{"id":"W3043809140","doi":"10.48550/arxiv.2007.06727","title":"Inertial Sensing Meets Artificial Intelligence: Opportunity or Challenge?","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Inertial navigation system; Computer science; Inertial measurement unit; Sensor fusion; Artificial intelligence; Big data; Inertial frame of reference; Systems engineering; Real-time computing; Data mining; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001061763,0.0003940975,0.0004172444,0.0002658268,0.0001229935,0.00006194587,0.0005620148,0.0006271222,0.000210649],"category_scores_gemma":[0.0001626043,0.0004313093,0.0001670454,0.0004937311,0.0001479436,0.0001162889,0.0006315126,0.000917171,0.0001133543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002060199,"about_ca_system_score_gemma":0.0001443837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004818526,"about_ca_topic_score_gemma":0.0001320569,"domain_scores_codex":[0.9985803,0.00005275683,0.0003130134,0.0005987021,0.00009256918,0.0003625972],"domain_scores_gemma":[0.9989741,0.0000569894,0.00009762195,0.00056198,0.0001086168,0.0002006807],"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":[0.0001272757,0.00005280654,0.00002453845,0.0003988474,0.0002134888,0.001418177,0.0006670943,0.9165421,0.0002890381,0.05950243,0.000767707,0.01999657],"study_design_scores_gemma":[0.00009671938,0.00005409204,0.00001165344,0.00009894874,0.00008869635,0.000005577761,0.00117735,0.9445029,0.007600414,0.04399525,0.001690119,0.0006782777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1513998,0.0001318305,0.8322399,0.0007386382,0.002156899,0.0005515708,0.0000754826,0.004472394,0.008233471],"genre_scores_gemma":[0.9983281,0.0007738779,0.0003849748,0.00005520114,0.0002190204,4.749681e-7,0.00005089023,0.00005726025,0.0001302343],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8469282,"threshold_uncertainty_score":0.9998139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1831882941408964,"score_gpt":0.2141414835496147,"score_spread":0.03095318940871825,"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."}}