{"id":"W4308909100","doi":"10.1002/aisy.202200229","title":"Adaptive 3D Printing for In Situ Adjustment of Mechanical Properties","year":2022,"lang":"en","type":"article","venue":"Advanced Intelligent Systems","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Extrusion; 3D printing; Consistency (knowledge bases); Convolutional neural network; Computer science; Fused deposition modeling; Deep learning; Artificial intelligence; Variance (accounting); Engineering drawing; Mechanical engineering; Materials science; Engineering; Composite material","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":[0.0001876199,0.0003666498,0.0002202442,0.0002350639,0.00009900279,0.000364296,0.0005771954,0.0004177267,0.001568369],"category_scores_gemma":[0.0005804429,0.0002366535,0.0002978812,0.0002514801,0.0002511425,0.0004217671,0.0003526603,0.0004337814,0.0003865481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003575234,"about_ca_system_score_gemma":0.0002672858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007598519,"about_ca_topic_score_gemma":0.002111235,"domain_scores_codex":[0.9997954,0.00001305309,0.000008408909,0.0000437069,0.0001247242,0.00001473313],"domain_scores_gemma":[0.9997316,0.00007596277,0.00005535983,0.00006877826,0.00005956396,0.000008634211],"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":[0.00009530914,0.00006794258,0.001952209,0.0001733721,0.00003978597,0.0001756534,0.0000850773,0.05943733,0.7115435,0.001801069,0.001611588,0.2230172],"study_design_scores_gemma":[0.000008864041,0.00008042147,0.002728209,0.000009531464,0.00002575366,0.0002244667,0.00001401901,0.5286053,0.4611123,0.0007383004,0.006418568,0.00003423624],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1067102,0.0004766289,0.88272,0.0001417162,0.0001191113,0.00003881436,0.0001916934,0.003348188,0.006253711],"genre_scores_gemma":[0.8034027,0.0003147408,0.1916487,0.0001533567,0.00002170024,0.0000403111,0.0001935562,0.0001446802,0.004080207],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001568369,"threshold_uncertainty_score":0.005246758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03400373611395605,"score_gpt":0.2374578384013301,"score_spread":0.203454102287374,"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."}}