{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009288994,0.000291774,0.0001706229,0.0008256768,0.0002463374,0.0007561838,0.0005342232,0.0001678423,0.0003088167],"category_scores_gemma":[0.001794711,0.0001803388,0.000229452,0.001154289,0.0002019514,0.000584446,0.0002997603,0.0002999427,0.0001153143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001908627,"about_ca_system_score_gemma":0.00235644,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.5377299,"about_ca_topic_score_gemma":0.6129621,"domain_scores_codex":[0.9997597,0.0000456763,0.00002063823,0.00006620977,0.00007868477,0.00002913045],"domain_scores_gemma":[0.9992625,0.0001234002,0.0001022005,0.0001496915,0.000333695,0.00002855458],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007994914,0.0001006716,0.2824239,0.00006983936,0.00009833556,0.00009791677,0.0003024,0.54957,0.003434385,0.001264705,0.001912238,0.1606455],"study_design_scores_gemma":[0.00000987881,0.00002007694,0.1091491,0.0000208457,0.00003468584,0.00002928729,0.0001785206,0.8865438,0.0009650397,0.0004079264,0.002622055,0.0000187703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9497563,0.0002945575,0.04285102,0.0001574543,0.00002283718,0.00009423606,0.003131669,0.0006458967,0.00304594],"genre_scores_gemma":[0.9692207,0.0001767886,0.02735123,0.0000152562,0.000005086538,0.00003382897,0.002762137,0.00002426541,0.0004107793],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5377299,"threshold_uncertainty_score":0.929985,"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."}}