{"id":"W2953571830","doi":"10.22260/isarc2019/0174","title":"3D Printing Architectural Freeform Elements: Challenges and Opportunities in Manufacturing for Industry 4.0","year":2019,"lang":"en","type":"article","venue":"Proceedings of the ... ISARC","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"3D printing; Digitization; Manufacturing engineering; Resource (disambiguation); Manufacturing; Variety (cybernetics); Computer science; Emerging technologies; Engineering; Architectural engineering; Business; Telecommunications; Mechanical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002493549,0.0001722517,0.0002091822,0.0001451956,0.00004246937,0.00002494057,0.000328924,0.0001443331,0.000007992834],"category_scores_gemma":[0.00006826409,0.0001316962,0.00004703829,0.00003189061,0.00007593028,0.0001402069,0.0003001307,0.000428827,9.159112e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000388695,"about_ca_system_score_gemma":0.000004866738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005560114,"about_ca_topic_score_gemma":0.000003036461,"domain_scores_codex":[0.9991506,0.000002096376,0.0002460648,0.0001829578,0.0001179878,0.0003003383],"domain_scores_gemma":[0.9996815,0.00006280082,0.00009552906,0.0001071416,0.00002908028,0.00002400154],"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":[0.00004713214,0.00005333328,0.02033901,0.004655994,0.0001854331,8.433582e-7,0.004393304,0.0005229341,0.01015119,0.01073886,0.0001701345,0.9487419],"study_design_scores_gemma":[0.0009665221,0.0001322912,0.08592375,0.00109551,0.00002701658,0.00002019197,0.008924318,0.002798297,0.8759496,0.01803142,0.005559763,0.0005713691],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990612,0.0003064692,0.000007442617,0.0004989163,0.0001111241,0.0003528213,0.000006236507,0.0002380259,0.007867006],"genre_scores_gemma":[0.9976323,0.0001713911,0.001896473,0.00001398615,0.00003890758,0.00004152694,6.335602e-7,0.00002868867,0.0001760778],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9481705,"threshold_uncertainty_score":0.5370414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03243645754385448,"score_gpt":0.2204930421700985,"score_spread":0.188056584626244,"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."}}