{"id":"W2230084392","doi":"10.1080/08109028.2015.1095979","title":"Patterns of innovative outputs across climate zones: the geography of innovation","year":2015,"lang":"en","type":"article","venue":"Prometheus","topic":"Economic Growth and Productivity","field":"Economics, Econometrics and Finance","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Arizona State University; Georgia Institute of Technology; Maastricht Economic and Social Research Institute on Innovation and Technology, United Nations University; University of Toronto; Yale University","keywords":"Temperate climate; Human capital; Adaptation (eye); Natural (archaeology); Climate change; Economic geography; Order (exchange); Process (computing); Technological change; Environmental resource management; Geography; Business; Ecology; Computer science; Economics; Economic growth; Biology; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002797556,0.0001145606,0.0003736914,0.0002851289,0.00004657029,0.00001878826,0.0002643552,0.00006807437,0.00002838085],"category_scores_gemma":[0.0005692181,0.0001000625,0.00005283538,0.001372309,0.00009879097,0.0002162214,0.0001137925,0.0001308565,0.00003737492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000243778,"about_ca_system_score_gemma":0.00002552079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000251993,"about_ca_topic_score_gemma":0.000008427518,"domain_scores_codex":[0.9985575,0.00002856765,0.0008735638,0.0002606027,0.000039079,0.0002406689],"domain_scores_gemma":[0.9981403,0.00006617085,0.001022306,0.0003956861,0.0003474129,0.00002813745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000295615,0.00008107142,0.8777426,0.00006582325,0.00005401626,1.47765e-7,0.001593605,0.000007846821,0.00003896648,0.1168063,0.00004748632,0.003532568],"study_design_scores_gemma":[0.00100179,0.0002853301,0.85185,0.00002861284,0.000005066483,0.000002773673,0.0006581126,0.0001001956,0.007379285,0.1327504,0.005655526,0.0002829341],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9904847,0.0005415917,0.003370042,0.000464542,0.0003727551,0.000260891,0.0005205145,0.00001487287,0.003970095],"genre_scores_gemma":[0.9990953,0.00004531439,0.0006170252,0.00007915742,0.0000573656,0.0000275433,0.00002190491,0.00001361558,0.00004276725],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02589259,"threshold_uncertainty_score":0.408043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06435387472537443,"score_gpt":0.2705055056319167,"score_spread":0.2061516309065423,"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."}}