{"id":"W2746298346","doi":"10.1016/j.jglr.2017.08.001","title":"Biomonitoring using invasive species in a large Lake: Dreissena distribution maps hypoxic zones","year":2017,"lang":"en","type":"article","venue":"Journal of Great Lakes Research","topic":"Aquatic Invertebrate Ecology and Behavior","field":"Environmental Science","cited_by":54,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Michigan Department of Natural Resources; U.S. Geological Survey; University of Michigan; U.S. Department of the Interior; U.S. Environmental Protection Agency","keywords":"Dreissena; Hypoxia (environmental); Eutrophication; Environmental science; Ecology; Zebra mussel; Profundal zone; Population; Structural basin; Oceanography; Nutrient; Geology; Biology; Bivalvia; Mollusca; Littoral zone","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":[0.0001603326,0.000222785,0.0001628816,0.0007131793,0.0005542237,0.0003778358,0.0002130281,0.0002231075,0.0004005805],"category_scores_gemma":[0.0002391201,0.000155638,0.0001659891,0.0007339042,0.000168907,0.0003890937,0.0004872365,0.0001237542,0.0000505621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005547812,"about_ca_system_score_gemma":0.0003296545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05451289,"about_ca_topic_score_gemma":0.2045014,"domain_scores_codex":[0.9999176,0.00001566584,0.000006399016,0.00003140145,0.00001428959,0.00001457192],"domain_scores_gemma":[0.9998668,0.00002140332,0.00003916572,0.000006781091,0.00003346685,0.00003222453],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001317654,0.00004015652,0.9718553,0.00001795846,0.00004694952,0.00006473025,0.0005619513,0.001155657,0.02188652,0.00002738071,0.00006954363,0.004142023],"study_design_scores_gemma":[0.000002568785,0.00002734213,0.996747,0.000001954167,0.00002144944,0.0000360523,0.0002875973,0.001903033,0.0008041299,0.00001583415,0.0001490078,0.000003987675],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996327,0.00001400599,0.00008832804,0.000006453506,3.341695e-7,0.000001887108,0.00009599346,0.000004437456,0.0001557162],"genre_scores_gemma":[0.9993304,0.00001826199,0.0003621274,0.000004608305,8.346937e-7,0.000004025116,0.0001059069,0.000001635366,0.0001722584],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05451289,"threshold_uncertainty_score":0.1083912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1015858701202801,"score_gpt":0.3676840786112878,"score_spread":0.2660982084910076,"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."}}