{"id":"W3035118309","doi":"10.1017/dsd.2020.160","title":"IDENTIFYING GAPS IN AUTOMATING THE ASSESSMENT OF TECHNOLOGY READINESS LEVELS","year":2020,"lang":"en","type":"article","venue":"Proceedings of the Design Society DESIGN Conference","topic":"Technology Assessment and Management","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Automation; Maturity (psychological); Process (computing); Data science; Scale (ratio); Emerging technologies; Computer science; Engineering; Risk analysis (engineering); Process management; Business; Psychology; Artificial intelligence; Geography","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":[0.0008317771,0.0002128105,0.000344711,0.00007606239,0.00009498635,0.00004433005,0.001186578,0.0001840341,0.00001318035],"category_scores_gemma":[0.0001348141,0.0001534417,0.0001160837,0.001084902,0.0002416815,0.0002150322,0.0003003548,0.0004667089,0.000001190503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007841834,"about_ca_system_score_gemma":0.00006454311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002983425,"about_ca_topic_score_gemma":3.366553e-7,"domain_scores_codex":[0.998668,0.00002032939,0.0004786314,0.0002452827,0.0002854489,0.0003023545],"domain_scores_gemma":[0.9992147,0.0001529072,0.000262135,0.00017177,0.0001718361,0.00002663409],"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":[0.000008942835,0.00007012446,0.007958967,0.001121003,0.0002668707,9.412146e-7,0.005302623,0.005550244,0.9141698,0.05571748,0.004931345,0.004901604],"study_design_scores_gemma":[0.0007355487,0.0001475449,0.009091991,0.0006086245,0.0001028572,0.000004589835,0.009072944,0.5100267,0.4536673,0.01601184,0.0001271434,0.0004029583],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09321777,0.0001970585,0.8995553,0.004079897,0.0001638623,0.001218246,0.000003841037,0.0006053117,0.0009587714],"genre_scores_gemma":[0.8969126,0.00008079237,0.1027871,0.00005713244,0.00001364691,0.0001081379,2.275688e-7,0.00002305082,0.0000173056],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8036949,"threshold_uncertainty_score":0.6257169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06376527906804959,"score_gpt":0.2783147213121272,"score_spread":0.2145494422440776,"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."}}