{"id":"W7020581282","doi":"","title":"Materials Acceleration Platform Accelerating Advanced Energy Materials Discovery by Integrating High-Throughput Methods with Artificial Intelligence ","year":2020,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universidad Nacional Autónoma de México; Lawrence Berkeley National Laboratory; Canadian Institute for Advanced Research; University of California, San Diego; Harvard University; U.S. Department of Energy","keywords":"Acceleration; Energy (signal processing); Energy consumption; Efficient energy use; Applications of artificial intelligence","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","scholarly_communication","insufficient_payload"],"consensus_categories":["scholarly_communication","insufficient_payload"],"category_scores_codex":[0.001095254,0.0008247842,0.0009468148,0.0001278425,0.0006131602,0.01545656,0.001505317,0.0002606395,0.004582452],"category_scores_gemma":[0.001056766,0.000652528,0.00009384466,0.0007616264,0.0003325868,0.0170495,0.0008571967,0.000414451,0.0008833484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008410814,"about_ca_system_score_gemma":0.0002083328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007837168,"about_ca_topic_score_gemma":0.000003173664,"domain_scores_codex":[0.9942588,0.0006389534,0.001657632,0.001531793,0.0008571108,0.001055668],"domain_scores_gemma":[0.9974541,0.0004545342,0.0009199345,0.0006202749,0.0001055946,0.0004455794],"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":[0.0005810244,0.00008020979,0.0001877102,0.00009643672,0.0000153947,0.00002010397,0.0001326885,0.0004587169,0.9725875,0.01511501,0.0002277674,0.01049745],"study_design_scores_gemma":[0.0002208975,0.0004117531,0.00004259321,0.0001516617,0.00002044193,0.0000178946,0.0002139044,0.0005800686,0.9863341,0.007468089,0.003669764,0.0008688664],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8100204,0.00005170702,0.1828355,0.001126718,0.0006601299,0.000448913,0.003323467,0.000806867,0.000726286],"genre_scores_gemma":[0.8621345,0.00001089898,0.1337969,0.00166233,0.0005640813,0.000110298,0.001422003,0.000168151,0.0001308452],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05211416,"threshold_uncertainty_score":0.9998946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03042226138499767,"score_gpt":0.2722871051527941,"score_spread":0.2418648437677964,"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."}}