{"id":"W4226287569","doi":"10.1002/ecs2.4020","title":"Estimating carbon stocks and stock changes in Interior Wetbelt forests of British Columbia, Canada","year":2022,"lang":"en","type":"article","venue":"Ecosphere","topic":"Peatlands and Wetlands Ecology","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Northern British Columbia; Positive Living North","funders":"","keywords":"Temperate climate; Biomass (ecology); Temperate rainforest; Environmental science; Forest inventory; Forestry; Carbon stock; Vegetation (pathology); Soil carbon; Stock (firearms); Rainforest; Carbon sequestration; Agroforestry; Ecology; Climate change; Geography; Forest management; Physical geography; Ecosystem; Soil water; Soil science; Biology; Carbon dioxide","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000108117,0.00004956127,0.0001449555,0.000004442629,0.00007384888,0.00001385779,0.0001163408,0.00002181756,0.004076096],"category_scores_gemma":[0.00001375178,0.00007974998,0.000009618996,0.00008922033,0.00003734288,0.00002473046,0.0003027813,0.0001077746,6.547079e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002054107,"about_ca_system_score_gemma":0.00003444746,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9756388,"about_ca_topic_score_gemma":0.9999197,"domain_scores_codex":[0.9993373,0.00003455967,0.0001295569,0.0001829486,0.0001256978,0.0001899279],"domain_scores_gemma":[0.9997714,0.00002713389,0.00006603448,0.00008744731,0.000002120552,0.00004586888],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00000412851,0.00001520506,0.9627801,0.000009465242,0.000003210624,0.00005104792,0.0001030401,0.0006432266,0.00007025211,1.55533e-7,0.01883029,0.01748991],"study_design_scores_gemma":[0.0003404724,0.0001523538,0.9859521,0.00001347016,0.000003238233,0.00004221856,0.0001938874,0.01086741,0.000005924067,0.00006498626,0.002264416,0.00009956717],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944171,0.0000778981,0.000002224728,0.00009200898,0.000133776,0.0001393172,0.00004510662,0.000006491252,0.005086038],"genre_scores_gemma":[0.9978452,0.000003260596,0.0002093161,0.00008673051,0.00001363048,0.00005437728,0.000007057581,0.000007859761,0.001772558],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0242809,"threshold_uncertainty_score":0.9968343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004355781962201096,"score_gpt":0.1835264225305218,"score_spread":0.1791706405683207,"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."}}