{"id":"W2150322300","doi":"10.1139/f10-162","title":"Modeling the response time of diatom assemblages to simulated water quality improvement and degradation in running waters","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Diatoms and Algae Research","field":"Materials Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières; Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; Groupe de recherche interuniversitaire en limnologie","keywords":"Eutrophication; Diatom; Trophic level; Nutrient; Environmental science; Trophic state index; Substrate (aquarium); Ecology; Water quality; Diversity index; Biology; Species richness","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.003868329,0.00006360305,0.0001479826,0.0001675955,0.0001900848,0.000172856,0.0002016127,0.00002078495,0.00006736076],"category_scores_gemma":[0.0001975457,0.00003367418,0.00001721469,0.0001459332,0.0002720664,0.0003193211,0.00002851256,0.00005228994,0.000001611182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002440327,"about_ca_system_score_gemma":0.0002506739,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01910373,"about_ca_topic_score_gemma":0.003686951,"domain_scores_codex":[0.9989942,0.00009969337,0.0003306209,0.0001108593,0.0002144941,0.0002501511],"domain_scores_gemma":[0.9995226,0.00009612814,0.00006895314,0.00006749127,0.00004762208,0.0001971538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004702825,0.00003893447,0.1792703,0.00006115494,0.00002339708,0.00004730932,0.05904844,0.003375952,0.7430267,0.0002336746,0.00007441765,0.01432944],"study_design_scores_gemma":[0.001947487,0.004076115,0.2491112,0.0007896586,0.00006026228,0.0000556896,0.05304115,0.1611116,0.520124,0.008332618,0.0004180601,0.0009322722],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984033,0.00007355796,0.00009293244,0.001255403,0.00005388465,0.00007945077,0.000001460776,0.000001216584,0.00003876795],"genre_scores_gemma":[0.9994319,0.000008977793,0.0004433175,0.00007348433,0.000008139595,8.437284e-7,1.448439e-7,0.00000278134,0.00003042479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2229028,"threshold_uncertainty_score":0.9874281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05483811968542821,"score_gpt":0.2849393498633876,"score_spread":0.2301012301779594,"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."}}