{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003842296,0.001417037,0.001295964,0.001736855,0.0004861488,0.002506897,0.00304695,0.001570762,0.003648671],"category_scores_gemma":[0.009558352,0.001195957,0.001595374,0.001301202,0.001662725,0.002606818,0.001290672,0.002392272,0.001404262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002869855,"about_ca_system_score_gemma":0.001130104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001518199,"about_ca_topic_score_gemma":0.00141745,"domain_scores_codex":[0.9972396,0.0007088565,0.0001354432,0.0007127741,0.0008789288,0.0003244797],"domain_scores_gemma":[0.9954176,0.002741395,0.0007803087,0.0004701645,0.0004157152,0.0001748529],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003152372,0.0003776083,0.00175678,0.0004798041,0.000173774,0.0001894509,0.0002100207,0.359616,0.1218265,0.4809819,0.001687167,0.03238577],"study_design_scores_gemma":[0.00004911689,0.0001026615,0.0003219286,0.00001934416,0.00004760926,0.00009902799,0.00001634659,0.9289602,0.01674597,0.04977134,0.003821474,0.00004500673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01808855,0.0002133768,0.9788257,0.0001192028,0.00004398251,0.00009057199,0.0001760458,0.0004550762,0.001987481],"genre_scores_gemma":[0.3976432,0.000899822,0.5861174,0.0002473768,0.00007086313,0.001749872,0.001160327,0.0007718225,0.01133916],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003842296,"threshold_uncertainty_score":0.02082241,"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."}}