{"id":"W7132488581","doi":"","title":"Machine learning-based power prediction for the icebreaker Henry Larsen using vessel motions and environmental data in open water conditions","year":2024,"lang":"en","type":"article","venue":"NPARC","topic":"Maritime Transport Emissions and Efficiency","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Time series; Series (stratigraphy); Power (physics); Open water; Root mean square; Coast guard; Random forest; Guard (computer science)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003660049,0.00008622176,0.00006950238,0.0000281007,0.0003321672,0.00008838286,0.0002654457,0.00003910752,0.009795788],"category_scores_gemma":[0.000007600921,0.00005482751,0.00002007833,0.00007416576,0.0001410397,0.0002998539,0.0002305368,0.00014186,0.00003270906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004864309,"about_ca_system_score_gemma":0.000009875614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003177675,"about_ca_topic_score_gemma":0.0000700161,"domain_scores_codex":[0.9992235,0.00002872246,0.0001366943,0.0003111554,0.000122257,0.0001776948],"domain_scores_gemma":[0.9996184,0.00005757437,0.00001180511,0.0002604969,0.000001008558,0.00005067631],"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":[0.0001194229,0.001504715,0.2710915,0.0001354287,0.0001111139,0.00008034227,0.002933369,0.1384532,0.5576135,0.000810923,0.01438212,0.01276436],"study_design_scores_gemma":[0.0003192893,0.00003833797,0.03330814,0.00002939907,0.00004050089,0.00001111421,0.0001220074,0.895506,0.000256652,0.0004128572,0.06985455,0.0001011935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9457223,0.0003104461,0.0309564,0.004789363,0.0003406862,0.001970319,0.004286605,0.0001174193,0.0115064],"genre_scores_gemma":[0.9974638,0.00002242293,0.0006832925,0.00008988359,0.00001330266,0.00003517831,0.000915609,0.00001349067,0.0007630039],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7570528,"threshold_uncertainty_score":0.9911094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02220845033499116,"score_gpt":0.2628828512069643,"score_spread":0.2406744008719731,"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."}}