{"id":"W1971585008","doi":"10.1016/s0045-2068(02)00018-4","title":"Making AppDNA using T4 DNA ligase","year":2002,"lang":"en","type":"article","venue":"Bioorganic Chemistry","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"Health Sciences Centre; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"DNA ligase; Chemistry; Sequencing by ligation; DNA; DNA clamp; In vitro recombination; DNA polymerase; Ligase chain reaction; Thermus aquaticus; Circular bacterial chromosome; DNA polymerase II; DNA Ligases; Primase; Biochemistry; Molecular biology; Molecular cloning; Genomic library; Biology; Complementary DNA; Polymerase chain reaction; Gene","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":[],"consensus_categories":[],"category_scores_codex":[0.00008314638,0.0002342303,0.0001791543,0.00001717009,0.0001251327,0.00003494579,0.0002217841,0.0002636717,0.00008496393],"category_scores_gemma":[0.00008148968,0.0002282481,0.0001424284,0.0001929881,0.0001064036,0.00000391969,0.00014603,0.0001198595,0.00001400953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004811308,"about_ca_system_score_gemma":0.00002385296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002586465,"about_ca_topic_score_gemma":0.000001597195,"domain_scores_codex":[0.9987977,0.00001633143,0.0002380311,0.0004863886,0.0001456443,0.0003159081],"domain_scores_gemma":[0.9991783,0.000005298944,0.0001236074,0.0005406931,0.00006479123,0.00008727811],"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":[0.000007863661,0.00005628042,0.000299259,0.00002109449,0.00004042393,0.00001032003,0.000008787705,0.000001024441,0.9967762,0.000001911317,0.0015716,0.001205287],"study_design_scores_gemma":[0.0001767438,0.00002269988,0.00002889439,0.00003059798,0.00004070532,0.0001160545,0.00003728096,0.0002628932,0.9849316,0.00001693913,0.01402535,0.0003102524],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944518,0.0008179881,0.0007849418,0.0001172461,0.00004080177,0.00007171108,0.00001424241,0.0001166921,0.003584557],"genre_scores_gemma":[0.9945833,0.0001199501,0.003223778,0.0002283812,0.0004932131,0.000002265262,0.00003605805,0.0000392857,0.001273739],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01245376,"threshold_uncertainty_score":0.9307686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03550159440065431,"score_gpt":0.2800782580780526,"score_spread":0.2445766636773983,"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."}}