{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001166929,0.0003580596,0.0004421007,0.0007305288,0.0001477599,0.00008660233,0.0006264392,0.0003226126,0.0001119189],"category_scores_gemma":[0.000440456,0.0003378092,0.00006016259,0.0004355078,0.0002076666,0.0002215563,0.0003696694,0.0002808245,0.00008399403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001261178,"about_ca_system_score_gemma":0.00001174775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003512211,"about_ca_topic_score_gemma":0.000001834182,"domain_scores_codex":[0.9985968,0.0000171503,0.0003890567,0.0003680726,0.0001236134,0.0005052622],"domain_scores_gemma":[0.9990292,0.0001633341,0.00007944657,0.0006781532,0.00003371996,0.00001612843],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000006306318,0.00001544575,0.00002251608,0.0002304855,0.0000920403,0.00001731395,0.00001868204,0.0003259679,0.05463216,0.002223564,0.001863261,0.9405522],"study_design_scores_gemma":[0.0002439871,0.00003554266,0.001720622,0.0001785676,0.00001971264,0.000004553164,0.0005341126,0.00009277256,0.962659,0.02384863,0.01033683,0.000325674],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9597863,0.0008497352,0.006003368,0.0001768317,0.0009430859,0.0003451772,0.00007637701,0.02910438,0.002714768],"genre_scores_gemma":[0.9847901,0.0006565877,0.01401395,0.00001833649,0.00001548735,0.0002456628,0.00002166475,0.00004018723,0.0001979644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9402266,"threshold_uncertainty_score":0.9999074,"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."}}