{"id":"W4388072373","doi":"10.2316/j.2023.206-0886","title":"PROBABILISTIC MODEL CHECKING METHOD FOR ROBOT PERFORMANCE OPTIMISATION, 461-470.","year":2023,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Software Testing and Debugging Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science; Probabilistic logic; Model checking; Robot; Statistical model; Artificial intelligence; Programming language","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0008666007,0.00007742762,0.0001133315,0.0002499694,0.00007816266,0.0001837188,0.0003909918,0.00003892042,4.973339e-7],"category_scores_gemma":[0.0004916634,0.0000692981,0.00004935237,0.0001534244,0.00001427866,0.000464743,0.00007840056,0.00007468182,0.000001247559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005471172,"about_ca_system_score_gemma":0.0000773356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001978392,"about_ca_topic_score_gemma":2.223387e-7,"domain_scores_codex":[0.9991044,0.00002066766,0.0003393207,0.0001180587,0.0003134903,0.0001039987],"domain_scores_gemma":[0.9986335,0.000258094,0.0003167774,0.00009517287,0.0006573168,0.00003911105],"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":[0.000005420564,0.0000172232,0.0002814386,0.00002128025,0.00002139471,0.000001728047,0.0003075855,0.9111461,0.000200198,0.009547905,0.001092998,0.07735674],"study_design_scores_gemma":[0.0001992364,0.00006179327,0.00174378,0.0001045849,0.000008449554,0.00006863492,0.000002899851,0.9285526,0.0003136807,0.06882981,0.00004300426,0.00007149949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007178605,0.00001936473,0.9894882,0.002175871,0.0003808401,0.00008554797,0.000001460965,0.0006396312,0.00003046857],"genre_scores_gemma":[0.3264152,0.00002700661,0.6733507,0.00007923957,0.00007905978,0.000005087645,0.000003766442,0.000005609465,0.00003435914],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3192366,"threshold_uncertainty_score":0.2825895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04118871263207958,"score_gpt":0.3299484346060279,"score_spread":0.2887597219739483,"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."}}