{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001063524,0.00009934256,0.00007773508,0.00004048445,0.0001207667,0.0000357805,0.0000655109,0.00007479292,0.000003557045],"category_scores_gemma":[0.00003560714,0.00009103249,0.00002359629,0.00005357112,0.00008966735,0.000001541421,0.00004105499,0.00002197317,0.000005573147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008276237,"about_ca_system_score_gemma":0.0000308409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001574419,"about_ca_topic_score_gemma":0.000001543402,"domain_scores_codex":[0.9995121,0.00000656984,0.0001669116,0.0001154984,0.00007594933,0.0001229411],"domain_scores_gemma":[0.9995894,0.000008767405,0.00008457331,0.0001779735,0.00009625916,0.00004299714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003769861,0.0005361409,0.008591719,0.0002978691,0.0003646986,9.647176e-7,0.001771858,0.02945452,0.6022046,0.002057963,0.008995946,0.3453467],"study_design_scores_gemma":[0.0006240273,0.0003917601,0.002789209,0.00001983799,0.00001442756,0.000002767114,0.0001461282,0.9740137,0.01415408,0.00001125012,0.007671162,0.0001616205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1239183,0.0001672779,0.8674689,0.0001926698,0.0003097835,0.000352927,0.00006634634,0.00001317744,0.007510591],"genre_scores_gemma":[0.7078037,0.00007477582,0.2912296,0.000533183,0.0002289334,0.00001023856,0.00006365072,0.00001157569,0.00004440832],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9445592,"threshold_uncertainty_score":0.3712197,"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."}}