{"id":"W2933852328","doi":"10.14257/ijunesst.2016.9.3.29","title":"An Investigation on Selection Mechanisms for Mobile Camera-based Cursor Manipulation","year":2016,"lang":"en","type":"article","venue":"International Journal of u- and e- Service Science and Technology","topic":"Open Education and E-Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"China Scholarship Council; University of Toronto","keywords":"Cursor (databases); Computer science; Selection (genetic algorithm); Computer graphics (images); Human–computer interaction; Computer vision; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006624645,0.00006443288,0.00007661924,0.0006235564,0.0001707705,0.0001662423,0.0005380059,0.00005373656,0.000005768054],"category_scores_gemma":[0.0001078682,0.0000462106,0.00001181592,0.0004782088,0.00009572838,0.001408247,0.00004050287,0.00007909829,0.000002976965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007192767,"about_ca_system_score_gemma":0.0002125712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001096728,"about_ca_topic_score_gemma":0.00001360159,"domain_scores_codex":[0.9991621,0.00001927055,0.0001874705,0.0001934819,0.000320978,0.0001167402],"domain_scores_gemma":[0.9983244,0.00005677661,0.0002144468,0.00007886835,0.001252303,0.00007319493],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004846595,0.00009412882,0.003283023,0.000008614017,0.00001454259,0.000001954088,0.0005520962,0.0004026077,0.3155591,0.4497468,0.00006830994,0.2302204],"study_design_scores_gemma":[0.003641972,0.005305146,0.01547317,0.0003954196,0.00002890023,0.000532057,0.004071316,0.3079767,0.3601911,0.2959555,0.00584783,0.0005808413],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8179756,0.00001324322,0.1409724,0.04032699,0.0005056915,0.0001093197,9.076477e-7,0.00004140873,0.00005443147],"genre_scores_gemma":[0.982076,0.00001528469,0.0165031,0.001317563,0.0000581918,0.00000957507,6.626898e-7,0.000003091244,0.00001647024],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3075741,"threshold_uncertainty_score":0.1884414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01967422427330715,"score_gpt":0.2972914637259202,"score_spread":0.277617239452613,"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."}}