{"id":"W3092368998","doi":"10.1371/journal.pone.0240140","title":"An empirical study on spatial–temporal dynamics and influencing factors of apple production in China","year":2020,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; Earmarked Fund for China Agriculture Research System; Federation for the Humanities and Social Sciences","keywords":"Production (economics); China; Context (archaeology); Economies of agglomeration; Economic geography; Loess plateau; Balance (ability); Agricultural economics; Natural resource economics; Economics; Geography; Environmental science; Economic growth; Biology; Macroeconomics","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":[0.00008450148,0.00006409182,0.0001361667,0.00001637281,0.00002819859,0.000008886886,0.00006969246,0.00002351225,0.00005211187],"category_scores_gemma":[0.00001241088,0.00004996985,0.00000713073,0.0001209107,0.000007147532,0.0001414189,0.00004416623,0.00006304775,0.00001060882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003790692,"about_ca_system_score_gemma":0.000002571634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002930295,"about_ca_topic_score_gemma":0.01119393,"domain_scores_codex":[0.9993572,0.00003830817,0.0001367826,0.0001992729,0.0001863942,0.00008201815],"domain_scores_gemma":[0.9997889,0.000006526609,0.00004757243,0.00009891704,0.000002205207,0.00005582249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002155423,0.0008759348,0.9907442,0.00003594041,0.00000853482,0.000001061593,0.005901136,0.0003798132,0.001897998,1.334744e-7,0.000001880193,0.000131842],"study_design_scores_gemma":[0.0001590779,0.0005042104,0.9729413,0.0000201048,0.00001233206,7.816387e-8,0.001116382,0.02166735,0.0035074,0.000008121953,8.007914e-7,0.00006283707],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994256,0.00000234803,0.00000435225,0.0002117522,0.000009651464,0.0002724253,0.000005007856,0.00001628438,0.00005257843],"genre_scores_gemma":[0.9998825,0.00000156339,0.00003829963,0.00003204756,0.0000233215,0.000005215787,0.00001005165,0.000005679458,0.000001365483],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02128754,"threshold_uncertainty_score":0.6246475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03147383292302879,"score_gpt":0.2386434167700508,"score_spread":0.207169583847022,"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."}}