{"id":"W4402876691","doi":"10.2172/2447562","title":"Leveraging Additive Manufacturing and Coatings for Turbine Thermal Management","year":2024,"lang":"en","type":"article","venue":"","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Siemens (Canada)","funders":"National Energy Technology Laboratory; U.S. Department of Energy","keywords":"Turbine; Thermal management of electronic devices and systems; Manufacturing engineering; Thermal; Computer science; Process engineering; Mechanical engineering; Engineering","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":[0.0004823899,0.0005792072,0.0002743555,0.0007572158,0.000335895,0.00129509,0.0005025723,0.0006346194,0.002047232],"category_scores_gemma":[0.0005642024,0.0003419322,0.0004612935,0.0004013159,0.0003865563,0.001084645,0.0008999432,0.001078589,0.001027402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003535084,"about_ca_system_score_gemma":0.0003047152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002969228,"about_ca_topic_score_gemma":0.001388037,"domain_scores_codex":[0.9991384,0.00004001001,0.00002971421,0.00006792275,0.0006708744,0.00005310311],"domain_scores_gemma":[0.9997157,0.00006555988,0.00004878804,0.00005408378,0.00009292726,0.00002287672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000448301,0.00005137968,0.000282941,0.0004705405,0.00002430731,0.0002489489,0.00008222862,0.002575847,0.8552057,0.006428957,0.00243934,0.132145],"study_design_scores_gemma":[0.00001314932,0.000323711,0.0009840095,0.00007327742,0.00003796054,0.0008022973,0.00003771617,0.008080775,0.8303326,0.001887171,0.1573916,0.00003574521],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.246543,0.108389,0.4642783,0.003382826,0.007215763,0.0003025047,0.0003515728,0.003289507,0.1662477],"genre_scores_gemma":[0.6395817,0.04432913,0.2622491,0.001109606,0.001227767,0.000101784,0.0002939422,0.0006058386,0.050501],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002047232,"threshold_uncertainty_score":0.006848693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01182514485426806,"score_gpt":0.2158034994215977,"score_spread":0.2039783545673297,"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."}}