{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001291068,0.00003864251,0.00007163968,0.00001787565,0.0001587688,6.75853e-7,0.000142181,0.0000149228,0.01007927],"category_scores_gemma":[0.000002302739,0.00002269298,0.00003338099,0.00004035905,0.0000515641,0.0000246227,0.0004267933,0.00006584852,0.00001538314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002218216,"about_ca_system_score_gemma":0.000004048786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00288405,"about_ca_topic_score_gemma":0.001716483,"domain_scores_codex":[0.9995769,0.0001825255,0.00006657781,0.00005269579,0.00004191282,0.000079433],"domain_scores_gemma":[0.9997864,0.00005158578,0.00003353075,0.0001200986,9.424876e-7,0.000007482728],"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":[0.0001614168,0.000403144,0.2563899,8.639431e-7,0.00001778511,0.000001648192,0.006340778,0.004297341,0.7318957,0.0001376509,0.0001873663,0.0001663689],"study_design_scores_gemma":[0.0002463722,0.0008662621,0.9477379,0.000001173674,0.000004172226,9.770414e-7,0.000120259,0.00004649881,0.05022991,0.000309801,0.000398467,0.00003822774],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986402,5.347519e-7,4.059133e-7,0.00007845322,0.00002611047,0.00008405471,0.00004877788,0.00000218094,0.001119298],"genre_scores_gemma":[0.9998612,0.000003608272,0.000002578664,0.000043619,0.00000176459,0.000002984608,0.00003789241,0.000001354809,0.0000450141],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.691348,"threshold_uncertainty_score":0.9908257,"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."}}