{"id":"W4400275447","doi":"10.1109/tmrb.2024.3422652","title":"Label-Free Adaptive Gaussian Sample Consensus Framework for Learning From Perfect and Imperfect Demonstrations","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Robotics and Bionics","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; China Scholarship Council; Canada Foundation for Innovation","keywords":"Imperfect; Gaussian; Sample (material); Computer science; Artificial intelligence; Econometrics; Machine learning; Mathematics; Physics; Philosophy","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003578261,0.00017647,0.0001920357,0.0000985008,0.0005112851,0.000277198,0.0002244353,0.0002224857,0.00002771195],"category_scores_gemma":[0.0002628238,0.0001440261,0.0000743648,0.0002503041,0.0001567971,0.00006874865,0.000009721901,0.0009130003,0.000005380634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002253578,"about_ca_system_score_gemma":0.0001696315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001158614,"about_ca_topic_score_gemma":0.00006438849,"domain_scores_codex":[0.9986647,0.0001040283,0.0002148493,0.0004541335,0.000305938,0.0002562936],"domain_scores_gemma":[0.9968032,0.002622155,0.00003275108,0.0002282624,0.0000391312,0.0002745584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004211101,0.0002121701,0.00005895232,0.00009149116,0.000254758,0.00005485221,0.00107199,0.02953125,0.0001109751,0.1993903,0.0002488477,0.7689323],"study_design_scores_gemma":[0.0003618817,0.0005041931,0.00003157241,0.0002087025,0.00005674754,0.00003491293,0.00007154109,0.9754753,0.0001078712,0.02217302,0.0007906664,0.0001836504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001940225,0.0008246482,0.9867698,0.009232243,0.0007873382,0.0001499667,0.00005562337,0.00020968,0.00003050993],"genre_scores_gemma":[0.5529882,0.0005891311,0.4459765,0.0002148756,0.0001307584,0.00001583847,0.000005777846,0.00001643914,0.00006252719],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.945944,"threshold_uncertainty_score":0.5873213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02222466351777169,"score_gpt":0.2815202385697144,"score_spread":0.2592955750519427,"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."}}