{"id":"W2388634744","doi":"","title":"Influencing Factor of Investment in China from Perspective of City Space","year":2014,"lang":"en","type":"article","venue":"Resource Development & Market","topic":"Regional Economic and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"Investment (military); Statistic; Economics; Gross private domestic investment; China; Capital expenditure; Capital (architecture); Fixed investment; Scale (ratio); Human capital; Monetary economics; Economic geography; Macroeconomics; Capital formation; Return on investment; Economic growth; Geography; Finance; Financial capital; Open-ended investment company; Production (economics); Statistics; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000275819,0.000211721,0.000180095,0.001269194,0.000469142,0.001290305,0.0002100969,0.0001629965,0.00247854],"category_scores_gemma":[0.001030862,0.0001342937,0.0003269939,0.00254148,0.0005871839,0.0004245395,0.0007052987,0.0002690084,0.0001504056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001977226,"about_ca_system_score_gemma":0.001036291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03703272,"about_ca_topic_score_gemma":0.05042319,"domain_scores_codex":[0.9996344,0.00006894756,0.00002143008,0.00005607333,0.0001019699,0.0001171116],"domain_scores_gemma":[0.9992606,0.0001203638,0.000257558,0.0000476873,0.0001421246,0.0001717279],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002936482,0.00002109012,0.983209,0.00002005798,0.00007560295,0.0003015119,0.0004797829,0.003775475,0.0004914057,0.004567336,0.0004450357,0.006584441],"study_design_scores_gemma":[0.000003344839,0.00002119922,0.9913973,0.000009240321,0.00003106826,0.00007576033,0.0008585364,0.004677931,0.0001686348,0.0008858167,0.001862182,0.000009061468],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9940891,0.0001141849,0.0004005789,0.0001880974,0.000004547442,0.000006522376,0.0001401976,0.000009318152,0.005047623],"genre_scores_gemma":[0.999345,0.000058313,0.00007576282,0.000005361601,0.000003377678,0.00000200547,0.00007201348,0.00000148575,0.0004367482],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03703272,"threshold_uncertainty_score":0.07363433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01588508286003807,"score_gpt":0.1935730666935843,"score_spread":0.1776879838335462,"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."}}