{"id":"W2790455659","doi":"10.1080/01441647.2018.1426651","title":"Designing computable general equilibrium models for transportation applications","year":2018,"lang":"en","type":"article","venue":"Transport Reviews","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computable general equilibrium; Computer science; Operations research; Management science; Economics; Transport engineering; Macroeconomics; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008300475,0.0001496054,0.0002902057,0.00006921044,0.0004796904,0.00003054859,0.0001940288,0.0001059998,0.0001275383],"category_scores_gemma":[0.000006932401,0.0001519673,0.0001561859,0.0003936588,0.000160733,0.0004320447,5.283857e-7,0.00006755446,0.00003417487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003281045,"about_ca_system_score_gemma":0.0001304734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002619655,"about_ca_topic_score_gemma":0.0008281473,"domain_scores_codex":[0.9985262,0.0000642618,0.0005451439,0.0003200979,0.0002117666,0.0003325233],"domain_scores_gemma":[0.9992571,0.00004615244,0.0001725967,0.0001681061,0.0002217626,0.0001342735],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003874434,0.0006688288,0.01932207,0.001397795,0.0001755266,0.000006361106,0.1091069,0.3011015,0.003900303,0.4149473,0.02054946,0.1284365],"study_design_scores_gemma":[0.0004699539,0.00006543096,0.0008514572,0.0000945469,0.000142942,2.678197e-7,0.000246715,0.009726284,0.0003476046,0.003832699,0.983879,0.0003430536],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003593802,0.001140325,0.9875705,0.0002375773,0.0001656913,0.001896022,0.00008232512,0.0002132447,0.005100474],"genre_scores_gemma":[0.5250289,0.002697117,0.4605451,0.0007812275,0.001620816,0.001695626,0.00269892,0.00007701211,0.004855262],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9633296,"threshold_uncertainty_score":0.6197047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07612948014089387,"score_gpt":0.3460915031283436,"score_spread":0.2699620229874498,"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."}}