{"id":"W3176162164","doi":"","title":"Machine Learning Techniques for Ship Performance Predictions in Open Water and Ice","year":2021,"lang":"en","type":"article","venue":"NPARC","topic":"Maritime Transport Emissions and Efficiency","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Open water; Computer science; Environmental science; Artificial intelligence; Meteorology; Marine engineering; Engineering; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0008483135,0.0009274371,0.0004866818,0.0006859635,0.0002381992,0.0005969751,0.00067153,0.0006619968,0.0008000405],"category_scores_gemma":[0.002663996,0.0002954694,0.0005161394,0.0007613178,0.0002841048,0.0007177856,0.0003976877,0.001138795,0.0002941896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006876889,"about_ca_system_score_gemma":0.0006556229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009718247,"about_ca_topic_score_gemma":0.006306462,"domain_scores_codex":[0.9998326,0.00004482043,0.00001502701,0.00004781637,0.00003541742,0.00002431528],"domain_scores_gemma":[0.9989994,0.0006961387,0.0001006396,0.00004562834,0.000136167,0.00002212456],"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":[0.00002089804,0.00003701058,0.001636312,0.0000220822,0.00002250996,0.0000203608,0.0000136848,0.95559,0.0005259389,0.0006534672,0.0002715685,0.04118631],"study_design_scores_gemma":[6.915936e-7,0.000005599192,0.0001706041,0.000001955819,0.000001059537,0.000001870832,0.000002287771,0.9990503,0.0001579004,0.0005449193,0.00006155245,0.000001305735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1352266,0.001326674,0.8582256,0.0005188631,0.00007536339,0.00005830401,0.0003979064,0.00134883,0.002821802],"genre_scores_gemma":[0.9272293,0.0004162704,0.0697377,0.00009718937,0.00005388457,0.00009015443,0.0005281107,0.00004934,0.001798123],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009718247,"threshold_uncertainty_score":0.01932335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01302754419191941,"score_gpt":0.236966664837315,"score_spread":0.2239391206453956,"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."}}