{"id":"W4231186245","doi":"10.1515/iupac.79.1028","title":"Cloning Vector","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"History and advancements in chemistry","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Cloning (programming); Vector (molecular biology); Computer science; Biology; Computational biology; Genetics; Programming language; 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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001966614,0.0005485151,0.0005862404,0.00005887215,0.0001971894,0.00003817975,0.0006947848,0.0005713358,0.05501032],"category_scores_gemma":[0.0005261602,0.0004691279,0.0002040289,0.00007785534,0.0001987779,0.00008830426,0.0002015407,0.0008105101,0.000008321768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001064201,"about_ca_system_score_gemma":0.0006206954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001203383,"about_ca_topic_score_gemma":0.00003596198,"domain_scores_codex":[0.9972723,0.00001572042,0.0005175254,0.0006939244,0.0009680277,0.0005325369],"domain_scores_gemma":[0.9979057,0.0001059115,0.0003625306,0.001196992,0.0002156589,0.0002132155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009395392,0.0001567399,0.000002481569,0.0006657185,0.000108272,0.000133085,0.000006864988,7.933034e-7,0.004851133,0.000002752864,0.9919813,0.001996861],"study_design_scores_gemma":[0.000775379,0.00002121989,1.745107e-7,0.001041699,0.00009352944,0.00001764052,0.00001625054,5.229925e-7,0.0082656,0.00004272299,0.9891322,0.0005931223],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000114803,0.001108876,0.00003649747,0.00009025897,0.0008058213,0.00004680678,0.9958153,0.0001251331,0.001856511],"genre_scores_gemma":[0.0000202553,0.0005264187,0.00006754137,0.0001284269,0.002675633,0.00001900143,0.9857412,0.00006244108,0.01075906],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05500199,"threshold_uncertainty_score":0.9997761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01328547552373333,"score_gpt":0.3868243507383583,"score_spread":0.373538875214625,"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."}}