{"id":"W3003204374","doi":"10.1109/iros40897.2019.8967694","title":"Benchmarking and Workload Analysis of Robot Dynamics Algorithms","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Benchmarking; Computer science; Implementation; Software; Robot; Robotics; Workload; Suite; Motion control; Software framework; Task (project management); Artificial intelligence; Computer engineering; Algorithm; Control engineering; Software development; Software engineering; Software construction; Engineering; Systems engineering; Operating system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003925028,0.001616614,0.0009473021,0.002012127,0.0007948959,0.001434102,0.002889936,0.0009025171,0.002814866],"category_scores_gemma":[0.02165962,0.0005118243,0.0006506872,0.002785932,0.0008654436,0.002021523,0.001307252,0.001200464,0.001179851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001270839,"about_ca_system_score_gemma":0.001647339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004464163,"about_ca_topic_score_gemma":0.003165373,"domain_scores_codex":[0.9917578,0.00197275,0.0008489678,0.001329114,0.003185189,0.0009061315],"domain_scores_gemma":[0.9855981,0.006125311,0.0007094769,0.003274732,0.003713085,0.0005792542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00361771,0.001526681,0.03336718,0.002110637,0.0003998802,0.0005738665,0.001099036,0.4567597,0.03756801,0.01883839,0.0612057,0.3829331],"study_design_scores_gemma":[0.0001812434,0.0008511099,0.01023203,0.00007397537,0.00005956118,0.0001754722,0.0003598314,0.9424403,0.02702517,0.005772113,0.01277073,0.00005846221],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8595199,0.002922392,0.08746061,0.0007791588,0.0007077229,0.0004117168,0.004688095,0.02342787,0.0200825],"genre_scores_gemma":[0.9142884,0.0005051253,0.07043249,0.0001744869,0.00007859762,0.0003976766,0.01031597,0.001875642,0.001931675],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004464163,"threshold_uncertainty_score":0.02075779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002828366365236519,"score_gpt":0.1766313303102917,"score_spread":0.1738029639450552,"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."}}