{"id":"W2240873096","doi":"10.4028/www.scientific.net/amr.1120-1121.677","title":"Improvement in Corrosion and Adhesion Resistance of a Al<sub>2</sub>O<sub>3</sub>-CeO<sub>2</sub> Nanocomposite Coating on the Aluminum Alloy AA6061 via Surface Pretreatment","year":2015,"lang":"en","type":"article","venue":"Advanced materials research","topic":"Corrosion Behavior and Inhibition","field":"Materials Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Université du Québec à Chicoutimi","keywords":"Materials science; Coating; Scanning electron microscope; Dielectric spectroscopy; Corrosion; Oxide; Contact angle; Fourier transform infrared spectroscopy; Nuclear chemistry; Composite material; Adhesion; Metallurgy; Chemical engineering; Electrochemistry; Chemistry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.01081804,0.002133019,0.002769676,0.001086799,0.001859016,0.001233031,0.002189182,0.001017885,0.0005094112],"category_scores_gemma":[0.001442963,0.0018248,0.0004783672,0.002095205,0.001421445,0.002042537,0.002658911,0.001118945,0.0006469996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002472716,"about_ca_system_score_gemma":0.000833003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00024148,"about_ca_topic_score_gemma":0.001708066,"domain_scores_codex":[0.9786144,0.003504948,0.00404333,0.004012104,0.005955637,0.003869588],"domain_scores_gemma":[0.9890738,0.001699867,0.002054257,0.003433879,0.00239457,0.001343695],"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":[0.006375919,0.001647714,0.000213406,0.000730943,0.00002976467,0.0001879091,0.001430316,0.0004512448,0.9797882,0.0004893809,0.005655933,0.002999291],"study_design_scores_gemma":[0.006382606,0.003655023,0.001364579,0.002981673,0.0001483704,0.00005068783,0.0003803874,0.0005040282,0.9776348,0.0008125001,0.004253341,0.001832027],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9839854,0.002100174,0.0004024708,0.001051183,0.002495168,0.007745795,0.001264405,0.0004573198,0.0004980796],"genre_scores_gemma":[0.9896449,0.004122114,0.001851005,0.0003174344,0.0004045032,0.001773237,0.0006065186,0.000444397,0.0008359079],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01045934,"threshold_uncertainty_score":0.9998038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0375044351333232,"score_gpt":0.3140676786497081,"score_spread":0.2765632435163849,"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."}}