{"id":"W4386799654","doi":"10.1016/j.dib.2023.109590","title":"A multimodal tactile dataset for dynamic texture classification","year":2023,"lang":"en","type":"article","venue":"Data in Brief","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; Lakehead University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Lakehead University","keywords":"Tactile sensor; Artificial intelligence; Computer science; Texture (cosmology); Computer vision; Robot; Robotics; Pattern recognition (psychology); Acceleration; Image (mathematics); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007285397,0.00008623153,0.00009219906,0.0001319848,0.0001159011,0.00006945463,0.0005628874,0.00005608704,0.00008524103],"category_scores_gemma":[0.001202549,0.00008645768,0.00001826056,0.0003709172,0.00003758975,0.0006887667,0.0001495644,0.0001622614,0.0004871866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003348049,"about_ca_system_score_gemma":0.00002826716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001280987,"about_ca_topic_score_gemma":0.0003600939,"domain_scores_codex":[0.9989292,0.00004272723,0.0001731939,0.0005103627,0.0001245534,0.0002199449],"domain_scores_gemma":[0.9983315,0.0005088971,0.00006191715,0.001044982,0.00001146537,0.0000412307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009292854,0.0001807539,0.0002506875,0.00003025038,0.000004767149,0.00003637434,0.000168311,0.00006959704,0.386982,0.0006897541,0.5919727,0.01952192],"study_design_scores_gemma":[0.0003174731,0.00001704954,0.006156841,0.000008920457,0.000005507512,0.00001431726,0.0001314506,0.2965586,0.003236298,0.00009573932,0.6933501,0.0001077101],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.347766,0.000008891478,0.00450241,0.008652162,0.002600194,0.00208838,0.6320189,0.0005985998,0.001764446],"genre_scores_gemma":[0.9389527,0.0000296857,0.0001464709,0.0007325211,0.00006266146,0.00007507666,0.05939459,0.00001775229,0.0005885477],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5911867,"threshold_uncertainty_score":0.626196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1278987808644132,"score_gpt":0.3865138124705024,"score_spread":0.2586150316060891,"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."}}