{"id":"W6976374051","doi":"10.60692/6tv1x-mfw03","title":"Use of Multivariate Statistical Analysis for Detecting Spatial and Seasonal Attributes of Surface Water Quality","year":2020,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Principal component analysis; Multivariate statistics; Water quality; Linear discriminant analysis; Surface water; Multivariate analysis; Curse of dimensionality; Statistical analysis; Spatial variability","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005294199,0.00009096042,0.0002694378,0.0000286195,0.00006285168,0.00004096069,0.00005948086,0.00004811071,0.00008679075],"category_scores_gemma":[0.00005577883,0.0000655868,0.00006629682,0.0001227629,0.00005964893,0.000373972,0.00008449338,0.00003649591,0.0000242708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003975779,"about_ca_system_score_gemma":0.000005411978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004728255,"about_ca_topic_score_gemma":0.000004061419,"domain_scores_codex":[0.9987326,0.0001440223,0.0006224259,0.0001137006,0.0002491745,0.0001380295],"domain_scores_gemma":[0.9994573,0.00005060403,0.0002635366,0.0001041883,0.00003843391,0.00008591359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001611088,0.000003137071,0.9440055,0.000350209,0.0001396422,1.355286e-7,0.04225959,0.01222485,0.0004768659,0.0001288269,0.000007893856,0.0002422136],"study_design_scores_gemma":[0.0006730502,0.00007346787,0.8517076,0.00001502661,0.000145448,7.2369e-7,0.00179533,0.1200732,0.02521726,0.000001565771,0.0001579028,0.0001393898],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5809156,1.115942e-7,0.418197,0.00006267239,0.00001645954,0.0001363003,0.0006481826,0.00001269312,0.00001094223],"genre_scores_gemma":[0.992941,2.067143e-8,0.006921235,0.00005607626,0.000008122241,0.000006244737,0.00005636024,0.000002680702,0.000008299776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4120253,"threshold_uncertainty_score":0.2674552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1201782584833881,"score_gpt":0.2831560906689386,"score_spread":0.1629778321855505,"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."}}