{"id":"W2004751953","doi":"10.2166/ws.2009.020","title":"Prediction of watermain failure frequencies using multiple and Poisson regression","year":2009,"lang":"en","type":"article","venue":"Water Science & Technology Water Supply","topic":"Water Systems and Optimization","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Poisson regression; Poisson distribution; Regression analysis; Regression; Statistics; Econometrics; Linear regression; Mathematics; Computer science; Population","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":[0.0002957432,0.0002005158,0.0002324928,0.0008392636,0.0002141838,0.00006889139,0.0003286412,0.0002149716,0.000008942296],"category_scores_gemma":[0.000008339925,0.0001145606,0.00002752984,0.0003722744,0.0004025363,0.0007371638,0.0001014769,0.0001479849,0.000006594073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007036767,"about_ca_system_score_gemma":0.0000096559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003304777,"about_ca_topic_score_gemma":0.00001803781,"domain_scores_codex":[0.9985598,0.0000143044,0.0003329549,0.0003339541,0.0002209523,0.0005380087],"domain_scores_gemma":[0.9994639,0.000002873761,0.00002905683,0.0003428052,0.00009790112,0.00006343854],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000003743683,0.0000104082,0.01246575,0.00002585691,0.000004003361,0.00000602465,0.001808916,0.004564444,0.9802737,0.00005389037,0.00005412499,0.0007291075],"study_design_scores_gemma":[0.0002561779,0.0000916388,0.0009440368,0.00007630717,0.000009272626,0.00006906736,0.0001494413,0.05588915,0.9410561,0.0007737476,0.0005375313,0.0001474751],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906367,0.00006492541,0.007557712,0.0007282902,0.0002723157,0.0002098695,0.000009460705,0.0004402645,0.00008043177],"genre_scores_gemma":[0.9935266,0.00001258956,0.006311819,0.00001220456,0.00003016921,0.000008035242,0.00001703405,0.00001494343,0.00006663157],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0513247,"threshold_uncertainty_score":0.4671647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009670190482314144,"score_gpt":0.1904388653246092,"score_spread":0.1807686748422951,"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."}}