{"id":"W2077229100","doi":"10.1016/j.catena.2014.05.025","title":"Modeling peatland carbon stock in a delineated portion of the Nayshkootayaow river watershed in Far North, Ontario using an integrated GIS and remote sensing approach","year":2014,"lang":"en","type":"article","venue":"CATENA","topic":"Peatlands and Wetlands Ecology","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Forest Research Institute","funders":"","keywords":"Peat; Environmental science; Greenhouse gas; Watershed; Hydrology (agriculture); Carbon sink; Carbon fibers; Physical geography; Geology; Geography; Climate change; Oceanography","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":[],"consensus_categories":[],"category_scores_codex":[0.0003251857,0.0001377965,0.0002252975,0.00006963949,0.00005132858,0.00001232547,0.000104475,0.0001036828,0.0000060204],"category_scores_gemma":[0.00001979434,0.00009350836,0.00002509813,0.0002245317,0.00009872878,0.00009135057,0.00009267497,0.0002063052,3.643112e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002158045,"about_ca_system_score_gemma":0.00002156208,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3598645,"about_ca_topic_score_gemma":0.4931057,"domain_scores_codex":[0.9988987,0.0001299223,0.0002973986,0.0003085646,0.0001155509,0.0002498229],"domain_scores_gemma":[0.9996509,0.000009032453,0.00007472892,0.0001991436,0.00001324983,0.00005293992],"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":[0.00006406807,0.00003868303,0.8213463,0.000007165782,0.000004946538,0.000005707816,0.003706992,0.1647997,0.004950601,4.622584e-7,0.000001119053,0.00507416],"study_design_scores_gemma":[0.0004893536,0.00003371107,0.261875,0.00001578224,0.000009534789,0.00001729597,0.00006959754,0.7372144,0.0001239531,0.00004339497,0.00001252037,0.00009541959],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953709,0.000006317741,0.004161241,0.00002950701,0.00003250492,0.0001887924,0.000001149161,0.000009782866,0.0001998508],"genre_scores_gemma":[0.9981152,0.000003999826,0.0017439,0.00004266474,0.00001082882,5.543696e-7,0.00004302634,0.0000108842,0.0000289398],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5724147,"threshold_uncertainty_score":0.6443982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02140708997157385,"score_gpt":0.2087292590349453,"score_spread":0.1873221690633715,"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."}}