{"id":"W2012815033","doi":"10.3394/0380-1330(2008)34[245:essflc]2.0.co;2","title":"Evaluating Sampling Strategies for Larval Cisco (Coregonus artedi)","year":2008,"lang":"en","type":"article","venue":"Journal of Great Lakes Research","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Natural Resources and Forestry","funders":"Ministry of Natural Resources; U.S. Geological Survey; Michigan State University; Cisco Systems","keywords":"Larva; Sampling (signal processing); Coregonus; Environmental science; Abundance (ecology); Fishery; Biology; Oceanography; Ecology; Computer science; Geology; Fish <Actinopterygii>; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003709934,0.0006154648,0.000257374,0.0007198433,0.0006799424,0.0004670247,0.0006273432,0.000581625,0.0007055253],"category_scores_gemma":[0.01192584,0.0002702946,0.0002067094,0.0004032307,0.0003502402,0.0005301191,0.0005499015,0.0002209181,0.0001632199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001902453,"about_ca_system_score_gemma":0.002229595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0638512,"about_ca_topic_score_gemma":0.181719,"domain_scores_codex":[0.9986612,0.0006707747,0.00009366175,0.0002171691,0.0002389956,0.0001181657],"domain_scores_gemma":[0.9945776,0.002739795,0.0008148945,0.0001765039,0.001340739,0.0003504149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004253512,0.0006030514,0.8821118,0.0002157703,0.0001340429,0.0001216865,0.001419237,0.005395504,0.02153833,0.000331623,0.0003446477,0.08353081],"study_design_scores_gemma":[0.0002537347,0.006622409,0.9191065,0.00009960311,0.0004120174,0.0001928847,0.003526113,0.04260895,0.02575502,0.0003787919,0.001004203,0.00003970051],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980203,0.00007630456,0.001360305,0.00001999424,0.000003155515,0.0001000621,0.00003430753,0.000008326925,0.0003773129],"genre_scores_gemma":[0.9921672,0.00009054138,0.007155818,0.00003330564,0.000001848766,0.0001247586,0.0000930605,0.000003415194,0.0003300018],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0638512,"threshold_uncertainty_score":0.1269591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2876731969692462,"score_gpt":0.4393724469714667,"score_spread":0.1516992500022205,"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."}}