{"id":"W2163123197","doi":"10.1093/nar/gkg151","title":"Inferring functional constraints and divergence in protein families using 3D mapping of phylogenetic information","year":2003,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; Dalhousie University","funders":"","keywords":"Biology; Divergence (linguistics); Functional divergence; Computational biology; Constraint (computer-aided design); Phylogenetic tree; Function (biology); Rhodopsin; Sequence (biology); Rate of evolution; Molecular evolution; Phylogenetics; Genetics; Evolutionary biology; Genome; Gene; Biochemistry; Gene family","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":[0.002084198,0.0004841648,0.0007892468,0.003874305,0.000868168,0.001051676,0.000668629,0.0008309077,0.0004537198],"category_scores_gemma":[0.008870038,0.0004478148,0.0008391232,0.002152917,0.0007902175,0.001261412,0.001011496,0.0007336991,0.0002378715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000882886,"about_ca_system_score_gemma":0.0004796556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002460375,"about_ca_topic_score_gemma":0.002693489,"domain_scores_codex":[0.998862,0.0005764104,0.00007003771,0.0002163913,0.0002028281,0.00007242355],"domain_scores_gemma":[0.9948249,0.003387831,0.0008132763,0.0005406331,0.0002524718,0.0001808656],"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":[0.001031877,0.0003257694,0.3350967,0.0005455865,0.0006058082,0.001026849,0.00187767,0.2617775,0.1780956,0.01731622,0.0009894202,0.2013111],"study_design_scores_gemma":[0.00004436602,0.0001144705,0.1015083,0.00003947238,0.00009256061,0.0007353039,0.0005909679,0.8424061,0.01666477,0.03541251,0.002313753,0.00007753978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8472713,0.0003677564,0.1506868,0.0001497998,0.000005373503,0.00002351652,0.0005689174,0.0003484744,0.0005780927],"genre_scores_gemma":[0.9302767,0.0001714449,0.06859292,0.000022669,0.00000577735,0.00003039368,0.0007897847,0.00004460047,0.00006573488],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003874305,"threshold_uncertainty_score":0.01102239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03235444772878344,"score_gpt":0.2908106243252278,"score_spread":0.2584561765964443,"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."}}