{"id":"W2159448912","doi":"10.1007/s11024-015-9281-6","title":"Nanotechnology in Mexico: Key Findings Based on OECD Criteria","year":2015,"lang":"en","type":"article","venue":"Minerva","topic":"Business, Innovation, and Economy","field":"Economics, Econometrics and Finance","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Consejo Nacional de Ciencia y Tecnología; University of Manchester; University of California Institute for Mexico and the United States; National Science Foundation","keywords":"Key (lock); Regional science; Higher education; Political science; Business; Economic growth; Geography; Economics; Computer 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":[{"model":"gemma","categories":["sts"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0006978186,0.0001880495,0.000323608,0.003633376,0.0006829209,0.001252251,0.0002681886,0.0003244074,0.004052602],"category_scores_gemma":[0.002583169,0.0000569026,0.0004025713,0.004061135,0.0003554015,0.0005669176,0.001030444,0.0002701557,0.0002374974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001171156,"about_ca_system_score_gemma":0.001213268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07969571,"about_ca_topic_score_gemma":0.09233584,"domain_scores_codex":[0.999542,0.0000835895,0.00002613161,0.00005934267,0.0001148456,0.0001741001],"domain_scores_gemma":[0.9975123,0.0008322584,0.000798361,0.0001028815,0.0005959227,0.0001582445],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001879289,0.00008873316,0.9712495,0.000143921,0.00009107785,0.0002974166,0.0005344763,0.0008375803,0.0003228522,0.008034699,0.003359238,0.01485267],"study_design_scores_gemma":[0.000006206048,0.00003418167,0.9888411,0.00006381649,0.00008237245,0.0001318723,0.002211057,0.0003621714,0.0002901389,0.0006628452,0.007308486,0.000005743659],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9606836,0.001741141,0.0004026332,0.0006575255,0.00001229902,0.00003163759,0.005426315,0.00002078008,0.03102402],"genre_scores_gemma":[0.99263,0.001253319,0.0003762379,0.00004845906,0.00002174251,0.0000468289,0.002719672,0.000008126924,0.002895514],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.999317,"threshold_uncertainty_score":0.1584637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05721326205569208,"score_gpt":0.2352144863972538,"score_spread":0.1780012243415617,"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."}}