{"id":"W4312721522","doi":"10.1109/igarss46834.2022.9884019","title":"Assessing Temporal and Spatial Variations of Vegetation Degradation in Southwest China Based on Multi-Source Remote Sensing Data","year":2022,"lang":"en","type":"article","venue":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Vegetation (pathology); Shrubland; Grassland; Environmental science; Grassland degradation; China; Physical geography; Elevation (ballistics); Land degradation; Restoration ecology; Spatial ecology; Ecosystem; Geography; Remote sensing; Ecology; Land use","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":[0.001269104,0.0002696575,0.0002581702,0.0003129819,0.0006575846,0.0002278528,0.0004350476,0.0000899299,0.00002801193],"category_scores_gemma":[0.0001985534,0.0002643861,0.00005078466,0.0007669542,0.0003226518,0.0005978417,0.000581443,0.000515973,0.000003861537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003756808,"about_ca_system_score_gemma":0.0000768239,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02189278,"about_ca_topic_score_gemma":0.003344597,"domain_scores_codex":[0.9965708,0.0003561512,0.0005464142,0.000980605,0.001210199,0.0003358601],"domain_scores_gemma":[0.9987135,0.0001900183,0.0004530095,0.0004961874,0.00004463919,0.0001026238],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001032052,0.0001730182,0.004945918,0.00003612936,0.0000292056,0.0001061265,0.002897314,0.1798379,0.2906486,0.00001019743,0.0001569725,0.5210554],"study_design_scores_gemma":[0.0005776093,0.00007219629,0.0616835,0.0001101235,0.00002256231,0.0001374358,0.0003877798,0.9351135,0.001045517,0.0001087104,0.0004632793,0.0002777438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7152675,0.00002238835,0.279625,0.002179983,0.001645961,0.0003640569,0.00005381917,0.00005578059,0.0007855046],"genre_scores_gemma":[0.9294019,0.00002006563,0.06949025,0.0003268481,0.00009763525,7.421333e-8,0.0002580866,0.00002616038,0.0003789847],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7552757,"threshold_uncertainty_score":0.9999808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02124836861987464,"score_gpt":0.2691405685023155,"score_spread":0.2478921998824409,"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."}}