{"id":"W4313125492","doi":"10.21272/jes.2022.9(1).h3","title":"Automated Decision-Making with TOPSIS for Water Analysis","year":2022,"lang":"en","type":"article","venue":"Journal of Engineering Sciences","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"TOPSIS; Confusion; Computer science; Process (computing); Data mining; Set (abstract data type); Process engineering; Operations research; Mathematics; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001176343,0.00007709045,0.000162764,0.0002900255,0.0002500762,0.00005809159,0.0005910021,0.00001618393,0.0001355297],"category_scores_gemma":[0.0001145017,0.00004645596,0.00008796497,0.000936187,0.00008589511,0.0002323864,0.0002036673,0.00010729,0.000001866122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001475177,"about_ca_system_score_gemma":0.000008841296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001032374,"about_ca_topic_score_gemma":0.000001787158,"domain_scores_codex":[0.9988377,0.00001422476,0.0002369998,0.0001360028,0.0005534933,0.0002215624],"domain_scores_gemma":[0.9995728,0.0001652601,0.0001061547,0.000111953,0.00001392426,0.00002995601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001214864,0.00001532183,0.0138038,0.00000206425,0.00005195972,0.000008552042,0.0002150284,0.9800766,0.005010147,0.00001970826,0.000203971,0.0005807456],"study_design_scores_gemma":[0.0009315471,0.003076674,0.1146127,0.0001138767,0.000688691,0.0003654157,0.002344941,0.7917147,0.06782719,0.002780281,0.0145625,0.0009815441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9781075,0.00001774216,0.02122173,0.0002589113,0.0002123086,0.00004833154,0.000001788148,0.0001040871,0.00002766401],"genre_scores_gemma":[0.898139,8.30993e-7,0.1018066,0.000009728209,0.00001921673,0.000006723332,1.479197e-7,0.000004609585,0.00001311189],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1883619,"threshold_uncertainty_score":0.1923409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01453412252958444,"score_gpt":0.2670461168790603,"score_spread":0.2525119943494759,"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."}}