{"id":"W2107275056","doi":"10.1139/f03-127","title":"Determining long-term water quality change in the presence of climate variability: Lake Tahoe (U.S.A.)","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"California State Water Resources Control Board; U.S. Environmental Protection Agency","keywords":"Secchi disk; Environmental science; Precipitation; Water quality; Hydrology (agriculture); Term (time); Climate change; Eutrophication; Ecology; Meteorology; Oceanography; Geography; Geology; Nutrient","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002934075,0.0003665717,0.0002445977,0.00026677,0.0004965554,0.0007465926,0.0005194682,0.0006996501,0.0007512748],"category_scores_gemma":[0.001327366,0.0003289494,0.0003119605,0.000473086,0.0003296112,0.0008421579,0.0004332842,0.0004026805,0.0001017428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002489852,"about_ca_system_score_gemma":0.001493509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2936106,"about_ca_topic_score_gemma":0.3229302,"domain_scores_codex":[0.9999093,0.00001845373,0.00000613124,0.00003192718,0.00001303515,0.00002131145],"domain_scores_gemma":[0.9996712,0.00008627044,0.00008075827,0.00001894792,0.00008631998,0.0000564749],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001649155,0.0001300533,0.2957024,0.00003563679,0.0001137973,0.0002737369,0.0002436326,0.6904514,0.003931438,0.001054384,0.0008852551,0.007013331],"study_design_scores_gemma":[0.00001471384,0.00006427834,0.06738479,0.000004222004,0.00002399312,0.00002474141,0.00009732934,0.9311152,0.0006598121,0.000250701,0.0003381513,0.00002209899],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975916,0.00001805429,0.001530739,0.0001200537,0.000004079968,0.000006762228,0.0002816541,0.00002900798,0.000418099],"genre_scores_gemma":[0.998772,0.00002016075,0.0007145405,0.000009876352,0.000002354255,0.000007975043,0.0002154399,0.000004580766,0.0002530431],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2936106,"threshold_uncertainty_score":0.5838033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03755271771115869,"score_gpt":0.2589253314650273,"score_spread":0.2213726137538686,"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."}}