{"id":"W1971849548","doi":"10.5194/bg-12-323-2015","title":"Atmospheric inversion of surface carbon flux with consideration of the spatial distribution of US crop production and consumption","year":2015,"lang":"en","type":"article","venue":"Biogeosciences","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Ministry of Science and Technology of the People's Republic of China; Canadian Foundation for Climate and Atmospheric Sciences","keywords":"Environmental science; Sink (geography); Carbon sink; Primary production; Spatial distribution; Atmospheric sciences; Carbon flux; Spatial variability; Biomass (ecology); Ecosystem; Agronomy; Ecology; Geography; Geology; Mathematics; Remote sensing","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001949812,0.00006818885,0.00009599099,0.000001149253,0.00005620555,0.000004375611,0.00007910241,0.00003399499,0.00001630538],"category_scores_gemma":[0.00003988697,0.00004354758,0.00001496857,0.0001897852,0.001438384,0.0001260372,0.00007993305,0.00002855176,7.117974e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000652737,"about_ca_system_score_gemma":0.00002063392,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007801638,"about_ca_topic_score_gemma":0.0005345988,"domain_scores_codex":[0.9992142,0.00004491056,0.0001559158,0.0001649439,0.0003384337,0.00008157883],"domain_scores_gemma":[0.9996055,0.0000100336,0.0002181202,0.0001175454,0.0000130571,0.00003575662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003078427,0.00003796944,0.9439558,0.000008892575,0.000002164968,8.500243e-8,0.0002112814,0.02808019,0.02695586,0.00001348255,0.00001851195,0.0006849908],"study_design_scores_gemma":[0.0001770984,0.0002386526,0.921773,0.00002503754,0.00002046926,0.000005078252,0.0002932652,0.04403598,0.03323432,0.00007125583,0.00005366679,0.00007222078],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985685,0.00003853729,0.0009491908,0.00007186369,0.0001139598,0.0001587387,0.000005070175,0.000004448107,0.00008969095],"genre_scores_gemma":[0.9978182,0.00002449059,0.002065751,0.00000555273,0.000004657459,0.00000101216,0.000003364876,0.000002328652,0.00007464508],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02218283,"threshold_uncertainty_score":0.9988055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01121101541428885,"score_gpt":0.1949149755225482,"score_spread":0.1837039601082594,"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."}}