{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003867655,0.00167671,0.0009189567,0.000662885,0.0007166987,0.001053374,0.0009652407,0.001738724,0.002411612],"category_scores_gemma":[0.008015182,0.000431307,0.0008310937,0.0007148978,0.0005122235,0.001913028,0.0009214024,0.002336543,0.00086081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001752867,"about_ca_system_score_gemma":0.001695588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05461118,"about_ca_topic_score_gemma":0.039137,"domain_scores_codex":[0.998897,0.0003208307,0.00009993483,0.0003043761,0.0002158994,0.000162047],"domain_scores_gemma":[0.995865,0.002248635,0.0002157044,0.0004127068,0.001097489,0.0001604638],"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":[0.001197426,0.0009272547,0.02938533,0.0003109394,0.0004344223,0.0001742164,0.00009847571,0.7958671,0.007345784,0.0006680026,0.006455231,0.1571359],"study_design_scores_gemma":[0.00001825249,0.0001752153,0.004633901,0.00002475891,0.00004251816,0.00001489608,0.00004765297,0.9887062,0.005537899,0.0004369387,0.0003434574,0.00001822263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9120793,0.007844691,0.06537417,0.001956027,0.0006428552,0.0001295705,0.002028149,0.003218681,0.006726565],"genre_scores_gemma":[0.9852764,0.0003127515,0.01116605,0.0001555708,0.00004458004,0.00003763773,0.001669504,0.00004637288,0.001291065],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05461118,"threshold_uncertainty_score":0.1085866,"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."}}