{"id":"W6981019023","doi":"","title":"Detection of errors in multiple genome alignments using machine learning approaches","year":2018,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Astro and Planetary Science","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Multiple sequence alignment; Phylogenetic tree; Genome; Sequence alignment; Sequence (biology); Artificial neural network; Smith–Waterman algorithm","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":[0.003302816,0.001145785,0.001056742,0.003294053,0.0006277927,0.001288219,0.001244768,0.001268525,0.0008552754],"category_scores_gemma":[0.01153195,0.0005061096,0.001023493,0.001912841,0.0004432166,0.001822177,0.0008896912,0.00131569,0.0008767094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007681692,"about_ca_system_score_gemma":0.000785612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001957429,"about_ca_topic_score_gemma":0.002677741,"domain_scores_codex":[0.9972851,0.0006926227,0.0002379429,0.0008487669,0.0008046772,0.0001309357],"domain_scores_gemma":[0.9921024,0.004727127,0.001498944,0.0007619959,0.0007954417,0.0001140291],"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":[0.0003267112,0.0004311303,0.05570862,0.0004218155,0.000489427,0.0005707993,0.0002542433,0.3217815,0.03348532,0.002695732,0.003891209,0.5799436],"study_design_scores_gemma":[0.000005623008,0.00004412769,0.002943475,0.00002876122,0.00002143065,0.0001128241,0.00004279981,0.9821148,0.01049228,0.003250689,0.0009230698,0.00002002962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1178137,0.0007403434,0.871973,0.0003424294,0.0001076459,0.0001143394,0.0005935451,0.007325018,0.0009899597],"genre_scores_gemma":[0.4605155,0.0004007513,0.5361687,0.0001624225,0.00005377755,0.0001231611,0.001465683,0.000250297,0.0008597056],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003302816,"threshold_uncertainty_score":0.01746714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03317316364947149,"score_gpt":0.2273403817326957,"score_spread":0.1941672180832242,"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."}}