{"id":"W3212780401","doi":"10.1016/j.trd.2021.103074","title":"Reducing emissions in international transport: A supply chain perspective","year":2021,"lang":"en","type":"article","venue":"Transportation Research Part D Transport and Environment","topic":"Maritime Transport Emissions and Efficiency","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Supply chain; Container (type theory); Investment (military); Econometric model; Industrial organization; Panel data; Bilateral trade; Gravity model of trade; International trade; Multimodal transport; Estimation; Business; Environmental economics; Economics; Environmental science; Transport engineering; China; Econometrics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001804407,0.001650584,0.0008379866,0.002197896,0.001393127,0.00541794,0.00171831,0.004035685,0.01526467],"category_scores_gemma":[0.00236155,0.0006239164,0.001171259,0.005002078,0.002561614,0.009156076,0.001677113,0.002653661,0.000790413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005385196,"about_ca_system_score_gemma":0.004884819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01863625,"about_ca_topic_score_gemma":0.01716975,"domain_scores_codex":[0.998484,0.0005452511,0.00005706751,0.0001659155,0.0003778498,0.0003700697],"domain_scores_gemma":[0.9989153,0.0004394324,0.0001160445,0.00006683981,0.0003851306,0.0000772597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00009599276,0.0003185756,0.001493479,0.000532621,0.0001460026,0.0001650672,0.0002660096,0.1639816,0.00199314,0.7824876,0.00652374,0.04199619],"study_design_scores_gemma":[0.00003076801,0.0003096428,0.001412683,0.0005900926,0.000151429,0.00009718721,0.001867698,0.05732631,0.004298813,0.8622835,0.07157672,0.00005523552],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.08839547,0.03486261,0.3515057,0.09212497,0.002776018,0.0002595184,0.0009201319,0.0002417702,0.4289138],"genre_scores_gemma":[0.8655844,0.04094582,0.03416682,0.002924083,0.001499164,0.000205232,0.0003381511,0.0001756641,0.05416067],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01863625,"threshold_uncertainty_score":0.05106539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02636248523139207,"score_gpt":0.2976045738680971,"score_spread":0.2712420886367051,"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."}}