{"id":"W178275036","doi":"","title":"SOUNDVIEW: SENSING COLOR IMAGES BY KINESTHETIC AUDIO","year":2003,"lang":"en","type":"article","venue":"","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Color space; Color image; Computer graphics (images); Image (mathematics); Image processing","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":[0.0005386634,0.0005193841,0.0003817955,0.0005029258,0.0002649381,0.0009790069,0.001241489,0.0005217392,0.01044409],"category_scores_gemma":[0.001187574,0.0003297756,0.0002307838,0.000235911,0.0008102703,0.001166909,0.001279236,0.0005091609,0.001206253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003183812,"about_ca_system_score_gemma":0.0004121132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009148856,"about_ca_topic_score_gemma":0.001061008,"domain_scores_codex":[0.9996124,0.00005356563,0.0000181113,0.00009680483,0.0001655917,0.00005348947],"domain_scores_gemma":[0.9995298,0.0002089633,0.00003313069,0.00006837646,0.00006988077,0.00008985422],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009840541,0.0002322029,0.0007044724,0.0003432453,0.00002847494,0.0001420391,0.0001995402,0.0008304925,0.8668833,0.003300028,0.002802822,0.1235493],"study_design_scores_gemma":[0.0007730904,0.004685734,0.01193023,0.0001346119,0.0001670194,0.001634776,0.0002616585,0.06821392,0.8635102,0.005142084,0.04331463,0.0002319307],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.339465,0.001264459,0.6276085,0.0003524348,0.0006165119,0.001129231,0.00164206,0.01172177,0.01620006],"genre_scores_gemma":[0.5809566,0.0006413297,0.4039857,0.0002722048,0.0000906708,0.001070329,0.001012672,0.0005496586,0.01142091],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01044409,"threshold_uncertainty_score":0.03493899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0259079870629206,"score_gpt":0.2751781695530391,"score_spread":0.2492701824901185,"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."}}