{"id":"W2741398865","doi":"10.36334/modsim.2015.l16.haque","title":"Assessment of water quality in Hawkesbury-Nepean River in Sydney using water quality index and multivariate analysis","year":2015,"lang":"en","type":"article","venue":"Weber, T., McPhee, M.J. and Anderssen, R.S. (eds) MODSIM2015, 21st International Congress on Modelling and Simulation","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"WaterNSW","keywords":"Index (typography); Water quality; Multivariate statistics; Quality (philosophy); Multivariate analysis; Environmental science; Hydrology (agriculture); Statistics; Computer science; Engineering; Mathematics; Geotechnical engineering; Biology; Physics","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":[0.0003967537,0.0002180513,0.0002109927,0.0009741352,0.0003226988,0.0005010539,0.0002025164,0.0001573402,0.000506899],"category_scores_gemma":[0.0005057009,0.0001319096,0.0001760696,0.001549467,0.0003142165,0.0002221364,0.000365654,0.0001777304,0.000106056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001081276,"about_ca_system_score_gemma":0.001050691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1929353,"about_ca_topic_score_gemma":0.3754699,"domain_scores_codex":[0.9997192,0.00005508124,0.00001552866,0.00006359038,0.0001187791,0.00002782974],"domain_scores_gemma":[0.9997447,0.0000399045,0.00005691724,0.00001373505,0.0001100447,0.00003470188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009463452,0.0000940567,0.9154379,0.0001275576,0.0001318716,0.0004278302,0.001345534,0.002023867,0.0142225,0.0001850226,0.0008585413,0.06505072],"study_design_scores_gemma":[0.000002016915,0.00005632712,0.9935037,0.00001083778,0.00001469797,0.00007152554,0.0006328737,0.003534312,0.001118244,0.00003702763,0.00100741,0.00001099675],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997195,0.0001846474,0.000917463,0.00002841739,0.00000388816,0.0000310191,0.0003311794,0.00001169941,0.001296596],"genre_scores_gemma":[0.9928768,0.0002549695,0.004044729,0.00001608221,0.000004148985,0.00003836315,0.0006790537,0.000004653881,0.002081111],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1929353,"threshold_uncertainty_score":0.3836246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09090862074178321,"score_gpt":0.3717594916126888,"score_spread":0.2808508708709057,"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."}}