{"id":"W2137662760","doi":"10.1109/icra.2012.6225207","title":"Slip prediction using Hidden Markov models: Multidimensional sensor data to symbolic temporal pattern learning","year":2012,"lang":"en","type":"article","venue":"","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"McGill University","keywords":"Hidden Markov model; Classifier (UML); Computer science; Pattern recognition (psychology); Artificial intelligence; Slip (aerodynamics); Tactile sensor; Cluster analysis; Probabilistic logic; Strain gauge; Engineering; Robot; Structural engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000199147,0.0001548505,0.0001436097,0.000153772,0.0001288654,0.00002003508,0.0001034181,0.00005970433,0.00007527023],"category_scores_gemma":[0.00002660249,0.0001482628,0.00003342983,0.0002053719,0.00001014783,0.0005724519,0.0001180459,0.0001572357,0.000008675203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005228837,"about_ca_system_score_gemma":0.00000667269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001125929,"about_ca_topic_score_gemma":0.00001420002,"domain_scores_codex":[0.9990212,0.00003656839,0.0001892354,0.0001884114,0.0001950488,0.000369518],"domain_scores_gemma":[0.9994968,0.00005227912,0.00002176097,0.0002652585,0.00003488146,0.0001289787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005410203,0.0002386963,0.3890456,0.0002121683,0.0009143621,0.000004438816,0.004811538,0.1292546,0.1058852,0.0001887461,0.03971872,0.3296718],"study_design_scores_gemma":[0.0002439261,0.00002058649,0.05905233,0.00002954217,0.00002815213,0.00001043598,0.0004674019,0.9336833,0.0007200267,0.000006854134,0.005468635,0.0002687546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8778291,0.0001912976,0.1188104,0.00009728286,0.0003826159,0.0001905353,0.00002752203,0.0005969943,0.001874212],"genre_scores_gemma":[0.9838436,0.00002516155,0.01548954,0.0001025577,0.0002951765,0.000008660304,0.00008836427,0.00003489695,0.0001120396],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8044288,"threshold_uncertainty_score":0.6045983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06418754707922206,"score_gpt":0.2609680608618601,"score_spread":0.1967805137826381,"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."}}