{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05043372,0.0004568064,0.0006065764,0.004312101,0.00150477,0.008094132,0.001993472,0.001368547,0.003002581],"category_scores_gemma":[0.1270063,0.0003910003,0.0006989397,0.004174564,0.002051963,0.007892589,0.004483774,0.002269719,0.0007327002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005036539,"about_ca_system_score_gemma":0.01177224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005176541,"about_ca_topic_score_gemma":0.005595417,"domain_scores_codex":[0.957431,0.02489878,0.003196,0.002263757,0.009925635,0.002284865],"domain_scores_gemma":[0.8495176,0.08435339,0.01534807,0.005914284,0.03823872,0.006628079],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002328091,0.0006464651,0.3414742,0.001860041,0.0001332393,0.0003110227,0.02426765,0.003196615,0.002208414,0.02709305,0.009488806,0.5890875],"study_design_scores_gemma":[0.0001403143,0.003425982,0.5361456,0.007193239,0.0004042369,0.001218675,0.2539707,0.04611494,0.01047695,0.05437658,0.08587788,0.0006549191],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8460108,0.004954369,0.03103333,0.08108994,0.0002396286,0.0003631874,0.000474238,0.0006196967,0.03521481],"genre_scores_gemma":[0.9918236,0.0004433554,0.006527287,0.0005707942,0.00002072974,0.0001006046,0.000081198,0.00001974943,0.0004127513],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9495663,"threshold_uncertainty_score":0.2667221,"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."}}