{"id":"W3022077551","doi":"10.1016/j.ijhydene.2020.03.226","title":"Selection of phase change materials, metal foams and geometries for improving metal hydride performance","year":2020,"lang":"en","type":"article","venue":"International Journal of Hydrogen Energy","topic":"Hydrogen Storage and Materials","field":"Materials Science","cited_by":85,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Higher Education Discipline Innovation Project; China Scholarship Council; National Natural Science Foundation of China","keywords":"Hydride; Metal; Selection (genetic algorithm); Materials science; Phase (matter); Material selection; Metallurgy; Chemistry; Composite material; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005287994,0.0001576739,0.0004032936,0.0002036469,0.00005682436,0.0000759692,0.0003312087,0.00006261003,0.0002683294],"category_scores_gemma":[0.0001545272,0.0001342765,0.0001127122,0.0001082541,0.00006295391,0.0007723765,0.00009977881,0.00004450201,0.000003269374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004063729,"about_ca_system_score_gemma":0.00006175786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001158211,"about_ca_topic_score_gemma":0.000006131081,"domain_scores_codex":[0.9984595,0.0000512006,0.0006913313,0.0001833074,0.000427633,0.0001870416],"domain_scores_gemma":[0.9986323,0.00005100063,0.000749129,0.00006106443,0.0003864223,0.0001201571],"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":[0.0006244667,0.00007028341,0.0000292149,0.00004479204,0.0001333703,0.00001721425,0.0002121452,0.00004865469,0.9926114,0.0002788066,0.00002693393,0.005902704],"study_design_scores_gemma":[0.001395542,0.0007691313,0.00001221132,0.0000234915,0.0001012756,0.0002447839,0.0000298317,0.0008810845,0.991325,0.00009178925,0.004997786,0.0001280617],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960154,0.0004631492,0.002054507,0.0002491467,0.000960877,0.00008918436,0.0001411214,0.00001604702,0.00001052497],"genre_scores_gemma":[0.9972008,0.0001354058,0.001122467,0.0002139798,0.001251804,0.00001755466,0.0000173232,0.00002224726,0.00001843055],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005774642,"threshold_uncertainty_score":0.5475638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02940439465956335,"score_gpt":0.2735679663925069,"score_spread":0.2441635717329436,"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."}}