{"id":"W2063871508","doi":"10.1111/j.1365-2656.2008.01512.x","title":"Towards a predictive framework for predator risk effects: the interaction of landscape features and prey escape tactics","year":2008,"lang":"en","type":"article","venue":"Journal of Animal Ecology","topic":"Ichthyology and Marine Biology","field":"Environmental Science","cited_by":241,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Predation; Seagrass; Context (archaeology); Biology; Ecology; Habitat; Sympatric speciation; Apex predator; Interspecific competition; Foraging; Predator; Fishery","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":[],"consensus_categories":[],"category_scores_codex":[0.000333955,0.00008809905,0.0002325032,0.00003535394,0.0001235504,0.000003395833,0.0001460927,0.0002005131,0.0001540344],"category_scores_gemma":[0.0009551222,0.00005459303,0.00007385638,0.00005401538,0.0002915773,0.0001186938,0.0001087755,0.0003761578,0.000004055481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003617838,"about_ca_system_score_gemma":0.0000299883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001913616,"about_ca_topic_score_gemma":0.00008301069,"domain_scores_codex":[0.999257,0.0001737233,0.0002385548,0.0001085163,0.00007157254,0.0001506935],"domain_scores_gemma":[0.9985728,0.0008597337,0.0004109391,0.00007577879,0.00003564089,0.00004507405],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00717338,0.0002860104,0.9734396,0.00003075197,0.0003130955,0.00002986762,0.00203127,0.0002187292,0.003403879,0.0006893055,0.009957504,0.00242661],"study_design_scores_gemma":[0.0005615053,0.007358494,0.9853629,0.000007589447,0.0001147113,0.0009530486,0.0002046357,0.0002561808,0.001345636,0.002636532,0.001143661,0.00005505914],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997137,0.0001284696,0.001124368,0.0004304576,0.0004275509,0.0001952146,0.000007776503,0.000004162754,0.0005450068],"genre_scores_gemma":[0.9958247,0.0003179921,0.003479796,0.0001726641,0.0001573448,0.000008597671,8.56309e-7,0.000005745767,0.00003232539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01192335,"threshold_uncertainty_score":0.2226239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006591922660407069,"score_gpt":0.2493560361140786,"score_spread":0.2427641134536715,"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."}}