{"id":"W4285208639","doi":"10.1039/9781839163838","title":"Energy Materials Discovery","year":2022,"lang":"en","type":"book","venue":"","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Renewable energy; Context (archaeology); Computer science; Sustainability; Emerging technologies; Energy transition; Data science; Engineering; Nanotechnology; Artificial intelligence; Electrical engineering","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":[0.0009968341,0.0006997048,0.0006907735,0.001323377,0.001434528,0.004439323,0.001892007,0.002486724,0.06182994],"category_scores_gemma":[0.001722862,0.0005632634,0.0007979106,0.001509222,0.001130263,0.004240749,0.003082475,0.003071793,0.03521753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00169756,"about_ca_system_score_gemma":0.001612267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005254005,"about_ca_topic_score_gemma":0.001248775,"domain_scores_codex":[0.9988791,0.0001349401,0.00004146385,0.0001758922,0.0006399205,0.0001287083],"domain_scores_gemma":[0.9995902,0.0001053235,0.0000196357,0.00008487399,0.0001297919,0.00007016634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003514457,0.00005702183,0.0001974174,0.001432774,0.00003547653,0.0002670051,0.0001966791,0.0006725952,0.00446746,0.30847,0.4329433,0.2512251],"study_design_scores_gemma":[0.000002934446,0.000008721426,0.00003069793,0.0001252405,0.000002377131,0.0001050341,0.00002777641,0.0001631763,0.000952145,0.01884107,0.9797357,0.00000506734],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.003417047,0.1184797,0.03245871,0.01981896,0.01834115,0.0003262729,0.002112649,0.001191473,0.8038539],"genre_scores_gemma":[0.03516714,0.1460174,0.03466462,0.01017092,0.003849305,0.0004283447,0.004653732,0.0006614851,0.7643871],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.06182994,"threshold_uncertainty_score":0.2068418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008901035849844289,"score_gpt":0.2317698430805573,"score_spread":0.222868807230713,"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."}}