{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007816371,0.0005505512,0.0003917328,0.0003561798,0.0001972935,0.001645871,0.0003233326,0.0006086247,0.002591721],"category_scores_gemma":[0.00861466,0.0002797713,0.0003388679,0.0002068473,0.0004785873,0.001403342,0.0007376594,0.0003274669,0.0004300189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002109935,"about_ca_system_score_gemma":0.0002143681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001686158,"about_ca_topic_score_gemma":0.001447049,"domain_scores_codex":[0.9991865,0.0002041953,0.000085484,0.0002432706,0.0001921181,0.0000883774],"domain_scores_gemma":[0.9962544,0.001519165,0.001257469,0.0006221692,0.0001675662,0.0001791753],"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":[0.004482056,0.000427662,0.05406304,0.0005053729,0.0002246091,0.0005826729,0.002360598,0.03391817,0.8120815,0.001594809,0.000579633,0.08917993],"study_design_scores_gemma":[0.0003063632,0.007441632,0.6542314,0.0001380105,0.000310828,0.002012842,0.002189071,0.1014995,0.2222205,0.004108376,0.005268615,0.0002729965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912174,0.00007616825,0.007575114,0.00002907003,0.000007625012,0.000008891948,0.00003847949,0.0001151056,0.0009321944],"genre_scores_gemma":[0.9959959,0.00006752896,0.003444726,0.00001433645,0.000002545885,0.000006675632,0.0001024383,0.00004260513,0.0003232968],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002591721,"threshold_uncertainty_score":0.008670211,"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."}}