{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002220772,0.00004987981,0.00006438719,0.00001332654,0.0001525718,0.0000269924,0.0001022207,0.00002921647,0.00427429],"category_scores_gemma":[0.00001041078,0.00003772785,0.00001131629,0.00007341399,0.00003816001,0.0001400041,0.0001255622,0.00009032235,0.000009629042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001999655,"about_ca_system_score_gemma":0.000005526239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008213043,"about_ca_topic_score_gemma":0.00007273596,"domain_scores_codex":[0.9995141,0.00001367511,0.00009234012,0.0001701015,0.00006209096,0.0001477104],"domain_scores_gemma":[0.9998502,0.0000144166,0.000008866004,0.0000811408,0.000003723055,0.0000416641],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005233552,0.0002350053,0.7108448,0.0000666014,0.000004499734,0.0000149594,0.001139782,0.0007499339,0.1910707,0.0003326696,0.0008127847,0.09467594],"study_design_scores_gemma":[0.0009559738,0.0003001573,0.1843956,0.0001217546,0.00002201046,0.00004482036,0.000181847,0.09442077,0.165133,0.002649097,0.5513387,0.0004362914],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7777278,0.00002422863,0.001918071,0.001032696,0.00002692167,0.0003441899,0.000008558877,0.00004896124,0.2188686],"genre_scores_gemma":[0.9840035,0.00006802978,0.009716362,0.00006886514,0.000008058715,0.00005460047,0.00002518065,0.000005695802,0.006049726],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.550526,"threshold_uncertainty_score":0.9966359,"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."}}