{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001387361,0.0003129474,0.0002574516,0.0003609049,0.0008554342,0.0009665544,0.0007928356,0.0005224063,0.001145],"category_scores_gemma":[0.0004483447,0.0003855772,0.0004318064,0.0005603055,0.0004632165,0.0003285843,0.0003201069,0.0002390191,0.0001212332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007989684,"about_ca_system_score_gemma":0.006372556,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9596083,"about_ca_topic_score_gemma":0.9726661,"domain_scores_codex":[0.9999142,0.00001025369,0.000004942279,0.00002713258,0.00001538054,0.00002815184],"domain_scores_gemma":[0.9998792,0.00003004902,0.00001589779,0.000006100104,0.00003888473,0.00002984719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001985387,0.0001469716,0.1875088,0.00006187756,0.000118955,0.0003367439,0.0004991071,0.7989185,0.00356654,0.0009847335,0.0007784906,0.006880728],"study_design_scores_gemma":[0.0000600051,0.00002927375,0.1146763,0.00001176751,0.00004154046,0.00002923143,0.0007171712,0.8828149,0.0005217852,0.0002210588,0.0008511799,0.00002578952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975688,0.00002461998,0.0003921017,0.00004086672,0.000002174408,0.00001265721,0.0005394904,0.00003046373,0.001388864],"genre_scores_gemma":[0.9982316,0.00002541949,0.0006013716,0.000006179341,0.000001151181,0.00001009456,0.0003078452,0.000005227241,0.0008110454],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04039168,"threshold_uncertainty_score":0.08125907,"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."}}