{"id":"W3048911062","doi":"10.3233/sji-200663","title":"The use of combined Landsat and Radarsat data for urban ecosystem accounting in Canada","year":2020,"lang":"en","type":"article","venue":"Statistical Journal of the IAOS","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada","funders":"Canadian Space Agency; Strong","keywords":"Urban ecosystem; Remote sensing; Environmental resource management; Random forest; Ecosystem; Geospatial analysis; Environmental science; Ecosystem services; Geography; Impervious surface; Cartography; Urban planning; Computer science; Ecology; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0008674894,0.0002910322,0.0002448014,0.003277609,0.001036299,0.001357138,0.0005353596,0.0001060043,0.001285422],"category_scores_gemma":[0.00241697,0.0001690514,0.0003134682,0.006224191,0.0002162255,0.0004872796,0.0006649253,0.0002724324,0.0002258854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02162608,"about_ca_system_score_gemma":0.02924997,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9934019,"about_ca_topic_score_gemma":0.99722,"domain_scores_codex":[0.9991061,0.00007407396,0.00004265137,0.00008823656,0.0005549549,0.0001339601],"domain_scores_gemma":[0.9978916,0.000124855,0.0001047361,0.00007475544,0.001687777,0.0001163032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001892374,0.0001186905,0.5780653,0.0003141515,0.0003100326,0.000249383,0.0009103105,0.02253525,0.005212221,0.004864493,0.01467178,0.3725593],"study_design_scores_gemma":[0.00002569184,0.00003725464,0.8965933,0.0001489533,0.0001506344,0.00009578597,0.002087175,0.060121,0.004035678,0.0008921642,0.03573964,0.00007265681],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.901874,0.002227177,0.0149197,0.001159787,0.00005923992,0.0004122393,0.03748616,0.0004255255,0.04143613],"genre_scores_gemma":[0.9543704,0.001161266,0.02636576,0.0000991287,0.00001244011,0.00009030627,0.01050603,0.00005695344,0.007337641],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02162608,"threshold_uncertainty_score":0.1569089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03646710907137764,"score_gpt":0.2218114943780896,"score_spread":0.185344385306712,"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."}}