{"id":"W4400992218","doi":"10.23977/jaip.2024.070302","title":"Design of Key Technologies for Robot End Effectors","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Industrial Technology and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Key (lock); Robot end effector; Computer science; Robot; Human–computer interaction; Artificial intelligence; Computer security","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":[0.001670202,0.0001244447,0.0002900807,0.0003040886,0.00004252703,0.00005913444,0.0002753352,0.0002719105,0.00001461941],"category_scores_gemma":[0.002556398,0.0001035426,0.0001192806,0.0003522577,0.00008694697,0.0004716023,0.00001534257,0.0005387967,0.00002080662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000645495,"about_ca_system_score_gemma":0.00007073476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006070506,"about_ca_topic_score_gemma":0.000001485723,"domain_scores_codex":[0.9987633,0.00007317204,0.0007083098,0.0000956795,0.000171121,0.0001883767],"domain_scores_gemma":[0.9971476,0.002251776,0.0002171347,0.0001339186,0.0002257494,0.00002384052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005307877,0.00007553261,0.000003860544,0.0001889903,0.0005384371,0.00008894483,0.0005694509,0.145943,0.1797438,0.02989307,0.0009027537,0.6415214],"study_design_scores_gemma":[0.00005996047,0.0008799807,6.825145e-7,0.0003119395,0.0002514928,0.0002624806,0.003189229,0.1248335,0.8354581,0.01786112,0.01670699,0.0001844931],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002603716,0.004287551,0.988593,0.001134983,0.002557178,0.0003233124,0.000003108702,0.0002493508,0.0002477542],"genre_scores_gemma":[0.987537,0.000276757,0.01186262,0.000007142096,0.0002652001,0.00001555583,2.145239e-7,0.0000207476,0.00001472741],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9849333,"threshold_uncertainty_score":0.4222344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05588464101422634,"score_gpt":0.3131073649896139,"score_spread":0.2572227239753876,"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."}}