{"id":"W4412073179","doi":"10.1038/s41467-025-60499-6","title":"Machine learning-assisted high-throughput prediction and experimental validation of high-responsivity extreme ultraviolet detectors","year":2025,"lang":"en","type":"article","venue":"Nature Communications","topic":"Photocathodes and Microchannel Plates","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"RMIT University; University of Wollongong; Australian National Fabrication Facility; Australian Nuclear Science and Technology Organisation; US-UK Fulbright Commission; Ontario Ministry of Natural Resources and Forestry; Australian Synchrotron","keywords":"Responsivity; Throughput; Extreme ultraviolet; Detector; Computer science; Ultraviolet; Extreme learning machine; Ultraviolet radiation; Ultraviolet a; Optoelectronics; Machine learning; Physics; Optics; Telecommunications; Artificial neural network; Chemistry; Medicine; Wireless","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001405542,0.0001243201,0.0001588243,0.0001322161,0.0001657593,0.00002139273,0.0002566341,0.0002073264,0.000009654285],"category_scores_gemma":[0.00007319079,0.0001296161,0.00003485037,0.0002592065,0.00007616426,0.0001111836,0.0001128175,0.000538291,0.000001319598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006551065,"about_ca_system_score_gemma":0.00001529424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001850673,"about_ca_topic_score_gemma":0.0001259178,"domain_scores_codex":[0.9993879,0.00009782478,0.0002088251,0.0001286457,0.00007174522,0.0001050131],"domain_scores_gemma":[0.9991075,0.0002074748,0.00005107215,0.0005408492,0.00006381708,0.00002928352],"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":[0.00004327337,0.0001300042,0.001611358,0.00004826765,0.00009167536,3.075741e-7,0.0004927422,0.003510518,0.9902511,0.001909693,0.0004134665,0.001497569],"study_design_scores_gemma":[0.000671231,0.00005742675,0.02531012,0.00009782938,0.00006577896,0.000006765633,0.0002033009,0.01612497,0.9522599,0.0002955586,0.004739953,0.0001671664],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9856489,0.01123097,0.001782526,0.0001425855,0.0002349411,0.0002585102,0.0001261935,0.0002326949,0.0003427226],"genre_scores_gemma":[0.995965,0.0008397765,0.002709002,0.00002008884,0.00001160195,0.00004213589,0.0003353973,0.0000153589,0.00006164575],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03799122,"threshold_uncertainty_score":0.5285593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01485078139050046,"score_gpt":0.2589083603409625,"score_spread":0.2440575789504621,"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."}}