{"id":"W2519066912","doi":"10.1039/c6ra13194h","title":"Preparation and characterization of superhydrophobic composite coatings on a magnesium–lithium alloy","year":2016,"lang":"en","type":"article","venue":"RSC Advances","topic":"Surface Modification and Superhydrophobicity","field":"Materials Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Zhejiang University","keywords":"Composite number; Characterization (materials science); Lithium (medication); Magnesium; Alloy; Materials science; Magnesium alloy; Metallurgy; Chemical engineering; Nanotechnology; Composite material; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001830343,0.000116463,0.0001719562,0.00005083594,0.00008731859,0.0000270305,0.00009871944,0.00004511639,0.0002480755],"category_scores_gemma":[0.00003159134,0.00007990407,0.00002371786,0.00008574229,0.0001397121,0.0004715178,0.00002631756,0.00002673832,0.00007756295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002371018,"about_ca_system_score_gemma":0.00001746226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001264674,"about_ca_topic_score_gemma":0.00000477402,"domain_scores_codex":[0.9990259,0.00008201763,0.0002604853,0.0002870973,0.0002039919,0.0001404489],"domain_scores_gemma":[0.9994537,0.00008254247,0.0001327258,0.0001986034,0.00008003417,0.0000523656],"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":[0.0001087097,0.00004260481,0.002406653,0.00002349997,8.546675e-7,4.638295e-7,0.0003570039,0.00001491293,0.9957087,0.0003658834,0.000006586771,0.0009641055],"study_design_scores_gemma":[0.000344489,0.0001621916,0.01072248,0.00003417531,0.000005856712,0.000003155668,0.00002499613,0.0001929601,0.9854891,0.0001284546,0.002771001,0.0001211519],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974014,0.00006791401,0.001313055,0.0003730373,0.0001786365,0.0001896647,0.00004100414,0.00006420661,0.00037114],"genre_scores_gemma":[0.9985911,0.00009541493,0.0005759351,0.0001093649,0.00003234777,0.00001681503,0.0000142781,0.00001015644,0.0005546303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01021963,"threshold_uncertainty_score":0.3258393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01272683913147069,"score_gpt":0.2591120089488378,"score_spread":0.2463851698173671,"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."}}