{"id":"W4410027493","doi":"10.1109/jstars.2025.3566611","title":"Large Scale Land Cover Mapping in Ontario, Canada, Using a Deep Learning Framework","year":2025,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Scale (ratio); Land cover; Cover (algebra); Remote sensing; Computer science; Land use; Cartography; Geology; Geography; Engineering; Civil engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001495669,0.000507166,0.0001838095,0.0009606718,0.000917772,0.0009787094,0.0006328259,0.0003400487,0.001422915],"category_scores_gemma":[0.0005449981,0.0002141375,0.000382454,0.00166027,0.000423577,0.0004483883,0.0005169612,0.0003439494,0.0003575004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01606773,"about_ca_system_score_gemma":0.01637862,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9848821,"about_ca_topic_score_gemma":0.9944351,"domain_scores_codex":[0.9998362,0.000008044377,0.000003760133,0.00003788715,0.00005416541,0.00005986097],"domain_scores_gemma":[0.9998326,0.00001629795,0.00001718206,0.00001298908,0.00009413486,0.00002670983],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004130148,0.0001812425,0.2935328,0.0004580923,0.000319379,0.001381133,0.001234004,0.3060108,0.01744807,0.009572453,0.03581022,0.3336388],"study_design_scores_gemma":[0.00003381494,0.00002451536,0.2437127,0.00009574948,0.00008980269,0.0001439091,0.002036565,0.7187349,0.004680916,0.003033396,0.02733284,0.00008088107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9131218,0.001356801,0.03063238,0.001777512,0.00006736122,0.000246051,0.02300151,0.001178755,0.02861791],"genre_scores_gemma":[0.969654,0.0004660668,0.01590753,0.0001001921,0.00001214333,0.00003145814,0.006652408,0.00006189607,0.007114383],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01606773,"threshold_uncertainty_score":0.1165801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01209127807321068,"score_gpt":0.2052862212966259,"score_spread":0.1931949432234152,"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."}}