{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07948495,0.0009980182,0.001530269,0.005732955,0.002388662,0.00701082,0.004600947,0.006751606,0.03025135],"category_scores_gemma":[0.3375468,0.001234716,0.002956778,0.008457891,0.002983107,0.01003305,0.004157268,0.009497265,0.02296235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002679853,"about_ca_system_score_gemma":0.01129121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04516079,"about_ca_topic_score_gemma":0.07982264,"domain_scores_codex":[0.9591801,0.01625643,0.007859778,0.001586972,0.0134569,0.001659843],"domain_scores_gemma":[0.6997992,0.09878397,0.02230557,0.04117822,0.1310481,0.006884955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000114966,0.00002187599,0.007609902,0.0002727497,0.0001226338,0.00007232567,0.0002745627,0.00007851918,0.000249783,0.003764156,0.957807,0.02961142],"study_design_scores_gemma":[0.00005779555,0.00006554242,0.02537883,0.002003556,0.0001992995,0.0002005732,0.002104602,0.0003861937,0.0009573275,0.01640634,0.9520484,0.0001914869],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.003439469,0.006730597,0.01817379,0.7838704,0.1123657,0.0004890332,0.03485294,0.001374826,0.03870322],"genre_scores_gemma":[0.06197339,0.01251287,0.05819166,0.5898226,0.1495588,0.00293149,0.04201791,0.004351609,0.07863958],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.07948495,"threshold_uncertainty_score":0.4203616,"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."}}