{"id":"W3009571181","doi":"10.1002/sys.21533","title":"Technology readiness levels: Shortcomings and improvement opportunities","year":2020,"lang":"en","type":"article","venue":"Systems Engineering","topic":"Technology Assessment and Management","field":"Engineering","cited_by":158,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Technology readiness level; Implementation; Best practice; Maturity (psychological); Liberian dollar; Scale (ratio); Engineering management; Engineering; Management science; Process management; Knowledge management; Computer science; Systems engineering; Business; Psychology; Management; Economics; Software engineering; Finance","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006272306,0.0001876764,0.0002278482,0.0001946491,0.00002961673,0.00003851575,0.0001496375,0.000120631,0.000005419671],"category_scores_gemma":[0.00001040297,0.0002003935,0.000019902,0.0001808018,0.00002230072,0.0001149949,0.00009704304,0.0001655161,0.000004452339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000408635,"about_ca_system_score_gemma":0.000004552343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004000226,"about_ca_topic_score_gemma":4.263765e-7,"domain_scores_codex":[0.9992536,0.000002414335,0.0002238203,0.0001759677,0.00008719243,0.0002569565],"domain_scores_gemma":[0.9997194,0.00001051229,0.00002132952,0.0001566577,0.00001546854,0.0000766224],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001119047,0.00004498463,0.002227104,0.006564062,0.001456107,0.0004914252,0.002221472,0.2287658,0.2697144,0.3428997,0.01464452,0.1309592],"study_design_scores_gemma":[0.001070482,0.0002712469,0.0008112612,0.000320021,0.0000929333,0.0000648894,0.004128475,0.7487029,0.02727231,0.0001394211,0.2156899,0.001436085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6131186,0.006996388,0.3571654,0.003527163,0.002383978,0.001257852,0.00004146948,0.01186687,0.003642209],"genre_scores_gemma":[0.9988975,0.00007891181,0.000671553,0.00002973746,0.00008273451,0.0000930495,0.000003717099,0.00004279391,0.0001000077],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5199372,"threshold_uncertainty_score":0.8171811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03008430333514671,"score_gpt":0.1975335984877485,"score_spread":0.1674492951526018,"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."}}