{"id":"W4308966209","doi":"10.2166/ws.2022.389","title":"The use of PCA and ANN to improve evaluation of the WQIclassic, development of a new index, and prediction of WQI, Coastel Constantinois, northern coast of eastern Algeria","year":2022,"lang":"en","type":"article","venue":"Water Science & Technology Water Supply","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Index (typography); Water quality; Principal component analysis; Artificial neural network; Principal (computer security); Quality (philosophy); Artificial intelligence; Statistics; Mathematics; Computer science; Ecology","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":[0.001284507,0.0006132106,0.0002989552,0.001400461,0.0002549452,0.001055307,0.0002779332,0.0002874909,0.0006282916],"category_scores_gemma":[0.002126227,0.0001397242,0.0004104858,0.001178668,0.0001978906,0.0005282772,0.0003862167,0.0003706409,0.0001568848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009290813,"about_ca_system_score_gemma":0.0009238287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03073005,"about_ca_topic_score_gemma":0.03122747,"domain_scores_codex":[0.9994597,0.0001935294,0.00004168215,0.0001096242,0.0001503463,0.00004515248],"domain_scores_gemma":[0.9993491,0.0002190561,0.00009168669,0.00004150416,0.0002672963,0.00003131919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003811318,0.0003643044,0.2171177,0.0002309931,0.000394235,0.00018706,0.0002648802,0.3190148,0.01404912,0.001093843,0.002065447,0.4448366],"study_design_scores_gemma":[0.000008984476,0.0001013823,0.117602,0.00001796032,0.00004111607,0.00002236273,0.0001073877,0.8753471,0.005395344,0.0003159845,0.001017801,0.00002262625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9364443,0.0004164729,0.05723402,0.000207172,0.00005044713,0.0000853021,0.0004388468,0.0004637881,0.00465971],"genre_scores_gemma":[0.97973,0.0001163307,0.01885859,0.00001510403,0.00001037918,0.0000315492,0.0002697634,0.00001429416,0.0009540013],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03073005,"threshold_uncertainty_score":0.06110233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03330518302764816,"score_gpt":0.2471242498918667,"score_spread":0.2138190668642186,"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."}}