{"id":"W1972978561","doi":"10.1139/s06-008","title":"Artificial neural networks and time series modelling of TP concentration in boreal streams: a comparative approach","year":2006,"lang":"en","type":"article","venue":"Journal of Environmental Engineering and Science","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Alberta","keywords":"Environmental science; Watershed; Taiga; Boreal; STREAMS; Hydrology (agriculture); Nutrient; Snow; Biomass (ecology); Ecology; Atmospheric sciences; Meteorology; Computer science; Biology; Machine learning; Geology; Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002748437,0.00007673536,0.0001413291,0.00004080825,0.00006765777,0.00001420459,0.00006837893,0.0000212668,0.000007017294],"category_scores_gemma":[0.000002422625,0.00006466787,0.0000142936,0.00009233545,0.0005317443,0.0003729335,0.0000666882,0.0000865695,3.515403e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004060773,"about_ca_system_score_gemma":0.000001731433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002800307,"about_ca_topic_score_gemma":0.000001522201,"domain_scores_codex":[0.9993568,0.0000119304,0.0002167121,0.0001157551,0.0001543193,0.0001444654],"domain_scores_gemma":[0.9998258,0.00001652212,0.00008472805,0.00003607543,0.000001409906,0.00003542381],"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":[0.00002070421,0.00004051913,0.02299205,0.000002702151,0.000003228242,0.000003098249,0.0003167635,0.96899,0.007286254,0.00007076982,0.000004749261,0.0002691386],"study_design_scores_gemma":[0.0001223527,0.0000926209,0.08364474,0.000006596357,0.000007716626,0.00001750457,0.0001272168,0.9149961,0.0008385117,0.00007588286,0.000008862183,0.00006190173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931582,0.0001288175,0.006204973,0.00002637803,0.00002582226,0.00005189515,0.000001150723,0.000002450862,0.0004002652],"genre_scores_gemma":[0.9985381,0.00004884801,0.001373099,0.000005435809,0.00001747817,8.299707e-7,7.465238e-7,0.000002153224,0.00001336018],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06065269,"threshold_uncertainty_score":0.2637079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009417045483588618,"score_gpt":0.1790005935302649,"score_spread":0.1695835480466762,"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."}}