{"id":"W2139699415","doi":"10.1145/958432.958445","title":"TorqueBAR","year":2003,"lang":"en","type":"article","venue":"","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Kinesthetic learning; Computer science; Haptic technology; Mobile device; Human–computer interaction; Inertia; Object (grammar); Hammer; Exploit; Simulation; Artificial intelligence; Computer vision; Engineering","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":[0.0002829776,0.0009420736,0.0004819002,0.0007693762,0.0005817716,0.001702771,0.001811649,0.001379261,0.09421995],"category_scores_gemma":[0.002304404,0.0003778628,0.0003680431,0.0004695721,0.0004310271,0.0017827,0.001431592,0.0005933291,0.02558021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002537174,"about_ca_system_score_gemma":0.0003876636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006007453,"about_ca_topic_score_gemma":0.0005344684,"domain_scores_codex":[0.9996106,0.00003756648,0.00002399239,0.00007182778,0.0001991137,0.00005691873],"domain_scores_gemma":[0.9989666,0.0002386407,0.00007769866,0.0002203713,0.0003563126,0.0001404998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001657964,0.0003640178,0.001172857,0.001590702,0.00003638642,0.001448433,0.0007543159,0.002650023,0.242612,0.03055612,0.1168717,0.6002856],"study_design_scores_gemma":[0.0002257115,0.001223367,0.003322167,0.0002725101,0.00008928769,0.002490246,0.0003592629,0.01969282,0.1410382,0.007276501,0.8238483,0.0001615157],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.06632677,0.002729944,0.6438659,0.001911907,0.007733698,0.001418468,0.005165152,0.06066858,0.2101796],"genre_scores_gemma":[0.5211298,0.001746838,0.1596044,0.001832405,0.0006267158,0.0009295699,0.004454544,0.003261005,0.3064148],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.09421995,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05219212877557822,"score_gpt":0.2961529643357942,"score_spread":0.243960835560216,"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."}}