{"id":"W2388868774","doi":"","title":"Forest health evaluation for tending of recreational forest in Xishan Forest Farm in Beijing city","year":2014,"lang":"en","type":"article","venue":"Zhongnan Linye Keji Daxue xuebao","topic":"Advanced Decision-Making Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"L'Alliance Boviteq","funders":"","keywords":"Robinia; Beijing; Agroforestry; Forest management; Tree health; Recreation; Thinning; Forest health; Forestry; Urban forest; Pruning; Forest farming; Forest restoration; Environmental science; Forest ecology; Geography; China; Ecology; Ecosystem; Biology; Agronomy","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004148877,0.0002901327,0.0005594298,0.0008300617,0.0001738874,0.0001072789,0.001112145,0.0001671704,0.000005037926],"category_scores_gemma":[0.002551059,0.000297682,0.00013179,0.001052627,0.00008182837,0.0008181153,0.0003394831,0.0002892621,0.000003589247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005507738,"about_ca_system_score_gemma":0.0003403629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003355863,"about_ca_topic_score_gemma":0.01344083,"domain_scores_codex":[0.9963591,0.0002477762,0.001111969,0.0008245045,0.0008124467,0.0006442074],"domain_scores_gemma":[0.9967808,0.00120594,0.0006332122,0.0009044659,0.0003449256,0.0001307317],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009124362,0.0003329972,0.3918881,0.0001504109,0.00001438043,0.000004955327,0.0008554728,0.05301775,0.0002972066,0.2390969,0.0003403094,0.3139103],"study_design_scores_gemma":[0.00112829,0.0003270521,0.1562466,0.0009116282,0.000005538335,0.000007581049,0.00003551433,0.5989605,0.0003305878,0.2402205,0.001504614,0.0003215842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2136442,0.0001323009,0.7832583,0.0005488182,0.0002708437,0.001353446,0.00001463437,0.0001538145,0.0006237076],"genre_scores_gemma":[0.7665651,0.00001494854,0.2328101,0.000130254,0.00011289,0.0002482248,0.00005346641,0.00002880988,0.0000361878],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5529209,"threshold_uncertainty_score":0.9999475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05325355867242984,"score_gpt":0.3666981425687578,"score_spread":0.313444583896328,"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."}}