{"id":"W4409717369","doi":"10.1016/j.jenvman.2025.125338","title":"A sustainable industrial waste control with AI for predicting CO2 for climate change monitoring","year":2025,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University Canada West","funders":"Murata Science Foundation; Universiti Teknologi Petronas; Universiti Kebangsaan Malaysia; Liverpool School of Tropical Medicine","keywords":"Climate change; Environmental science; Climate change mitigation; Sustainable development; Environmental monitoring; Control (management); Environmental engineering; Environmental resource management; Environmental planning; Waste management; Engineering; Computer science; Ecology","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":[0.0009632958,0.0001848798,0.0002628958,0.00009681992,0.0003457549,0.00006442385,0.0002321268,0.00006504695,0.00002292086],"category_scores_gemma":[0.00002765836,0.0001568611,0.0001296195,0.0001106531,0.00008314881,0.0004015005,0.0001865857,0.0001732847,0.000002591833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005871108,"about_ca_system_score_gemma":0.000005274843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000012829,"about_ca_topic_score_gemma":5.76646e-7,"domain_scores_codex":[0.9984081,0.00003662741,0.0004738624,0.0002312133,0.0003395705,0.0005105732],"domain_scores_gemma":[0.999255,0.0001192519,0.0003778499,0.0001476056,0.000008367814,0.00009193103],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004597532,0.0009262584,0.8122602,0.0008531334,0.0008756871,0.0001121778,0.001472406,0.02019739,0.002034557,0.0009963777,0.002657277,0.153017],"study_design_scores_gemma":[0.1106701,0.01816082,0.3502139,0.006415224,0.006291848,0.0001410284,0.1387361,0.03370509,0.02389085,0.005452657,0.3024897,0.003832622],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9694104,0.0001813444,0.02428944,0.00107713,0.0009985392,0.002670396,0.00004262353,0.00002860079,0.001301557],"genre_scores_gemma":[0.9940711,0.00004938713,0.00343257,0.0001345572,0.0006646332,0.0002068785,0.000002060211,0.00002328953,0.001415555],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4620462,"threshold_uncertainty_score":0.6396612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02246644321597284,"score_gpt":0.2543066973068275,"score_spread":0.2318402540908547,"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."}}