{"id":"W2991072261","doi":"10.1101/856526","title":"Statistically-estimated tree biomass, stem density, and basal area for the upper Midwestern United States at the time of Euro-American settlement","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Forest ecology and management","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Royal University","funders":"College of Engineering, Michigan State University; Michigan State University; University of Notre Dame; Minnesota Department of Natural Resources; National Science Foundation","keywords":"Biomass (ecology); Basal area; Settlement (finance); Geography; Vegetation (pathology); Scale (ratio); Environmental science; Physical geography; Raw data; Forestry; Statistics; Ecology; Cartography; Mathematics; Computer science; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001024606,0.00022004,0.0001798144,0.0008826976,0.0001454504,0.0005175995,0.0003105125,0.000187194,0.002405845],"category_scores_gemma":[0.001527313,0.0001138913,0.0001856165,0.001330274,0.0001500952,0.0003492178,0.0005172227,0.0002122975,0.0004692041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006038017,"about_ca_system_score_gemma":0.0003279615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1246781,"about_ca_topic_score_gemma":0.1633174,"domain_scores_codex":[0.9997643,0.00005514386,0.00002165691,0.00008179275,0.00004497823,0.00003216841],"domain_scores_gemma":[0.9991584,0.0001185907,0.0002151004,0.0001364473,0.0003228135,0.0000486354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001271377,0.00004216889,0.9700062,0.00002537229,0.0000744422,0.00007123595,0.0003445876,0.004785672,0.0007882706,0.0003357964,0.003568494,0.01983064],"study_design_scores_gemma":[0.000006558279,0.00001750902,0.9906652,0.00002345837,0.00001554579,0.00002257697,0.0005360891,0.002613583,0.0004806237,0.0001667303,0.005440698,0.00001150316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9702145,0.0001195317,0.001250823,0.00009974578,0.00001288895,0.00001141498,0.02558552,0.00005625772,0.002649262],"genre_scores_gemma":[0.9637779,0.0001084357,0.002424018,0.00004815872,0.000008012174,0.00003149705,0.0318604,0.00002429287,0.001717142],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1246781,"threshold_uncertainty_score":0.2479047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01255167299004682,"score_gpt":0.2149134750332364,"score_spread":0.2023618020431896,"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."}}