{"id":"W4323849257","doi":"10.1101/2023.03.09.531574","title":"Evolutionary constraint and innovation across hundreds of placental mammals","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Museum of Nature","funders":"High Performance Research Computing, Texas A and M University; Science for Life Laboratory; Broad Institute","keywords":"Biology; Evolutionary biology; Genome; Constraint (computer-aided design); Phylogenetics; Computational biology; Phenotype; Gene; Genetics","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":[0.0007094321,0.0002315508,0.0005011093,0.001668086,0.0004337345,0.001150779,0.000388667,0.0003304913,0.002562845],"category_scores_gemma":[0.002784327,0.0002382971,0.0003082405,0.002277739,0.000897439,0.0006803258,0.001103587,0.0004220007,0.0003415657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003862416,"about_ca_system_score_gemma":0.0002345465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00112676,"about_ca_topic_score_gemma":0.001441948,"domain_scores_codex":[0.9995162,0.00008271728,0.00002264686,0.0002301323,0.0001130574,0.00003516621],"domain_scores_gemma":[0.9986305,0.0006294317,0.0003359556,0.0001948288,0.00009734971,0.0001118754],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008017569,0.00004811234,0.631751,0.0008260899,0.00126413,0.0008173348,0.002417057,0.013391,0.2591863,0.008874809,0.0010715,0.07955085],"study_design_scores_gemma":[0.00001967934,0.00007963896,0.9694942,0.00005947545,0.0001401582,0.0006348424,0.0003933315,0.01051748,0.005165199,0.007459242,0.006002665,0.00003421572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9924656,0.0008733046,0.003768113,0.0001021488,0.000004127617,0.000003868532,0.0009232784,0.0001079591,0.001751567],"genre_scores_gemma":[0.9959943,0.0002926703,0.002342333,0.00003812085,0.00001091722,0.000006090524,0.001035761,0.00003529373,0.0002444438],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002562845,"threshold_uncertainty_score":0.008573592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02006253448951315,"score_gpt":0.2467794313319892,"score_spread":0.226716896842476,"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."}}