{"id":"W7132159528","doi":"","title":"Predicting icebreaker resistance using machine learning and scale model testing","year":2025,"lang":"en","type":"article","venue":"NPARC","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hull; Propulsion; Scale (ratio); Arctic; Multivariate statistics; Sea ice; Predictive modelling","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003073347,0.001507942,0.0008160896,0.001426313,0.0005039693,0.001117786,0.001645388,0.001178359,0.00166997],"category_scores_gemma":[0.008371629,0.0003495849,0.001281906,0.0009781049,0.0006070244,0.001363782,0.001023358,0.001669012,0.0007132235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008840519,"about_ca_system_score_gemma":0.000896753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01332141,"about_ca_topic_score_gemma":0.01472403,"domain_scores_codex":[0.9989815,0.0003085845,0.00008319209,0.0003207413,0.000178624,0.0001271949],"domain_scores_gemma":[0.9940577,0.00379558,0.0004235594,0.0006375205,0.0008053536,0.0002803923],"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":[0.0002221484,0.0003603838,0.03449053,0.00006884508,0.0001477137,0.0001261749,0.00004461932,0.8984705,0.001444545,0.0004560188,0.003280223,0.06088845],"study_design_scores_gemma":[0.000006888653,0.000045901,0.00185038,0.000004587655,0.000005003135,0.000008404796,0.00001371484,0.9970523,0.0005117426,0.0003241837,0.0001716922,0.000005330057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8733264,0.0008941669,0.1153558,0.0005123521,0.0002078544,0.0001567387,0.002801005,0.003589853,0.003155821],"genre_scores_gemma":[0.967774,0.00009580499,0.02574079,0.0000967616,0.00004335514,0.00007467074,0.005040919,0.00009186707,0.001041785],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01332141,"threshold_uncertainty_score":0.02648771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01376433751276817,"score_gpt":0.2207537146330427,"score_spread":0.2069893771202745,"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."}}