{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001940894,0.0002585059,0.0003228866,0.00005754257,0.00007403533,0.00007025019,0.0001387586,0.0001801276,0.000157814],"category_scores_gemma":[0.00002615228,0.0002185634,0.0001120256,0.00001939991,0.00001941959,0.00006147374,0.0002802364,0.00059539,0.000005887845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004716394,"about_ca_system_score_gemma":0.00002606113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000070334,"about_ca_topic_score_gemma":0.00001374008,"domain_scores_codex":[0.9990213,0.00001371132,0.000293081,0.0003078778,0.0001107824,0.000253242],"domain_scores_gemma":[0.9996066,0.00005731373,0.00002146386,0.0002092433,0.00004709875,0.0000582426],"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":[0.0000306718,0.00001257808,0.0001105577,0.001535126,0.0001379606,0.000001856116,0.000721237,0.9891539,0.0001645088,0.001697754,0.002107291,0.004326535],"study_design_scores_gemma":[0.0004474371,0.000009719182,0.000009147075,0.00009761695,0.00004623256,0.000002212878,0.000009016119,0.976879,0.002051598,0.01470418,0.005502972,0.0002408848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005173889,0.0003757264,0.9347239,0.0003505712,0.0003414755,0.0007532416,0.0004499084,0.0009405327,0.05689075],"genre_scores_gemma":[0.8818618,0.0008884097,0.06983215,0.0003381075,0.0002406899,0.0001497266,0.003135458,0.0001016635,0.04345196],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8766879,"threshold_uncertainty_score":0.8912758,"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."}}