{"id":"W2716243114","doi":"10.17760/d20248546","title":"Robotic grasping in cluttered scenes","year":2017,"lang":"en","type":"dissertation","venue":"","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"GRASP; Artificial intelligence; Kinematics; Computer vision; Computer science; Mobile robot; Convolutional neural network; Porting; Engineering; Robot; Simulation","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000355817,0.0006926232,0.0005943414,0.0003622667,0.000433868,0.0007895988,0.0004301477,0.0006813175,0.002484393],"category_scores_gemma":[0.001209324,0.0004723213,0.0004735585,0.0003596242,0.0008982696,0.001320439,0.001276499,0.0004677416,0.0007412526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005721718,"about_ca_system_score_gemma":0.0003797234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001972798,"about_ca_topic_score_gemma":0.001895676,"domain_scores_codex":[0.9997008,0.00004312693,0.0000099313,0.00008017758,0.00009949744,0.00006652586],"domain_scores_gemma":[0.9995853,0.0001932209,0.00005854945,0.00006699591,0.00005206205,0.00004392564],"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":[0.0003523141,0.0001292581,0.001404031,0.0003757528,0.00008699961,0.00133206,0.0005955098,0.3459009,0.3454734,0.008774404,0.002769942,0.2928055],"study_design_scores_gemma":[0.00003621301,0.0004288864,0.009949221,0.00008678241,0.00003558392,0.001498315,0.0005680597,0.8704418,0.07616596,0.02879447,0.01190151,0.00009322228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3538642,0.001657134,0.6224548,0.0003478679,0.00007464072,0.00007398751,0.00008226093,0.002115831,0.01932934],"genre_scores_gemma":[0.8461595,0.001388006,0.1417007,0.0001277258,0.00003759138,0.00004140604,0.0001784625,0.0002441965,0.01012231],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002484393,"threshold_uncertainty_score":0.008311093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02503085300707903,"score_gpt":0.27258186043625,"score_spread":0.247551007429171,"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."}}