{"id":"W2019487873","doi":"10.1890/07-0764.1","title":"MEASURING THE IMPACT OF DYNAMIC ANTIPREDATOR TRAITS ON PREDATOR–PREY–RESOURCE INTERACTIONS","year":2008,"lang":"en","type":"article","venue":"Ecology","topic":"Animal Ecology and Behavior Studies","field":"Environmental Science","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Toronto Zoo","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Predation; Predator; Trait; Ecology; Population; Resource (disambiguation); Functional response; Food chain; Biology; Computer science; Demography","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001057184,0.0001049826,0.0001603348,0.00002672062,0.0003289866,0.000002103037,0.0002115061,0.00006229958,0.00232354],"category_scores_gemma":[0.00006460273,0.00006885151,0.00009948301,0.0001001035,0.0004709322,0.00006983029,0.0001448863,0.0001852254,0.0003944621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001637062,"about_ca_system_score_gemma":0.00001516683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006111654,"about_ca_topic_score_gemma":0.001197796,"domain_scores_codex":[0.9992606,0.00008949504,0.0001570685,0.0001902667,0.00008089343,0.0002216559],"domain_scores_gemma":[0.9995539,0.0001755577,0.00008640471,0.0001415547,0.000006152227,0.00003648827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008576259,0.0003197523,0.9842289,0.000001389389,0.00005888772,0.00001340195,0.0009256167,0.001170379,0.00569775,0.00001544194,0.006355771,0.001126955],"study_design_scores_gemma":[0.0001561835,0.0003977821,0.9984199,0.000002137302,0.00001488149,0.00005742928,0.00009092868,0.0001541185,0.0002542513,0.00002296803,0.0003625821,0.00006680612],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919434,0.00001323865,0.000008487343,0.0001437416,0.00008355548,0.0001431662,0.00001206541,0.00002268673,0.007629696],"genre_scores_gemma":[0.9991468,0.000013417,0.00003616253,0.00006800959,0.00001398563,0.00002146296,0.000001885116,0.00000742556,0.0006908602],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01419104,"threshold_uncertainty_score":0.9985884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02696220026204751,"score_gpt":0.2761041056260641,"score_spread":0.2491419053640166,"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."}}