{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002106105,0.00005327078,0.00005793146,0.00002447378,0.000110091,0.000004459673,0.0001410254,0.0000283918,0.007146263],"category_scores_gemma":[0.00001067141,0.00004352553,0.00001616932,0.0001910986,0.00005957096,0.00007516861,0.00003689027,0.00004811093,0.0002007754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002251432,"about_ca_system_score_gemma":0.000003578602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009106833,"about_ca_topic_score_gemma":0.00008622064,"domain_scores_codex":[0.9994176,0.00001648196,0.0001276466,0.0001782351,0.0001440414,0.0001159634],"domain_scores_gemma":[0.9996563,0.000005355483,0.00002951901,0.000164685,0.000006429424,0.0001377239],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001561339,0.0001142466,0.4349411,0.00000666276,9.796319e-7,2.98084e-7,0.0008348848,0.001202948,0.4867252,0.0001203519,0.0002854453,0.07575233],"study_design_scores_gemma":[0.0003420109,0.0008577838,0.229774,0.00004546913,0.00001389099,0.000003350435,0.0004026387,0.5030431,0.1058133,0.0003580087,0.158894,0.0004525172],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9079092,0.000004323839,0.03643468,0.00008027622,0.00001655698,0.0001005437,5.2422e-7,0.00004614363,0.0554078],"genre_scores_gemma":[0.9953126,0.000001196404,0.002993564,0.00009222522,0.00001953381,0.000003986379,0.000005767163,0.000005393542,0.001565679],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5018401,"threshold_uncertainty_score":0.9937614,"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."}}