{"id":"W3184866665","doi":"10.1177/03091325211033652","title":"Geographies of infrastructure III: Infrastructure with Chinese characteristics","year":2021,"lang":"en","type":"article","venue":"Progress in Human Geography","topic":"China's Socioeconomic Reforms and Governance","field":"Social Sciences","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Canada Excellence Research Chairs, Government of Canada","keywords":"China; Critical infrastructure; Urban infrastructure; Transport infrastructure; Economic geography; Diversity (politics); State (computer science); Business; Regional science; Geography; Economic growth; Environmental planning; Political science; Urban planning; Transport engineering; Engineering; Civil engineering; Economics; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002426157,0.0002382272,0.0004518494,0.0002364848,0.0003684629,0.0001258753,0.0003800755,0.0001852881,0.0002792955],"category_scores_gemma":[0.00003033302,0.0001847227,0.0001765493,0.0009732686,0.001413171,0.0003205813,0.0001043895,0.000352187,7.083777e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004557621,"about_ca_system_score_gemma":0.0001411726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005709883,"about_ca_topic_score_gemma":0.002200118,"domain_scores_codex":[0.9983237,0.00007456198,0.0004262922,0.0003668534,0.0003769072,0.0004316574],"domain_scores_gemma":[0.9989186,0.00003899724,0.0004011699,0.0003447383,0.0001904765,0.000106034],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002606924,0.00006281293,0.9743124,0.00005886165,0.00005930851,0.00002234457,0.007926723,0.000008493421,0.000009222794,0.007930492,0.00007661415,0.009506622],"study_design_scores_gemma":[0.000611736,0.00005052421,0.9768095,0.00008868956,0.00001559337,0.000003385637,0.001298873,0.000004178008,0.00002800102,0.01488174,0.005969787,0.0002379824],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945049,0.001743603,0.000008714731,0.000201773,0.0002196503,0.0002343481,0.00004650783,0.0000481945,0.002992255],"genre_scores_gemma":[0.9981408,0.0006696163,0.0007595227,0.00006733665,0.0002088841,0.0000326836,0.0000383771,0.00001985258,0.00006296321],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00926864,"threshold_uncertainty_score":0.7532775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003957612626880734,"score_gpt":0.259463361253399,"score_spread":0.2555057486265183,"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."}}