{"id":"W2154348147","doi":"10.1093/bioinformatics/bts721","title":"Fragment recruitment on metabolic pathways: comparative metabolic profiling of metagenomes and metatranscriptomes","year":2013,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Leibniz-Gemeinschaft; Deutsche Forschungsgemeinschaft","keywords":"KEGG; Metagenomics; Profiling (computer programming); Perl; Metabolic pathway; Computational biology; Biology; Java; Computer science; Bioinformatics; Gene; Transcriptome; Genetics; World Wide Web; Programming language","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":[0.0002879516,0.0002928776,0.0005069442,0.0001100169,0.00008925864,0.00005932376,0.0002079114,0.0001343943,0.00003436474],"category_scores_gemma":[0.0000139271,0.0002257491,0.0001457947,0.0001221974,0.0001293279,0.00002775108,0.0001082632,0.0001174316,0.0000358327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007459311,"about_ca_system_score_gemma":0.00005414792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001080743,"about_ca_topic_score_gemma":0.000002627505,"domain_scores_codex":[0.9985175,0.00004235045,0.0007141497,0.0001773529,0.0002212951,0.0003274034],"domain_scores_gemma":[0.999005,0.00002429029,0.0003177925,0.0004015864,0.0001095228,0.0001418034],"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":[0.0004385315,0.001121543,0.002525358,0.001646856,0.00707911,0.000002705652,0.02436677,0.003279692,0.502048,0.1171589,0.0123208,0.3280117],"study_design_scores_gemma":[0.002809993,0.001349767,0.005405114,0.00009712789,0.0004888962,0.0000243436,0.00706305,0.0220974,0.8361112,0.001845714,0.1214631,0.001244244],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9630926,0.01579923,0.009598533,0.0001133999,0.0003357658,0.002487754,0.0001394755,0.00003292655,0.008400339],"genre_scores_gemma":[0.9406764,0.002518206,0.05560512,0.0005555105,0.00009121895,0.0002143092,0.0001540729,0.00002106448,0.0001640862],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3340632,"threshold_uncertainty_score":0.9205782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04910625613312687,"score_gpt":0.2687357045996056,"score_spread":0.2196294484664787,"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."}}