{"id":"W4391786693","doi":"10.28924/2291-8639-22-2024-30","title":"Application of Deep Belief Network in Weather Modeling: PM2.5 Concentration in Thailand","year":2024,"lang":"en","type":"article","venue":"International Journal of Analysis and Applications","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Kementerian Sumber Asli dan Alam Sekitar; Prince of Songkla University","keywords":"Deep belief network; Mathematics; Meteorology; Artificial intelligence; Computer science; Artificial neural network; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000439095,0.00005112887,0.0001233294,0.0001222821,0.00002000541,0.00002951754,0.0001307072,0.00003115174,0.00004593508],"category_scores_gemma":[0.00001215249,0.00004504269,0.00006566974,0.0005392644,0.00003409128,0.0001236396,0.00002879209,0.0000962102,0.00000312143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007548439,"about_ca_system_score_gemma":0.000008910114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002945563,"about_ca_topic_score_gemma":0.0002154033,"domain_scores_codex":[0.999153,0.00002413509,0.0004219643,0.0001140777,0.0002182982,0.00006848125],"domain_scores_gemma":[0.9996921,0.00004833888,0.0001333423,0.00006047617,0.00003151996,0.00003416418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001058517,0.00004665958,0.3279203,0.000003856244,0.0001085132,0.00000173109,0.0004335666,0.6168791,0.0006755117,0.001762666,0.000006730458,0.05215081],"study_design_scores_gemma":[0.0001423354,0.00001389925,0.04536965,0.00003238435,0.00008137708,0.000005031567,0.0001559489,0.9478061,0.0000871779,0.005501444,0.0007442112,0.00006046438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4969352,0.0004329706,0.5014893,0.0003649472,0.00004211103,0.00007255688,0.000003325521,0.000004339383,0.0006553174],"genre_scores_gemma":[0.9984659,0.0001466204,0.001192859,0.00001557089,0.0001396582,0.00001189879,0.000006810837,0.000003461441,0.00001724913],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5015307,"threshold_uncertainty_score":0.1836788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01129599956407596,"score_gpt":0.2794384933222917,"score_spread":0.2681424937582157,"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."}}