{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003108276,0.001120844,0.001564705,0.0007828593,0.0006226778,0.0009766676,0.002747738,0.001908179,0.001719597],"category_scores_gemma":[0.0105456,0.0007009392,0.0007147279,0.0008067678,0.001924507,0.001723045,0.00220547,0.002639171,0.000577117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001249956,"about_ca_system_score_gemma":0.002022513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008548339,"about_ca_topic_score_gemma":0.008264044,"domain_scores_codex":[0.9983224,0.0005152759,0.00008310788,0.0004460056,0.0004621825,0.0001709395],"domain_scores_gemma":[0.9949468,0.002737228,0.0006111508,0.0005368801,0.0009386305,0.0002292644],"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":[0.0001319344,0.0000527673,0.0004401238,0.00008014332,0.00003470363,0.00007630654,0.0001178894,0.9321284,0.00263855,0.008571226,0.0008865564,0.05484147],"study_design_scores_gemma":[0.000007349268,0.00002024153,0.00003989801,0.00000279742,0.000002194037,0.000004864454,0.000003711946,0.9966456,0.0003592169,0.002785004,0.0001243867,0.000004694371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007651247,0.0001336441,0.9909622,0.0001368154,0.00002044936,0.0000364346,0.0000330227,0.0005171687,0.0005089623],"genre_scores_gemma":[0.7753989,0.0002297704,0.218061,0.0003171389,0.0001198074,0.000370679,0.0003845462,0.0002640063,0.004854207],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008548339,"threshold_uncertainty_score":0.01699716,"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."}}