{"id":"W2810476984","doi":"10.2175/193864718823774057","title":"<i>AMI Data and Rate Studies</i> – <i>Seizing Opportunities</i> … <i>Carefully!</i>","year":2018,"lang":"en","type":"article","venue":"Proceedings of the Water Environment Federation","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Stantec (Canada)","funders":"","keywords":"Data science; Computer science","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":[0.001714997,0.0002903843,0.0002603133,0.0000331852,0.0008104075,0.0001043488,0.0005953148,0.00008951876,0.0002908953],"category_scores_gemma":[0.0000763982,0.0001771999,0.00003943258,0.00006697558,0.0009910654,0.001072117,0.002544165,0.0001975518,0.0001678549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002149604,"about_ca_system_score_gemma":0.000006703116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003091549,"about_ca_topic_score_gemma":0.00001471274,"domain_scores_codex":[0.9975942,0.00005998709,0.0004848692,0.0008654295,0.0005183112,0.0004771671],"domain_scores_gemma":[0.9990837,0.00004619271,0.0002496117,0.0004762406,0.00001767894,0.000126589],"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":[0.00006779705,0.0001498357,0.02382476,0.0001257149,0.00009723102,0.000002043769,0.006083551,0.00005080114,0.945533,0.0001101802,0.009048947,0.01490609],"study_design_scores_gemma":[0.0009491081,0.0002631943,0.007790606,0.0001349107,0.0001640817,0.00003008666,0.003433555,0.002172925,0.7962579,0.001777776,0.186384,0.0006418995],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859272,0.0000751119,0.0001988336,0.006140399,0.0001801587,0.0007459704,0.00002507763,0.00004162045,0.006665598],"genre_scores_gemma":[0.9939899,0.0007750759,0.0008088922,0.002654778,0.0001593381,0.00004272971,0.00002380987,0.00004198561,0.001503534],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1773351,"threshold_uncertainty_score":0.7226003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06856120452780318,"score_gpt":0.2691772635018519,"score_spread":0.2006160589740488,"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."}}