{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001488793,0.0004122427,0.0003574813,0.00001844212,0.0005612117,0.00006882451,0.0002936665,0.0004056608,0.0000173022],"category_scores_gemma":[0.00005531459,0.0004036365,0.00009235198,0.000235501,0.0008269042,0.000476116,0.0005437798,0.0001989713,0.00008738465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004628442,"about_ca_system_score_gemma":0.00001277595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001575296,"about_ca_topic_score_gemma":0.00256005,"domain_scores_codex":[0.9974269,0.00009526989,0.000362025,0.0008278583,0.000138998,0.00114893],"domain_scores_gemma":[0.9988154,0.0001913654,0.0001962543,0.0003283369,0.0000150121,0.0004536196],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000005737104,0.0001414725,0.9475348,0.0000200177,0.00003021008,0.000003804787,0.001518278,0.00003079303,0.02288049,0.0002713989,0.0006127929,0.02695023],"study_design_scores_gemma":[0.001190579,0.000226271,0.9922758,0.00002064055,0.00004522049,0.00003246922,0.001826033,0.00155245,0.0006922519,0.001017931,0.0009700688,0.0001502493],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939087,0.0001821242,0.002040992,0.0005534945,0.0001685681,0.00156084,0.001326516,0.00008521065,0.0001735699],"genre_scores_gemma":[0.994212,0.002504958,0.0008258541,0.001212092,0.0001605024,0.0006915269,0.0003473959,0.00003540552,0.00001024512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04474105,"threshold_uncertainty_score":0.9998416,"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."}}