{"id":"W2291644810","doi":"10.1002/ece3.2046","title":"Are more diverse parts of the mammalian skull more labile?","year":2016,"lang":"en","type":"article","venue":"Ecology and Evolution","topic":"Morphological variations and asymmetry","field":"Mathematics","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Skull; Trait; Biology; Evolutionary biology; Morphometrics; Stabilizing selection; Lability; Variation (astronomy); Diversification (marketing strategy); Natural selection; Selection (genetic algorithm); Allometry; Ecology; Genetic variation; Anatomy; Genetics; Artificial intelligence; Computer science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004029661,0.0003610396,0.000460999,0.001327759,0.0004017626,0.0009820468,0.0004494335,0.0005190891,0.002830612],"category_scores_gemma":[0.002529006,0.0002877753,0.000313332,0.001000588,0.001544279,0.0009118892,0.0008594893,0.0003800356,0.0002834406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001701227,"about_ca_system_score_gemma":0.0001446933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001393076,"about_ca_topic_score_gemma":0.002573241,"domain_scores_codex":[0.9996763,0.00005916867,0.00002321697,0.0001179944,0.00008077324,0.00004248376],"domain_scores_gemma":[0.9982654,0.0003132561,0.0009853173,0.0001984769,0.00009683199,0.0001405803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000422658,0.00005156976,0.7463491,0.0001661663,0.0004361737,0.0004135495,0.001181482,0.002444075,0.193928,0.001821593,0.0001480345,0.05263764],"study_design_scores_gemma":[0.000003095915,0.00005264574,0.9954137,0.000007868059,0.00002323822,0.0004986727,0.0003268102,0.0007455345,0.002028202,0.000695864,0.000191596,0.00001278584],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985562,0.0001398361,0.000674332,0.00003611798,0.000001675873,0.000001585148,0.00004367438,0.000006437672,0.0005400472],"genre_scores_gemma":[0.9992906,0.00008370527,0.0004272388,0.00002395405,0.000006044836,0.000002160708,0.00004736245,0.000005636723,0.0001132962],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002830612,"threshold_uncertainty_score":0.00946939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02818796836509264,"score_gpt":0.2643312663161009,"score_spread":0.2361432979510082,"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."}}