{"id":"W2140440391","doi":"10.1109/iros.1995.525914","title":"Efficient edge detection from tactile data","year":2002,"lang":"en","type":"article","venue":"","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Tactile sensor; Computer science; Edge detection; Orientation (vector space); Enhanced Data Rates for GSM Evolution; Computer vision; Artificial intelligence; Elasticity (physics); Contact force; Image processing; Computational complexity theory; Image (mathematics); Algorithm; Mathematics; Geometry; Physics; Robot","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":[0.0003049192,0.0005269372,0.0008333046,0.001210268,0.0003010468,0.0006647238,0.0009410542,0.0007940517,0.002674963],"category_scores_gemma":[0.001448555,0.0003411547,0.0003265519,0.0009894826,0.0002932392,0.001315869,0.0009863366,0.0006055012,0.001351286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001771494,"about_ca_system_score_gemma":0.0003490911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003947267,"about_ca_topic_score_gemma":0.0006848955,"domain_scores_codex":[0.9996506,0.00003109148,0.00001975729,0.00006985669,0.000191752,0.0000369455],"domain_scores_gemma":[0.9993423,0.0002617593,0.00006984481,0.0001048517,0.0001908621,0.0000304656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002668531,0.00009241803,0.0004822496,0.0001626119,0.00002216259,0.00014951,0.00006796695,0.01813907,0.190021,0.003599898,0.002452453,0.7845438],"study_design_scores_gemma":[0.00004197162,0.000208773,0.001961122,0.00002307617,0.00001868623,0.0006713584,0.00005805201,0.854408,0.1270846,0.008682796,0.006795929,0.00004563091],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01014159,0.0001032799,0.9883983,0.00002892925,0.00001850723,0.00003049093,0.00005090482,0.0007698632,0.0004581958],"genre_scores_gemma":[0.1098862,0.0002042897,0.8878466,0.00007640872,0.00003495575,0.00009597748,0.000280705,0.0001075543,0.001467341],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002674963,"threshold_uncertainty_score":0.008948684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05904112345664005,"score_gpt":0.2253759807723937,"score_spread":0.1663348573157536,"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."}}