{"id":"W2509827291","doi":"10.1534/genetics.116.191973","title":"Estimation of Gene Insertion/Deletion Rates with Missing Data","year":2016,"lang":"en","type":"article","venue":"Genetics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences; Innovative Research Group Project of the National Natural Science Foundation of China","keywords":"Biology; Genetics; Genome; Gene; Missing data; Computational biology; Documentation; Gene Annotation; Computer science; Machine learning","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":[0.01748279,0.001264301,0.001780671,0.001861694,0.001123911,0.002157841,0.003517983,0.002469889,0.002928687],"category_scores_gemma":[0.05458948,0.001235288,0.002538115,0.00226043,0.001799991,0.003050878,0.002052162,0.003668411,0.001139688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001135289,"about_ca_system_score_gemma":0.001221964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002909783,"about_ca_topic_score_gemma":0.002820331,"domain_scores_codex":[0.9956148,0.002541787,0.0002267244,0.001091943,0.0003482401,0.0001766393],"domain_scores_gemma":[0.9676911,0.02465636,0.002258118,0.004264973,0.0007050743,0.0004244004],"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":[0.0009905084,0.0001792444,0.07131276,0.0007064452,0.0009912208,0.0007893779,0.0007560937,0.7900755,0.008163348,0.04203581,0.003048809,0.08095095],"study_design_scores_gemma":[0.00006600774,0.0001072103,0.00490277,0.00006858137,0.0000896912,0.0004433442,0.0001158067,0.9329675,0.0045031,0.05434969,0.00230303,0.00008322679],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1173712,0.0006479517,0.8764619,0.000464653,0.00006529686,0.00008626513,0.002443624,0.00183741,0.0006217442],"genre_scores_gemma":[0.5954295,0.0006296786,0.3938243,0.0002216271,0.00008030883,0.0004019622,0.007142104,0.0006074132,0.001663124],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01748279,"threshold_uncertainty_score":0.0924589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02579183028456765,"score_gpt":0.2642380601426239,"score_spread":0.2384462298580562,"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."}}