{"id":"W1966674957","doi":"10.1016/j.watres.2008.02.016","title":"A time series model for influent temperature estimation: Application to dynamic temperature modelling of an aerated lagoon","year":2008,"lang":"en","type":"article","venue":"Water Research","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"Universidad Autónoma de San Luis Potosí; Consejo Nacional de Ciencia y Tecnología","keywords":"Calibration; Environmental science; Aeration; Air temperature; Mean squared error; Series (stratigraphy); Time series; Facultative; Wastewater; Mathematics; Statistics; Environmental engineering; Meteorology; Engineering; Ecology; Geography","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":[0.0007100688,0.0001442014,0.0001696395,0.0001333065,0.0003901165,0.00005226749,0.0004895899,0.0001901209,0.00002137758],"category_scores_gemma":[0.00003590345,0.0001079114,0.00003235156,0.0003818154,0.0002863145,0.0004410429,0.0003069589,0.0002787031,0.0001816641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002117384,"about_ca_system_score_gemma":0.00001993215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001055597,"about_ca_topic_score_gemma":0.0000155253,"domain_scores_codex":[0.998216,0.0000825942,0.0002523941,0.0004327951,0.000573615,0.0004425599],"domain_scores_gemma":[0.9991837,0.00001425459,0.00002572207,0.0005733176,0.0001100828,0.0000929512],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006450027,0.00004119519,0.00004901442,0.00001859637,0.000003306808,0.000001084649,0.001780706,0.4955652,0.5021499,0.00001428492,0.0001944224,0.0001178135],"study_design_scores_gemma":[0.0001157123,0.0001260664,0.00009479873,0.00001125134,0.000001736932,0.000004287796,0.00006426188,0.5518093,0.4460519,0.001511371,0.000105983,0.0001033645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.989446,0.000006435903,0.008137009,0.001253127,0.00001546803,0.0009328434,0.0000279672,0.0001578516,0.00002323733],"genre_scores_gemma":[0.9510453,0.000004495743,0.04629026,0.0000176119,0.00001621637,0.0002983751,0.00009735578,0.00002589311,0.002204436],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05624405,"threshold_uncertainty_score":0.44005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.067343422156773,"score_gpt":0.3357520127282997,"score_spread":0.2684085905715268,"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."}}