{"id":"W4414832496","doi":"10.1016/j.cep.2025.110581","title":"Deep-learning-aided modifier adaptation: synergies with process intensification","year":2025,"lang":"en","type":"article","venue":"Chemical Engineering and Processing - Process Intensification","topic":"Semiconductor materials and devices","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Convergence (economics); Adaptation (eye); Process (computing); Artificial neural network; Constraint (computer-aided design); Matching (statistics)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001410544,0.0003370462,0.0003265741,0.0001959267,0.0001180337,0.0002293346,0.0001827398,0.0001934025,0.000007504065],"category_scores_gemma":[0.0002729349,0.0003026889,0.00002859339,0.0005719415,0.00009484043,0.0004503759,0.00001803061,0.0003362207,0.000004263814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006167082,"about_ca_system_score_gemma":0.00004960876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004859678,"about_ca_topic_score_gemma":5.814942e-7,"domain_scores_codex":[0.9986263,0.00001002148,0.000405035,0.0004440064,0.0002046914,0.0003099683],"domain_scores_gemma":[0.9989101,0.00006526809,0.0000953626,0.0001922564,0.00064545,0.00009158293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002009847,0.00006269126,0.0002840595,0.006953734,0.0001459172,0.000003678379,0.00526818,0.3584321,0.5937334,0.0007431619,0.0001019997,0.03407016],"study_design_scores_gemma":[0.0003124769,0.00002092796,0.0003106853,0.0006894349,0.00006835855,0.00002846026,0.001824267,0.7678084,0.2278562,0.0003583182,0.0003117093,0.000410741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9220986,0.003336364,0.07172538,0.0002013099,0.0002458785,0.0002488856,0.000002007031,0.001394279,0.0007472619],"genre_scores_gemma":[0.9977158,0.00009313972,0.001672,0.00006763723,0.00009495238,0.000169622,0.00007297878,0.00006037656,0.0000534781],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4093764,"threshold_uncertainty_score":0.9999425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01061069346733908,"score_gpt":0.2255871552281894,"score_spread":0.2149764617608503,"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."}}