{"id":"W2090762136","doi":"10.1109/mnano.2012.2237312","title":"Nanotechnology Public Funding and Impact Analysis: A Tale of Two Decades (1991-2010)","year":2013,"lang":"en","type":"article","venue":"IEEE Nanotechnology Magazine","topic":"University-Industry-Government Innovation Models","field":"Business, Management and Accounting","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Societal impact of nanotechnology; Government (linguistics); Technology transfer; Investment (military); Nanotechnology; Business; Political science; Engineering; Politics; International trade; Materials science","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003367807,0.0003378577,0.0005992317,0.003367597,0.0001971559,0.0001468056,0.0006502914,0.0009177921,0.0006844552],"category_scores_gemma":[0.0001878153,0.000324562,0.0001515435,0.004975885,0.0005176885,0.001768779,0.0004599134,0.0007262452,0.0002931744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001306857,"about_ca_system_score_gemma":0.00003230052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005922866,"about_ca_topic_score_gemma":0.0001881836,"domain_scores_codex":[0.9979712,0.00001533228,0.0005127786,0.0005538563,0.0003212953,0.0006255045],"domain_scores_gemma":[0.9982817,0.00005381274,0.0006354887,0.0006413861,0.0003604071,0.00002719148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00005449638,0.0004145392,0.3506075,0.0001440692,0.001937571,0.00004349897,0.00003969305,0.0006396236,0.4581193,0.1583996,0.009800773,0.01979937],"study_design_scores_gemma":[0.02435011,0.001019062,0.3378098,0.0004329612,0.007912813,0.0002988864,0.003110788,0.1956819,0.1061754,0.1976825,0.117784,0.007741707],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9847947,0.00007409618,0.00616915,0.006546563,0.0002057107,0.0003743066,0.00001117628,0.000514884,0.001309402],"genre_scores_gemma":[0.9977922,0.00001587097,0.0008004753,0.000428737,0.000110406,0.00002371433,0.00003264751,0.00003277982,0.0007632137],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3519439,"threshold_uncertainty_score":0.9999207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02604805604584257,"score_gpt":0.2500146374176441,"score_spread":0.2239665813718015,"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."}}