{"id":"W1752227541","doi":"","title":"Trend analysis of science and technology R&D networks","year":2010,"lang":"en","type":"article","venue":"","topic":"Innovation Policy and R&D","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ignorance; China; Order (exchange); Political science; Sociology of scientific knowledge; Scale (ratio); Regional science; Economic growth; Business; Social science; Sociology; Geography; Economics; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.00156493,0.0001763859,0.0003536832,0.005399208,0.0004288717,0.001284567,0.0005188929,0.0005717735,0.004567022],"category_scores_gemma":[0.01401985,0.0001708669,0.0005309929,0.005792437,0.0003189146,0.001910692,0.0005417598,0.0004572963,0.0007172385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009609886,"about_ca_system_score_gemma":0.0004491568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009649187,"about_ca_topic_score_gemma":0.005806203,"domain_scores_codex":[0.9990841,0.0002310563,0.00005557993,0.0002222207,0.0002628443,0.0001441646],"domain_scores_gemma":[0.99268,0.003530408,0.001296743,0.0005287301,0.001695328,0.0002689376],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006132071,0.0001478539,0.6164268,0.0002651409,0.0004854387,0.000644377,0.001528003,0.1241596,0.003389284,0.1267903,0.009762532,0.1157875],"study_design_scores_gemma":[0.00004817846,0.0002178581,0.4706798,0.00008522988,0.0001659618,0.0005958566,0.001711982,0.4589744,0.002047631,0.03992146,0.02548609,0.00006567666],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9606982,0.0009833449,0.01628802,0.0007528767,0.00003954023,0.00006912116,0.003801882,0.0001413618,0.01722568],"genre_scores_gemma":[0.9914302,0.0005116244,0.002710727,0.00002046782,0.00002540794,0.00005612557,0.002115581,0.00002241811,0.003107364],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9946008,"threshold_uncertainty_score":0.01918602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01590810736440169,"score_gpt":0.2302276500315654,"score_spread":0.2143195426671637,"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."}}