{"id":"W2954508569","doi":"10.1007/s00442-019-04450-9","title":"Performance trade-offs in wild mice","year":2019,"lang":"en","type":"article","venue":"Oecologia","topic":"Physiological and biochemical adaptations","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Biology; Peromyscus; Sprint; Trade-off; Foraging; Correlation; Arboreal locomotion; Bivariate analysis; Ecology; Population; Predation; Zoology; Statistics; Habitat; Demography; Mathematics; Computer science","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":[0.000559376,0.001558975,0.0009668538,0.002644622,0.0005100554,0.001250492,0.0008424288,0.001317228,0.006091606],"category_scores_gemma":[0.0008169677,0.0005983656,0.0007886686,0.0007059749,0.001393877,0.0007998275,0.001054258,0.001750977,0.0009221411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007624737,"about_ca_system_score_gemma":0.0003655454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001385695,"about_ca_topic_score_gemma":0.001411253,"domain_scores_codex":[0.9993283,0.0001034774,0.00006628781,0.0002323716,0.0001156309,0.000154012],"domain_scores_gemma":[0.9980012,0.0002803112,0.0005725534,0.0002639603,0.0001150569,0.00076688],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.006445265,0.0006253757,0.004261992,0.00007772471,0.0001172722,0.00042026,0.00008458561,0.0004420146,0.9816962,0.0005499421,0.0003528011,0.004926517],"study_design_scores_gemma":[0.0008289251,0.01304423,0.268115,0.0001398724,0.0008443803,0.003460545,0.0009545101,0.005949507,0.695383,0.003516274,0.007398529,0.0003653023],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954722,0.0007758302,0.0009889537,0.0001957176,0.00005412483,0.00001214217,0.00136748,0.0001583443,0.0009752534],"genre_scores_gemma":[0.9883772,0.0007669785,0.001441321,0.0002138587,0.00004757423,0.0001056679,0.001922851,0.0004270796,0.006697475],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006091606,"threshold_uncertainty_score":0.02037841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009626205875455986,"score_gpt":0.2009710902127697,"score_spread":0.1913448843373137,"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."}}