{"id":"W2606223706","doi":"10.1016/j.rse.2017.03.045","title":"Multi-scale analysis of relationship between imperviousness and urban tree height using airborne remote sensing","year":2017,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Impervious surface; Remote sensing; Lidar; Environmental science; Scale (ratio); Land cover; Spatial ecology; Physical geography; Geography; Land use; Cartography; 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":[0.0002651024,0.0002242931,0.000215017,0.0006637391,0.0002305887,0.000278495,0.0002173881,0.0002059065,0.0005277243],"category_scores_gemma":[0.0004401189,0.0001460467,0.000349345,0.0006190196,0.000142435,0.0003454802,0.000200453,0.0001749871,0.0001065412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001846523,"about_ca_system_score_gemma":0.0001577046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01185714,"about_ca_topic_score_gemma":0.02284117,"domain_scores_codex":[0.9998991,0.00001877827,0.000005394145,0.00003047993,0.00002469223,0.00002158185],"domain_scores_gemma":[0.9996344,0.0001682311,0.00005775527,0.00003521846,0.0000746536,0.00002979094],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004213314,0.0004662314,0.8253144,0.00008845003,0.0004138694,0.0002938001,0.0003277877,0.04060424,0.07121868,0.0003898085,0.0006876969,0.05977362],"study_design_scores_gemma":[0.000006292843,0.00003674928,0.9224753,0.00000248027,0.00003931669,0.00005026989,0.0001686129,0.07597236,0.001025796,0.00008911923,0.0001218094,0.00001182217],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998307,0.00002951254,0.001288382,0.00001102429,0.000004287499,0.000002641593,0.000111888,0.00002671904,0.0002186312],"genre_scores_gemma":[0.9987016,0.0000112153,0.001079754,0.000003751362,0.000003203732,0.000002429731,0.0001199496,0.000002970029,0.00007508387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01185714,"threshold_uncertainty_score":0.02357626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04083280515408558,"score_gpt":0.2639550823066847,"score_spread":0.2231222771525991,"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."}}