{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001648619,0.0008968872,0.0008083146,0.001758449,0.00035714,0.001887765,0.0008406334,0.001305425,0.001719788],"category_scores_gemma":[0.003697593,0.0004554596,0.001482033,0.001732249,0.001485705,0.001355589,0.0009620397,0.003300613,0.0008334388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000936862,"about_ca_system_score_gemma":0.0008717095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00161224,"about_ca_topic_score_gemma":0.001026524,"domain_scores_codex":[0.9990786,0.0002991754,0.00008716211,0.0001896041,0.0003064831,0.0000389915],"domain_scores_gemma":[0.9982036,0.00125113,0.0001023373,0.0001543737,0.0002611635,0.00002731157],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004854686,0.00014354,0.002533335,0.001702449,0.0002906859,0.0004434344,0.0003330879,0.1541475,0.004859351,0.4957197,0.008908342,0.33087],"study_design_scores_gemma":[0.00001874215,0.0001145975,0.002072032,0.000478163,0.00004307163,0.0004227742,0.00005679012,0.3877241,0.003998681,0.5366265,0.06836476,0.00007983335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.003227209,0.01853569,0.961519,0.003122354,0.0003214236,0.00007682026,0.0002494256,0.0004816795,0.0124665],"genre_scores_gemma":[0.2012798,0.05181374,0.7311964,0.001597405,0.001611986,0.0007871288,0.0004748206,0.0002330408,0.01100571],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.001887765,"threshold_uncertainty_score":0.008718848,"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."}}