{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001334699,0.00006286675,0.0001466117,0.00004646481,0.00006045417,0.000009412129,0.00003279945,0.00003179288,0.0004167141],"category_scores_gemma":[0.00003920356,0.00003381976,0.00002345381,0.00003878541,0.0001006601,0.00004024122,0.000004341696,0.0002074622,0.000004031863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002132962,"about_ca_system_score_gemma":0.00006337833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003120378,"about_ca_topic_score_gemma":0.000001241461,"domain_scores_codex":[0.9995265,0.00001140228,0.0002228062,0.00006612311,0.00008398388,0.00008920838],"domain_scores_gemma":[0.9992286,0.0004812166,0.00007308283,0.0000204742,0.0001395981,0.0000570074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00439772,0.0005674039,0.08719112,0.000901304,0.006023592,0.001436704,0.006317007,0.01289485,0.3805975,0.3648027,0.08171021,0.05315992],"study_design_scores_gemma":[0.01851074,0.006417368,0.004266215,0.004473375,0.001403988,0.03488987,0.001900627,0.06419096,0.02586548,0.3330669,0.5041391,0.0008754295],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9601985,0.001747298,0.02044206,0.01007792,0.0003830507,0.00002953278,0.00001406501,0.00001713001,0.007090464],"genre_scores_gemma":[0.9983541,0.00006313746,0.0005824778,0.0004133997,0.0003491707,0.000001217297,0.00001614996,0.000004641415,0.0002156673],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4224289,"threshold_uncertainty_score":0.4562728,"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."}}