{"id":"W2622297308","doi":"10.1109/iccar.2017.7942759","title":"Texture roughness estimation using dynamic tactile sensing","year":2017,"lang":"en","type":"article","venue":"","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tactile sensor; Slippage; Computer vision; Surface finish; Artificial intelligence; Computer science; Scale (ratio); Object (grammar); Texture (cosmology); Robot; Engineering; Image (mathematics); Mechanical engineering; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002538193,0.0004961584,0.0004245458,0.00092861,0.0001846775,0.0006229381,0.0004057412,0.0005522454,0.001029769],"category_scores_gemma":[0.001885038,0.000237614,0.0003894395,0.0004792831,0.0003098191,0.0007024077,0.0004074164,0.0003423951,0.000264944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003194691,"about_ca_system_score_gemma":0.0002175079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001606363,"about_ca_topic_score_gemma":0.001605205,"domain_scores_codex":[0.9997464,0.00002535437,0.00001276409,0.00007637198,0.0001113166,0.00002778004],"domain_scores_gemma":[0.9994501,0.0002200029,0.00009709346,0.00007480696,0.0001306927,0.00002727933],"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":[0.0003531493,0.000134511,0.006454944,0.0001950022,0.00007251116,0.0001805246,0.0001193768,0.1284499,0.3092415,0.001156458,0.001202304,0.5524399],"study_design_scores_gemma":[0.00001813069,0.0001854818,0.01211431,0.00001713406,0.00002766174,0.0002730168,0.00004689858,0.9530638,0.03123783,0.001834415,0.001140606,0.00004058884],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2083344,0.0005096504,0.7875654,0.0001316182,0.0000821435,0.0000654367,0.00009630834,0.0009919758,0.00222313],"genre_scores_gemma":[0.8749382,0.0001895726,0.123824,0.00004866857,0.00003467257,0.00002876176,0.0001074916,0.00005109926,0.0007776108],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001606363,"threshold_uncertainty_score":0.00344491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05978537421134188,"score_gpt":0.3547152400448902,"score_spread":0.2949298658335483,"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."}}