{"id":"W4401690893","doi":"10.1101/2024.08.16.607980","title":"Target interception in virtual reality is better for natural versus unnatural trajectory shapes and orientations","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Western University","funders":"","keywords":"Interception; Trajectory; Natural (archaeology); Virtual reality; Computer science; Human–computer interaction; Computer vision; Computer graphics (images); Artificial intelligence; Physics; Geology; Biology; Ecology; Paleontology; Astronomy","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002010786,0.0003882961,0.0003375557,0.0003211995,0.00005605972,0.0001675847,0.0001547702,0.0003925735,0.00001846013],"category_scores_gemma":[0.00007569452,0.0003931312,0.0001087885,0.000289173,0.00006754284,0.0001271111,0.0001151907,0.0006043123,0.0000105004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003802213,"about_ca_system_score_gemma":0.00008332648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002773976,"about_ca_topic_score_gemma":0.00003840083,"domain_scores_codex":[0.9984245,0.00004505253,0.0004257502,0.0005895902,0.0001837918,0.0003313412],"domain_scores_gemma":[0.9992915,0.00009748361,0.00006873986,0.0002919606,0.0001485659,0.0001017972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004732734,0.0001890004,0.003455226,0.004124006,0.0009298249,0.00006214916,0.000620908,0.1685409,0.8081788,0.005083878,0.008171475,0.0001706023],"study_design_scores_gemma":[0.002534868,0.0001640087,0.05477479,0.001080568,0.0002849436,2.761592e-8,0.00004082904,0.8736809,0.06272741,0.00006708384,0.002819419,0.001825097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9686477,0.0008576292,0.02529423,0.0003714929,0.003247162,0.0006252279,0.0005082863,0.0004455115,0.00000280505],"genre_scores_gemma":[0.9954861,0.0001308221,0.003733657,0.00008867193,0.0003185,0.0001221367,0.000004634699,0.0001121896,0.00000327641],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7454514,"threshold_uncertainty_score":0.9998521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01564142581462147,"score_gpt":0.2364296565181796,"score_spread":0.2207882307035581,"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."}}