{"id":"W4403922372","doi":"10.1145/3702319","title":"An Investigation of Multimodal Kinematic Template Matching for Ray Pointing Prediction for Target Selection in VR","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Computer-Human Interaction","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Kinematics; Selection (genetic algorithm); Computer science; Matching (statistics); Artificial intelligence; Template matching; Computer vision; Mathematics; Physics; Image (mathematics); Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000504717,0.0002124609,0.000249967,0.0009192772,0.0002777613,0.0001991309,0.0004047841,0.0001504916,0.000008170071],"category_scores_gemma":[0.00001858732,0.0002275142,0.0001500171,0.0004958229,0.00003640621,0.001332556,0.00001028862,0.0003575996,0.000003422594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002075043,"about_ca_system_score_gemma":0.00004302414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001151797,"about_ca_topic_score_gemma":0.00007363924,"domain_scores_codex":[0.9983031,0.00009264227,0.0006045522,0.0005886664,0.0001571167,0.0002539654],"domain_scores_gemma":[0.9988446,0.0004550444,0.0001795682,0.0003228833,0.0001490693,0.00004887881],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001697671,0.0006837875,0.0004921862,0.001090457,0.0002310088,0.000004336212,0.005787016,0.3908254,0.2172712,0.008493979,0.0002163145,0.3747345],"study_design_scores_gemma":[0.0005849285,0.0009471967,0.003402744,0.0005711294,0.00002373543,0.00002851381,0.00007592399,0.9492255,0.02437579,0.0204492,0.000123771,0.0001915401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2289121,0.0000072148,0.7685317,0.0003669655,0.001077818,0.000527601,0.00001786518,0.0005538785,0.000004803365],"genre_scores_gemma":[0.6720116,0.000001665465,0.3276064,0.00003146433,0.0001124451,0.0001755201,0.00002947379,0.00002027749,0.00001118139],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5584001,"threshold_uncertainty_score":0.9277762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02564837697537234,"score_gpt":0.3123002958854843,"score_spread":0.286651918910112,"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."}}