{"id":"W4318976646","doi":"10.1002/ldr.4627","title":"Nonlinear trends of vegetation changes in different geomorphologic zones and land use types of the Yangtze River basin, China","year":2023,"lang":"en","type":"article","venue":"Land Degradation and Development","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Algoma University","funders":"National Key Research and Development Program of China; Priority Academic Program Development of Jiangsu Higher Education Institutions; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Vegetation (pathology); Grassland; Environmental science; Normalized Difference Vegetation Index; Afforestation; Physical geography; Land use; Land degradation; Altitude (triangle); Hydrology (agriculture); Vegetation type; China; Drainage basin; Wetland; Climate change; Agroforestry; Geography; Geology; Ecology","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":[0.0004614664,0.0002712635,0.0001753044,0.00151149,0.0003388491,0.0005420971,0.0002208913,0.0001829602,0.0009337127],"category_scores_gemma":[0.000772666,0.000180086,0.0003523054,0.001691598,0.0003945455,0.0004688962,0.0005338239,0.0001805069,0.00009868496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007439943,"about_ca_system_score_gemma":0.0007803444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04191209,"about_ca_topic_score_gemma":0.04633201,"domain_scores_codex":[0.9997649,0.00004569493,0.00002417085,0.00006137312,0.00005402787,0.00004989747],"domain_scores_gemma":[0.9995978,0.00008019853,0.00008223446,0.00003638252,0.000144008,0.00005928138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003787193,0.00001384106,0.9871994,0.00003989223,0.0001146164,0.0001608783,0.0004652866,0.002324048,0.001298468,0.0002537564,0.0004040774,0.007687861],"study_design_scores_gemma":[0.000002097401,0.000009498709,0.9960713,0.000004220284,0.00001356322,0.00002380899,0.0003061819,0.003056132,0.00008460595,0.00006381139,0.0003594796,0.00000527765],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987859,0.00009467384,0.0001710948,0.0000831912,0.000002980848,0.000004311498,0.0004154741,0.00001108354,0.0004313919],"genre_scores_gemma":[0.9993266,0.00004363852,0.0000715363,0.000007132372,0.000002728651,0.000004722552,0.0003392506,0.000001737999,0.000202734],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04191209,"threshold_uncertainty_score":0.08333623,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0177045295570286,"score_gpt":0.2247025727701245,"score_spread":0.2069980432130959,"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."}}