{"id":"W11686315","doi":"","title":"M-057 Bluetoothデバイスの観測履歴を用いた雑踏検出と位置情報を併用した応用アプリケーションの試案(M分野:ユビキタス・モバイルコンピューティング,一般論文)","year":2010,"lang":"en","type":"article","venue":"情報科学技術フォーラム講演論文集","topic":"Blood Coagulation and Thrombosis Mechanisms","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bluetooth; Computer science; Telecommunications; Wireless","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":[0.0001082914,0.0003720724,0.000132216,0.0004004622,0.0008847716,0.000209299,0.0002000228,0.0002376061,0.00418191],"category_scores_gemma":[0.0002525505,0.0001167202,0.0001638417,0.0003920565,0.0003296803,0.0000623696,0.0002385999,0.0001536459,0.0006400827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001052007,"about_ca_system_score_gemma":0.000654935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1356317,"about_ca_topic_score_gemma":0.2138463,"domain_scores_codex":[0.999894,0.00001664196,0.000005413138,0.00002720575,0.00002404948,0.0000326436],"domain_scores_gemma":[0.9998832,0.00001699051,0.00002344205,0.000009828179,0.00002248414,0.00004403214],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001775737,0.0002618158,0.6740831,0.0001339672,0.0002089573,0.01014553,0.001818263,0.0003666222,0.2488389,0.0007893777,0.001839083,0.05973884],"study_design_scores_gemma":[0.0000796358,0.0008073009,0.9618931,0.00002312677,0.00008796137,0.01279766,0.0005561329,0.0004126295,0.01104432,0.00007646882,0.01220167,0.00002007809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998254,0.0001326746,0.0003005974,0.0000390914,0.000004512178,0.00001398871,0.0001304401,0.000007653275,0.001117086],"genre_scores_gemma":[0.9935659,0.0001788582,0.001913968,0.00006143193,0.000008567192,0.00002046139,0.0006121294,0.000007952563,0.00363068],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1356317,"threshold_uncertainty_score":0.2696844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01918774067835054,"score_gpt":0.2847262032872843,"score_spread":0.2655384626089337,"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."}}