{"id":"W2765928085","doi":"10.3390/rs9111098","title":"Circa 2010 Land Cover of Canada: Local Optimization Methodology and Product Development","year":2017,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":125,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Natural Resources Canada","funders":"Natural Resources Canada; Canadian Space Agency; U.S. Forest Service; National Oceanic and Atmospheric Administration","keywords":"Land cover; Thematic Mapper; Thematic map; Remote sensing; Geography; Land use; Cartography; Computer science; Environmental resource management; Environmental science; Satellite imagery; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003396686,0.0001166154,0.00018488,0.00001372806,0.0003128963,0.00002903485,0.00009778983,0.00006546139,0.00002393587],"category_scores_gemma":[0.0002671377,0.00009530324,0.00001426566,0.00004293863,0.000225697,0.00008603469,0.0001614668,0.0001071954,0.000004968854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001964852,"about_ca_system_score_gemma":0.00009303655,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1160816,"about_ca_topic_score_gemma":0.1336739,"domain_scores_codex":[0.9990193,0.00008227026,0.0001788053,0.0002808152,0.0002281187,0.0002106913],"domain_scores_gemma":[0.9993433,0.00005181085,0.0001946121,0.0003143229,0.00002437499,0.00007161208],"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":[0.00002982763,0.00001077846,0.001661726,0.00003496357,0.00003746826,0.00005575981,0.0006684919,0.2493847,0.04180704,0.000002827095,0.003100972,0.7032055],"study_design_scores_gemma":[0.0005378385,0.00003004256,0.1619516,0.0001287322,0.00004031405,0.0004434199,0.00007506328,0.6943349,0.1310461,0.00009209943,0.01079677,0.0005231124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6789801,0.00006384752,0.3076046,0.001252912,0.0006866253,0.0002599219,0.000001334034,0.00002539643,0.01112528],"genre_scores_gemma":[0.6355844,0.00001256469,0.3635462,0.00008708585,0.00004283189,1.721906e-9,0.000003053879,0.00001035469,0.0007136021],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7026824,"threshold_uncertainty_score":0.8898045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0251071951984222,"score_gpt":0.2335811721562347,"score_spread":0.2084739769578125,"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."}}