{"id":"W4308118421","doi":"10.3390/toxins14110749","title":"Impact of Stagnation on the Diversity of Cyanobacteria in Drinking Water Treatment Plant Sludge","year":2022,"lang":"en","type":"article","venue":"Toxins","topic":"Microbial Community Ecology and Physiology","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; McGill University; McGill Genome Centre; National Research Council Canada; Polytechnique Montréal","funders":"Groupe de recherche interuniversitaire en limnologie; Génome Québec; National Research Council Canada; Polytechnique Montréal; Natural Sciences and Engineering Research Council of Canada; Université du Québec à Montréal; Genome Canada","keywords":"Cyanobacteria; Microcystin; Microcystis; Water treatment; Sewage treatment; Biology; Microcystis aeruginosa; Environmental science; Botany; Pulp and paper industry; Environmental engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004391843,0.0003217615,0.0005255305,0.0002803157,0.000375924,0.0006958274,0.0002090788,0.0002975389,0.0003698945],"category_scores_gemma":[0.000552051,0.0001482812,0.0003383389,0.000262516,0.0003972404,0.0004147346,0.0007255473,0.0002950263,0.00008532307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006490199,"about_ca_system_score_gemma":0.0004865969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002451716,"about_ca_topic_score_gemma":0.002310143,"domain_scores_codex":[0.9995963,0.00007944948,0.00004854538,0.00008860527,0.0001242464,0.00006283485],"domain_scores_gemma":[0.9996355,0.00005248161,0.0001118578,0.00002314872,0.0001012307,0.00007577936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003382955,0.00004348987,0.01938881,0.00006153977,0.00001755025,0.00009684281,0.0001448405,0.0002224206,0.9765525,0.00001503243,0.00002563374,0.003092914],"study_design_scores_gemma":[0.00002036211,0.0033833,0.4078622,0.0000200873,0.00009853625,0.000345019,0.000837214,0.002248646,0.5837586,0.0001409452,0.001246482,0.00003860704],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996117,0.00009738134,0.0001110103,0.00001664969,0.00000224587,0.000005071254,0.00006699621,0.000008841464,0.00008006392],"genre_scores_gemma":[0.999297,0.00008568642,0.0002757242,0.00002096434,0.000002310345,0.000007727163,0.000168427,0.000004762066,0.0001374493],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002451716,"threshold_uncertainty_score":0.004874885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02368820048259643,"score_gpt":0.2345979085421738,"score_spread":0.2109097080595773,"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."}}