{"id":"W2544103171","doi":"10.1186/s13015-017-0101-4","title":"Aligning coding sequences with frameshift extension penalties","year":2017,"lang":"en","type":"article","venue":"Algorithms for Molecular Biology","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Canada Research Chairs; Université de Sherbrooke","keywords":"Frameshift mutation; Coding (social sciences); Extension (predicate logic); Gene; Homologous chromosome; Encoding (memory); Efficient algorithm","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008807561,0.0009212426,0.0006477942,0.001140706,0.0004455642,0.0005483655,0.0008699744,0.0007201284,0.002498623],"category_scores_gemma":[0.002965334,0.0002217174,0.0006431065,0.00144674,0.0003747204,0.000685123,0.0007087664,0.0008221301,0.001350449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003760403,"about_ca_system_score_gemma":0.0008824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001264784,"about_ca_topic_score_gemma":0.001287172,"domain_scores_codex":[0.9991757,0.0001951569,0.00006461999,0.0002357933,0.0002584462,0.00007020812],"domain_scores_gemma":[0.9987113,0.0004993089,0.0002112939,0.0002079181,0.0003102276,0.00005983995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005265889,0.0002215422,0.006349471,0.0004676387,0.0001588571,0.0002901619,0.0001716246,0.1785689,0.1000007,0.01066274,0.004008426,0.6985732],"study_design_scores_gemma":[0.00005752925,0.0002134981,0.002578786,0.00003465729,0.00005510591,0.0004514031,0.00006848518,0.9224485,0.04776149,0.01970619,0.006595909,0.00002848219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05267472,0.000266306,0.9439727,0.00004835975,0.0000402818,0.00009390915,0.0001412801,0.002072217,0.0006902389],"genre_scores_gemma":[0.1867327,0.0001571641,0.8104975,0.00004624888,0.00003620719,0.0001148524,0.000851356,0.0003746068,0.001189573],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002498623,"threshold_uncertainty_score":0.008358717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02500415426522971,"score_gpt":0.3100925058355515,"score_spread":0.2850883515703218,"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."}}