{"id":"W4281570720","doi":"10.1017/pds.2022.133","title":"Cardinal Maturity Determination of Technology Development: Medical Device Development Case Study","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Design Society","topic":"Technology Assessment and Management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Maturity (psychological); Consistency (knowledge bases); Cardinality (data modeling); Selection (genetic algorithm); Computer science; Capability Maturity Model; Multiple-criteria decision analysis; Class (philosophy); Technology development; Process management; Operations research; Risk analysis (engineering); Engineering; Artificial intelligence; Data mining; Business; Manufacturing engineering; Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001003802,0.0001373879,0.0002080608,0.0001022059,0.0002777846,0.000007377757,0.0005500229,0.0001067942,0.00001901576],"category_scores_gemma":[0.00002848907,0.0001220634,0.00006866425,0.0005851745,0.00007767989,0.0000741883,0.0006464282,0.0003518602,5.650391e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002153128,"about_ca_system_score_gemma":0.00007936131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003358173,"about_ca_topic_score_gemma":0.000003171432,"domain_scores_codex":[0.9987702,0.000009329377,0.0003549699,0.0001716202,0.0004991138,0.0001948147],"domain_scores_gemma":[0.9996222,0.00002366453,0.0001348732,0.00009966642,0.00009489631,0.000024759],"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":[0.0001311117,0.00490103,0.230971,0.0046159,0.004970137,0.0004973018,0.1954166,0.001214999,0.05023717,0.008535859,0.02139577,0.4771132],"study_design_scores_gemma":[0.0051211,0.0007472356,0.01844984,0.0002833539,0.0006782656,0.002708618,0.3469035,0.0234618,0.56664,0.002168221,0.03098521,0.001852929],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.993813,0.0000886943,0.004780274,0.0001251944,0.0001295034,0.0005947221,9.411483e-7,0.0002488691,0.0002187427],"genre_scores_gemma":[0.9532974,0.000005600495,0.04635051,0.00001537077,0.000006771962,0.0002695096,5.320355e-7,0.00001628095,0.00003805472],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5164028,"threshold_uncertainty_score":0.4977602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01709839060481131,"score_gpt":0.2421804377697593,"score_spread":0.225082047164948,"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."}}