{"id":"W2481767437","doi":"10.4018/978-1-59904-141-4.ch010","title":"Multimodal Human Localization Using Bayesian Network Sensor Fusion","year":2007,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Robustness (evolution); Modalities; Bayesian network; Artificial intelligence; Microphone; Computer vision; Sensor fusion; Videoconferencing; Modular design; Beamforming; Pattern recognition (psychology); Speech recognition; Multimedia","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002310529,0.0004850578,0.0004164131,0.0001039546,0.0005299537,0.0003386477,0.0006903367,0.0005431446,0.00002088631],"category_scores_gemma":[0.000008534378,0.0005038817,0.000180232,0.00007137353,0.00009085874,0.0001583899,0.000379897,0.0003106201,0.00005256423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003519781,"about_ca_system_score_gemma":0.000196113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006488025,"about_ca_topic_score_gemma":0.00009785482,"domain_scores_codex":[0.9974896,0.00002290785,0.0005075475,0.0007674621,0.0005914745,0.0006210406],"domain_scores_gemma":[0.9985456,0.000020084,0.0003936673,0.0006306075,0.0001797392,0.0002302778],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001921198,0.00001578543,0.000197239,0.00007369771,0.00005732423,0.0003609894,0.0001103126,0.002519308,0.0003866143,0.9140425,0.0006654646,0.0815516],"study_design_scores_gemma":[0.001348521,0.0002589304,0.00009912581,0.002336859,0.0001573837,0.0004596864,0.00001409697,0.08367828,0.003582461,0.8780771,0.02722139,0.002766161],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001012769,0.0001674143,0.5133093,0.00000937054,0.0004680398,0.0001739887,0.000005102124,0.0002500514,0.4855155],"genre_scores_gemma":[0.3041379,0.000009130114,0.6246215,0.005038441,0.006739944,0.000007998648,0.00003826044,0.000282208,0.05912461],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4263909,"threshold_uncertainty_score":0.9997413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02903113331558026,"score_gpt":0.2790781345757901,"score_spread":0.2500470012602098,"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."}}