{"id":"W2765680700","doi":"10.1080/10402381.2017.1379574","title":"Climate as a driver of increasing algal production in Lake of the Woods, Ontario, Canada","year":2017,"lang":"en","type":"article","venue":"Lake and Reservoir Management","topic":"Aquatic Ecosystems and Phytoplankton Dynamics","field":"Environmental Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Golder Associates (Canada); Queen's University; Ministry of the Environment, Conservation and Parks","funders":"Fisheries and Oceans Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Tributary; Environmental science; Algal bloom; Climate change; Diatom; Chlorophyll a; Eutrophication; Precipitation; Biomass (ecology); Phosphorus; Trophic state index; Oceanography; Ecology; Phytoplankton; Nutrient; Geography; Geology; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003132815,0.0000645242,0.0001115334,0.00001169758,0.0001462749,0.00001435147,0.0002291774,0.00002108246,0.0002127438],"category_scores_gemma":[0.00001658717,0.00004484788,0.00001861327,0.00004055158,0.0000821084,0.00008174815,0.0004265854,0.00005933891,0.000001685797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006609063,"about_ca_system_score_gemma":0.00001852368,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8380712,"about_ca_topic_score_gemma":0.9996051,"domain_scores_codex":[0.9993024,0.00003443164,0.0001738605,0.0001454825,0.0002157699,0.0001279996],"domain_scores_gemma":[0.9994068,0.000009085447,0.0001670457,0.0003892034,0.000004100549,0.00002380629],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002265201,0.00002642813,0.9966107,0.00009939451,0.00001346056,0.000007247097,0.0002738938,0.001023945,0.00003713145,0.0003982585,0.000485918,0.001000976],"study_design_scores_gemma":[0.0001763034,0.00001337495,0.9807267,0.0001293133,0.00001190204,0.000002372162,0.0001275958,0.0004105558,0.00007716106,0.0002428157,0.01802619,0.00005571413],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9679924,0.000008771071,0.000001750357,0.0002872529,0.0001466665,0.0002421012,0.00001153314,0.000001580016,0.03130789],"genre_scores_gemma":[0.9976004,0.00003368294,0.0001495755,0.00002329083,0.000008998431,0.000005955942,0.000002923038,0.000003554823,0.002171604],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1615339,"threshold_uncertainty_score":0.2329395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005927291724726435,"score_gpt":0.1978697389178766,"score_spread":0.1919424471931502,"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."}}