{"id":"W4401168380","doi":"10.2139/ssrn.4912167","title":"Printing of Low-Viscosity Materials Using Tomographic Additive Manufacturing","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Viscosity; 3D printing; Materials science; Process engineering; Composite material; 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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00142272,0.0006403552,0.0007923402,0.0007910643,0.0001610131,0.0002009564,0.0008052292,0.0005522365,0.00005057586],"category_scores_gemma":[0.00007786881,0.0006221256,0.0003973276,0.0001569163,0.0001518951,0.00009660585,0.001395446,0.007468144,0.00001742344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001105675,"about_ca_system_score_gemma":0.000620067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007300356,"about_ca_topic_score_gemma":0.0000442522,"domain_scores_codex":[0.9958018,0.00008846602,0.0008388447,0.0005014262,0.0003691307,0.002400331],"domain_scores_gemma":[0.998852,0.00008294152,0.0004075608,0.0005002304,0.00008729059,0.00007003757],"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":[0.0002956333,0.0004387245,0.0005963164,0.01991275,0.02241636,0.0005016674,0.002725549,0.169332,0.1613338,0.09989539,0.0006488944,0.5219029],"study_design_scores_gemma":[0.0001467798,0.00004420948,0.0002474639,0.001709619,0.0001802649,0.0002307697,0.000472619,0.0008299432,0.6705017,0.3250093,0.00005979264,0.0005674875],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9832116,0.002657589,0.01065087,0.00003978061,0.001654794,0.0002764998,0.0001251637,0.001010204,0.0003734809],"genre_scores_gemma":[0.9944079,0.003871779,0.0009224283,0.00000371017,0.0005416658,0.00001533064,0.00002371966,0.0001556018,0.00005785378],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5213355,"threshold_uncertainty_score":0.999623,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009686066556501767,"score_gpt":0.2274780841276401,"score_spread":0.2177920175711383,"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."}}