores_gemma":[0.0008382545,0.0002817304,0.0003900543,0.0004578315,0.0008454516,0.001282255,0.001071356,0.0007045195,0.02811153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000803255,"about_ca_system_score_gemma":0.0007587449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001359375,"about_ca_topic_score_gemma":0.002538851,"domain_scores_codex":[0.9997172,0.00002718683,0.00002306427,0.00008217913,0.00009645316,0.00005391618],"domain_scores_gemma":[0.9994922,0.00006681306,0.00004392385,0.0001339876,0.0001943293,0.00006868491],"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":[0.002255811,0.000758183,0.007287588,0.001167337,0.0001084336,0.001480342,0.001210291,0.001766922,0.1618224,0.1293895,0.1085294,0.5842239],"study_design_scores_gemma":[0.000569362,0.001803869,0.007947066,0.0001673635,0.0002113147,0.004195113,0.0009934197,0.006104383,0.2698707,0.03020017,0.6777821,0.0001552138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1940909,0.002367042,0.04632417,0.002139105,0.001774627,0.0003903593,0.002825869,0.003019246,0.7470686],"genre_scores_gemma":[0.5012863,0.001096632,0.04341615,0.001397764,0.0002674879,0.000263314,0.002862197,0.0003015236,0.4491087],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06313013,"threshold_uncertainty_score":0.2111914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0088452430649507,"score_gpt":0.2063853597862006,"score_spread":0.1975401167212499,"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."}}