{"id":"W2951992010","doi":"10.1038/ismej.2017.58","title":"Characterising and predicting cyanobacterial blooms in an 8-year amplicon sequencing time course","year":2017,"lang":"en","type":"article","venue":"The ISME Journal","topic":"Aquatic Ecosystems and Phytoplankton Dynamics","field":"Environmental Science","cited_by":123,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Université du Québec à Montréal; National Research Council Canada; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; European Commission; Australian Government","keywords":"Bloom; Algal bloom; Eutrophication; Microcystis; Cyanobacteria; Amplicon sequencing; Biology; Operational taxonomic unit; Ecology; Microcystin; Microcystis aeruginosa; Phytoplankton; Bacteria; Nutrient","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":[0.0009886291,0.000095372,0.000138759,0.00001264488,0.000687808,0.0004224245,0.0003165479,0.00004890913,0.0003271847],"category_scores_gemma":[0.00002903495,0.00006683473,0.0000225305,0.00001879982,0.0001061326,0.0005731605,0.0001291057,0.000261388,0.0000545561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000102187,"about_ca_system_score_gemma":0.00001833578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002084684,"about_ca_topic_score_gemma":0.0003955283,"domain_scores_codex":[0.9991745,0.00008515611,0.0002260744,0.0001283397,0.0001673329,0.0002185947],"domain_scores_gemma":[0.9992839,0.00003014838,0.0003121949,0.0002719251,0.000004936872,0.00009697263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002183981,0.00002128742,0.9362889,0.000005431772,0.000009855756,0.00002448736,0.001839478,0.0001996433,0.05843536,0.00002844327,0.00001837194,0.003106928],"study_design_scores_gemma":[0.0004577435,0.00006328859,0.9644983,0.0001105417,0.00001682387,0.0003906996,0.0002351358,0.0333176,0.000187985,0.0004543511,0.0001373337,0.0001301367],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984447,0.000006468861,0.00005389112,0.0002727121,0.0002482294,0.00009437236,0.000004994847,0.000008490058,0.0008661938],"genre_scores_gemma":[0.9992524,0.0000102628,0.0001530504,0.00004751276,0.0003915071,0.000001205645,0.00000116854,0.00001118363,0.0001316652],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05824737,"threshold_uncertainty_score":0.5290133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01298704690376556,"score_gpt":0.2450182079686726,"score_spread":0.2320311610649071,"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."}}