{"id":"W2032424390","doi":"10.1145/2702123.2702185","title":"Grip Change as an Information Side Channel for Mobile Touch Interaction","year":2015,"lang":"en","type":"article","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Mobile device; Tilt sensor; Channel (broadcasting); Point (geometry); Smartwatch; Tilt (camera); Mobile interaction; Motion (physics); Human–computer interaction; Computer vision; Simulation; Real-time computing; Engineering; Embedded system; Telecommunications; Wearable computer","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":[0.0004153562,0.0008074632,0.0004916884,0.0004214053,0.0002531938,0.001258428,0.000662728,0.0008005275,0.004278682],"category_scores_gemma":[0.002155784,0.0004108795,0.0004850962,0.0002548188,0.0004675194,0.001649853,0.0008537409,0.0009430997,0.0007600014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003405092,"about_ca_system_score_gemma":0.0003339938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00190318,"about_ca_topic_score_gemma":0.001737313,"domain_scores_codex":[0.9995964,0.00006379552,0.00001711232,0.00009528505,0.0001781631,0.00004919282],"domain_scores_gemma":[0.9988195,0.0007075172,0.0001075991,0.0001378752,0.0001647378,0.00006267948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.002226991,0.0004991872,0.01505442,0.0004464204,0.0001294344,0.001213433,0.0005996487,0.3757184,0.3684642,0.01309138,0.004855446,0.2177011],"study_design_scores_gemma":[0.00001737196,0.0002131213,0.002473728,0.00001441907,0.00002413027,0.0001546248,0.00002646734,0.9645033,0.02980507,0.001616438,0.001120185,0.00003118266],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2551408,0.0005714326,0.7289283,0.0004086432,0.0002540938,0.0001362697,0.0005122,0.004185491,0.009862727],"genre_scores_gemma":[0.9692909,0.0001631046,0.02737944,0.00009180807,0.00005396917,0.00004006103,0.0001137132,0.0001131518,0.002753831],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004278682,"threshold_uncertainty_score":0.01431364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06793888484387334,"score_gpt":0.3292745117421858,"score_spread":0.2613356268983125,"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."}}