{"id":"W4391487254","doi":"10.1016/j.heliyon.2024.e25534","title":"Digital transformation, productive services agglomeration and innovation performance","year":2024,"lang":"en","type":"article","venue":"Heliyon","topic":"Energy, Environment, Economic Growth","field":"Economics, Econometrics and Finance","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European External Action Service; Federation for the Humanities and Social Sciences","keywords":"Digital transformation; Economies of agglomeration; Transformation (genetics); Industrial organization; Index (typography); China; Service (business); Service innovation; Construct (python library); Business; Computer science; Economics; Economic system; Marketing; Economic growth; Political science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001070709,0.000214362,0.0002094445,0.00170373,0.0007049607,0.001826457,0.0002242782,0.0003271246,0.003630642],"category_scores_gemma":[0.003809791,0.00005453735,0.0004781459,0.002790503,0.001237197,0.001691765,0.001422822,0.0003547851,0.0004523495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002447027,"about_ca_system_score_gemma":0.002142747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02200852,"about_ca_topic_score_gemma":0.02245954,"domain_scores_codex":[0.9992384,0.0001523184,0.00005168312,0.00009440174,0.0002263852,0.0002367879],"domain_scores_gemma":[0.9973223,0.0008149855,0.0007389304,0.0001983461,0.000458584,0.0004669777],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001325024,0.000185914,0.9215259,0.0001184554,0.0001245937,0.0002222946,0.001615108,0.01404593,0.0007531972,0.01851109,0.0008566785,0.04190833],"study_design_scores_gemma":[0.00002197967,0.0002175884,0.9578552,0.00005882614,0.0001101219,0.0001074428,0.005289165,0.01844098,0.001723475,0.009661932,0.006484163,0.00002919301],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9816034,0.0002582473,0.001462782,0.0002742063,0.000006314265,0.00001931137,0.0002024078,0.00002122825,0.0161521],"genre_scores_gemma":[0.9992779,0.00007057635,0.0001236254,0.000006506899,0.000002898734,0.000004164121,0.00007648893,0.000001529772,0.0004363816],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02200852,"threshold_uncertainty_score":0.0437609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01346810068214996,"score_gpt":0.1912244555763701,"score_spread":0.1777563548942201,"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."}}