{"id":"W2995010809","doi":"10.1155/2019/7219047","title":"Estimating Productivity Loss Cost according to Severity of Vehicle Crash Injury","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Crash; Tobit model; Productivity; Willingness to pay; Transport engineering; Engineering; Econometrics; Economics; Computer science; Economic growth","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.00197054,0.0005381274,0.0003500376,0.003429332,0.000148508,0.0008841365,0.0005040833,0.000558274,0.001458006],"category_scores_gemma":[0.01065131,0.0002464,0.0009947342,0.001782024,0.0002641499,0.0009593848,0.0008030414,0.0005479786,0.0002524056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007704716,"about_ca_system_score_gemma":0.0003640283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005903066,"about_ca_topic_score_gemma":0.002888444,"domain_scores_codex":[0.9987902,0.0004672916,0.0001509004,0.0001705275,0.0002670982,0.0001541387],"domain_scores_gemma":[0.9938927,0.003329037,0.001505081,0.00043199,0.0006447478,0.00019648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003455463,0.0002218547,0.9161282,0.0000631048,0.0002810585,0.0002923193,0.0001849971,0.05539081,0.0007834874,0.0008936425,0.0003607063,0.02505417],"study_design_scores_gemma":[0.00001984618,0.0004978861,0.7539177,0.00003555201,0.0001441825,0.000455814,0.0007629708,0.2415096,0.0007193338,0.001178649,0.0007093577,0.00004913143],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9907872,0.0001632161,0.007668478,0.00006698376,0.0000085831,0.00005497133,0.0004372516,0.00001920604,0.0007940501],"genre_scores_gemma":[0.9979331,0.00005782978,0.001291026,0.000006198336,0.000003684875,0.00002253934,0.0004443034,0.000002244405,0.0002389501],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005903066,"threshold_uncertainty_score":0.01173741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0306280194560617,"score_gpt":0.2406207593406423,"score_spread":0.2099927398845806,"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."}}