{"id":"W2120183722","doi":"10.1016/j.cpc.2015.03.015","title":"PyDII: A python framework for computing equilibrium intrinsic point defect concentrations and extrinsic solute site preferences in intermetallic compounds","year":2015,"lang":"en","type":"article","venue":"Computer Physics Communications","topic":"Intermetallics and Advanced Alloy Properties","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Office of Fossil Energy; Queen's University; Basic Energy Sciences; National Energy Research Scientific Computing Center; U.S. Department of Energy","keywords":"Python (programming language); Intermetallic; Computer science; Crystallographic defect; Algorithm; Computational science; Thermodynamics; Materials science; Statistical physics; Physics; Chemistry; Crystallography; Programming language","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001913169,0.0002476067,0.0003432079,0.00009256902,0.00009706488,0.0001611115,0.0005673904,0.00007383986,0.000001480193],"category_scores_gemma":[0.0000322609,0.0002549201,0.0001087064,0.0002104033,0.0002116293,0.0003928446,0.0006111925,0.0003873599,0.000008965461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001555292,"about_ca_system_score_gemma":0.00005380381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001638724,"about_ca_topic_score_gemma":0.00005078677,"domain_scores_codex":[0.9987978,0.00009041607,0.000417747,0.0002508409,0.0001090295,0.0003341084],"domain_scores_gemma":[0.998381,0.0004163231,0.00008958083,0.0008465546,0.0001519063,0.000114623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007470616,0.0008037908,0.00319203,0.0005881721,0.0006700399,0.000004527536,0.01945409,0.1690974,0.008255948,0.6413487,0.002351978,0.1541587],"study_design_scores_gemma":[0.0005621339,0.0001041834,0.0006182202,0.000257383,0.00002997699,0.00000552569,0.0001272269,0.9551921,0.0002110773,0.03824616,0.004308743,0.0003372387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2724482,0.00325515,0.7210875,0.0003058504,0.0006349647,0.0007511372,0.0000279148,0.0004062092,0.00108307],"genre_scores_gemma":[0.8109648,0.0002106793,0.1883716,0.0001135662,0.000146842,0.00006257334,0.00008070598,0.00003905682,0.00001011462],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7860947,"threshold_uncertainty_score":0.9999903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06285677067216981,"score_gpt":0.2816770733297841,"score_spread":0.2188203026576143,"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."}}