{"id":"W4391042413","doi":"10.61784/wjms240168","title":"APPLICATION PROGRESS OF MATERIALS GENOME TECHNOLOGY IN THE FIELD OF NEW ENERGY MATERIALS","year":2024,"lang":"en","type":"article","venue":"World Journal of Materials Science","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Throughput; Big data; Computer science; Field (mathematics); Characterization (materials science); Data science; Nanotechnology; Systems engineering; Engineering; Materials science; Data mining","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":[0.002145666,0.0007205534,0.0006408496,0.002273171,0.0008033178,0.002058202,0.0007835452,0.00167732,0.00349257],"category_scores_gemma":[0.00226416,0.0003665628,0.001015387,0.002899268,0.001166342,0.003307426,0.001460628,0.00228018,0.001540662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001591771,"about_ca_system_score_gemma":0.002739872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001517331,"about_ca_topic_score_gemma":0.001113135,"domain_scores_codex":[0.9989384,0.0002684643,0.00006703714,0.0002233564,0.0004225598,0.00008023571],"domain_scores_gemma":[0.998759,0.0004970721,0.00009292851,0.0001488487,0.000384631,0.0001173488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001337778,0.0001266586,0.005256637,0.002432124,0.0000807481,0.0004379508,0.0007461107,0.004917931,0.0717157,0.1736074,0.02738488,0.7131599],"study_design_scores_gemma":[0.00004499883,0.0002334091,0.005114076,0.0006272169,0.0001271238,0.001597187,0.0007021248,0.01280146,0.07649633,0.08834687,0.8137442,0.0001649347],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.04598735,0.2309959,0.5899594,0.03749107,0.006472546,0.0004558902,0.002137807,0.003026192,0.08347395],"genre_scores_gemma":[0.1991718,0.2705053,0.4982209,0.005933946,0.002044331,0.0004128255,0.003736264,0.0005717012,0.01940295],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.00349257,"threshold_uncertainty_score":0.01168376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00841346656735523,"score_gpt":0.297573559612996,"score_spread":0.2891600930456408,"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."}}