{"id":"W4411389194","doi":"10.3389/fenvs.2025.1552159","title":"Digital industry agglomeration and inclusive green growth: Synergies and path exploration","year":2025,"lang":"en","type":"article","venue":"Frontiers in Environmental Science","topic":"Energy, Environment, Economic Growth","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Federation for the Humanities and Social Sciences","keywords":"Economies of agglomeration; Path (computing); Green growth; Business; Industrial organization; Natural resource economics; Environmental science; Economic geography; Economics; Computer science; Sustainable development; Ecology; Economic growth; Biology","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":[0.0003340992,0.0001459498,0.0002105554,0.0003612695,0.000208103,0.0001486691,0.0001968368,0.0001206547,0.00001729931],"category_scores_gemma":[0.00004924563,0.0001803502,0.00001911746,0.0002347099,0.0008240095,0.001982322,0.0003564658,0.0001679312,0.00001378352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004916911,"about_ca_system_score_gemma":0.00001312689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006511396,"about_ca_topic_score_gemma":0.000005642501,"domain_scores_codex":[0.9987389,0.000008809456,0.0003475207,0.0005971486,0.00005573615,0.000251848],"domain_scores_gemma":[0.999576,0.00001452976,0.0001489135,0.0001793878,0.000001515084,0.00007965112],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00000859493,0.00006005664,0.9664071,0.000010998,0.00001093732,0.000002540413,0.0005492624,0.0002117708,0.0003924739,0.01996685,0.0002008755,0.01217855],"study_design_scores_gemma":[0.0009044767,0.00007808007,0.8794531,0.00003352913,0.000006021708,0.00000418572,0.002777082,0.01015216,0.001094198,0.09942636,0.005582347,0.0004884711],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9812414,0.001309835,0.004094832,0.0007851149,0.0003409213,0.0001809708,0.00006159055,0.00001393974,0.01197142],"genre_scores_gemma":[0.9967175,0.000831069,0.001277639,0.0001835387,0.00002032142,0.00002701027,0.00001257153,0.000008972092,0.0009213776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.086954,"threshold_uncertainty_score":0.7354468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007556080832225453,"score_gpt":0.1827300599582765,"score_spread":0.1751739791260511,"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."}}