{"id":"W4291163510","doi":"10.1016/j.heliyon.2022.e10168","title":"The triple helix in developed countries: when knowledge meets innovation?","year":2022,"lang":"en","type":"article","venue":"Heliyon","topic":"University-Industry-Government Innovation Models","field":"Business, Management and Accounting","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Narodowym Centrum Nauki","keywords":"Triple helix; Inefficiency; Data envelopment analysis; Milestone; Government (linguistics); Order (exchange); Index (typography); Helix (gastropod); Value (mathematics); Sample (material); Economics; Econometrics; Mathematics; Statistics; Computer science; Geography; Physics; Finance; Microeconomics; Cartography","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009755119,0.0001443495,0.0001335135,0.0004641666,0.0008883725,0.0002186373,0.0004579358,0.00007497107,0.001145113],"category_scores_gemma":[0.0001277393,0.0001368405,0.00002450622,0.002563364,0.00004280893,0.0007941061,0.0005609161,0.0004311697,0.0002494862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003932819,"about_ca_system_score_gemma":0.0001276175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000742513,"about_ca_topic_score_gemma":0.0002328299,"domain_scores_codex":[0.9985552,0.00002874629,0.0003819202,0.0002492279,0.0004919415,0.0002929271],"domain_scores_gemma":[0.9991215,0.00006492456,0.0002596071,0.0002495079,0.0002986784,0.000005736527],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002328839,0.0002666345,0.05423449,0.0002321171,0.00005276466,0.00002609614,0.0009555448,0.0005638775,0.0004579299,0.8386026,0.0999665,0.004408556],"study_design_scores_gemma":[0.000850983,0.000007068422,0.00432249,0.00003054601,0.000008237044,8.92445e-7,0.001288928,0.0009355919,0.00005771644,0.001768217,0.9905427,0.0001865941],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.822886,0.0005463478,0.000721742,0.02506525,0.001861291,0.001012338,0.0000242859,0.0002606767,0.147622],"genre_scores_gemma":[0.9688148,0.00004197809,0.0001188176,0.006459334,0.0004636351,0.00008391537,0.00008079429,0.00003734331,0.02389942],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8905762,"threshold_uncertainty_score":0.999768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03239709837189576,"score_gpt":0.2431544244524093,"score_spread":0.2107573260805136,"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."}}