{"id":"W1964609162","doi":"10.5547/issn0195-6574-ej-vol26-no1-4","title":"Combining Top-Down and Bottom-Up Approaches To Energy-Economy Modeling Using Discrete Choice Methods","year":2005,"lang":"en","type":"article","venue":"The Energy Journal","topic":"Climate Change Policy and Economics","field":"Economics, Econometrics and Finance","cited_by":129,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Cogeneration; Discrete choice; Subsidy; Top-down and bottom-up design; Economics; Carbon tax; Key (lock); Econometrics; Computer science; Environmental economics; Industrial engineering; Engineering; Greenhouse gas; Electricity generation","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.00474404,0.001107128,0.001593633,0.001135721,0.0005955921,0.002297382,0.002010925,0.001248073,0.003123409],"category_scores_gemma":[0.00926529,0.0009847915,0.001952671,0.001399019,0.001338043,0.001604311,0.001521799,0.002134361,0.0003438765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001995498,"about_ca_system_score_gemma":0.001463419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01916137,"about_ca_topic_score_gemma":0.01491335,"domain_scores_codex":[0.996682,0.002370134,0.0001243336,0.0002964066,0.0003857154,0.0001414939],"domain_scores_gemma":[0.9892848,0.009460919,0.0003596754,0.0003776618,0.0003787297,0.0001382564],"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":[0.00001949992,0.00003599053,0.0007958729,0.00003239993,0.00007943347,0.00002594751,0.00004973371,0.9650728,0.0001161498,0.0277563,0.0001187399,0.005897081],"study_design_scores_gemma":[0.00000654254,0.000007890078,0.00007874807,0.00000305853,0.000007457748,0.000002099572,0.000006290789,0.9841878,0.00006186203,0.01545786,0.000175459,0.000004887623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01269298,0.0001160634,0.9853125,0.0002127325,0.00001969155,0.00005758347,0.0001282256,0.00007784225,0.001382337],"genre_scores_gemma":[0.6240579,0.0005740946,0.3702699,0.0002225028,0.00008258841,0.0007733351,0.0004247983,0.00006123178,0.00353369],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01916137,"threshold_uncertainty_score":0.03809971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3137856512257245,"score_gpt":0.3166486388407327,"score_spread":0.002862987615008195,"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."}}