{"id":"W3003746554","doi":"10.3389/fgene.2019.01396","title":"MAC: Merging Assemblies by Using Adjacency Algebraic Model and Classification","year":2020,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"National Natural Science Foundation of China","keywords":"Adjacency list; Computer science; Merge (version control); Contiguity; Graph; Sequence assembly; Ranking (information retrieval); Theoretical computer science; Data mining; Algorithm; Artificial intelligence; Information retrieval; Biology","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":[0.00006241819,0.000147367,0.0001525104,0.00003283617,0.00006603102,0.00002260771,0.000128426,0.0001055173,9.269318e-7],"category_scores_gemma":[0.00002566765,0.0001636738,0.00003233067,0.0000852702,0.00007197812,0.000001062014,0.0001202834,0.0000668584,5.559962e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001311339,"about_ca_system_score_gemma":0.00004032663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003108278,"about_ca_topic_score_gemma":0.000003746776,"domain_scores_codex":[0.9991451,0.000028617,0.0001913055,0.0003462115,0.00008206363,0.00020674],"domain_scores_gemma":[0.9996662,0.00000322061,0.00006254254,0.0001535567,0.00003235731,0.00008214641],"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":[0.00002347194,0.00001593413,0.06580374,0.0000210065,0.00004044996,5.094029e-7,0.0002966055,0.005662618,0.91369,0.00002013451,0.008897303,0.00552822],"study_design_scores_gemma":[0.001370403,0.0002995701,0.01858831,0.00002062029,0.00009897442,0.000005518889,0.001465043,0.841125,0.1072458,0.001132415,0.02774141,0.0009070082],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9405424,0.01939702,0.03919161,0.0003283265,0.0001437761,0.0001403064,0.0000270315,0.000003757685,0.0002257694],"genre_scores_gemma":[0.9577748,0.00486988,0.0367976,0.0003724973,0.00008283358,0.000007829809,0.00002477734,0.00002451597,0.00004533509],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8354623,"threshold_uncertainty_score":0.6674424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02634174953491874,"score_gpt":0.2442676821106956,"score_spread":0.2179259325757769,"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."}}