{"id":"W2131982632","doi":"10.1016/j.rse.2003.08.017","title":"Remote sensing in BOREAS: Lessons learned","year":2003,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":103,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University; University of Toronto; University of Lethbridge","funders":"Agriculture and Agri-Food Canada; Natural Resources Canada; Canadian Space Agency; Natural Sciences and Engineering Research Council of Canada; U.S. Forest Service; U.S. Geological Survey; National Aeronautics and Space Administration; Canadian Forest Service; Parks Canada; U.S. Environmental Protection Agency; National Science Foundation","keywords":"Biome; Taiga; Biosphere; Boreal; Remote sensing; Environmental science; Vegetation (pathology); Land cover; Climate change; Environmental resource management; Geography; Land use; Ecosystem; Ecology; Forestry","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007638945,0.0004345646,0.0005201655,0.0001076762,0.0001448053,0.00003159363,0.0001878343,0.0002666154,0.0001854121],"category_scores_gemma":[0.0002485565,0.0004042559,0.0001768142,0.000421341,0.0004344507,0.0001225957,0.0001820121,0.0004788621,0.0003637031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007708869,"about_ca_system_score_gemma":0.00002121652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002131775,"about_ca_topic_score_gemma":0.0003024918,"domain_scores_codex":[0.9965137,0.0004120132,0.000670267,0.0008684505,0.0007912956,0.00074426],"domain_scores_gemma":[0.99844,0.0001237968,0.000317919,0.0009045326,0.000008654383,0.0002051105],"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":[0.00003050585,0.00005951015,0.0002302423,0.00002001835,0.00002782387,0.0001949434,0.0007188594,0.02365592,0.2226562,0.00003177086,0.0005676501,0.7518066],"study_design_scores_gemma":[0.004775215,0.0004801158,0.08998764,0.001423575,0.0002741244,0.00168187,0.001687164,0.3805548,0.282704,0.02523823,0.2071236,0.004069633],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8907306,0.0001543594,0.03610807,0.002322896,0.0003772886,0.0006672716,0.000004715682,0.0001161514,0.06951867],"genre_scores_gemma":[0.7457229,0.0002630512,0.2525944,0.0002080006,0.00004179909,5.799251e-9,0.000008433009,0.00006283958,0.001098542],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.747737,"threshold_uncertainty_score":0.9998409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02170562319347257,"score_gpt":0.2461398855923111,"score_spread":0.2244342623988386,"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."}}