{"id":"W4385828898","doi":"10.1088/2515-7620/acf0a3","title":"Machine learning for accurate methane concentration predictions: short-term training, long-term results","year":2023,"lang":"en","type":"article","venue":"Environmental Research Communications","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Canada First Research Excellence Fund","keywords":"Term (time); Methane emissions; Training (meteorology); Methane; Artificial neural network; Computer science; Machine learning; Long short term memory; Training set; Artificial intelligence; Deep learning; Recurrent neural network; Meteorology; Chemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001888831,0.0002609678,0.0002253781,0.0000444102,0.001455094,0.0000825959,0.001267067,0.0001520909,0.0006428469],"category_scores_gemma":[0.0002686458,0.0002732482,0.0001345516,0.0005630515,0.001518011,0.0004702452,0.001585621,0.0008008153,0.0008867366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000901218,"about_ca_system_score_gemma":0.00002529185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008461497,"about_ca_topic_score_gemma":0.000127133,"domain_scores_codex":[0.9965366,0.0005361105,0.0005437218,0.0006186788,0.0008939783,0.0008709056],"domain_scores_gemma":[0.9973521,0.0008285251,0.000117828,0.001388128,0.000006597493,0.0003068463],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005326314,0.001553616,0.6076713,0.00004547979,0.0002568061,0.00003211573,0.009452178,0.1304939,0.04828266,0.0004815901,0.002503393,0.1986944],"study_design_scores_gemma":[0.001568863,0.000537947,0.6804292,0.00004644097,0.00005443241,0.00001968368,0.001859472,0.2805164,0.0004337187,0.0005405357,0.03344007,0.0005532142],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.96742,0.0007756669,0.01345767,0.002654377,0.0002037887,0.003175376,0.0003518813,0.000523387,0.0114378],"genre_scores_gemma":[0.9778542,0.007679874,0.004150057,0.00004772208,0.00005162625,0.0007114289,0.00223988,0.00006684122,0.007198393],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1981411,"threshold_uncertainty_score":0.999972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1168876462430755,"score_gpt":0.3744102062424288,"score_spread":0.2575225599993533,"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."}}