{"id":"W4382134652","doi":"10.3390/coatings13071140","title":"Principles of Machine Learning and Its Application to Thermal Barrier Coatings","year":2023,"lang":"en","type":"article","venue":"Coatings","topic":"High-Temperature Coating Behaviors","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"National Research Council Canada","keywords":"Thermal conductivity; Machine learning; Artificial intelligence; Artificial neural network; Gradient boosting; Support vector machine; Thermal barrier coating; Python (programming language); Computer science; Algorithm; Materials science; Random forest; Composite material","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003468997,0.0001549754,0.000190081,0.0001472788,0.0001544538,0.00002395627,0.0001449102,0.00006538519,0.00001684582],"category_scores_gemma":[0.0002210636,0.000165768,0.00003031749,0.0005017438,0.00001959298,0.00006019277,0.0001506006,0.0002731304,0.00004913244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003231478,"about_ca_system_score_gemma":0.00001276703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001619947,"about_ca_topic_score_gemma":0.000006676577,"domain_scores_codex":[0.9990108,0.00003305584,0.0002883984,0.000213943,0.0002158891,0.0002379407],"domain_scores_gemma":[0.999524,0.00009722735,0.00007215837,0.0001387647,0.00007058655,0.00009728227],"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":[0.000003109324,0.000004175189,0.008423908,0.00006746439,0.000003959813,0.00000157041,0.002560317,0.02765636,0.9603469,0.0002610529,0.00006157123,0.0006095961],"study_design_scores_gemma":[0.0004943609,0.0001453757,0.01586869,0.0001370438,0.00003706116,0.00001064965,0.0004970142,0.07694739,0.8829323,0.00001023765,0.0223519,0.000567944],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979708,0.00008662847,0.0004224959,0.00007076203,0.00008099472,0.0003029026,0.00002390287,0.0006529753,0.0003885776],"genre_scores_gemma":[0.9982331,0.000006664222,0.0005769354,0.00002514197,0.00004864153,0.0001227765,0.00003707993,0.00006209571,0.000887611],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07741459,"threshold_uncertainty_score":0.6759821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01208504542013996,"score_gpt":0.243652966812079,"score_spread":0.231567921391939,"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."}}