{"id":"W4410470708","doi":"10.71070/jcbm.v4i1.100","title":"Plasmid Copy Number Control with Gradient Boosting","year":2024,"lang":"en","type":"article","venue":"Journal of Computational Biology and Medicine","topic":"Electrostatics and Colloid Interactions","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Boosting (machine learning); Plasmid; Computer science; Biology; Computational biology; Genetics; Artificial intelligence; Gene","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":[0.001709071,0.0006716803,0.0007538121,0.0004386373,0.0003132632,0.0006754491,0.001117293,0.0006722862,0.0006258192],"category_scores_gemma":[0.003356425,0.0003310011,0.0004034673,0.0004248492,0.000511263,0.0006557888,0.0007579182,0.0009900852,0.000343115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006058779,"about_ca_system_score_gemma":0.0008427985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001008669,"about_ca_topic_score_gemma":0.0008043668,"domain_scores_codex":[0.9992431,0.0001911055,0.0000354748,0.0001860799,0.0002807323,0.00006344806],"domain_scores_gemma":[0.9989763,0.000401585,0.0001534056,0.0001366181,0.0002881305,0.00004396293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003804809,0.000283764,0.001995792,0.0001745952,0.00009644867,0.0001012758,0.0001010686,0.600435,0.1591306,0.01323296,0.002917475,0.2211505],"study_design_scores_gemma":[0.000008836359,0.00003860965,0.0001489978,0.000002466764,0.000007677724,0.0000150086,0.000001604533,0.9811438,0.01610447,0.001598411,0.0009207481,0.000009385779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0217486,0.0001677684,0.9759418,0.00009811638,0.00005974471,0.00004715584,0.00003324847,0.0009689006,0.0009346006],"genre_scores_gemma":[0.6444101,0.0002016079,0.3528418,0.00019482,0.00005936345,0.0001943174,0.0001389901,0.0002255783,0.001733384],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001709071,"threshold_uncertainty_score":0.009038568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006388706391211638,"score_gpt":0.2977841254697738,"score_spread":0.2913954190785622,"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."}}