{"id":"W1975982655","doi":"10.1007/s10295-010-0915-2","title":"Construction and functional screening of a metagenomic library using a T7 RNA polymerase-based expression cosmid vector","year":2010,"lang":"en","type":"article","venue":"Journal of Industrial Microbiology & Biotechnology","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Cosmid; Biology; Shuttle vector; Heterologous expression; Metagenomics; Escherichia coli; Gene; Genomic library; RNA polymerase; Insert (composites); T7 RNA polymerase; Computational biology; Molecular biology; Genetics; Vector (molecular biology); Recombinant DNA; Bacteriophage","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":[],"consensus_categories":[],"category_scores_codex":[0.0002801791,0.000184547,0.0003651242,0.0003507828,0.00007191256,0.0000112552,0.00015747,0.001070544,0.00004254792],"category_scores_gemma":[0.0001924632,0.0001590994,0.0001271022,0.0001752935,0.0004628779,0.00001520503,0.00009329166,0.0006825169,4.603781e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008176864,"about_ca_system_score_gemma":0.0001942429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009651572,"about_ca_topic_score_gemma":0.000001888168,"domain_scores_codex":[0.9988809,0.00008532058,0.0005297236,0.0002584987,0.00004240348,0.000203123],"domain_scores_gemma":[0.9990439,0.00001495557,0.00058982,0.0002057971,0.0000852838,0.00006018016],"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":[0.0005427668,0.00004476084,0.0005686145,0.000007384283,0.0000979731,0.000002569302,0.000003966521,0.00009673556,0.9917411,0.00004813616,0.0004356931,0.006410256],"study_design_scores_gemma":[0.001454726,0.0004010545,0.00008627705,0.00004119943,0.0000678515,0.001201788,0.00002962709,0.00001653871,0.9839409,0.00001042713,0.01261024,0.0001393755],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903769,0.0008819749,0.006304,0.0005859748,0.001656007,0.0001148061,0.00006083241,0.00001592569,0.000003551938],"genre_scores_gemma":[0.9749525,0.00009030955,0.02379201,0.00005527267,0.001031513,0.000001174252,0.00004119445,0.0000209404,0.00001504322],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01748801,"threshold_uncertainty_score":0.8257015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01400280931053578,"score_gpt":0.2044330517090978,"score_spread":0.190430242398562,"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."}}