{"id":"W4411690384","doi":"10.1016/j.rineng.2025.105833","title":"Planning for offshore wind: An integrated smart approach combining NREL classification and TOPSIS","year":2025,"lang":"en","type":"article","venue":"Results in Engineering","topic":"Maritime Transport Emissions and Efficiency","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"TOPSIS; Marine engineering; Offshore wind power; Submarine pipeline; Computer science; Engineering; Environmental science; Operations research; Artificial intelligence; Systems engineering; Wind power; Electrical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003171483,0.00009471636,0.0001066072,0.00007810633,0.00006337486,0.00002190488,0.0001035143,0.00006740705,0.0000100128],"category_scores_gemma":[0.00006396686,0.00008989924,0.00001611152,0.0002697927,0.00002543367,0.0001014305,0.0000243449,0.0001189965,8.391445e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000062204,"about_ca_system_score_gemma":0.000007485304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008126053,"about_ca_topic_score_gemma":0.000006811748,"domain_scores_codex":[0.9992732,0.000008335168,0.0002086269,0.0002577309,0.0000720649,0.0001800454],"domain_scores_gemma":[0.9997301,0.00005547099,0.0000215671,0.0001380159,0.000004381809,0.00005053631],"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":[0.0001631055,0.0002371905,0.101046,0.0001700271,0.0000137684,0.0000045563,0.002800231,0.8415121,0.01008569,0.0009991231,0.0004731967,0.04249502],"study_design_scores_gemma":[0.0005084851,0.00002479883,0.1721578,0.0001001333,0.000005679372,0.000001027058,0.0003942414,0.8213636,0.0001281377,0.00001603528,0.0051873,0.0001127352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8712373,0.0000720388,0.1114132,0.0001109668,0.0001106082,0.000366518,0.00002200352,0.0001095821,0.0165577],"genre_scores_gemma":[0.9855231,0.000005735274,0.01410992,0.000014595,0.000006662374,0.00001860297,0.00007359975,0.000007740789,0.0002400167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1142858,"threshold_uncertainty_score":0.3665985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01621036603855071,"score_gpt":0.2448512122565055,"score_spread":0.2286408462179547,"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."}}