{"id":"W4410923531","doi":"10.1002/admt.202402168","title":"Automatic Exposure Volumetric Additive Manufacturing","year":2025,"lang":"en","type":"article","venue":"Advanced Materials Technologies","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; National Research Council Canada","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Environmental science; Process engineering; Artificial intelligence; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004618955,0.0006713354,0.0003971591,0.0008295268,0.0003085707,0.0008703235,0.001016024,0.0004164437,0.009113827],"category_scores_gemma":[0.001306773,0.0005166105,0.0003578284,0.0005174981,0.0003997598,0.0005419418,0.001289971,0.0007503282,0.00203537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003398751,"about_ca_system_score_gemma":0.0004736802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003563556,"about_ca_topic_score_gemma":0.0006091287,"domain_scores_codex":[0.9991002,0.00005973527,0.00005213593,0.0001594724,0.0005544776,0.00007399544],"domain_scores_gemma":[0.9988253,0.0003400787,0.0001211784,0.0004452657,0.0002382226,0.00002990523],"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":[0.0001210762,0.0001050491,0.00127883,0.0004478178,0.00003148579,0.0002006589,0.0001925001,0.00868059,0.7094731,0.004686554,0.004870538,0.2699118],"study_design_scores_gemma":[0.00002476049,0.0002853831,0.003702826,0.00004788676,0.00002656066,0.0007500543,0.00005236989,0.07094615,0.8747555,0.001280789,0.04804011,0.00008759942],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1000997,0.0007982025,0.8659118,0.0001389837,0.0002585482,0.0003013444,0.0006139913,0.01395655,0.01792085],"genre_scores_gemma":[0.5520974,0.0004185246,0.4356497,0.0001653539,0.0000712004,0.0003130242,0.0008156777,0.001350592,0.009118463],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009113827,"threshold_uncertainty_score":0.03048879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005360718979778242,"score_gpt":0.2148057079375241,"score_spread":0.2094449889577458,"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."}}