{"id":"W2601627885","doi":"10.1007/s00773-017-0447-9","title":"Mathematical programming basis for ship resistance reduction through the optimization of design waterline","year":2017,"lang":"en","type":"article","venue":"Journal of Marine Science and Technology","topic":"Ship Hydrodynamics and Maneuverability","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia Hospital","funders":"Istanbul Teknik Üniversitesi","keywords":"Waterline; Hull; Reduction (mathematics); Froude number; Marine engineering; Computer science; Naval architecture; Mathematical optimization; Engineering; Mathematics","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":[0.001241799,0.001029211,0.001076604,0.0008079523,0.0003832196,0.001506604,0.0009141685,0.0008969936,0.004199381],"category_scores_gemma":[0.002528255,0.0006068358,0.0009623679,0.0007644745,0.0008182162,0.001002031,0.000952685,0.001758139,0.0006023495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008143383,"about_ca_system_score_gemma":0.001377375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003147648,"about_ca_topic_score_gemma":0.003076269,"domain_scores_codex":[0.9996905,0.0001396592,0.00001103895,0.00004460704,0.00008171273,0.00003241325],"domain_scores_gemma":[0.9994115,0.0003505566,0.00005443497,0.00003026609,0.0001298177,0.00002339648],"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":[0.00001222308,0.00004044261,0.0001302259,0.00006579363,0.00001653218,0.00002242348,0.00003015873,0.9300432,0.000664557,0.05663877,0.001116445,0.01121927],"study_design_scores_gemma":[0.000002773228,0.0000102247,0.00002722008,0.000007498877,0.000003322368,0.000003263195,0.000005432409,0.9883659,0.00008016397,0.0109094,0.0005823719,0.000002493803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004536569,0.0002285165,0.986478,0.0002671982,0.00004430015,0.00003102125,0.00005074447,0.00004204225,0.008321513],"genre_scores_gemma":[0.461067,0.00171867,0.515398,0.0003527248,0.0002609849,0.0006782369,0.000304095,0.0003348684,0.01988556],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004199381,"threshold_uncertainty_score":0.01404834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02104238549852553,"score_gpt":0.2636693617293915,"score_spread":0.242626976230866,"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."}}