{"id":"W2887108287","doi":"10.1080/14634988.2018.1508935","title":"Autotrophic and heterotrophic indicators of ecological impairment in Toronto Harbour and coastal Lake Ontario","year":2018,"lang":"en","type":"article","venue":"Aquatic Ecosystem Health & Management","topic":"Aquatic Ecosystems and Phytoplankton Dynamics","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"Environment Canada","keywords":"Phytoplankton; Bay; Environmental science; Harbour; Chlorophyll a; Biomass (ecology); Oceanography; Eutrophication; Productivity; Water quality; Ecology; Nutrient; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002825831,0.0003039597,0.0003007496,0.001229613,0.001661673,0.0009268076,0.0004812252,0.0002639332,0.0007870316],"category_scores_gemma":[0.0008127507,0.0002875962,0.0002894359,0.002918402,0.0008330271,0.0003038719,0.0009271415,0.0002126226,0.0001238675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01263967,"about_ca_system_score_gemma":0.008456447,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9681721,"about_ca_topic_score_gemma":0.9933786,"domain_scores_codex":[0.9994695,0.0000347345,0.00002878515,0.00007011105,0.0002420187,0.0001549166],"domain_scores_gemma":[0.9989424,0.00005776425,0.0002632593,0.00002939533,0.0005053298,0.0002017034],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001255287,0.00001707084,0.9877626,0.00005572837,0.00004431129,0.000194169,0.003524593,0.0001628866,0.004177415,0.00006901482,0.0005068939,0.003359817],"study_design_scores_gemma":[0.000001095399,0.00001050465,0.9985771,0.000004247428,0.00000575329,0.00001666541,0.0008141517,0.0001006048,0.00009298356,0.000005270269,0.0003679404,0.000003635124],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975665,0.0001880029,0.00007668289,0.00004452385,0.000002708366,0.00002186715,0.0008469045,0.000005043917,0.001247677],"genre_scores_gemma":[0.9973968,0.0001554514,0.0002293103,0.00002700533,0.000002906718,0.00002134312,0.0008108642,0.000003547961,0.00135281],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03182793,"threshold_uncertainty_score":0.09170765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00663759097411129,"score_gpt":0.2345138322649366,"score_spread":0.2278762412908253,"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."}}