{"id":"W2890720538","doi":"10.1093/bioinformatics/bty773","title":"SCOP: a novel scaffolding algorithm based on contig classification and optimization","year":2018,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Contig; Computer science; Scaffold; Spurious relationship; Graph; Sequence assembly; Algorithm; Cuboid; Pattern recognition (psychology); Artificial intelligence; Theoretical computer science; Genome; Machine learning; Biology; Mathematics; Genetics; Programming language","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":[0.001160309,0.002759021,0.001907436,0.004040543,0.002239656,0.001715373,0.002705821,0.001592501,0.006374551],"category_scores_gemma":[0.003498149,0.0009909293,0.002083717,0.004368458,0.001157598,0.002351227,0.002522143,0.002126441,0.003311137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009405396,"about_ca_system_score_gemma":0.00281026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007491695,"about_ca_topic_score_gemma":0.009343939,"domain_scores_codex":[0.9988917,0.0001323518,0.0000768109,0.0003846795,0.0003849356,0.0001295038],"domain_scores_gemma":[0.9981903,0.0005609104,0.0001770957,0.0003713802,0.0005526787,0.0001475381],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006439191,0.0003562376,0.006354223,0.0007505087,0.0002019572,0.0004486068,0.0004213214,0.1550093,0.02882742,0.009936512,0.05087993,0.7461701],"study_design_scores_gemma":[0.0001462816,0.0001886773,0.001470118,0.00005034608,0.00007747769,0.0002658339,0.000160687,0.9500801,0.01510181,0.01485491,0.01754239,0.00006135171],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02188879,0.000499521,0.948112,0.0002094418,0.0001288231,0.0002690066,0.001520116,0.02468093,0.002691276],"genre_scores_gemma":[0.07761005,0.0003102099,0.9021803,0.0002480971,0.0001028828,0.0004712367,0.01170209,0.003541533,0.003833541],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007491695,"threshold_uncertainty_score":0.02132493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0188849520969795,"score_gpt":0.2417094541896047,"score_spread":0.2228245020926252,"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."}}