{"id":"W2525560477","doi":"10.1139/cjfr-2016-0209","title":"Mapping site index and volume increment from forest inventory, Landsat, and ecological variables in Tahoe National Forest, California, USA","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Pacific Northwest Research Station; U.S. Forest Service; U.S. Department of Agriculture","keywords":"Forest inventory; Mean squared error; Environmental science; National forest; Elevation (ballistics); Forestry; Physical geography; Forest management; Hydrology (agriculture); Geography; Ecology; Mathematics; Statistics; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000434499,0.0002989353,0.0001643612,0.001788562,0.000546272,0.0003939427,0.0003768588,0.0001251518,0.002492115],"category_scores_gemma":[0.0009566839,0.0002002346,0.0001169072,0.002403507,0.0001061901,0.0002471285,0.0002636462,0.0002381922,0.0005161739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009682555,"about_ca_system_score_gemma":0.00163641,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.6160794,"about_ca_topic_score_gemma":0.8733255,"domain_scores_codex":[0.9997348,0.0000274059,0.00002136901,0.00007851334,0.0001077664,0.00003017522],"domain_scores_gemma":[0.9992887,0.00004985702,0.0001042811,0.00003711552,0.0004491233,0.00007100533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009603037,0.0001109012,0.8861046,0.0001276471,0.000102157,0.0001542216,0.0005205004,0.002317231,0.001650153,0.0003710034,0.04555646,0.06288907],"study_design_scores_gemma":[0.00001014873,0.00001045399,0.9896241,0.00002077063,0.00001796549,0.00005111949,0.0002297118,0.002205823,0.0001321969,0.00003396785,0.007655795,0.000007889769],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9039566,0.0006491346,0.002977613,0.0001596978,0.00004006175,0.0002044183,0.07493184,0.0002615168,0.01681907],"genre_scores_gemma":[0.8953015,0.000622082,0.01935664,0.00005733082,0.00003383632,0.0003608151,0.07437856,0.0000561922,0.009833051],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6160794,"threshold_uncertainty_score":0.7723631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0341915263358905,"score_gpt":0.2698494603386227,"score_spread":0.2356579340027322,"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."}}