{"id":"W4405546361","doi":"10.1080/09640568.2024.2413879","title":"The impact of industrial agglomeration on the synergistic evolution of the energy big data ecosystem: empirical findings from China","year":2024,"lang":"en","type":"article","venue":"Journal of Environmental Planning and Management","topic":"Energy, Environment, Economic Growth","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Economies of agglomeration; Sustainable development; Economic geography; China; Sustainability; Environmental economics; Big data; Urban agglomeration; Business; Economics; Natural resource economics; Industrial organization; Ecology; Geography; Economic growth; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000830967,0.0001301719,0.0002339811,0.0001092627,0.0001370219,0.00006753199,0.0004448137,0.00005742594,0.00003742663],"category_scores_gemma":[0.0000367312,0.00007484962,0.0001281688,0.00007627552,0.00009681263,0.0001371105,0.0002547239,0.0001807439,0.000005836395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003401707,"about_ca_system_score_gemma":0.00001018511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001268181,"about_ca_topic_score_gemma":0.000005687904,"domain_scores_codex":[0.9988099,0.00005408376,0.0006910117,0.0002156773,0.0000958222,0.0001335337],"domain_scores_gemma":[0.9987887,0.0001811296,0.0005895384,0.0004027365,0.000001220977,0.00003672532],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007236469,0.00101678,0.6395508,0.0001621117,0.006615533,0.00006447136,0.002050729,0.1116159,0.003392288,0.1543879,0.05719941,0.02322039],"study_design_scores_gemma":[0.001012087,0.0004703938,0.9439937,0.00046831,0.0001625026,0.00001503975,0.0006671327,0.02241018,0.0003637208,0.008818441,0.02135527,0.0002632274],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934661,0.002991997,0.001035837,0.0004861119,0.0006869615,0.0001031125,0.0003166532,0.000002630964,0.0009105692],"genre_scores_gemma":[0.998714,0.0008683128,0.00001762336,0.00001953015,0.0002028922,0.000003303145,0.00001270438,0.00001319801,0.0001484036],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3044429,"threshold_uncertainty_score":0.3052279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05588055466738785,"score_gpt":0.2384119234063206,"score_spread":0.1825313687389327,"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."}}