{"id":"W2980484446","doi":"10.5194/isprs-archives-xlii-4-w18-827-2019","title":"REDUCING THE TIME TO GET EMERGENCY ASSISTANCE FOR ACCIDENT VEHICLES ON THE ROAD THROUGH AN INTELLIGENT TRANSPORTATION SYSTEM","year":2019,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"IoT and GPS-based Vehicle Safety Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"General Packet Radio Service; Collision; Computer science; Global Positioning System; Real-time computing; ALARM; Intelligent transportation system; Transport engineering; Computer security; Simulation; Embedded system; Engineering; Wireless; Telecommunications","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":[0.0001995238,0.0004959745,0.0004826624,0.000754396,0.0005030864,0.0007021286,0.0007223977,0.0005213611,0.002319342],"category_scores_gemma":[0.0007974405,0.000140918,0.0003240859,0.0004155498,0.0001154659,0.0006872257,0.0005290325,0.0002781322,0.0006489098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003278133,"about_ca_system_score_gemma":0.0006808998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002725883,"about_ca_topic_score_gemma":0.002080266,"domain_scores_codex":[0.9996456,0.00004880681,0.00003189548,0.00008308528,0.000118058,0.00007252731],"domain_scores_gemma":[0.9995512,0.00006991714,0.00006742364,0.00003666169,0.0002149086,0.00005979873],"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":[0.001884899,0.00123805,0.04607416,0.001150273,0.0002713462,0.001472803,0.000598149,0.09647281,0.1183359,0.003324259,0.02358215,0.7055953],"study_design_scores_gemma":[0.0001837054,0.002479922,0.04414429,0.0001193014,0.0004833153,0.001346829,0.000962532,0.8518137,0.06715699,0.001849051,0.02928882,0.0001714792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.614426,0.002160314,0.3562285,0.0009943974,0.0007174778,0.0005361694,0.0006412819,0.00639602,0.01789985],"genre_scores_gemma":[0.9775438,0.0002652866,0.01849874,0.00007585845,0.00004354518,0.00008200181,0.0003010368,0.00002181719,0.003167945],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002725883,"threshold_uncertainty_score":0.007758975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01564084269906412,"score_gpt":0.2491676713676583,"score_spread":0.2335268286685942,"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."}}