{"id":"W2150099576","doi":"10.1111/gcb.12479","title":"Patterns in <scp>CH<sub>4</sub></scp> and <scp>CO<sub>2</sub></scp> concentrations across boreal rivers: Major drivers and implications for fluvial greenhouse emissions under climate change scenarios","year":2013,"lang":"en","type":"article","venue":"Global Change Biology","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":167,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Hydro-Québec","keywords":"Greenhouse gas; Fluvial; Boreal; Environmental science; Climate change; STREAMS; Atmospheric sciences; Global warming; Hydrology (agriculture); Methane; Carbon dioxide; Physical geography; Ecology; Geology; Geography; Structural basin","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001734464,0.000161362,0.0001289381,0.0003298758,0.0004033437,0.0006437832,0.0003236422,0.0002830407,0.0009464699],"category_scores_gemma":[0.0003751827,0.00009362895,0.000240948,0.0006311541,0.0004843216,0.0002183451,0.0001975569,0.0001671967,0.00007488149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004211239,"about_ca_system_score_gemma":0.0012054,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.8228167,"about_ca_topic_score_gemma":0.8756282,"domain_scores_codex":[0.999912,0.00001234756,0.00000454857,0.00003814032,0.00001170483,0.00002126886],"domain_scores_gemma":[0.9997749,0.00004144399,0.00005622247,0.00001495304,0.00007726588,0.00003520077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000748823,0.00002992524,0.976035,0.00002070596,0.00007552937,0.00007977248,0.0004277682,0.01139658,0.00591336,0.0002342632,0.0003514092,0.005360797],"study_design_scores_gemma":[0.000003276845,0.000008402101,0.9872985,0.000002714295,0.000008970745,0.00002060474,0.0003022781,0.01184938,0.0001857244,0.00004142737,0.0002720712,0.000006607791],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987563,0.0000343437,0.000265776,0.00003636576,5.958575e-7,0.000004966807,0.0005180398,0.00001589666,0.000367789],"genre_scores_gemma":[0.9992326,0.00002615072,0.0002070248,0.000008154816,4.909286e-7,0.000003597321,0.0003044125,0.000002183713,0.0002152556],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8228167,"threshold_uncertainty_score":0.3564535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01973250129064105,"score_gpt":0.2572431598398237,"score_spread":0.2375106585491826,"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."}}