{"id":"W2973832355","doi":"10.1016/j.jag.2019.101956","title":"Update and spatial extension of strategic forest inventories using time series remote sensing and modeling","year":2019,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency; Ministry of Forests, Lands and Natural Resource Operations; Western Canada Research Grid","keywords":"Geography; Forest inventory; Sustainable forest management; Random forest; Remote sensing; Forest management; Environmental resource management; Cartography; Environmental science; Computer science; Forestry; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002499921,0.00007978512,0.0001215729,0.00007335909,0.00005959256,0.00007145818,0.00004581302,0.00004455733,0.00002096921],"category_scores_gemma":[0.0000143374,0.00007312183,0.0000184817,0.0000646034,0.0000605694,0.0005814666,0.0000513323,0.00007720857,0.000007225066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002039087,"about_ca_system_score_gemma":0.00001531351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002541849,"about_ca_topic_score_gemma":0.0000547405,"domain_scores_codex":[0.9991767,0.00000984798,0.0003851196,0.00008328254,0.0002751582,0.00006984759],"domain_scores_gemma":[0.999411,0.00002188884,0.0003552662,0.00006212275,0.0001056722,0.00004404887],"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":[0.0006811785,0.00002804618,0.009290867,0.00008199162,0.00009617896,0.000002803111,0.003476757,0.4271238,0.0851743,0.003479301,0.00002883091,0.4705359],"study_design_scores_gemma":[0.0003858752,0.00003455851,0.01396566,0.00004849746,0.00001252578,0.00006407042,0.0003326082,0.9773681,0.0007581411,0.006647445,0.0003061131,0.00007647974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9704419,0.00001446226,0.02852661,0.0002617885,0.00007733864,0.00009557696,0.000002123404,0.000006278197,0.0005739545],"genre_scores_gemma":[0.9770344,0.00009745039,0.02265921,0.0001315304,0.0000381799,2.981347e-8,0.00001585632,0.000004862995,0.00001846298],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5502442,"threshold_uncertainty_score":0.2981822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02535093366854037,"score_gpt":0.23347727403455,"score_spread":0.2081263403660096,"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."}}