{"id":"W4388556952","doi":"10.20944/preprints202311.0581.v1","title":"Achieving 2.2 GPA Ultra-High Strength in Low-Alloy Steel Using a Direct Quenching and Partitioning Process","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Microstructure and Mechanical Properties of Steels","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Materials science; Microstructure; Austenite; Ultimate tensile strength; Metallurgy; Alloy; Martensite; Quenching (fluorescence); Cementite; Volume fraction; Toughness; Tempering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001960697,0.0003746897,0.0002443087,0.0001885846,0.0001780182,0.000199353,0.0004748264,0.0003363006,0.0006808112],"category_scores_gemma":[0.000192949,0.0002515664,0.0002530519,0.0001525909,0.0003062405,0.0003610634,0.0003013214,0.0005018887,0.0002613024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003764277,"about_ca_system_score_gemma":0.0002637815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009077949,"about_ca_topic_score_gemma":0.002251026,"domain_scores_codex":[0.9997503,0.00001209561,0.000008312929,0.00007290803,0.0001275818,0.00002868181],"domain_scores_gemma":[0.9999044,0.00001018894,0.00003231104,0.00001235604,0.00003171287,0.000009101414],"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":[0.0000136702,0.00000529112,0.0001260169,0.00004225807,0.000002639709,0.00003316042,0.00001743283,0.0001621256,0.9979006,0.0001122565,0.00004011252,0.00154442],"study_design_scores_gemma":[0.00001105086,0.000145611,0.001793023,0.00000253069,0.000008087619,0.000117824,0.000008855995,0.002230552,0.9940144,0.00004548621,0.001615971,0.000006599796],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9462385,0.000941285,0.04847944,0.0001020433,0.00003905128,0.00009410369,0.0001516021,0.0004170328,0.00353688],"genre_scores_gemma":[0.980309,0.0002505066,0.01739934,0.00004344191,0.000009606454,0.00003750083,0.0001363182,0.00004895973,0.001765491],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009077949,"threshold_uncertainty_score":0.002731204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07225443267061095,"score_gpt":0.2990198499814203,"score_spread":0.2267654173108094,"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."}}