{"id":"W2770836247","doi":"10.1021/acssynbio.7b00412","title":"An Improved Whole-Cell Biosensor for the Discovery of Lignin-Transforming Enzymes in Functional Metagenomic Screens","year":2017,"lang":"en","type":"letter","venue":"ACS Synthetic Biology","topic":"Biochemical and biochemical processes","field":"Biochemistry, Genetics and Molecular Biology","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canada Foundation for Innovation; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Biosensor; Syringaldehyde; Lignin; Vanillin; Fosmid; Chemistry; High-throughput screening; Metagenomics; Biotechnology; Computational biology; Biology; Biochemistry; Gene; Organic chemistry","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0003108267,0.0004607641,0.0005725545,0.00007705958,0.0001401437,0.00004654446,0.001010391,0.001732543,0.000006301304],"category_scores_gemma":[0.0002039257,0.0003044371,0.0003343298,0.0000524435,0.0008351706,0.00001048383,0.0001603852,0.0006310212,0.00000412231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001799971,"about_ca_system_score_gemma":0.0001756187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008049804,"about_ca_topic_score_gemma":0.00002725362,"domain_scores_codex":[0.9978142,0.00008805764,0.0005278079,0.0008967902,0.0001020879,0.000571007],"domain_scores_gemma":[0.9982334,0.0002640277,0.0003758093,0.0009463743,0.0001311764,0.00004923448],"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":[0.0001656171,0.00008537318,0.00005035641,0.0002124278,0.0001277992,0.000002074012,0.000008271936,8.235854e-7,0.9588506,0.00003315565,0.03954744,0.0009160962],"study_design_scores_gemma":[0.0004728533,0.0003678537,0.00001542503,0.00003571652,0.0001293059,0.00001420759,0.00002215332,0.00001375305,0.8004016,0.000293342,0.1978867,0.0003471516],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5226815,0.06267472,0.008514758,0.3841383,0.003289498,0.006245187,0.01010096,0.0001140297,0.002241104],"genre_scores_gemma":[0.9696211,0.0004505086,0.0003576799,0.02323093,0.002206286,0.0001769959,0.002534917,0.00007109092,0.001350523],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4469396,"threshold_uncertainty_score":0.9999408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01779429264056202,"score_gpt":0.2533488186558349,"score_spread":0.2355545260152729,"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."}}