{"id":"W2965160631","doi":"10.1134/s1875372819020045","title":"Comparing the Efficiency of River Water Quality Parameterization by Different Methods Under a Significant Human-Induced Impact","year":2019,"lang":"en","type":"article","venue":"Geography and Natural Resources","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Water quality; Environmental science; Hydrometeorology; Aquatic ecosystem; Pollution; Scale (ratio); Hydrology (agriculture); STREAMS; Quality (philosophy); Ecosystem; Water resource management; Ecology; Geography; Meteorology; Precipitation; Computer science; Geology; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005967591,0.0009988048,0.0008273638,0.00305742,0.0002551721,0.0009663168,0.0005374597,0.0006891412,0.0004232498],"category_scores_gemma":[0.01181295,0.0002328986,0.000840778,0.001639548,0.0003278124,0.0007579193,0.000747853,0.0004095085,0.000207315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004152414,"about_ca_system_score_gemma":0.0004708819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004908805,"about_ca_topic_score_gemma":0.003667172,"domain_scores_codex":[0.997044,0.001588534,0.0001865671,0.0005181806,0.0005279243,0.0001347141],"domain_scores_gemma":[0.9908213,0.006544183,0.0007312747,0.0006800076,0.001064825,0.0001584173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003251128,0.0007506793,0.3237837,0.0006311138,0.001564496,0.0001337579,0.0006627256,0.2293141,0.01747182,0.0006088745,0.0007403853,0.4210872],"study_design_scores_gemma":[0.0001142701,0.001542058,0.263787,0.00005268688,0.0003876952,0.0001669347,0.00048617,0.7103305,0.02119745,0.0009132881,0.0008588963,0.0001630879],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9377328,0.0008400207,0.05817815,0.00008974082,0.00002650319,0.00009110729,0.0004321659,0.0006136452,0.001995854],"genre_scores_gemma":[0.9727544,0.0002207707,0.02609071,0.00001617759,0.00001500681,0.00005026457,0.0005047615,0.00004816171,0.0002997637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005967591,"threshold_uncertainty_score":0.03156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02952823628443485,"score_gpt":0.3339974103962662,"score_spread":0.3044691741118313,"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."}}