{"id":"W2218362467","doi":"10.1109/robot.1992.220098","title":"Constructive recognizability for task-directed robot programming","year":2003,"lang":"en","type":"article","venue":"","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institute for Advanced Research; Intel Corporation","keywords":"Task (project management); Computer science; Robot; Human–computer interaction; Focus (optics); Artificial intelligence; Mobile robot; Constructive; Vocabulary; Programming language; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0001369448,0.00008910109,0.0001063116,0.00003716634,0.0000600547,0.00002894358,0.00003285195,0.00005141275,0.00026463],"category_scores_gemma":[0.0002115179,0.00008819248,0.00004367276,0.000138021,0.0000185819,0.00007835386,0.000003185594,0.00008190321,0.00002204293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000367913,"about_ca_system_score_gemma":0.00001030503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003741105,"about_ca_topic_score_gemma":0.000009644977,"domain_scores_codex":[0.9994703,0.00002763502,0.0001431114,0.0001303685,0.00005041957,0.0001781467],"domain_scores_gemma":[0.9996952,0.00008412988,0.00001625437,0.00009404421,0.00006161305,0.00004872117],"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":[0.00002759584,0.00008124272,0.0214463,0.0002669381,0.0002046794,0.000002125167,0.001038455,0.7211564,0.004699734,0.01852156,0.004157698,0.2283973],"study_design_scores_gemma":[0.001776787,0.00009406258,0.008448051,0.00003547683,0.0000498335,0.00002688728,0.0008481676,0.7658575,0.009431059,0.002250534,0.2102951,0.0008865652],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01296731,0.00007485981,0.9484047,0.00002409023,0.0005876648,0.0006002264,6.450874e-7,0.001765196,0.0355753],"genre_scores_gemma":[0.8777341,0.000001589488,0.1218025,0.00001663938,0.00003027426,0.00004761772,0.000008590002,0.00002125722,0.0003374014],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8647668,"threshold_uncertainty_score":0.3596385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02044972732115136,"score_gpt":0.2367455116474307,"score_spread":0.2162957843262794,"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."}}