{"id":"W3118362308","doi":"10.3390/toxins13010025","title":"Can Cyanobacterial Diversity in the Source Predict the Diversity in Sludge and the Risk of Toxin Release in a Drinking Water Treatment Plant?","year":2021,"lang":"en","type":"article","venue":"Toxins","topic":"Aquatic Ecosystems and Phytoplankton Dynamics","field":"Environmental Science","cited_by":28,"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":"Microcystis; Cyanobacteria; Biology; Microcystis aeruginosa; Metagenomics; Bloom; Bacteroidetes; Botany; Microcystin; Proteobacteria; Microbiology; Ecology; Bacteria; 16S ribosomal RNA","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":[0.001098788,0.000362338,0.0005845596,0.0009162137,0.0002374303,0.0009841092,0.0002488735,0.000894394,0.0004979026],"category_scores_gemma":[0.002229143,0.0002984435,0.0005035549,0.0007872803,0.000385643,0.0007555374,0.0004927642,0.000526523,0.0003316098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005472954,"about_ca_system_score_gemma":0.0003785295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003823364,"about_ca_topic_score_gemma":0.005276734,"domain_scores_codex":[0.9994102,0.0001269409,0.00004815794,0.0001475422,0.0001651376,0.0001020693],"domain_scores_gemma":[0.9984381,0.0003025901,0.0008254579,0.00006870361,0.0002012761,0.0001639293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001176007,0.00006115773,0.9862347,0.00004286159,0.0001074562,0.00007219658,0.00007042482,0.0007872154,0.007218895,0.00002481838,0.00009716892,0.005165546],"study_design_scores_gemma":[0.00000328127,0.000121881,0.9934217,0.00001726384,0.0000452248,0.0001079675,0.0002865657,0.004106825,0.001462152,0.0002112287,0.0002058714,0.00001003549],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976767,0.0008849361,0.00069199,0.0002313058,0.000005506192,0.000008078,0.0002546189,0.000006972629,0.0002400149],"genre_scores_gemma":[0.9989854,0.0003008018,0.0003131652,0.00005926094,0.000009382292,0.0000045604,0.0001979608,0.000002175684,0.0001272968],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003823364,"threshold_uncertainty_score":0.007602215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01136097261037005,"score_gpt":0.1845298957126784,"score_spread":0.1731689231023084,"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."}}