{"id":"W3111304167","doi":"10.1007/978-3-030-52693-1_8","title":"Advanced Disturbance Observer-Based Failure Detection for Force Sensor","year":2020,"lang":"en","type":"book-chapter","venue":"Advances in industrial control","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Mechatronics; Disturbance (geology); Engineering; Contact force; Reliability (semiconductor); Control engineering; Computer science; Simulation; Physics","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":[0.0001096514,0.0004343642,0.0006320719,0.0001309727,0.00007988556,0.00004179719,0.000182552,0.0005674218,0.00007748741],"category_scores_gemma":[0.0002588642,0.0004858751,0.0002111512,0.00007790195,0.00003625855,0.0003050816,0.00001024175,0.0009615548,0.00002809434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000231789,"about_ca_system_score_gemma":0.00003873884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001602357,"about_ca_topic_score_gemma":0.0001003595,"domain_scores_codex":[0.9984749,0.00002532451,0.0005551391,0.0004234609,0.0002128306,0.0003083628],"domain_scores_gemma":[0.9990018,0.0003861975,0.0002164395,0.0002345863,0.00006423888,0.00009672189],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004216851,0.000003651763,0.0000403898,0.0001107859,0.000040693,0.000007545356,0.00001479336,0.9201424,0.0003351945,0.002633172,0.0001544077,0.07609533],"study_design_scores_gemma":[0.006765989,0.0001827634,0.00001213541,0.0003087545,0.00006597347,0.000001039485,0.00001536981,0.1871819,0.0002705753,0.0010535,0.8035131,0.000628994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001339435,0.007696678,0.8973446,0.0008567844,0.006404986,0.005839898,0.0001781763,0.001341427,0.08020347],"genre_scores_gemma":[0.9758289,0.0001183293,0.001188987,0.0002489737,0.00211773,0.0003150158,0.0001746279,0.0002340184,0.01977346],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9756949,"threshold_uncertainty_score":0.9997593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02973113094791103,"score_gpt":0.232348533646756,"score_spread":0.202617402698845,"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."}}