{"id":"W4388179111","doi":"10.1111/deci.12619","title":"The role of generative design and additive manufacturing capabilities in developing human–AI symbiosis: Evidence from multiple case studies","year":2023,"lang":"en","type":"article","venue":"Decision Sciences","topic":"Design Education and Practice","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Generative grammar; Computer science; Knowledge management; Set (abstract data type); Coronavirus disease 2019 (COVID-19); Valuation (finance); Mechanism (biology); Artificial intelligence; Business","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04262828,0.0004638603,0.0004082638,0.00288976,0.004657161,0.005870963,0.002465833,0.002204734,0.003771045],"category_scores_gemma":[0.06504817,0.000586412,0.000508087,0.00223751,0.0128894,0.005699837,0.006404446,0.002460653,0.0002978639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005358155,"about_ca_system_score_gemma":0.003579124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00304541,"about_ca_topic_score_gemma":0.00727293,"domain_scores_codex":[0.9616089,0.03261321,0.0009344239,0.001095425,0.002684127,0.001063904],"domain_scores_gemma":[0.7858677,0.1864806,0.009205988,0.01194922,0.004705175,0.001791233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0004607105,0.002728441,0.1039951,0.001528046,0.0001740711,0.004553779,0.6954117,0.003735944,0.002342335,0.08468426,0.001538531,0.09884708],"study_design_scores_gemma":[0.000344779,0.001666561,0.05654467,0.002307988,0.0002024224,0.002819268,0.8277532,0.01101808,0.007140293,0.04312037,0.04692115,0.0001612131],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9773069,0.0005505638,0.0077236,0.0008354649,0.00001080278,0.0002873007,0.00003351498,0.00001116749,0.01324068],"genre_scores_gemma":[0.9941112,0.0002710311,0.004857819,0.00009077124,0.000002502402,0.0001688297,0.00001521992,0.000005894057,0.0004767089],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04262828,"threshold_uncertainty_score":0.2254425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.126551323755345,"score_gpt":0.3790460093854764,"score_spread":0.2524946856301314,"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."}}