{"id":"W2014287870","doi":"10.1007/s00477-011-0483-7","title":"Modeling locally varying anisotropy of CO2 emissions in the United States","year":2011,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Natural Resources; University of Alberta","funders":"","keywords":"Covariance; Kriging; Anisotropy; Nonlinear system; Variogram; Gaussian; Context (archaeology); Scaling; Computer science; Covariance function; Mathematics; Mathematical optimization; Applied mathematics; Statistical physics; Statistics; Physics; Geometry; Geology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001180493,0.00009809196,0.000199036,0.0003016963,0.000209562,0.00002785093,0.0002409094,0.00004448221,0.0002924897],"category_scores_gemma":[0.00005014311,0.00007524645,0.00003899788,0.0002686413,0.0001922833,0.00009164412,0.0001460484,0.0003241663,0.000022673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007056397,"about_ca_system_score_gemma":0.0000142148,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01110821,"about_ca_topic_score_gemma":0.0000973936,"domain_scores_codex":[0.9988343,0.00008327243,0.0003765326,0.0002695827,0.0001446919,0.0002915843],"domain_scores_gemma":[0.9993979,0.0001636072,0.00008827545,0.0002514846,0.000008925781,0.00008975228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004303068,0.003386205,0.7408773,0.00010144,0.0005214126,0.00005652653,0.01713366,0.1518013,0.0005402014,0.07953195,0.0001258413,0.005493833],"study_design_scores_gemma":[0.0006113117,0.0004693883,0.06619015,0.00003257332,0.00001641703,0.000002298239,0.004845109,0.8637612,0.00001987183,0.06382381,0.00007014762,0.0001577148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8378321,0.0004474572,0.1604934,0.00006134355,0.00001723685,0.0001972338,0.0003038427,0.000003342271,0.000644105],"genre_scores_gemma":[0.9949609,0.003412257,0.001386469,0.00002018715,0.00001301806,0.0000304214,0.0001373127,0.0000090454,0.00003039441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7119599,"threshold_uncertainty_score":0.9954769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09838592634184089,"score_gpt":0.3088968724450761,"score_spread":0.2105109461032352,"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."}}