{"id":"W2990272075","doi":"","title":"Enhancement of greenhouse gases associated with Canadian forest fire using multi sensor data","year":2008,"lang":"en","type":"article","venue":"37th COSPAR Scientific Assembly","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Greenhouse gas; Environmental science; Greenhouse effect; Atmospheric sciences; Meteorology; Remote sensing; Climate change; Global warming; Geography; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008291,0.0006538261,0.00033053,0.000940407,0.0007129014,0.0006454988,0.0005635988,0.0003853211,0.0006973652],"category_scores_gemma":[0.001323474,0.0002594121,0.0003236799,0.001662485,0.0001631699,0.0006992425,0.0003388529,0.0005422047,0.0001090759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002701411,"about_ca_system_score_gemma":0.003186502,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6587496,"about_ca_topic_score_gemma":0.8294343,"domain_scores_codex":[0.9996676,0.00002827662,0.000008754244,0.00005474002,0.0001809896,0.00005960959],"domain_scores_gemma":[0.9993222,0.0001190109,0.00005071596,0.00005116489,0.0004127943,0.00004414775],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001935105,0.0004474182,0.293734,0.0005679736,0.0005094867,0.0002914039,0.0003928912,0.1692809,0.1634809,0.001479039,0.01534232,0.3525385],"study_design_scores_gemma":[0.0001184747,0.0001223228,0.5173703,0.00005984093,0.0003548118,0.00007936388,0.000222575,0.4067331,0.05820956,0.000665467,0.01593472,0.0001294211],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9376516,0.002212553,0.02880472,0.001115202,0.0003239304,0.0001568367,0.01181621,0.001495117,0.01642396],"genre_scores_gemma":[0.9730436,0.0007211882,0.02121829,0.00009645306,0.00005841499,0.00003336996,0.00332388,0.00007251935,0.001432256],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3412504,"threshold_uncertainty_score":0.6865201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04346671352611543,"score_gpt":0.2355316615469187,"score_spread":0.1920649480208033,"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."}}