{"id":"W1995145742","doi":"10.1109/icinfa.2014.6932827","title":"Wi-Fi positioning based on deep learning","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Hidden Markov model; Artificial intelligence; Deep learning; Coherence (philosophical gambling strategy); Artificial neural network; Wireless; Set (abstract data type); Deep neural networks; Pattern recognition (psychology); Markov process; Computer vision; Real-time computing; Machine learning; Telecommunications; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.00006572357,0.00007386221,0.00006533621,0.00008441353,0.00007640945,0.00002767932,0.00006659196,0.00006424585,0.0001899437],"category_scores_gemma":[0.00006077206,0.00006875532,0.00002456097,0.0001104443,0.00001323169,0.00004030312,0.000007421326,0.0001234307,0.0001569029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002386976,"about_ca_system_score_gemma":0.000001550988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001823367,"about_ca_topic_score_gemma":0.000002565823,"domain_scores_codex":[0.9996217,0.00001185378,0.00007930653,0.00007736855,0.00007762624,0.0001321582],"domain_scores_gemma":[0.9998042,0.00004679585,0.000008302013,0.0001052495,0.00001596197,0.00001949607],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001895792,0.000004606264,0.0008144776,0.00001346268,0.000003859498,7.543503e-7,0.00002869211,0.9618078,0.0008315785,0.01347404,0.0002783263,0.02274046],"study_design_scores_gemma":[0.000147927,0.00004159466,0.00043531,0.00001319463,0.000002670153,5.874863e-7,0.00004405751,0.9627551,0.03134738,0.0003281964,0.004779094,0.0001048448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01006534,0.00001205558,0.8415352,0.0000689693,0.000106399,0.00003444052,2.068462e-7,0.00216365,0.1460137],"genre_scores_gemma":[0.9967286,0.00000283225,0.002728756,0.0001962939,0.00003094046,0.000005368393,0.00000898486,0.00001782279,0.0002804181],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9866632,"threshold_uncertainty_score":0.2803761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002873085814080363,"score_gpt":0.1720010715915549,"score_spread":0.1691279857774746,"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."}}