{"id":"W2661683454","doi":"10.1299/jsmermd.2008._2p1-b03_1","title":"2P1-B03 Development of sufferer detection system with a cellular phone","year":2008,"lang":"en","type":"article","venue":"The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)","topic":"Seismology and Earthquake Studies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"La Cité Collégiale","funders":"","keywords":"ALARM; Computer science; Position (finance); Component (thermodynamics); Acoustics; False alarm; Phone; Real-time computing; Mobile phone; Telecommunications; Simulation; Electrical engineering; Artificial intelligence; Physics; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003925921,0.0004499228,0.0004664302,0.0005588183,0.0003611157,0.0005108981,0.0009490833,0.0008534319,0.01016996],"category_scores_gemma":[0.0005878135,0.0002359364,0.0002717074,0.0002418595,0.0001589354,0.000571502,0.0005838002,0.0004007052,0.005820884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002710962,"about_ca_system_score_gemma":0.0005463031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001531758,"about_ca_topic_score_gemma":0.001383652,"domain_scores_codex":[0.9994618,0.00007933871,0.00002839645,0.00009944652,0.0002655316,0.00006542151],"domain_scores_gemma":[0.9995167,0.00004099982,0.00002089603,0.00005274109,0.0003069809,0.00006178348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007890143,0.0002861606,0.01259669,0.0004881517,0.00008804265,0.001061136,0.0005280937,0.001932508,0.5206465,0.005888797,0.03962662,0.4160682],"study_design_scores_gemma":[0.0004737225,0.003644977,0.03763542,0.0001581342,0.0003513839,0.007527672,0.0004278796,0.1314231,0.5378647,0.001397141,0.2787752,0.0003207409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.152262,0.000827105,0.7748345,0.0008945393,0.0008086408,0.001060071,0.001033365,0.02278176,0.04549798],"genre_scores_gemma":[0.6031614,0.0004772539,0.3108505,0.001169058,0.0002795556,0.000772275,0.001671795,0.0003581892,0.08125991],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01016996,"threshold_uncertainty_score":0.03402185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02050327284960147,"score_gpt":0.2005468126654869,"score_spread":0.1800435398158854,"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."}}