{"id":"W2229807060","doi":"10.4271/2001-01-2654","title":"Synthetic Vision Databases for Runway Incursion Avoidance","year":2001,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Concordia University; University of Denver","keywords":"Runway; Computer science; Database; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001343132,0.00143442,0.001506408,0.002662092,0.0006911607,0.003226086,0.002847053,0.001543119,0.02127282],"category_scores_gemma":[0.004875055,0.000608766,0.001292187,0.002876814,0.000362926,0.002610274,0.001577662,0.001369363,0.01701045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001417432,"about_ca_system_score_gemma":0.001159462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01840159,"about_ca_topic_score_gemma":0.01420217,"domain_scores_codex":[0.998318,0.0002163805,0.0001457019,0.0005329569,0.0006491127,0.0001380428],"domain_scores_gemma":[0.9986264,0.0001851383,0.00007093994,0.0005190371,0.0005200578,0.00007845501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001116272,0.000464433,0.001454253,0.0005616021,0.0001996193,0.0003074095,0.00007969751,0.03988201,0.009951831,0.006705629,0.2944632,0.6448141],"study_design_scores_gemma":[0.0001324272,0.0002575198,0.004617799,0.0001999415,0.00008062244,0.0004890775,0.0003124889,0.7847474,0.02238882,0.01893464,0.1677266,0.0001125733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04265856,0.01294653,0.6219223,0.002947853,0.001990938,0.001253008,0.1722994,0.100342,0.04363948],"genre_scores_gemma":[0.2683855,0.004990968,0.2857012,0.0007524485,0.0003558095,0.0008978102,0.4196253,0.002092785,0.01719814],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02127282,"threshold_uncertainty_score":0.07116461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0115494744466143,"score_gpt":0.2479413520911936,"score_spread":0.2363918776445793,"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."}}