{"id":"W4416715446","doi":"10.1016/j.ijrmhm.2025.107569","title":"Machine learning-based service life prediction of cemented carbide micro-drills","year":2025,"lang":"en","type":"article","venue":"International Journal of Refractory Metals and Hard Materials","topic":"Advanced materials and composites","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"MD Precision (Canada)","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Cemented carbide; Service life; Microstructure; Carbide; Grain size; Service (business)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003757582,0.0005859268,0.0005535207,0.001023455,0.0002132002,0.0005070135,0.0005344911,0.0006961451,0.0007887126],"category_scores_gemma":[0.001165589,0.0002312812,0.0003720664,0.0004183777,0.0002538931,0.0005398497,0.0002267664,0.0004812057,0.0003002658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005593686,"about_ca_system_score_gemma":0.0005002124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006236943,"about_ca_topic_score_gemma":0.005777008,"domain_scores_codex":[0.9998179,0.00002001165,0.00001518619,0.0000523644,0.00006043722,0.00003421813],"domain_scores_gemma":[0.9991463,0.0003621036,0.0001230121,0.00003943284,0.000283622,0.00004548679],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006668766,0.0002238393,0.01919349,0.0001316152,0.00003680386,0.0001226059,0.000029555,0.8592688,0.01925559,0.0003873688,0.0006904055,0.09999312],"study_design_scores_gemma":[0.00000225229,0.00002969854,0.001767397,0.00000187412,0.000003185648,0.000008264882,0.000005414595,0.9955069,0.00254881,0.00006856891,0.00005412331,0.000003427978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9234262,0.0006767688,0.07309766,0.00009621526,0.00004455179,0.00004154355,0.0003933879,0.0009057817,0.001317822],"genre_scores_gemma":[0.9942293,0.00006248842,0.0050449,0.00000536606,0.000004209282,0.0000102486,0.0001784338,0.00000733925,0.0004576194],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006236943,"threshold_uncertainty_score":0.01240128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01036104615256397,"score_gpt":0.2358610857844567,"score_spread":0.2255000396318927,"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."}}