{"id":"W2294484138","doi":"10.1145/2818346.2820756","title":"Different Strokes and Different Folks","year":2015,"lang":"en","type":"article","venue":"","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gesture; Context (archaeology); Computer science; Robot; Human–computer interaction; Computer vision; Haptic technology; Cover (algebra); Object (grammar); Gesture recognition; Artificial intelligence; Channel (broadcasting); Engineering","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":[0.000006730422,0.00008570423,0.00008941877,0.0000391771,0.00005746779,0.00005964641,0.00006342707,0.00002331832,0.0001614124],"category_scores_gemma":[0.0001183165,0.00005552641,0.00002615989,0.00002722729,0.0000341705,0.0001077493,0.00004226546,0.00008704245,0.00007243123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001855663,"about_ca_system_score_gemma":0.000004850065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001867599,"about_ca_topic_score_gemma":0.00003278676,"domain_scores_codex":[0.9994298,0.00003632128,0.00008649662,0.0001841194,0.000130009,0.0001332545],"domain_scores_gemma":[0.9995824,0.0001068786,0.00002149247,0.0001313074,0.00001236198,0.00014556],"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":[0.00008218767,0.0004196014,0.0217839,0.000009070883,0.00001101182,0.00002133948,0.001071968,0.000006688549,0.9322714,0.02577709,0.01063988,0.007905911],"study_design_scores_gemma":[0.0005133189,0.0001618794,0.01357918,0.000006249786,0.00001155715,0.00005540537,0.0006476304,0.0008174968,0.9673558,0.001315153,0.01536153,0.0001748308],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9671562,0.000004411211,0.0002307704,0.0005617498,0.0003702065,0.00006302,0.000004940494,0.00007605315,0.03153269],"genre_scores_gemma":[0.9896938,0.00001414047,0.00001314027,0.0004280613,0.00005105519,0.000005960954,4.924992e-7,0.000006147,0.009787203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03508441,"threshold_uncertainty_score":0.2264301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09014515592219298,"score_gpt":0.3016161568420997,"score_spread":0.2114710009199068,"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."}}