{"id":"W4405618751","doi":"10.1007/978-3-031-77571-0_12","title":"Filtering Data from Motion Sensors with Rich Features for Monitoring Brushing Behaviors","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Motion (physics); Computer vision; Computer science; Motion sensors; Artificial intelligence","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":[0.0001769494,0.0004238464,0.0005222106,0.0001413127,0.00009099596,0.0001307252,0.0002714265,0.001044585,0.000004341222],"category_scores_gemma":[0.000009463257,0.0003647951,0.00004214182,0.00005186674,0.00004375948,0.00009233371,0.0001163318,0.001129533,0.000001976381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000984861,"about_ca_system_score_gemma":0.000008252016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001016917,"about_ca_topic_score_gemma":0.0002517301,"domain_scores_codex":[0.9986721,0.000009392686,0.0003385306,0.000554253,0.0001140079,0.0003117436],"domain_scores_gemma":[0.9990388,0.0002596219,0.00007456438,0.0005629674,0.00001955489,0.000044466],"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":[0.00002834135,0.000003233008,0.0008417967,0.0003973566,0.0002432948,0.00008349279,0.0002299956,0.9763871,0.00007233009,0.0007246335,0.0001170829,0.02087135],"study_design_scores_gemma":[0.0004433194,0.00007759823,0.0006467499,0.004841693,0.0003889741,0.0001026911,0.00003843353,0.9829448,0.00013023,0.002514838,0.006737932,0.001132718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04900847,0.1842946,0.7426679,0.0001377686,0.009412121,0.002904005,0.0010941,0.002884139,0.007596896],"genre_scores_gemma":[0.9953298,0.0007838039,0.0007176842,0.000004627783,0.001381573,0.00003900888,0.0003796134,0.0001677358,0.001196139],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9463214,"threshold_uncertainty_score":0.9998804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01944045165338171,"score_gpt":0.2318809692195563,"score_spread":0.2124405175661746,"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."}}