{"id":"W1964886109","doi":"10.1016/j.ecolmodel.2014.09.009","title":"Foraging and predation risk for larval cisco (Coregonus artedi) in Lake Superior: A modelling synthesis of empirical survey data","year":2014,"lang":"en","type":"article","venue":"Ecological Modelling","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Natural Resources and Forestry","funders":"U.S. Fish and Wildlife Service","keywords":"Predation; Zooplankton; Foraging; Ichthyoplankton; Water column; Coregonus; Smelt; Biology; Fishery; Larva; Bay; Capelin; Ecology; Environmental science; Oceanography; Fish <Actinopterygii>","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002137901,0.0001374518,0.0003080637,0.00004555168,0.0001913192,0.00001794694,0.0002732263,0.0001211827,0.00009529087],"category_scores_gemma":[0.0006157272,0.0001185713,0.00003017182,0.000121993,0.0001705024,0.0002547273,0.0005071468,0.0001388105,0.000008151344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003110249,"about_ca_system_score_gemma":0.000004148606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001199031,"about_ca_topic_score_gemma":0.01037034,"domain_scores_codex":[0.9984967,0.0002132741,0.0003382086,0.0005266295,0.0001169964,0.0003081936],"domain_scores_gemma":[0.9980388,0.001527232,0.0001136225,0.0002638228,0.0000109562,0.00004560411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004758954,0.00008064803,0.4649108,0.0000130452,0.00001135425,5.802257e-7,0.00009370376,0.5338427,0.0000020376,0.00003228205,0.0002227525,0.0007424742],"study_design_scores_gemma":[0.0002341977,0.00007040899,0.2189718,0.000008776212,0.00002777956,3.530063e-7,0.00003655829,0.7774937,0.000007821201,0.002788044,0.0002533146,0.0001072442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6471446,0.00001025608,0.3519677,0.0001021062,0.00003736148,0.000265005,0.0000587981,0.00001866886,0.0003955461],"genre_scores_gemma":[0.9837468,0.0001627463,0.01583593,0.00009709554,0.00001967078,0.00006212956,0.00004698366,0.000009376236,0.00001925035],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3366022,"threshold_uncertainty_score":0.5786892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09414437469445991,"score_gpt":0.282567520181127,"score_spread":0.1884231454866671,"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."}}