{"id":"W2808688853","doi":"10.3390/rs10060956","title":"Temporal Variability of MODIS Phenological Indices in the Temperate Rainforest of Northern Patagonia","year":2018,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Fondo Nacional de Desarrollo Científico y Tecnológico; Natural Sciences and Engineering Research Council of Canada; U.S. Geological Survey; National Aeronautics and Space Administration","keywords":"Enhanced vegetation index; Normalized Difference Vegetation Index; Climatology; Phenology; Environmental science; Moderate-resolution imaging spectroradiometer; Vegetation (pathology); Temperate climate; Physical geography; Climate change; Geography; Ecology; Satellite; Vegetation Index; Geology; Oceanography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002443784,0.0001730153,0.0001820056,0.0005817908,0.000216958,0.0007029269,0.0002255509,0.0001301088,0.0004693504],"category_scores_gemma":[0.0006564648,0.0001227605,0.00015136,0.001141449,0.0002163509,0.0001947169,0.0002840737,0.0001554201,0.0001021166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000465861,"about_ca_system_score_gemma":0.0003535761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09359741,"about_ca_topic_score_gemma":0.0991236,"domain_scores_codex":[0.9998728,0.00002272799,0.000008326087,0.00004510743,0.00002071072,0.00003031586],"domain_scores_gemma":[0.999684,0.00004402236,0.0001321101,0.00002445799,0.00007367551,0.00004174812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000162426,0.00002786397,0.9843887,0.00003919023,0.00009374665,0.000291673,0.0006629204,0.001463503,0.004329103,0.00005643936,0.0008361028,0.007648395],"study_design_scores_gemma":[0.000002775511,0.000007454193,0.9988889,0.000002847439,0.000005955335,0.00002839503,0.0001446862,0.0005589968,0.00006260443,0.000006729102,0.0002884215,0.000002304157],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998434,0.0000814361,0.00008717003,0.00002901493,0.0000028146,0.0000044977,0.0007611049,0.00001685188,0.0005830418],"genre_scores_gemma":[0.9986511,0.00006039618,0.0001844485,0.0000144514,0.000005142524,0.00001334007,0.0008947539,0.000004435602,0.0001720115],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09359741,"threshold_uncertainty_score":0.1861053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03056053147349016,"score_gpt":0.255720901237362,"score_spread":0.2251603697638718,"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."}}