{"id":"W2079674913","doi":"10.4236/ti.2013.44027","title":"A Study on Chinese Regional Scientific Innovation Efficiency with a Perspective of Synergy Degree","year":2013,"lang":"en","type":"article","venue":"Technology and Investment","topic":"Regional Economic and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Perspective (graphical); Degree (music); Process (computing); Computer science; Industrial organization; Economics; Artificial intelligence; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.002298874,0.000303829,0.0005422304,0.00385603,0.0005829117,0.001314892,0.0004686422,0.000258207,0.001415162],"category_scores_gemma":[0.004934549,0.0001929415,0.001240169,0.004072641,0.0009223273,0.001880063,0.001098032,0.0002788712,0.00009243506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002949156,"about_ca_system_score_gemma":0.002049879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01816274,"about_ca_topic_score_gemma":0.0133744,"domain_scores_codex":[0.9986477,0.000353254,0.0001336905,0.0002295384,0.0003435542,0.0002922028],"domain_scores_gemma":[0.9971139,0.001168661,0.0005711759,0.0002761839,0.0006467533,0.0002233004],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001893137,0.0001077364,0.8001534,0.0002686085,0.0008962185,0.001199774,0.003801018,0.08727318,0.003736647,0.05366754,0.0007878522,0.04791861],"study_design_scores_gemma":[0.00002891116,0.0002178576,0.8984419,0.00005111653,0.0004163181,0.000408531,0.003341499,0.08307958,0.002008038,0.009011852,0.002943542,0.00005085717],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9897584,0.0002982382,0.003531514,0.0001545972,0.000003363809,0.00001258928,0.0001015193,0.00001152622,0.006128318],"genre_scores_gemma":[0.9993881,0.0000857719,0.0003182786,0.00000485011,0.000002346977,0.000003969394,0.00003955836,0.000001290836,0.0001556968],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.996144,"threshold_uncertainty_score":0.03611404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03777640122650919,"score_gpt":0.2299177084645778,"score_spread":0.1921413072380686,"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."}}