{"id":"W4413017399","doi":"10.22215/etd/2025-16511","title":"(Im)perfect Precision: Design for Manufacturing and Assembly (DfMA) Workflows for Exterior Retrofit Wall Panels using Industrial Robotics","year":2025,"lang":"en","type":"dissertation","venue":"","topic":"Innovations in Concrete and Construction Materials","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Workflow; Robotics; Engineering; Manufacturing engineering; Engineering drawing; Design for manufacturability; Systems engineering; Artificial intelligence; Mechanical engineering; Computer science; Robot; Database","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007180175,0.0005829403,0.0002346606,0.0003220574,0.0004150777,0.001019849,0.0006879862,0.0003738423,0.004938513],"category_scores_gemma":[0.000685958,0.000352441,0.0004845102,0.0001785001,0.0004635168,0.0005204441,0.0007603467,0.0005005503,0.001570781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005722937,"about_ca_system_score_gemma":0.001471765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002056705,"about_ca_topic_score_gemma":0.004318416,"domain_scores_codex":[0.999509,0.00006330475,0.00002411215,0.00009939253,0.0002446923,0.00005950307],"domain_scores_gemma":[0.9997248,0.00004535208,0.00004825766,0.00006943783,0.00008438379,0.00002776988],"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":[0.0001634168,0.0001792073,0.00247366,0.0004267544,0.00003042708,0.000274202,0.001153366,0.2591842,0.1806842,0.03272411,0.00516583,0.5175407],"study_design_scores_gemma":[0.00008335028,0.001087488,0.005525638,0.0001305326,0.00005356751,0.000748112,0.0007374667,0.6677721,0.1245066,0.01181416,0.1874287,0.0001123253],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0285659,0.0001017189,0.9567634,0.00006387704,0.00003088338,0.000170356,0.00005763627,0.001953314,0.01229291],"genre_scores_gemma":[0.1800318,0.0001773515,0.8089367,0.00002905505,0.000005799628,0.0001777549,0.0001232239,0.0003082729,0.01021013],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004938513,"threshold_uncertainty_score":0.01652098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07979732088190972,"score_gpt":0.2933486071392034,"score_spread":0.2135512862572936,"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."}}