{"id":"W3211625894","doi":"10.1111/2041-210x.13756","title":"<i>allodb</i> : An R package for biomass estimation at globally distributed extratropical forest plots","year":2021,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Forest ecology and management","field":"Environmental Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Biodiversity Monitoring Institute; University of Alberta; Wilfrid Laurier University","funders":"Smithsonian Institution","keywords":"Tree allometry; Biomass (ecology); Range (aeronautics); Environmental science; Forest inventory; Extratropical cyclone; Taiga; Ecology; Tree (set theory); Mathematics; Forest management; Meteorology; Geography; Biology; Agroforestry; Biomass partitioning","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.0007490656,0.0001043962,0.0001553346,0.00002844731,0.0002149964,0.00001169538,0.00009733863,0.0002029038,0.0001851139],"category_scores_gemma":[0.0003155841,0.000106299,0.00003278446,0.0001631261,0.000216539,0.0001790509,0.000208189,0.00007774326,0.00002848563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004025574,"about_ca_system_score_gemma":0.00001523075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005961889,"about_ca_topic_score_gemma":0.01695466,"domain_scores_codex":[0.9986937,0.0003874252,0.0002077701,0.0003581383,0.00006080637,0.0002921741],"domain_scores_gemma":[0.9995102,0.0001881664,0.000059494,0.0001656998,0.00001017861,0.00006626236],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001341897,0.0002701748,0.9552933,0.00002365191,0.000017411,0.00002398613,0.00006821816,0.003291051,0.005383381,0.02950279,0.001499398,0.004492446],"study_design_scores_gemma":[0.0005618298,0.0001814155,0.88667,0.00000256797,0.00002398091,0.00001785668,0.00002556516,0.02806613,0.000493837,0.0821858,0.001666282,0.0001047286],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.507647,0.000033518,0.4912528,0.0003728142,0.0002098015,0.0001917742,0.00001301862,0.00002304099,0.0002561789],"genre_scores_gemma":[0.7717963,0.00001108699,0.2276079,0.0001701254,0.00001579568,0.00008379642,0.0001463825,0.000005721157,0.0001628061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2641494,"threshold_uncertainty_score":0.94611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0154189035013573,"score_gpt":0.31526501491401,"score_spread":0.2998461114126527,"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."}}