{"id":"W2587515503","doi":"10.1016/j.isprsjprs.2016.12.014","title":"Estimating urban vegetation fraction across 25 cities in pan-Pacific using Landsat time series data","year":2017,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":65,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Urbanization; Vegetation (pathology); Geography; Physical geography; Population; Normalized Difference Vegetation Index; Environmental science; Remote sensing; Climate change; Ecology","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":[0.0003519981,0.0003634802,0.0002391748,0.001845105,0.0005780829,0.000783857,0.0003980561,0.0002432971,0.0005342063],"category_scores_gemma":[0.0006644369,0.0003089764,0.0005099093,0.002659214,0.0003310191,0.0005581285,0.0004242924,0.000229312,0.00009920266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001239497,"about_ca_system_score_gemma":0.001121616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.377384,"about_ca_topic_score_gemma":0.4766456,"domain_scores_codex":[0.9998525,0.00001610699,0.00001181949,0.00005769718,0.00002745849,0.0000344228],"domain_scores_gemma":[0.9996836,0.00006769966,0.00007278471,0.0000334256,0.00009651029,0.00004594947],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009976196,0.00008351951,0.972244,0.00003234851,0.0001816205,0.0001367485,0.0003412619,0.00989497,0.0009057315,0.0001310428,0.0005118852,0.01543707],"study_design_scores_gemma":[0.000007202257,0.000008533265,0.9805469,0.000007134646,0.00006758256,0.00002755412,0.0008587719,0.01769864,0.000324857,0.00005738021,0.0003887157,0.000006817718],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986537,0.00004256417,0.0002311744,0.00001642608,0.000001161528,0.000006647472,0.0007734767,0.00001779595,0.0002570372],"genre_scores_gemma":[0.9972001,0.00006728448,0.0008557507,0.000004836262,0.000001800408,0.00001085131,0.001640535,0.000004491481,0.0002142882],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.377384,"threshold_uncertainty_score":0.7503748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0249644112796845,"score_gpt":0.286494041025791,"score_spread":0.2615296297461065,"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."}}