{"id":"W4251520346","doi":"10.1038/npre.2012.6965","title":"Quantitative Models for Efficient Cloning of Different Vectors with Various Clone sites","year":2012,"lang":"en","type":"preprint","venue":"Nature Precedings","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Cloning (programming); clone (Java method); Restriction enzyme; Transformation (genetics); Ligation; Computational biology; Restriction digest; Restriction site; Biology; Cloning vector; Molecular biology; Vector (molecular biology); Library; Molecular cloning; Genetics; DNA; Recombinant DNA; Gene; Computer science; Peptide sequence","routes":{"ca_aff":true,"ca_fund":true,"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"],"consensus_categories":[],"category_scores_codex":[0.0001628053,0.0003561528,0.0003880674,0.00008271973,0.00004558147,0.00002199036,0.0002528624,0.0007924008,0.000003085872],"category_scores_gemma":[0.000093228,0.0002906029,0.0001772158,0.00005847567,0.00005364604,0.000002099809,0.0003277573,0.0005849858,2.48753e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002806318,"about_ca_system_score_gemma":0.00004637935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001021111,"about_ca_topic_score_gemma":0.00001372881,"domain_scores_codex":[0.9986871,0.00001776697,0.0002531822,0.0005018331,0.0002013381,0.0003387721],"domain_scores_gemma":[0.9990752,0.00005495038,0.0002041192,0.0003352067,0.0002373078,0.00009320806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005221681,0.0001482703,0.0006651842,0.0007739976,0.0004335604,5.020192e-7,0.001127495,0.136092,0.8583448,0.001345059,0.0002873568,0.0002596203],"study_design_scores_gemma":[0.000740081,0.0006105956,0.001008422,0.0003244945,0.0002695455,0.000007262705,0.0001077602,0.02844707,0.9671089,0.0004414653,0.0003180138,0.0006163878],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8294789,0.007564212,0.1614799,0.00003454428,0.0004729366,0.0006035156,0.0001028204,0.00002319153,0.0002399538],"genre_scores_gemma":[0.9816046,0.0001321889,0.01738855,0.0000221301,0.0002547849,0.0001043036,0.0003726806,0.00006255101,0.0000582083],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1521257,"threshold_uncertainty_score":0.9999546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01207125834601249,"score_gpt":0.3066429475428827,"score_spread":0.2945716891968703,"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."}}