{"id":"W2158760256","doi":"10.5267/j.dsl.2014.12.004","title":"Selection of industrial robot using axiomatic design principles in fuzzy environment","year":2015,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Axiomatic design; Fuzzy logic; Computer science; Axiom; Engineering; Artificial intelligence; Biochemical engineering; Management science; Mathematics; Manufacturing engineering","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":[0.001195095,0.00007547046,0.0001103803,0.0003985595,0.00004356501,0.00004183204,0.0001647013,0.00003795206,0.00001729919],"category_scores_gemma":[0.0002083631,0.00007255538,0.00001762542,0.0006974679,0.00007189903,0.0003142805,0.00002445993,0.000109324,0.00002031093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002526097,"about_ca_system_score_gemma":0.00003272648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009018911,"about_ca_topic_score_gemma":8.785234e-7,"domain_scores_codex":[0.9988361,0.00004316448,0.0002801477,0.0001512716,0.0005125232,0.0001767869],"domain_scores_gemma":[0.9996626,0.00007889307,0.00005432708,0.0001181374,0.00001350506,0.00007255397],"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":[0.000006079235,0.000003729338,0.004118326,9.682066e-7,8.214843e-7,0.000001096408,0.0001831001,0.8492841,0.1429424,0.00001036506,0.00003136037,0.003417628],"study_design_scores_gemma":[0.000332682,0.00001603955,0.007951975,0.00003763228,0.000002167038,0.000003938544,0.00006833635,0.9823012,0.009066907,0.00006410188,0.00006691338,0.0000881017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5217037,0.000003762543,0.4779912,0.00002936819,0.0001375709,0.00007245075,2.456589e-8,0.00001870917,0.00004327404],"genre_scores_gemma":[0.9420637,0.000001069332,0.05785621,0.00003839804,0.00002871257,0.000002157406,2.29596e-7,0.000007563802,0.000001961495],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.42036,"threshold_uncertainty_score":0.2958722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2013710368962373,"score_gpt":0.2947791459196853,"score_spread":0.09340810902344801,"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."}}