{"id":"W2038601075","doi":"10.1126/science.1174301","title":"Positive Selection of Tyrosine Loss in Metazoan Evolution","year":2009,"lang":"en","type":"article","venue":"Science","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":95,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; University of Toronto; Mount Sinai Hospital","funders":"National Institute of General Medical Sciences","keywords":"Multicellular organism; Biology; Evolutionary biology; Receptor tyrosine kinase; Selection (genetic algorithm); Tyrosine; Lineage (genetic); Phenotype; Organism; Computational biology; Gene; Genetics; Computer science; Signal transduction; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002200753,0.00004218596,0.0000471583,0.00007041317,0.00003819738,0.000005156367,0.0001042112,0.00003258731,0.000003614271],"category_scores_gemma":[0.00005599363,0.00004130353,0.00001831049,0.0004148192,0.0001592138,0.000004845913,0.00001890908,0.00002856921,0.000001884845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003059912,"about_ca_system_score_gemma":0.0001062286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001263708,"about_ca_topic_score_gemma":0.00005771391,"domain_scores_codex":[0.9994894,0.00001454986,0.0000915278,0.0001680775,0.0001127849,0.000123661],"domain_scores_gemma":[0.9997458,0.000001465361,0.000034853,0.00009133543,0.00009745751,0.00002908345],"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":[0.0000140649,0.00003582221,0.009013021,8.928702e-7,8.627272e-7,1.574646e-7,0.00001732494,0.0007403205,0.9878075,0.00142045,0.00001709757,0.0009324941],"study_design_scores_gemma":[0.0001703544,0.0002631549,0.7242692,0.000005676826,0.000002379674,0.000008298362,0.00001773512,0.004172316,0.2701993,0.000718874,0.0001088513,0.00006394526],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9881387,0.0001677198,0.01031643,0.0001334179,0.00004816766,0.00005709324,0.000002703488,0.000003505421,0.001132196],"genre_scores_gemma":[0.9985562,0.0000195468,0.001152332,0.00007515761,0.00001889962,7.501621e-7,0.000005493614,0.000001428452,0.0001701679],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7176082,"threshold_uncertainty_score":0.1684309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003804146537482121,"score_gpt":0.2521783379463893,"score_spread":0.2483741914089072,"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."}}