{"id":"W7161828162","doi":"10.82308/7963","title":"Vision-guided automatic grasping using SARAH","year":2005,"lang":"en","type":"dissertation","venue":"","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"GRASP; Object (grammar); Revolute joint; Correctness; Robot; SMT placement equipment; Machine vision; Robotics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008540165,0.0002607109,0.0002701137,0.0002676098,0.0000981693,0.0001010751,0.0001150167,0.0002433911,0.00191679],"category_scores_gemma":[0.00002635332,0.0002747789,0.000102826,0.0001893054,0.000004326886,0.0001686757,0.000006685288,0.0003082391,0.0002932445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001246114,"about_ca_system_score_gemma":0.00002648041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000191752,"about_ca_topic_score_gemma":0.00006317846,"domain_scores_codex":[0.9989435,0.00001877893,0.0004079216,0.0001838568,0.0002162303,0.0002297249],"domain_scores_gemma":[0.9996035,0.00002519443,0.00007511375,0.0001910425,0.00004113954,0.00006401291],"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":[8.317512e-7,0.000005994322,0.00003617988,0.0002700916,0.00004031547,0.000002920873,0.0004221325,0.9850768,0.002315111,0.0002012066,0.001793557,0.009834819],"study_design_scores_gemma":[0.0001150606,0.000004895317,0.003897973,0.0002914295,0.00002879054,0.000005384964,0.0003068547,0.9933726,0.0003358492,0.00002092335,0.001284055,0.0003361733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5405712,0.001177554,0.04627807,0.0000317885,0.003456554,0.0005916122,3.517456e-7,0.003871886,0.4040209],"genre_scores_gemma":[0.9769762,0.0000331305,0.008612298,0.00004034518,0.0003569275,0.000008626762,0.000325543,0.0001588121,0.01348814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4364049,"threshold_uncertainty_score":0.9999704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02962046558135353,"score_gpt":0.3120899347169239,"score_spread":0.2824694691355704,"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."}}