{"id":"W4410777258","doi":"10.1016/j.mseb.2025.118450","title":"Machine learning-driven optimization of physical properties in Al-Ga Co-doped ZnO films for hydrogen production applications","year":2025,"lang":"en","type":"article","venue":"Materials Science and Engineering B","topic":"ZnO doping and properties","field":"Materials Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Deanship of Scientific Research, University of Jordan; Jordan University of Science and Technology","keywords":"Materials science; Doping; Hydrogen production; Hydrogen; Production (economics); Nanotechnology; Chemical engineering; Computer science; Optoelectronics; Chemistry; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005601762,0.00009502134,0.0001755753,0.0001554222,0.0001462965,0.00008366536,0.0001547749,0.0000244875,0.00001007164],"category_scores_gemma":[0.0002033461,0.00007655709,0.0000128463,0.0002761314,0.0001686759,0.0002345913,0.00004937168,0.00003369661,0.000002564007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002701369,"about_ca_system_score_gemma":0.00006371659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004858144,"about_ca_topic_score_gemma":0.000001064914,"domain_scores_codex":[0.999202,0.00001813829,0.0001938545,0.0002583977,0.0001412047,0.0001863895],"domain_scores_gemma":[0.9996791,0.00001847306,0.00005006661,0.0001282264,0.00009961834,0.00002454639],"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":[0.00001278389,0.00001586389,0.00004286342,0.0001145303,0.000001019257,3.266495e-8,0.0001810626,0.3903221,0.6091228,0.0001260744,0.000007734722,0.00005311557],"study_design_scores_gemma":[0.0001011609,0.00004079878,0.00008360512,0.00008282829,0.000004819812,8.294518e-7,0.00002929656,0.2312223,0.7678459,0.00001545243,0.0005010698,0.00007191391],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962632,0.0001060714,0.002705567,0.0001753548,0.0001637507,0.0004508474,0.00001232314,0.00007164148,0.00005124198],"genre_scores_gemma":[0.9975058,0.00002798773,0.002047266,0.0000174397,0.00003444277,0.0002346416,0.000007378022,0.000007869797,0.0001171116],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1590998,"threshold_uncertainty_score":0.3121908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01385043231162495,"score_gpt":0.2368538305410843,"score_spread":0.2230033982294593,"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."}}