{"id":"W4220870978","doi":"10.1016/j.aeaoa.2022.100163","title":"Analyzing spatio-temporal patterns in atmospheric carbon dioxide concentration across Iran from 2003 to 2020","year":2022,"lang":"en","type":"article","venue":"Atmospheric Environment X","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Iran National Science Foundation","keywords":"SCIAMACHY; Environmental science; Vegetation (pathology); Precipitation; Abundance (ecology); Carbon dioxide; Abiotic component; Greenhouse gas; Climate change; Atmospheric sciences; Carbon dioxide in Earth's atmosphere; Climatology; Physical geography; Ecology; Geography; Oceanography; Geology; Meteorology; Troposphere; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002769006,0.0002345606,0.0001598918,0.0008320271,0.0001987995,0.0003469102,0.0002417009,0.0002274804,0.0003145669],"category_scores_gemma":[0.0003871335,0.00009055475,0.0003737937,0.001301913,0.0001121902,0.0002171674,0.0001851814,0.0001947012,0.0001137391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006724948,"about_ca_system_score_gemma":0.0006837936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08499059,"about_ca_topic_score_gemma":0.1078207,"domain_scores_codex":[0.9998822,0.00001201488,0.00001147092,0.00003602948,0.00003187177,0.00002645081],"domain_scores_gemma":[0.9997479,0.00002397754,0.0000780308,0.00001482889,0.0001100011,0.00002525216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009631101,0.00003860055,0.9791862,0.00005482327,0.0000988453,0.0001485173,0.0002221838,0.002874485,0.001928345,0.0001329437,0.001639106,0.01357973],"study_design_scores_gemma":[0.000002019019,0.00001399501,0.9956881,0.000005781169,0.00002458676,0.00004498372,0.0002172088,0.002450618,0.0004067881,0.00002854147,0.001112021,0.000005255387],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9936349,0.0003023848,0.0004005118,0.0001259839,0.0000148737,0.00001079263,0.004328494,0.00004402983,0.001138001],"genre_scores_gemma":[0.9933749,0.0002266734,0.0007245418,0.00002580389,0.0000125529,0.0000179275,0.005285687,0.000004445768,0.0003273718],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08499059,"threshold_uncertainty_score":0.1689918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006239711587986913,"score_gpt":0.2038762743507567,"score_spread":0.1976365627627698,"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."}}