{"id":"W2945751924","doi":"10.1142/s0218339019500128","title":"PROBABILISTIC MODELING AND ANALYSIS OF DNA FRAGMENTATION","year":2019,"lang":"en","type":"article","venue":"Journal of Biological Systems","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Shotgun sequencing; Fragmentation (computing); DNA; Probabilistic logic; DNA fragmentation; Computational biology; DNA sequencing; Shotgun; Computer science; Biology; Algorithm; Mathematics; Genetics; Gene; Artificial intelligence","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":[0.0003095524,0.00006385294,0.0002392284,0.00006622176,0.00001518603,0.000006952956,0.00009358884,0.0001295541,0.000007162605],"category_scores_gemma":[0.00003920684,0.00004206186,0.0001146659,0.0001212212,0.00003549568,0.000001782697,0.00003341821,0.00004889692,5.11862e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006419682,"about_ca_system_score_gemma":0.00001278362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007334258,"about_ca_topic_score_gemma":7.53979e-7,"domain_scores_codex":[0.9993218,0.00006908988,0.0003532738,0.0001217677,0.00006254593,0.00007155281],"domain_scores_gemma":[0.999446,0.00001404647,0.000264902,0.0001182077,0.0001204586,0.00003634993],"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":[0.00004717001,0.00004698312,0.02491411,0.00001820544,0.0003579816,6.141739e-7,0.00001049172,0.006310852,0.9669298,0.0009991974,0.00002652332,0.0003380593],"study_design_scores_gemma":[0.005876113,0.02081512,0.181014,0.0005340339,0.00468567,0.0007265782,0.002176995,0.4861877,0.2688654,0.004534615,0.02216184,0.002421857],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9755532,0.000721136,0.02337825,0.00002649493,0.00003921117,0.0001461783,0.000009688462,0.000002342364,0.000123477],"genre_scores_gemma":[0.9990571,0.0001997224,0.0006189045,0.00002742001,0.00003572994,0.000005461123,0.00003260823,0.000002841245,0.00002018794],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6980644,"threshold_uncertainty_score":0.1715233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01868427910015307,"score_gpt":0.277559609868664,"score_spread":0.2588753307685109,"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."}}