{"id":"W4376115103","doi":"10.3390/su15107787","title":"Carbon Footprint Analysis of the Freight Transport Sector Using a Multi-Region Input–Output Model (MRIO) from 2000 to 2014: Evidence from Industrial Countries","year":2023,"lang":"en","type":"article","venue":"Sustainability","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Carbon footprint; Gross domestic product; Urbanization; Business; Population; China; Productivity; Supply chain; Product (mathematics); Natural resource economics; Greenhouse gas; Agricultural economics; Industrial organization; Economics; Economic growth; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009449595,0.0003726709,0.0006373195,0.0001560304,0.0002468619,0.00003573362,0.0007365155,0.0002752294,0.0003215972],"category_scores_gemma":[0.0007756536,0.0002943906,0.0004253239,0.001859732,0.0007544333,0.0002254344,0.0006344236,0.0003529707,0.000009882294],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005552939,"about_ca_system_score_gemma":0.0004364267,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1203421,"about_ca_topic_score_gemma":0.00746333,"domain_scores_codex":[0.996393,0.0003720046,0.0007117814,0.0009767618,0.0008603514,0.0006860581],"domain_scores_gemma":[0.9976178,0.0002978981,0.0002279211,0.001498542,0.00008027292,0.000277515],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001828238,0.0001249597,0.7688925,0.00001569517,0.0001078162,0.000008448737,0.005828337,0.2234907,0.0008690072,0.000003017229,0.00002582794,0.0004508882],"study_design_scores_gemma":[0.0003825517,0.00004543084,0.8935546,0.00002187492,0.0004743516,1.702776e-7,0.001515471,0.1001383,0.002174645,0.001286188,0.0001063378,0.0003000126],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951145,0.00004482576,0.002076435,0.0009865469,0.0001310011,0.001337406,0.0001992648,0.00007595518,0.00003404716],"genre_scores_gemma":[0.9991044,0.0000120469,0.0003479503,0.00008385177,0.00003967762,0.00005016122,0.00002416729,0.00002572799,0.00031198],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1246622,"threshold_uncertainty_score":0.9999508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05559535151001572,"score_gpt":0.2798273094121613,"score_spread":0.2242319579021456,"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."}}