{"id":"W1985355819","doi":"10.1016/j.watres.2015.01.006","title":"Temperature diagnostic to identify high risk areas and optimize Legionella pneumophila surveillance in hot water distribution systems","year":2015,"lang":"en","type":"article","venue":"Water Research","topic":"Legionella and Acanthamoeba research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":100,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Cégep de Rimouski; Polytechnique Montréal; Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Legionella pneumophila; Environmental science; Legionella; Risk assessment; Profiling (computer programming); Water cooling; Environmental engineering; Environmental health; Medicine; Biology; Engineering; Computer science; Bacteria","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002652573,0.0001967897,0.0002344681,0.0001592726,0.0001877798,0.0002921837,0.0003426105,0.0002498799,0.0000051921],"category_scores_gemma":[0.0005427933,0.0001252167,0.00004033724,0.0002090231,0.0001764302,0.00001411685,0.0006188311,0.0004814803,0.0001578668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008723218,"about_ca_system_score_gemma":0.00007044735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002774765,"about_ca_topic_score_gemma":0.0001944615,"domain_scores_codex":[0.9967834,0.000718184,0.0002634883,0.0006638002,0.0006358302,0.0009353152],"domain_scores_gemma":[0.9985675,0.00004867153,0.00001767405,0.000498514,0.0004518081,0.0004158191],"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":[0.0007510094,0.0001321461,0.05014934,0.00008859292,0.00004420342,0.0001927817,0.0004899999,0.001578587,0.9290404,0.00003926751,0.01736709,0.0001265739],"study_design_scores_gemma":[0.003349269,0.001084297,0.08291426,0.0001402759,0.00001053929,0.0001343227,0.0006990582,0.0003108662,0.8653693,0.0003174847,0.04486701,0.0008032898],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969827,0.0005915405,0.00004548026,0.00132913,0.0002027441,0.0006557365,0.00009961621,0.0000127173,0.00008031695],"genre_scores_gemma":[0.9965793,0.0005011367,0.00001318945,0.00002884584,0.000392451,0.0001575588,0.0009159167,0.00003236362,0.001379257],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06367108,"threshold_uncertainty_score":0.510619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03099996104431774,"score_gpt":0.3226164633729655,"score_spread":0.2916165023286478,"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."}}