{"id":"W2978611233","doi":"10.3390/rs11192286","title":"Comparison and Assessment of Regional and Global Land Cover Datasets for Use in CLASS over Canada","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Forest Service; Natural Resources Canada; Environment and Climate Change Canada","funders":"Canadian Forest Service; Natural Resources Canada; Canadian Space Agency; U.S. Forest Service","keywords":"Land cover; Tundra; Remote sensing; Environmental science; Physical geography; Satellite; Snow cover; Taiga; Climatology; Snow; Land use; Geography; Arctic; Meteorology; Forestry; Geology","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":[0.001191947,0.0003808696,0.0002757527,0.002448716,0.001021103,0.001541177,0.0009032179,0.0002151219,0.001274268],"category_scores_gemma":[0.004304518,0.0001570659,0.0003902641,0.005820614,0.0002218608,0.0006565365,0.0006676548,0.0003627623,0.0003007531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01976635,"about_ca_system_score_gemma":0.01676569,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9905657,"about_ca_topic_score_gemma":0.9929537,"domain_scores_codex":[0.9990309,0.00007560349,0.00004847593,0.0001227451,0.0005330394,0.0001891594],"domain_scores_gemma":[0.9957901,0.0002560757,0.0002466353,0.0002306043,0.003201925,0.0002747144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004959617,0.0001692144,0.8059303,0.0002286235,0.0003204351,0.0001497959,0.0009459896,0.0137014,0.003772099,0.00266318,0.02817312,0.1434499],"study_design_scores_gemma":[0.0000347287,0.00002192472,0.9662561,0.00007562713,0.00006043446,0.00003570271,0.001016828,0.01485339,0.001366057,0.0001584973,0.01608007,0.00004064341],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8585331,0.0004074919,0.002897013,0.0006586697,0.00003426706,0.0003323263,0.1209698,0.0004504839,0.01571695],"genre_scores_gemma":[0.89578,0.0005614451,0.009788499,0.0001958568,0.000009270304,0.0002040093,0.09051631,0.0001138396,0.002830746],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01976635,"threshold_uncertainty_score":0.1434156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01896310151512738,"score_gpt":0.2811858512574845,"score_spread":0.2622227497423572,"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."}}