{"id":"W2547075996","doi":"10.1016/j.sbi.2016.10.013","title":"Bridging the physical scales in evolutionary biology: from protein sequence space to fitness of organisms and populations","year":2016,"lang":"en","type":"review","venue":"Current Opinion in Structural Biology","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":87,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"National Institute of General Medical Sciences; Defense Advanced Research Projects Agency; National Institutes of Health","keywords":"Epistasis; Biology; Population; Fitness landscape; Bridging (networking); Evolutionary biology; Genetic Fitness; Selection (genetic algorithm); Computational biology; Scale (ratio); Systems biology; Genetics; Biological evolution; Computer science; Gene; Artificial intelligence; Physics","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":[0.001458617,0.001165776,0.002275378,0.001690538,0.0003716043,0.002285275,0.002156977,0.003174487,0.0029176],"category_scores_gemma":[0.002184242,0.0004616164,0.0004645661,0.003136414,0.003057645,0.005393168,0.001837606,0.003591867,0.001504778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001311906,"about_ca_system_score_gemma":0.001596567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001577555,"about_ca_topic_score_gemma":0.002332751,"domain_scores_codex":[0.9996132,0.00008817954,0.000036669,0.00009269231,0.0001336595,0.0000354693],"domain_scores_gemma":[0.9989088,0.0006702739,0.0001301609,0.00003558554,0.0001754663,0.00007967344],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007287126,0.0000301576,0.0003223943,0.01060513,0.0001063873,0.000134249,0.0001148639,0.0006868729,0.001533777,0.02344566,0.02855148,0.9343963],"study_design_scores_gemma":[0.00001981785,0.00006240374,0.001786409,0.004364158,0.0001309919,0.000950767,0.0002152844,0.0003967836,0.0005957103,0.04049001,0.9509214,0.00006636777],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0000904368,0.9974459,0.000401124,0.001319869,0.0003033829,0.000001331426,0.00001065268,0.000005963088,0.0004213729],"genre_scores_gemma":[0.0009859183,0.9973447,0.0003062965,0.0005239267,0.0005600409,0.000003466018,0.00001734289,0.000002358206,0.0002559445],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003174487,"threshold_uncertainty_score":0.00976032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06099774343666182,"score_gpt":0.4017797698859192,"score_spread":0.3407820264492574,"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."}}