{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004210467,0.0001005574,0.0001040023,0.0001019824,0.0001519414,0.00013587,0.0002879568,0.00003963293,0.00001972119],"category_scores_gemma":[0.0001251988,0.00008614647,0.00003706375,0.0003121706,0.00001257424,0.0003053384,0.0001522871,0.0001867267,0.000127477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000488301,"about_ca_system_score_gemma":0.00003243079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008055452,"about_ca_topic_score_gemma":0.00005583723,"domain_scores_codex":[0.9990648,0.0001223802,0.0001264151,0.0002474613,0.0002181673,0.0002207475],"domain_scores_gemma":[0.9994899,0.00002847037,0.00005444165,0.0002382073,0.0000547411,0.0001342076],"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":[0.00001113964,0.00007488581,0.01686535,0.00001394288,0.00002193555,0.00002913431,0.001125909,0.6161641,0.003773536,0.006019705,0.0001703731,0.35573],"study_design_scores_gemma":[0.0002144037,0.00007720313,0.00107828,0.000005228587,0.00000383648,0.00005737861,0.00002353987,0.9944378,0.0003941474,0.0006769176,0.002907541,0.0001237725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05325403,0.00008728162,0.9388348,0.0002568552,0.0003702592,0.00003726009,1.681283e-8,0.0004616996,0.006697802],"genre_scores_gemma":[0.95403,0.000001126166,0.04320931,0.00009715402,0.00007599005,0.000001076971,5.955218e-7,0.000008751859,0.002576015],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.900776,"threshold_uncertainty_score":0.3512951,"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."}}