{"id":"W4206622071","doi":"10.3389/fmats.2021.816610","title":"Exploring the Compositional Space of High-Entropy Alloys for Cost-Effective High-Temperature Applications","year":2022,"lang":"en","type":"article","venue":"Frontiers in Materials","topic":"High Entropy Alloys Studies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Thermal expansion; Thermal conductivity; Thermodynamics; Solid solution strengthening; High entropy alloys; Solid solution; Metallurgy; Composite material; Microstructure","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":[0.0003948475,0.0005033538,0.0004207466,0.0005377834,0.0003353837,0.0008432431,0.0005509054,0.0005717349,0.002205417],"category_scores_gemma":[0.0006675103,0.0003851284,0.0006183093,0.0003542968,0.0003974205,0.0006841736,0.0003651925,0.0005630903,0.0003021415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004431718,"about_ca_system_score_gemma":0.0007690936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001205832,"about_ca_topic_score_gemma":0.002495492,"domain_scores_codex":[0.9999189,0.00001911766,0.000002747256,0.00001104699,0.00003350445,0.00001466168],"domain_scores_gemma":[0.9998729,0.00006806624,0.00001848859,0.000009770325,0.00001911019,0.00001172551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009842071,0.00008660701,0.001734053,0.0002873543,0.00004899409,0.0001780382,0.00004018047,0.9395691,0.02429344,0.01780679,0.0008897377,0.01496738],"study_design_scores_gemma":[0.00001687043,0.00005918512,0.0003300105,0.00001691176,0.00001584062,0.00002747014,0.00003339214,0.9883667,0.002335832,0.007504847,0.001286595,0.000006299331],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6575437,0.005721812,0.299469,0.001083329,0.0001338881,0.0001172283,0.0005120769,0.0004867524,0.03493226],"genre_scores_gemma":[0.9379526,0.001426193,0.05758323,0.0001392467,0.00002417779,0.0001090543,0.0003171781,0.0001092467,0.00233909],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002205417,"threshold_uncertainty_score":0.007377923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01351889294817815,"score_gpt":0.2132642276402057,"score_spread":0.1997453346920275,"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."}}