{"id":"W3010085372","doi":"","title":"Carbon Source and Sink Distribution in Canada's Forests and Wetlands Based on Remote Sensing, Forest Inventory, Large Fire Polygons, Drainage Class, Topography, and Climate","year":2004,"lang":"en","type":"article","venue":"AGUFM","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Wetland; Forest inventory; Carbon sink; Environmental science; Sink (geography); Climate change; Hydrology (agriculture); Geography; Distribution (mathematics); Drainage; Remote sensing; Forestry; Physical geography; Environmental resource management; Forest management; Geology; Cartography; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002840451,0.0001827177,0.0001846256,0.00003758931,0.0001367745,0.00003987285,0.00005902321,0.00008149473,0.000003175446],"category_scores_gemma":[0.00004263919,0.0001749141,0.00001950135,0.000167642,0.00009797015,0.00007207847,0.00009191623,0.0001414197,0.000001693109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004440353,"about_ca_system_score_gemma":0.00003883808,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7887539,"about_ca_topic_score_gemma":0.9829333,"domain_scores_codex":[0.9987456,0.00007393437,0.0001724309,0.000366154,0.0002227405,0.0004191653],"domain_scores_gemma":[0.9995002,0.00006068415,0.00007161547,0.0002031967,0.000003393924,0.0001608666],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003976631,0.00002201873,0.9904554,0.00006280767,0.00000431416,0.0000899485,0.00009808794,0.0007659023,0.0001461883,0.0000375258,0.00007657019,0.008201484],"study_design_scores_gemma":[0.001009483,0.0001103956,0.6589056,0.0001323466,0.000009056875,0.00001915848,0.00004230884,0.3382567,0.00009075152,0.0001174646,0.001122515,0.0001842372],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983531,0.0001155962,0.00006199344,0.0005342315,0.00007153608,0.0002918318,0.00004421192,0.00002542292,0.0005020549],"genre_scores_gemma":[0.9996388,0.00002348496,0.00004045082,0.0002023679,0.00001844463,0.000001583244,0.00004688863,0.00001699337,0.00001096561],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3374908,"threshold_uncertainty_score":0.7132788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002791164795734104,"score_gpt":0.1755856547701291,"score_spread":0.172794489974395,"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."}}