{"id":"W4239626646","doi":"10.7551/mitpress/2022.003.0008","title":"Multiple Alignment","year":2000,"lang":"en","type":"book-chapter","venue":"Computational Molecular Biology","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00001667163,0.0002030234,0.0001743598,0.00008217803,0.00002848927,0.000007774553,0.00008820311,0.0002176967,0.0002031434],"category_scores_gemma":[0.000006146816,0.0002260005,0.00006375324,0.000008703571,0.00005346576,0.000009888206,0.00001988253,0.0001437807,0.0001559761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005668637,"about_ca_system_score_gemma":0.0000128763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.627395e-7,"about_ca_topic_score_gemma":4.052713e-7,"domain_scores_codex":[0.9994184,0.00000609059,0.0001794264,0.0001994834,0.00006814007,0.0001284027],"domain_scores_gemma":[0.9997209,0.00005445648,0.00003374834,0.0001212525,0.00002768865,0.00004195104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000002413381,0.000002145187,3.599331e-7,0.000008288314,0.00004316058,0.000009192241,0.000003611759,0.7691221,0.000028591,0.2262248,0.0001753051,0.004380067],"study_design_scores_gemma":[0.0001643061,0.00002623081,0.000004113018,0.0000168734,0.00001754918,0.000008241573,2.387182e-7,0.0710453,0.00009336416,0.8743291,0.05403322,0.000261416],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000009937759,0.0005227465,0.7902405,0.00001548531,0.0001795435,0.0000948766,0.00005985177,0.0001696115,0.2087074],"genre_scores_gemma":[0.7653182,0.0005311059,0.1852027,0.0006449014,0.0004126744,0.00004795357,0.009184339,0.0003359375,0.03832225],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7653082,"threshold_uncertainty_score":0.9216033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009032046004557877,"score_gpt":0.2221915059010773,"score_spread":0.2131594598965195,"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."}}