{"id":"W4285794869","doi":"10.21203/rs.3.rs-1800847/v1","title":"Planning Lane Changes using Advance Visual and Haptic Information","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Haptic technology; Sensory cue; Computer science; Crossmodal; Perception; Visual perception; Computer vision; Simulation; Psychology; Artificial intelligence; Neuroscience","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":[0.0002425268,0.0002667964,0.0001084364,0.0001816012,0.00008058351,0.0004510844,0.0001653813,0.000177838,0.001552617],"category_scores_gemma":[0.00239838,0.0001552057,0.0001025681,0.00007716376,0.0001802822,0.0003887544,0.0002822942,0.0001729409,0.0001601373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001201532,"about_ca_system_score_gemma":0.0003345895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008289847,"about_ca_topic_score_gemma":0.001051356,"domain_scores_codex":[0.9998461,0.00003750781,0.000008483555,0.0000310595,0.00004937668,0.00002754508],"domain_scores_gemma":[0.9990168,0.0004219337,0.0002538652,0.00008649899,0.0001121916,0.0001086788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.005032486,0.000784005,0.02662779,0.000278676,0.00006095699,0.0002965846,0.0004450202,0.02273907,0.7552957,0.001942399,0.0003095609,0.1861877],"study_design_scores_gemma":[0.0003224853,0.009033397,0.4240839,0.0001272966,0.0001963335,0.0006651091,0.001151482,0.2151328,0.3365497,0.006834456,0.005757225,0.0001456472],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.982137,0.00006880295,0.01573722,0.00003024651,0.000008319526,0.00002063831,0.00002703203,0.00008026784,0.001890458],"genre_scores_gemma":[0.997047,0.00003126851,0.002594526,0.000005414285,0.000002871307,0.000003773952,0.0000197652,0.0000056664,0.0002897862],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001552617,"threshold_uncertainty_score":0.005194008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1519010714795266,"score_gpt":0.5426192263419611,"score_spread":0.3907181548624345,"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."}}