{"id":"W2914647775","doi":"10.3940/rina.ijme.2018.a4.500","title":"An Application of Machine Learning to Shipping Emission Inventory","year":2018,"lang":"en","type":"article","venue":"","topic":"Maritime Transport Emissions and Efficiency","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Emission inventory; Set (abstract data type); Computer science; Quality (philosophy); Model selection; Data set; Key (lock); Selection (genetic algorithm); Environmental science; Operations research; Machine learning; Artificial intelligence; Engineering; Meteorology; Air quality index","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001086366,0.0007410531,0.0005793174,0.0007280136,0.0003548084,0.000753857,0.0006416857,0.0006172986,0.001012544],"category_scores_gemma":[0.004063413,0.0003151516,0.0006137842,0.000703142,0.0003356021,0.0007484262,0.0005157511,0.0007655023,0.0002962739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000855231,"about_ca_system_score_gemma":0.0008506779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01031985,"about_ca_topic_score_gemma":0.005919414,"domain_scores_codex":[0.9994946,0.0002118106,0.00002910581,0.00008114,0.0001471502,0.00003631007],"domain_scores_gemma":[0.9986838,0.0009481483,0.00007036568,0.00008472695,0.0001942023,0.00001883104],"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":[0.00001603045,0.00004029577,0.001949222,0.00002242954,0.00002815866,0.00003149316,0.00001655624,0.9576981,0.0005131778,0.002047623,0.0002674907,0.03736935],"study_design_scores_gemma":[0.000001139256,0.000009348598,0.0002273376,0.000002629932,0.000001870779,0.000005704131,0.000002241366,0.997731,0.0003208764,0.001425522,0.0002691849,0.000003029678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05733537,0.0003377428,0.9360801,0.0003697063,0.00005000917,0.00008631274,0.0002287198,0.0009372488,0.004574814],"genre_scores_gemma":[0.7424431,0.0003011797,0.2535127,0.0001401255,0.00006110111,0.0001635461,0.0003226905,0.00009029857,0.002965378],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01031985,"threshold_uncertainty_score":0.02051955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008756983726506304,"score_gpt":0.2452615150280036,"score_spread":0.2365045313014973,"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."}}