{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00002377802,0.00004381669,0.00003887833,0.00002631343,0.00003166714,0.00002224926,0.00008181032,0.00002544945,0.00372428],"category_scores_gemma":[0.00001233609,0.00004285406,0.000008902819,0.00006335065,0.000002824528,0.00004434197,0.00002206116,0.00007130771,0.001072905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001479061,"about_ca_system_score_gemma":3.852042e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004094022,"about_ca_topic_score_gemma":0.00002396613,"domain_scores_codex":[0.9997078,0.000006508864,0.00006517367,0.00009239666,0.00005613364,0.00007202019],"domain_scores_gemma":[0.9997016,0.00001848554,0.00000638681,0.0002433534,0.000004552441,0.00002565103],"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":[3.144046e-7,0.000005461532,0.00005953771,0.000001725549,0.000005590209,5.647187e-7,0.00007436831,0.9743533,0.002650474,0.00001009406,0.003247978,0.01959063],"study_design_scores_gemma":[0.00006957066,0.000002127426,0.001891427,0.000001868784,0.000002933969,5.785387e-7,0.00002253421,0.9777328,0.0008468918,0.000001873899,0.01937174,0.0000556461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1719536,0.0002434474,0.7308198,0.0000478057,0.0008382703,0.00008015191,0.00000156442,0.001026324,0.09498902],"genre_scores_gemma":[0.9987172,0.000004155347,0.000495817,0.00002031102,0.0001014909,0.000001025125,0.0000153817,0.0000106201,0.0006339902],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8267636,"threshold_uncertainty_score":0.9997049,"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."}}