{"id":"W328969963","doi":"10.1023/a:1021237709617","title":"Inferring long-term nutrient changes in southeastern Ontario lakes: comparing paleolimnological and mass-balance models","year":2002,"lang":"en","type":"article","venue":"Hydrobiologia","topic":"Aquatic Ecosystems and Phytoplankton Dynamics","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Trent University; Queen's University","funders":"National Oceanic and Atmospheric Administration; Ministry of Environment","keywords":"Diatom; Eutrophication; Environmental science; Paleolimnology; Watershed; Ecology; Proxy (statistics); Nutrient; Physical geography; Geography; Statistics; Biology; Mathematics; Computer science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001558599,0.000173128,0.000270765,0.00003064036,0.00006376165,0.00002765951,0.0001792886,0.0001267704,0.0005328095],"category_scores_gemma":[0.000006786698,0.0001395787,0.0000255269,0.00007825041,0.000114931,0.00008953301,0.000160734,0.0002074346,0.0001476764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001239097,"about_ca_system_score_gemma":0.000002290811,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001196155,"about_ca_topic_score_gemma":0.126036,"domain_scores_codex":[0.998932,0.00004655375,0.0002232004,0.0003565448,0.00008952858,0.0003521517],"domain_scores_gemma":[0.9996016,0.0000399226,0.00008537171,0.0001972817,0.00000241452,0.00007343482],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000006291664,0.00004139756,0.9938227,0.00001218233,0.000004479488,0.00001813258,0.0009025513,0.004444127,0.0004505886,0.000106597,0.000006362292,0.0001846188],"study_design_scores_gemma":[0.0005249961,0.0001035681,0.6000676,0.00009495276,0.000005789106,0.00002839912,0.00005605486,0.3980858,0.00003825805,0.0006079063,0.000105105,0.0002816231],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931639,0.0001497149,0.0009466291,0.00004060546,0.0000755983,0.0002207903,0.000005622445,0.00004143479,0.005355663],"genre_scores_gemma":[0.9991502,0.00005359068,0.0002119778,0.00006352019,0.00001881866,0.00002447695,0.000009201595,0.000007332682,0.0004609434],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3937551,"threshold_uncertainty_score":0.8899115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03395755653493106,"score_gpt":0.2100933877221366,"score_spread":0.1761358311872055,"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."}}