{"id":"W2233705203","doi":"10.1186/s40168-015-0146-x","title":"Metatranscriptomic analysis of diverse microbial communities reveals core metabolic pathways and microbiome-specific functionality","year":2016,"lang":"en","type":"article","venue":"Microbiome","topic":"Microbial Community Ecology and Physiology","field":"Environmental Science","cited_by":180,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; University of Toronto; Hospital for Sick Children","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Ontario Genomics Institute; Government of Ontario; Canadian Institutes of Health Research; Genome Canada; Ontario Genomics; Compute Canada; Juvenile Diabetes Research Foundation International","keywords":"Biology; Microbiome; Metagenomics; Computational biology; Microbial ecology; Genome; Taxonomic rank; KEGG; Metabolic pathway; Evolutionary biology; Identification (biology); Taxon; Genetics; Ecology; Gene; Transcriptome; Bacteria","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006033183,0.0002993639,0.0007676969,0.0003496108,0.0003683358,0.00001708009,0.0005739346,0.0002058769,0.01321827],"category_scores_gemma":[0.000009706624,0.0002340503,0.0003104859,0.0009208836,0.001788736,0.0002105766,0.0005249067,0.000197628,0.0002605259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009684113,"about_ca_system_score_gemma":0.00001495511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009130689,"about_ca_topic_score_gemma":0.002158649,"domain_scores_codex":[0.9981362,0.0004700028,0.000525164,0.0003578207,0.00008384732,0.0004269863],"domain_scores_gemma":[0.9986214,0.0002800236,0.0002469348,0.0007147954,0.00003916273,0.00009769612],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001230218,0.0001587825,0.01899963,0.00001406736,0.000662033,0.000001674999,0.001122728,0.00001164618,0.9704277,0.0003857337,0.007341439,0.0007515237],"study_design_scores_gemma":[0.002913062,0.0002532316,0.6727784,0.00005818662,0.00218303,0.00006724169,0.0008976909,0.00001092573,0.1320236,0.001680773,0.1860111,0.00112285],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968488,0.0004600776,0.0002272094,0.0001627756,0.0002342113,0.000213364,0.001531328,0.00004173851,0.0002804611],"genre_scores_gemma":[0.9976979,0.0004426448,0.0005878152,0.0004320703,0.00002181607,0.000008539671,0.0003254166,0.00001831514,0.0004654987],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8384042,"threshold_uncertainty_score":0.9876838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04179105165085897,"score_gpt":0.2295061101572428,"score_spread":0.1877150585063838,"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."}}