{"id":"W4402984307","doi":"10.1007/s00267-024-02057-2","title":"Long-term Evaluation of Machine Learning Based Methods for Air Emission Monitoring","year":2024,"lang":"en","type":"article","venue":"Environmental Management","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Cenovus Energy (Canada); University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Term (time); Nature Conservation; Environmental science; Computer science; Artificial intelligence; Remote sensing; Ecology; Geography; Physics; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002112391,0.0001662971,0.0001333485,0.00006361283,0.0001529474,0.00002287496,0.000151227,0.00004766035,0.0005963109],"category_scores_gemma":[0.00002472616,0.000159354,0.0001088218,0.0001114064,0.00007147804,0.0001410201,0.000211907,0.0001233568,0.0000533459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005202158,"about_ca_system_score_gemma":0.000002909733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001250041,"about_ca_topic_score_gemma":2.478843e-7,"domain_scores_codex":[0.9983437,0.0002004103,0.0002828352,0.0004015061,0.0005376508,0.0002338762],"domain_scores_gemma":[0.9995307,0.0001194582,0.00008145536,0.0001994721,0.000001813583,0.00006712094],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002224364,0.000100227,0.0913744,0.0001892694,0.00004945951,0.000003112422,0.0002421861,0.0296198,0.01953148,0.00001100816,0.0000358759,0.8588209],"study_design_scores_gemma":[0.000945861,0.0002383137,0.3068739,0.0004097784,0.0004142889,0.000002420421,0.000308767,0.5930377,0.09030718,0.0002954106,0.006733136,0.0004333542],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7949255,0.0009942754,0.200094,0.0001049953,0.0007954864,0.000993968,0.000008963119,0.0001351035,0.001947722],"genre_scores_gemma":[0.9656045,0.00003909442,0.03271503,0.000008843106,0.00008293947,0.0001265322,0.00003538168,0.00003181713,0.001355897],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8583876,"threshold_uncertainty_score":0.6529186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04877234183622622,"score_gpt":0.3631637607289123,"score_spread":0.3143914188926861,"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."}}