{"id":"W4408430878","doi":"10.5194/egusphere-egu25-14039","title":"A Hybrid Machine Learning Model For Ship Speed Through Water: Solve And Predict","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning","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":[0.0006928361,0.0007837797,0.0006145812,0.0004914222,0.0004886853,0.0009705824,0.001462358,0.001460973,0.002472444],"category_scores_gemma":[0.001590371,0.0004395018,0.000657195,0.0005343806,0.000578455,0.001083824,0.0007585258,0.001283977,0.000508814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009914369,"about_ca_system_score_gemma":0.001290694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02077077,"about_ca_topic_score_gemma":0.01413119,"domain_scores_codex":[0.9997962,0.00004342814,0.00001143611,0.00006814909,0.00005244682,0.00002833749],"domain_scores_gemma":[0.9993811,0.000375467,0.00005563315,0.00002388297,0.000142777,0.00002120188],"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":[0.0000192517,0.0000269537,0.0007656308,0.00001369625,0.00001304512,0.00002232391,0.00001633924,0.98703,0.0002020527,0.001846592,0.000415171,0.009628926],"study_design_scores_gemma":[0.000002361425,0.000005294916,0.00005497035,0.000001158288,0.000001916116,0.000001828618,0.000001371897,0.9992853,0.00005268047,0.0004887479,0.0001026289,0.0000017077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.137803,0.000593061,0.8440226,0.00152003,0.0001668416,0.0001542247,0.0005680458,0.001061212,0.01411094],"genre_scores_gemma":[0.8907316,0.0003147412,0.09354467,0.0003445496,0.00008755372,0.0004695291,0.0007019152,0.00007099377,0.0137345],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02077077,"threshold_uncertainty_score":0.0412997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02278330299579822,"score_gpt":0.2482674417499912,"score_spread":0.225484138754193,"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."}}