{"id":"W2794435112","doi":"10.1002/ecy.2159","title":"A Canadian upland forest soil profile and carbon stocks database","year":2018,"lang":"en","type":"article","venue":"Ecology","topic":"Soil Carbon and Nitrogen Dynamics","field":"Agricultural and Biological Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Sport Centre Pacific; Canadian Forest Service","funders":"","keywords":"Soil carbon; Environmental science; Soil horizon; Silt; Soil science; USDA soil taxonomy; Soil survey; Soil texture; Peat; Soil organic matter; Hydrology (agriculture); Database; Forestry; Soil water; Soil classification; Ecology; Geology; Geography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001101186,0.001044036,0.0007999387,0.01116913,0.002536674,0.001876683,0.002421979,0.00045535,0.02115651],"category_scores_gemma":[0.003180876,0.0004047013,0.0007292465,0.0226048,0.0002709747,0.001201006,0.001166026,0.0006670039,0.006884527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02576629,"about_ca_system_score_gemma":0.04984856,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9875201,"about_ca_topic_score_gemma":0.9927958,"domain_scores_codex":[0.9987149,0.00003967456,0.0001070079,0.0001498341,0.0007603269,0.0002283002],"domain_scores_gemma":[0.9921932,0.0001295577,0.0002676107,0.0002785339,0.006620489,0.0005105548],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001442964,0.00005327748,0.0330176,0.0007941308,0.0001249477,0.0001755882,0.0002767398,0.001737836,0.0007079885,0.005354952,0.9024926,0.05512015],"study_design_scores_gemma":[0.00005352262,0.00001432178,0.1089093,0.0002362912,0.00008870356,0.0000739716,0.0003608565,0.003153515,0.000768097,0.0007493244,0.885489,0.0001029798],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.004053155,0.0002837859,0.0006751133,0.0001798145,0.00002210482,0.0001767353,0.9815567,0.0003301668,0.01272243],"genre_scores_gemma":[0.0148441,0.0006577342,0.005027961,0.0001486429,0.00001086663,0.0002527729,0.9708551,0.0001051781,0.008097676],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02576629,"threshold_uncertainty_score":0.1869484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009703231602058549,"score_gpt":0.2013158608318882,"score_spread":0.1916126292298296,"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."}}