{"id":"W2320352178","doi":"10.1139/cjfr-2016-0041","title":"Watershed-scale forest biomass distribution in a perhumid temperate rainforest as driven by topographic, soil, and disturbance variables","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of Experimental Program to Stimulate Competitive Research; National Science Foundation","keywords":"Environmental science; Rainforest; Biomass (ecology); Temperate rainforest; Watershed; Disturbance (geology); Temperate climate; Biome; Temperate forest; Riparian zone; Hydrology (agriculture); Ecology; Ecosystem; Geology; Habitat","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":[0.0002443611,0.00009243294,0.0001376097,0.0004954925,0.0002163261,0.0003892545,0.000127476,0.0001274837,0.0006383869],"category_scores_gemma":[0.0004297919,0.00008928316,0.0001301998,0.0005587728,0.0002089219,0.0002746093,0.0002869047,0.00009967849,0.00008101066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004021872,"about_ca_system_score_gemma":0.0002508732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04217739,"about_ca_topic_score_gemma":0.08995594,"domain_scores_codex":[0.9999305,0.00001593698,0.000005175935,0.00002284936,0.000008732145,0.00001683417],"domain_scores_gemma":[0.9998241,0.00004833947,0.00004955694,0.00001363371,0.00002330733,0.000040996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005080437,0.00003126771,0.9926329,0.00000692856,0.00002572532,0.00009358267,0.0003744771,0.001399896,0.001851356,0.00007418518,0.00003764448,0.003421186],"study_design_scores_gemma":[0.000001158026,0.0000111209,0.9978922,0.000002118846,0.000005162537,0.00003336417,0.0004092328,0.001489522,0.00005608723,0.00004264471,0.00005532811,0.00000211915],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9998181,0.00001122945,0.00002986142,0.0000023316,1.677558e-7,6.539412e-7,0.00005035447,0.000001512212,0.00008579023],"genre_scores_gemma":[0.9997244,0.00001705922,0.00007355247,0.000001885493,3.506826e-7,0.000001609328,0.0001041442,4.040803e-7,0.00007647314],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04217739,"threshold_uncertainty_score":0.08386374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01226638723765691,"score_gpt":0.250553167017061,"score_spread":0.2382867797794041,"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."}}