{"id":"W2343521063","doi":"10.22215/timreview/1011","title":"A Proposed Approach for Idea Selection in Front End of Innovation Activities","year":2016,"lang":"en","type":"article","venue":"Technology Innovation Management Review","topic":"Design Education and Practice","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Front and back ends; Selection (genetic algorithm); Computer science; Front (military); Business; Process management; Engineering; Artificial intelligence; Mechanical engineering; Operating system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0007607387,0.0001073234,0.0001995681,0.001280062,0.00002103647,0.000006021469,0.0001161493,0.00008350793,0.00007498993],"category_scores_gemma":[0.0001755762,0.00008898767,0.00001370272,0.002990796,0.00003745618,0.0002534207,0.00001637902,0.00008212896,0.000008214752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001158871,"about_ca_system_score_gemma":0.00001948438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001188907,"about_ca_topic_score_gemma":0.000001706784,"domain_scores_codex":[0.9990146,0.00002148618,0.0005863396,0.0001512826,0.00009496645,0.0001312922],"domain_scores_gemma":[0.9994042,0.00003309745,0.0002160042,0.0001534552,0.0001891203,0.000004146364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000009337125,0.00007099658,0.0003060466,0.003879755,0.00005573512,1.45952e-7,0.00001416178,0.0000245694,0.0183726,0.423788,0.005673815,0.5478048],"study_design_scores_gemma":[0.003172443,0.0002837106,0.003460015,0.005213696,0.0002456367,0.00001741805,0.0004582846,0.002344973,0.08558054,0.0469538,0.8512351,0.001034381],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01247173,0.002915393,0.9557962,0.003907303,0.0001880859,0.003348196,0.000006409598,0.0006545989,0.02071206],"genre_scores_gemma":[0.9232018,0.007980078,0.0651811,0.0004483177,0.00003135701,0.001350875,0.00006489033,0.00003857915,0.001703009],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9107301,"threshold_uncertainty_score":0.3628812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02666276558588152,"score_gpt":0.2893884130510002,"score_spread":0.2627256474651187,"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."}}