{"id":"W4225741268","doi":"10.5750/ijme.v160ia4.1073","title":"AN APPLICATION OF MACHINE LEARNING TO SHIPPING EMISSION INVENTORY","year":2021,"lang":"en","type":"article","venue":"The International Journal of Maritime Engineering","topic":"Maritime Transport Emissions and Efficiency","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"International Association of Maritime Universities","keywords":"Set (abstract data type); Emission inventory; Computer science; Model selection; Data set; Quality (philosophy); Environmental science; Operations research; Machine learning; Meteorology; Artificial intelligence; Engineering; Air quality index; Geography","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":[0.001071847,0.0007461353,0.0006002391,0.0007473664,0.0003493548,0.0007789203,0.0006485633,0.0006152475,0.00100387],"category_scores_gemma":[0.003874288,0.000311439,0.0006257813,0.0007275243,0.0003277502,0.0007371518,0.0005159112,0.0007612801,0.0003018858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008243906,"about_ca_system_score_gemma":0.0008605337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01009374,"about_ca_topic_score_gemma":0.005782091,"domain_scores_codex":[0.9994833,0.0002100401,0.00003023076,0.00008352667,0.0001555908,0.00003723678],"domain_scores_gemma":[0.9986956,0.0009268338,0.00007142296,0.0000829718,0.0002047153,0.00001851849],"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":[0.00001749364,0.00004360756,0.002098558,0.00002527374,0.00003067625,0.00003577225,0.00001782095,0.9535802,0.0005690683,0.002048831,0.0002832678,0.04124939],"study_design_scores_gemma":[0.00000114095,0.00001004572,0.0002405333,0.000002899154,0.000001973498,0.000006161354,0.000002383905,0.9977817,0.000333576,0.001349789,0.0002667209,0.000003147542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05531874,0.0003498847,0.9381291,0.0003456221,0.00005155034,0.00008975342,0.0002314165,0.0009715618,0.004512382],"genre_scores_gemma":[0.7372354,0.0003125212,0.2587025,0.0001378128,0.00006114414,0.0001655218,0.0003353796,0.00008802069,0.002961776],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01009374,"threshold_uncertainty_score":0.02007002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005417148154557534,"score_gpt":0.2194420612640964,"score_spread":0.2140249131095389,"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."}}