{"id":"W3173726734","doi":"10.1139/cjfas-2020-0113","title":"As the prey thickens: rainbow trout select prey based upon width not length","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"EcoMetrix","funders":"","keywords":"Rainbow trout; Predation; Foraging; Biology; Optimal foraging theory; Trout; Ecology; Invertebrate; Predator; Selection (genetic algorithm); Fish <Actinopterygii>; Fishery","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001358764,0.0002478508,0.0003800198,0.000265379,0.0003104959,0.0008901805,0.0004669793,0.0004026596,0.002220032],"category_scores_gemma":[0.002473575,0.0002108012,0.0005713317,0.00031024,0.0005266849,0.0007075075,0.000542673,0.0004258206,0.0002879016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000719021,"about_ca_system_score_gemma":0.0006307632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0170004,"about_ca_topic_score_gemma":0.03272367,"domain_scores_codex":[0.9996792,0.00008749534,0.00001286136,0.0001455691,0.00004420305,0.00003068714],"domain_scores_gemma":[0.9993687,0.0002505588,0.0002104147,0.00006352106,0.00004815842,0.00005866005],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005912361,0.0001124026,0.9052325,0.0001476056,0.0005900144,0.0002592175,0.0003501565,0.0346204,0.02255439,0.007422501,0.001364454,0.02675517],"study_design_scores_gemma":[0.00006557564,0.0002350655,0.7940347,0.00004797195,0.0002150747,0.0005348089,0.0002672826,0.1870706,0.002063091,0.01327902,0.002136265,0.00005051765],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9826712,0.000165111,0.01481836,0.0002553575,0.000008324377,0.00001666864,0.0004938088,0.00006433018,0.001506831],"genre_scores_gemma":[0.9949142,0.00006201094,0.003518009,0.00008201268,0.000004890114,0.00001397941,0.0003285849,0.00001209844,0.001064274],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0170004,"threshold_uncertainty_score":0.03380293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01650280498565645,"score_gpt":0.2110409755787054,"score_spread":0.194538170593049,"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."}}