{"id":"W4244887662","doi":"10.26434/chemrxiv.7580021.v1","title":"Single Crystal Automated Refinement (SCAR): A Data-Driven Method for Solving Inorganic Structures","year":2019,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Crystal (programming language); Crystal structure; Algorithm; Computer science; Crystal structure prediction; Set (abstract data type); Crystallography; Diffraction; Single crystal; Mixing (physics); Chemistry; Materials science; Physics; Optics","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":[0.002128119,0.001443356,0.001588831,0.001830469,0.001051506,0.001221645,0.003516977,0.001234511,0.007002502],"category_scores_gemma":[0.002801334,0.0009493523,0.001670494,0.001813155,0.0007257177,0.001757232,0.001528401,0.002358002,0.002248003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005496219,"about_ca_system_score_gemma":0.002477661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002288101,"about_ca_topic_score_gemma":0.004656946,"domain_scores_codex":[0.9989184,0.0002453317,0.00007412817,0.0002218094,0.0004873302,0.0000530078],"domain_scores_gemma":[0.9985974,0.00055114,0.0001480044,0.0004062288,0.000257039,0.00004015075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002797601,0.0002875534,0.003325979,0.001422254,0.0004879647,0.0003359645,0.0004284791,0.1104867,0.04703921,0.06151567,0.05549983,0.7188906],"study_design_scores_gemma":[0.0001754869,0.0001022033,0.0005264152,0.00006974224,0.0000570167,0.0002740848,0.00009493133,0.8766195,0.03450797,0.03893553,0.0485659,0.00007138224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004520409,0.0001975333,0.9866192,0.0001206333,0.00004129705,0.0000783527,0.0005588382,0.00694701,0.0009168343],"genre_scores_gemma":[0.02124433,0.0002050696,0.9754598,0.00006468142,0.00001745593,0.000135467,0.001299345,0.0008930708,0.0006806907],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007002502,"threshold_uncertainty_score":0.0234257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04296426728615411,"score_gpt":0.334284744883338,"score_spread":0.2913204775971839,"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."}}