{"id":"W4410386569","doi":"10.1002/eng2.70140","title":"Critical Factors Governing the Frictional Coefficient in <scp>Mg</scp> Alloys—Learn From Machine Learning","year":2025,"lang":"en","type":"article","venue":"Engineering Reports","topic":"Magnesium Alloys: Properties and Applications","field":"Materials Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Friction coefficient; Chemistry; Materials science; Composite material","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003826782,0.0001583772,0.0001703061,0.00006355968,0.0002119979,0.0001317292,0.000160913,0.00006689454,0.0001924955],"category_scores_gemma":[0.001935895,0.0001189345,0.00005623895,0.000240437,0.00003583827,0.00008642438,0.0001372716,0.0003184662,0.00002543029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008115501,"about_ca_system_score_gemma":0.00004912639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005849787,"about_ca_topic_score_gemma":0.00002511113,"domain_scores_codex":[0.9986689,0.00002694119,0.0003754715,0.0003523655,0.0002741952,0.0003020881],"domain_scores_gemma":[0.9988747,0.000664115,0.00007174801,0.0002827842,0.00004695377,0.00005972658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003916707,0.0001431249,0.04066044,0.00007433294,0.00001925118,0.0001042615,0.001265623,0.403832,0.5463195,0.005716197,0.001650337,0.0002109911],"study_design_scores_gemma":[0.0003347433,0.00006348131,0.1850425,0.000226537,0.00007039069,0.00006363375,0.001502159,0.2488002,0.06615113,0.0003245466,0.497065,0.0003556353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9861599,0.0006327992,0.009838695,0.0003932942,0.0008358482,0.0001523775,0.00001117721,0.0002035065,0.001772355],"genre_scores_gemma":[0.997177,0.000009794534,0.0005656255,0.00004718852,0.00008444351,0.00005465063,0.0000226408,0.00001850987,0.002020155],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4954146,"threshold_uncertainty_score":0.485001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007466503261900473,"score_gpt":0.2170300317245348,"score_spread":0.2095635284626343,"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."}}