{"id":"W2334860033","doi":"10.3989/collectbot.2015.v34.008","title":"Espacios verdes urbanos y diversidad vegetal a diferentes escalas espacio-temporales: el ejemplo de Beijing, China","year":2015,"lang":"es","type":"article","venue":"Collectanea Botanica","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; Thompson Rivers University","keywords":"Geography; Humanities; Beijing; China; Forestry; Art; Archaeology","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005813157,0.0007773091,0.0008771209,0.0001037157,0.0007887212,0.0006162553,0.001237653,0.0004671269,0.001599832],"category_scores_gemma":[0.0002037377,0.0006903179,0.0003589161,0.000928345,0.0001871391,0.0007043802,0.0009209106,0.0004662927,0.003100592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001160732,"about_ca_system_score_gemma":0.0003054597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001456469,"about_ca_topic_score_gemma":0.001258671,"domain_scores_codex":[0.9953975,0.0003742878,0.0007188881,0.00111341,0.001031531,0.001364357],"domain_scores_gemma":[0.997276,0.000196018,0.0004129091,0.0009517276,0.00005585214,0.001107479],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005959205,0.0009021197,0.9620949,0.0003527349,0.0004020052,0.0004091455,0.005767709,0.0004276258,0.002814894,0.0001987128,0.02552812,0.0005060845],"study_design_scores_gemma":[0.01491305,0.006760146,0.66825,0.003097844,0.002042338,0.0006881299,0.007164171,0.08429218,0.008910092,0.002325343,0.1946666,0.006890029],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.978514,0.002469684,0.00004190836,0.001203064,0.0007039689,0.0008021895,0.0001675235,0.0002339047,0.01586382],"genre_scores_gemma":[0.99698,0.0005326368,0.0002380823,0.0001929173,0.0003779476,0.00005653988,0.00002803643,0.00009718398,0.001496702],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2938449,"threshold_uncertainty_score":0.9995548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01428734236423329,"score_gpt":0.2358099893778642,"score_spread":0.2215226470136309,"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."}}