{"id":"W3095888362","doi":"10.1155/2020/8892781","title":"Government Subsidies and Revenue Sharing Decisions for Port and Shipping Service Supply Chain in Emission Control Areas","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Maritime Transport Emissions and Efficiency","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Business; Subsidy; Revenue sharing; Port (circuit theory); Revenue; Supply chain; Incentive; Service (business); Government (linguistics); Industrial organization; Profit (economics); Finance; Economics; Marketing; Microeconomics; 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":[0.0002563063,0.0000972331,0.0001931469,0.0000213109,0.00007938088,0.00001170143,0.00007816825,0.00003859067,0.00005174048],"category_scores_gemma":[0.00005609316,0.00008531171,0.00003747521,0.000137158,0.0000237516,0.000296836,0.000004471533,0.0001134761,3.139285e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004340682,"about_ca_system_score_gemma":0.0000105228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000020245,"about_ca_topic_score_gemma":0.0002078926,"domain_scores_codex":[0.9990042,0.000008620224,0.0004204585,0.0001780799,0.000252726,0.0001359428],"domain_scores_gemma":[0.9995054,0.00008043816,0.0001852294,0.00004917695,0.00001808125,0.0001616537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001024972,0.0001043685,0.8119988,0.0001282357,0.0000151818,0.00007127548,0.00773563,0.0554807,0.08892224,0.00006266142,0.00006682023,0.03438908],"study_design_scores_gemma":[0.002109642,0.0001762765,0.9903462,0.0002885544,0.0000393685,0.000009550527,0.00104063,0.003059402,0.0007563082,0.0004870175,0.001557865,0.0001291711],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913421,0.0002238015,0.006265853,0.001843276,0.00003122924,0.0002086592,0.00004460332,0.000004285986,0.00003622192],"genre_scores_gemma":[0.9954827,0.0002962342,0.003895133,0.0002635268,0.00002180514,0.000005983803,0.000006776742,0.00000890085,0.00001890663],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1783474,"threshold_uncertainty_score":0.3478911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01137235197741832,"score_gpt":0.2307586265310932,"score_spread":0.2193862745536748,"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."}}