{"id":"W4362723615","doi":"10.1016/j.jglr.2023.03.013","title":"Environmental drivers of spatial and temporal water quality variability in four coastal wetlands of Lake Ontario","year":2023,"lang":"en","type":"article","venue":"Journal of Great Lakes Research","topic":"Aquatic Ecosystems and Phytoplankton Dynamics","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Tech University; Toronto and Region Conservation Authority; Environment and Climate Change Canada","funders":"","keywords":"Wetland; Water quality; Tributary; Bay; Hydrology (agriculture); Environmental science; Seiche; Marsh; Macrophyte; Shore; Water level; Oceanography; Ecology; Geology; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.00032903,0.0001352948,0.0002775087,0.0008708971,0.001580341,0.001212997,0.0005663219,0.0003636235,0.001016605],"category_scores_gemma":[0.001444937,0.0002921818,0.0003437079,0.001859635,0.000997696,0.0004398119,0.0011384,0.0002549872,0.00008686956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007967578,"about_ca_system_score_gemma":0.006436062,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9294378,"about_ca_topic_score_gemma":0.9787284,"domain_scores_codex":[0.999681,0.00004714553,0.00002219111,0.0000693567,0.00005598376,0.0001243349],"domain_scores_gemma":[0.9987062,0.0002574649,0.0003229257,0.00004320623,0.0004118679,0.000258447],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001080813,0.00002382577,0.9932654,0.00001686738,0.00005603974,0.000107439,0.002403292,0.0002992027,0.001474189,0.000121126,0.0002284304,0.001896117],"study_design_scores_gemma":[0.000002039762,0.00000341502,0.9985427,0.000002175174,0.000005886529,0.000008348869,0.00103356,0.000223863,0.0000195126,0.00001491747,0.0001406084,0.000002950699],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992913,0.00002601159,0.00004429053,0.00004086208,9.376259e-7,0.000005874358,0.0002170263,0.000002226332,0.0003714738],"genre_scores_gemma":[0.9994441,0.00002345264,0.00006557633,0.000008236876,8.33109e-7,0.000007205589,0.0001491215,0.00000181127,0.0002995912],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07056224,"threshold_uncertainty_score":0.1419556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03698132197469762,"score_gpt":0.2944965918716887,"score_spread":0.2575152698969911,"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."}}