{"id":"W4391544902","doi":"10.1016/j.jclepro.2024.141043","title":"Catalyzing net-zero carbon strategies: Enhancing CO2 flux Prediction from underground coal fires using optimized machine learning models","year":2024,"lang":"en","type":"article","venue":"Journal of Cleaner Production","topic":"Coal Properties and Utilization","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Henan Polytechnic University; State Key Laboratory Cultivation Base for Gas Geology and Gas Control; Department of Education of Liaoning Province; National Natural Science Foundation of China","keywords":"Particle swarm optimization; Artificial neural network; Environmental science; Coal; Mean squared error; Computer science; Machine learning; Engineering; Waste management; Mathematics; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.0003940684,0.0001843274,0.0002474748,0.0002042,0.0001224652,0.0002711139,0.00007964942,0.0000980913,0.00003250315],"category_scores_gemma":[0.0000388309,0.0001647071,0.00009087447,0.000180672,0.00002530755,0.001391291,0.00002310388,0.0005296904,0.000001482332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003071451,"about_ca_system_score_gemma":0.00008120146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004319521,"about_ca_topic_score_gemma":0.00006635294,"domain_scores_codex":[0.9986522,0.00006517697,0.0005767093,0.000204711,0.0003068551,0.0001943576],"domain_scores_gemma":[0.9995341,0.00003816028,0.0001299584,0.0001187946,0.0001177754,0.00006116828],"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":[0.00007558725,0.00001163832,0.00003247513,0.0001327891,0.0001161205,0.00001003571,0.001074518,0.9311515,0.06345753,0.00001406438,0.0001125826,0.00381112],"study_design_scores_gemma":[0.0002447089,0.00007811093,0.00004676973,0.0003979551,0.0001433927,0.0001196267,0.001034659,0.9805651,0.01561684,0.0007011305,0.0008980728,0.0001536245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7701411,0.00612151,0.2199465,0.00007716386,0.002938738,0.0001103894,0.000003767137,0.0002105882,0.0004502855],"genre_scores_gemma":[0.9965831,0.0007070507,0.0009894972,0.000003846005,0.001447806,0.000001505803,0.00003256279,0.00006080603,0.0001738302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.226442,"threshold_uncertainty_score":0.6716561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02672753835666962,"score_gpt":0.2247070692418218,"score_spread":0.1979795308851521,"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."}}