{"id":"W3203081940","doi":"10.1051/e3sconf/202130802005","title":"Evaluation of MODIS-based Vegetation Restoration After the 2008 Wenchuan Earthquake","year":2021,"lang":"en","type":"article","venue":"E3S Web of Conferences","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Vegetation (pathology); Landslide; Natural hazard; Environmental science; Remote sensing; Restoration ecology; Physical geography; Geology; Normalized Difference Vegetation Index; Hydrology (agriculture); Geography; Seismology; Ecology; Geotechnical engineering; Climate change","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":[],"consensus_categories":[],"category_scores_codex":[0.0006981083,0.00007396531,0.00009828345,0.00001715819,0.00004403527,0.00002087721,0.00009827565,0.000053521,0.0008357005],"category_scores_gemma":[0.0001788369,0.00004736138,0.0000425544,0.0002197662,0.0001804234,0.0001173038,0.00002161346,0.00006237801,0.00003332053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003899824,"about_ca_system_score_gemma":0.0003268918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001168896,"about_ca_topic_score_gemma":0.002083527,"domain_scores_codex":[0.9983345,0.0003182394,0.0002083039,0.0001534625,0.000903926,0.00008158805],"domain_scores_gemma":[0.9993372,0.00006668227,0.0001681191,0.0002002127,0.0002066844,0.00002109073],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001435619,0.000433709,0.1666786,0.00009135451,0.0001002608,0.000007799485,0.004856869,0.2186604,0.3264329,0.001411944,0.004508051,0.2766745],"study_design_scores_gemma":[0.0002668928,0.00005173007,0.858764,0.00005961528,0.00007174764,0.000001886175,0.0002118396,0.09197016,0.04554676,0.001244768,0.001720404,0.00009018063],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9723549,0.0002016265,0.0002237,0.0006848188,0.0001357361,0.0001585782,0.000002387702,0.000008021048,0.02623025],"genre_scores_gemma":[0.9991879,0.000010568,0.0005997191,0.0000444233,0.00002366625,0.000003750505,0.00001487104,0.000002720851,0.0001123893],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6920854,"threshold_uncertainty_score":0.9150334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02573294961746869,"score_gpt":0.252616304402448,"score_spread":0.2268833547849793,"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."}}