{"id":"W2750013145","doi":"","title":"グリーン輸送システムのモードシフトへの炭素低減効果の評価【Powered by NICT】","year":2016,"lang":"ja","type":"article","venue":"Journal of Advanced Transportation","topic":"Military Technology and Strategies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Aeronautics; Aerospace engineering; Engineering; Environmental science; Systems engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002099551,0.0002818461,0.0004553697,0.0002009495,0.00006277075,0.00001179349,0.0002700024,0.0003010753,0.0002829285],"category_scores_gemma":[0.00004348646,0.0002132174,0.0002272635,0.0002144549,0.0001156842,0.001058107,0.00000170689,0.0003893514,0.00002711077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001112775,"about_ca_system_score_gemma":0.0000631826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003948707,"about_ca_topic_score_gemma":0.0000378998,"domain_scores_codex":[0.9981154,0.00003013913,0.001030519,0.0001847455,0.0003011541,0.0003380497],"domain_scores_gemma":[0.9989343,0.0001247699,0.0003847195,0.0002165464,0.0002073926,0.0001322224],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001443085,0.0003503006,0.002519841,0.0004553228,0.0009732206,0.0004161314,0.005619677,0.0231606,0.7993708,0.01550071,0.008275717,0.1419146],"study_design_scores_gemma":[0.03752788,0.008521675,0.3338983,0.007836456,0.002133247,0.0004125569,0.01814623,0.0002058569,0.2119298,0.1610258,0.2136782,0.004684],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.953858,0.0183157,0.02156642,0.001790358,0.002233174,0.0002047501,0.0002401311,0.0001457192,0.001645741],"genre_scores_gemma":[0.9863037,0.01027618,0.002881516,0.00002668605,0.0001284317,0.00000312879,0.00001558824,0.00004012539,0.0003246301],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.587441,"threshold_uncertainty_score":0.8694751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004916072688546676,"score_gpt":0.2133579776355112,"score_spread":0.2084419049469645,"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."}}