{"id":"W3195628996","doi":"10.2166/wqrj.2021.003","title":"A genetic algorithm-based support vector machine to estimate the transverse mixing coefficient in streams","year":2021,"lang":"en","type":"article","venue":"Water Quality Research Journal","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Support vector machine; Genetic algorithm; Algorithm; Computer science; Range (aeronautics); Mixing (physics); Principal component analysis; Pattern recognition (psychology); Data mining; Artificial intelligence; Machine learning; Engineering; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001157342,0.0006798665,0.000822939,0.001040015,0.0003329511,0.000638567,0.0007720219,0.0008730765,0.0005678385],"category_scores_gemma":[0.002550078,0.0002811197,0.0006030265,0.0007703508,0.0002742802,0.0004768762,0.0003040571,0.000704549,0.000155808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005995962,"about_ca_system_score_gemma":0.0009242028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01116192,"about_ca_topic_score_gemma":0.005321458,"domain_scores_codex":[0.9996282,0.0001240273,0.00003340826,0.0000857773,0.00007496613,0.00005372451],"domain_scores_gemma":[0.9988101,0.0006666907,0.0000940759,0.00003357253,0.0003615296,0.00003415408],"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":[0.0001082777,0.0001021905,0.004117436,0.00004305747,0.00007479823,0.00005358854,0.00003738052,0.8512343,0.002880211,0.0005627103,0.0004240455,0.1403621],"study_design_scores_gemma":[0.000003148278,0.00001683545,0.0002289779,0.000001760076,0.000003523357,0.000003321632,0.000003489083,0.9993219,0.0002829426,0.000101137,0.00003064951,0.000002265411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2595065,0.0005004425,0.7373001,0.0002222738,0.00006355935,0.00009508614,0.00009313485,0.0009932736,0.001225668],"genre_scores_gemma":[0.9041643,0.00009314377,0.09481168,0.0000477093,0.00001667231,0.0000983904,0.0001174141,0.00001947334,0.0006312865],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01116192,"threshold_uncertainty_score":0.02219391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05402259241513809,"score_gpt":0.3868587408933636,"score_spread":0.3328361484782256,"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."}}