{"id":"W3162249423","doi":"10.2139/ssrn.3431694","title":"Quantifying Mileage Runs","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Experimental Behavioral Economics Studies","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University; Western University","funders":"","keywords":"Geography; Computer science; Environmental science","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":[0.001218491,0.0007407763,0.000666851,0.002496635,0.0004881109,0.002333149,0.0007954771,0.0009647516,0.005435956],"category_scores_gemma":[0.009600771,0.0002951417,0.0004751006,0.001161631,0.0007299957,0.002271783,0.001103041,0.0007291905,0.0005171884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009748293,"about_ca_system_score_gemma":0.0005212048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001958312,"about_ca_topic_score_gemma":0.001823903,"domain_scores_codex":[0.9992154,0.0002258918,0.00003366232,0.0002636408,0.0001482783,0.0001130565],"domain_scores_gemma":[0.996566,0.001853772,0.000571222,0.0004358977,0.0002741219,0.0002991533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006753161,0.0002063507,0.03826036,0.0002025097,0.0003782134,0.0001773995,0.0002794563,0.7424127,0.00870627,0.1373946,0.002632182,0.06867457],"study_design_scores_gemma":[0.00001716498,0.0002433475,0.02095024,0.00007205305,0.00007914091,0.0001183136,0.0001855083,0.8975098,0.004575186,0.07306553,0.003119307,0.00006444316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.801082,0.0008786136,0.1611828,0.0002776396,0.0001161237,0.00009132776,0.001927234,0.0008904781,0.0335537],"genre_scores_gemma":[0.9871706,0.00008508861,0.009867192,0.00001694002,0.00001978065,0.00003102409,0.0004724281,0.00007357432,0.002263377],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005435956,"threshold_uncertainty_score":0.01818502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03370892055022889,"score_gpt":0.3395899483515852,"score_spread":0.3058810278013563,"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."}}