{"id":"W2288761778","doi":"10.1109/robio.2015.7418921","title":"Robotic grasp detection using extreme learning machine","year":2015,"lang":"en","type":"article","venue":"","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"GRASP; Artificial intelligence; Computer science; Extreme learning machine; Classifier (UML); Object detection; Benchmark (surveying); Computer vision; Grippers; Histogram; Object (grammar); Pattern recognition (psychology); Machine learning; Engineering; Artificial neural network; Image (mathematics)","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":[0.0007037873,0.000724773,0.001200541,0.001556645,0.0003927356,0.000596401,0.001368584,0.001044377,0.001233225],"category_scores_gemma":[0.001540448,0.0004750227,0.001017208,0.000795061,0.0005951866,0.0008719665,0.001250398,0.0008095985,0.0004834098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005818456,"about_ca_system_score_gemma":0.0004212501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008518602,"about_ca_topic_score_gemma":0.0009059254,"domain_scores_codex":[0.9992979,0.00009971348,0.00004051282,0.0002101844,0.0002705844,0.00008113849],"domain_scores_gemma":[0.9992402,0.0002162124,0.0001774231,0.000150003,0.000167063,0.0000492177],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001790352,0.0001487924,0.002218263,0.0001001834,0.00009849436,0.0003936931,0.00007655891,0.2665403,0.0405029,0.002969521,0.002229507,0.6845428],"study_design_scores_gemma":[0.000005423242,0.00006572736,0.0008427959,0.000005252884,0.000006201711,0.0001156169,0.000009181021,0.9892561,0.006147511,0.002968761,0.0005628314,0.00001454884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02581764,0.0001428057,0.9718301,0.00005892973,0.00002199793,0.00003149832,0.0000249991,0.001241971,0.0008300401],"genre_scores_gemma":[0.5873864,0.0001592334,0.4087684,0.0001325972,0.00005037504,0.0001309188,0.0002110444,0.00008929058,0.003071799],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001556645,"threshold_uncertainty_score":0.004221618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07174289087635585,"score_gpt":0.2683711390960014,"score_spread":0.1966282482196455,"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."}}