{"id":"W2978698363","doi":"10.22260/isarc2019/0070","title":"Adaptive Automation Strategies for Robotic Prefabrication of Parametrized Mass Timber Building Components","year":2019,"lang":"en","type":"article","venue":"Proceedings of the ... ISARC","topic":"Innovations in Concrete and Construction Materials","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Prefabrication; Automation; Modular design; Mass customization; Workflow; Engineering; Computer science; Systems engineering; Software engineering; Architectural engineering; Manufacturing engineering; Personalization; Civil engineering; World Wide Web; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003241338,0.000521693,0.0002387961,0.0004054818,0.0002733751,0.0006309755,0.0006836692,0.0003942316,0.003067923],"category_scores_gemma":[0.0005090318,0.0002837348,0.0003471525,0.0002440605,0.0005537663,0.0006339593,0.0007507249,0.0004458147,0.0006045809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000288205,"about_ca_system_score_gemma":0.0002006826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005183949,"about_ca_topic_score_gemma":0.0009278326,"domain_scores_codex":[0.9997095,0.00003798589,0.00001591332,0.00006385143,0.0001332851,0.00003936621],"domain_scores_gemma":[0.9997413,0.00008800734,0.00005938915,0.00005766848,0.00004037494,0.00001331557],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001372765,0.0001580795,0.001203832,0.0002911963,0.00003550748,0.0003800291,0.0006952024,0.1311112,0.6421793,0.0142425,0.0006648691,0.208901],"study_design_scores_gemma":[0.00005587629,0.0009736095,0.005458099,0.00007777975,0.00005795373,0.0006749125,0.000438998,0.6329708,0.3123996,0.01470828,0.03209744,0.00008678349],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1698015,0.000453718,0.8175376,0.00006296094,0.00003644483,0.0001353141,0.00002723131,0.00078852,0.01115677],"genre_scores_gemma":[0.8511171,0.0002290328,0.1447696,0.00002523038,0.000008077291,0.00007562306,0.00003840978,0.00007263311,0.003664286],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003067923,"threshold_uncertainty_score":0.0102632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01693950031495472,"score_gpt":0.228801013876182,"score_spread":0.2118615135612273,"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."}}