{"id":"W2145829069","doi":"10.1002/hyp.10733","title":"Estimating stream solute loads from fixed frequency sampling regimes: the importance of considering multiple solutes and seasonal fluxes in the design of long‐term stream monitoring networks","year":2015,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of the Environment, Conservation and Parks; Trent University","funders":"Ministry of Environment","keywords":"Sampling (signal processing); Environmental science; STREAMS; Magnitude (astronomy); Seasonality; Spring (device); Hydrology (agriculture); Atmospheric sciences; Mean squared error; Wetland; Soil science; Mathematics; Ecology; Statistics; Biology; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002975707,0.0002795561,0.0005059897,0.0007166376,0.0006238722,0.0008605554,0.0007026875,0.0003204708,0.0001859265],"category_scores_gemma":[0.007444954,0.0002722216,0.0002418027,0.0008063398,0.0002922088,0.0007067864,0.0004042583,0.0002286797,0.00005948762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009856344,"about_ca_system_score_gemma":0.001151115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06932089,"about_ca_topic_score_gemma":0.1541224,"domain_scores_codex":[0.9992079,0.0003290439,0.00007058385,0.0001510286,0.0001930893,0.00004831507],"domain_scores_gemma":[0.9974132,0.001149716,0.0005777554,0.0002319811,0.0005619163,0.00006545176],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002958082,0.0001143998,0.7525969,0.0001273467,0.0003042839,0.00006575356,0.0003015306,0.1189785,0.02843553,0.0001855171,0.0003270583,0.09826741],"study_design_scores_gemma":[0.00003784057,0.0001867783,0.6762869,0.0000287112,0.0001198326,0.00005666204,0.0001632802,0.3136498,0.008072008,0.0004979192,0.0008604313,0.00003980329],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9360474,0.0001689843,0.06238461,0.00005365935,0.000007267698,0.0001257014,0.0004032983,0.0001863751,0.0006226889],"genre_scores_gemma":[0.9525764,0.00007296901,0.04664264,0.00001732505,0.000008973327,0.0001145872,0.0003885879,0.00001659295,0.0001619281],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06932089,"threshold_uncertainty_score":0.1378348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06959094770203372,"score_gpt":0.2723122535270515,"score_spread":0.2027213058250177,"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."}}