{"id":"W7098950590","doi":"","title":"ARVEE: AUTOMATIC ROAD GEOMETRY EXTRACTION SYSTEM FOR MOBILE MAPPING","year":2014,"lang":"en","type":"article","venue":"","topic":"Plant and Biological Electrophysiology Studies","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mobile mapping; Georeference; Line (geometry); Line segment; Digital mapping; Geographic information system; Road surface; Feature extraction","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0004060308,0.001090698,0.000982074,0.002359968,0.0002848659,0.0006418445,0.001431494,0.0006867226,0.008156187],"category_scores_gemma":[0.0007796907,0.0004682883,0.0007222549,0.0009843118,0.0001758696,0.0009458394,0.001062501,0.0006564986,0.006231953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003869739,"about_ca_system_score_gemma":0.0004271741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00452714,"about_ca_topic_score_gemma":0.004817485,"domain_scores_codex":[0.9995775,0.00003258665,0.00002468902,0.0001316314,0.0001826723,0.00005100465],"domain_scores_gemma":[0.9997811,0.00002929938,0.00002418044,0.00005511009,0.00009340755,0.00001692001],"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":[0.0005191999,0.0002088983,0.004748335,0.000458943,0.0002761282,0.0005070301,0.0002796547,0.02627282,0.1097692,0.00278691,0.1021503,0.7520226],"study_design_scores_gemma":[0.0003794975,0.0003510002,0.02311531,0.0001251424,0.0001442281,0.001108595,0.0002572603,0.5525405,0.1786982,0.004827179,0.2381532,0.0002998819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01939418,0.0003640887,0.6866524,0.00007894897,0.00009068081,0.000376909,0.01084302,0.2785124,0.003687359],"genre_scores_gemma":[0.1762751,0.0004196646,0.7482623,0.0002381476,0.00005819841,0.0008180777,0.05538922,0.005080022,0.01345925],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008156187,"threshold_uncertainty_score":0.02728516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01839877705803284,"score_gpt":0.219473616052702,"score_spread":0.2010748389946691,"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."}}