{"id":"W4410711545","doi":"10.1016/j.ceramint.2025.05.091","title":"Predicting ultimate tensile strength of SiC/SiC mini-composites via machine learning","year":2025,"lang":"en","type":"article","venue":"Ceramics International","topic":"Advanced ceramic materials synthesis","field":"Materials Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"Materials and Manufacturing Directorate; Air Force Research Laboratory; National Research Council Canada; National Science Foundation","keywords":"Materials science; Ultimate tensile strength; Composite material; Silicon carbide","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003091413,0.0001953787,0.0002919511,0.000175496,0.0001467619,0.00008550395,0.0005027712,0.00008164269,0.001095667],"category_scores_gemma":[0.0004254557,0.0001941992,0.00007665536,0.0001209644,0.0001490719,0.0002614117,0.0002466191,0.000155456,0.00004104536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001357391,"about_ca_system_score_gemma":0.0000388609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001664763,"about_ca_topic_score_gemma":0.00002604096,"domain_scores_codex":[0.9984171,0.00007555699,0.0005512902,0.0003743155,0.0003248334,0.0002569115],"domain_scores_gemma":[0.9988968,0.0002624937,0.0003259219,0.0002392796,0.0002344704,0.00004102614],"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":[0.00007488555,0.00008749239,0.01122935,0.00005586387,0.00004984095,0.000004267046,0.0001596981,0.005048346,0.9734943,0.006512066,0.00003013409,0.003253766],"study_design_scores_gemma":[0.0004718233,0.0000344844,0.002799069,0.0001409837,0.00003243286,0.00001284969,0.00009487208,0.1197658,0.8721181,0.002591648,0.001738201,0.0001997422],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9759835,0.00006873212,0.01436381,0.0004299526,0.002220835,0.0001418918,0.0001671071,0.0001598912,0.006464306],"genre_scores_gemma":[0.984371,0.00002089755,0.01414015,0.00007831358,0.0001368654,0.00001487741,0.0001099489,0.00002335161,0.00110456],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1147175,"threshold_uncertainty_score":0.9998175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008036478517323795,"score_gpt":0.2572193520771981,"score_spread":0.2491828735598743,"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."}}