{"id":"W2982444119","doi":"10.3390/toxins11110620","title":"Meteorological and Nutrient Conditions Influence Microcystin Congeners in Freshwaters","year":2019,"lang":"en","type":"article","venue":"Toxins","topic":"Aquatic Ecosystems and Phytoplankton Dynamics","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; University of Ottawa; University of Waterloo; Environment and Climate Change Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère de l’Environnement, de la Protection de la nature et des Parcs","keywords":"Nutrient; Environmental science; Biomagnification; Microcystin; Cyanotoxin; Ecology; Biology; Environmental chemistry; Cyanobacteria; Trophic level; Chemistry","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":[0.0002132664,0.0001860539,0.0002050171,0.0004527878,0.0002653335,0.0003721618,0.0001065871,0.0001428938,0.0008501516],"category_scores_gemma":[0.0008867068,0.000124082,0.0001847094,0.000650152,0.0002483044,0.000364281,0.0003547386,0.0001146765,0.0001340096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003516645,"about_ca_system_score_gemma":0.0003380066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01651175,"about_ca_topic_score_gemma":0.0455112,"domain_scores_codex":[0.9998254,0.00003557521,0.00001857073,0.00006347123,0.00003328267,0.00002367787],"domain_scores_gemma":[0.9994337,0.00009447982,0.0002587917,0.00002827022,0.0001053649,0.00007937477],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000943284,0.00001326234,0.9805602,0.00002268998,0.00004166927,0.00009847931,0.0001991081,0.0001746687,0.01551665,0.00002389879,0.0000530317,0.003202011],"study_design_scores_gemma":[3.894304e-7,0.00001172493,0.999522,9.717519e-7,0.0000043445,0.00001300804,0.0000869549,0.00006270711,0.0002180337,0.000008889808,0.00006961363,0.000001309642],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992681,0.0001213822,0.00005565945,0.00001429668,0.000001752257,0.000002636544,0.0001568276,0.000003891302,0.0003753059],"genre_scores_gemma":[0.9994123,0.00008141661,0.0001059355,0.000007992022,0.000002296847,0.000002630974,0.0001279575,0.000003054569,0.0002565534],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01651175,"threshold_uncertainty_score":0.03283125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004488507883147759,"score_gpt":0.2049473148742682,"score_spread":0.2004588069911205,"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."}}