{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001439749,0.00007707894,0.0001146822,0.00002078071,0.00003121118,0.00001337406,0.0000907743,0.00005241209,0.001379677],"category_scores_gemma":[0.000008531092,0.00006459811,0.00001577833,0.00008574018,0.00008197112,0.00007832242,0.00007588149,0.00007590433,0.0006090967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006923775,"about_ca_system_score_gemma":0.000004574635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004995325,"about_ca_topic_score_gemma":0.001479109,"domain_scores_codex":[0.9993783,0.00002625809,0.000145543,0.0001925316,0.00008914431,0.0001682088],"domain_scores_gemma":[0.9997386,0.00003691994,0.00003669453,0.0001232738,0.000002064155,0.00006247825],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000007849991,0.00003722222,0.9440393,0.000009347196,0.000004430098,0.00001037876,0.0002700877,0.003578548,0.04929531,0.002144706,0.000495082,0.0001077261],"study_design_scores_gemma":[0.00082026,0.0001381981,0.9695005,0.00002929225,0.000007563373,0.00002673013,0.0001936304,0.0141537,0.002352637,0.001322903,0.01118393,0.0002706834],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947472,0.00001969761,0.00003210515,0.0001296636,0.00005127557,0.0002054782,0.00002452216,0.00001462789,0.004775483],"genre_scores_gemma":[0.9990337,0.00001052332,0.0003034099,0.0002769442,0.000005241273,0.0000125146,0.000008928218,0.000004334371,0.0003443432],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04694267,"threshold_uncertainty_score":0.9995332,"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."}}