{"id":"W4308748381","doi":"10.1016/j.dib.2022.108734","title":"Dataset of top-down nitrogen oxides fire emission estimation in northeastern Asia","year":2022,"lang":"en","type":"article","venue":"Data in Brief","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"NOx; Environmental science; Nitrogen oxides; Atmospheric sciences; Smoke; Human health; Emission inventory; Radiative transfer; Atmosphere (unit); Air pollution; Meteorology; Satellite; Combustion; Air quality index; Geography; Chemistry; Geology; Engineering","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007654165,0.00008651292,0.0001413571,0.00004240718,0.00004834912,0.00001239409,0.000760954,0.00002574969,0.00109126],"category_scores_gemma":[0.0001173629,0.00008951352,0.00001014061,0.0003057661,0.00003704755,0.0004383265,0.001370514,0.0001303242,0.00006964296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001410036,"about_ca_system_score_gemma":0.00001165862,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01583565,"about_ca_topic_score_gemma":0.002304983,"domain_scores_codex":[0.9986951,0.0001533544,0.0003088193,0.0003421325,0.0003349008,0.0001656475],"domain_scores_gemma":[0.9988716,0.00006755911,0.0001114612,0.0009083326,0.000001309474,0.00003975287],"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":[0.00004297115,0.0002170429,0.9269361,0.00004934931,0.00000411167,0.00004663094,0.0002289246,0.003768332,0.003658482,0.00000407924,0.02577104,0.03927291],"study_design_scores_gemma":[0.0008518922,0.0001243569,0.277327,0.00007895456,0.000009584776,0.00004261628,0.0001910899,0.6376768,0.001611224,0.0001449121,0.08164811,0.0002935208],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9882439,0.00003961518,0.00004718735,0.000106967,0.0000665964,0.0002761649,0.01109172,0.000008921012,0.0001189766],"genre_scores_gemma":[0.9770528,0.000002347487,0.0005098448,0.00004928376,0.000006537242,0.00002821593,0.0223254,0.000009202435,0.00001636172],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6496091,"threshold_uncertainty_score":0.9998219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01324430716855004,"score_gpt":0.2490152233202743,"score_spread":0.2357709161517242,"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."}}