{"id":"W3102450234","doi":"10.1515/ijnsns-2017-0270","title":"Drive-train selection criteria for <i>n</i>-dof manipulators: basis for modular serial robots library","year":2020,"lang":"en","type":"article","venue":"International Journal of Nonlinear Sciences and Numerical Simulation","topic":"Modular Robots and Swarm Intelligence","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Department of Science and Technology, Republic of the Philippines","keywords":"Modular design; Payload (computing); Computer science; Selection (genetic algorithm); Robot; Control engineering; Joint (building); Robotics; Degrees of freedom (physics and chemistry); Simulation; Control theory (sociology); Engineering; Control (management); Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.000171665,0.000102293,0.0001602947,0.00007957128,0.0000766638,0.0001616032,0.0002145523,0.00005123004,0.00005324965],"category_scores_gemma":[0.0001326657,0.00008735494,0.00009832587,0.0001416662,0.00003604128,0.0006290433,0.00002109514,0.0000728926,9.609328e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002123599,"about_ca_system_score_gemma":0.00002816929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002332701,"about_ca_topic_score_gemma":3.407571e-7,"domain_scores_codex":[0.9990816,0.00001697439,0.0003661909,0.0001446233,0.000261971,0.0001286667],"domain_scores_gemma":[0.9994394,0.0001465438,0.0001134355,0.00002686353,0.00016896,0.0001047847],"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":[0.0001300906,0.00001996071,0.0004491239,0.00001736279,0.00003763329,0.000001294417,0.0001487917,0.9783675,0.005226689,0.0002414798,0.0001684969,0.01519158],"study_design_scores_gemma":[0.0003190022,0.0003388623,0.0005076413,0.00002421556,0.00001366204,0.000009071662,0.0000287931,0.9884393,0.003906447,0.0005952268,0.005714116,0.0001036162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1370206,0.00009173867,0.8606383,0.001283475,0.0007338609,0.0001479662,0.00002976608,0.00003083385,0.00002346783],"genre_scores_gemma":[0.9143426,0.00003744072,0.08386593,0.0002713358,0.001448845,0.000003334921,0.00001002001,0.00001447814,0.000006047434],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.777322,"threshold_uncertainty_score":0.3562231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04379414497002859,"score_gpt":0.3150321157679105,"score_spread":0.2712379707978819,"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."}}