{"id":"W2135838413","doi":"10.4319/lo.2013.58.5.1736","title":"Nutrients and water temperature are significant predictors of cyanobacterial biomass in a 1147 lakes data set","year":2013,"lang":"en","type":"article","venue":"Limnology and Oceanography","topic":"Aquatic Ecosystems and Phytoplankton Dynamics","field":"Environmental Science","cited_by":245,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Groupe de recherche interuniversitaire en limnologie","keywords":"Nutrient; Environmental science; Cyanobacteria; Biomass (ecology); Linear regression; Explained variation; Water column; Nitrogen; Regression analysis; Phosphorus; Ecology; Atmospheric sciences; Biology; Mathematics; Chemistry; Statistics; Geology","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":[0.0001353775,0.0001224336,0.0002098312,0.00007781175,0.00005464363,0.00002038689,0.0001912305,0.0001636828,0.0001441055],"category_scores_gemma":[0.000008666183,0.00008285552,0.00001824158,0.0001353084,0.0003072314,0.0002045859,0.0002443875,0.0001010824,0.00001143614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004561998,"about_ca_system_score_gemma":0.000002589298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001778448,"about_ca_topic_score_gemma":0.0004444326,"domain_scores_codex":[0.9991148,0.00006407782,0.0002204533,0.0003071459,0.00007947451,0.0002140456],"domain_scores_gemma":[0.9995441,0.00003256866,0.00007061265,0.0002844787,0.000005191343,0.00006300628],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002109377,0.00004152954,0.9839063,0.00003494541,0.00001998599,0.000003859002,0.0004032772,9.994236e-7,0.01506298,0.0000235897,0.0004270547,0.00005436407],"study_design_scores_gemma":[0.0005496605,0.0001309943,0.9952685,0.0000341362,0.00001541201,0.000009661086,0.0002771788,0.0004120306,0.00135918,0.0004426687,0.001358579,0.0001420492],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990891,0.000064272,0.000003449915,0.0001535515,0.000153906,0.0002325338,0.0001996976,0.00001232531,0.00009122497],"genre_scores_gemma":[0.9996343,0.0000775295,0.00005385682,0.00004381605,0.00002444884,0.000007539608,0.0001299733,0.000006126636,0.00002244202],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0137038,"threshold_uncertainty_score":0.337875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008377106612207702,"score_gpt":0.1939896554151266,"score_spread":0.1856125488029189,"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."}}