{"id":"W3023220159","doi":"10.2172/1616513","title":"Basic Research Needs Workshop on Synthesis Science for Energy Relevant Technology","year":2016,"lang":"en","type":"report","venue":"","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Sandia National Laboratories; Brookhaven National Laboratory; Argonne National Laboratory; University of Dayton; University of Illinois at Urbana-Champaign; Pacific Northwest National Laboratory; Basic Energy Sciences; Advanced Research Projects Agency - Energy; Northwestern University; York University; Iowa State University; Drexel University; Washington State University; University of California, Los Angeles; State University of New York; Advanced Research Projects Agency; SLAC National Accelerator Laboratory; Harvard University; University of Limerick; U.S. Department of Energy; Brown University; Office of Science; Princeton University; Johns Hopkins University; Florida State University; National Science Foundation; Yale University; Massachusetts Institute of Technology","keywords":"SAFER; Computer science; Nanotechnology; Computer security","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":["metaresearch","metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["sts","insufficient_payload"],"category_scores_codex":[0.02685008,0.0006531236,0.00105843,0.004733586,0.001543334,0.000789371,0.005065628,0.0008887118,0.004010975],"category_scores_gemma":[0.0427953,0.0004360977,0.0001804362,0.003428736,0.004719448,0.0003685367,0.001573246,0.0006823839,0.001266208],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001521404,"about_ca_system_score_gemma":0.005903306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000330452,"about_ca_topic_score_gemma":0.00005717505,"domain_scores_codex":[0.9885389,0.0004408791,0.001045002,0.002314195,0.005204577,0.002456418],"domain_scores_gemma":[0.9884751,0.004608805,0.0006467107,0.002773332,0.003148394,0.000347629],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001268182,0.0001106721,0.00003234221,0.0002316397,0.00001123085,0.00002378867,0.00004622164,0.00005439738,0.8438,0.04908624,0.06153684,0.04493982],"study_design_scores_gemma":[0.0001622135,0.0002881692,0.00002544355,0.001436813,0.00002470215,0.00007635892,0.0001154146,0.0002134425,0.4782348,0.007563959,0.5111156,0.0007430264],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.06178848,0.001150466,0.03043645,0.02693035,0.03096632,0.003846426,0.0007728736,0.003631467,0.8404772],"genre_scores_gemma":[0.5652746,0.003651491,0.04611345,0.0006205888,0.005837662,0.005410713,0.00002828506,0.0006561423,0.3724071],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.5034861,"threshold_uncertainty_score":0.9998091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08560642775906009,"score_gpt":0.3979924000631214,"score_spread":0.3123859723040613,"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."}}