{"id":"W2006250096","doi":"10.1038/npre.2012.6965.1","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":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Cloning (programming); clone (Java method); Restriction enzyme; Transformation (genetics); Ligation; Computational biology; Restriction digest; Biology; Restriction site; Molecular biology; Vector (molecular biology); Genetics; DNA; Computer science; Recombinant DNA; 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":[0.00395819,0.001236533,0.001194495,0.001666728,0.0004482733,0.002271263,0.002727828,0.0013139,0.003352229],"category_scores_gemma":[0.009500536,0.001001159,0.001348828,0.001098605,0.001619568,0.002341386,0.001161242,0.002019266,0.001098822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0029511,"about_ca_system_score_gemma":0.001092101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001616865,"about_ca_topic_score_gemma":0.001333968,"domain_scores_codex":[0.9975872,0.0006803932,0.0001065772,0.0005839414,0.0007510964,0.000290846],"domain_scores_gemma":[0.9952645,0.002927385,0.0007952261,0.0004184078,0.0004139623,0.0001804275],"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":[0.0002978788,0.0003641312,0.001848708,0.0003808725,0.0001616344,0.0001775973,0.0001694768,0.4744656,0.09452511,0.3971696,0.001391014,0.02904841],"study_design_scores_gemma":[0.00003187518,0.00007206163,0.0002763588,0.00001313781,0.0000335993,0.00006361497,0.00001182629,0.9555516,0.01043578,0.03151798,0.001961146,0.00003100896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03035372,0.0002157608,0.9663246,0.0001348474,0.0000409342,0.00009753013,0.0001631941,0.0004611446,0.002208227],"genre_scores_gemma":[0.5622444,0.0007314046,0.4234001,0.0002228232,0.00005575603,0.001300664,0.000930566,0.0006413235,0.01047296],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00395819,"threshold_uncertainty_score":0.02141184,"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."}}