{"id":"W4391427441","doi":"10.1016/j.ijhydene.2024.01.198","title":"Tailoring the hydrogen production behavior of Al-Zn-Sn alloys through their as-solidified microstructures","year":2024,"lang":"en","type":"article","venue":"International Journal of Hydrogen Energy","topic":"Aluminum Alloy Microstructure Properties","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Fundo de Apoio ao Ensino, à Pesquisa e Extensão, Universidade Estadual de Campinas; Laboratório Nacional de Nanotecnologia; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Ministério da Ciência, Tecnologia e Inovação; Centro Nacional de Pesquisa em Energia e Materiais; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Microstructure; Hydrogen; Alloy; Hydrogen production; Materials science; Metallurgy; Chemistry","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":[0.00008917119,0.0001354338,0.0001113448,0.000162363,0.0001353541,0.0002972214,0.0001823038,0.0001470555,0.0005935857],"category_scores_gemma":[0.0001988272,0.0001174024,0.00008067038,0.0001549366,0.0001480842,0.0001601833,0.0001161419,0.0001567404,0.0001414583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003250956,"about_ca_system_score_gemma":0.0001791121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001200348,"about_ca_topic_score_gemma":0.0041008,"domain_scores_codex":[0.9999427,0.000004446603,0.000004100506,0.00001319713,0.00002323153,0.00001234495],"domain_scores_gemma":[0.9999292,0.000006701112,0.00001814315,0.000004994235,0.00003181739,0.000009213021],"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":[0.00008221401,0.00001445564,0.0004857221,0.00003104567,0.000003718546,0.00002376559,0.00002355188,0.0004935944,0.9970844,0.0001597712,0.00004579976,0.001551865],"study_design_scores_gemma":[0.000008433384,0.0001144239,0.002360617,0.000003137286,0.000008650775,0.0000183442,0.00003334473,0.003461717,0.9928628,0.0000340363,0.001089253,0.000005328287],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982216,0.0002216022,0.0006371482,0.00001905964,0.00001766006,0.000006276385,0.00007643562,0.00003275132,0.0007675539],"genre_scores_gemma":[0.998742,0.0001088386,0.0005768919,0.000005034877,0.000002290575,0.000004657557,0.00003607419,0.000007205934,0.0005170343],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001200348,"threshold_uncertainty_score":0.002386689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01236907045552222,"score_gpt":0.2387988811076164,"score_spread":0.2264298106520941,"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."}}