{"id":"W4310901422","doi":"10.1002/ece3.9582","title":"Different proxies, different stories? Imperfect correlations and different determinants of fitness in bighorn sheep","year":2022,"lang":"en","type":"article","venue":"Ecology and Evolution","topic":"Animal Behavior and Reproduction","field":"Agricultural and Biological Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Environment and Parks; Université de Sherbrooke; Alberta Conservation Association","keywords":"Ovis canadensis; Biology; Genetic Fitness; Reproductive success; Proxy (statistics); Population; Ecology; Demography; Biological evolution; Statistics; Mathematics; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004799861,0.0003111556,0.0004504283,0.0009279613,0.0003296605,0.001286201,0.0003712251,0.0005496806,0.0007241078],"category_scores_gemma":[0.01166839,0.0002541867,0.0002775423,0.0008392676,0.00125828,0.001049145,0.0008170974,0.0005861609,0.00008419572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003239741,"about_ca_system_score_gemma":0.0001930568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002563047,"about_ca_topic_score_gemma":0.004196581,"domain_scores_codex":[0.9985352,0.0008572294,0.0001054176,0.0002524797,0.0001617017,0.00008797437],"domain_scores_gemma":[0.9869141,0.006966668,0.003429245,0.001418515,0.0008116073,0.0004598667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007788381,0.00001891992,0.9915828,0.00002574037,0.0002223877,0.0001017717,0.0004681982,0.0006116871,0.001549154,0.000562999,0.00008475811,0.004693677],"study_design_scores_gemma":[0.000001148024,0.0000348147,0.9977336,0.000009568394,0.00003178727,0.00005541341,0.000219139,0.0009450617,0.0001661349,0.0006862581,0.000108482,0.000008655352],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983402,0.0004008764,0.0007062369,0.0001040443,0.000007141577,0.000002257254,0.00005778798,0.000004025686,0.0003773318],"genre_scores_gemma":[0.9997054,0.00002919447,0.0001518032,0.00001579486,0.00000599858,0.000001594561,0.00003290766,0.000001792151,0.00005556863],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004799861,"threshold_uncertainty_score":0.02538437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01569054643339535,"score_gpt":0.227496704008951,"score_spread":0.2118061575755556,"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."}}